id	sid	tid	token	lemma	pos
brj-23253	1	1	peer	peer	NOUN
brj-23253	1	2	-	-	PUNCT
brj-23253	1	3	review	review	NOUN
brj-23253	1	4	article	article	NOUN
brj-23253	1	5	peer	peer	NOUN
brj-23253	1	6	-	-	PUNCT
brj-23253	1	7	reviewed	review	VERB
brj-23253	1	8	article	article	NOUN
brj-23253	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	1	10	li	li	PROPN
brj-23253	1	11	et	et	PROPN
brj-23253	1	12	al	al	PROPN
brj-23253	1	13	.	.	PROPN
brj-23253	2	1	(	(	PUNCT
brj-23253	2	2	2024	2024	NUM
brj-23253	2	3	)	)	PUNCT
brj-23253	2	4	.	.	PUNCT
brj-23253	3	1	“	"	PUNCT
brj-23253	3	2	alfalfa	alfalfa	X
brj-23253	3	3	protein	protein	NOUN
brj-23253	3	4	with	with	ADP
brj-23253	3	5	vis	vis	X
brj-23253	3	6	/	/	SYM
brj-23253	3	7	nir	nir	NOUN
brj-23253	3	8	,	,	PUNCT
brj-23253	3	9	”	"	PUNCT
brj-23253	3	10	bioresources	bioresource	NOUN
brj-23253	3	11	19(2	19(2	NUM
brj-23253	3	12	)	)	PUNCT
brj-23253	3	13	,	,	PUNCT
brj-23253	3	14	3808	3808	NUM
brj-23253	3	15	-	-	SYM
brj-23253	3	16	3825	3825	NUM
brj-23253	3	17	.	.	PUNCT
brj-23253	4	1	3808	3808	NUM
brj-23253	4	2	detection	detection	NOUN
brj-23253	4	3	of	of	ADP
brj-23253	4	4	protein	protein	NOUN
brj-23253	4	5	content	content	NOUN
brj-23253	4	6	in	in	ADP
brj-23253	4	7	alfalfa	alfalfa	NOUN
brj-23253	4	8	using	use	VERB
brj-23253	4	9	visible/	visible/	NUM
brj-23253	4	10	near	near	ADV
brj-23253	4	11	-	-	PUNCT
brj-23253	4	12	infrared	infrared	ADJ
brj-23253	4	13	spectroscopy	spectroscopy	NOUN
brj-23253	4	14	technology	technology	PROPN
brj-23253	4	15	jie	jie	PROPN
brj-23253	4	16	li	li	PROPN
brj-23253	4	17	,	,	PUNCT
brj-23253	4	18	a	a	DET
brj-23253	4	19	guifang	guifang	PROPN
brj-23253	4	20	wu	wu	PROPN
brj-23253	4	21	,	,	PUNCT
brj-23253	4	22	a	a	PRON
brj-23253	4	23	,	,	PUNCT
brj-23253	4	24	*	*	PUNCT
brj-23253	4	25	fang	fang	PROPN
brj-23253	4	26	guo	guo	PROPN
brj-23253	4	27	,	,	PUNCT
brj-23253	4	28	a	a	PRON
brj-23253	4	29	,	,	PUNCT
brj-23253	4	30	*	*	PUNCT
brj-23253	4	31	lei	lei	PROPN
brj-23253	4	32	han	han	PROPN
brj-23253	4	33	,	,	PUNCT
brj-23253	4	34	a	a	DET
brj-23253	4	35	haowen	haowen	NOUN
brj-23253	4	36	xiao	xiao	PROPN
brj-23253	4	37	,	,	PUNCT
brj-23253	4	38	b	b	PROPN
brj-23253	4	39	yang	yang	PROPN
brj-23253	4	40	cao	cao	PROPN
brj-23253	4	41	,	,	PUNCT
brj-23253	4	42	b	b	PROPN
brj-23253	4	43	huihe	huihe	PROPN
brj-23253	4	44	yang	yang	PROPN
brj-23253	4	45	,	,	PUNCT
brj-23253	4	46	a	a	PRON
brj-23253	4	47	and	and	CCONJ
brj-23253	4	48	shubin	shubin	PROPN
brj-23253	4	49	yan	yan	PROPN
brj-23253	5	1	a	a	PRON
brj-23253	5	2	in	in	ADP
brj-23253	5	3	this	this	DET
brj-23253	5	4	study	study	NOUN
brj-23253	5	5	,	,	PUNCT
brj-23253	5	6	a	a	DET
brj-23253	5	7	quantitative	quantitative	ADJ
brj-23253	5	8	model	model	NOUN
brj-23253	5	9	was	be	AUX
brj-23253	5	10	developed	develop	VERB
brj-23253	5	11	using	use	VERB
brj-23253	5	12	near	near	ADV
brj-23253	5	13	-	-	PUNCT
brj-23253	5	14	infrared	infrared	ADJ
brj-23253	5	15	spectroscopy	spectroscopy	NOUN
brj-23253	5	16	to	to	PART
brj-23253	5	17	analyze	analyze	VERB
brj-23253	5	18	protein	protein	NOUN
brj-23253	5	19	content	content	NOUN
brj-23253	5	20	in	in	ADP
brj-23253	5	21	dried	dry	VERB
brj-23253	5	22	purple	purple	ADJ
brj-23253	5	23	alfalfa	alfalfa	NOUN
brj-23253	5	24	,	,	PUNCT
brj-23253	5	25	employing	employ	VERB
brj-23253	5	26	preprocessing	preprocessing	NOUN
brj-23253	5	27	methods	method	NOUN
brj-23253	5	28	(	(	PUNCT
brj-23253	5	29	sg	sg	PROPN
brj-23253	5	30	,	,	PUNCT
brj-23253	5	31	snv	snv	PROPN
brj-23253	5	32	,	,	PUNCT
brj-23253	5	33	msc	msc	PROPN
brj-23253	5	34	,	,	PUNCT
brj-23253	5	35	fd	fd	PROPN
brj-23253	5	36	)	)	PUNCT
brj-23253	5	37	and	and	CCONJ
brj-23253	5	38	variable	variable	ADJ
brj-23253	5	39	selection	selection	NOUN
brj-23253	5	40	algorithms	algorithm	NOUN
brj-23253	5	41	(	(	PUNCT
brj-23253	5	42	cars	car	NOUN
brj-23253	5	43	,	,	PUNCT
brj-23253	5	44	iriv	iriv	PROPN
brj-23253	5	45	)	)	PUNCT
brj-23253	5	46	to	to	PART
brj-23253	5	47	optimize	optimize	VERB
brj-23253	5	48	spectra	spectra	PROPN
brj-23253	5	49	.	.	PUNCT
brj-23253	6	1	models	model	NOUN
brj-23253	6	2	using	use	VERB
brj-23253	6	3	elm	elm	PROPN
brj-23253	6	4	,	,	PUNCT
brj-23253	6	5	plsr	plsr	NOUN
brj-23253	6	6	,	,	PUNCT
brj-23253	6	7	svm	svm	ADJ
brj-23253	6	8	,	,	PUNCT
brj-23253	6	9	and	and	CCONJ
brj-23253	6	10	lstm	lstm	NOUN
brj-23253	6	11	were	be	AUX
brj-23253	6	12	tested	test	VERB
brj-23253	6	13	;	;	PUNCT
brj-23253	6	14	the	the	DET
brj-23253	6	15	msc	msc	PROPN
brj-23253	6	16	-	-	PUNCT
brj-23253	6	17	cars	car	NOUN
brj-23253	6	18	-	-	PUNCT
brj-23253	6	19	plsr	plsr	NOUN
brj-23253	6	20	-	-	PUNCT
brj-23253	6	21	svm	svm	NOUN
brj-23253	6	22	model	model	NOUN
brj-23253	6	23	achieved	achieve	VERB
brj-23253	6	24	the	the	DET
brj-23253	6	25	highest	high	ADJ
brj-23253	6	26	accuracy	accuracy	NOUN
brj-23253	6	27	,	,	PUNCT
brj-23253	6	28	with	with	ADP
brj-23253	6	29	a	a	DET
brj-23253	6	30	calibration	calibration	NOUN
brj-23253	6	31	determination	determination	NOUN
brj-23253	6	32	coefficient	coefficient	NOUN
brj-23253	6	33	(	(	PUNCT
brj-23253	6	34	r²	r²	NOUN
brj-23253	6	35	)	)	PUNCT
brj-23253	6	36	of	of	ADP
brj-23253	6	37	0.9982	0.9982	NUM
brj-23253	6	38	and	and	CCONJ
brj-23253	6	39	root	root	NOUN
brj-23253	6	40	mean	mean	ADJ
brj-23253	6	41	square	square	ADJ
brj-23253	6	42	error	error	NOUN
brj-23253	6	43	(	(	PUNCT
brj-23253	6	44	rmse	rmse	NOUN
brj-23253	6	45	)	)	PUNCT
brj-23253	6	46	of	of	ADP
brj-23253	6	47	0.1088	0.1088	NUM
brj-23253	6	48	,	,	PUNCT
brj-23253	6	49	and	and	CCONJ
brj-23253	6	50	a	a	DET
brj-23253	6	51	prediction	prediction	NOUN
brj-23253	6	52	r²	r²	NOUN
brj-23253	6	53	of	of	ADP
brj-23253	6	54	0.9645	0.9645	NUM
brj-23253	6	55	with	with	ADP
brj-23253	6	56	rmse	rmse	NOUN
brj-23253	6	57	of	of	ADP
brj-23253	6	58	0.5230	0.5230	NUM
brj-23253	6	59	,	,	PUNCT
brj-23253	6	60	offering	offer	VERB
brj-23253	6	61	a	a	DET
brj-23253	6	62	precise	precise	ADJ
brj-23253	6	63	and	and	CCONJ
brj-23253	6	64	reliable	reliable	ADJ
brj-23253	6	65	method	method	NOUN
brj-23253	6	66	for	for	ADP
brj-23253	6	67	protein	protein	NOUN
brj-23253	6	68	content	content	NOUN
brj-23253	6	69	prediction	prediction	NOUN
brj-23253	6	70	.	.	PUNCT
brj-23253	7	1	doi	doi	NOUN
brj-23253	7	2	:	:	PUNCT
brj-23253	7	3	10.15376	10.15376	NUM
brj-23253	7	4	/	/	SYM
brj-23253	7	5	biores.19.2.3808	biores.19.2.3808	NOUN
brj-23253	7	6	-	-	PUNCT
brj-23253	7	7	3825	3825	NUM
brj-23253	7	8	keywords	keyword	NOUN
brj-23253	7	9	:	:	PUNCT
brj-23253	7	10	quantitative	quantitative	ADJ
brj-23253	7	11	detection	detection	NOUN
brj-23253	7	12	;	;	PUNCT
brj-23253	7	13	near	near	ADV
brj-23253	7	14	-	-	PUNCT
brj-23253	7	15	infrared	infrared	ADJ
brj-23253	7	16	spectroscopy	spectroscopy	NOUN
brj-23253	7	17	;	;	PUNCT
brj-23253	7	18	machine	machine	NOUN
brj-23253	7	19	learning	learning	NOUN
brj-23253	7	20	;	;	PUNCT
brj-23253	7	21	protein	protein	NOUN
brj-23253	7	22	content	content	NOUN
brj-23253	7	23	;	;	PUNCT
brj-23253	7	24	alfalfa	alfalfa	PROPN
brj-23253	7	25	hay	hay	PROPN
brj-23253	7	26	contact	contact	NOUN
brj-23253	7	27	information	information	NOUN
brj-23253	7	28	:	:	PUNCT
brj-23253	7	29	a	a	X
brj-23253	7	30	:	:	PUNCT
brj-23253	7	31	college	college	NOUN
brj-23253	7	32	of	of	ADP
brj-23253	7	33	mechanical	mechanical	ADJ
brj-23253	7	34	&	&	CCONJ
brj-23253	7	35	electrical	electrical	ADJ
brj-23253	7	36	engineering	engineering	NOUN
brj-23253	7	37	,	,	PUNCT
brj-23253	7	38	inner	inner	PROPN
brj-23253	7	39	mongolia	mongolia	PROPN
brj-23253	7	40	agricultural	agricultural	PROPN
brj-23253	7	41	university	university	PROPN
brj-23253	7	42	,	,	PUNCT
brj-23253	7	43	hohhot	hohhot	ADJ
brj-23253	7	44	,	,	PUNCT
brj-23253	7	45	010018	010018	NUM
brj-23253	7	46	,	,	PUNCT
brj-23253	7	47	p.r	p.r	PROPN
brj-23253	7	48	.	.	PROPN
brj-23253	7	49	china	china	PROPN
brj-23253	7	50	;	;	PUNCT
brj-23253	7	51	b	b	X
brj-23253	7	52	:	:	PUNCT
brj-23253	7	53	inner	inner	PROPN
brj-23253	7	54	mongolia	mongolia	PROPN
brj-23253	7	55	autonomous	autonomous	PROPN
brj-23253	7	56	region	region	PROPN
brj-23253	7	57	agricultural	agricultural	ADJ
brj-23253	7	58	and	and	CCONJ
brj-23253	7	59	pastoral	pastoral	ADJ
brj-23253	7	60	technology	technology	NOUN
brj-23253	7	61	extension	extension	NOUN
brj-23253	7	62	center	center	NOUN
brj-23253	7	63	,	,	PUNCT
brj-23253	7	64	hohhot	hohhot	ADJ
brj-23253	7	65	,	,	PUNCT
brj-23253	7	66	010010	010010	NUM
brj-23253	7	67	,	,	PUNCT
brj-23253	7	68	p.r	p.r	PROPN
brj-23253	7	69	.	.	PROPN
brj-23253	7	70	china	china	PROPN
brj-23253	7	71	;	;	PUNCT
brj-23253	7	72	*	*	PUNCT
brj-23253	7	73	corresponding	correspond	VERB
brj-23253	7	74	authors	author	NOUN
brj-23253	7	75	:	:	PUNCT
brj-23253	7	76	wgfsara@126.com	wgfsara@126.com	PROPN
brj-23253	7	77	and	and	CCONJ
brj-23253	7	78	jennifer_guo@imau.edu.cn	jennifer_guo@imau.edu.cn	PROPN
brj-23253	7	79	introduction	introduction	NOUN
brj-23253	7	80	purple	purple	ADJ
brj-23253	7	81	alfalfa	alfalfa	NOUN
brj-23253	7	82	,	,	PUNCT
brj-23253	7	83	a	a	DET
brj-23253	7	84	perennial	perennial	ADJ
brj-23253	7	85	herbaceous	herbaceous	ADJ
brj-23253	7	86	plant	plant	NOUN
brj-23253	7	87	belonging	belong	VERB
brj-23253	7	88	to	to	ADP
brj-23253	7	89	the	the	DET
brj-23253	7	90	legume	legume	NOUN
brj-23253	7	91	family	family	NOUN
brj-23253	7	92	,	,	PUNCT
brj-23253	7	93	is	be	AUX
brj-23253	7	94	widely	widely	ADV
brj-23253	7	95	considered	consider	VERB
brj-23253	7	96	to	to	PART
brj-23253	7	97	have	have	AUX
brj-23253	7	98	originated	originate	VERB
brj-23253	7	99	in	in	ADP
brj-23253	7	100	the	the	DET
brj-23253	7	101	near	near	PROPN
brj-23253	7	102	east	east	PROPN
brj-23253	7	103	region	region	NOUN
brj-23253	7	104	,	,	PUNCT
brj-23253	7	105	including	include	VERB
brj-23253	7	106	iran	iran	PROPN
brj-23253	7	107	,	,	PUNCT
brj-23253	7	108	anatolia	anatolia	PROPN
brj-23253	7	109	,	,	PUNCT
brj-23253	7	110	the	the	DET
brj-23253	7	111	turkmen	turkmen	PROPN
brj-23253	7	112	plateau	plateau	NOUN
brj-23253	7	113	,	,	PUNCT
brj-23253	7	114	and	and	CCONJ
brj-23253	7	115	the	the	DET
brj-23253	7	116	transcaucasus	transcaucasus	NOUN
brj-23253	7	117	(	(	PUNCT
brj-23253	7	118	bedaf	bedaf	PROPN
brj-23253	7	119	et	et	PROPN
brj-23253	7	120	al	al	PROPN
brj-23253	7	121	.	.	PROPN
brj-23253	7	122	2008	2008	NUM
brj-23253	7	123	)	)	PUNCT
brj-23253	7	124	.	.	PUNCT
brj-23253	8	1	it	it	PRON
brj-23253	8	2	boasts	boast	VERB
brj-23253	8	3	high	high	ADJ
brj-23253	8	4	production	production	NOUN
brj-23253	8	5	potential	potential	ADJ
brj-23253	8	6	and	and	CCONJ
brj-23253	8	7	nutritional	nutritional	ADJ
brj-23253	8	8	value	value	NOUN
brj-23253	8	9	,	,	PUNCT
brj-23253	8	10	making	make	VERB
brj-23253	8	11	it	it	PRON
brj-23253	8	12	one	one	NUM
brj-23253	8	13	of	of	ADP
brj-23253	8	14	the	the	DET
brj-23253	8	15	most	most	ADV
brj-23253	8	16	extensively	extensively	ADV
brj-23253	8	17	cultivated	cultivate	VERB
brj-23253	8	18	forage	forage	NOUN
brj-23253	8	19	crops	crop	NOUN
brj-23253	8	20	worldwide	worldwide	ADV
brj-23253	8	21	and	and	CCONJ
brj-23253	8	22	earning	earn	VERB
brj-23253	8	23	it	it	PRON
brj-23253	8	24	the	the	DET
brj-23253	8	25	reputation	reputation	NOUN
brj-23253	8	26	of	of	ADP
brj-23253	8	27	"	"	PUNCT
brj-23253	8	28	the	the	DET
brj-23253	8	29	king	king	NOUN
brj-23253	8	30	of	of	ADP
brj-23253	8	31	pastures	pasture	NOUN
brj-23253	8	32	.	.	PUNCT
brj-23253	8	33	"	"	PUNCT
brj-23253	9	1	beyond	beyond	ADP
brj-23253	9	2	its	its	PRON
brj-23253	9	3	nutritional	nutritional	ADJ
brj-23253	9	4	benefits	benefit	NOUN
brj-23253	9	5	,	,	PUNCT
brj-23253	9	6	purple	purple	ADJ
brj-23253	9	7	alfalfa	alfalfa	NOUN
brj-23253	9	8	plays	play	VERB
brj-23253	9	9	a	a	DET
brj-23253	9	10	crucial	crucial	ADJ
brj-23253	9	11	role	role	NOUN
brj-23253	9	12	in	in	ADP
brj-23253	9	13	nitrogen	nitrogen	NOUN
brj-23253	9	14	fixation	fixation	NOUN
brj-23253	9	15	,	,	PUNCT
brj-23253	9	16	enhancing	enhance	VERB
brj-23253	9	17	soil	soil	NOUN
brj-23253	9	18	fertility	fertility	NOUN
brj-23253	9	19	and	and	CCONJ
brj-23253	9	20	aiding	aid	VERB
brj-23253	9	21	in	in	ADP
brj-23253	9	22	the	the	DET
brj-23253	9	23	reduction	reduction	NOUN
brj-23253	9	24	of	of	ADP
brj-23253	9	25	chemical	chemical	ADJ
brj-23253	9	26	fertilizers	fertilizer	NOUN
brj-23253	9	27	'	'	PART
brj-23253	9	28	use	use	NOUN
brj-23253	9	29	,	,	PUNCT
brj-23253	9	30	which	which	PRON
brj-23253	9	31	is	be	AUX
brj-23253	9	32	significant	significant	ADJ
brj-23253	9	33	for	for	ADP
brj-23253	9	34	the	the	DET
brj-23253	9	35	sustainable	sustainable	ADJ
brj-23253	9	36	development	development	NOUN
brj-23253	9	37	of	of	ADP
brj-23253	9	38	agriculture	agriculture	NOUN
brj-23253	9	39	(	(	PUNCT
brj-23253	9	40	ye	ye	INTJ
brj-23253	9	41	et	et	PROPN
brj-23253	9	42	al	al	PROPN
brj-23253	9	43	.	.	PROPN
brj-23253	9	44	2022	2022	NUM
brj-23253	9	45	)	)	PUNCT
brj-23253	9	46	.	.	PUNCT
brj-23253	10	1	purple	purple	ADJ
brj-23253	10	2	alfalfa	alfalfa	NOUN
brj-23253	10	3	is	be	AUX
brj-23253	10	4	vital	vital	ADJ
brj-23253	10	5	in	in	ADP
brj-23253	10	6	the	the	DET
brj-23253	10	7	development	development	NOUN
brj-23253	10	8	of	of	ADP
brj-23253	10	9	grasslands	grassland	NOUN
brj-23253	10	10	and	and	CCONJ
brj-23253	10	11	livestock	livestock	NOUN
brj-23253	10	12	industries	industry	NOUN
brj-23253	10	13	,	,	PUNCT
brj-23253	10	14	especially	especially	ADV
brj-23253	10	15	in	in	ADP
brj-23253	10	16	arid	arid	NOUN
brj-23253	10	17	and	and	CCONJ
brj-23253	10	18	semi	semi	ADJ
brj-23253	10	19	-	-	ADJ
brj-23253	10	20	arid	arid	ADJ
brj-23253	10	21	regions	region	NOUN
brj-23253	10	22	(	(	PUNCT
brj-23253	10	23	cao	cao	PROPN
brj-23253	10	24	et	et	PROPN
brj-23253	10	25	al	al	PROPN
brj-23253	10	26	.	.	PROPN
brj-23253	10	27	2011	2011	NUM
brj-23253	10	28	)	)	PUNCT
brj-23253	10	29	.	.	PUNCT
brj-23253	11	1	alfalfa	alfalfa	PROPN
brj-23253	11	2	hay	hay	PROPN
brj-23253	11	3	is	be	AUX
brj-23253	11	4	a	a	DET
brj-23253	11	5	critical	critical	ADJ
brj-23253	11	6	feed	feed	NOUN
brj-23253	11	7	source	source	NOUN
brj-23253	11	8	for	for	ADP
brj-23253	11	9	dairy	dairy	NOUN
brj-23253	11	10	cows	cow	NOUN
brj-23253	11	11	and	and	CCONJ
brj-23253	11	12	other	other	ADJ
brj-23253	11	13	livestock	livestock	NOUN
brj-23253	11	14	,	,	PUNCT
brj-23253	11	15	significantly	significantly	ADV
brj-23253	11	16	impacting	impact	VERB
brj-23253	11	17	animal	animal	NOUN
brj-23253	11	18	health	health	NOUN
brj-23253	11	19	and	and	CCONJ
brj-23253	11	20	productivity	productivity	NOUN
brj-23253	11	21	due	due	ADP
brj-23253	11	22	to	to	ADP
brj-23253	11	23	its	its	PRON
brj-23253	11	24	protein	protein	NOUN
brj-23253	11	25	content	content	NOUN
brj-23253	11	26	(	(	PUNCT
brj-23253	11	27	fustini	fustini	PROPN
brj-23253	11	28	et	et	PROPN
brj-23253	11	29	al	al	PROPN
brj-23253	11	30	.	.	PROPN
brj-23253	11	31	2017	2017	NUM
brj-23253	11	32	)	)	PUNCT
brj-23253	11	33	.	.	PUNCT
brj-23253	12	1	improper	improper	ADJ
brj-23253	12	2	drying	dry	VERB
brj-23253	12	3	methods	method	NOUN
brj-23253	12	4	can	can	AUX
brj-23253	12	5	degrade	degrade	VERB
brj-23253	12	6	the	the	DET
brj-23253	12	7	nutritional	nutritional	ADJ
brj-23253	12	8	quality	quality	NOUN
brj-23253	12	9	of	of	ADP
brj-23253	12	10	alfalfa	alfalfa	NOUN
brj-23253	12	11	,	,	PUNCT
brj-23253	12	12	diminishing	diminish	VERB
brj-23253	12	13	its	its	PRON
brj-23253	12	14	feed	feed	NOUN
brj-23253	12	15	value	value	NOUN
brj-23253	12	16	and	and	CCONJ
brj-23253	12	17	potentially	potentially	ADV
brj-23253	12	18	leading	lead	VERB
brj-23253	12	19	to	to	ADP
brj-23253	12	20	livestock	livestock	NOUN
brj-23253	12	21	poisoning	poisoning	NOUN
brj-23253	12	22	,	,	PUNCT
brj-23253	12	23	affecting	affect	VERB
brj-23253	12	24	the	the	DET
brj-23253	12	25	quality	quality	NOUN
brj-23253	12	26	of	of	ADP
brj-23253	12	27	dairy	dairy	NOUN
brj-23253	12	28	products	product	NOUN
brj-23253	12	29	.	.	PUNCT
brj-23253	13	1	however	however	ADV
brj-23253	13	2	,	,	PUNCT
brj-23253	13	3	current	current	ADJ
brj-23253	13	4	methods	method	NOUN
brj-23253	13	5	for	for	ADP
brj-23253	13	6	assessing	assess	VERB
brj-23253	13	7	the	the	DET
brj-23253	13	8	protein	protein	NOUN
brj-23253	13	9	content	content	NOUN
brj-23253	13	10	in	in	ADP
brj-23253	13	11	alfalfa	alfalfa	PROPN
brj-23253	13	12	hay	hay	PROPN
brj-23253	13	13	have	have	VERB
brj-23253	13	14	limitations	limitation	NOUN
brj-23253	13	15	,	,	PUNCT
brj-23253	13	16	especially	especially	ADV
brj-23253	13	17	in	in	ADP
brj-23253	13	18	terms	term	NOUN
brj-23253	13	19	of	of	ADP
brj-23253	13	20	rapid	rapid	ADJ
brj-23253	13	21	and	and	CCONJ
brj-23253	13	22	non	non	ADJ
brj-23253	13	23	-	-	ADJ
brj-23253	13	24	destructive	destructive	ADJ
brj-23253	13	25	testing	testing	NOUN
brj-23253	13	26	.	.	PUNCT
brj-23253	14	1	therefore	therefore	ADV
brj-23253	14	2	,	,	PUNCT
brj-23253	14	3	developing	develop	VERB
brj-23253	14	4	a	a	DET
brj-23253	14	5	new	new	ADJ
brj-23253	14	6	technology	technology	NOUN
brj-23253	14	7	for	for	ADP
brj-23253	14	8	the	the	DET
brj-23253	14	9	quick	quick	ADJ
brj-23253	14	10	and	and	CCONJ
brj-23253	14	11	accurate	accurate	ADJ
brj-23253	14	12	assessment	assessment	NOUN
brj-23253	14	13	of	of	ADP
brj-23253	14	14	protein	protein	NOUN
brj-23253	14	15	content	content	NOUN
brj-23253	14	16	in	in	ADP
brj-23253	14	17	purple	purple	ADJ
brj-23253	14	18	alfalfa	alfalfa	NOUN
brj-23253	14	19	hay	hay	NOUN
brj-23253	14	20	is	be	AUX
brj-23253	14	21	particularly	particularly	ADV
brj-23253	14	22	important	important	ADJ
brj-23253	14	23	.	.	PUNCT
brj-23253	15	1	near	near	ADP
brj-23253	15	2	-	-	PUNCT
brj-23253	15	3	infrared	infrared	ADJ
brj-23253	15	4	spectroscopy	spectroscopy	NOUN
brj-23253	15	5	(	(	PUNCT
brj-23253	15	6	nir	nir	NOUN
brj-23253	15	7	)	)	PUNCT
brj-23253	15	8	is	be	AUX
brj-23253	15	9	a	a	DET
brj-23253	15	10	non	non	ADJ
brj-23253	15	11	-	-	ADJ
brj-23253	15	12	destructive	destructive	ADJ
brj-23253	15	13	analytical	analytical	ADJ
brj-23253	15	14	method	method	NOUN
brj-23253	15	15	capable	capable	ADJ
brj-23253	15	16	of	of	ADP
brj-23253	15	17	detecting	detect	VERB
brj-23253	15	18	different	different	ADJ
brj-23253	15	19	absorbance	absorbance	NOUN
brj-23253	15	20	frequencies	frequency	NOUN
brj-23253	15	21	of	of	ADP
brj-23253	15	22	specific	specific	ADJ
brj-23253	15	23	molecules	molecule	NOUN
brj-23253	15	24	within	within	ADP
brj-23253	15	25	substances	substance	NOUN
brj-23253	15	26	.	.	PUNCT
brj-23253	16	1	its	its	PRON
brj-23253	16	2	rapid	rapid	ADJ
brj-23253	16	3	and	and	CCONJ
brj-23253	16	4	non	non	ADJ
brj-23253	16	5	-	-	ADJ
brj-23253	16	6	destructive	destructive	ADJ
brj-23253	16	7	nature	nature	NOUN
brj-23253	16	8	makes	make	VERB
brj-23253	16	9	it	it	PRON
brj-23253	16	10	particularly	particularly	ADV
brj-23253	16	11	well	well	ADV
brj-23253	16	12	-	-	PUNCT
brj-23253	16	13	suited	suited	ADJ
brj-23253	16	14	for	for	ADP
brj-23253	16	15	analyzing	analyze	VERB
brj-23253	16	16	functional	functional	ADJ
brj-23253	16	17	peer	peer	NOUN
brj-23253	16	18	-	-	PUNCT
brj-23253	16	19	reviewed	review	VERB
brj-23253	16	20	article	article	NOUN
brj-23253	16	21	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	16	22	li	li	PROPN
brj-23253	16	23	et	et	PROPN
brj-23253	16	24	al	al	PROPN
brj-23253	16	25	.	.	PROPN
brj-23253	17	1	(	(	PUNCT
brj-23253	17	2	2024	2024	NUM
brj-23253	17	3	)	)	PUNCT
brj-23253	17	4	.	.	PUNCT
brj-23253	18	1	“	"	PUNCT
brj-23253	18	2	alfalfa	alfalfa	X
brj-23253	18	3	protein	protein	NOUN
brj-23253	18	4	with	with	ADP
brj-23253	18	5	vis	vis	X
brj-23253	18	6	/	/	SYM
brj-23253	18	7	nir	nir	NOUN
brj-23253	18	8	,	,	PUNCT
brj-23253	18	9	”	"	PUNCT
brj-23253	18	10	bioresources	bioresource	NOUN
brj-23253	18	11	19(2	19(2	NUM
brj-23253	18	12	)	)	PUNCT
brj-23253	18	13	,	,	PUNCT
brj-23253	18	14	3808	3808	NUM
brj-23253	18	15	-	-	SYM
brj-23253	18	16	3825	3825	NUM
brj-23253	18	17	.	.	PUNCT
brj-23253	19	1	3809	3809	NUM
brj-23253	19	2	groups	group	NOUN
brj-23253	19	3	in	in	ADP
brj-23253	19	4	proteins	protein	NOUN
brj-23253	19	5	,	,	PUNCT
brj-23253	19	6	such	such	ADJ
brj-23253	19	7	as	as	ADP
brj-23253	19	8	amide	amide	NOUN
brj-23253	19	9	groups	group	NOUN
brj-23253	19	10	,	,	PUNCT
brj-23253	19	11	whose	whose	DET
brj-23253	19	12	infrared	infrared	ADJ
brj-23253	19	13	absorbance	absorbance	NOUN
brj-23253	19	14	band	band	NOUN
brj-23253	19	15	characteristics	characteristic	NOUN
brj-23253	19	16	can	can	AUX
brj-23253	19	17	accurately	accurately	ADV
brj-23253	19	18	reflect	reflect	VERB
brj-23253	19	19	the	the	DET
brj-23253	19	20	content	content	NOUN
brj-23253	19	21	and	and	CCONJ
brj-23253	19	22	quality	quality	NOUN
brj-23253	19	23	of	of	ADP
brj-23253	19	24	proteins	protein	NOUN
brj-23253	19	25	(	(	PUNCT
brj-23253	19	26	huck	huck	NOUN
brj-23253	19	27	et	et	PROPN
brj-23253	19	28	al	al	PROPN
brj-23253	19	29	.	.	PROPN
brj-23253	19	30	2020	2020	NUM
brj-23253	19	31	)	)	PUNCT
brj-23253	19	32	.	.	PUNCT
brj-23253	20	1	by	by	ADP
brj-23253	20	2	measuring	measure	VERB
brj-23253	20	3	the	the	DET
brj-23253	20	4	interaction	interaction	NOUN
brj-23253	20	5	between	between	ADP
brj-23253	20	6	the	the	DET
brj-23253	20	7	sample	sample	NOUN
brj-23253	20	8	and	and	CCONJ
brj-23253	20	9	near	near	ADV
brj-23253	20	10	-	-	PUNCT
brj-23253	20	11	infrared	infrared	ADJ
brj-23253	20	12	light	light	NOUN
brj-23253	20	13	within	within	ADP
brj-23253	20	14	a	a	DET
brj-23253	20	15	specific	specific	ADJ
brj-23253	20	16	wavelength	wavelength	NOUN
brj-23253	20	17	range	range	NOUN
brj-23253	20	18	,	,	PUNCT
brj-23253	20	19	materials	material	NOUN
brj-23253	20	20	with	with	ADP
brj-23253	20	21	different	different	ADJ
brj-23253	20	22	components	component	NOUN
brj-23253	20	23	exhibit	exhibit	VERB
brj-23253	20	24	unique	unique	ADJ
brj-23253	20	25	spectral	spectral	ADJ
brj-23253	20	26	features	feature	NOUN
brj-23253	20	27	regarding	regard	VERB
brj-23253	20	28	light	light	ADJ
brj-23253	20	29	absorption	absorption	NOUN
brj-23253	20	30	,	,	PUNCT
brj-23253	20	31	scattering	scattering	NOUN
brj-23253	20	32	,	,	PUNCT
brj-23253	20	33	and	and	CCONJ
brj-23253	20	34	reflection	reflection	NOUN
brj-23253	20	35	.	.	PUNCT
brj-23253	21	1	the	the	DET
brj-23253	21	2	varying	vary	VERB
brj-23253	21	3	concentrations	concentration	NOUN
brj-23253	21	4	of	of	ADP
brj-23253	21	5	the	the	DET
brj-23253	21	6	same	same	ADJ
brj-23253	21	7	component	component	NOUN
brj-23253	21	8	are	be	AUX
brj-23253	21	9	indicated	indicate	VERB
brj-23253	21	10	by	by	ADP
brj-23253	21	11	different	different	ADJ
brj-23253	21	12	intensities	intensity	NOUN
brj-23253	21	13	of	of	ADP
brj-23253	21	14	characteristic	characteristic	ADJ
brj-23253	21	15	absorbance	absorbance	NOUN
brj-23253	21	16	peaks	peak	NOUN
brj-23253	21	17	(	(	PUNCT
brj-23253	21	18	hell	hell	INTJ
brj-23253	21	19	et	et	PROPN
brj-23253	21	20	al	al	PROPN
brj-23253	21	21	.	.	PROPN
brj-23253	21	22	2016	2016	NUM
brj-23253	21	23	)	)	PUNCT
brj-23253	21	24	.	.	PUNCT
brj-23253	22	1	analyzing	analyze	VERB
brj-23253	22	2	these	these	DET
brj-23253	22	3	features	feature	NOUN
brj-23253	22	4	provides	provide	VERB
brj-23253	22	5	chemical	chemical	NOUN
brj-23253	22	6	and	and	CCONJ
brj-23253	22	7	physical	physical	ADJ
brj-23253	22	8	information	information	NOUN
brj-23253	22	9	about	about	ADP
brj-23253	22	10	agricultural	agricultural	ADJ
brj-23253	22	11	products	product	NOUN
brj-23253	22	12	.	.	PUNCT
brj-23253	23	1	recently	recently	ADV
brj-23253	23	2	,	,	PUNCT
brj-23253	23	3	nir	nir	ADJ
brj-23253	23	4	spectroscopy	spectroscopy	NOUN
brj-23253	23	5	has	have	AUX
brj-23253	23	6	been	be	AUX
brj-23253	23	7	applied	apply	VERB
brj-23253	23	8	in	in	ADP
brj-23253	23	9	various	various	ADJ
brj-23253	23	10	fields	field	NOUN
brj-23253	23	11	,	,	PUNCT
brj-23253	23	12	including	include	VERB
brj-23253	23	13	food	food	NOUN
brj-23253	23	14	,	,	PUNCT
brj-23253	23	15	pharmaceutical	pharmaceutical	NOUN
brj-23253	23	16	,	,	PUNCT
brj-23253	23	17	and	and	CCONJ
brj-23253	23	18	chemical	chemical	NOUN
brj-23253	23	19	engineering	engineering	NOUN
brj-23253	23	20	(	(	PUNCT
brj-23253	23	21	lopes	lope	NOUN
brj-23253	23	22	et	et	PROPN
brj-23253	23	23	al	al	PROPN
brj-23253	23	24	.	.	PROPN
brj-23253	23	25	2015	2015	NUM
brj-23253	23	26	)	)	PUNCT
brj-23253	23	27	,	,	PUNCT
brj-23253	23	28	tea	tea	NOUN
brj-23253	23	29	(	(	PUNCT
brj-23253	23	30	shen	shen	PROPN
brj-23253	23	31	et	et	PROPN
brj-23253	23	32	al	al	PROPN
brj-23253	23	33	.	.	PROPN
brj-23253	23	34	2022	2022	NUM
brj-23253	23	35	)	)	PUNCT
brj-23253	23	36	,	,	PUNCT
brj-23253	23	37	wood	wood	NOUN
brj-23253	23	38	(	(	PUNCT
brj-23253	23	39	acuna	acuna	PROPN
brj-23253	23	40	-	-	PUNCT
brj-23253	23	41	gutierrez	gutierrez	PROPN
brj-23253	23	42	et	et	PROPN
brj-23253	23	43	al	al	PROPN
brj-23253	23	44	.	.	PROPN
brj-23253	23	45	2021	2021	NUM
brj-23253	23	46	)	)	PUNCT
brj-23253	23	47	,	,	PUNCT
brj-23253	23	48	and	and	CCONJ
brj-23253	23	49	feed	feed	NOUN
brj-23253	23	50	.	.	PUNCT
brj-23253	24	1	there	there	PRON
brj-23253	24	2	are	be	VERB
brj-23253	24	3	also	also	ADV
brj-23253	24	4	reports	report	NOUN
brj-23253	24	5	of	of	ADP
brj-23253	24	6	using	use	VERB
brj-23253	24	7	infrared	infrared	ADJ
brj-23253	24	8	spectroscopy	spectroscopy	NOUN
brj-23253	24	9	to	to	PART
brj-23253	24	10	detect	detect	VERB
brj-23253	24	11	molds	mold	NOUN
brj-23253	24	12	in	in	ADP
brj-23253	24	13	food	food	NOUN
brj-23253	24	14	(	(	PUNCT
brj-23253	24	15	ma	ma	PROPN
brj-23253	24	16	et	et	PROPN
brj-23253	24	17	al	al	PROPN
brj-23253	24	18	.	.	PROPN
brj-23253	24	19	2023	2023	NUM
brj-23253	24	20	)	)	PUNCT
brj-23253	24	21	.	.	PUNCT
brj-23253	25	1	near	near	ADV
brj-23253	25	2	-	-	PUNCT
brj-23253	25	3	infrared	infrared	ADJ
brj-23253	25	4	(	(	PUNCT
brj-23253	25	5	nir	nir	NOUN
brj-23253	25	6	)	)	PUNCT
brj-23253	25	7	spectroscopy	spectroscopy	NOUN
brj-23253	25	8	has	have	AUX
brj-23253	25	9	been	be	AUX
brj-23253	25	10	employed	employ	VERB
brj-23253	25	11	in	in	ADP
brj-23253	25	12	the	the	DET
brj-23253	25	13	grain	grain	NOUN
brj-23253	25	14	and	and	CCONJ
brj-23253	25	15	feed	feed	NOUN
brj-23253	25	16	industries	industry	NOUN
brj-23253	25	17	to	to	PART
brj-23253	25	18	determine	determine	VERB
brj-23253	25	19	the	the	DET
brj-23253	25	20	content	content	NOUN
brj-23253	25	21	of	of	ADP
brj-23253	25	22	moisture	moisture	NOUN
brj-23253	25	23	,	,	PUNCT
brj-23253	25	24	protein	protein	NOUN
brj-23253	25	25	,	,	PUNCT
brj-23253	25	26	fiber	fiber	NOUN
brj-23253	25	27	,	,	PUNCT
brj-23253	25	28	and	and	CCONJ
brj-23253	25	29	fat	fat	ADJ
brj-23253	25	30	.	.	PUNCT
brj-23253	26	1	by	by	ADP
brj-23253	26	2	establishing	establish	VERB
brj-23253	26	3	a	a	DET
brj-23253	26	4	relationship	relationship	NOUN
brj-23253	26	5	model	model	NOUN
brj-23253	26	6	between	between	ADP
brj-23253	26	7	the	the	DET
brj-23253	26	8	moisture	moisture	NOUN
brj-23253	26	9	content	content	NOUN
brj-23253	26	10	and	and	CCONJ
brj-23253	26	11	the	the	DET
brj-23253	26	12	spectral	spectral	ADJ
brj-23253	26	13	characteristics	characteristic	NOUN
brj-23253	26	14	of	of	ADP
brj-23253	26	15	samples	sample	NOUN
brj-23253	26	16	,	,	PUNCT
brj-23253	26	17	the	the	DET
brj-23253	26	18	moisture	moisture	NOUN
brj-23253	26	19	content	content	NOUN
brj-23253	26	20	in	in	ADP
brj-23253	26	21	grains	grain	NOUN
brj-23253	26	22	and	and	CCONJ
brj-23253	26	23	feeds	feed	NOUN
brj-23253	26	24	can	can	AUX
brj-23253	26	25	be	be	AUX
brj-23253	26	26	determined	determine	VERB
brj-23253	26	27	,	,	PUNCT
brj-23253	26	28	providing	provide	VERB
brj-23253	26	29	a	a	DET
brj-23253	26	30	method	method	NOUN
brj-23253	26	31	for	for	ADP
brj-23253	26	32	the	the	DET
brj-23253	26	33	rapid	rapid	ADJ
brj-23253	26	34	and	and	CCONJ
brj-23253	26	35	accurate	accurate	ADJ
brj-23253	26	36	assessment	assessment	NOUN
brj-23253	26	37	of	of	ADP
brj-23253	26	38	their	their	PRON
brj-23253	26	39	dryness	dryness	NOUN
brj-23253	26	40	(	(	PUNCT
brj-23253	26	41	phetpan	phetpan	NOUN
brj-23253	26	42	2019	2019	NUM
brj-23253	26	43	)	)	PUNCT
brj-23253	26	44	.	.	PUNCT
brj-23253	27	1	due	due	ADP
brj-23253	27	2	to	to	ADP
brj-23253	27	3	their	their	PRON
brj-23253	27	4	chemical	chemical	NOUN
brj-23253	27	5	bond	bond	NOUN
brj-23253	27	6	structures	structure	NOUN
brj-23253	27	7	,	,	PUNCT
brj-23253	27	8	proteins	protein	NOUN
brj-23253	27	9	produce	produce	VERB
brj-23253	27	10	specific	specific	ADJ
brj-23253	27	11	spectral	spectral	ADJ
brj-23253	27	12	features	feature	NOUN
brj-23253	27	13	in	in	ADP
brj-23253	27	14	light	light	ADJ
brj-23253	27	15	absorption	absorption	NOUN
brj-23253	27	16	and	and	CCONJ
brj-23253	27	17	scattering	scattering	NOUN
brj-23253	27	18	,	,	PUNCT
brj-23253	27	19	enabling	enable	VERB
brj-23253	27	20	the	the	DET
brj-23253	27	21	prediction	prediction	NOUN
brj-23253	27	22	of	of	ADP
brj-23253	27	23	protein	protein	NOUN
brj-23253	27	24	content	content	NOUN
brj-23253	27	25	in	in	ADP
brj-23253	27	26	grains	grain	NOUN
brj-23253	27	27	and	and	CCONJ
brj-23253	27	28	feeds	feed	NOUN
brj-23253	27	29	,	,	PUNCT
brj-23253	27	30	which	which	PRON
brj-23253	27	31	is	be	AUX
brj-23253	27	32	crucial	crucial	ADJ
brj-23253	27	33	for	for	ADP
brj-23253	27	34	feed	feed	NOUN
brj-23253	27	35	production	production	NOUN
brj-23253	27	36	and	and	CCONJ
brj-23253	27	37	grain	grain	NOUN
brj-23253	27	38	quality	quality	NOUN
brj-23253	27	39	control	control	NOUN
brj-23253	27	40	(	(	PUNCT
brj-23253	27	41	masithoh	masithoh	NOUN
brj-23253	27	42	et	et	NOUN
brj-23253	27	43	al	al	PROPN
brj-23253	27	44	.	.	PROPN
brj-23253	27	45	2020	2020	NUM
brj-23253	27	46	)	)	PUNCT
brj-23253	27	47	.	.	PUNCT
brj-23253	28	1	nir	nir	ADJ
brj-23253	28	2	spectroscopy	spectroscopy	NOUN
brj-23253	28	3	can	can	AUX
brj-23253	28	4	determine	determine	VERB
brj-23253	28	5	fiber	fiber	NOUN
brj-23253	28	6	content	content	NOUN
brj-23253	28	7	through	through	ADP
brj-23253	28	8	specific	specific	ADJ
brj-23253	28	9	spectral	spectral	ADJ
brj-23253	28	10	responses	response	NOUN
brj-23253	28	11	generated	generate	VERB
brj-23253	28	12	by	by	ADP
brj-23253	28	13	the	the	DET
brj-23253	28	14	fiber	fiber	NOUN
brj-23253	28	15	components	component	NOUN
brj-23253	28	16	in	in	ADP
brj-23253	28	17	samples	sample	NOUN
brj-23253	28	18	(	(	PUNCT
brj-23253	28	19	chen	chen	PROPN
brj-23253	28	20	et	et	PROPN
brj-23253	28	21	al	al	PROPN
brj-23253	28	22	.	.	PROPN
brj-23253	28	23	2020	2020	NUM
brj-23253	28	24	)	)	PUNCT
brj-23253	28	25	.	.	PUNCT
brj-23253	29	1	similarly	similarly	ADV
brj-23253	29	2	,	,	PUNCT
brj-23253	29	3	it	it	PRON
brj-23253	29	4	can	can	AUX
brj-23253	29	5	rapidly	rapidly	ADV
brj-23253	29	6	determine	determine	VERB
brj-23253	29	7	fat	fat	ADJ
brj-23253	29	8	content	content	NOUN
brj-23253	29	9	by	by	ADP
brj-23253	29	10	utilizing	utilize	VERB
brj-23253	29	11	the	the	DET
brj-23253	29	12	optical	optical	ADJ
brj-23253	29	13	properties	property	NOUN
brj-23253	29	14	of	of	ADP
brj-23253	29	15	fats	fat	NOUN
brj-23253	29	16	,	,	PUNCT
brj-23253	29	17	offering	offer	VERB
brj-23253	29	18	key	key	ADJ
brj-23253	29	19	data	datum	NOUN
brj-23253	29	20	support	support	NOUN
brj-23253	29	21	for	for	ADP
brj-23253	29	22	feed	feed	NOUN
brj-23253	29	23	formulation	formulation	NOUN
brj-23253	29	24	(	(	PUNCT
brj-23253	29	25	bilal	bilal	PROPN
brj-23253	29	26	et	et	PROPN
brj-23253	29	27	al	al	PROPN
brj-23253	29	28	.	.	PROPN
brj-23253	29	29	2020	2020	NUM
brj-23253	29	30	)	)	PUNCT
brj-23253	29	31	.	.	PUNCT
brj-23253	30	1	discovering	discover	VERB
brj-23253	30	2	the	the	DET
brj-23253	30	3	links	link	NOUN
brj-23253	30	4	and	and	CCONJ
brj-23253	30	5	patterns	pattern	NOUN
brj-23253	30	6	between	between	ADP
brj-23253	30	7	nir	nir	ADJ
brj-23253	30	8	spectroscopy	spectroscopy	NOUN
brj-23253	30	9	and	and	CCONJ
brj-23253	30	10	agricultural	agricultural	ADJ
brj-23253	30	11	products	product	NOUN
brj-23253	30	12	allows	allow	VERB
brj-23253	30	13	for	for	ADP
brj-23253	30	14	the	the	DET
brj-23253	30	15	practical	practical	ADJ
brj-23253	30	16	analysis	analysis	NOUN
brj-23253	30	17	and	and	CCONJ
brj-23253	30	18	detection	detection	NOUN
brj-23253	30	19	of	of	ADP
brj-23253	30	20	agricultural	agricultural	ADJ
brj-23253	30	21	product	product	NOUN
brj-23253	30	22	quality	quality	NOUN
brj-23253	30	23	(	(	PUNCT
brj-23253	30	24	cortés	cortés	NOUN
brj-23253	30	25	et	et	PROPN
brj-23253	30	26	al	al	PROPN
brj-23253	30	27	.	.	PROPN
brj-23253	30	28	2019	2019	NUM
brj-23253	30	29	)	)	PUNCT
brj-23253	30	30	.	.	PUNCT
brj-23253	31	1	with	with	ADP
brj-23253	31	2	the	the	DET
brj-23253	31	3	rapid	rapid	ADJ
brj-23253	31	4	advancement	advancement	NOUN
brj-23253	31	5	of	of	ADP
brj-23253	31	6	computer	computer	NOUN
brj-23253	31	7	science	science	NOUN
brj-23253	31	8	and	and	CCONJ
brj-23253	31	9	artificial	artificial	ADJ
brj-23253	31	10	intelligence	intelligence	NOUN
brj-23253	31	11	,	,	PUNCT
brj-23253	31	12	machine	machine	NOUN
brj-23253	31	13	learning	learning	NOUN
brj-23253	31	14	algorithms	algorithm	NOUN
brj-23253	31	15	are	be	AUX
brj-23253	31	16	increasingly	increasingly	ADV
brj-23253	31	17	applied	apply	VERB
brj-23253	31	18	to	to	ADP
brj-23253	31	19	the	the	DET
brj-23253	31	20	processing	processing	NOUN
brj-23253	31	21	of	of	ADP
brj-23253	31	22	near	near	ADV
brj-23253	31	23	-	-	PUNCT
brj-23253	31	24	infrared	infrared	ADJ
brj-23253	31	25	(	(	PUNCT
brj-23253	31	26	nir	nir	NOUN
brj-23253	31	27	)	)	PUNCT
brj-23253	31	28	spectroscopy	spectroscopy	NOUN
brj-23253	31	29	data	datum	NOUN
brj-23253	31	30	.	.	PUNCT
brj-23253	32	1	as	as	ADP
brj-23253	32	2	a	a	DET
brj-23253	32	3	non	non	ADJ
brj-23253	32	4	-	-	ADJ
brj-23253	32	5	destructive	destructive	ADJ
brj-23253	32	6	analytical	analytical	ADJ
brj-23253	32	7	technique	technique	NOUN
brj-23253	32	8	,	,	PUNCT
brj-23253	32	9	nir	nir	ADJ
brj-23253	32	10	spectroscopy	spectroscopy	NOUN
brj-23253	32	11	obtains	obtain	VERB
brj-23253	32	12	chemical	chemical	ADJ
brj-23253	32	13	and	and	CCONJ
brj-23253	32	14	physical	physical	ADJ
brj-23253	32	15	information	information	NOUN
brj-23253	32	16	about	about	ADP
brj-23253	32	17	samples	sample	NOUN
brj-23253	32	18	by	by	ADP
brj-23253	32	19	measuring	measure	VERB
brj-23253	32	20	their	their	PRON
brj-23253	32	21	absorbance	absorbance	NOUN
brj-23253	32	22	and	and	CCONJ
brj-23253	32	23	scattering	scatter	VERB
brj-23253	32	24	spectra	spectra	NOUN
brj-23253	32	25	(	(	PUNCT
brj-23253	32	26	mishra	mishra	PROPN
brj-23253	32	27	et	et	PROPN
brj-23253	32	28	al	al	PROPN
brj-23253	32	29	.	.	PROPN
brj-23253	32	30	2019	2019	NUM
brj-23253	32	31	;	;	PUNCT
brj-23253	32	32	cortés	cortés	NOUN
brj-23253	32	33	et	et	PROPN
brj-23253	32	34	al	al	PROPN
brj-23253	32	35	.	.	PROPN
brj-23253	32	36	2019	2019	NUM
brj-23253	32	37	;	;	PUNCT
brj-23253	32	38	zhang	zhang	PROPN
brj-23253	32	39	et	et	PROPN
brj-23253	32	40	al	al	PROPN
brj-23253	32	41	.	.	PROPN
brj-23253	32	42	2020	2020	NUM
brj-23253	32	43	)	)	PUNCT
brj-23253	32	44	.	.	PUNCT
brj-23253	33	1	the	the	DET
brj-23253	33	2	application	application	NOUN
brj-23253	33	3	of	of	ADP
brj-23253	33	4	machine	machine	NOUN
brj-23253	33	5	learning	learning	NOUN
brj-23253	33	6	in	in	ADP
brj-23253	33	7	the	the	DET
brj-23253	33	8	analysis	analysis	NOUN
brj-23253	33	9	and	and	CCONJ
brj-23253	33	10	modeling	modeling	NOUN
brj-23253	33	11	of	of	ADP
brj-23253	33	12	nir	nir	ADJ
brj-23253	33	13	spectroscopy	spectroscopy	NOUN
brj-23253	33	14	data	datum	NOUN
brj-23253	33	15	can	can	AUX
brj-23253	33	16	achieve	achieve	VERB
brj-23253	33	17	various	various	ADJ
brj-23253	33	18	objectives	objective	NOUN
brj-23253	33	19	,	,	PUNCT
brj-23253	33	20	such	such	ADJ
brj-23253	33	21	as	as	ADP
brj-23253	33	22	prediction	prediction	NOUN
brj-23253	33	23	and	and	CCONJ
brj-23253	33	24	classification	classification	NOUN
brj-23253	33	25	(	(	PUNCT
brj-23253	33	26	ciza	ciza	NOUN
brj-23253	33	27	et	et	PROPN
brj-23253	33	28	al	al	PROPN
brj-23253	33	29	.	.	PROPN
brj-23253	33	30	2019	2019	NUM
brj-23253	33	31	)	)	PUNCT
brj-23253	33	32	,	,	PUNCT
brj-23253	33	33	feature	feature	NOUN
brj-23253	33	34	extraction	extraction	NOUN
brj-23253	33	35	and	and	CCONJ
brj-23253	33	36	dimensionality	dimensionality	NOUN
brj-23253	33	37	reduction	reduction	NOUN
brj-23253	33	38	,	,	PUNCT
brj-23253	33	39	anomaly	anomaly	NOUN
brj-23253	33	40	detection	detection	NOUN
brj-23253	33	41	,	,	PUNCT
brj-23253	33	42	and	and	CCONJ
brj-23253	33	43	quality	quality	NOUN
brj-23253	33	44	control	control	NOUN
brj-23253	33	45	(	(	PUNCT
brj-23253	33	46	gao	gao	PROPN
brj-23253	33	47	et	et	PROPN
brj-23253	33	48	al	al	PROPN
brj-23253	33	49	.	.	PROPN
brj-23253	33	50	2018	2018	NUM
brj-23253	33	51	)	)	PUNCT
brj-23253	33	52	,	,	PUNCT
brj-23253	33	53	significantly	significantly	ADV
brj-23253	33	54	enhancing	enhance	VERB
brj-23253	33	55	the	the	DET
brj-23253	33	56	efficiency	efficiency	NOUN
brj-23253	33	57	and	and	CCONJ
brj-23253	33	58	accuracy	accuracy	NOUN
brj-23253	33	59	of	of	ADP
brj-23253	33	60	material	material	NOUN
brj-23253	33	61	analysis	analysis	NOUN
brj-23253	33	62	and	and	CCONJ
brj-23253	33	63	testing	testing	NOUN
brj-23253	33	64	.	.	PUNCT
brj-23253	34	1	in	in	ADP
brj-23253	34	2	the	the	DET
brj-23253	34	3	construction	construction	NOUN
brj-23253	34	4	of	of	ADP
brj-23253	34	5	a	a	DET
brj-23253	34	6	quantitative	quantitative	ADJ
brj-23253	34	7	detection	detection	NOUN
brj-23253	34	8	model	model	NOUN
brj-23253	34	9	for	for	ADP
brj-23253	34	10	purple	purple	ADJ
brj-23253	34	11	alfalfa	alfalfa	NOUN
brj-23253	34	12	,	,	PUNCT
brj-23253	34	13	the	the	DET
brj-23253	34	14	preprocessing	preprocessing	NOUN
brj-23253	34	15	of	of	ADP
brj-23253	34	16	spectral	spectral	ADJ
brj-23253	34	17	data	datum	NOUN
brj-23253	34	18	and	and	CCONJ
brj-23253	34	19	the	the	DET
brj-23253	34	20	extraction	extraction	NOUN
brj-23253	34	21	of	of	ADP
brj-23253	34	22	characteristic	characteristic	ADJ
brj-23253	34	23	wavelengths	wavelength	NOUN
brj-23253	34	24	are	be	AUX
brj-23253	34	25	of	of	ADP
brj-23253	34	26	paramount	paramount	ADJ
brj-23253	34	27	importance	importance	NOUN
brj-23253	34	28	.	.	PUNCT
brj-23253	35	1	effective	effective	ADJ
brj-23253	35	2	preprocessing	preprocessing	NOUN
brj-23253	35	3	of	of	ADP
brj-23253	35	4	spectral	spectral	ADJ
brj-23253	35	5	data	datum	NOUN
brj-23253	35	6	can	can	AUX
brj-23253	35	7	eliminate	eliminate	VERB
brj-23253	35	8	or	or	CCONJ
brj-23253	35	9	reduce	reduce	VERB
brj-23253	35	10	the	the	DET
brj-23253	35	11	interference	interference	NOUN
brj-23253	35	12	from	from	ADP
brj-23253	35	13	non	non	ADJ
brj-23253	35	14	-	-	ADJ
brj-23253	35	15	target	target	ADJ
brj-23253	35	16	factors	factor	NOUN
brj-23253	35	17	such	such	ADJ
brj-23253	35	18	as	as	ADP
brj-23253	35	19	noise	noise	NOUN
brj-23253	35	20	and	and	CCONJ
brj-23253	35	21	baseline	baseline	NOUN
brj-23253	35	22	drift	drift	NOUN
brj-23253	35	23	,	,	PUNCT
brj-23253	35	24	thereby	thereby	ADV
brj-23253	35	25	enhancing	enhance	VERB
brj-23253	35	26	the	the	DET
brj-23253	35	27	accuracy	accuracy	NOUN
brj-23253	35	28	and	and	CCONJ
brj-23253	35	29	repeatability	repeatability	NOUN
brj-23253	35	30	of	of	ADP
brj-23253	35	31	the	the	DET
brj-23253	35	32	analysis	analysis	NOUN
brj-23253	35	33	(	(	PUNCT
brj-23253	35	34	saly	saly	PROPN
brj-23253	35	35	et	et	PROPN
brj-23253	35	36	al	al	PROPN
brj-23253	35	37	.	.	PROPN
brj-23253	35	38	2010	2010	NUM
brj-23253	35	39	)	)	PUNCT
brj-23253	35	40	.	.	PUNCT
brj-23253	36	1	feature	feature	NOUN
brj-23253	36	2	extraction	extraction	NOUN
brj-23253	36	3	plays	play	VERB
brj-23253	36	4	a	a	DET
brj-23253	36	5	crucial	crucial	ADJ
brj-23253	36	6	role	role	NOUN
brj-23253	36	7	in	in	ADP
brj-23253	36	8	identifying	identify	VERB
brj-23253	36	9	the	the	DET
brj-23253	36	10	most	most	ADV
brj-23253	36	11	representative	representative	ADJ
brj-23253	36	12	and	and	CCONJ
brj-23253	36	13	relevant	relevant	ADJ
brj-23253	36	14	information	information	NOUN
brj-23253	36	15	from	from	ADP
brj-23253	36	16	complex	complex	ADJ
brj-23253	36	17	spectral	spectral	ADJ
brj-23253	36	18	data	datum	NOUN
brj-23253	36	19	,	,	PUNCT
brj-23253	36	20	pinpointing	pinpoint	VERB
brj-23253	36	21	the	the	DET
brj-23253	36	22	characteristic	characteristic	ADJ
brj-23253	36	23	wavelengths	wavelength	NOUN
brj-23253	36	24	closely	closely	ADV
brj-23253	36	25	related	relate	VERB
brj-23253	36	26	to	to	ADP
brj-23253	36	27	protein	protein	NOUN
brj-23253	36	28	content	content	NOUN
brj-23253	36	29	with	with	ADP
brj-23253	36	30	greater	great	ADJ
brj-23253	36	31	precision	precision	NOUN
brj-23253	36	32	(	(	PUNCT
brj-23253	36	33	zhang	zhang	X
brj-23253	36	34	et	et	PROPN
brj-23253	36	35	al	al	PROPN
brj-23253	36	36	.	.	PROPN
brj-23253	36	37	2023	2023	NUM
brj-23253	36	38	)	)	PUNCT
brj-23253	36	39	.	.	PUNCT
brj-23253	37	1	these	these	DET
brj-23253	37	2	characteristic	characteristic	ADJ
brj-23253	37	3	wavelengths	wavelength	NOUN
brj-23253	37	4	are	be	AUX
brj-23253	37	5	key	key	ADJ
brj-23253	37	6	to	to	ADP
brj-23253	37	7	building	build	VERB
brj-23253	37	8	a	a	DET
brj-23253	37	9	high	high	ADJ
brj-23253	37	10	-	-	PUNCT
brj-23253	37	11	accuracy	accuracy	NOUN
brj-23253	37	12	quantitative	quantitative	ADJ
brj-23253	37	13	detection	detection	NOUN
brj-23253	37	14	model	model	NOUN
brj-23253	37	15	.	.	PUNCT
brj-23253	38	1	compared	compare	VERB
brj-23253	38	2	to	to	ADP
brj-23253	38	3	conventional	conventional	ADJ
brj-23253	38	4	models	model	NOUN
brj-23253	38	5	,	,	PUNCT
brj-23253	38	6	models	model	NOUN
brj-23253	38	7	employing	employ	VERB
brj-23253	38	8	characteristic	characteristic	ADJ
brj-23253	38	9	wavelengths	wavelength	NOUN
brj-23253	38	10	significantly	significantly	ADV
brj-23253	38	11	improve	improve	VERB
brj-23253	38	12	precision	precision	NOUN
brj-23253	38	13	and	and	CCONJ
brj-23253	38	14	efficiency	efficiency	NOUN
brj-23253	38	15	by	by	ADP
brj-23253	38	16	precisely	precisely	ADV
brj-23253	38	17	identifying	identify	VERB
brj-23253	38	18	specific	specific	ADJ
brj-23253	38	19	wavelengths	wavelength	NOUN
brj-23253	38	20	closely	closely	ADV
brj-23253	38	21	associated	associate	VERB
brj-23253	38	22	with	with	ADP
brj-23253	38	23	target	target	NOUN
brj-23253	38	24	attributes	attribute	NOUN
brj-23253	38	25	,	,	PUNCT
brj-23253	38	26	such	such	ADJ
brj-23253	38	27	as	as	ADP
brj-23253	38	28	protein	protein	NOUN
brj-23253	38	29	content	content	NOUN
brj-23253	38	30	.	.	PUNCT
brj-23253	39	1	this	this	DET
brj-23253	39	2	approach	approach	NOUN
brj-23253	39	3	reduces	reduce	VERB
brj-23253	39	4	the	the	DET
brj-23253	39	5	need	need	NOUN
brj-23253	39	6	to	to	PART
brj-23253	39	7	process	process	VERB
brj-23253	39	8	redundant	redundant	ADJ
brj-23253	39	9	information	information	NOUN
brj-23253	39	10	,	,	PUNCT
brj-23253	39	11	allowing	allow	VERB
brj-23253	39	12	the	the	DET
brj-23253	39	13	model	model	NOUN
brj-23253	39	14	to	to	PART
brj-23253	39	15	focus	focus	VERB
brj-23253	39	16	more	more	ADV
brj-23253	39	17	on	on	ADP
brj-23253	39	18	key	key	ADJ
brj-23253	39	19	data	datum	NOUN
brj-23253	39	20	.	.	PUNCT
brj-23253	40	1	consequently	consequently	ADV
brj-23253	40	2	,	,	PUNCT
brj-23253	40	3	under	under	ADP
brj-23253	40	4	similar	similar	ADJ
brj-23253	40	5	conditions	condition	NOUN
brj-23253	40	6	,	,	PUNCT
brj-23253	40	7	it	it	PRON
brj-23253	40	8	achieves	achieve	VERB
brj-23253	40	9	higher	high	ADJ
brj-23253	40	10	predictive	predictive	ADJ
brj-23253	40	11	performance	performance	NOUN
brj-23253	40	12	and	and	CCONJ
brj-23253	40	13	stability	stability	NOUN
brj-23253	40	14	with	with	ADP
brj-23253	40	15	lower	low	ADJ
brj-23253	40	16	peer	peer	NOUN
brj-23253	40	17	-	-	PUNCT
brj-23253	40	18	reviewed	review	VERB
brj-23253	40	19	article	article	NOUN
brj-23253	40	20	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	40	21	li	li	PROPN
brj-23253	40	22	et	et	PROPN
brj-23253	40	23	al	al	PROPN
brj-23253	40	24	.	.	PROPN
brj-23253	41	1	(	(	PUNCT
brj-23253	41	2	2024	2024	NUM
brj-23253	41	3	)	)	PUNCT
brj-23253	41	4	.	.	PUNCT
brj-23253	42	1	“	"	PUNCT
brj-23253	42	2	alfalfa	alfalfa	X
brj-23253	42	3	protein	protein	NOUN
brj-23253	42	4	with	with	ADP
brj-23253	42	5	vis	vis	X
brj-23253	42	6	/	/	SYM
brj-23253	42	7	nir	nir	NOUN
brj-23253	42	8	,	,	PUNCT
brj-23253	42	9	”	"	PUNCT
brj-23253	42	10	bioresources	bioresource	NOUN
brj-23253	42	11	19(2	19(2	NUM
brj-23253	42	12	)	)	PUNCT
brj-23253	42	13	,	,	PUNCT
brj-23253	42	14	3808	3808	NUM
brj-23253	42	15	-	-	SYM
brj-23253	42	16	3825	3825	NUM
brj-23253	42	17	.	.	PUNCT
brj-23253	43	1	3810	3810	NUM
brj-23253	43	2	computational	computational	ADJ
brj-23253	43	3	costs	cost	NOUN
brj-23253	43	4	(	(	PUNCT
brj-23253	43	5	li	li	PROPN
brj-23253	43	6	et	et	PROPN
brj-23253	43	7	al	al	PROPN
brj-23253	43	8	.	.	PROPN
brj-23253	43	9	2023	2023	NUM
brj-23253	43	10	)	)	PUNCT
brj-23253	43	11	.	.	PUNCT
brj-23253	44	1	this	this	DET
brj-23253	44	2	study	study	NOUN
brj-23253	44	3	employed	employ	VERB
brj-23253	44	4	a	a	DET
brj-23253	44	5	variety	variety	NOUN
brj-23253	44	6	of	of	ADP
brj-23253	44	7	machine	machine	NOUN
brj-23253	44	8	learning	learning	NOUN
brj-23253	44	9	algorithms	algorithm	NOUN
brj-23253	44	10	,	,	PUNCT
brj-23253	44	11	including	include	VERB
brj-23253	44	12	extreme	extreme	ADJ
brj-23253	44	13	learning	learning	NOUN
brj-23253	44	14	machine	machine	NOUN
brj-23253	44	15	(	(	PUNCT
brj-23253	44	16	elm	elm	PROPN
brj-23253	44	17	)	)	PUNCT
brj-23253	44	18	,	,	PUNCT
brj-23253	44	19	partial	partial	ADJ
brj-23253	44	20	least	least	ADJ
brj-23253	44	21	squares	square	NOUN
brj-23253	44	22	regression	regression	NOUN
brj-23253	44	23	(	(	PUNCT
brj-23253	44	24	plsr	plsr	PROPN
brj-23253	44	25	)	)	PUNCT
brj-23253	44	26	,	,	PUNCT
brj-23253	44	27	support	support	VERB
brj-23253	44	28	vector	vector	NOUN
brj-23253	44	29	machine	machine	NOUN
brj-23253	44	30	(	(	PUNCT
brj-23253	44	31	svm	svm	PROPN
brj-23253	44	32	)	)	PUNCT
brj-23253	44	33	,	,	PUNCT
brj-23253	44	34	and	and	CCONJ
brj-23253	44	35	long	long	ADJ
brj-23253	44	36	short	short	ADJ
brj-23253	44	37	-	-	PUNCT
brj-23253	44	38	term	term	NOUN
brj-23253	44	39	memory	memory	NOUN
brj-23253	44	40	(	(	PUNCT
brj-23253	44	41	lstm	lstm	NOUN
brj-23253	44	42	)	)	PUNCT
brj-23253	44	43	networks	network	NOUN
brj-23253	44	44	.	.	PUNCT
brj-23253	45	1	the	the	DET
brj-23253	45	2	elm	elm	PROPN
brj-23253	45	3	algorithm	algorithm	NOUN
brj-23253	45	4	demonstrated	demonstrate	VERB
brj-23253	45	5	advantages	advantage	NOUN
brj-23253	45	6	in	in	ADP
brj-23253	45	7	providing	provide	VERB
brj-23253	45	8	fast	fast	ADJ
brj-23253	45	9	learning	learning	NOUN
brj-23253	45	10	speed	speed	NOUN
brj-23253	45	11	and	and	CCONJ
brj-23253	45	12	high	high	ADJ
brj-23253	45	13	generalization	generalization	NOUN
brj-23253	45	14	capability	capability	NOUN
brj-23253	45	15	,	,	PUNCT
brj-23253	45	16	while	while	SCONJ
brj-23253	45	17	plsr	plsr	NOUN
brj-23253	45	18	is	be	AUX
brj-23253	45	19	suited	suit	VERB
brj-23253	45	20	for	for	ADP
brj-23253	45	21	handling	handle	VERB
brj-23253	45	22	high	high	ADJ
brj-23253	45	23	-	-	PUNCT
brj-23253	45	24	dimensional	dimensional	ADJ
brj-23253	45	25	data	datum	NOUN
brj-23253	45	26	.	.	PUNCT
brj-23253	46	1	svm	svm	PROPN
brj-23253	46	2	showed	show	VERB
brj-23253	46	3	strong	strong	ADJ
brj-23253	46	4	performance	performance	NOUN
brj-23253	46	5	in	in	ADP
brj-23253	46	6	small	small	ADJ
brj-23253	46	7	sample	sample	NOUN
brj-23253	46	8	sizes	size	NOUN
brj-23253	46	9	,	,	PUNCT
brj-23253	46	10	non	non	ADJ
brj-23253	46	11	-	-	ADJ
brj-23253	46	12	linearity	linearity	ADJ
brj-23253	46	13	,	,	PUNCT
brj-23253	46	14	and	and	CCONJ
brj-23253	46	15	high	high	ADV
brj-23253	46	16	-	-	PUNCT
brj-23253	46	17	dimensional	dimensional	ADJ
brj-23253	46	18	pattern	pattern	NOUN
brj-23253	46	19	recognition	recognition	NOUN
brj-23253	46	20	,	,	PUNCT
brj-23253	46	21	and	and	CCONJ
brj-23253	46	22	the	the	DET
brj-23253	46	23	lstm	lstm	PROPN
brj-23253	46	24	network	network	NOUN
brj-23253	46	25	excelled	excel	VERB
brj-23253	46	26	in	in	ADP
brj-23253	46	27	processing	processing	NOUN
brj-23253	46	28	time	time	NOUN
brj-23253	46	29	-	-	PUNCT
brj-23253	46	30	series	series	NOUN
brj-23253	46	31	data	datum	NOUN
brj-23253	46	32	.	.	PUNCT
brj-23253	47	1	the	the	DET
brj-23253	47	2	combination	combination	NOUN
brj-23253	47	3	of	of	ADP
brj-23253	47	4	these	these	DET
brj-23253	47	5	methods	method	NOUN
brj-23253	47	6	enabled	enable	VERB
brj-23253	47	7	the	the	DET
brj-23253	47	8	study	study	NOUN
brj-23253	47	9	to	to	PART
brj-23253	47	10	predict	predict	VERB
brj-23253	47	11	the	the	DET
brj-23253	47	12	protein	protein	NOUN
brj-23253	47	13	content	content	NOUN
brj-23253	47	14	in	in	ADP
brj-23253	47	15	purple	purple	ADJ
brj-23253	47	16	alfalfa	alfalfa	NOUN
brj-23253	47	17	hay	hay	NOUN
brj-23253	47	18	more	more	ADV
brj-23253	47	19	accurately	accurately	ADV
brj-23253	47	20	and	and	CCONJ
brj-23253	47	21	efficiently	efficiently	ADV
brj-23253	47	22	.	.	PUNCT
brj-23253	48	1	this	this	DET
brj-23253	48	2	approach	approach	NOUN
brj-23253	48	3	offers	offer	VERB
brj-23253	48	4	new	new	ADJ
brj-23253	48	5	perspectives	perspective	NOUN
brj-23253	48	6	and	and	CCONJ
brj-23253	48	7	methods	method	NOUN
brj-23253	48	8	for	for	ADP
brj-23253	48	9	related	related	ADJ
brj-23253	48	10	research	research	NOUN
brj-23253	48	11	.	.	PUNCT
brj-23253	49	1	compared	compare	VERB
brj-23253	49	2	to	to	ADP
brj-23253	49	3	the	the	DET
brj-23253	49	4	authors	author	NOUN
brj-23253	49	5	’	'	PUNCT
brj-23253	49	6	earlier	early	ADJ
brj-23253	49	7	article	article	NOUN
brj-23253	49	8	published	publish	VERB
brj-23253	49	9	in	in	ADP
brj-23253	49	10	bioresources	bioresource	NOUN
brj-23253	49	11	in	in	ADP
brj-23253	49	12	2023	2023	NUM
brj-23253	49	13	,	,	PUNCT
brj-23253	49	14	vol	vol	NOUN
brj-23253	49	15	.	.	PROPN
brj-23253	49	16	18	18	NUM
brj-23253	49	17	,	,	PUNCT
brj-23253	49	18	pages	page	NOUN
brj-23253	49	19	5399	5399	NUM
brj-23253	49	20	-	-	SYM
brj-23253	49	21	5416	5416	NUM
brj-23253	49	22	,	,	PUNCT
brj-23253	49	23	the	the	DET
brj-23253	49	24	study	study	NOUN
brj-23253	49	25	displays	display	VERB
brj-23253	49	26	several	several	ADJ
brj-23253	49	27	differences	difference	NOUN
brj-23253	49	28	and	and	CCONJ
brj-23253	49	29	innovations	innovation	NOUN
brj-23253	49	30	.	.	PUNCT
brj-23253	50	1	significantly	significantly	ADV
brj-23253	50	2	,	,	PUNCT
brj-23253	50	3	the	the	DET
brj-23253	50	4	spectrometer	spectrometer	NOUN
brj-23253	50	5	and	and	CCONJ
brj-23253	50	6	detection	detection	NOUN
brj-23253	50	7	range	range	NOUN
brj-23253	50	8	used	use	VERB
brj-23253	50	9	in	in	ADP
brj-23253	50	10	this	this	DET
brj-23253	50	11	study	study	NOUN
brj-23253	50	12	differ	differ	VERB
brj-23253	50	13	from	from	ADP
brj-23253	50	14	previous	previous	ADJ
brj-23253	50	15	research	research	NOUN
brj-23253	50	16	.	.	PUNCT
brj-23253	51	1	observations	observation	NOUN
brj-23253	51	2	were	be	AUX
brj-23253	51	3	made	make	VERB
brj-23253	51	4	with	with	ADP
brj-23253	51	5	the	the	DET
brj-23253	51	6	quality	quality	NOUN
brj-23253	51	7	spec	spec	PROPN
brj-23253	51	8	pro	pro	ADV
brj-23253	51	9	visible	visible	ADJ
brj-23253	51	10	/	/	SYM
brj-23253	51	11	near	near	ADV
brj-23253	51	12	-	-	PUNCT
brj-23253	51	13	infrared	infrared	ADJ
brj-23253	51	14	spectrometer	spectrometer	NOUN
brj-23253	51	15	from	from	ADP
brj-23253	51	16	asd	asd	PROPN
brj-23253	51	17	inc	inc	PROPN
brj-23253	51	18	.	.	PROPN
brj-23253	51	19	,	,	PUNCT
brj-23253	51	20	usa	usa	PROPN
brj-23253	51	21	.	.	PUNCT
brj-23253	52	1	it	it	PRON
brj-23253	52	2	has	have	VERB
brj-23253	52	3	a	a	DET
brj-23253	52	4	detection	detection	NOUN
brj-23253	52	5	range	range	NOUN
brj-23253	52	6	of	of	ADP
brj-23253	52	7	350	350	NUM
brj-23253	52	8	to	to	PART
brj-23253	52	9	1830	1830	NUM
brj-23253	52	10	nm	nm	NOUN
brj-23253	52	11	,	,	PUNCT
brj-23253	52	12	which	which	PRON
brj-23253	52	13	is	be	AUX
brj-23253	52	14	better	well	ADV
brj-23253	52	15	suited	suited	ADJ
brj-23253	52	16	for	for	ADP
brj-23253	52	17	capturing	capture	VERB
brj-23253	52	18	spectral	spectral	ADJ
brj-23253	52	19	information	information	NOUN
brj-23253	52	20	related	relate	VERB
brj-23253	52	21	to	to	ADP
brj-23253	52	22	protein	protein	NOUN
brj-23253	52	23	content	content	NOUN
brj-23253	52	24	.	.	PUNCT
brj-23253	53	1	moreover	moreover	ADV
brj-23253	53	2	,	,	PUNCT
brj-23253	53	3	in	in	ADP
brj-23253	53	4	terms	term	NOUN
brj-23253	53	5	of	of	ADP
brj-23253	53	6	research	research	NOUN
brj-23253	53	7	focus	focus	NOUN
brj-23253	53	8	,	,	PUNCT
brj-23253	53	9	this	this	DET
brj-23253	53	10	study	study	NOUN
brj-23253	53	11	concentrated	concentrate	VERB
brj-23253	53	12	on	on	ADP
brj-23253	53	13	the	the	DET
brj-23253	53	14	quantitative	quantitative	ADJ
brj-23253	53	15	analysis	analysis	NOUN
brj-23253	53	16	of	of	ADP
brj-23253	53	17	protein	protein	NOUN
brj-23253	53	18	content	content	NOUN
brj-23253	53	19	in	in	ADP
brj-23253	53	20	alfalfa	alfalfa	NOUN
brj-23253	53	21	using	use	VERB
brj-23253	53	22	visible	visible	ADJ
brj-23253	53	23	/	/	SYM
brj-23253	53	24	near	near	ADV
brj-23253	53	25	-	-	PUNCT
brj-23253	53	26	infrared	infrared	ADJ
brj-23253	53	27	spectroscopy	spectroscopy	NOUN
brj-23253	53	28	,	,	PUNCT
brj-23253	53	29	rather	rather	ADV
brj-23253	53	30	than	than	ADP
brj-23253	53	31	merely	merely	ADV
brj-23253	53	32	classifying	classify	VERB
brj-23253	53	33	alfalfa	alfalfa	NOUN
brj-23253	53	34	's	's	PART
brj-23253	53	35	moldy	moldy	ADJ
brj-23253	53	36	state	state	NOUN
brj-23253	53	37	or	or	CCONJ
brj-23253	53	38	drying	dry	VERB
brj-23253	53	39	method	method	NOUN
brj-23253	53	40	.	.	PUNCT
brj-23253	54	1	a	a	DET
brj-23253	54	2	significant	significant	ADJ
brj-23253	54	3	correlation	correlation	NOUN
brj-23253	54	4	was	be	AUX
brj-23253	54	5	found	find	VERB
brj-23253	54	6	between	between	ADP
brj-23253	54	7	visible	visible	ADJ
brj-23253	54	8	/	/	SYM
brj-23253	54	9	near	near	ADV
brj-23253	54	10	-	-	PUNCT
brj-23253	54	11	infrared	infrared	ADJ
brj-23253	54	12	spectroscopy	spectroscopy	NOUN
brj-23253	54	13	and	and	CCONJ
brj-23253	54	14	the	the	DET
brj-23253	54	15	protein	protein	NOUN
brj-23253	54	16	content	content	NOUN
brj-23253	54	17	in	in	ADP
brj-23253	54	18	alfalfa	alfalfa	NOUN
brj-23253	54	19	,	,	PUNCT
brj-23253	54	20	enabling	enable	VERB
brj-23253	54	21	rapid	rapid	ADJ
brj-23253	54	22	and	and	CCONJ
brj-23253	54	23	accurate	accurate	ADJ
brj-23253	54	24	detection	detection	NOUN
brj-23253	54	25	of	of	ADP
brj-23253	54	26	protein	protein	NOUN
brj-23253	54	27	in	in	ADP
brj-23253	54	28	dried	dry	VERB
brj-23253	54	29	alfalfa	alfalfa	NOUN
brj-23253	54	30	.	.	PUNCT
brj-23253	55	1	additionally	additionally	ADV
brj-23253	55	2	,	,	PUNCT
brj-23253	55	3	in	in	ADP
brj-23253	55	4	the	the	DET
brj-23253	55	5	establishment	establishment	NOUN
brj-23253	55	6	of	of	ADP
brj-23253	55	7	machine	machine	NOUN
brj-23253	55	8	learning	learning	NOUN
brj-23253	55	9	models	model	NOUN
brj-23253	55	10	,	,	PUNCT
brj-23253	55	11	this	this	DET
brj-23253	55	12	work	work	NOUN
brj-23253	55	13	introduced	introduce	VERB
brj-23253	55	14	different	different	ADJ
brj-23253	55	15	algorithms	algorithm	NOUN
brj-23253	55	16	and	and	CCONJ
brj-23253	55	17	modeling	modeling	NOUN
brj-23253	55	18	methods	method	NOUN
brj-23253	55	19	from	from	ADP
brj-23253	55	20	the	the	DET
brj-23253	55	21	aforementioned	aforementioned	ADJ
brj-23253	55	22	study	study	NOUN
brj-23253	55	23	and	and	CCONJ
brj-23253	55	24	employed	employ	VERB
brj-23253	55	25	optimization	optimization	NOUN
brj-23253	55	26	algorithms	algorithm	NOUN
brj-23253	55	27	to	to	PART
brj-23253	55	28	enhance	enhance	VERB
brj-23253	55	29	prediction	prediction	NOUN
brj-23253	55	30	accuracy	accuracy	NOUN
brj-23253	55	31	.	.	PUNCT
brj-23253	56	1	in	in	ADP
brj-23253	56	2	summary	summary	NOUN
brj-23253	56	3	,	,	PUNCT
brj-23253	56	4	this	this	DET
brj-23253	56	5	study	study	NOUN
brj-23253	56	6	successfully	successfully	ADV
brj-23253	56	7	enhanced	enhance	VERB
brj-23253	56	8	the	the	DET
brj-23253	56	9	accuracy	accuracy	NOUN
brj-23253	56	10	and	and	CCONJ
brj-23253	56	11	efficiency	efficiency	NOUN
brj-23253	56	12	of	of	ADP
brj-23253	56	13	protein	protein	NOUN
brj-23253	56	14	content	content	NOUN
brj-23253	56	15	detection	detection	NOUN
brj-23253	56	16	in	in	ADP
brj-23253	56	17	purple	purple	ADJ
brj-23253	56	18	alfalfa	alfalfa	NOUN
brj-23253	56	19	hay	hay	NOUN
brj-23253	56	20	by	by	ADP
brj-23253	56	21	utilizing	utilize	VERB
brj-23253	56	22	advanced	advanced	ADJ
brj-23253	56	23	spectrometric	spectrometric	ADJ
brj-23253	56	24	equipment	equipment	NOUN
brj-23253	56	25	and	and	CCONJ
brj-23253	56	26	innovative	innovative	ADJ
brj-23253	56	27	data	datum	NOUN
brj-23253	56	28	processing	processing	NOUN
brj-23253	56	29	and	and	CCONJ
brj-23253	56	30	modeling	model	VERB
brj-23253	56	31	techniques	technique	NOUN
brj-23253	56	32	.	.	PUNCT
brj-23253	57	1	the	the	DET
brj-23253	57	2	objectives	objective	NOUN
brj-23253	57	3	of	of	ADP
brj-23253	57	4	this	this	DET
brj-23253	57	5	research	research	NOUN
brj-23253	57	6	were	be	AUX
brj-23253	57	7	as	as	SCONJ
brj-23253	57	8	follows	follow	VERB
brj-23253	57	9	:	:	PUNCT
brj-23253	57	10	(	(	PUNCT
brj-23253	57	11	1	1	X
brj-23253	57	12	)	)	PUNCT
brj-23253	57	13	conditioning	conditioning	NOUN
brj-23253	57	14	and	and	CCONJ
brj-23253	57	15	conventional	conventional	ADJ
brj-23253	57	16	protein	protein	NOUN
brj-23253	57	17	content	content	NOUN
brj-23253	57	18	analysis	analysis	NOUN
brj-23253	57	19	of	of	ADP
brj-23253	57	20	alfalfa	alfalfa	PROPN
brj-23253	57	21	hay	hay	PROPN
brj-23253	57	22	;	;	PUNCT
brj-23253	57	23	(	(	PUNCT
brj-23253	57	24	2	2	X
brj-23253	57	25	)	)	PUNCT
brj-23253	57	26	preprocessing	preprocessing	NOUN
brj-23253	57	27	and	and	CCONJ
brj-23253	57	28	average	average	ADJ
brj-23253	57	29	spectral	spectral	ADJ
brj-23253	57	30	analysis	analysis	NOUN
brj-23253	57	31	of	of	ADP
brj-23253	57	32	dried	dry	VERB
brj-23253	57	33	alfalfa	alfalfa	NOUN
brj-23253	57	34	;	;	PUNCT
brj-23253	57	35	(	(	PUNCT
brj-23253	57	36	3	3	X
brj-23253	57	37	)	)	PUNCT
brj-23253	57	38	determining	determine	VERB
brj-23253	57	39	the	the	DET
brj-23253	57	40	optimal	optimal	ADJ
brj-23253	57	41	characteristic	characteristic	ADJ
brj-23253	57	42	wavelengths	wavelength	NOUN
brj-23253	57	43	using	use	VERB
brj-23253	57	44	competitive	competitive	ADJ
brj-23253	57	45	adaptive	adaptive	ADJ
brj-23253	57	46	reweighted	reweighte	VERB
brj-23253	57	47	sampling	sampling	NOUN
brj-23253	57	48	and	and	CCONJ
brj-23253	57	49	iteratively	iteratively	ADV
brj-23253	57	50	retains	retain	VERB
brj-23253	57	51	informative	informative	ADJ
brj-23253	57	52	variables	variable	NOUN
brj-23253	57	53	algorithms	algorithm	NOUN
brj-23253	57	54	;	;	PUNCT
brj-23253	57	55	(	(	PUNCT
brj-23253	57	56	4	4	X
brj-23253	57	57	)	)	PUNCT
brj-23253	57	58	constructing	construct	VERB
brj-23253	57	59	quantitative	quantitative	ADJ
brj-23253	57	60	detection	detection	NOUN
brj-23253	57	61	models	model	NOUN
brj-23253	57	62	for	for	ADP
brj-23253	57	63	alfalfa	alfalfa	NOUN
brj-23253	57	64	using	use	VERB
brj-23253	57	65	extreme	extreme	ADJ
brj-23253	57	66	learning	learn	VERB
brj-23253	57	67	machine	machine	NOUN
brj-23253	57	68	and	and	CCONJ
brj-23253	57	69	partial	partial	ADJ
brj-23253	57	70	least	least	ADJ
brj-23253	57	71	squares	square	NOUN
brj-23253	57	72	regression	regression	NOUN
brj-23253	57	73	;	;	PUNCT
brj-23253	57	74	(	(	PUNCT
brj-23253	57	75	5	5	X
brj-23253	57	76	)	)	PUNCT
brj-23253	57	77	improving	improve	VERB
brj-23253	57	78	model	model	NOUN
brj-23253	57	79	predictive	predictive	ADJ
brj-23253	57	80	capability	capability	NOUN
brj-23253	57	81	by	by	ADP
brj-23253	57	82	using	use	VERB
brj-23253	57	83	principal	principal	ADJ
brj-23253	57	84	components	component	NOUN
brj-23253	57	85	derived	derive	VERB
brj-23253	57	86	from	from	ADP
brj-23253	57	87	plsr	plsr	NOUN
brj-23253	57	88	as	as	ADP
brj-23253	57	89	independent	independent	ADJ
brj-23253	57	90	variables	variable	NOUN
brj-23253	57	91	,	,	PUNCT
brj-23253	57	92	with	with	ADP
brj-23253	57	93	support	support	NOUN
brj-23253	57	94	vector	vector	NOUN
brj-23253	57	95	machine	machine	NOUN
brj-23253	57	96	(	(	PUNCT
brj-23253	57	97	svm	svm	PROPN
brj-23253	57	98	)	)	PUNCT
brj-23253	57	99	and	and	CCONJ
brj-23253	57	100	long	long	ADJ
brj-23253	57	101	short	short	ADJ
brj-23253	57	102	-	-	PUNCT
brj-23253	57	103	term	term	NOUN
brj-23253	57	104	memory	memory	NOUN
brj-23253	57	105	(	(	PUNCT
brj-23253	57	106	lstm	lstm	NOUN
brj-23253	57	107	)	)	PUNCT
brj-23253	57	108	networks	network	NOUN
brj-23253	57	109	for	for	ADP
brj-23253	57	110	regression	regression	NOUN
brj-23253	57	111	prediction	prediction	NOUN
brj-23253	57	112	.	.	PUNCT
brj-23253	58	1	experimental	experimental	ADJ
brj-23253	58	2	preparation	preparation	NOUN
brj-23253	58	3	of	of	ADP
brj-23253	58	4	experimental	experimental	ADJ
brj-23253	58	5	samples	sample	NOUN
brj-23253	58	6	the	the	DET
brj-23253	58	7	samples	sample	NOUN
brj-23253	58	8	utilized	utilize	VERB
brj-23253	58	9	in	in	ADP
brj-23253	58	10	this	this	DET
brj-23253	58	11	study	study	NOUN
brj-23253	58	12	were	be	AUX
brj-23253	58	13	sourced	source	VERB
brj-23253	58	14	from	from	ADP
brj-23253	58	15	the	the	DET
brj-23253	58	16	experimental	experimental	ADJ
brj-23253	58	17	fields	field	NOUN
brj-23253	58	18	of	of	ADP
brj-23253	58	19	inner	inner	ADJ
brj-23253	58	20	mongolia	mongolia	PROPN
brj-23253	58	21	agricultural	agricultural	PROPN
brj-23253	58	22	university	university	PROPN
brj-23253	58	23	.	.	PUNCT
brj-23253	59	1	to	to	PART
brj-23253	59	2	ensure	ensure	VERB
brj-23253	59	3	the	the	DET
brj-23253	59	4	accuracy	accuracy	NOUN
brj-23253	59	5	and	and	CCONJ
brj-23253	59	6	rigor	rigor	NOUN
brj-23253	59	7	of	of	ADP
brj-23253	59	8	the	the	DET
brj-23253	59	9	experiment	experiment	NOUN
brj-23253	59	10	,	,	PUNCT
brj-23253	59	11	while	while	SCONJ
brj-23253	59	12	minimizing	minimize	VERB
brj-23253	59	13	sampling	sample	VERB
brj-23253	59	14	errors	error	NOUN
brj-23253	59	15	,	,	PUNCT
brj-23253	59	16	a	a	DET
brj-23253	59	17	strict	strict	ADJ
brj-23253	59	18	sample	sample	NOUN
brj-23253	59	19	selection	selection	NOUN
brj-23253	59	20	and	and	CCONJ
brj-23253	59	21	processing	processing	NOUN
brj-23253	59	22	protocol	protocol	NOUN
brj-23253	59	23	was	be	AUX
brj-23253	59	24	adopted	adopt	VERB
brj-23253	59	25	.	.	PUNCT
brj-23253	60	1	specifically	specifically	ADV
brj-23253	60	2	,	,	PUNCT
brj-23253	60	3	prior	prior	ADV
brj-23253	60	4	to	to	ADP
brj-23253	60	5	sampling	sample	VERB
brj-23253	60	6	,	,	PUNCT
brj-23253	60	7	impurities	impurity	NOUN
brj-23253	60	8	such	such	ADJ
brj-23253	60	9	as	as	ADP
brj-23253	60	10	weeds	weed	NOUN
brj-23253	60	11	,	,	PUNCT
brj-23253	60	12	nails	nail	NOUN
brj-23253	60	13	,	,	PUNCT
brj-23253	60	14	artificially	artificially	ADV
brj-23253	60	15	damaged	damage	VERB
brj-23253	60	16	specimens	specimen	NOUN
brj-23253	60	17	,	,	PUNCT
brj-23253	60	18	and	and	CCONJ
brj-23253	60	19	decayed	decayed	ADJ
brj-23253	60	20	alfalfa	alfalfa	NOUN
brj-23253	60	21	were	be	AUX
brj-23253	60	22	removed	remove	VERB
brj-23253	60	23	.	.	PUNCT
brj-23253	61	1	samples	sample	NOUN
brj-23253	61	2	with	with	ADP
brj-23253	61	3	similar	similar	ADJ
brj-23253	61	4	plant	plant	NOUN
brj-23253	61	5	heights	height	NOUN
brj-23253	61	6	and	and	CCONJ
brj-23253	61	7	leaf	leaf	NOUN
brj-23253	61	8	areas	area	NOUN
brj-23253	61	9	were	be	AUX
brj-23253	61	10	selected	select	VERB
brj-23253	61	11	(	(	PUNCT
brj-23253	61	12	average	average	ADJ
brj-23253	61	13	plant	plant	NOUN
brj-23253	61	14	height	height	NOUN
brj-23253	61	15	ranged	range	VERB
brj-23253	61	16	from	from	ADP
brj-23253	61	17	71.85	71.85	NUM
brj-23253	61	18	cm	cm	NOUN
brj-23253	61	19	to	to	ADP
brj-23253	61	20	82.31	82.31	NUM
brj-23253	61	21	cm	cm	NOUN
brj-23253	61	22	,	,	PUNCT
brj-23253	61	23	leaf	leaf	NOUN
brj-23253	61	24	length	length	NOUN
brj-23253	61	25	from	from	ADP
brj-23253	61	26	1.25	1.25	NUM
brj-23253	61	27	cm	cm	NOUN
brj-23253	61	28	to	to	ADP
brj-23253	61	29	2.25	2.25	NUM
brj-23253	61	30	cm	cm	NOUN
brj-23253	61	31	,	,	PUNCT
brj-23253	61	32	and	and	CCONJ
brj-23253	61	33	leaf	leaf	NOUN
brj-23253	61	34	width	width	NOUN
brj-23253	61	35	from	from	ADP
brj-23253	61	36	1	1	NUM
brj-23253	61	37	cm	cm	NOUN
brj-23253	61	38	to	to	ADP
brj-23253	61	39	2.5	2.5	NUM
brj-23253	61	40	cm	cm	NOUN
brj-23253	61	41	)	)	PUNCT
brj-23253	61	42	.	.	PUNCT
brj-23253	62	1	the	the	DET
brj-23253	62	2	initial	initial	ADJ
brj-23253	62	3	moisture	moisture	NOUN
brj-23253	62	4	content	content	NOUN
brj-23253	62	5	of	of	ADP
brj-23253	62	6	the	the	DET
brj-23253	62	7	harvested	harvest	VERB
brj-23253	62	8	alfalfa	alfalfa	NOUN
brj-23253	62	9	was	be	AUX
brj-23253	62	10	80±3	80±3	NUM
brj-23253	62	11	%	%	NOUN
brj-23253	62	12	.	.	PUNCT
brj-23253	63	1	drying	dry	VERB
brj-23253	63	2	treatments	treatment	NOUN
brj-23253	63	3	included	include	VERB
brj-23253	63	4	peer	peer	NOUN
brj-23253	63	5	-	-	PUNCT
brj-23253	63	6	reviewed	review	VERB
brj-23253	63	7	article	article	NOUN
brj-23253	63	8	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	63	9	li	li	PROPN
brj-23253	63	10	et	et	PROPN
brj-23253	63	11	al	al	PROPN
brj-23253	63	12	.	.	PROPN
brj-23253	64	1	(	(	PUNCT
brj-23253	64	2	2024	2024	NUM
brj-23253	64	3	)	)	PUNCT
brj-23253	64	4	.	.	PUNCT
brj-23253	65	1	“	"	PUNCT
brj-23253	65	2	alfalfa	alfalfa	X
brj-23253	65	3	protein	protein	NOUN
brj-23253	65	4	with	with	ADP
brj-23253	65	5	vis	vis	X
brj-23253	65	6	/	/	SYM
brj-23253	65	7	nir	nir	NOUN
brj-23253	65	8	,	,	PUNCT
brj-23253	65	9	”	"	PUNCT
brj-23253	65	10	bioresources	bioresource	NOUN
brj-23253	65	11	19(2	19(2	NUM
brj-23253	65	12	)	)	PUNCT
brj-23253	65	13	,	,	PUNCT
brj-23253	65	14	3808	3808	NUM
brj-23253	65	15	-	-	SYM
brj-23253	65	16	3825	3825	NUM
brj-23253	65	17	.	.	PUNCT
brj-23253	66	1	3811	3811	NUM
brj-23253	66	2	both	both	DET
brj-23253	66	3	natural	natural	ADJ
brj-23253	66	4	sun	sun	NOUN
brj-23253	66	5	drying	dry	VERB
brj-23253	66	6	and	and	CCONJ
brj-23253	66	7	ventilated	ventilate	VERB
brj-23253	66	8	shade	shade	NOUN
brj-23253	66	9	drying	dry	VERB
brj-23253	66	10	after	after	ADP
brj-23253	66	11	mold	mold	NOUN
brj-23253	66	12	formation	formation	NOUN
brj-23253	66	13	.	.	PUNCT
brj-23253	67	1	during	during	ADP
brj-23253	67	2	the	the	DET
brj-23253	67	3	drying	dry	VERB
brj-23253	67	4	process	process	NOUN
brj-23253	67	5	,	,	PUNCT
brj-23253	67	6	a	a	DET
brj-23253	67	7	dsh-50	dsh-50	ADV
brj-23253	67	8	-	-	PUNCT
brj-23253	67	9	10	10	NUM
brj-23253	67	10	electronic	electronic	ADJ
brj-23253	67	11	moisture	moisture	NOUN
brj-23253	67	12	meter	meter	NOUN
brj-23253	67	13	was	be	AUX
brj-23253	67	14	used	use	VERB
brj-23253	67	15	to	to	PART
brj-23253	67	16	measure	measure	VERB
brj-23253	67	17	the	the	DET
brj-23253	67	18	moisture	moisture	NOUN
brj-23253	67	19	content	content	NOUN
brj-23253	67	20	every	every	DET
brj-23253	67	21	two	two	NUM
brj-23253	67	22	hours	hour	NOUN
brj-23253	67	23	,	,	PUNCT
brj-23253	67	24	to	to	PART
brj-23253	67	25	monitor	monitor	VERB
brj-23253	67	26	the	the	DET
brj-23253	67	27	drying	dry	VERB
brj-23253	67	28	process	process	NOUN
brj-23253	67	29	,	,	PUNCT
brj-23253	67	30	and	and	CCONJ
brj-23253	67	31	to	to	PART
brj-23253	67	32	ensure	ensure	VERB
brj-23253	67	33	that	that	SCONJ
brj-23253	67	34	the	the	DET
brj-23253	67	35	final	final	ADJ
brj-23253	67	36	moisture	moisture	NOUN
brj-23253	67	37	content	content	NOUN
brj-23253	67	38	of	of	ADP
brj-23253	67	39	alfalfa	alfalfa	NOUN
brj-23253	67	40	stabilized	stabilize	VERB
brj-23253	67	41	between	between	ADP
brj-23253	67	42	15	15	NUM
brj-23253	67	43	%	%	NOUN
brj-23253	67	44	and	and	CCONJ
brj-23253	67	45	20	20	NUM
brj-23253	67	46	%	%	NOUN
brj-23253	67	47	.	.	PUNCT
brj-23253	68	1	naturally	naturally	ADV
brj-23253	68	2	sun	sun	NOUN
brj-23253	68	3	-	-	PUNCT
brj-23253	68	4	dried	dry	VERB
brj-23253	68	5	alfalfa	alfalfa	NOUN
brj-23253	68	6	was	be	AUX
brj-23253	68	7	placed	place	VERB
brj-23253	68	8	under	under	ADP
brj-23253	68	9	direct	direct	ADJ
brj-23253	68	10	sunlight	sunlight	NOUN
brj-23253	68	11	,	,	PUNCT
brj-23253	68	12	while	while	SCONJ
brj-23253	68	13	moldy	moldy	ADJ
brj-23253	68	14	alfalfa	alfalfa	NOUN
brj-23253	68	15	was	be	AUX
brj-23253	68	16	sealed	seal	VERB
brj-23253	68	17	in	in	ADP
brj-23253	68	18	ziplock	ziplock	NOUN
brj-23253	68	19	bags	bag	NOUN
brj-23253	68	20	and	and	CCONJ
brj-23253	68	21	stored	store	VERB
brj-23253	68	22	in	in	ADP
brj-23253	68	23	the	the	DET
brj-23253	68	24	laboratory	laboratory	NOUN
brj-23253	68	25	until	until	SCONJ
brj-23253	68	26	ventilated	ventilate	VERB
brj-23253	68	27	shade	shade	NOUN
brj-23253	68	28	drying	dry	VERB
brj-23253	68	29	was	be	AUX
brj-23253	68	30	performed	perform	VERB
brj-23253	68	31	after	after	ADP
brj-23253	68	32	mold	mold	NOUN
brj-23253	68	33	formation	formation	NOUN
brj-23253	68	34	.	.	PUNCT
brj-23253	69	1	after	after	ADP
brj-23253	69	2	drying	dry	VERB
brj-23253	69	3	,	,	PUNCT
brj-23253	69	4	alfalfa	alfalfa	NOUN
brj-23253	69	5	samples	sample	NOUN
brj-23253	69	6	were	be	AUX
brj-23253	69	7	ground	grind	VERB
brj-23253	69	8	into	into	ADP
brj-23253	69	9	a	a	DET
brj-23253	69	10	fine	fine	ADJ
brj-23253	69	11	powder	powder	NOUN
brj-23253	69	12	(	(	PUNCT
brj-23253	69	13	100	100	NUM
brj-23253	69	14	mesh	mesh	NOUN
brj-23253	69	15	)	)	PUNCT
brj-23253	69	16	using	use	VERB
brj-23253	69	17	a	a	DET
brj-23253	69	18	pulverizer	pulverizer	NOUN
brj-23253	69	19	,	,	PUNCT
brj-23253	69	20	and	and	CCONJ
brj-23253	69	21	15	15	NUM
brj-23253	69	22	g	g	NOUN
brj-23253	69	23	was	be	AUX
brj-23253	69	24	weighed	weigh	VERB
brj-23253	69	25	and	and	CCONJ
brj-23253	69	26	stored	store	VERB
brj-23253	69	27	in	in	ADP
brj-23253	69	28	ziplock	ziplock	NOUN
brj-23253	69	29	bags	bag	NOUN
brj-23253	69	30	.	.	PUNCT
brj-23253	70	1	a	a	DET
brj-23253	70	2	total	total	NOUN
brj-23253	70	3	of	of	ADP
brj-23253	70	4	120	120	NUM
brj-23253	70	5	alfalfa	alfalfa	NOUN
brj-23253	70	6	hay	hay	NOUN
brj-23253	70	7	samples	sample	NOUN
brj-23253	70	8	were	be	AUX
brj-23253	70	9	prepared	prepare	VERB
brj-23253	70	10	,	,	PUNCT
brj-23253	70	11	and	and	CCONJ
brj-23253	70	12	the	the	DET
brj-23253	70	13	protein	protein	NOUN
brj-23253	70	14	content	content	NOUN
brj-23253	70	15	of	of	ADP
brj-23253	70	16	these	these	DET
brj-23253	70	17	samples	sample	NOUN
brj-23253	70	18	was	be	AUX
brj-23253	70	19	determined	determine	VERB
brj-23253	70	20	using	use	VERB
brj-23253	70	21	a	a	DET
brj-23253	70	22	kjeltec	kjeltec	ADJ
brj-23253	70	23	8420	8420	NUM
brj-23253	70	24	automatic	automatic	ADJ
brj-23253	70	25	kjeldahl	kjeldahl	NOUN
brj-23253	70	26	apparatus	apparatus	NOUN
brj-23253	70	27	and	and	CCONJ
brj-23253	70	28	recorded	record	VERB
brj-23253	70	29	.	.	PUNCT
brj-23253	71	1	infrared	infrared	PROPN
brj-23253	71	2	spectral	spectral	PROPN
brj-23253	71	3	acquisition	acquisition	NOUN
brj-23253	71	4	the	the	DET
brj-23253	71	5	spectrometer	spectrometer	NOUN
brj-23253	71	6	used	use	VERB
brj-23253	71	7	in	in	ADP
brj-23253	71	8	this	this	DET
brj-23253	71	9	study	study	NOUN
brj-23253	71	10	was	be	AUX
brj-23253	71	11	the	the	DET
brj-23253	71	12	quality	quality	NOUN
brj-23253	71	13	spec	spec	NOUN
brj-23253	71	14	pro	pro	X
brj-23253	71	15	from	from	ADP
brj-23253	71	16	analytical	analytical	ADJ
brj-23253	71	17	spectral	spectral	ADJ
brj-23253	71	18	devices	device	NOUN
brj-23253	71	19	,	,	PUNCT
brj-23253	71	20	inc	inc	PROPN
brj-23253	71	21	.	.	PROPN
brj-23253	72	1	(	(	PUNCT
brj-23253	72	2	asd	asd	PROPN
brj-23253	72	3	)	)	PUNCT
brj-23253	72	4	,	,	PUNCT
brj-23253	72	5	usa	usa	PROPN
brj-23253	72	6	.	.	PUNCT
brj-23253	73	1	the	the	DET
brj-23253	73	2	wavelength	wavelength	NOUN
brj-23253	73	3	range	range	NOUN
brj-23253	73	4	of	of	ADP
brj-23253	73	5	the	the	DET
brj-23253	73	6	spectrometer	spectrometer	NOUN
brj-23253	73	7	was	be	AUX
brj-23253	73	8	350	350	NUM
brj-23253	73	9	to	to	ADP
brj-23253	73	10	1830	1830	NUM
brj-23253	73	11	nm	nm	NOUN
brj-23253	73	12	,	,	PUNCT
brj-23253	73	13	with	with	ADP
brj-23253	73	14	a	a	DET
brj-23253	73	15	spectral	spectral	ADJ
brj-23253	73	16	sampling	sampling	NOUN
brj-23253	73	17	interval	interval	NOUN
brj-23253	73	18	of	of	ADP
brj-23253	73	19	1	1	NUM
brj-23253	73	20	nm	nm	NOUN
brj-23253	73	21	.	.	PUNCT
brj-23253	74	1	the	the	DET
brj-23253	74	2	visible	visible	ADJ
brj-23253	74	3	-	-	PUNCT
brj-23253	74	4	near	near	ADV
brj-23253	74	5	infrared	infrared	ADJ
brj-23253	74	6	spectrometer	spectrometer	NOUN
brj-23253	74	7	was	be	AUX
brj-23253	74	8	preheated	preheat	VERB
brj-23253	74	9	for	for	ADP
brj-23253	74	10	30	30	NUM
brj-23253	74	11	minutes	minute	NOUN
brj-23253	74	12	before	before	ADP
brj-23253	74	13	collecting	collect	VERB
brj-23253	74	14	dark	dark	ADJ
brj-23253	74	15	and	and	CCONJ
brj-23253	74	16	reference	reference	NOUN
brj-23253	74	17	spectra	spectra	NOUN
brj-23253	74	18	for	for	ADP
brj-23253	74	19	calibration	calibration	NOUN
brj-23253	74	20	.	.	PUNCT
brj-23253	75	1	measurements	measurement	NOUN
brj-23253	75	2	were	be	AUX
brj-23253	75	3	taken	take	VERB
brj-23253	75	4	in	in	ADP
brj-23253	75	5	a	a	DET
brj-23253	75	6	dark	dark	ADJ
brj-23253	75	7	environment	environment	NOUN
brj-23253	75	8	(	(	PUNCT
brj-23253	75	9	dark	dark	PROPN
brj-23253	75	10	box	box	PROPN
brj-23253	75	11	)	)	PUNCT
brj-23253	75	12	to	to	PART
brj-23253	75	13	avoid	avoid	VERB
brj-23253	75	14	stray	stray	ADJ
brj-23253	75	15	light	light	ADJ
brj-23253	75	16	interference	interference	NOUN
brj-23253	75	17	.	.	PUNCT
brj-23253	76	1	the	the	DET
brj-23253	76	2	fiber	fiber	NOUN
brj-23253	76	3	optic	optic	NOUN
brj-23253	76	4	probe	probe	NOUN
brj-23253	76	5	was	be	AUX
brj-23253	76	6	placed	place	VERB
brj-23253	76	7	12	12	NUM
brj-23253	76	8	cm	cm	NOUN
brj-23253	76	9	above	above	ADP
brj-23253	76	10	the	the	DET
brj-23253	76	11	sample	sample	NOUN
brj-23253	76	12	's	's	PART
brj-23253	76	13	surface	surface	NOUN
brj-23253	76	14	vertically	vertically	ADV
brj-23253	76	15	.	.	PUNCT
brj-23253	77	1	each	each	DET
brj-23253	77	2	petri	petri	ADJ
brj-23253	77	3	dish	dish	NOUN
brj-23253	77	4	sample	sample	NOUN
brj-23253	77	5	was	be	AUX
brj-23253	77	6	measured	measure	VERB
brj-23253	77	7	three	three	NUM
brj-23253	77	8	times	time	NOUN
brj-23253	77	9	,	,	PUNCT
brj-23253	77	10	generating	generate	VERB
brj-23253	77	11	three	three	NUM
brj-23253	77	12	sets	set	NOUN
brj-23253	77	13	of	of	ADP
brj-23253	77	14	spectral	spectral	ADJ
brj-23253	77	15	data	datum	NOUN
brj-23253	77	16	per	per	ADP
brj-23253	77	17	sample	sample	NOUN
brj-23253	77	18	.	.	PUNCT
brj-23253	78	1	the	the	DET
brj-23253	78	2	average	average	NOUN
brj-23253	78	3	of	of	ADP
brj-23253	78	4	these	these	DET
brj-23253	78	5	three	three	NUM
brj-23253	78	6	sets	set	NOUN
brj-23253	78	7	was	be	AUX
brj-23253	78	8	taken	take	VERB
brj-23253	78	9	as	as	ADP
brj-23253	78	10	the	the	DET
brj-23253	78	11	spectral	spectral	ADJ
brj-23253	78	12	reflectance	reflectance	NOUN
brj-23253	78	13	test	test	NOUN
brj-23253	78	14	value	value	NOUN
brj-23253	78	15	for	for	ADP
brj-23253	78	16	the	the	DET
brj-23253	78	17	alfalfa	alfalfa	NOUN
brj-23253	78	18	sample	sample	NOUN
brj-23253	78	19	.	.	PUNCT
brj-23253	79	1	after	after	ADP
brj-23253	79	2	collection	collection	NOUN
brj-23253	79	3	,	,	PUNCT
brj-23253	79	4	the	the	DET
brj-23253	79	5	spectral	spectral	ADJ
brj-23253	79	6	data	datum	NOUN
brj-23253	79	7	were	be	AUX
brj-23253	79	8	imported	import	VERB
brj-23253	79	9	into	into	ADP
brj-23253	79	10	a	a	DET
brj-23253	79	11	computer	computer	NOUN
brj-23253	79	12	for	for	ADP
brj-23253	79	13	analysis	analysis	NOUN
brj-23253	79	14	using	use	VERB
brj-23253	79	15	viewspecpro	viewspecpro	ADJ
brj-23253	79	16	software	software	NOUN
brj-23253	79	17	,	,	PUNCT
brj-23253	79	18	resulting	result	VERB
brj-23253	79	19	in	in	ADP
brj-23253	79	20	an	an	DET
brj-23253	79	21	average	average	ADJ
brj-23253	79	22	spectrum	spectrum	NOUN
brj-23253	79	23	reflectance	reflectance	NOUN
brj-23253	79	24	in	in	ADP
brj-23253	79	25	the	the	DET
brj-23253	79	26	wavelength	wavelength	NOUN
brj-23253	79	27	range	range	NOUN
brj-23253	79	28	of	of	ADP
brj-23253	79	29	350	350	NUM
brj-23253	79	30	to	to	PART
brj-23253	79	31	1830	1830	NUM
brj-23253	79	32	nm	nm	NOUN
brj-23253	79	33	.	.	PUNCT
brj-23253	80	1	fig	fig	NOUN
brj-23253	80	2	.	.	PUNCT
brj-23253	81	1	1	1	X
brj-23253	81	2	.	.	X
brj-23253	81	3	reflectivity	reflectivity	NOUN
brj-23253	81	4	curve	curve	NOUN
brj-23253	81	5	of	of	ADP
brj-23253	81	6	alfalfa	alfalfa	NOUN
brj-23253	81	7	hay	hay	NOUN
brj-23253	81	8	samples	sample	NOUN
brj-23253	81	9	to	to	PART
brj-23253	81	10	improve	improve	VERB
brj-23253	81	11	the	the	DET
brj-23253	81	12	accuracy	accuracy	NOUN
brj-23253	81	13	of	of	ADP
brj-23253	81	14	visible	visible	ADJ
brj-23253	81	15	/	/	SYM
brj-23253	81	16	near	near	ADV
brj-23253	81	17	-	-	PUNCT
brj-23253	81	18	infrared	infrared	ADJ
brj-23253	81	19	spectroscopy	spectroscopy	NOUN
brj-23253	81	20	measurements	measurement	NOUN
brj-23253	81	21	and	and	CCONJ
brj-23253	81	22	enhance	enhance	VERB
brj-23253	81	23	the	the	DET
brj-23253	81	24	signal	signal	NOUN
brj-23253	81	25	-	-	PUNCT
brj-23253	81	26	to	to	ADP
brj-23253	81	27	-	-	PUNCT
brj-23253	81	28	noise	noise	NOUN
brj-23253	81	29	ratio	ratio	NOUN
brj-23253	81	30	of	of	ADP
brj-23253	81	31	the	the	DET
brj-23253	81	32	spectra	spectra	ADJ
brj-23253	81	33	,	,	PUNCT
brj-23253	81	34	noisy	noisy	ADJ
brj-23253	81	35	spectra	spectra	NOUN
brj-23253	81	36	in	in	ADP
brj-23253	81	37	the	the	DET
brj-23253	81	38	350	350	NUM
brj-23253	81	39	to	to	PART
brj-23253	81	40	449	449	NUM
brj-23253	81	41	nm	nm	ADJ
brj-23253	81	42	range	range	NOUN
brj-23253	81	43	were	be	AUX
brj-23253	81	44	excluded	exclude	VERB
brj-23253	81	45	.	.	PUNCT
brj-23253	82	1	thus	thus	ADV
brj-23253	82	2	,	,	PUNCT
brj-23253	82	3	the	the	DET
brj-23253	82	4	effective	effective	ADJ
brj-23253	82	5	wavelength	wavelength	NOUN
brj-23253	82	6	range	range	NOUN
brj-23253	82	7	was	be	AUX
brj-23253	82	8	450	450	NUM
brj-23253	82	9	to	to	ADP
brj-23253	82	10	1830	1830	NUM
brj-23253	82	11	nm	nm	NOUN
brj-23253	82	12	,	,	PUNCT
brj-23253	82	13	as	as	SCONJ
brj-23253	82	14	shown	show	VERB
brj-23253	82	15	in	in	ADP
brj-23253	82	16	fig	fig	NOUN
brj-23253	82	17	.	.	PUNCT
brj-23253	83	1	peer	peer	NOUN
brj-23253	83	2	-	-	PUNCT
brj-23253	83	3	reviewed	review	VERB
brj-23253	83	4	article	article	NOUN
brj-23253	83	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	83	6	li	li	PROPN
brj-23253	83	7	et	et	PROPN
brj-23253	83	8	al	al	PROPN
brj-23253	83	9	.	.	PROPN
brj-23253	84	1	(	(	PUNCT
brj-23253	84	2	2024	2024	NUM
brj-23253	84	3	)	)	PUNCT
brj-23253	84	4	.	.	PUNCT
brj-23253	85	1	“	"	PUNCT
brj-23253	85	2	alfalfa	alfalfa	X
brj-23253	85	3	protein	protein	NOUN
brj-23253	85	4	with	with	ADP
brj-23253	85	5	vis	vis	X
brj-23253	85	6	/	/	SYM
brj-23253	85	7	nir	nir	NOUN
brj-23253	85	8	,	,	PUNCT
brj-23253	85	9	”	"	PUNCT
brj-23253	85	10	bioresources	bioresource	NOUN
brj-23253	85	11	19(2	19(2	NUM
brj-23253	85	12	)	)	PUNCT
brj-23253	85	13	,	,	PUNCT
brj-23253	85	14	3808	3808	NUM
brj-23253	85	15	-	-	SYM
brj-23253	85	16	3825	3825	NUM
brj-23253	85	17	.	.	PUNCT
brj-23253	86	1	3812	3812	NUM
brj-23253	86	2	1	1	NUM
brj-23253	86	3	.	.	PUNCT
brj-23253	87	1	in	in	ADP
brj-23253	87	2	the	the	DET
brj-23253	87	3	graph	graph	NOUN
brj-23253	87	4	,	,	PUNCT
brj-23253	87	5	each	each	DET
brj-23253	87	6	curve	curve	NOUN
brj-23253	87	7	corresponds	correspond	VERB
brj-23253	87	8	to	to	ADP
brj-23253	87	9	the	the	DET
brj-23253	87	10	spectral	spectral	ADJ
brj-23253	87	11	reflectance	reflectance	NOUN
brj-23253	87	12	test	test	NOUN
brj-23253	87	13	values	value	NOUN
brj-23253	87	14	of	of	ADP
brj-23253	87	15	a	a	DET
brj-23253	87	16	sample	sample	NOUN
brj-23253	87	17	from	from	ADP
brj-23253	87	18	450	450	NUM
brj-23253	87	19	to	to	ADP
brj-23253	87	20	1830	1830	NUM
brj-23253	87	21	nm	nm	NOUN
brj-23253	87	22	,	,	PUNCT
brj-23253	87	23	with	with	ADP
brj-23253	87	24	a	a	DET
brj-23253	87	25	total	total	NOUN
brj-23253	87	26	of	of	ADP
brj-23253	87	27	120	120	NUM
brj-23253	87	28	curves	curve	NOUN
brj-23253	87	29	.	.	PUNCT
brj-23253	88	1	during	during	ADP
brj-23253	88	2	the	the	DET
brj-23253	88	3	measurement	measurement	NOUN
brj-23253	88	4	process	process	NOUN
brj-23253	88	5	,	,	PUNCT
brj-23253	88	6	dark	dark	ADJ
brj-23253	88	7	and	and	CCONJ
brj-23253	88	8	reference	reference	NOUN
brj-23253	88	9	spectra	spectra	NOUN
brj-23253	88	10	were	be	AUX
brj-23253	88	11	collected	collect	VERB
brj-23253	88	12	every	every	DET
brj-23253	88	13	10	10	NUM
brj-23253	88	14	minutes	minute	NOUN
brj-23253	88	15	for	for	ADP
brj-23253	88	16	recalibration	recalibration	NOUN
brj-23253	88	17	to	to	PART
brj-23253	88	18	ensure	ensure	VERB
brj-23253	88	19	measurement	measurement	NOUN
brj-23253	88	20	accuracy	accuracy	NOUN
brj-23253	88	21	.	.	PUNCT
brj-23253	89	1	pretreatment	pretreatment	NOUN
brj-23253	89	2	of	of	ADP
brj-23253	89	3	the	the	DET
brj-23253	89	4	spectral	spectral	ADJ
brj-23253	89	5	data	datum	NOUN
brj-23253	89	6	due	due	ADP
brj-23253	89	7	to	to	ADP
brj-23253	89	8	the	the	DET
brj-23253	89	9	susceptibility	susceptibility	NOUN
brj-23253	89	10	of	of	ADP
brj-23253	89	11	spectral	spectral	ADJ
brj-23253	89	12	data	datum	NOUN
brj-23253	89	13	to	to	ADP
brj-23253	89	14	instrument	instrument	NOUN
brj-23253	89	15	noise	noise	NOUN
brj-23253	89	16	and	and	CCONJ
brj-23253	89	17	surrounding	surround	VERB
brj-23253	89	18	environmental	environmental	ADJ
brj-23253	89	19	factors	factor	NOUN
brj-23253	89	20	,	,	PUNCT
brj-23253	89	21	the	the	DET
brj-23253	89	22	original	original	ADJ
brj-23253	89	23	spectral	spectral	ADJ
brj-23253	89	24	curves	curve	NOUN
brj-23253	89	25	of	of	ADP
brj-23253	89	26	alfalfa	alfalfa	NOUN
brj-23253	89	27	hay	hay	NOUN
brj-23253	89	28	often	often	ADV
brj-23253	89	29	contain	contain	VERB
brj-23253	89	30	numerous	numerous	ADJ
brj-23253	89	31	spikes	spike	NOUN
brj-23253	89	32	,	,	PUNCT
brj-23253	89	33	which	which	PRON
brj-23253	89	34	can	can	AUX
brj-23253	89	35	impact	impact	VERB
brj-23253	89	36	subsequent	subsequent	ADJ
brj-23253	89	37	model	model	NOUN
brj-23253	89	38	building	building	NOUN
brj-23253	89	39	.	.	PUNCT
brj-23253	90	1	therefore	therefore	ADV
brj-23253	90	2	,	,	PUNCT
brj-23253	90	3	it	it	PRON
brj-23253	90	4	is	be	AUX
brj-23253	90	5	necessary	necessary	ADJ
brj-23253	90	6	to	to	PART
brj-23253	90	7	preprocess	preprocess	VERB
brj-23253	90	8	the	the	DET
brj-23253	90	9	average	average	ADJ
brj-23253	90	10	spectrum	spectrum	NOUN
brj-23253	90	11	to	to	PART
brj-23253	90	12	eliminate	eliminate	VERB
brj-23253	90	13	machine	machine	NOUN
brj-23253	90	14	noise	noise	NOUN
brj-23253	90	15	and	and	CCONJ
brj-23253	90	16	baseline	baseline	NOUN
brj-23253	90	17	drift	drift	NOUN
brj-23253	90	18	.	.	PUNCT
brj-23253	91	1	this	this	DET
brj-23253	91	2	study	study	NOUN
brj-23253	91	3	selected	select	VERB
brj-23253	91	4	savitzky	savitzky	NOUN
brj-23253	91	5	-	-	PUNCT
brj-23253	91	6	golay	golay	NOUN
brj-23253	91	7	(	(	PUNCT
brj-23253	91	8	sg	sg	NOUN
brj-23253	91	9	)	)	PUNCT
brj-23253	91	10	convolution	convolution	NOUN
brj-23253	91	11	smoothing	smoothing	NOUN
brj-23253	91	12	,	,	PUNCT
brj-23253	91	13	standard	standard	ADJ
brj-23253	91	14	normal	normal	ADJ
brj-23253	91	15	variate	variate	NOUN
brj-23253	91	16	(	(	PUNCT
brj-23253	91	17	snv	snv	PROPN
brj-23253	91	18	)	)	PUNCT
brj-23253	91	19	,	,	PUNCT
brj-23253	91	20	multiplicative	multiplicative	ADJ
brj-23253	91	21	scatter	scatter	NOUN
brj-23253	91	22	correction	correction	NOUN
brj-23253	91	23	(	(	PUNCT
brj-23253	91	24	msc	msc	PROPN
brj-23253	91	25	)	)	PUNCT
brj-23253	91	26	,	,	PUNCT
brj-23253	91	27	and	and	CCONJ
brj-23253	91	28	first	first	ADJ
brj-23253	91	29	derivative	derivative	ADJ
brj-23253	91	30	(	(	PUNCT
brj-23253	91	31	fd	fd	NOUN
brj-23253	91	32	)	)	PUNCT
brj-23253	91	33	algorithms	algorithm	NOUN
brj-23253	91	34	for	for	ADP
brj-23253	91	35	preprocessing	preprocesse	VERB
brj-23253	91	36	.	.	PUNCT
brj-23253	92	1	sg	sg	ADP
brj-23253	92	2	smoothing	smoothing	NOUN
brj-23253	92	3	enhances	enhance	VERB
brj-23253	92	4	the	the	DET
brj-23253	92	5	smoothness	smoothness	NOUN
brj-23253	92	6	of	of	ADP
brj-23253	92	7	the	the	DET
brj-23253	92	8	spectrum	spectrum	NOUN
brj-23253	92	9	,	,	PUNCT
brj-23253	92	10	reducing	reduce	VERB
brj-23253	92	11	noise	noise	NOUN
brj-23253	92	12	interference	interference	NOUN
brj-23253	92	13	(	(	PUNCT
brj-23253	92	14	jiao	jiao	PROPN
brj-23253	92	15	et	et	PROPN
brj-23253	92	16	al	al	PROPN
brj-23253	92	17	.	.	PROPN
brj-23253	92	18	2020	2020	NUM
brj-23253	92	19	)	)	PUNCT
brj-23253	92	20	.	.	PUNCT
brj-23253	93	1	snv	snv	PROPN
brj-23253	93	2	is	be	AUX
brj-23253	93	3	primarily	primarily	ADV
brj-23253	93	4	used	use	VERB
brj-23253	93	5	to	to	PART
brj-23253	93	6	address	address	VERB
brj-23253	93	7	surface	surface	NOUN
brj-23253	93	8	scattering	scatter	VERB
brj-23253	93	9	effects	effect	NOUN
brj-23253	93	10	and	and	CCONJ
brj-23253	93	11	variations	variation	NOUN
brj-23253	93	12	in	in	ADP
brj-23253	93	13	light	light	ADJ
brj-23253	93	14	intensity	intensity	NOUN
brj-23253	93	15	on	on	ADP
brj-23253	93	16	the	the	DET
brj-23253	93	17	spectrum	spectrum	NOUN
brj-23253	93	18	(	(	PUNCT
brj-23253	93	19	oliveri	oliveri	PROPN
brj-23253	93	20	et	et	PROPN
brj-23253	93	21	al	al	PROPN
brj-23253	93	22	.	.	PROPN
brj-23253	93	23	2019	2019	NUM
brj-23253	93	24	)	)	PUNCT
brj-23253	93	25	.	.	PUNCT
brj-23253	94	1	msc	msc	PROPN
brj-23253	94	2	is	be	AUX
brj-23253	94	3	employed	employ	VERB
brj-23253	94	4	to	to	PART
brj-23253	94	5	eliminate	eliminate	VERB
brj-23253	94	6	the	the	DET
brj-23253	94	7	impacts	impact	NOUN
brj-23253	94	8	of	of	ADP
brj-23253	94	9	particle	particle	NOUN
brj-23253	94	10	size	size	NOUN
brj-23253	94	11	and	and	CCONJ
brj-23253	94	12	scattering	scattering	NOUN
brj-23253	94	13	caused	cause	VERB
brj-23253	94	14	by	by	ADP
brj-23253	94	15	particle	particle	NOUN
brj-23253	94	16	inhomogeneity	inhomogeneity	NOUN
brj-23253	94	17	(	(	PUNCT
brj-23253	94	18	makino	makino	PROPN
brj-23253	94	19	et	et	PROPN
brj-23253	94	20	al	al	PROPN
brj-23253	94	21	.	.	PROPN
brj-23253	94	22	2016	2016	NUM
brj-23253	94	23	)	)	PUNCT
brj-23253	94	24	.	.	PUNCT
brj-23253	95	1	the	the	DET
brj-23253	95	2	first	first	ADJ
brj-23253	95	3	derivative	derivative	ADJ
brj-23253	95	4	operation	operation	NOUN
brj-23253	95	5	(	(	PUNCT
brj-23253	95	6	fd	fd	X
brj-23253	95	7	)	)	PUNCT
brj-23253	95	8	eliminates	eliminate	VERB
brj-23253	95	9	baseline	baseline	ADJ
brj-23253	95	10	shifts	shift	NOUN
brj-23253	95	11	(	(	PUNCT
brj-23253	95	12	yang	yang	PROPN
brj-23253	95	13	et	et	PROPN
brj-23253	95	14	al	al	PROPN
brj-23253	95	15	.	.	PROPN
brj-23253	95	16	2019	2019	NUM
brj-23253	95	17	)	)	PUNCT
brj-23253	95	18	.	.	PUNCT
brj-23253	96	1	this	this	DET
brj-23253	96	2	study	study	NOUN
brj-23253	96	3	preprocessed	preprocesse	VERB
brj-23253	96	4	spectral	spectral	ADJ
brj-23253	96	5	data	datum	NOUN
brj-23253	96	6	using	use	VERB
brj-23253	96	7	the	the	DET
brj-23253	96	8	unscrambler	unscrambler	NOUN
brj-23253	96	9	x10.4	x10.4	PROPN
brj-23253	96	10	and	and	CCONJ
brj-23253	96	11	matlab	matlab	PROPN
brj-23253	96	12	software	software	NOUN
brj-23253	96	13	.	.	PUNCT
brj-23253	97	1	the	the	DET
brj-23253	97	2	processed	process	VERB
brj-23253	97	3	spectral	spectral	ADJ
brj-23253	97	4	curve	curve	NOUN
brj-23253	97	5	is	be	AUX
brj-23253	97	6	shown	show	VERB
brj-23253	97	7	in	in	ADP
brj-23253	97	8	fig	fig	NOUN
brj-23253	97	9	.	.	PUNCT
brj-23253	98	1	2	2	X
brj-23253	98	2	.	.	X
brj-23253	98	3	(	(	PUNCT
brj-23253	98	4	a	a	X
brj-23253	98	5	)	)	PUNCT
brj-23253	98	6	(	(	PUNCT
brj-23253	98	7	b	b	X
brj-23253	98	8	)	)	PUNCT
brj-23253	98	9	(	(	PUNCT
brj-23253	98	10	c	c	X
brj-23253	98	11	)	)	PUNCT
brj-23253	98	12	(	(	PUNCT
brj-23253	98	13	d	d	X
brj-23253	98	14	)	)	PUNCT
brj-23253	98	15	fig	fig	NOUN
brj-23253	98	16	.	.	PUNCT
brj-23253	99	1	2	2	X
brj-23253	99	2	.	.	X
brj-23253	99	3	spectral	spectral	ADJ
brj-23253	99	4	curve	curve	NOUN
brj-23253	99	5	after	after	ADP
brj-23253	99	6	preprocessing：(a	preprocessing：(a	PROPN
brj-23253	99	7	)	)	PUNCT
brj-23253	99	8	sg	sg	PROPN
brj-23253	99	9	,	,	PUNCT
brj-23253	99	10	(	(	PUNCT
brj-23253	99	11	b	b	X
brj-23253	99	12	)	)	PUNCT
brj-23253	99	13	snv	snv	NOUN
brj-23253	99	14	,	,	PUNCT
brj-23253	99	15	(	(	PUNCT
brj-23253	99	16	c	c	X
brj-23253	99	17	)	)	PUNCT
brj-23253	99	18	msc	msc	PROPN
brj-23253	99	19	,	,	PUNCT
brj-23253	99	20	(	(	PUNCT
brj-23253	99	21	d	d	X
brj-23253	99	22	)	)	PUNCT
brj-23253	99	23	fd	fd	PROPN
brj-23253	99	24	.	.	PUNCT
brj-23253	99	25	peer	peer	NOUN
brj-23253	99	26	-	-	PUNCT
brj-23253	99	27	reviewed	review	VERB
brj-23253	99	28	article	article	NOUN
brj-23253	99	29	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	99	30	li	li	PROPN
brj-23253	99	31	et	et	PROPN
brj-23253	99	32	al	al	PROPN
brj-23253	99	33	.	.	PROPN
brj-23253	100	1	(	(	PUNCT
brj-23253	100	2	2024	2024	NUM
brj-23253	100	3	)	)	PUNCT
brj-23253	100	4	.	.	PUNCT
brj-23253	101	1	“	"	PUNCT
brj-23253	101	2	alfalfa	alfalfa	X
brj-23253	101	3	protein	protein	NOUN
brj-23253	101	4	with	with	ADP
brj-23253	101	5	vis	vis	X
brj-23253	101	6	/	/	SYM
brj-23253	101	7	nir	nir	NOUN
brj-23253	101	8	,	,	PUNCT
brj-23253	101	9	”	"	PUNCT
brj-23253	101	10	bioresources	bioresource	NOUN
brj-23253	101	11	19(2	19(2	NUM
brj-23253	101	12	)	)	PUNCT
brj-23253	101	13	,	,	PUNCT
brj-23253	101	14	3808	3808	NUM
brj-23253	101	15	-	-	SYM
brj-23253	101	16	3825	3825	NUM
brj-23253	101	17	.	.	PUNCT
brj-23253	102	1	3813	3813	NUM
brj-23253	102	2	the	the	DET
brj-23253	102	3	savitzky	savitzky	NOUN
brj-23253	102	4	-	-	PUNCT
brj-23253	102	5	golay	golay	NOUN
brj-23253	102	6	filtering	filter	VERB
brj-23253	102	7	algorithm	algorithm	NOUN
brj-23253	102	8	optimizes	optimize	VERB
brj-23253	102	9	the	the	DET
brj-23253	102	10	moving	move	VERB
brj-23253	102	11	average	average	ADJ
brj-23253	102	12	method	method	NOUN
brj-23253	102	13	and	and	CCONJ
brj-23253	102	14	is	be	AUX
brj-23253	102	15	extensively	extensively	ADV
brj-23253	102	16	utilized	utilize	VERB
brj-23253	102	17	for	for	ADP
brj-23253	102	18	data	datum	NOUN
brj-23253	102	19	denoising	denoise	VERB
brj-23253	102	20	and	and	CCONJ
brj-23253	102	21	smoothing	smoothing	NOUN
brj-23253	102	22	.	.	PUNCT
brj-23253	103	1	its	its	PRON
brj-23253	103	2	core	core	NOUN
brj-23253	103	3	advantage	advantage	NOUN
brj-23253	103	4	lies	lie	VERB
brj-23253	103	5	in	in	ADP
brj-23253	103	6	its	its	PRON
brj-23253	103	7	ability	ability	NOUN
brj-23253	103	8	to	to	PART
brj-23253	103	9	maintain	maintain	VERB
brj-23253	103	10	the	the	DET
brj-23253	103	11	original	original	ADJ
brj-23253	103	12	shape	shape	NOUN
brj-23253	103	13	and	and	CCONJ
brj-23253	103	14	width	width	NOUN
brj-23253	103	15	of	of	ADP
brj-23253	103	16	the	the	DET
brj-23253	103	17	signal	signal	NOUN
brj-23253	103	18	unaffected	unaffected	ADJ
brj-23253	103	19	during	during	ADP
brj-23253	103	20	filtering	filtering	NOUN
brj-23253	103	21	.	.	PUNCT
brj-23253	104	1	this	this	DET
brj-23253	104	2	algorithm	algorithm	NOUN
brj-23253	104	3	,	,	PUNCT
brj-23253	104	4	through	through	ADP
brj-23253	104	5	specific	specific	ADJ
brj-23253	104	6	computational	computational	ADJ
brj-23253	104	7	formulas	formula	NOUN
brj-23253	104	8	,	,	PUNCT
brj-23253	104	9	effectively	effectively	ADV
brj-23253	104	10	balances	balance	VERB
brj-23253	104	11	the	the	DET
brj-23253	104	12	relationship	relationship	NOUN
brj-23253	104	13	between	between	ADP
brj-23253	104	14	denoising	denoising	NOUN
brj-23253	104	15	and	and	CCONJ
brj-23253	104	16	the	the	DET
brj-23253	104	17	preservation	preservation	NOUN
brj-23253	104	18	of	of	ADP
brj-23253	104	19	signal	signal	ADJ
brj-23253	104	20	characteristics	characteristic	NOUN
brj-23253	104	21	in	in	ADP
brj-23253	104	22	signal	signal	ADJ
brj-23253	104	23	processing	processing	NOUN
brj-23253	104	24	.	.	PUNCT
brj-23253	105	1	standard	standard	ADJ
brj-23253	105	2	normal	normal	ADJ
brj-23253	105	3	variate	variate	NOUN
brj-23253	105	4	(	(	PUNCT
brj-23253	105	5	snv	snv	PROPN
brj-23253	105	6	)	)	PUNCT
brj-23253	105	7	transformation	transformation	NOUN
brj-23253	105	8	is	be	AUX
brj-23253	105	9	a	a	DET
brj-23253	105	10	preprocessing	preprocessing	NOUN
brj-23253	105	11	technique	technique	NOUN
brj-23253	105	12	aimed	aim	VERB
brj-23253	105	13	at	at	ADP
brj-23253	105	14	minimizing	minimize	VERB
brj-23253	105	15	the	the	DET
brj-23253	105	16	impact	impact	NOUN
brj-23253	105	17	of	of	ADP
brj-23253	105	18	sample	sample	NOUN
brj-23253	105	19	granularity	granularity	NOUN
brj-23253	105	20	,	,	PUNCT
brj-23253	105	21	surface	surface	NOUN
brj-23253	105	22	reflection	reflection	NOUN
brj-23253	105	23	properties	property	NOUN
brj-23253	105	24	,	,	PUNCT
brj-23253	105	25	and	and	CCONJ
brj-23253	105	26	path	path	NOUN
brj-23253	105	27	length	length	NOUN
brj-23253	105	28	differences	difference	NOUN
brj-23253	105	29	on	on	ADP
brj-23253	105	30	the	the	DET
brj-23253	105	31	reflectance	reflectance	NOUN
brj-23253	105	32	of	of	ADP
brj-23253	105	33	near	near	ADV
brj-23253	105	34	-	-	PUNCT
brj-23253	105	35	infrared	infrared	ADJ
brj-23253	105	36	spectroscopy	spectroscopy	NOUN
brj-23253	105	37	data	datum	NOUN
brj-23253	105	38	.	.	PUNCT
brj-23253	106	1	this	this	DET
brj-23253	106	2	method	method	NOUN
brj-23253	106	3	adjusts	adjust	VERB
brj-23253	106	4	the	the	DET
brj-23253	106	5	data	datum	NOUN
brj-23253	106	6	to	to	PART
brj-23253	106	7	ensure	ensure	VERB
brj-23253	106	8	the	the	DET
brj-23253	106	9	accuracy	accuracy	NOUN
brj-23253	106	10	and	and	CCONJ
brj-23253	106	11	consistency	consistency	NOUN
brj-23253	106	12	of	of	ADP
brj-23253	106	13	the	the	DET
brj-23253	106	14	analysis	analysis	NOUN
brj-23253	106	15	,	,	PUNCT
brj-23253	106	16	making	make	VERB
brj-23253	106	17	it	it	PRON
brj-23253	106	18	suitable	suitable	ADJ
brj-23253	106	19	for	for	ADP
brj-23253	106	20	improving	improve	VERB
brj-23253	106	21	the	the	DET
brj-23253	106	22	quality	quality	NOUN
brj-23253	106	23	of	of	ADP
brj-23253	106	24	near	near	ADV
brj-23253	106	25	-	-	PUNCT
brj-23253	106	26	infrared	infrared	ADJ
brj-23253	106	27	spectroscopy	spectroscopy	NOUN
brj-23253	106	28	data	datum	NOUN
brj-23253	106	29	.	.	PUNCT
brj-23253	107	1	multiplicative	multiplicative	ADJ
brj-23253	107	2	scatter	scatter	NOUN
brj-23253	107	3	correction	correction	NOUN
brj-23253	107	4	(	(	PUNCT
brj-23253	107	5	msc	msc	PROPN
brj-23253	107	6	)	)	PUNCT
brj-23253	107	7	is	be	AUX
brj-23253	107	8	a	a	DET
brj-23253	107	9	technique	technique	NOUN
brj-23253	107	10	for	for	ADP
brj-23253	107	11	enhancing	enhance	VERB
brj-23253	107	12	spectroscopic	spectroscopic	ADJ
brj-23253	107	13	data	datum	NOUN
brj-23253	107	14	,	,	PUNCT
brj-23253	107	15	primarily	primarily	ADV
brj-23253	107	16	by	by	ADP
brj-23253	107	17	reducing	reduce	VERB
brj-23253	107	18	spectral	spectral	ADJ
brj-23253	107	19	variability	variability	NOUN
brj-23253	107	20	caused	cause	VERB
brj-23253	107	21	by	by	ADP
brj-23253	107	22	sample	sample	NOUN
brj-23253	107	23	scattering	scattering	NOUN
brj-23253	107	24	,	,	PUNCT
brj-23253	107	25	thus	thus	ADV
brj-23253	107	26	enhancing	enhance	VERB
brj-23253	107	27	the	the	DET
brj-23253	107	28	correlation	correlation	NOUN
brj-23253	107	29	between	between	ADP
brj-23253	107	30	spectroscopic	spectroscopic	ADJ
brj-23253	107	31	data	datum	NOUN
brj-23253	107	32	and	and	CCONJ
brj-23253	107	33	analytical	analytical	ADJ
brj-23253	107	34	results	result	NOUN
brj-23253	107	35	.	.	PUNCT
brj-23253	108	1	this	this	DET
brj-23253	108	2	method	method	NOUN
brj-23253	108	3	standardizes	standardize	VERB
brj-23253	108	4	the	the	DET
brj-23253	108	5	data	datum	NOUN
brj-23253	108	6	through	through	ADP
brj-23253	108	7	necessary	necessary	ADJ
brj-23253	108	8	scaling	scaling	NOUN
brj-23253	108	9	and	and	CCONJ
brj-23253	108	10	shifting	shift	VERB
brj-23253	108	11	corrections	correction	NOUN
brj-23253	108	12	by	by	ADP
brj-23253	108	13	comparing	compare	VERB
brj-23253	108	14	all	all	DET
brj-23253	108	15	sample	sample	NOUN
brj-23253	108	16	spectra	spectra	NOUN
brj-23253	108	17	with	with	ADP
brj-23253	108	18	a	a	DET
brj-23253	108	19	selected	select	VERB
brj-23253	108	20	reference	reference	NOUN
brj-23253	108	21	spectrum	spectrum	NOUN
brj-23253	108	22	,	,	PUNCT
brj-23253	108	23	ideally	ideally	ADV
brj-23253	108	24	chosen	choose	VERB
brj-23253	108	25	based	base	VERB
brj-23253	108	26	on	on	ADP
brj-23253	108	27	the	the	DET
brj-23253	108	28	mean	mean	NOUN
brj-23253	108	29	of	of	ADP
brj-23253	108	30	all	all	DET
brj-23253	108	31	sample	sample	NOUN
brj-23253	108	32	spectra	spectra	NOUN
brj-23253	108	33	.	.	PUNCT
brj-23253	109	1	in	in	ADP
brj-23253	109	2	data	datum	NOUN
brj-23253	109	3	acquisition	acquisition	NOUN
brj-23253	109	4	,	,	PUNCT
brj-23253	109	5	it	it	PRON
brj-23253	109	6	is	be	AUX
brj-23253	109	7	challenging	challenge	VERB
brj-23253	109	8	to	to	PART
brj-23253	109	9	completely	completely	ADV
brj-23253	109	10	eliminate	eliminate	VERB
brj-23253	109	11	errors	error	NOUN
brj-23253	109	12	caused	cause	VERB
brj-23253	109	13	by	by	ADP
brj-23253	109	14	background	background	NOUN
brj-23253	109	15	color	color	NOUN
brj-23253	109	16	or	or	CCONJ
brj-23253	109	17	other	other	ADJ
brj-23253	109	18	factors	factor	NOUN
brj-23253	109	19	.	.	PUNCT
brj-23253	110	1	the	the	DET
brj-23253	110	2	application	application	NOUN
brj-23253	110	3	of	of	ADP
brj-23253	110	4	the	the	DET
brj-23253	110	5	first	first	ADJ
brj-23253	110	6	-	-	PUNCT
brj-23253	110	7	order	order	NOUN
brj-23253	110	8	derivative	derivative	NOUN
brj-23253	110	9	(	(	PUNCT
brj-23253	110	10	fd	fd	NOUN
brj-23253	110	11	)	)	PUNCT
brj-23253	110	12	algorithm	algorithm	NOUN
brj-23253	110	13	can	can	AUX
brj-23253	110	14	effectively	effectively	ADV
brj-23253	110	15	remove	remove	VERB
brj-23253	110	16	the	the	DET
brj-23253	110	17	influence	influence	NOUN
brj-23253	110	18	of	of	ADP
brj-23253	110	19	baseline	baseline	ADJ
brj-23253	110	20	drift	drift	NOUN
brj-23253	110	21	or	or	CCONJ
brj-23253	110	22	background	background	NOUN
brj-23253	110	23	noise	noise	NOUN
brj-23253	110	24	,	,	PUNCT
brj-23253	110	25	while	while	SCONJ
brj-23253	110	26	enhancing	enhance	VERB
brj-23253	110	27	the	the	DET
brj-23253	110	28	resolution	resolution	NOUN
brj-23253	110	29	and	and	CCONJ
brj-23253	110	30	sensitivity	sensitivity	NOUN
brj-23253	110	31	by	by	ADP
brj-23253	110	32	increasing	increase	VERB
brj-23253	110	33	the	the	DET
brj-23253	110	34	distinguishability	distinguishability	NOUN
brj-23253	110	35	of	of	ADP
brj-23253	110	36	overlapping	overlap	VERB
brj-23253	110	37	peaks	peak	NOUN
brj-23253	110	38	.	.	PUNCT
brj-23253	111	1	first	first	ADJ
brj-23253	111	2	-	-	PUNCT
brj-23253	111	3	order	order	NOUN
brj-23253	111	4	derivative	derivative	ADJ
brj-23253	111	5	processing	processing	NOUN
brj-23253	111	6	alters	alter	VERB
brj-23253	111	7	the	the	DET
brj-23253	111	8	shape	shape	NOUN
brj-23253	111	9	of	of	ADP
brj-23253	111	10	the	the	DET
brj-23253	111	11	spectrum	spectrum	NOUN
brj-23253	111	12	,	,	PUNCT
brj-23253	111	13	providing	provide	VERB
brj-23253	111	14	information	information	NOUN
brj-23253	111	15	through	through	ADP
brj-23253	111	16	emphasizing	emphasize	VERB
brj-23253	111	17	the	the	DET
brj-23253	111	18	rate	rate	NOUN
brj-23253	111	19	of	of	ADP
brj-23253	111	20	change	change	NOUN
brj-23253	111	21	rather	rather	ADV
brj-23253	111	22	than	than	ADP
brj-23253	111	23	the	the	DET
brj-23253	111	24	absolute	absolute	ADJ
brj-23253	111	25	intensity	intensity	NOUN
brj-23253	111	26	.	.	PUNCT
brj-23253	112	1	this	this	DET
brj-23253	112	2	aids	aid	NOUN
brj-23253	112	3	in	in	ADP
brj-23253	112	4	the	the	DET
brj-23253	112	5	identification	identification	NOUN
brj-23253	112	6	and	and	CCONJ
brj-23253	112	7	quantitative	quantitative	ADJ
brj-23253	112	8	analysis	analysis	NOUN
brj-23253	112	9	of	of	ADP
brj-23253	112	10	specific	specific	ADJ
brj-23253	112	11	components	component	NOUN
brj-23253	112	12	,	,	PUNCT
brj-23253	112	13	although	although	SCONJ
brj-23253	112	14	it	it	PRON
brj-23253	112	15	may	may	AUX
brj-23253	112	16	complicate	complicate	VERB
brj-23253	112	17	data	datum	NOUN
brj-23253	112	18	interpretation	interpretation	NOUN
brj-23253	112	19	.	.	PUNCT
brj-23253	113	1	characteristic	characteristic	ADJ
brj-23253	113	2	wavelength	wavelength	NOUN
brj-23253	113	3	selection	selection	NOUN
brj-23253	113	4	feature	feature	NOUN
brj-23253	113	5	extraction	extraction	NOUN
brj-23253	113	6	plays	play	VERB
brj-23253	113	7	a	a	DET
brj-23253	113	8	pivotal	pivotal	ADJ
brj-23253	113	9	role	role	NOUN
brj-23253	113	10	in	in	ADP
brj-23253	113	11	near	near	ADV
brj-23253	113	12	-	-	PUNCT
brj-23253	113	13	infrared	infrared	ADJ
brj-23253	113	14	spectroscopy	spectroscopy	NOUN
brj-23253	113	15	analysis	analysis	NOUN
brj-23253	113	16	(	(	PUNCT
brj-23253	113	17	jo	jo	PROPN
brj-23253	113	18	et	et	PROPN
brj-23253	113	19	al	al	PROPN
brj-23253	113	20	.	.	PROPN
brj-23253	113	21	2020	2020	NUM
brj-23253	113	22	)	)	PUNCT
brj-23253	113	23	,	,	PUNCT
brj-23253	113	24	enabling	enable	VERB
brj-23253	113	25	the	the	DET
brj-23253	113	26	extraction	extraction	NOUN
brj-23253	113	27	of	of	ADP
brj-23253	113	28	crucial	crucial	ADJ
brj-23253	113	29	information	information	NOUN
brj-23253	113	30	from	from	ADP
brj-23253	113	31	complex	complex	ADJ
brj-23253	113	32	spectral	spectral	ADJ
brj-23253	113	33	data	datum	NOUN
brj-23253	113	34	related	relate	VERB
brj-23253	113	35	to	to	ADP
brj-23253	113	36	the	the	DET
brj-23253	113	37	properties	property	NOUN
brj-23253	113	38	of	of	ADP
brj-23253	113	39	the	the	DET
brj-23253	113	40	substances	substance	NOUN
brj-23253	113	41	under	under	ADP
brj-23253	113	42	investigation	investigation	NOUN
brj-23253	113	43	.	.	PUNCT
brj-23253	114	1	this	this	DET
brj-23253	114	2	process	process	NOUN
brj-23253	114	3	reduces	reduce	VERB
brj-23253	114	4	data	datum	NOUN
brj-23253	114	5	dimensionality	dimensionality	NOUN
brj-23253	114	6	,	,	PUNCT
brj-23253	114	7	simplifies	simplify	VERB
brj-23253	114	8	the	the	DET
brj-23253	114	9	model	model	NOUN
brj-23253	114	10	-	-	PUNCT
brj-23253	114	11	building	building	NOUN
brj-23253	114	12	process	process	NOUN
brj-23253	114	13	,	,	PUNCT
brj-23253	114	14	and	and	CCONJ
brj-23253	114	15	enhances	enhance	VERB
brj-23253	114	16	the	the	DET
brj-23253	114	17	accuracy	accuracy	NOUN
brj-23253	114	18	of	of	ADP
brj-23253	114	19	predictions	prediction	NOUN
brj-23253	114	20	(	(	PUNCT
brj-23253	114	21	mei	mei	PROPN
brj-23253	114	22	et	et	PROPN
brj-23253	114	23	al	al	PROPN
brj-23253	114	24	.	.	PROPN
brj-23253	114	25	2019	2019	NUM
brj-23253	114	26	)	)	PUNCT
brj-23253	114	27	.	.	PUNCT
brj-23253	115	1	in	in	ADP
brj-23253	115	2	this	this	DET
brj-23253	115	3	study	study	NOUN
brj-23253	115	4	,	,	PUNCT
brj-23253	115	5	aimed	aim	VERB
brj-23253	115	6	at	at	ADP
brj-23253	115	7	facilitating	facilitate	VERB
brj-23253	115	8	rapid	rapid	ADJ
brj-23253	115	9	and	and	CCONJ
brj-23253	115	10	non	non	ADJ
brj-23253	115	11	-	-	ADJ
brj-23253	115	12	destructive	destructive	ADJ
brj-23253	115	13	detection	detection	NOUN
brj-23253	115	14	of	of	ADP
brj-23253	115	15	nutritional	nutritional	ADJ
brj-23253	115	16	substances	substance	NOUN
brj-23253	115	17	in	in	ADP
brj-23253	115	18	purple	purple	ADJ
brj-23253	115	19	alfalfa	alfalfa	NOUN
brj-23253	115	20	,	,	PUNCT
brj-23253	115	21	the	the	DET
brj-23253	115	22	collected	collect	VERB
brj-23253	115	23	spectral	spectral	ADJ
brj-23253	115	24	data	datum	NOUN
brj-23253	115	25	in	in	ADP
brj-23253	115	26	the	the	DET
brj-23253	115	27	450	450	NUM
brj-23253	115	28	to	to	PART
brj-23253	115	29	1830	1830	NUM
brj-23253	115	30	nm	nm	PRON
brj-23253	115	31	range	range	NOUN
brj-23253	115	32	underwent	underwent	NOUN
brj-23253	115	33	preprocessing	preprocessing	NOUN
brj-23253	115	34	using	use	VERB
brj-23253	115	35	the	the	DET
brj-23253	115	36	four	four	NUM
brj-23253	115	37	different	different	ADJ
brj-23253	115	38	methods	method	NOUN
brj-23253	115	39	that	that	PRON
brj-23253	115	40	were	be	AUX
brj-23253	115	41	described	describe	VERB
brj-23253	115	42	earlier	early	ADV
brj-23253	115	43	.	.	PUNCT
brj-23253	116	1	this	this	PRON
brj-23253	116	2	was	be	AUX
brj-23253	116	3	followed	follow	VERB
brj-23253	116	4	by	by	ADP
brj-23253	116	5	integrating	integrate	VERB
brj-23253	116	6	competitive	competitive	ADJ
brj-23253	116	7	adaptive	adaptive	ADJ
brj-23253	116	8	reweighted	reweighte	VERB
brj-23253	116	9	sampling	sample	VERB
brj-23253	116	10	(	(	PUNCT
brj-23253	116	11	cars	car	NOUN
brj-23253	116	12	)	)	PUNCT
brj-23253	116	13	and	and	CCONJ
brj-23253	116	14	iteratively	iteratively	ADV
brj-23253	116	15	retains	retain	VERB
brj-23253	116	16	informative	informative	ADJ
brj-23253	116	17	variables	variable	NOUN
brj-23253	116	18	(	(	PUNCT
brj-23253	116	19	iriv	iriv	ADJ
brj-23253	116	20	)	)	PUNCT
brj-23253	116	21	algorithms	algorithm	NOUN
brj-23253	116	22	to	to	PART
brj-23253	116	23	extract	extract	VERB
brj-23253	116	24	characteristic	characteristic	ADJ
brj-23253	116	25	wavelengths	wavelength	NOUN
brj-23253	116	26	.	.	PUNCT
brj-23253	117	1	competitive	competitive	ADJ
brj-23253	117	2	adaptive	adaptive	ADJ
brj-23253	117	3	reweighted	reweighte	VERB
brj-23253	117	4	sampling	sample	VERB
brj-23253	117	5	(	(	PUNCT
brj-23253	117	6	cars	car	NOUN
brj-23253	117	7	)	)	PUNCT
brj-23253	117	8	the	the	DET
brj-23253	117	9	competitive	competitive	ADJ
brj-23253	117	10	adaptive	adaptive	ADJ
brj-23253	117	11	reweighted	reweighte	VERB
brj-23253	117	12	sampling	sample	VERB
brj-23253	117	13	(	(	PUNCT
brj-23253	117	14	cars	car	NOUN
brj-23253	117	15	)	)	PUNCT
brj-23253	117	16	algorithm	algorithm	NOUN
brj-23253	117	17	is	be	AUX
brj-23253	117	18	a	a	DET
brj-23253	117	19	feature	feature	NOUN
brj-23253	117	20	selection	selection	NOUN
brj-23253	117	21	method	method	NOUN
brj-23253	117	22	based	base	VERB
brj-23253	117	23	on	on	ADP
brj-23253	117	24	competitive	competitive	ADJ
brj-23253	117	25	neural	neural	ADJ
brj-23253	117	26	networks	network	NOUN
brj-23253	117	27	.	.	PUNCT
brj-23253	118	1	it	it	PRON
brj-23253	118	2	selects	select	VERB
brj-23253	118	3	feature	feature	NOUN
brj-23253	118	4	wavelengths	wavelength	NOUN
brj-23253	118	5	highly	highly	ADV
brj-23253	118	6	relevant	relevant	ADJ
brj-23253	118	7	to	to	ADP
brj-23253	118	8	the	the	DET
brj-23253	118	9	target	target	NOUN
brj-23253	118	10	variable	variable	NOUN
brj-23253	118	11	through	through	ADP
brj-23253	118	12	a	a	DET
brj-23253	118	13	competitive	competitive	ADJ
brj-23253	118	14	,	,	PUNCT
brj-23253	118	15	adaptive	adaptive	ADJ
brj-23253	118	16	approach	approach	NOUN
brj-23253	118	17	.	.	PUNCT
brj-23253	119	1	the	the	DET
brj-23253	119	2	algorithm	algorithm	NOUN
brj-23253	119	3	iteratively	iteratively	ADV
brj-23253	119	4	adjusts	adjust	VERB
brj-23253	119	5	weights	weight	NOUN
brj-23253	119	6	based	base	VERB
brj-23253	119	7	on	on	ADP
brj-23253	119	8	the	the	DET
brj-23253	119	9	interaction	interaction	NOUN
brj-23253	119	10	and	and	CCONJ
brj-23253	119	11	importance	importance	NOUN
brj-23253	119	12	of	of	ADP
brj-23253	119	13	feature	feature	NOUN
brj-23253	119	14	wavelengths	wavelength	NOUN
brj-23253	119	15	,	,	PUNCT
brj-23253	119	16	thereby	thereby	ADV
brj-23253	119	17	selecting	select	VERB
brj-23253	119	18	the	the	DET
brj-23253	119	19	most	most	ADV
brj-23253	119	20	representative	representative	ADJ
brj-23253	119	21	wavelengths	wavelength	NOUN
brj-23253	119	22	(	(	PUNCT
brj-23253	119	23	xie	xie	PROPN
brj-23253	119	24	et	et	PROPN
brj-23253	119	25	al	al	PROPN
brj-23253	119	26	.	.	PROPN
brj-23253	119	27	2022	2022	NUM
brj-23253	119	28	)	)	PUNCT
brj-23253	119	29	.	.	PUNCT
brj-23253	120	1	the	the	DET
brj-23253	120	2	analysis	analysis	NOUN
brj-23253	120	3	process	process	NOUN
brj-23253	120	4	of	of	ADP
brj-23253	120	5	the	the	DET
brj-23253	120	6	cars	car	NOUN
brj-23253	120	7	algorithm	algorithm	NOUN
brj-23253	120	8	is	be	AUX
brj-23253	120	9	as	as	SCONJ
brj-23253	120	10	follows	follow	VERB
brj-23253	120	11	:	:	PUNCT
brj-23253	120	12	(	(	PUNCT
brj-23253	120	13	1	1	NUM
brj-23253	120	14	)	)	PUNCT
brj-23253	120	15	.	.	PUNCT
brj-23253	121	1	monte	monte	PROPN
brj-23253	121	2	carlo	carlo	PROPN
brj-23253	121	3	model	model	NOUN
brj-23253	121	4	sampling	sample	VERB
brj-23253	121	5	:	:	PUNCT
brj-23253	121	6	the	the	DET
brj-23253	121	7	dataset	dataset	NOUN
brj-23253	121	8	is	be	AUX
brj-23253	121	9	randomly	randomly	ADV
brj-23253	121	10	divided	divide	VERB
brj-23253	121	11	for	for	ADP
brj-23253	121	12	model	model	NOUN
brj-23253	121	13	construction	construction	NOUN
brj-23253	121	14	,	,	PUNCT
brj-23253	121	15	with	with	ADP
brj-23253	121	16	a	a	DET
brj-23253	121	17	split	split	ADJ
brj-23253	121	18	ratio	ratio	NOUN
brj-23253	121	19	of	of	ADP
brj-23253	121	20	80	80	NUM
brj-23253	121	21	%	%	NOUN
brj-23253	121	22	to	to	PART
brj-23253	121	23	90	90	NUM
brj-23253	121	24	%	%	NOUN
brj-23253	121	25	,	,	PUNCT
brj-23253	121	26	to	to	PART
brj-23253	121	27	establish	establish	VERB
brj-23253	121	28	a	a	DET
brj-23253	121	29	pls	pls	NOUN
brj-23253	121	30	(	(	PUNCT
brj-23253	121	31	partial	partial	ADJ
brj-23253	121	32	least	least	ADJ
brj-23253	121	33	squares	square	NOUN
brj-23253	121	34	)	)	PUNCT
brj-23253	121	35	model	model	NOUN
brj-23253	121	36	.	.	PUNCT
brj-23253	122	1	this	this	DET
brj-23253	122	2	process	process	NOUN
brj-23253	122	3	yields	yield	VERB
brj-23253	122	4	the	the	DET
brj-23253	122	5	regression	regression	NOUN
brj-23253	122	6	coefficient	coefficient	NOUN
brj-23253	122	7	for	for	ADP
brj-23253	122	8	the	the	DET
brj-23253	122	9	ith	ith	PROPN
brj-23253	122	10	wavelength	wavelength	NOUN
brj-23253	122	11	.	.	PUNCT
brj-23253	123	1	(	(	PUNCT
brj-23253	123	2	2	2	NUM
brj-23253	123	3	)	)	PUNCT
brj-23253	123	4	.	.	PUNCT
brj-23253	124	1	exponential	exponential	ADJ
brj-23253	124	2	decay	decay	NOUN
brj-23253	124	3	wavelength	wavelength	NOUN
brj-23253	124	4	selection	selection	NOUN
brj-23253	124	5	:	:	PUNCT
brj-23253	124	6	the	the	DET
brj-23253	124	7	method	method	NOUN
brj-23253	124	8	uses	use	VERB
brj-23253	124	9	an	an	DET
brj-23253	124	10	exponentially	exponentially	ADV
brj-23253	124	11	decreasing	decrease	VERB
brj-23253	124	12	function	function	NOUN
brj-23253	124	13	(	(	PUNCT
brj-23253	124	14	edf	edf	PROPN
brj-23253	124	15	)	)	PUNCT
brj-23253	124	16	to	to	PART
brj-23253	124	17	forcibly	forcibly	ADV
brj-23253	124	18	eliminate	eliminate	VERB
brj-23253	124	19	wavelengths	wavelength	NOUN
brj-23253	124	20	having	have	VERB
brj-23253	124	21	relatively	relatively	ADV
brj-23253	124	22	small	small	ADJ
brj-23253	124	23	peer	peer	NOUN
brj-23253	124	24	-	-	PUNCT
brj-23253	124	25	reviewed	review	VERB
brj-23253	124	26	article	article	NOUN
brj-23253	124	27	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	124	28	li	li	PROPN
brj-23253	124	29	et	et	PROPN
brj-23253	124	30	al	al	PROPN
brj-23253	124	31	.	.	PROPN
brj-23253	125	1	(	(	PUNCT
brj-23253	125	2	2024	2024	NUM
brj-23253	125	3	)	)	PUNCT
brj-23253	125	4	.	.	PUNCT
brj-23253	126	1	“	"	PUNCT
brj-23253	126	2	alfalfa	alfalfa	X
brj-23253	126	3	protein	protein	NOUN
brj-23253	126	4	with	with	ADP
brj-23253	126	5	vis	vis	X
brj-23253	126	6	/	/	SYM
brj-23253	126	7	nir	nir	NOUN
brj-23253	126	8	,	,	PUNCT
brj-23253	126	9	”	"	PUNCT
brj-23253	126	10	bioresources	bioresource	NOUN
brj-23253	126	11	19(2	19(2	NUM
brj-23253	126	12	)	)	PUNCT
brj-23253	126	13	,	,	PUNCT
brj-23253	126	14	3808	3808	NUM
brj-23253	126	15	-	-	SYM
brj-23253	126	16	3825	3825	NUM
brj-23253	126	17	.	.	PUNCT
brj-23253	127	1	3814	3814	NUM
brj-23253	127	2	absolute	absolute	ADJ
brj-23253	127	3	weight	weight	NOUN
brj-23253	127	4	in	in	ADP
brj-23253	127	5	regression	regression	NOUN
brj-23253	127	6	coefficients	coefficient	NOUN
brj-23253	127	7	.	.	PUNCT
brj-23253	128	1	the	the	DET
brj-23253	128	2	retention	retention	NOUN
brj-23253	128	3	rate	rate	NOUN
brj-23253	128	4	of	of	ADP
brj-23253	128	5	variables	variable	NOUN
brj-23253	128	6	is	be	AUX
brj-23253	128	7	.	.	PUNCT
brj-23253	129	1	where	where	SCONJ
brj-23253	129	2	‘	'	PUNCT
brj-23253	129	3	j	j	NOUN
brj-23253	129	4	’	'	PUNCT
brj-23253	129	5	denotes	denote	VERB
brj-23253	129	6	the	the	DET
brj-23253	129	7	jth	jth	PROPN
brj-23253	129	8	monte	monte	PROPN
brj-23253	129	9	carlo	carlo	PROPN
brj-23253	129	10	sampling	sampling	NOUN
brj-23253	129	11	,	,	PUNCT
brj-23253	129	12	‘	'	PUNCT
brj-23253	129	13	n	n	CCONJ
brj-23253	129	14	’	'	PUNCT
brj-23253	129	15	represents	represent	VERB
brj-23253	129	16	the	the	DET
brj-23253	129	17	total	total	ADJ
brj-23253	129	18	number	number	NOUN
brj-23253	129	19	of	of	ADP
brj-23253	129	20	monte	monte	PROPN
brj-23253	129	21	carlo	carlo	PROPN
brj-23253	129	22	samplings	sampling	NOUN
brj-23253	129	23	,	,	PUNCT
brj-23253	129	24	and	and	CCONJ
brj-23253	129	25	the	the	DET
brj-23253	129	26	parameters	parameter	NOUN
brj-23253	129	27	‘	'	PUNCT
brj-23253	129	28	a	a	PRON
brj-23253	129	29	’	'	PUNCT
brj-23253	129	30	and	and	CCONJ
brj-23253	129	31	‘	'	PUNCT
brj-23253	129	32	b	b	X
brj-23253	129	33	’	'	PUNCT
brj-23253	129	34	are	be	AUX
brj-23253	129	35	constants	constant	NOUN
brj-23253	129	36	.	.	PUNCT
brj-23253	130	1	(	(	PUNCT
brj-23253	130	2	3	3	NUM
brj-23253	130	3	)	)	PUNCT
brj-23253	130	4	.	.	PUNCT
brj-23253	131	1	adaptive	adaptive	ADJ
brj-23253	131	2	reweighting	reweighting	NOUN
brj-23253	131	3	sampling	sampling	NOUN
brj-23253	131	4	:	:	PUNCT
brj-23253	131	5	selection	selection	NOUN
brj-23253	131	6	is	be	AUX
brj-23253	131	7	conducted	conduct	VERB
brj-23253	131	8	using	use	VERB
brj-23253	131	9	the	the	DET
brj-23253	131	10	evaluation	evaluation	NOUN
brj-23253	131	11	weights	weight	NOUN
brj-23253	131	12	as	as	ADP
brj-23253	131	13	in	in	ADP
brj-23253	131	14	.	.	PUNCT
brj-23253	132	1	(	(	PUNCT
brj-23253	132	2	4	4	NUM
brj-23253	132	3	)	)	PUNCT
brj-23253	132	4	.	.	PUNCT
brj-23253	133	1	cyclic	cyclic	ADJ
brj-23253	133	2	iteration	iteration	NOUN
brj-23253	133	3	:	:	PUNCT
brj-23253	133	4	the	the	DET
brj-23253	133	5	process	process	NOUN
brj-23253	133	6	involves	involve	VERB
brj-23253	133	7	iterative	iterative	ADJ
brj-23253	133	8	calculations	calculation	NOUN
brj-23253	133	9	based	base	VERB
brj-23253	133	10	on	on	ADP
brj-23253	133	11	a	a	DET
brj-23253	133	12	set	set	ADJ
brj-23253	133	13	number	number	NOUN
brj-23253	133	14	of	of	ADP
brj-23253	133	15	cycle	cycle	NOUN
brj-23253	133	16	iterations	iteration	NOUN
brj-23253	133	17	.	.	PUNCT
brj-23253	134	1	the	the	DET
brj-23253	134	2	optimal	optimal	ADJ
brj-23253	134	3	set	set	NOUN
brj-23253	134	4	of	of	ADP
brj-23253	134	5	variables	variable	NOUN
brj-23253	134	6	is	be	AUX
brj-23253	134	7	based	base	VERB
brj-23253	134	8	on	on	ADP
brj-23253	134	9	the	the	DET
brj-23253	134	10	minimum	minimum	NOUN
brj-23253	134	11	crossvalidation	crossvalidation	NOUN
brj-23253	134	12	root	root	NOUN
brj-23253	134	13	mean	mean	VERB
brj-23253	134	14	square	square	NOUN
brj-23253	134	15	error	error	NOUN
brj-23253	134	16	,	,	PUNCT
brj-23253	134	17	representing	represent	VERB
brj-23253	134	18	the	the	DET
brj-23253	134	19	desired	desire	VERB
brj-23253	134	20	characteristic	characteristic	ADJ
brj-23253	134	21	variables	variable	NOUN
brj-23253	134	22	.	.	PUNCT
brj-23253	135	1	(	(	PUNCT
brj-23253	135	2	a	a	X
brj-23253	135	3	)	)	PUNCT
brj-23253	135	4	(	(	PUNCT
brj-23253	135	5	b	b	X
brj-23253	135	6	)	)	PUNCT
brj-23253	135	7	(	(	PUNCT
brj-23253	135	8	c	c	X
brj-23253	135	9	)	)	PUNCT
brj-23253	135	10	(	(	PUNCT
brj-23253	135	11	d	d	X
brj-23253	135	12	)	)	PUNCT
brj-23253	135	13	fig	fig	NOUN
brj-23253	135	14	.	.	PUNCT
brj-23253	136	1	3	3	X
brj-23253	136	2	.	.	X
brj-23253	136	3	selection	selection	NOUN
brj-23253	136	4	of	of	ADP
brj-23253	136	5	characteristic	characteristic	ADJ
brj-23253	136	6	wavelengths	wavelength	NOUN
brj-23253	136	7	after	after	ADP
brj-23253	136	8	cars：(a	cars：(a	NOUN
brj-23253	136	9	)	)	PUNCT
brj-23253	136	10	sg	sg	NOUN
brj-23253	136	11	-	-	PUNCT
brj-23253	136	12	cars	car	NOUN
brj-23253	136	13	,	,	PUNCT
brj-23253	136	14	(	(	PUNCT
brj-23253	136	15	b	b	X
brj-23253	136	16	)	)	PUNCT
brj-23253	136	17	snv	snv	NOUN
brj-23253	136	18	-	-	PUNCT
brj-23253	136	19	cars	car	NOUN
brj-23253	136	20	,	,	PUNCT
brj-23253	136	21	(	(	PUNCT
brj-23253	136	22	c	c	X
brj-23253	136	23	)	)	PUNCT
brj-23253	136	24	msc	msc	NOUN
brj-23253	136	25	-	-	PUNCT
brj-23253	136	26	cars	car	NOUN
brj-23253	136	27	,	,	PUNCT
brj-23253	136	28	(	(	PUNCT
brj-23253	136	29	d	d	X
brj-23253	136	30	)	)	PUNCT
brj-23253	136	31	fd	fd	NOUN
brj-23253	136	32	-	-	PUNCT
brj-23253	136	33	cars	car	NOUN
brj-23253	136	34	when	when	SCONJ
brj-23253	136	35	applying	apply	VERB
brj-23253	136	36	the	the	DET
brj-23253	136	37	cars	car	NOUN
brj-23253	136	38	method	method	NOUN
brj-23253	136	39	for	for	ADP
brj-23253	136	40	extracting	extract	VERB
brj-23253	136	41	feature	feature	NOUN
brj-23253	136	42	wavelengths	wavelength	NOUN
brj-23253	136	43	from	from	ADP
brj-23253	136	44	preprocessed	preprocesse	VERB
brj-23253	136	45	spectral	spectral	ADJ
brj-23253	136	46	data	datum	NOUN
brj-23253	136	47	using	use	VERB
brj-23253	136	48	four	four	NUM
brj-23253	136	49	different	different	ADJ
brj-23253	136	50	methods	method	NOUN
brj-23253	136	51	,	,	PUNCT
brj-23253	136	52	the	the	DET
brj-23253	136	53	monte	monte	PROPN
brj-23253	136	54	carlo	carlo	NOUN
brj-23253	136	55	sampling	sampling	NOUN
brj-23253	136	56	was	be	AUX
brj-23253	136	57	set	set	VERB
brj-23253	136	58	to	to	ADP
brj-23253	136	59	50	50	NUM
brj-23253	136	60	times	time	NOUN
brj-23253	136	61	,	,	PUNCT
brj-23253	136	62	employing	employ	VERB
brj-23253	136	63	a	a	DET
brj-23253	136	64	10	10	NUM
brj-23253	136	65	-	-	ADJ
brj-23253	136	66	fold	fold	ADJ
brj-23253	136	67	cross	cross	NOUN
brj-23253	136	68	-	-	NOUN
brj-23253	136	69	validation	validation	NOUN
brj-23253	136	70	.	.	PUNCT
brj-23253	137	1	the	the	DET
brj-23253	137	2	process	process	NOUN
brj-23253	137	3	of	of	ADP
brj-23253	137	4	variable	variable	ADJ
brj-23253	137	5	reduction	reduction	NOUN
brj-23253	137	6	exhibited	exhibit	VERB
brj-23253	137	7	an	an	DET
brj-23253	137	8	exponential	exponential	ADJ
brj-23253	137	9	decay	decay	NOUN
brj-23253	137	10	,	,	PUNCT
brj-23253	137	11	with	with	ADP
brj-23253	137	12	a	a	DET
brj-23253	137	13	rapid	rapid	ADJ
brj-23253	137	14	decrease	decrease	NOUN
brj-23253	137	15	in	in	ADP
brj-23253	137	16	the	the	DET
brj-23253	137	17	number	number	NOUN
brj-23253	137	18	of	of	ADP
brj-23253	137	19	variables	variable	NOUN
brj-23253	137	20	in	in	ADP
brj-23253	137	21	the	the	DET
brj-23253	137	22	initial	initial	ADJ
brj-23253	137	23	phase	phase	NOUN
brj-23253	137	24	and	and	CCONJ
brj-23253	137	25	a	a	DET
brj-23253	137	26	much	much	ADV
brj-23253	137	27	slower	slow	ADJ
brj-23253	137	28	decrease	decrease	NOUN
brj-23253	137	29	in	in	ADP
brj-23253	137	30	the	the	DET
brj-23253	137	31	second	second	ADJ
brj-23253	137	32	phase	phase	NOUN
brj-23253	137	33	,	,	PUNCT
brj-23253	137	34	indicating	indicate	VERB
brj-23253	137	35	"	"	PUNCT
brj-23253	137	36	rough	rough	ADJ
brj-23253	137	37	"	"	PUNCT
brj-23253	137	38	and	and	CCONJ
brj-23253	137	39	"	"	PUNCT
brj-23253	137	40	fine	fine	ADJ
brj-23253	137	41	"	"	PUNCT
brj-23253	137	42	selection	selection	NOUN
brj-23253	137	43	stages	stage	NOUN
brj-23253	137	44	(	(	PUNCT
brj-23253	137	45	chen	chen	PROPN
brj-23253	137	46	et	et	PROPN
brj-23253	137	47	al	al	PROPN
brj-23253	137	48	.	.	PROPN
brj-23253	137	49	2020	2020	NUM
brj-23253	137	50	;	;	PUNCT
brj-23253	137	51	li	li	PROPN
brj-23253	137	52	et	et	PROPN
brj-23253	137	53	al	al	PROPN
brj-23253	137	54	.	.	PROPN
brj-23253	137	55	2022	2022	NUM
brj-23253	137	56	)	)	PUNCT
brj-23253	137	57	.	.	PUNCT
brj-23253	138	1	the	the	DET
brj-23253	138	2	change	change	NOUN
brj-23253	138	3	in	in	ADP
brj-23253	138	4	the	the	DET
brj-23253	138	5	10	10	NUM
brj-23253	138	6	-	-	ADJ
brj-23253	138	7	fold	fold	ADJ
brj-23253	138	8	crossvalidation	crossvalidation	NOUN
brj-23253	138	9	root	root	NOUN
brj-23253	138	10	mean	mean	VERB
brj-23253	138	11	square	square	ADJ
brj-23253	138	12	error	error	NOUN
brj-23253	138	13	initially	initially	ADV
brj-23253	138	14	decreases	decrease	VERB
brj-23253	138	15	and	and	CCONJ
brj-23253	138	16	then	then	ADV
brj-23253	138	17	gradually	gradually	ADV
brj-23253	138	18	increases	increase	VERB
brj-23253	138	19	,	,	PUNCT
brj-23253	138	20	suggesting	suggest	VERB
brj-23253	138	21	that	that	SCONJ
brj-23253	138	22	less	less	ADV
brj-23253	138	23	relevant	relevant	ADJ
brj-23253	138	24	wavelengths	wavelength	NOUN
brj-23253	138	25	to	to	ADP
brj-23253	138	26	protein	protein	NOUN
brj-23253	138	27	content	content	NOUN
brj-23253	138	28	in	in	ADP
brj-23253	138	29	alfalfa	alfalfa	PROPN
brj-23253	138	30	spectral	spectral	PROPN
brj-23253	138	31	data	datum	NOUN
brj-23253	138	32	are	be	AUX
brj-23253	138	33	discarded	discard	VERB
brj-23253	138	34	initially	initially	ADV
brj-23253	138	35	,	,	PUNCT
brj-23253	138	36	and	and	CCONJ
brj-23253	138	37	later	later	ADV
brj-23253	138	38	,	,	PUNCT
brj-23253	138	39	due	due	ADP
brj-23253	138	40	to	to	ADP
brj-23253	138	41	high	high	ADJ
brj-23253	138	42	selectivity	selectivity	NOUN
brj-23253	138	43	,	,	PUNCT
brj-23253	138	44	some	some	DET
brj-23253	138	45	critical	critical	ADJ
brj-23253	138	46	parameters	parameter	NOUN
brj-23253	138	47	are	be	AUX
brj-23253	138	48	excluded	exclude	VERB
brj-23253	138	49	,	,	PUNCT
brj-23253	138	50	leading	lead	VERB
brj-23253	138	51	to	to	ADP
brj-23253	138	52	a	a	DET
brj-23253	138	53	gradual	gradual	ADJ
brj-23253	138	54	increase	increase	NOUN
brj-23253	138	55	in	in	ADP
brj-23253	138	56	error	error	NOUN
brj-23253	138	57	.	.	PUNCT
brj-23253	139	1	the	the	DET
brj-23253	139	2	best	good	ADJ
brj-23253	139	3	iteration	iteration	NOUN
brj-23253	139	4	numbers	number	NOUN
brj-23253	139	5	for	for	ADP
brj-23253	139	6	different	different	ADJ
brj-23253	139	7	preprocessing	preprocessing	NOUN
brj-23253	139	8	methods	method	NOUN
brj-23253	139	9	were	be	AUX
brj-23253	139	10	as	as	SCONJ
brj-23253	139	11	follows	follow	VERB
brj-23253	139	12	:	:	PUNCT
brj-23253	140	1	sg	sg	ADP
brj-23253	140	2	convolution	convolution	NOUN
brj-23253	140	3	smoothing	smooth	VERB
brj-23253	140	4	(	(	PUNCT
brj-23253	140	5	18	18	NUM
brj-23253	140	6	iterations	iteration	NOUN
brj-23253	140	7	,	,	PUNCT
brj-23253	140	8	96	96	NUM
brj-23253	140	9	feature	feature	NOUN
brj-23253	140	10	wavelengths	wavelength	NOUN
brj-23253	140	11	,	,	PUNCT
brj-23253	140	12	6.95	6.95	NUM
brj-23253	140	13	%	%	NOUN
brj-23253	140	14	of	of	ADP
brj-23253	140	15	the	the	DET
brj-23253	140	16	full	full	ADJ
brj-23253	140	17	spectrum	spectrum	NOUN
brj-23253	140	18	)	)	PUNCT
brj-23253	140	19	;	;	PUNCT
brj-23253	140	20	standard	standard	ADJ
brj-23253	140	21	normal	normal	ADJ
brj-23253	140	22	variate	variate	NOUN
brj-23253	140	23	transformation	transformation	NOUN
brj-23253	140	24	(	(	PUNCT
brj-23253	140	25	22	22	NUM
brj-23253	140	26	peer	peer	NOUN
brj-23253	140	27	-	-	PUNCT
brj-23253	140	28	reviewed	review	VERB
brj-23253	140	29	article	article	NOUN
brj-23253	140	30	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	140	31	li	li	PROPN
brj-23253	140	32	et	et	PROPN
brj-23253	140	33	al	al	PROPN
brj-23253	140	34	.	.	PROPN
brj-23253	141	1	(	(	PUNCT
brj-23253	141	2	2024	2024	NUM
brj-23253	141	3	)	)	PUNCT
brj-23253	141	4	.	.	PUNCT
brj-23253	142	1	“	"	PUNCT
brj-23253	142	2	alfalfa	alfalfa	X
brj-23253	142	3	protein	protein	NOUN
brj-23253	142	4	with	with	ADP
brj-23253	142	5	vis	vis	X
brj-23253	142	6	/	/	SYM
brj-23253	142	7	nir	nir	NOUN
brj-23253	142	8	,	,	PUNCT
brj-23253	142	9	”	"	PUNCT
brj-23253	142	10	bioresources	bioresource	NOUN
brj-23253	142	11	19(2	19(2	NUM
brj-23253	142	12	)	)	PUNCT
brj-23253	142	13	,	,	PUNCT
brj-23253	142	14	3808	3808	NUM
brj-23253	142	15	-	-	SYM
brj-23253	142	16	3825	3825	NUM
brj-23253	142	17	.	.	PUNCT
brj-23253	143	1	3815	3815	NUM
brj-23253	143	2	iterations	iteration	NOUN
brj-23253	143	3	,	,	PUNCT
brj-23253	143	4	143	143	NUM
brj-23253	143	5	feature	feature	NOUN
brj-23253	143	6	wavelengths	wavelength	NOUN
brj-23253	143	7	,	,	PUNCT
brj-23253	143	8	10.35	10.35	NUM
brj-23253	143	9	%	%	NOUN
brj-23253	143	10	of	of	ADP
brj-23253	143	11	the	the	DET
brj-23253	143	12	full	full	ADJ
brj-23253	143	13	spectrum	spectrum	NOUN
brj-23253	143	14	)	)	PUNCT
brj-23253	143	15	;	;	PUNCT
brj-23253	143	16	multiplicative	multiplicative	ADJ
brj-23253	143	17	scatter	scatter	NOUN
brj-23253	143	18	correction	correction	NOUN
brj-23253	143	19	(	(	PUNCT
brj-23253	143	20	21	21	NUM
brj-23253	143	21	iterations	iteration	NOUN
brj-23253	143	22	,	,	PUNCT
brj-23253	143	23	96	96	NUM
brj-23253	143	24	feature	feature	NOUN
brj-23253	143	25	wavelengths	wavelength	NOUN
brj-23253	143	26	,	,	PUNCT
brj-23253	143	27	6.95	6.95	NUM
brj-23253	143	28	%	%	NOUN
brj-23253	143	29	of	of	ADP
brj-23253	143	30	the	the	DET
brj-23253	143	31	full	full	ADJ
brj-23253	143	32	spectrum	spectrum	NOUN
brj-23253	143	33	)	)	PUNCT
brj-23253	143	34	;	;	PUNCT
brj-23253	143	35	first	first	ADJ
brj-23253	143	36	derivative	derivative	ADJ
brj-23253	143	37	operation	operation	NOUN
brj-23253	143	38	(	(	PUNCT
brj-23253	143	39	19	19	NUM
brj-23253	143	40	iterations	iteration	NOUN
brj-23253	143	41	,	,	PUNCT
brj-23253	143	42	163	163	NUM
brj-23253	143	43	feature	feature	NOUN
brj-23253	143	44	wavelengths	wavelength	NOUN
brj-23253	143	45	,	,	PUNCT
brj-23253	143	46	11.8	11.8	NUM
brj-23253	143	47	%	%	NOUN
brj-23253	143	48	of	of	ADP
brj-23253	143	49	the	the	DET
brj-23253	143	50	full	full	ADJ
brj-23253	143	51	spectrum	spectrum	NOUN
brj-23253	143	52	)	)	PUNCT
brj-23253	143	53	.	.	PUNCT
brj-23253	144	1	the	the	DET
brj-23253	144	2	feature	feature	NOUN
brj-23253	144	3	wavelengths	wavelength	NOUN
brj-23253	144	4	extracted	extract	VERB
brj-23253	144	5	by	by	ADP
brj-23253	144	6	different	different	ADJ
brj-23253	144	7	preprocessing	preprocessing	NOUN
brj-23253	144	8	methods	method	NOUN
brj-23253	144	9	are	be	AUX
brj-23253	144	10	illustrated	illustrate	VERB
brj-23253	144	11	in	in	ADP
brj-23253	144	12	fig	fig	NOUN
brj-23253	144	13	.	.	PUNCT
brj-23253	145	1	3	3	X
brj-23253	145	2	.	.	X
brj-23253	145	3	the	the	DET
brj-23253	145	4	blue	blue	ADJ
brj-23253	145	5	line	line	NOUN
brj-23253	145	6	represents	represent	VERB
brj-23253	145	7	the	the	DET
brj-23253	145	8	average	average	ADJ
brj-23253	145	9	spectral	spectral	ADJ
brj-23253	145	10	data	datum	NOUN
brj-23253	145	11	after	after	ADP
brj-23253	145	12	preprocessing	preprocessing	NOUN
brj-23253	145	13	,	,	PUNCT
brj-23253	145	14	and	and	CCONJ
brj-23253	145	15	the	the	DET
brj-23253	145	16	red	red	ADJ
brj-23253	145	17	circles	circle	NOUN
brj-23253	145	18	denote	denote	VERB
brj-23253	145	19	the	the	DET
brj-23253	145	20	characteristic	characteristic	ADJ
brj-23253	145	21	wavelengths	wavelength	NOUN
brj-23253	145	22	extracted	extract	VERB
brj-23253	145	23	by	by	ADP
brj-23253	145	24	the	the	DET
brj-23253	145	25	cars	car	NOUN
brj-23253	145	26	algorithm	algorithm	NOUN
brj-23253	145	27	.	.	PUNCT
brj-23253	146	1	iterative	iterative	NOUN
brj-23253	146	2	retention	retention	NOUN
brj-23253	146	3	of	of	ADP
brj-23253	146	4	information	information	NOUN
brj-23253	146	5	variables	variable	NOUN
brj-23253	146	6	method	method	NOUN
brj-23253	146	7	(	(	PUNCT
brj-23253	146	8	iriv	iriv	PROPN
brj-23253	146	9	)	)	PUNCT
brj-23253	146	10	the	the	DET
brj-23253	146	11	iteratively	iteratively	ADV
brj-23253	146	12	retains	retain	VERB
brj-23253	146	13	informative	informative	ADJ
brj-23253	146	14	variables	variable	NOUN
brj-23253	146	15	(	(	PUNCT
brj-23253	146	16	iriv	iriv	ADJ
brj-23253	146	17	)	)	PUNCT
brj-23253	146	18	algorithm	algorithm	NOUN
brj-23253	146	19	is	be	AUX
brj-23253	146	20	a	a	DET
brj-23253	146	21	method	method	NOUN
brj-23253	146	22	used	use	VERB
brj-23253	146	23	for	for	ADP
brj-23253	146	24	feature	feature	NOUN
brj-23253	146	25	selection	selection	NOUN
brj-23253	146	26	to	to	PART
brj-23253	146	27	identify	identify	VERB
brj-23253	146	28	the	the	DET
brj-23253	146	29	most	most	ADV
brj-23253	146	30	relevant	relevant	ADJ
brj-23253	146	31	subset	subset	NOUN
brj-23253	146	32	of	of	ADP
brj-23253	146	33	variables	variable	NOUN
brj-23253	146	34	from	from	ADP
brj-23253	146	35	a	a	DET
brj-23253	146	36	large	large	ADJ
brj-23253	146	37	pool	pool	NOUN
brj-23253	146	38	about	about	ADP
brj-23253	146	39	a	a	DET
brj-23253	146	40	target	target	NOUN
brj-23253	146	41	variable	variable	NOUN
brj-23253	146	42	(	(	PUNCT
brj-23253	146	43	yu	yu	PROPN
brj-23253	146	44	et	et	PROPN
brj-23253	146	45	al	al	PROPN
brj-23253	146	46	.	.	PROPN
brj-23253	146	47	2018	2018	NUM
brj-23253	146	48	)	)	PUNCT
brj-23253	146	49	.	.	PUNCT
brj-23253	147	1	it	it	PRON
brj-23253	147	2	is	be	AUX
brj-23253	147	3	particularly	particularly	ADV
brj-23253	147	4	suitable	suitable	ADJ
brj-23253	147	5	for	for	ADP
brj-23253	147	6	wavelength	wavelength	NOUN
brj-23253	147	7	selection	selection	NOUN
brj-23253	147	8	in	in	ADP
brj-23253	147	9	spectral	spectral	ADJ
brj-23253	147	10	analysis	analysis	NOUN
brj-23253	147	11	.	.	PUNCT
brj-23253	148	1	the	the	DET
brj-23253	148	2	basic	basic	ADJ
brj-23253	148	3	process	process	NOUN
brj-23253	148	4	of	of	ADP
brj-23253	148	5	the	the	DET
brj-23253	148	6	iriv	iriv	ADJ
brj-23253	148	7	algorithm	algorithm	NOUN
brj-23253	148	8	can	can	AUX
brj-23253	148	9	be	be	AUX
brj-23253	148	10	outlined	outline	VERB
brj-23253	148	11	as	as	SCONJ
brj-23253	148	12	follows	follow	VERB
brj-23253	148	13	:	:	PUNCT
brj-23253	148	14	iterative	iterative	NOUN
brj-23253	148	15	retention	retention	NOUN
brj-23253	148	16	of	of	ADP
brj-23253	148	17	informative	informative	ADJ
brj-23253	148	18	variables	variable	NOUN
brj-23253	148	19	:	:	PUNCT
brj-23253	148	20	a	a	DET
brj-23253	148	21	subset	subset	NOUN
brj-23253	148	22	of	of	ADP
brj-23253	148	23	variables	variable	NOUN
brj-23253	148	24	is	be	AUX
brj-23253	148	25	selected	select	VERB
brj-23253	148	26	from	from	ADP
brj-23253	148	27	the	the	DET
brj-23253	148	28	current	current	ADJ
brj-23253	148	29	variable	variable	NOUN
brj-23253	148	30	set	set	VERB
brj-23253	148	31	in	in	ADP
brj-23253	148	32	each	each	DET
brj-23253	148	33	iteration	iteration	NOUN
brj-23253	148	34	.	.	PUNCT
brj-23253	149	1	these	these	DET
brj-23253	149	2	variables	variable	NOUN
brj-23253	149	3	are	be	AUX
brj-23253	149	4	chosen	choose	VERB
brj-23253	149	5	because	because	SCONJ
brj-23253	149	6	they	they	PRON
brj-23253	149	7	maximally	maximally	ADV
brj-23253	149	8	retain	retain	VERB
brj-23253	149	9	relevant	relevant	ADJ
brj-23253	149	10	information	information	NOUN
brj-23253	149	11	about	about	ADP
brj-23253	149	12	the	the	DET
brj-23253	149	13	target	target	NOUN
brj-23253	149	14	variable	variable	NOUN
brj-23253	149	15	.	.	PUNCT
brj-23253	150	1	assessment	assessment	NOUN
brj-23253	150	2	of	of	ADP
brj-23253	150	3	variable	variable	ADJ
brj-23253	150	4	importance	importance	NOUN
brj-23253	150	5	:	:	PUNCT
brj-23253	150	6	the	the	DET
brj-23253	150	7	importance	importance	NOUN
brj-23253	150	8	of	of	ADP
brj-23253	150	9	each	each	DET
brj-23253	150	10	selected	select	VERB
brj-23253	150	11	variable	variable	NOUN
brj-23253	150	12	is	be	AUX
brj-23253	150	13	evaluated	evaluate	VERB
brj-23253	150	14	.	.	PUNCT
brj-23253	151	1	this	this	PRON
brj-23253	151	2	is	be	AUX
brj-23253	151	3	typically	typically	ADV
brj-23253	151	4	done	do	VERB
brj-23253	151	5	by	by	ADP
brj-23253	151	6	examining	examine	VERB
brj-23253	151	7	each	each	DET
brj-23253	151	8	variable	variable	NOUN
brj-23253	151	9	's	's	PART
brj-23253	151	10	contribution	contribution	NOUN
brj-23253	151	11	to	to	ADP
brj-23253	151	12	the	the	DET
brj-23253	151	13	model	model	NOUN
brj-23253	151	14	's	's	PART
brj-23253	151	15	predictive	predictive	ADJ
brj-23253	151	16	performance	performance	NOUN
brj-23253	151	17	.	.	PUNCT
brj-23253	152	1	the	the	DET
brj-23253	152	2	assessment	assessment	NOUN
brj-23253	152	3	is	be	AUX
brj-23253	152	4	often	often	ADV
brj-23253	152	5	based	base	VERB
brj-23253	152	6	on	on	ADP
brj-23253	152	7	statistical	statistical	ADJ
brj-23253	152	8	indicators	indicator	NOUN
brj-23253	152	9	such	such	ADJ
brj-23253	152	10	as	as	ADP
brj-23253	152	11	the	the	DET
brj-23253	152	12	magnitude	magnitude	NOUN
brj-23253	152	13	of	of	ADP
brj-23253	152	14	regression	regression	NOUN
brj-23253	152	15	coefficients	coefficient	NOUN
brj-23253	152	16	,	,	PUNCT
brj-23253	152	17	the	the	DET
brj-23253	152	18	impact	impact	NOUN
brj-23253	152	19	of	of	ADP
brj-23253	152	20	variables	variable	NOUN
brj-23253	152	21	on	on	ADP
brj-23253	152	22	model	model	NOUN
brj-23253	152	23	prediction	prediction	NOUN
brj-23253	152	24	error	error	NOUN
brj-23253	152	25	,	,	PUNCT
brj-23253	152	26	and	and	CCONJ
brj-23253	152	27	the	the	DET
brj-23253	152	28	consistency	consistency	NOUN
brj-23253	152	29	of	of	ADP
brj-23253	152	30	model	model	NOUN
brj-23253	152	31	performance	performance	NOUN
brj-23253	152	32	across	across	ADP
brj-23253	152	33	different	different	ADJ
brj-23253	152	34	datasets	dataset	NOUN
brj-23253	152	35	.	.	PUNCT
brj-23253	153	1	the	the	DET
brj-23253	153	2	assessment	assessment	NOUN
brj-23253	153	3	is	be	AUX
brj-23253	153	4	often	often	ADV
brj-23253	153	5	based	base	VERB
brj-23253	153	6	on	on	ADP
brj-23253	153	7	statistical	statistical	ADJ
brj-23253	153	8	indicators	indicator	NOUN
brj-23253	153	9	such	such	ADJ
brj-23253	153	10	as	as	ADP
brj-23253	153	11	the	the	DET
brj-23253	153	12	magnitude	magnitude	NOUN
brj-23253	153	13	of	of	ADP
brj-23253	153	14	regression	regression	NOUN
brj-23253	153	15	coefficients	coefficient	NOUN
brj-23253	153	16	,	,	PUNCT
brj-23253	153	17	the	the	DET
brj-23253	153	18	impact	impact	NOUN
brj-23253	153	19	of	of	ADP
brj-23253	153	20	variables	variable	NOUN
brj-23253	153	21	on	on	ADP
brj-23253	153	22	model	model	NOUN
brj-23253	153	23	prediction	prediction	NOUN
brj-23253	153	24	error	error	NOUN
brj-23253	153	25	,	,	PUNCT
brj-23253	153	26	and	and	CCONJ
brj-23253	153	27	the	the	DET
brj-23253	153	28	consistency	consistency	NOUN
brj-23253	153	29	of	of	ADP
brj-23253	153	30	model	model	NOUN
brj-23253	153	31	performance	performance	NOUN
brj-23253	153	32	across	across	ADP
brj-23253	153	33	different	different	ADJ
brj-23253	153	34	datasets	dataset	NOUN
brj-23253	153	35	.	.	PUNCT
brj-23253	154	1	cyclic	cyclic	ADJ
brj-23253	154	2	iteration	iteration	NOUN
brj-23253	154	3	and	and	CCONJ
brj-23253	154	4	optimization	optimization	NOUN
brj-23253	154	5	:	:	PUNCT
brj-23253	154	6	the	the	DET
brj-23253	154	7	above	above	ADJ
brj-23253	154	8	process	process	NOUN
brj-23253	154	9	is	be	AUX
brj-23253	154	10	repeated	repeat	VERB
brj-23253	154	11	for	for	ADP
brj-23253	154	12	a	a	DET
brj-23253	154	13	predetermined	predetermine	VERB
brj-23253	154	14	number	number	NOUN
brj-23253	154	15	of	of	ADP
brj-23253	154	16	iterations	iteration	NOUN
brj-23253	154	17	or	or	CCONJ
brj-23253	154	18	until	until	SCONJ
brj-23253	154	19	specific	specific	ADJ
brj-23253	154	20	stopping	stopping	NOUN
brj-23253	154	21	criteria	criterion	NOUN
brj-23253	154	22	are	be	AUX
brj-23253	154	23	met	meet	VERB
brj-23253	154	24	(	(	PUNCT
brj-23253	154	25	such	such	ADJ
brj-23253	154	26	as	as	ADP
brj-23253	154	27	minimization	minimization	NOUN
brj-23253	154	28	of	of	ADP
brj-23253	154	29	cross	cross	ADJ
brj-23253	154	30	-	-	ADJ
brj-23253	154	31	validation	validation	ADJ
brj-23253	154	32	error	error	NOUN
brj-23253	154	33	)	)	PUNCT
brj-23253	154	34	.	.	PUNCT
brj-23253	155	1	after	after	ADP
brj-23253	155	2	each	each	DET
brj-23253	155	3	iteration	iteration	NOUN
brj-23253	155	4	,	,	PUNCT
brj-23253	155	5	the	the	DET
brj-23253	155	6	variable	variable	ADJ
brj-23253	155	7	set	set	NOUN
brj-23253	155	8	is	be	AUX
brj-23253	155	9	updated	update	VERB
brj-23253	155	10	,	,	PUNCT
brj-23253	155	11	removing	remove	VERB
brj-23253	155	12	those	those	PRON
brj-23253	155	13	deemed	deem	VERB
brj-23253	155	14	unimportant	unimportant	ADJ
brj-23253	155	15	or	or	CCONJ
brj-23253	155	16	contributing	contribute	VERB
brj-23253	155	17	less	less	ADJ
brj-23253	155	18	to	to	ADP
brj-23253	155	19	the	the	DET
brj-23253	155	20	prediction	prediction	NOUN
brj-23253	155	21	of	of	ADP
brj-23253	155	22	the	the	DET
brj-23253	155	23	target	target	NOUN
brj-23253	155	24	variable	variable	NOUN
brj-23253	155	25	.	.	PUNCT
brj-23253	156	1	determination	determination	NOUN
brj-23253	156	2	of	of	ADP
brj-23253	156	3	the	the	DET
brj-23253	156	4	final	final	ADJ
brj-23253	156	5	feature	feature	NOUN
brj-23253	156	6	set	set	NOUN
brj-23253	156	7	:	:	PUNCT
brj-23253	156	8	the	the	DET
brj-23253	156	9	variable	variable	ADJ
brj-23253	156	10	set	set	NOUN
brj-23253	156	11	obtained	obtain	VERB
brj-23253	156	12	at	at	ADP
brj-23253	156	13	the	the	DET
brj-23253	156	14	end	end	NOUN
brj-23253	156	15	of	of	ADP
brj-23253	156	16	the	the	DET
brj-23253	156	17	iterative	iterative	NOUN
brj-23253	156	18	process	process	NOUN
brj-23253	156	19	represents	represent	VERB
brj-23253	156	20	the	the	DET
brj-23253	156	21	selected	select	VERB
brj-23253	156	22	feature	feature	NOUN
brj-23253	156	23	variables	variable	NOUN
brj-23253	156	24	.	.	PUNCT
brj-23253	157	1	these	these	DET
brj-23253	157	2	variables	variable	NOUN
brj-23253	157	3	are	be	AUX
brj-23253	157	4	considered	consider	VERB
brj-23253	157	5	the	the	DET
brj-23253	157	6	most	most	ADV
brj-23253	157	7	important	important	ADJ
brj-23253	157	8	for	for	ADP
brj-23253	157	9	predicting	predict	VERB
brj-23253	157	10	the	the	DET
brj-23253	157	11	target	target	NOUN
brj-23253	157	12	variable	variable	NOUN
brj-23253	157	13	and	and	CCONJ
brj-23253	157	14	can	can	AUX
brj-23253	157	15	be	be	AUX
brj-23253	157	16	used	use	VERB
brj-23253	157	17	in	in	ADP
brj-23253	157	18	subsequent	subsequent	ADJ
brj-23253	157	19	data	datum	NOUN
brj-23253	157	20	analysis	analysis	NOUN
brj-23253	157	21	or	or	CCONJ
brj-23253	157	22	modeling	modeling	NOUN
brj-23253	157	23	processes	process	NOUN
brj-23253	157	24	.	.	PUNCT
brj-23253	158	1	the	the	DET
brj-23253	158	2	iteratively	iteratively	ADV
brj-23253	158	3	retains	retain	VERB
brj-23253	158	4	informative	informative	ADJ
brj-23253	158	5	variables	variable	NOUN
brj-23253	158	6	(	(	PUNCT
brj-23253	158	7	iriv	iriv	ADJ
brj-23253	158	8	)	)	PUNCT
brj-23253	158	9	algorithm	algorithm	NOUN
brj-23253	158	10	effectively	effectively	ADV
brj-23253	158	11	selects	select	VERB
brj-23253	158	12	the	the	DET
brj-23253	158	13	most	most	ADV
brj-23253	158	14	crucial	crucial	ADJ
brj-23253	158	15	subset	subset	NOUN
brj-23253	158	16	of	of	ADP
brj-23253	158	17	variables	variable	NOUN
brj-23253	158	18	from	from	ADP
brj-23253	158	19	a	a	DET
brj-23253	158	20	large	large	ADJ
brj-23253	158	21	set	set	NOUN
brj-23253	158	22	through	through	ADP
brj-23253	158	23	an	an	DET
brj-23253	158	24	iterative	iterative	NOUN
brj-23253	158	25	process	process	NOUN
brj-23253	158	26	,	,	PUNCT
brj-23253	158	27	enhancing	enhance	VERB
brj-23253	158	28	the	the	DET
brj-23253	158	29	model	model	NOUN
brj-23253	158	30	's	's	PART
brj-23253	158	31	explanatory	explanatory	ADJ
brj-23253	158	32	power	power	NOUN
brj-23253	158	33	and	and	CCONJ
brj-23253	158	34	predictive	predictive	ADJ
brj-23253	158	35	accuracy	accuracy	NOUN
brj-23253	158	36	.	.	PUNCT
brj-23253	159	1	this	this	PRON
brj-23253	159	2	is	be	AUX
brj-23253	159	3	particularly	particularly	ADV
brj-23253	159	4	applicable	applicable	ADJ
brj-23253	159	5	to	to	ADP
brj-23253	159	6	spectral	spectral	ADJ
brj-23253	159	7	data	datum	NOUN
brj-23253	159	8	and	and	CCONJ
brj-23253	159	9	other	other	ADJ
brj-23253	159	10	high	high	ADJ
brj-23253	159	11	-	-	PUNCT
brj-23253	159	12	dimensional	dimensional	ADJ
brj-23253	159	13	data	datum	NOUN
brj-23253	159	14	analyses	analysis	NOUN
brj-23253	159	15	.	.	PUNCT
brj-23253	160	1	the	the	DET
brj-23253	160	2	core	core	NOUN
brj-23253	160	3	of	of	ADP
brj-23253	160	4	the	the	DET
brj-23253	160	5	iriv	iriv	ADJ
brj-23253	160	6	method	method	NOUN
brj-23253	160	7	in	in	ADP
brj-23253	160	8	feature	feature	NOUN
brj-23253	160	9	wavelength	wavelength	NOUN
brj-23253	160	10	selection	selection	NOUN
brj-23253	160	11	involves	involve	VERB
brj-23253	160	12	iterative	iterative	ADJ
brj-23253	160	13	feature	feature	NOUN
brj-23253	160	14	selection	selection	NOUN
brj-23253	160	15	.	.	PUNCT
brj-23253	161	1	in	in	ADP
brj-23253	161	2	each	each	DET
brj-23253	161	3	iteration	iteration	NOUN
brj-23253	161	4	,	,	PUNCT
brj-23253	161	5	it	it	PRON
brj-23253	161	6	assesses	assess	VERB
brj-23253	161	7	the	the	DET
brj-23253	161	8	impact	impact	NOUN
brj-23253	161	9	of	of	ADP
brj-23253	161	10	remaining	remain	VERB
brj-23253	161	11	features	feature	NOUN
brj-23253	161	12	on	on	ADP
brj-23253	161	13	the	the	DET
brj-23253	161	14	model	model	NOUN
brj-23253	161	15	's	's	PART
brj-23253	161	16	performance	performance	NOUN
brj-23253	161	17	and	and	CCONJ
brj-23253	161	18	selects	select	VERB
brj-23253	161	19	those	those	PRON
brj-23253	161	20	that	that	PRON
brj-23253	161	21	minimize	minimize	VERB
brj-23253	161	22	the	the	DET
brj-23253	161	23	prediction	prediction	NOUN
brj-23253	161	24	error	error	NOUN
brj-23253	161	25	(	(	PUNCT
brj-23253	161	26	mean	mean	ADJ
brj-23253	161	27	square	square	ADJ
brj-23253	161	28	error	error	NOUN
brj-23253	161	29	)	)	PUNCT
brj-23253	161	30	.	.	PUNCT
brj-23253	162	1	this	this	DET
brj-23253	162	2	process	process	NOUN
brj-23253	162	3	is	be	AUX
brj-23253	162	4	repeated	repeat	VERB
brj-23253	162	5	until	until	SCONJ
brj-23253	162	6	the	the	DET
brj-23253	162	7	maximum	maximum	ADJ
brj-23253	162	8	number	number	NOUN
brj-23253	162	9	of	of	ADP
brj-23253	162	10	iterations	iteration	NOUN
brj-23253	162	11	is	be	AUX
brj-23253	162	12	reached	reach	VERB
brj-23253	162	13	,	,	PUNCT
brj-23253	162	14	or	or	CCONJ
brj-23253	162	15	no	no	DET
brj-23253	162	16	remaining	remain	VERB
brj-23253	162	17	features	feature	NOUN
brj-23253	162	18	are	be	AUX
brj-23253	162	19	left	leave	VERB
brj-23253	162	20	(	(	PUNCT
brj-23253	162	21	xu	xu	INTJ
brj-23253	162	22	et	et	PROPN
brj-23253	162	23	al	al	PROPN
brj-23253	162	24	.	.	PROPN
brj-23253	162	25	2019	2019	NUM
brj-23253	162	26	)	)	PUNCT
brj-23253	162	27	.	.	PUNCT
brj-23253	163	1	in	in	ADP
brj-23253	163	2	this	this	DET
brj-23253	163	3	study	study	NOUN
brj-23253	163	4	,	,	PUNCT
brj-23253	163	5	the	the	DET
brj-23253	163	6	feature	feature	NOUN
brj-23253	163	7	wavelengths	wavelength	NOUN
brj-23253	163	8	obtained	obtain	VERB
brj-23253	163	9	from	from	ADP
brj-23253	163	10	sg	sg	ADP
brj-23253	163	11	convolution	convolution	NOUN
brj-23253	163	12	smoothing	smoothing	NOUN
brj-23253	163	13	were	be	AUX
brj-23253	163	14	59	59	NUM
brj-23253	163	15	(	(	PUNCT
brj-23253	163	16	4.27	4.27	NUM
brj-23253	163	17	%	%	NOUN
brj-23253	163	18	of	of	ADP
brj-23253	163	19	the	the	DET
brj-23253	163	20	full	full	ADJ
brj-23253	163	21	spectrum	spectrum	NOUN
brj-23253	163	22	)	)	PUNCT
brj-23253	163	23	,	,	PUNCT
brj-23253	163	24	from	from	ADP
brj-23253	163	25	standard	standard	ADJ
brj-23253	163	26	normal	normal	ADJ
brj-23253	163	27	variate	variate	NOUN
brj-23253	163	28	transformation	transformation	NOUN
brj-23253	163	29	were	be	AUX
brj-23253	163	30	69	69	NUM
brj-23253	163	31	(	(	PUNCT
brj-23253	163	32	5.00	5.00	NUM
brj-23253	163	33	%	%	NOUN
brj-23253	163	34	of	of	ADP
brj-23253	163	35	the	the	DET
brj-23253	163	36	full	full	ADJ
brj-23253	163	37	spectrum	spectrum	NOUN
brj-23253	163	38	)	)	PUNCT
brj-23253	163	39	,	,	PUNCT
brj-23253	163	40	from	from	ADP
brj-23253	163	41	multiplicative	multiplicative	ADJ
brj-23253	163	42	scatter	scatter	NOUN
brj-23253	163	43	correction	correction	NOUN
brj-23253	163	44	were	be	AUX
brj-23253	163	45	57	57	NUM
brj-23253	163	46	(	(	PUNCT
brj-23253	163	47	4.13	4.13	NUM
brj-23253	163	48	%	%	NOUN
brj-23253	163	49	of	of	ADP
brj-23253	163	50	the	the	DET
brj-23253	163	51	full	full	ADJ
brj-23253	163	52	spectrum	spectrum	NOUN
brj-23253	163	53	)	)	PUNCT
brj-23253	163	54	,	,	PUNCT
brj-23253	163	55	and	and	CCONJ
brj-23253	163	56	from	from	ADP
brj-23253	163	57	the	the	DET
brj-23253	163	58	first	first	ADJ
brj-23253	163	59	derivative	derivative	ADJ
brj-23253	163	60	operation	operation	NOUN
brj-23253	163	61	were	be	AUX
brj-23253	163	62	51	51	NUM
brj-23253	163	63	(	(	PUNCT
brj-23253	163	64	3.69	3.69	NUM
brj-23253	163	65	%	%	NOUN
brj-23253	163	66	of	of	ADP
brj-23253	163	67	the	the	DET
brj-23253	163	68	full	full	ADJ
brj-23253	163	69	spectrum	spectrum	NOUN
brj-23253	163	70	)	)	PUNCT
brj-23253	163	71	.	.	PUNCT
brj-23253	164	1	the	the	DET
brj-23253	164	2	results	result	NOUN
brj-23253	164	3	of	of	ADP
brj-23253	164	4	feature	feature	NOUN
brj-23253	164	5	wavelength	wavelength	NOUN
brj-23253	164	6	selection	selection	NOUN
brj-23253	164	7	using	use	VERB
brj-23253	164	8	the	the	DET
brj-23253	164	9	iriv	iriv	ADJ
brj-23253	164	10	method	method	NOUN
brj-23253	164	11	after	after	SCONJ
brj-23253	164	12	various	various	ADJ
brj-23253	164	13	preprocessing	preprocessing	NOUN
brj-23253	164	14	methods	method	NOUN
brj-23253	164	15	are	be	AUX
brj-23253	164	16	shown	show	VERB
brj-23253	164	17	in	in	ADP
brj-23253	164	18	fig	fig	NOUN
brj-23253	164	19	.	.	PUNCT
brj-23253	165	1	4	4	X
brj-23253	165	2	.	.	X
brj-23253	165	3	the	the	DET
brj-23253	165	4	blue	blue	ADJ
brj-23253	165	5	line	line	NOUN
brj-23253	165	6	represents	represent	VERB
brj-23253	165	7	the	the	DET
brj-23253	165	8	average	average	ADJ
brj-23253	165	9	spectral	spectral	ADJ
brj-23253	165	10	data	datum	NOUN
brj-23253	165	11	after	after	ADP
brj-23253	165	12	preprocessing	preprocessing	NOUN
brj-23253	165	13	,	,	PUNCT
brj-23253	165	14	and	and	CCONJ
brj-23253	165	15	the	the	DET
brj-23253	165	16	red	red	ADJ
brj-23253	165	17	circles	circle	NOUN
brj-23253	165	18	denote	denote	VERB
brj-23253	165	19	the	the	DET
brj-23253	165	20	characteristic	characteristic	ADJ
brj-23253	165	21	wavelengths	wavelength	NOUN
brj-23253	165	22	extracted	extract	VERB
brj-23253	165	23	by	by	ADP
brj-23253	165	24	the	the	DET
brj-23253	165	25	iriv	iriv	ADJ
brj-23253	165	26	algorithm	algorithm	NOUN
brj-23253	165	27	.	.	PUNCT
brj-23253	166	1	peer	peer	NOUN
brj-23253	166	2	-	-	PUNCT
brj-23253	166	3	reviewed	review	VERB
brj-23253	166	4	article	article	NOUN
brj-23253	166	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	166	6	li	li	PROPN
brj-23253	166	7	et	et	PROPN
brj-23253	166	8	al	al	PROPN
brj-23253	166	9	.	.	PROPN
brj-23253	167	1	(	(	PUNCT
brj-23253	167	2	2024	2024	NUM
brj-23253	167	3	)	)	PUNCT
brj-23253	167	4	.	.	PUNCT
brj-23253	168	1	“	"	PUNCT
brj-23253	168	2	alfalfa	alfalfa	X
brj-23253	168	3	protein	protein	NOUN
brj-23253	168	4	with	with	ADP
brj-23253	168	5	vis	vis	X
brj-23253	168	6	/	/	SYM
brj-23253	168	7	nir	nir	NOUN
brj-23253	168	8	,	,	PUNCT
brj-23253	168	9	”	"	PUNCT
brj-23253	168	10	bioresources	bioresource	NOUN
brj-23253	168	11	19(2	19(2	NUM
brj-23253	168	12	)	)	PUNCT
brj-23253	168	13	,	,	PUNCT
brj-23253	168	14	3808	3808	NUM
brj-23253	168	15	-	-	SYM
brj-23253	168	16	3825	3825	NUM
brj-23253	168	17	.	.	PUNCT
brj-23253	169	1	3816	3816	NUM
brj-23253	169	2	(	(	PUNCT
brj-23253	169	3	a	a	NOUN
brj-23253	169	4	)	)	PUNCT
brj-23253	169	5	(	(	PUNCT
brj-23253	169	6	b	b	X
brj-23253	169	7	)	)	PUNCT
brj-23253	169	8	(	(	PUNCT
brj-23253	169	9	c	c	X
brj-23253	169	10	)	)	PUNCT
brj-23253	169	11	(	(	PUNCT
brj-23253	169	12	d	d	X
brj-23253	169	13	)	)	PUNCT
brj-23253	169	14	fig	fig	NOUN
brj-23253	169	15	.	.	PUNCT
brj-23253	170	1	4	4	X
brj-23253	170	2	.	.	X
brj-23253	170	3	selection	selection	NOUN
brj-23253	170	4	of	of	ADP
brj-23253	170	5	characteristic	characteristic	ADJ
brj-23253	170	6	wavelengths	wavelength	NOUN
brj-23253	170	7	after	after	ADP
brj-23253	170	8	iriv：(a	iriv：(a	PROPN
brj-23253	170	9	)	)	PUNCT
brj-23253	170	10	sg	sg	NOUN
brj-23253	170	11	-	-	PUNCT
brj-23253	170	12	iriv	iriv	ADJ
brj-23253	170	13	,	,	PUNCT
brj-23253	170	14	(	(	PUNCT
brj-23253	170	15	b	b	X
brj-23253	170	16	)	)	PUNCT
brj-23253	170	17	snv	snv	PROPN
brj-23253	170	18	-	-	PUNCT
brj-23253	170	19	iriv	iriv	PROPN
brj-23253	170	20	,	,	PUNCT
brj-23253	170	21	(	(	PUNCT
brj-23253	170	22	c	c	X
brj-23253	170	23	)	)	PUNCT
brj-23253	170	24	msc	msc	PROPN
brj-23253	170	25	-	-	PUNCT
brj-23253	170	26	iriv	iriv	PROPN
brj-23253	170	27	,	,	PUNCT
brj-23253	170	28	(	(	PUNCT
brj-23253	170	29	d	d	X
brj-23253	170	30	)	)	PUNCT
brj-23253	170	31	fd	fd	PROPN
brj-23253	170	32	-	-	PUNCT
brj-23253	170	33	iriv	iriv	NOUN
brj-23253	170	34	.	.	PUNCT
brj-23253	171	1	based	base	VERB
brj-23253	171	2	on	on	ADP
brj-23253	171	3	the	the	DET
brj-23253	171	4	analysis	analysis	NOUN
brj-23253	171	5	results	result	NOUN
brj-23253	171	6	,	,	PUNCT
brj-23253	171	7	the	the	DET
brj-23253	171	8	identified	identify	VERB
brj-23253	171	9	feature	feature	NOUN
brj-23253	171	10	wavelengths	wavelength	NOUN
brj-23253	171	11	predominantly	predominantly	ADV
brj-23253	171	12	correspond	correspond	VERB
brj-23253	171	13	to	to	ADP
brj-23253	171	14	functional	functional	ADJ
brj-23253	171	15	groups	group	NOUN
brj-23253	171	16	such	such	ADJ
brj-23253	171	17	as	as	ADP
brj-23253	171	18	c	c	NOUN
brj-23253	171	19	-	-	PUNCT
brj-23253	171	20	h	h	NOUN
brj-23253	171	21	,	,	PUNCT
brj-23253	171	22	o	o	NOUN
brj-23253	171	23	-	-	NOUN
brj-23253	171	24	h	h	NOUN
brj-23253	171	25	,	,	PUNCT
brj-23253	171	26	n	n	CCONJ
brj-23253	171	27	-	-	PUNCT
brj-23253	171	28	h	h	NOUN
brj-23253	171	29	,	,	PUNCT
brj-23253	171	30	c	c	PROPN
brj-23253	171	31	=	=	SYM
brj-23253	171	32	o	o	NOUN
brj-23253	171	33	,	,	PUNCT
brj-23253	171	34	and	and	CCONJ
brj-23253	171	35	-cho	-cho	NOUN
brj-23253	171	36	.	.	PUNCT
brj-23253	172	1	the	the	DET
brj-23253	172	2	wavebands	waveband	NOUN
brj-23253	172	3	near	near	ADP
brj-23253	172	4	1100	1100	NUM
brj-23253	172	5	to	to	ADP
brj-23253	172	6	1160	1160	NUM
brj-23253	172	7	nm	nm	NOUN
brj-23253	172	8	and	and	CCONJ
brj-23253	172	9	1428	1428	NUM
brj-23253	172	10	to	to	ADP
brj-23253	172	11	1491	1491	NUM
brj-23253	172	12	nm	nm	NOUN
brj-23253	172	13	are	be	AUX
brj-23253	172	14	associated	associate	VERB
brj-23253	172	15	with	with	ADP
brj-23253	172	16	o	o	ADJ
brj-23253	172	17	-	-	ADJ
brj-23253	172	18	h	h	ADJ
brj-23253	172	19	groups	group	NOUN
brj-23253	172	20	(	(	PUNCT
brj-23253	172	21	rego	rego	PROPN
brj-23253	172	22	et	et	PROPN
brj-23253	172	23	al	al	PROPN
brj-23253	172	24	.	.	PROPN
brj-23253	172	25	2020	2020	NUM
brj-23253	172	26	)	)	PUNCT
brj-23253	172	27	,	,	PUNCT
brj-23253	172	28	related	relate	VERB
brj-23253	172	29	to	to	ADP
brj-23253	172	30	the	the	DET
brj-23253	172	31	moisture	moisture	NOUN
brj-23253	172	32	content	content	NOUN
brj-23253	172	33	in	in	ADP
brj-23253	172	34	the	the	DET
brj-23253	172	35	feed	feed	NOUN
brj-23253	172	36	;	;	PUNCT
brj-23253	172	37	absorption	absorption	NOUN
brj-23253	172	38	peaks	peak	NOUN
brj-23253	172	39	near	near	ADP
brj-23253	172	40	1470	1470	NUM
brj-23253	172	41	,	,	PUNCT
brj-23253	172	42	1500	1500	NUM
brj-23253	172	43	to	to	ADP
brj-23253	172	44	1530	1530	NUM
brj-23253	172	45	,	,	PUNCT
brj-23253	172	46	and	and	CCONJ
brj-23253	172	47	1640	1640	NUM
brj-23253	172	48	to	to	ADP
brj-23253	172	49	1680	1680	NUM
brj-23253	172	50	nm	nm	NOUN
brj-23253	172	51	correspond	correspond	ADV
brj-23253	172	52	to	to	ADP
brj-23253	172	53	the	the	DET
brj-23253	172	54	stretching	stretch	VERB
brj-23253	172	55	vibrations	vibration	NOUN
brj-23253	172	56	of	of	ADP
brj-23253	172	57	n	n	CCONJ
brj-23253	172	58	-	-	PUNCT
brj-23253	172	59	h	h	NOUN
brj-23253	172	60	groups	group	NOUN
brj-23253	172	61	,	,	PUNCT
brj-23253	172	62	which	which	PRON
brj-23253	172	63	are	be	AUX
brj-23253	172	64	related	relate	VERB
brj-23253	172	65	to	to	ADP
brj-23253	172	66	crude	crude	ADJ
brj-23253	172	67	protein	protein	NOUN
brj-23253	172	68	in	in	ADP
brj-23253	172	69	the	the	DET
brj-23253	172	70	feed	feed	NOUN
brj-23253	172	71	(	(	PUNCT
brj-23253	172	72	rego	rego	PROPN
brj-23253	172	73	et	et	PROPN
brj-23253	172	74	al	al	PROPN
brj-23253	172	75	.	.	PROPN
brj-23253	172	76	2020	2020	NUM
brj-23253	172	77	)	)	PUNCT
brj-23253	172	78	.	.	PUNCT
brj-23253	173	1	the	the	DET
brj-23253	173	2	selected	select	VERB
brj-23253	173	3	feature	feature	NOUN
brj-23253	173	4	wavelengths	wavelength	NOUN
brj-23253	173	5	reflect	reflect	VERB
brj-23253	173	6	the	the	DET
brj-23253	173	7	characteristic	characteristic	ADJ
brj-23253	173	8	absorption	absorption	NOUN
brj-23253	173	9	bands	band	NOUN
brj-23253	173	10	of	of	ADP
brj-23253	173	11	moisture	moisture	NOUN
brj-23253	173	12	,	,	PUNCT
brj-23253	173	13	protein	protein	NOUN
brj-23253	173	14	,	,	PUNCT
brj-23253	173	15	and	and	CCONJ
brj-23253	173	16	other	other	ADJ
brj-23253	173	17	substances	substance	NOUN
brj-23253	173	18	in	in	ADP
brj-23253	173	19	dried	dry	VERB
brj-23253	173	20	alfalfa	alfalfa	NOUN
brj-23253	173	21	.	.	PUNCT
brj-23253	174	1	in	in	ADP
brj-23253	174	2	subsequent	subsequent	ADJ
brj-23253	174	3	modeling	modeling	NOUN
brj-23253	174	4	,	,	PUNCT
brj-23253	174	5	these	these	DET
brj-23253	174	6	wavelengths	wavelength	NOUN
brj-23253	174	7	can	can	AUX
brj-23253	174	8	effectively	effectively	ADV
brj-23253	174	9	reduce	reduce	VERB
brj-23253	174	10	computational	computational	ADJ
brj-23253	174	11	load	load	NOUN
brj-23253	174	12	,	,	PUNCT
brj-23253	174	13	decrease	decrease	VERB
brj-23253	174	14	the	the	DET
brj-23253	174	15	redundancy	redundancy	NOUN
brj-23253	174	16	of	of	ADP
brj-23253	174	17	spectral	spectral	ADJ
brj-23253	174	18	data	datum	NOUN
brj-23253	174	19	,	,	PUNCT
brj-23253	174	20	and	and	CCONJ
brj-23253	174	21	improve	improve	VERB
brj-23253	174	22	model	model	NOUN
brj-23253	174	23	accuracy	accuracy	NOUN
brj-23253	174	24	.	.	PUNCT
brj-23253	175	1	the	the	DET
brj-23253	175	2	number	number	NOUN
brj-23253	175	3	of	of	ADP
brj-23253	175	4	feature	feature	NOUN
brj-23253	175	5	wavelengths	wavelength	NOUN
brj-23253	175	6	extracted	extract	VERB
brj-23253	175	7	using	use	VERB
brj-23253	175	8	different	different	ADJ
brj-23253	175	9	preprocessing	preprocessing	NOUN
brj-23253	175	10	methods	method	NOUN
brj-23253	175	11	and	and	CCONJ
brj-23253	175	12	feature	feature	NOUN
brj-23253	175	13	wavelength	wavelength	NOUN
brj-23253	175	14	extraction	extraction	NOUN
brj-23253	175	15	techniques	technique	NOUN
brj-23253	175	16	is	be	AUX
brj-23253	175	17	summarized	summarize	VERB
brj-23253	175	18	in	in	ADP
brj-23253	175	19	table	table	NOUN
brj-23253	175	20	1	1	NUM
brj-23253	175	21	.	.	PUNCT
brj-23253	175	22	peer	peer	NOUN
brj-23253	175	23	-	-	PUNCT
brj-23253	175	24	reviewed	review	VERB
brj-23253	175	25	article	article	NOUN
brj-23253	175	26	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	175	27	li	li	PROPN
brj-23253	175	28	et	et	PROPN
brj-23253	175	29	al	al	PROPN
brj-23253	175	30	.	.	PROPN
brj-23253	176	1	(	(	PUNCT
brj-23253	176	2	2024	2024	NUM
brj-23253	176	3	)	)	PUNCT
brj-23253	176	4	.	.	PUNCT
brj-23253	177	1	“	"	PUNCT
brj-23253	177	2	alfalfa	alfalfa	X
brj-23253	177	3	protein	protein	NOUN
brj-23253	177	4	with	with	ADP
brj-23253	177	5	vis	vis	X
brj-23253	177	6	/	/	SYM
brj-23253	177	7	nir	nir	NOUN
brj-23253	177	8	,	,	PUNCT
brj-23253	177	9	”	"	PUNCT
brj-23253	177	10	bioresources	bioresource	NOUN
brj-23253	177	11	19(2	19(2	NUM
brj-23253	177	12	)	)	PUNCT
brj-23253	177	13	,	,	PUNCT
brj-23253	177	14	3808	3808	NUM
brj-23253	177	15	-	-	SYM
brj-23253	177	16	3825	3825	NUM
brj-23253	177	17	.	.	PUNCT
brj-23253	178	1	3817	3817	NUM
brj-23253	178	2	table	table	NOUN
brj-23253	178	3	1	1	NUM
brj-23253	178	4	.	.	PUNCT
brj-23253	178	5	wavelength	wavelength	NOUN
brj-23253	178	6	selection	selection	NOUN
brj-23253	178	7	after	after	ADP
brj-23253	178	8	cars	car	NOUN
brj-23253	178	9	and	and	CCONJ
brj-23253	178	10	ivir	ivir	NOUN
brj-23253	178	11	pre	pre	ADJ
brj-23253	178	12	-	-	ADJ
brj-23253	178	13	processing	processing	ADJ
brj-23253	178	14	technique	technique	NOUN
brj-23253	178	15	method	method	NOUN
brj-23253	178	16	feature	feature	NOUN
brj-23253	178	17	variables	variable	VERB
brj-23253	178	18	number	number	NOUN
brj-23253	178	19	sg	sg	ADP
brj-23253	178	20	cars	car	NOUN
brj-23253	178	21	96	96	NUM
brj-23253	178	22	ivir	ivir	PROPN
brj-23253	178	23	59	59	NUM
brj-23253	178	24	snv	snv	PROPN
brj-23253	178	25	cars	car	NOUN
brj-23253	178	26	143	143	PRON
brj-23253	178	27	ivir	ivir	PROPN
brj-23253	178	28	69	69	NUM
brj-23253	178	29	msc	msc	PROPN
brj-23253	178	30	cars	car	NOUN
brj-23253	178	31	96	96	NUM
brj-23253	178	32	ivir	ivir	PROPN
brj-23253	178	33	57	57	NUM
brj-23253	179	1	fd	fd	PROPN
brj-23253	179	2	cars	car	NOUN
brj-23253	179	3	163	163	NUM
brj-23253	179	4	ivir	ivir	PROPN
brj-23253	179	5	51	51	NUM
brj-23253	179	6	establishment	establishment	NOUN
brj-23253	179	7	and	and	CCONJ
brj-23253	179	8	evaluation	evaluation	NOUN
brj-23253	179	9	of	of	ADP
brj-23253	179	10	the	the	DET
brj-23253	179	11	dried	dry	VERB
brj-23253	179	12	alfalfa	alfalfa	NOUN
brj-23253	179	13	protein	protein	NOUN
brj-23253	179	14	prediction	prediction	NOUN
brj-23253	179	15	model	model	NOUN
brj-23253	179	16	this	this	DET
brj-23253	179	17	study	study	NOUN
brj-23253	179	18	aimed	aim	VERB
brj-23253	179	19	to	to	PART
brj-23253	179	20	achieve	achieve	VERB
brj-23253	179	21	rapid	rapid	ADJ
brj-23253	179	22	and	and	CCONJ
brj-23253	179	23	non	non	ADJ
brj-23253	179	24	-	-	ADJ
brj-23253	179	25	destructive	destructive	ADJ
brj-23253	179	26	detection	detection	NOUN
brj-23253	179	27	of	of	ADP
brj-23253	179	28	nutritional	nutritional	ADJ
brj-23253	179	29	substances	substance	NOUN
brj-23253	179	30	in	in	ADP
brj-23253	179	31	purple	purple	ADJ
brj-23253	179	32	alfalfa	alfalfa	NOUN
brj-23253	179	33	using	use	VERB
brj-23253	179	34	near	near	ADV
brj-23253	179	35	-	-	PUNCT
brj-23253	179	36	infrared	infrared	ADJ
brj-23253	179	37	spectroscopy	spectroscopy	NOUN
brj-23253	179	38	technology	technology	NOUN
brj-23253	179	39	.	.	PUNCT
brj-23253	180	1	for	for	ADP
brj-23253	180	2	this	this	DET
brj-23253	180	3	purpose	purpose	NOUN
brj-23253	180	4	,	,	PUNCT
brj-23253	180	5	various	various	ADJ
brj-23253	180	6	machine	machine	NOUN
brj-23253	180	7	learning	learn	VERB
brj-23253	180	8	algorithms	algorithm	NOUN
brj-23253	180	9	,	,	PUNCT
brj-23253	180	10	including	include	VERB
brj-23253	180	11	partial	partial	ADJ
brj-23253	180	12	least	least	ADJ
brj-23253	180	13	squares	square	NOUN
brj-23253	180	14	regression	regression	NOUN
brj-23253	180	15	(	(	PUNCT
brj-23253	180	16	plsr	plsr	PROPN
brj-23253	180	17	)	)	PUNCT
brj-23253	180	18	,	,	PUNCT
brj-23253	180	19	extreme	extreme	ADJ
brj-23253	180	20	learning	learning	NOUN
brj-23253	180	21	machine	machine	NOUN
brj-23253	180	22	(	(	PUNCT
brj-23253	180	23	elm	elm	PROPN
brj-23253	180	24	)	)	PUNCT
brj-23253	180	25	,	,	PUNCT
brj-23253	180	26	support	support	VERB
brj-23253	180	27	vector	vector	NOUN
brj-23253	180	28	machine	machine	NOUN
brj-23253	180	29	(	(	PUNCT
brj-23253	180	30	svm	svm	PROPN
brj-23253	180	31	)	)	PUNCT
brj-23253	180	32	,	,	PUNCT
brj-23253	180	33	and	and	CCONJ
brj-23253	180	34	long	long	ADJ
brj-23253	180	35	shortterm	shortterm	PROPN
brj-23253	180	36	memory	memory	NOUN
brj-23253	180	37	networks	network	NOUN
brj-23253	180	38	(	(	PUNCT
brj-23253	180	39	lstm	lstm	PROPN
brj-23253	180	40	)	)	PUNCT
brj-23253	180	41	,	,	PUNCT
brj-23253	180	42	were	be	AUX
brj-23253	180	43	employed	employ	VERB
brj-23253	180	44	to	to	PART
brj-23253	180	45	establish	establish	VERB
brj-23253	180	46	and	and	CCONJ
brj-23253	180	47	evaluate	evaluate	VERB
brj-23253	180	48	a	a	DET
brj-23253	180	49	prediction	prediction	NOUN
brj-23253	180	50	model	model	NOUN
brj-23253	180	51	for	for	ADP
brj-23253	180	52	the	the	DET
brj-23253	180	53	protein	protein	NOUN
brj-23253	180	54	content	content	NOUN
brj-23253	180	55	in	in	ADP
brj-23253	180	56	purple	purple	ADJ
brj-23253	180	57	alfalfa	alfalfa	NOUN
brj-23253	180	58	.	.	PUNCT
brj-23253	181	1	initially	initially	ADV
brj-23253	181	2	,	,	PUNCT
brj-23253	181	3	prediction	prediction	NOUN
brj-23253	181	4	models	model	NOUN
brj-23253	181	5	were	be	AUX
brj-23253	181	6	developed	develop	VERB
brj-23253	181	7	using	use	VERB
brj-23253	181	8	the	the	DET
brj-23253	181	9	plsr	plsr	NOUN
brj-23253	181	10	method	method	NOUN
brj-23253	181	11	for	for	ADP
brj-23253	181	12	purple	purple	ADJ
brj-23253	181	13	alfalfa	alfalfa	NOUN
brj-23253	181	14	's	's	PART
brj-23253	181	15	full	full	ADJ
brj-23253	181	16	-	-	PUNCT
brj-23253	181	17	spectrum	spectrum	NOUN
brj-23253	181	18	and	and	CCONJ
brj-23253	181	19	feature	feature	NOUN
brj-23253	181	20	wavelength	wavelength	NOUN
brj-23253	181	21	spectral	spectral	ADJ
brj-23253	181	22	data	datum	NOUN
brj-23253	181	23	.	.	PUNCT
brj-23253	182	1	plsr	plsr	PROPN
brj-23253	182	2	is	be	AUX
brj-23253	182	3	a	a	DET
brj-23253	182	4	classic	classic	ADJ
brj-23253	182	5	regression	regression	NOUN
brj-23253	182	6	method	method	NOUN
brj-23253	182	7	that	that	PRON
brj-23253	182	8	establishes	establish	VERB
brj-23253	182	9	a	a	DET
brj-23253	182	10	linear	linear	ADJ
brj-23253	182	11	regression	regression	NOUN
brj-23253	182	12	model	model	NOUN
brj-23253	182	13	by	by	ADP
brj-23253	182	14	maximizing	maximize	VERB
brj-23253	182	15	the	the	DET
brj-23253	182	16	correlation	correlation	NOUN
brj-23253	182	17	between	between	ADP
brj-23253	182	18	input	input	NOUN
brj-23253	182	19	and	and	CCONJ
brj-23253	182	20	output	output	NOUN
brj-23253	182	21	variables	variable	NOUN
brj-23253	182	22	(	(	PUNCT
brj-23253	182	23	niu	niu	PROPN
brj-23253	182	24	et	et	PROPN
brj-23253	182	25	al	al	PROPN
brj-23253	182	26	.	.	PROPN
brj-23253	182	27	2021	2021	NUM
brj-23253	182	28	)	)	PUNCT
brj-23253	182	29	.	.	PUNCT
brj-23253	183	1	this	this	DET
brj-23253	183	2	study	study	NOUN
brj-23253	183	3	used	use	VERB
brj-23253	183	4	full	full	ADJ
brj-23253	183	5	-	-	PUNCT
brj-23253	183	6	spectrum	spectrum	NOUN
brj-23253	183	7	or	or	CCONJ
brj-23253	183	8	feature	feature	NOUN
brj-23253	183	9	wavelength	wavelength	NOUN
brj-23253	183	10	spectral	spectral	ADJ
brj-23253	183	11	data	datum	NOUN
brj-23253	183	12	as	as	ADP
brj-23253	183	13	input	input	NOUN
brj-23253	183	14	variables	variable	NOUN
brj-23253	183	15	.	.	PUNCT
brj-23253	184	1	protein	protein	NOUN
brj-23253	184	2	content	content	NOUN
brj-23253	184	3	was	be	AUX
brj-23253	184	4	used	use	VERB
brj-23253	184	5	as	as	ADP
brj-23253	184	6	the	the	DET
brj-23253	184	7	output	output	NOUN
brj-23253	184	8	variable	variable	NOUN
brj-23253	184	9	to	to	PART
brj-23253	184	10	build	build	VERB
brj-23253	184	11	a	a	DET
brj-23253	184	12	predictive	predictive	ADJ
brj-23253	184	13	model	model	NOUN
brj-23253	184	14	for	for	ADP
brj-23253	184	15	the	the	DET
brj-23253	184	16	nutritional	nutritional	ADJ
brj-23253	184	17	substances	substance	NOUN
brj-23253	184	18	in	in	ADP
brj-23253	184	19	purple	purple	ADJ
brj-23253	184	20	alfalfa	alfalfa	NOUN
brj-23253	184	21	.	.	PUNCT
brj-23253	185	1	establishing	establish	VERB
brj-23253	185	2	the	the	DET
brj-23253	185	3	plsr	plsr	PROPN
brj-23253	185	4	model	model	NOUN
brj-23253	185	5	involved	involve	VERB
brj-23253	185	6	two	two	NUM
brj-23253	185	7	steps	step	NOUN
brj-23253	185	8	:	:	PUNCT
brj-23253	185	9	model	model	NOUN
brj-23253	185	10	training	training	NOUN
brj-23253	185	11	and	and	CCONJ
brj-23253	185	12	model	model	NOUN
brj-23253	185	13	validation	validation	NOUN
brj-23253	185	14	.	.	PUNCT
brj-23253	186	1	the	the	DET
brj-23253	186	2	collected	collect	VERB
brj-23253	186	3	purple	purple	ADJ
brj-23253	186	4	alfalfa	alfalfa	NOUN
brj-23253	186	5	samples	sample	NOUN
brj-23253	186	6	were	be	AUX
brj-23253	186	7	divided	divide	VERB
brj-23253	186	8	into	into	ADP
brj-23253	186	9	a	a	DET
brj-23253	186	10	calibration	calibration	NOUN
brj-23253	186	11	set	set	VERB
brj-23253	186	12	and	and	CCONJ
brj-23253	186	13	a	a	DET
brj-23253	186	14	prediction	prediction	NOUN
brj-23253	186	15	set	set	NOUN
brj-23253	186	16	,	,	PUNCT
brj-23253	186	17	with	with	SCONJ
brj-23253	186	18	the	the	DET
brj-23253	186	19	calibration	calibration	NOUN
brj-23253	186	20	set	set	VERB
brj-23253	186	21	comprising	comprise	VERB
brj-23253	186	22	70	70	NUM
brj-23253	186	23	%	%	NOUN
brj-23253	186	24	of	of	ADP
brj-23253	186	25	the	the	DET
brj-23253	186	26	total	total	ADJ
brj-23253	186	27	samples	sample	NOUN
brj-23253	186	28	and	and	CCONJ
brj-23253	186	29	the	the	DET
brj-23253	186	30	prediction	prediction	NOUN
brj-23253	186	31	set	set	VERB
brj-23253	186	32	comprising	comprise	VERB
brj-23253	186	33	the	the	DET
brj-23253	186	34	remaining	remain	VERB
brj-23253	186	35	30	30	NUM
brj-23253	186	36	%	%	NOUN
brj-23253	186	37	.	.	PUNCT
brj-23253	187	1	the	the	DET
brj-23253	187	2	calibration	calibration	NOUN
brj-23253	187	3	set	set	NOUN
brj-23253	187	4	was	be	AUX
brj-23253	187	5	used	use	VERB
brj-23253	187	6	for	for	ADP
brj-23253	187	7	training	training	NOUN
brj-23253	187	8	and	and	CCONJ
brj-23253	187	9	optimizing	optimize	VERB
brj-23253	187	10	the	the	DET
brj-23253	187	11	model	model	NOUN
brj-23253	187	12	,	,	PUNCT
brj-23253	187	13	while	while	SCONJ
brj-23253	187	14	the	the	DET
brj-23253	187	15	prediction	prediction	NOUN
brj-23253	187	16	set	set	NOUN
brj-23253	187	17	was	be	AUX
brj-23253	187	18	used	use	VERB
brj-23253	187	19	to	to	PART
brj-23253	187	20	assess	assess	VERB
brj-23253	187	21	the	the	DET
brj-23253	187	22	model	model	NOUN
brj-23253	187	23	's	's	PART
brj-23253	187	24	generalizability	generalizability	NOUN
brj-23253	187	25	and	and	CCONJ
brj-23253	187	26	predictive	predictive	ADJ
brj-23253	187	27	accuracy	accuracy	NOUN
brj-23253	187	28	.	.	PUNCT
brj-23253	188	1	during	during	ADP
brj-23253	188	2	training	training	NOUN
brj-23253	188	3	,	,	PUNCT
brj-23253	188	4	the	the	DET
brj-23253	188	5	model	model	NOUN
brj-23253	188	6	's	's	PART
brj-23253	188	7	coefficients	coefficient	NOUN
brj-23253	188	8	and	and	CCONJ
brj-23253	188	9	intercept	intercept	NOUN
brj-23253	188	10	were	be	AUX
brj-23253	188	11	determined	determine	VERB
brj-23253	188	12	by	by	ADP
brj-23253	188	13	minimizing	minimize	VERB
brj-23253	188	14	the	the	DET
brj-23253	188	15	sum	sum	NOUN
brj-23253	188	16	of	of	ADP
brj-23253	188	17	squared	square	VERB
brj-23253	188	18	residuals	residual	NOUN
brj-23253	188	19	.	.	PUNCT
brj-23253	189	1	in	in	ADP
brj-23253	189	2	the	the	DET
brj-23253	189	3	optimization	optimization	NOUN
brj-23253	189	4	process	process	NOUN
brj-23253	189	5	,	,	PUNCT
brj-23253	189	6	the	the	DET
brj-23253	189	7	best	good	ADJ
brj-23253	189	8	number	number	NOUN
brj-23253	189	9	of	of	ADP
brj-23253	189	10	principal	principal	ADJ
brj-23253	189	11	components	component	NOUN
brj-23253	189	12	and	and	CCONJ
brj-23253	189	13	regularization	regularization	NOUN
brj-23253	189	14	parameters	parameter	NOUN
brj-23253	189	15	were	be	AUX
brj-23253	189	16	selected	select	VERB
brj-23253	189	17	through	through	ADP
brj-23253	189	18	cross	cross	NOUN
brj-23253	189	19	-	-	NOUN
brj-23253	189	20	validation	validation	NOUN
brj-23253	189	21	to	to	PART
brj-23253	189	22	enhance	enhance	VERB
brj-23253	189	23	the	the	DET
brj-23253	189	24	model	model	NOUN
brj-23253	189	25	's	's	PART
brj-23253	189	26	stability	stability	NOUN
brj-23253	189	27	and	and	CCONJ
brj-23253	189	28	generalizability	generalizability	NOUN
brj-23253	189	29	(	(	PUNCT
brj-23253	189	30	belini	belini	PROPN
brj-23253	189	31	et	et	PROPN
brj-23253	189	32	al	al	PROPN
brj-23253	189	33	.	.	PROPN
brj-23253	189	34	2011	2011	NUM
brj-23253	189	35	)	)	PUNCT
brj-23253	189	36	.	.	PUNCT
brj-23253	190	1	after	after	ADP
brj-23253	190	2	the	the	DET
brj-23253	190	3	training	training	NOUN
brj-23253	190	4	,	,	PUNCT
brj-23253	190	5	the	the	DET
brj-23253	190	6	model	model	NOUN
brj-23253	190	7	was	be	AUX
brj-23253	190	8	evaluated	evaluate	VERB
brj-23253	190	9	using	use	VERB
brj-23253	190	10	the	the	DET
brj-23253	190	11	prediction	prediction	NOUN
brj-23253	190	12	set	set	NOUN
brj-23253	190	13	.	.	PUNCT
brj-23253	191	1	the	the	DET
brj-23253	191	2	predictive	predictive	ADJ
brj-23253	191	3	accuracy	accuracy	NOUN
brj-23253	191	4	and	and	CCONJ
brj-23253	191	5	generalizability	generalizability	NOUN
brj-23253	191	6	of	of	ADP
brj-23253	191	7	the	the	DET
brj-23253	191	8	model	model	NOUN
brj-23253	191	9	were	be	AUX
brj-23253	191	10	assessed	assess	VERB
brj-23253	191	11	by	by	ADP
brj-23253	191	12	calculating	calculate	VERB
brj-23253	191	13	the	the	DET
brj-23253	191	14	determination	determination	NOUN
brj-23253	191	15	coefficient	coefficient	NOUN
brj-23253	191	16	(	(	PUNCT
brj-23253	191	17	r2	r2	PROPN
brj-23253	191	18	)	)	PUNCT
brj-23253	191	19	and	and	CCONJ
brj-23253	191	20	root	root	NOUN
brj-23253	191	21	mean	mean	NOUN
brj-23253	191	22	square	square	ADJ
brj-23253	191	23	error	error	NOUN
brj-23253	191	24	(	(	PUNCT
brj-23253	191	25	rmse	rmse	NOUN
brj-23253	191	26	)	)	PUNCT
brj-23253	191	27	for	for	ADP
brj-23253	191	28	both	both	CCONJ
brj-23253	191	29	the	the	DET
brj-23253	191	30	calibration	calibration	NOUN
brj-23253	191	31	and	and	CCONJ
brj-23253	191	32	prediction	prediction	NOUN
brj-23253	191	33	sets	set	NOUN
brj-23253	191	34	.	.	PUNCT
brj-23253	192	1	a	a	DET
brj-23253	192	2	determination	determination	NOUN
brj-23253	192	3	coefficient	coefficient	NOUN
brj-23253	192	4	closer	close	ADV
brj-23253	192	5	to	to	ADP
brj-23253	192	6	1	1	NUM
brj-23253	192	7	indicates	indicate	VERB
brj-23253	192	8	a	a	DET
brj-23253	192	9	better	well	ADJ
brj-23253	192	10	model	model	NOUN
brj-23253	192	11	fit	fit	ADJ
brj-23253	192	12	to	to	ADP
brj-23253	192	13	the	the	DET
brj-23253	192	14	data	datum	NOUN
brj-23253	192	15	,	,	PUNCT
brj-23253	192	16	and	and	CCONJ
brj-23253	192	17	a	a	DET
brj-23253	192	18	smaller	small	ADJ
brj-23253	192	19	rmse	rmse	NOUN
brj-23253	192	20	indicates	indicate	VERB
brj-23253	192	21	a	a	DET
brj-23253	192	22	lower	low	ADJ
brj-23253	192	23	prediction	prediction	NOUN
brj-23253	192	24	error	error	NOUN
brj-23253	192	25	.	.	PUNCT
brj-23253	193	1	the	the	DET
brj-23253	193	2	study	study	NOUN
brj-23253	193	3	also	also	ADV
brj-23253	193	4	employed	employ	VERB
brj-23253	193	5	the	the	DET
brj-23253	193	6	extreme	extreme	ADJ
brj-23253	193	7	learning	learning	NOUN
brj-23253	193	8	machine	machine	NOUN
brj-23253	193	9	(	(	PUNCT
brj-23253	193	10	elm	elm	NOUN
brj-23253	193	11	)	)	PUNCT
brj-23253	193	12	method	method	NOUN
brj-23253	193	13	for	for	ADP
brj-23253	193	14	model	model	NOUN
brj-23253	193	15	establishment	establishment	NOUN
brj-23253	193	16	.	.	PUNCT
brj-23253	194	1	elm	elm	NOUN
brj-23253	194	2	is	be	AUX
brj-23253	194	3	a	a	DET
brj-23253	194	4	nonlinear	nonlinear	ADJ
brj-23253	194	5	regression	regression	NOUN
brj-23253	194	6	method	method	NOUN
brj-23253	194	7	based	base	VERB
brj-23253	194	8	on	on	ADP
brj-23253	194	9	artificial	artificial	ADJ
brj-23253	194	10	neural	neural	ADJ
brj-23253	194	11	networks	network	NOUN
brj-23253	194	12	,	,	PUNCT
brj-23253	194	13	which	which	PRON
brj-23253	194	14	quickly	quickly	ADV
brj-23253	194	15	trains	train	VERB
brj-23253	194	16	the	the	DET
brj-23253	194	17	network	network	NOUN
brj-23253	194	18	to	to	PART
brj-23253	194	19	obtain	obtain	VERB
brj-23253	194	20	good	good	ADJ
brj-23253	194	21	predictive	predictive	ADJ
brj-23253	194	22	results	result	NOUN
brj-23253	194	23	by	by	ADP
brj-23253	194	24	randomly	randomly	ADV
brj-23253	194	25	generating	generate	VERB
brj-23253	194	26	initial	initial	ADJ
brj-23253	194	27	weights	weight	NOUN
brj-23253	194	28	and	and	CCONJ
brj-23253	194	29	biases	bias	NOUN
brj-23253	194	30	.	.	PUNCT
brj-23253	195	1	in	in	ADP
brj-23253	195	2	the	the	DET
brj-23253	195	3	elm	elm	NOUN
brj-23253	195	4	method	method	NOUN
brj-23253	195	5	,	,	PUNCT
brj-23253	195	6	the	the	DET
brj-23253	195	7	model	model	NOUN
brj-23253	195	8	's	's	PART
brj-23253	195	9	performance	performance	NOUN
brj-23253	195	10	is	be	AUX
brj-23253	195	11	optimized	optimize	VERB
brj-23253	195	12	by	by	ADP
brj-23253	195	13	adjusting	adjust	VERB
brj-23253	195	14	the	the	DET
brj-23253	195	15	number	number	NOUN
brj-23253	195	16	of	of	ADP
brj-23253	195	17	neurons	neuron	NOUN
brj-23253	195	18	in	in	ADP
brj-23253	195	19	the	the	DET
brj-23253	195	20	hidden	hide	VERB
brj-23253	195	21	layer	layer	NOUN
brj-23253	195	22	and	and	CCONJ
brj-23253	195	23	selecting	select	VERB
brj-23253	195	24	the	the	DET
brj-23253	195	25	activation	activation	NOUN
brj-23253	195	26	function	function	NOUN
brj-23253	195	27	.	.	PUNCT
brj-23253	196	1	during	during	ADP
brj-23253	196	2	training	training	NOUN
brj-23253	196	3	,	,	PUNCT
brj-23253	196	4	the	the	DET
brj-23253	196	5	outputs	output	NOUN
brj-23253	196	6	of	of	ADP
brj-23253	196	7	the	the	DET
brj-23253	196	8	hidden	hide	VERB
brj-23253	196	9	layer	layer	NOUN
brj-23253	196	10	neurons	neuron	NOUN
brj-23253	196	11	are	be	AUX
brj-23253	196	12	computed	compute	VERB
brj-23253	196	13	using	use	VERB
brj-23253	196	14	randomly	randomly	ADV
brj-23253	196	15	generated	generate	VERB
brj-23253	196	16	weights	weight	NOUN
brj-23253	196	17	and	and	CCONJ
brj-23253	196	18	biases	bias	NOUN
brj-23253	196	19	.	.	PUNCT
brj-23253	197	1	then	then	ADV
brj-23253	197	2	,	,	PUNCT
brj-23253	197	3	the	the	DET
brj-23253	197	4	weights	weight	NOUN
brj-23253	197	5	and	and	CCONJ
brj-23253	197	6	biases	bias	NOUN
brj-23253	197	7	of	of	ADP
brj-23253	197	8	the	the	DET
brj-23253	197	9	output	output	NOUN
brj-23253	197	10	layer	layer	NOUN
brj-23253	197	11	are	be	AUX
brj-23253	197	12	calculated	calculate	VERB
brj-23253	197	13	using	use	VERB
brj-23253	197	14	the	the	DET
brj-23253	197	15	least	least	ADJ
brj-23253	197	16	squares	square	NOUN
brj-23253	197	17	method	method	NOUN
brj-23253	197	18	.	.	PUNCT
brj-23253	198	1	the	the	DET
brj-23253	198	2	best	well	ADV
brj-23253	198	3	-	-	PUNCT
brj-23253	198	4	performing	perform	VERB
brj-23253	198	5	elm	elm	NOUN
brj-23253	198	6	model	model	NOUN
brj-23253	198	7	is	be	AUX
brj-23253	198	8	obtained	obtain	VERB
brj-23253	198	9	by	by	ADP
brj-23253	198	10	continuously	continuously	ADV
brj-23253	198	11	adjusting	adjust	VERB
brj-23253	198	12	the	the	DET
brj-23253	198	13	number	number	NOUN
brj-23253	198	14	of	of	ADP
brj-23253	198	15	neurons	neuron	NOUN
brj-23253	198	16	in	in	ADP
brj-23253	198	17	the	the	DET
brj-23253	198	18	hidden	hide	VERB
brj-23253	198	19	layer	layer	NOUN
brj-23253	198	20	and	and	CCONJ
brj-23253	198	21	the	the	DET
brj-23253	198	22	activation	activation	NOUN
brj-23253	198	23	function	function	NOUN
brj-23253	198	24	(	(	PUNCT
brj-23253	198	25	leuenberger	leuenberg	ADJ
brj-23253	198	26	and	and	CCONJ
brj-23253	198	27	peer	peer	NOUN
brj-23253	198	28	-	-	PUNCT
brj-23253	198	29	reviewed	review	VERB
brj-23253	198	30	article	article	NOUN
brj-23253	198	31	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	198	32	li	li	PROPN
brj-23253	198	33	et	et	PROPN
brj-23253	198	34	al	al	PROPN
brj-23253	198	35	.	.	PROPN
brj-23253	199	1	(	(	PUNCT
brj-23253	199	2	2024	2024	NUM
brj-23253	199	3	)	)	PUNCT
brj-23253	199	4	.	.	PUNCT
brj-23253	200	1	“	"	PUNCT
brj-23253	200	2	alfalfa	alfalfa	X
brj-23253	200	3	protein	protein	NOUN
brj-23253	200	4	with	with	ADP
brj-23253	200	5	vis	vis	X
brj-23253	200	6	/	/	SYM
brj-23253	200	7	nir	nir	NOUN
brj-23253	200	8	,	,	PUNCT
brj-23253	200	9	”	"	PUNCT
brj-23253	200	10	bioresources	bioresource	NOUN
brj-23253	200	11	19(2	19(2	NUM
brj-23253	200	12	)	)	PUNCT
brj-23253	200	13	,	,	PUNCT
brj-23253	200	14	3808	3808	NUM
brj-23253	200	15	-	-	SYM
brj-23253	200	16	3825	3825	NUM
brj-23253	200	17	.	.	PUNCT
brj-23253	201	1	3818	3818	NUM
brj-23253	201	2	kanevski	kanevski	PROPN
brj-23253	201	3	2015	2015	NUM
brj-23253	201	4	;	;	PUNCT
brj-23253	201	5	pradhan	pradhan	PROPN
brj-23253	201	6	et	et	PROPN
brj-23253	201	7	al	al	PROPN
brj-23253	201	8	.	.	PROPN
brj-23253	201	9	2019	2019	NUM
brj-23253	201	10	;	;	PUNCT
brj-23253	201	11	jiang	jiang	PROPN
brj-23253	201	12	et	et	PROPN
brj-23253	201	13	al	al	PROPN
brj-23253	201	14	.	.	PROPN
brj-23253	201	15	2020	2020	NUM
brj-23253	201	16	)	)	PUNCT
brj-23253	201	17	.	.	PUNCT
brj-23253	202	1	like	like	ADP
brj-23253	202	2	the	the	DET
brj-23253	202	3	plsr	plsr	NOUN
brj-23253	202	4	method	method	VERB
brj-23253	202	5	,	,	PUNCT
brj-23253	202	6	establishing	establish	VERB
brj-23253	202	7	the	the	DET
brj-23253	202	8	elm	elm	NOUN
brj-23253	202	9	model	model	NOUN
brj-23253	202	10	also	also	ADV
brj-23253	202	11	includes	include	VERB
brj-23253	202	12	training	training	NOUN
brj-23253	202	13	and	and	CCONJ
brj-23253	202	14	validation	validation	NOUN
brj-23253	202	15	steps	step	NOUN
brj-23253	202	16	,	,	PUNCT
brj-23253	202	17	with	with	ADP
brj-23253	202	18	the	the	DET
brj-23253	202	19	same	same	ADJ
brj-23253	202	20	distribution	distribution	NOUN
brj-23253	202	21	of	of	ADP
brj-23253	202	22	samples	sample	NOUN
brj-23253	202	23	in	in	ADP
brj-23253	202	24	the	the	DET
brj-23253	202	25	calibration	calibration	NOUN
brj-23253	202	26	and	and	CCONJ
brj-23253	202	27	prediction	prediction	NOUN
brj-23253	202	28	sets	set	NOUN
brj-23253	202	29	.	.	PUNCT
brj-23253	203	1	the	the	DET
brj-23253	203	2	optimal	optimal	ADJ
brj-23253	203	3	number	number	NOUN
brj-23253	203	4	of	of	ADP
brj-23253	203	5	hidden	hide	VERB
brj-23253	203	6	layer	layer	NOUN
brj-23253	203	7	neurons	neuron	NOUN
brj-23253	203	8	and	and	CCONJ
brj-23253	203	9	activation	activation	NOUN
brj-23253	203	10	function	function	NOUN
brj-23253	203	11	are	be	AUX
brj-23253	203	12	selected	select	VERB
brj-23253	203	13	through	through	ADP
brj-23253	203	14	cross	cross	NOUN
brj-23253	203	15	-	-	NOUN
brj-23253	203	16	validation	validation	NOUN
brj-23253	203	17	to	to	PART
brj-23253	203	18	improve	improve	VERB
brj-23253	203	19	the	the	DET
brj-23253	203	20	model	model	NOUN
brj-23253	203	21	's	's	PART
brj-23253	203	22	stability	stability	NOUN
brj-23253	203	23	and	and	CCONJ
brj-23253	203	24	predictive	predictive	ADJ
brj-23253	203	25	accuracy	accuracy	NOUN
brj-23253	203	26	.	.	PUNCT
brj-23253	204	1	after	after	ADP
brj-23253	204	2	the	the	DET
brj-23253	204	3	model	model	NOUN
brj-23253	204	4	training	training	NOUN
brj-23253	204	5	,	,	PUNCT
brj-23253	204	6	the	the	DET
brj-23253	204	7	model	model	NOUN
brj-23253	204	8	is	be	AUX
brj-23253	204	9	evaluated	evaluate	VERB
brj-23253	204	10	using	use	VERB
brj-23253	204	11	the	the	DET
brj-23253	204	12	prediction	prediction	NOUN
brj-23253	204	13	set	set	NOUN
brj-23253	204	14	.	.	PUNCT
brj-23253	205	1	the	the	DET
brj-23253	205	2	predictive	predictive	ADJ
brj-23253	205	3	accuracy	accuracy	NOUN
brj-23253	205	4	and	and	CCONJ
brj-23253	205	5	generalizability	generalizability	NOUN
brj-23253	205	6	of	of	ADP
brj-23253	205	7	the	the	DET
brj-23253	205	8	model	model	NOUN
brj-23253	205	9	are	be	AUX
brj-23253	205	10	assessed	assess	VERB
brj-23253	205	11	by	by	ADP
brj-23253	205	12	calculating	calculate	VERB
brj-23253	205	13	the	the	DET
brj-23253	205	14	determination	determination	NOUN
brj-23253	205	15	coefficient	coefficient	NOUN
brj-23253	205	16	(	(	PUNCT
brj-23253	205	17	r2	r2	PROPN
brj-23253	205	18	)	)	PUNCT
brj-23253	205	19	and	and	CCONJ
brj-23253	205	20	root	root	NOUN
brj-23253	205	21	mean	mean	NOUN
brj-23253	205	22	square	square	ADJ
brj-23253	205	23	error	error	NOUN
brj-23253	205	24	(	(	PUNCT
brj-23253	205	25	rmse	rmse	NOUN
brj-23253	205	26	)	)	PUNCT
brj-23253	205	27	for	for	ADP
brj-23253	205	28	both	both	CCONJ
brj-23253	205	29	the	the	DET
brj-23253	205	30	calibration	calibration	NOUN
brj-23253	205	31	and	and	CCONJ
brj-23253	205	32	prediction	prediction	NOUN
brj-23253	205	33	sets	set	NOUN
brj-23253	205	34	.	.	PUNCT
brj-23253	206	1	through	through	ADP
brj-23253	206	2	model	model	NOUN
brj-23253	206	3	establishment	establishment	NOUN
brj-23253	206	4	in	in	ADP
brj-23253	206	5	this	this	DET
brj-23253	206	6	study	study	NOUN
brj-23253	206	7	,	,	PUNCT
brj-23253	206	8	elm	elm	PROPN
brj-23253	206	9	and	and	CCONJ
brj-23253	206	10	plsr	plsr	PROPN
brj-23253	206	11	models	model	NOUN
brj-23253	206	12	based	base	VERB
brj-23253	206	13	on	on	ADP
brj-23253	206	14	full	full	ADJ
brj-23253	206	15	spectrum	spectrum	NOUN
brj-23253	206	16	and	and	CCONJ
brj-23253	206	17	feature	feature	NOUN
brj-23253	206	18	wavelengths	wavelength	NOUN
brj-23253	206	19	were	be	AUX
brj-23253	206	20	developed	develop	VERB
brj-23253	206	21	to	to	PART
brj-23253	206	22	predict	predict	VERB
brj-23253	206	23	the	the	DET
brj-23253	206	24	protein	protein	NOUN
brj-23253	206	25	content	content	NOUN
brj-23253	206	26	in	in	ADP
brj-23253	206	27	purple	purple	ADJ
brj-23253	206	28	alfalfa	alfalfa	NOUN
brj-23253	206	29	.	.	PUNCT
brj-23253	207	1	experimental	experimental	ADJ
brj-23253	207	2	results	result	NOUN
brj-23253	207	3	are	be	AUX
brj-23253	207	4	presented	present	VERB
brj-23253	207	5	in	in	ADP
brj-23253	207	6	tables	table	NOUN
brj-23253	207	7	2	2	NUM
brj-23253	207	8	and	and	CCONJ
brj-23253	207	9	3	3	NUM
brj-23253	207	10	.	.	X
brj-23253	207	11	analyzing	analyze	VERB
brj-23253	207	12	table	table	NOUN
brj-23253	207	13	1	1	NUM
brj-23253	207	14	from	from	ADP
brj-23253	207	15	the	the	DET
brj-23253	207	16	perspective	perspective	NOUN
brj-23253	207	17	of	of	ADP
brj-23253	207	18	spectral	spectral	ADJ
brj-23253	207	19	data	datum	NOUN
brj-23253	207	20	preprocessing	preprocessing	NOUN
brj-23253	207	21	,	,	PUNCT
brj-23253	207	22	it	it	PRON
brj-23253	207	23	is	be	AUX
brj-23253	207	24	observed	observe	VERB
brj-23253	207	25	that	that	SCONJ
brj-23253	207	26	the	the	DET
brj-23253	207	27	various	various	ADJ
brj-23253	207	28	preprocessing	preprocessing	NOUN
brj-23253	207	29	methods	method	NOUN
brj-23253	207	30	improved	improve	VERB
brj-23253	207	31	the	the	DET
brj-23253	207	32	accuracy	accuracy	NOUN
brj-23253	207	33	of	of	ADP
brj-23253	207	34	both	both	DET
brj-23253	207	35	calibration	calibration	NOUN
brj-23253	207	36	and	and	CCONJ
brj-23253	207	37	prediction	prediction	NOUN
brj-23253	207	38	sets	set	NOUN
brj-23253	207	39	of	of	ADP
brj-23253	207	40	the	the	DET
brj-23253	207	41	models	model	NOUN
brj-23253	207	42	.	.	PUNCT
brj-23253	208	1	from	from	ADP
brj-23253	208	2	the	the	DET
brj-23253	208	3	perspective	perspective	NOUN
brj-23253	208	4	of	of	ADP
brj-23253	208	5	feature	feature	NOUN
brj-23253	208	6	wavelengths	wavelength	NOUN
brj-23253	208	7	in	in	ADP
brj-23253	208	8	table	table	NOUN
brj-23253	208	9	3	3	NUM
brj-23253	208	10	,	,	PUNCT
brj-23253	208	11	it	it	PRON
brj-23253	208	12	was	be	AUX
brj-23253	208	13	noted	note	VERB
brj-23253	208	14	that	that	SCONJ
brj-23253	208	15	the	the	DET
brj-23253	208	16	number	number	NOUN
brj-23253	208	17	of	of	ADP
brj-23253	208	18	feature	feature	NOUN
brj-23253	208	19	wavelengths	wavelength	NOUN
brj-23253	208	20	extracted	extract	VERB
brj-23253	208	21	by	by	ADP
brj-23253	208	22	the	the	DET
brj-23253	208	23	ivir	ivir	PROPN
brj-23253	208	24	algorithm	algorithm	PROPN
brj-23253	208	25	was	be	AUX
brj-23253	208	26	significantly	significantly	ADV
brj-23253	208	27	less	less	ADJ
brj-23253	208	28	than	than	ADP
brj-23253	208	29	those	those	PRON
brj-23253	208	30	extracted	extract	VERB
brj-23253	208	31	by	by	ADP
brj-23253	208	32	the	the	DET
brj-23253	208	33	cars	car	NOUN
brj-23253	208	34	method	method	NOUN
brj-23253	208	35	.	.	PUNCT
brj-23253	209	1	moreover	moreover	ADV
brj-23253	209	2	,	,	PUNCT
brj-23253	209	3	the	the	DET
brj-23253	209	4	models	model	NOUN
brj-23253	209	5	using	use	VERB
brj-23253	209	6	the	the	DET
brj-23253	209	7	ivir	ivir	PROPN
brj-23253	209	8	algorithm	algorithm	PROPN
brj-23253	209	9	showed	show	VERB
brj-23253	209	10	lower	low	ADJ
brj-23253	209	11	calibration	calibration	NOUN
brj-23253	209	12	and	and	CCONJ
brj-23253	209	13	prediction	prediction	NOUN
brj-23253	209	14	set	set	NOUN
brj-23253	209	15	accuracies	accuracy	NOUN
brj-23253	209	16	compared	compare	VERB
brj-23253	209	17	to	to	ADP
brj-23253	209	18	other	other	ADJ
brj-23253	209	19	models	model	NOUN
brj-23253	209	20	,	,	PUNCT
brj-23253	209	21	possibly	possibly	ADV
brj-23253	209	22	due	due	ADP
brj-23253	209	23	to	to	ADP
brj-23253	209	24	the	the	DET
brj-23253	209	25	exclusion	exclusion	NOUN
brj-23253	209	26	of	of	ADP
brj-23253	209	27	wavelengths	wavelength	NOUN
brj-23253	209	28	highly	highly	ADV
brj-23253	209	29	relevant	relevant	ADJ
brj-23253	209	30	to	to	ADP
brj-23253	209	31	the	the	DET
brj-23253	209	32	protein	protein	NOUN
brj-23253	209	33	content	content	NOUN
brj-23253	209	34	in	in	ADP
brj-23253	209	35	dried	dry	VERB
brj-23253	209	36	alfalfa	alfalfa	NOUN
brj-23253	209	37	during	during	ADP
brj-23253	209	38	the	the	DET
brj-23253	209	39	ivir	ivir	PROPN
brj-23253	209	40	selection	selection	NOUN
brj-23253	209	41	process	process	NOUN
brj-23253	209	42	,	,	PUNCT
brj-23253	209	43	leading	lead	VERB
brj-23253	209	44	to	to	ADP
brj-23253	209	45	poorer	poor	ADJ
brj-23253	209	46	predictive	predictive	ADJ
brj-23253	209	47	accuracy	accuracy	NOUN
brj-23253	209	48	in	in	ADP
brj-23253	209	49	the	the	DET
brj-23253	209	50	calibration	calibration	NOUN
brj-23253	209	51	and	and	CCONJ
brj-23253	209	52	test	test	NOUN
brj-23253	209	53	sets	set	NOUN
brj-23253	209	54	.	.	PUNCT
brj-23253	210	1	from	from	ADP
brj-23253	210	2	the	the	DET
brj-23253	210	3	perspective	perspective	NOUN
brj-23253	210	4	of	of	ADP
brj-23253	210	5	model	model	NOUN
brj-23253	210	6	establishment	establishment	NOUN
brj-23253	210	7	,	,	PUNCT
brj-23253	210	8	table	table	NOUN
brj-23253	210	9	3	3	NUM
brj-23253	210	10	indicates	indicate	VERB
brj-23253	210	11	that	that	SCONJ
brj-23253	210	12	the	the	DET
brj-23253	210	13	msc	msc	PROPN
brj-23253	210	14	-	-	PUNCT
brj-23253	210	15	carsplsr	carsplsr	PROPN
brj-23253	210	16	model	model	NOUN
brj-23253	210	17	had	have	VERB
brj-23253	210	18	strong	strong	ADJ
brj-23253	210	19	predictive	predictive	ADJ
brj-23253	210	20	capability	capability	NOUN
brj-23253	210	21	,	,	PUNCT
brj-23253	210	22	with	with	ADP
brj-23253	210	23	a	a	DET
brj-23253	210	24	calibration	calibration	NOUN
brj-23253	210	25	set	set	VERB
brj-23253	210	26	root	root	NOUN
brj-23253	210	27	mean	mean	VERB
brj-23253	210	28	square	square	ADJ
brj-23253	210	29	error	error	NOUN
brj-23253	210	30	(	(	PUNCT
brj-23253	210	31	rmse	rmse	NOUN
brj-23253	210	32	)	)	PUNCT
brj-23253	210	33	of	of	ADP
brj-23253	210	34	0.1922	0.1922	NUM
brj-23253	210	35	,	,	PUNCT
brj-23253	210	36	a	a	DET
brj-23253	210	37	determination	determination	NOUN
brj-23253	210	38	coefficient	coefficient	NOUN
brj-23253	210	39	(	(	PUNCT
brj-23253	210	40	r2	r2	PROPN
brj-23253	210	41	)	)	PUNCT
brj-23253	210	42	of	of	ADP
brj-23253	210	43	0.9972	0.9972	NUM
brj-23253	210	44	,	,	PUNCT
brj-23253	210	45	a	a	DET
brj-23253	210	46	prediction	prediction	NOUN
brj-23253	210	47	set	set	VERB
brj-23253	210	48	rmse	rmse	NOUN
brj-23253	210	49	of	of	ADP
brj-23253	210	50	0.6581	0.6581	NUM
brj-23253	210	51	,	,	PUNCT
brj-23253	210	52	and	and	CCONJ
brj-23253	210	53	a	a	DET
brj-23253	210	54	determination	determination	NOUN
brj-23253	210	55	coefficient	coefficient	NOUN
brj-23253	210	56	of	of	ADP
brj-23253	210	57	0.9446	0.9446	NUM
brj-23253	210	58	.	.	PUNCT
brj-23253	211	1	in	in	ADP
brj-23253	211	2	establishing	establish	VERB
brj-23253	211	3	a	a	DET
brj-23253	211	4	prediction	prediction	NOUN
brj-23253	211	5	model	model	NOUN
brj-23253	211	6	for	for	ADP
brj-23253	211	7	the	the	DET
brj-23253	211	8	protein	protein	NOUN
brj-23253	211	9	value	value	NOUN
brj-23253	211	10	of	of	ADP
brj-23253	211	11	dried	dry	VERB
brj-23253	211	12	alfalfa	alfalfa	NOUN
brj-23253	211	13	,	,	PUNCT
brj-23253	211	14	it	it	PRON
brj-23253	211	15	is	be	AUX
brj-23253	211	16	evident	evident	ADJ
brj-23253	211	17	that	that	SCONJ
brj-23253	211	18	the	the	DET
brj-23253	211	19	plsr	plsr	PROPN
brj-23253	211	20	model	model	NOUN
brj-23253	211	21	performed	perform	VERB
brj-23253	211	22	better	well	ADV
brj-23253	211	23	than	than	ADP
brj-23253	211	24	the	the	DET
brj-23253	211	25	elm	elm	PROPN
brj-23253	211	26	model	model	NOUN
brj-23253	211	27	.	.	PUNCT
brj-23253	212	1	the	the	DET
brj-23253	212	2	prediction	prediction	NOUN
brj-23253	212	3	results	result	NOUN
brj-23253	212	4	show	show	VERB
brj-23253	212	5	that	that	SCONJ
brj-23253	212	6	the	the	DET
brj-23253	212	7	accuracy	accuracy	NOUN
brj-23253	212	8	of	of	ADP
brj-23253	212	9	the	the	DET
brj-23253	212	10	full	full	ADJ
brj-23253	212	11	-	-	PUNCT
brj-23253	212	12	spectrum	spectrum	NOUN
brj-23253	212	13	prediction	prediction	NOUN
brj-23253	212	14	model	model	NOUN
brj-23253	212	15	was	be	AUX
brj-23253	212	16	lower	low	ADJ
brj-23253	212	17	than	than	ADP
brj-23253	212	18	that	that	PRON
brj-23253	212	19	of	of	ADP
brj-23253	212	20	the	the	DET
brj-23253	212	21	feature	feature	NOUN
brj-23253	212	22	wavelength	wavelength	NOUN
brj-23253	212	23	prediction	prediction	NOUN
brj-23253	212	24	model	model	NOUN
brj-23253	212	25	,	,	PUNCT
brj-23253	212	26	and	and	CCONJ
brj-23253	212	27	the	the	DET
brj-23253	212	28	msc	msc	PROPN
brj-23253	212	29	-	-	PUNCT
brj-23253	212	30	cars	car	NOUN
brj-23253	212	31	-	-	PUNCT
brj-23253	212	32	plsr	plsr	NOUN
brj-23253	212	33	model	model	NOUN
brj-23253	212	34	improved	improve	VERB
brj-23253	212	35	the	the	DET
brj-23253	212	36	prediction	prediction	NOUN
brj-23253	212	37	accuracy	accuracy	NOUN
brj-23253	212	38	by	by	ADP
brj-23253	212	39	15.8	15.8	NUM
brj-23253	212	40	%	%	NOUN
brj-23253	212	41	and	and	CCONJ
brj-23253	212	42	reduced	reduce	VERB
brj-23253	212	43	the	the	DET
brj-23253	212	44	prediction	prediction	NOUN
brj-23253	212	45	set	set	VERB
brj-23253	212	46	rmse	rmse	NOUN
brj-23253	212	47	by	by	ADP
brj-23253	212	48	33.4	33.4	NUM
brj-23253	212	49	%	%	NOUN
brj-23253	212	50	compared	compare	VERB
brj-23253	212	51	to	to	ADP
brj-23253	212	52	the	the	DET
brj-23253	212	53	msc	msc	PROPN
brj-23253	212	54	-	-	PUNCT
brj-23253	212	55	plsr	plsr	PROPN
brj-23253	212	56	model	model	NOUN
brj-23253	212	57	.	.	PUNCT
brj-23253	213	1	this	this	PRON
brj-23253	213	2	demonstrates	demonstrate	VERB
brj-23253	213	3	that	that	SCONJ
brj-23253	213	4	extracting	extract	VERB
brj-23253	213	5	feature	feature	NOUN
brj-23253	213	6	wavelengths	wavelength	NOUN
brj-23253	213	7	significantly	significantly	ADV
brj-23253	213	8	simplified	simplify	VERB
brj-23253	213	9	the	the	DET
brj-23253	213	10	computational	computational	ADJ
brj-23253	213	11	model	model	NOUN
brj-23253	213	12	and	and	CCONJ
brj-23253	213	13	enhanced	enhance	VERB
brj-23253	213	14	prediction	prediction	NOUN
brj-23253	213	15	accuracy	accuracy	NOUN
brj-23253	213	16	.	.	PUNCT
brj-23253	214	1	table	table	NOUN
brj-23253	214	2	2	2	NUM
brj-23253	214	3	.	.	PUNCT
brj-23253	214	4	prediction	prediction	NOUN
brj-23253	214	5	results	result	NOUN
brj-23253	214	6	of	of	ADP
brj-23253	214	7	full	full	ADJ
brj-23253	214	8	-	-	PUNCT
brj-23253	214	9	spectrum	spectrum	NOUN
brj-23253	214	10	elm	elm	NOUN
brj-23253	214	11	and	and	CCONJ
brj-23253	214	12	plsr	plsr	PROPN
brj-23253	214	13	models	model	NOUN
brj-23253	214	14	using	use	VERB
brj-23253	214	15	different	different	ADJ
brj-23253	214	16	preprocessing	preprocessing	NOUN
brj-23253	214	17	methods	method	NOUN
brj-23253	214	18	model	model	NOUN
brj-23253	214	19	pretreatment	pretreatment	NOUN
brj-23253	214	20	calibration	calibration	NOUN
brj-23253	214	21	set	set	VERB
brj-23253	214	22	prediction	prediction	NOUN
brj-23253	214	23	set	set	VERB
brj-23253	214	24	elm	elm	PROPN
brj-23253	214	25	no	no	INTJ
brj-23253	214	26	without	without	ADP
brj-23253	214	27	1.5110	1.5110	NUM
brj-23253	214	28	0.8577	0.8577	NUM
brj-23253	214	29	1.5303	1.5303	NUM
brj-23253	214	30	0.6443	0.6443	NUM
brj-23253	214	31	sg	sg	ADP
brj-23253	214	32	0.9731	0.9731	NUM
brj-23253	214	33	0.9145	0.9145	NUM
brj-23253	215	1	1.5084	1.5084	NUM
brj-23253	215	2	0.6527	0.6527	NUM
brj-23253	215	3	snv	snv	PROPN
brj-23253	215	4	1.0325	1.0325	NUM
brj-23253	215	5	0.8963	0.8963	NUM
brj-23253	215	6	1.9715	1.9715	NUM
brj-23253	215	7	0.5823	0.5823	NUM
brj-23253	215	8	msc	msc	PROPN
brj-23253	215	9	1.1485	1.1485	NUM
brj-23253	215	10	0.9004	0.9004	NUM
brj-23253	215	11	1.1923	1.1923	NUM
brj-23253	215	12	0.7474	0.7474	NUM
brj-23253	215	13	fd	fd	X
brj-23253	215	14	1.0041	1.0041	NUM
brj-23253	215	15	0.8997	0.8997	NUM
brj-23253	215	16	2.1570	2.1570	NUM
brj-23253	215	17	0.5336	0.5336	NUM
brj-23253	215	18	plsr	plsr	NOUN
brj-23253	215	19	no	no	ADV
brj-23253	215	20	without	without	ADP
brj-23253	215	21	1.4201	1.4201	NUM
brj-23253	215	22	0.8443	0.8443	NUM
brj-23253	215	23	1.3363	1.3363	NUM
brj-23253	215	24	0.6850	0.6850	NUM
brj-23253	215	25	sg	sg	ADP
brj-23253	215	26	0.4354	0.4354	NUM
brj-23253	215	27	0.9744	0.9744	NUM
brj-23253	215	28	1.1533	1.1533	NUM
brj-23253	215	29	0.6997	0.6997	NUM
brj-23253	215	30	snv	snv	PROPN
brj-23253	215	31	0.4451	0.4451	NUM
brj-23253	215	32	0.9431	0.9431	NUM
brj-23253	215	33	1.1126	1.1126	NUM
brj-23253	215	34	0.7322	0.7322	NUM
brj-23253	215	35	msc	msc	PROPN
brj-23253	215	36	0.4126	0.4126	NUM
brj-23253	215	37	0.9752	0.9752	NUM
brj-23253	215	38	0.9875	0.9875	NUM
brj-23253	215	39	0.8155	0.8155	NUM
brj-23253	215	40	fd	fd	PROPN
brj-23253	215	41	0.5127	0.5127	NUM
brj-23253	215	42	0.9321	0.9321	NUM
brj-23253	215	43	1.3321	1.3321	NUM
brj-23253	215	44	0.6954	0.6954	NUM
brj-23253	215	45	peer	peer	NOUN
brj-23253	215	46	-	-	PUNCT
brj-23253	215	47	reviewed	review	VERB
brj-23253	215	48	article	article	NOUN
brj-23253	215	49	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	215	50	li	li	PROPN
brj-23253	215	51	et	et	PROPN
brj-23253	215	52	al	al	PROPN
brj-23253	215	53	.	.	PROPN
brj-23253	216	1	(	(	PUNCT
brj-23253	216	2	2024	2024	NUM
brj-23253	216	3	)	)	PUNCT
brj-23253	216	4	.	.	PUNCT
brj-23253	217	1	“	"	PUNCT
brj-23253	217	2	alfalfa	alfalfa	X
brj-23253	217	3	protein	protein	NOUN
brj-23253	217	4	with	with	ADP
brj-23253	217	5	vis	vis	X
brj-23253	217	6	/	/	SYM
brj-23253	217	7	nir	nir	NOUN
brj-23253	217	8	,	,	PUNCT
brj-23253	217	9	”	"	PUNCT
brj-23253	217	10	bioresources	bioresource	NOUN
brj-23253	217	11	19(2	19(2	NUM
brj-23253	217	12	)	)	PUNCT
brj-23253	217	13	,	,	PUNCT
brj-23253	217	14	3808	3808	NUM
brj-23253	217	15	-	-	SYM
brj-23253	217	16	3825	3825	NUM
brj-23253	217	17	.	.	PUNCT
brj-23253	218	1	3819	3819	NUM
brj-23253	218	2	based	base	VERB
brj-23253	218	3	on	on	ADP
brj-23253	218	4	these	these	DET
brj-23253	218	5	results	result	NOUN
brj-23253	218	6	,	,	PUNCT
brj-23253	218	7	to	to	PART
brj-23253	218	8	further	far	ADV
brj-23253	218	9	improve	improve	VERB
brj-23253	218	10	model	model	NOUN
brj-23253	218	11	accuracy	accuracy	NOUN
brj-23253	218	12	,	,	PUNCT
brj-23253	218	13	two	two	NUM
brj-23253	218	14	machine	machine	NOUN
brj-23253	218	15	learning	learn	VERB
brj-23253	218	16	algorithms	algorithm	NOUN
brj-23253	218	17	,	,	PUNCT
brj-23253	218	18	support	support	NOUN
brj-23253	218	19	vector	vector	NOUN
brj-23253	218	20	machine	machine	NOUN
brj-23253	218	21	(	(	PUNCT
brj-23253	218	22	svm	svm	PROPN
brj-23253	218	23	)	)	PUNCT
brj-23253	218	24	and	and	CCONJ
brj-23253	218	25	long	long	ADJ
brj-23253	218	26	short	short	ADJ
brj-23253	218	27	-	-	PUNCT
brj-23253	218	28	term	term	NOUN
brj-23253	218	29	memory	memory	NOUN
brj-23253	218	30	network	network	NOUN
brj-23253	218	31	(	(	PUNCT
brj-23253	218	32	lstm	lstm	PROPN
brj-23253	218	33	)	)	PUNCT
brj-23253	218	34	,	,	PUNCT
brj-23253	218	35	were	be	AUX
brj-23253	218	36	introduced	introduce	VERB
brj-23253	218	37	,	,	PUNCT
brj-23253	218	38	forming	form	VERB
brj-23253	218	39	two	two	NUM
brj-23253	218	40	new	new	ADJ
brj-23253	218	41	model	model	NOUN
brj-23253	218	42	combinations	combination	NOUN
brj-23253	218	43	:	:	PUNCT
brj-23253	218	44	msc	msc	PROPN
brj-23253	218	45	-	-	PUNCT
brj-23253	218	46	cars	car	NOUN
brj-23253	218	47	-	-	PUNCT
brj-23253	218	48	plsrsvm	plsrsvm	NOUN
brj-23253	218	49	and	and	CCONJ
brj-23253	218	50	msc	msc	PROPN
brj-23253	218	51	-	-	PUNCT
brj-23253	218	52	cars	car	NOUN
brj-23253	218	53	-	-	PUNCT
brj-23253	218	54	plsr	plsr	NOUN
brj-23253	218	55	-	-	PUNCT
brj-23253	218	56	lstm	lstm	NOUN
brj-23253	218	57	.	.	PUNCT
brj-23253	219	1	these	these	PRON
brj-23253	219	2	were	be	AUX
brj-23253	219	3	aimed	aim	VERB
brj-23253	219	4	at	at	ADP
brj-23253	219	5	utilizing	utilize	VERB
brj-23253	219	6	the	the	DET
brj-23253	219	7	strengths	strength	NOUN
brj-23253	219	8	of	of	ADP
brj-23253	219	9	each	each	DET
brj-23253	219	10	algorithm	algorithm	NOUN
brj-23253	219	11	to	to	PART
brj-23253	219	12	enhance	enhance	VERB
brj-23253	219	13	further	far	ADV
brj-23253	219	14	the	the	DET
brj-23253	219	15	accuracy	accuracy	NOUN
brj-23253	219	16	and	and	CCONJ
brj-23253	219	17	reliability	reliability	NOUN
brj-23253	219	18	of	of	ADP
brj-23253	219	19	predicting	predict	VERB
brj-23253	219	20	the	the	DET
brj-23253	219	21	protein	protein	NOUN
brj-23253	219	22	content	content	NOUN
brj-23253	219	23	in	in	ADP
brj-23253	219	24	dried	dry	VERB
brj-23253	219	25	alfalfa	alfalfa	NOUN
brj-23253	219	26	.	.	PUNCT
brj-23253	220	1	table	table	NOUN
brj-23253	220	2	3	3	NUM
brj-23253	220	3	.	.	PUNCT
brj-23253	220	4	prediction	prediction	NOUN
brj-23253	220	5	results	result	NOUN
brj-23253	220	6	of	of	ADP
brj-23253	220	7	elm	elm	NOUN
brj-23253	220	8	and	and	CCONJ
brj-23253	220	9	plsr	plsr	PROPN
brj-23253	220	10	models	model	NOUN
brj-23253	220	11	after	after	ADP
brj-23253	220	12	feature	feature	NOUN
brj-23253	220	13	variable	variable	ADJ
brj-23253	220	14	selection	selection	NOUN
brj-23253	220	15	model	model	NOUN
brj-23253	220	16	algorithm	algorithm	NOUN
brj-23253	220	17	combinations	combination	NOUN
brj-23253	220	18	feature	feature	NOUN
brj-23253	220	19	variables	variable	NOUN
brj-23253	220	20	number	number	NOUN
brj-23253	220	21	calibration	calibration	NOUN
brj-23253	220	22	set	set	VERB
brj-23253	220	23	prediction	prediction	NOUN
brj-23253	220	24	set	set	VERB
brj-23253	220	25	elm	elm	PROPN
brj-23253	220	26	sg	sg	NOUN
brj-23253	220	27	-	-	PUNCT
brj-23253	220	28	cars	car	NOUN
brj-23253	220	29	-	-	PUNCT
brj-23253	220	30	elm	elm	NOUN
brj-23253	220	31	96	96	NUM
brj-23253	220	32	0.2600	0.2600	NUM
brj-23253	220	33	0.9351	0.9351	NUM
brj-23253	220	34	1.3271	1.3271	NUM
brj-23253	220	35	0.7359	0.7359	NUM
brj-23253	220	36	sg	sg	NOUN
brj-23253	220	37	-	-	PUNCT
brj-23253	220	38	iriv	iriv	NOUN
brj-23253	220	39	-	-	PUNCT
brj-23253	220	40	elm	elm	NOUN
brj-23253	220	41	59	59	NUM
brj-23253	220	42	1.1354	1.1354	NUM
brj-23253	220	43	0.8021	0.8021	NUM
brj-23253	220	44	1.7059	1.7059	NUM
brj-23253	220	45	0.6059	0.6059	NUM
brj-23253	220	46	snv	snv	NOUN
brj-23253	220	47	-	-	PUNCT
brj-23253	220	48	cars	car	NOUN
brj-23253	220	49	-	-	PUNCT
brj-23253	220	50	elm	elm	NOUN
brj-23253	220	51	143	143	NOUN
brj-23253	220	52	0.1464	0.1464	NUM
brj-23253	221	1	0.9763	0.9763	NUM
brj-23253	221	2	1.0822	1.0822	NUM
brj-23253	221	3	0.7997	0.7997	NUM
brj-23253	221	4	snv	snv	PROPN
brj-23253	221	5	-	-	PUNCT
brj-23253	221	6	iriv	iriv	NOUN
brj-23253	221	7	-	-	PUNCT
brj-23253	221	8	elm	elm	NOUN
brj-23253	221	9	69	69	NUM
brj-23253	221	10	0.8813	0.8813	NUM
brj-23253	221	11	0.9015	0.9015	NUM
brj-23253	221	12	1.2114	1.2114	NUM
brj-23253	221	13	0.7908	0.7908	NUM
brj-23253	221	14	msc	msc	PROPN
brj-23253	221	15	-	-	PUNCT
brj-23253	221	16	cars	car	NOUN
brj-23253	221	17	-	-	PUNCT
brj-23253	221	18	elm	elm	NOUN
brj-23253	221	19	96	96	NUM
brj-23253	221	20	0.1903	0.1903	NUM
brj-23253	221	21	0.9655	0.9655	NUM
brj-23253	221	22	1.2099	1.2099	NUM
brj-23253	221	23	0.7204	0.7204	NUM
brj-23253	221	24	msc	msc	PROPN
brj-23253	221	25	-	-	PUNCT
brj-23253	221	26	iriv	iriv	ADJ
brj-23253	221	27	-	-	PUNCT
brj-23253	221	28	elm	elm	NOUN
brj-23253	221	29	57	57	NUM
brj-23253	221	30	1.3442	1.3442	NUM
brj-23253	221	31	0.7501	0.7501	NUM
brj-23253	221	32	2.1128	2.1128	NUM
brj-23253	221	33	0.7454	0.7454	NUM
brj-23253	221	34	fd	fd	NOUN
brj-23253	221	35	-	-	PUNCT
brj-23253	221	36	cars	car	NOUN
brj-23253	221	37	-	-	PUNCT
brj-23253	221	38	elm	elm	NOUN
brj-23253	221	39	163	163	NUM
brj-23253	221	40	0.2832	0.2832	NUM
brj-23253	221	41	0.9456	0.9456	NUM
brj-23253	221	42	1.5775	1.5775	NUM
brj-23253	221	43	0.6446	0.6446	NUM
brj-23253	221	44	fd	fd	VERB
brj-23253	221	45	-	-	PUNCT
brj-23253	221	46	iriv	iriv	NOUN
brj-23253	221	47	-	-	PUNCT
brj-23253	221	48	elm	elm	NOUN
brj-23253	221	49	51	51	NUM
brj-23253	221	50	1.3402	1.3402	NUM
brj-23253	221	51	0.7318	0.7318	NUM
brj-23253	221	52	2.1308	2.1308	NUM
brj-23253	221	53	0.7442	0.7442	NUM
brj-23253	221	54	plsr	plsr	NOUN
brj-23253	221	55	sg	sg	PROPN
brj-23253	221	56	-	-	PUNCT
brj-23253	221	57	cars	car	NOUN
brj-23253	221	58	-	-	PUNCT
brj-23253	221	59	plsr	plsr	NOUN
brj-23253	221	60	96	96	NUM
brj-23253	221	61	0.2044	0.2044	NUM
brj-23253	221	62	0.9930	0.9930	NUM
brj-23253	221	63	0.7581	0.7581	NUM
brj-23253	221	64	0.9362	0.9362	NUM
brj-23253	221	65	sg	sg	PROPN
brj-23253	221	66	-	-	PUNCT
brj-23253	221	67	iriv	iriv	NOUN
brj-23253	221	68	-	-	PUNCT
brj-23253	221	69	plsr	plsr	ADJ
brj-23253	221	70	59	59	NUM
brj-23253	221	71	1.4871	1.4871	NUM
brj-23253	221	72	0.6725	0.6725	NUM
brj-23253	221	73	2.0921	2.0921	NUM
brj-23253	221	74	0.7237	0.7237	NUM
brj-23253	221	75	snv	snv	NOUN
brj-23253	221	76	-	-	PUNCT
brj-23253	221	77	cars	car	NOUN
brj-23253	221	78	-	-	PUNCT
brj-23253	221	79	plsr	plsr	NOUN
brj-23253	221	80	143	143	NUM
brj-23253	221	81	0.0983	0.0983	NUM
brj-23253	221	82	0.9953	0.9953	NUM
brj-23253	221	83	0.7651	0.7651	NUM
brj-23253	221	84	0.9212	0.9212	NUM
brj-23253	221	85	snv	snv	PROPN
brj-23253	221	86	-	-	PUNCT
brj-23253	221	87	iriv	iriv	NOUN
brj-23253	221	88	-	-	PUNCT
brj-23253	221	89	plsr	plsr	NOUN
brj-23253	221	90	69	69	NUM
brj-23253	221	91	1.3304	1.3304	NUM
brj-23253	221	92	0.7655	0.7655	NUM
brj-23253	221	93	2.193	2.193	NUM
brj-23253	221	94	0.7315	0.7315	NUM
brj-23253	221	95	msc	msc	PROPN
brj-23253	221	96	-	-	PUNCT
brj-23253	221	97	cars	car	NOUN
brj-23253	221	98	-	-	PUNCT
brj-23253	221	99	plsr	plsr	NOUN
brj-23253	221	100	96	96	NUM
brj-23253	221	101	0.1922	0.1922	NUM
brj-23253	221	102	0.9972	0.9972	NUM
brj-23253	221	103	0.6581	0.6581	NUM
brj-23253	221	104	0.9446	0.9446	NUM
brj-23253	221	105	msc	msc	PROPN
brj-23253	221	106	-	-	PUNCT
brj-23253	221	107	iriv	iriv	ADJ
brj-23253	221	108	-	-	PUNCT
brj-23253	221	109	plsr	plsr	NOUN
brj-23253	221	110	57	57	NUM
brj-23253	221	111	1.2184	1.2184	NUM
brj-23253	221	112	0.7808	0.7808	NUM
brj-23253	221	113	2.8432	2.8432	NUM
brj-23253	221	114	0.6124	0.6124	NUM
brj-23253	221	115	fd	fd	NOUN
brj-23253	221	116	-	-	PUNCT
brj-23253	221	117	cars	car	NOUN
brj-23253	221	118	-	-	PUNCT
brj-23253	221	119	plsr	plsr	NOUN
brj-23253	221	120	163	163	NUM
brj-23253	221	121	0.1940	0.1940	NUM
brj-23253	221	122	0.9939	0.9939	NUM
brj-23253	221	123	1.3465	1.3465	NUM
brj-23253	221	124	0.7723	0.7723	NUM
brj-23253	221	125	fd	fd	ADJ
brj-23253	221	126	-	-	PUNCT
brj-23253	221	127	iriv	iriv	NOUN
brj-23253	221	128	-	-	PUNCT
brj-23253	221	129	plsr	plsr	NOUN
brj-23253	221	130	51	51	NUM
brj-23253	221	131	1.5201	1.5201	NUM
brj-23253	221	132	0.7133	0.7133	NUM
brj-23253	221	133	2.6244	2.6244	NUM
brj-23253	221	134	0.5982	0.5982	NUM
brj-23253	221	135	the	the	DET
brj-23253	221	136	msc	msc	PROPN
brj-23253	221	137	-	-	PUNCT
brj-23253	221	138	cars	car	NOUN
brj-23253	221	139	-	-	PUNCT
brj-23253	221	140	plsr	plsr	NOUN
brj-23253	221	141	-	-	PUNCT
brj-23253	221	142	svm	svm	NOUN
brj-23253	221	143	model	model	NOUN
brj-23253	221	144	combines	combine	VERB
brj-23253	221	145	the	the	DET
brj-23253	221	146	feature	feature	NOUN
brj-23253	221	147	extraction	extraction	NOUN
brj-23253	221	148	and	and	CCONJ
brj-23253	221	149	data	datum	NOUN
brj-23253	221	150	preprocessing	preprocesse	VERB
brj-23253	221	151	capabilities	capability	NOUN
brj-23253	221	152	of	of	ADP
brj-23253	221	153	the	the	DET
brj-23253	221	154	msc	msc	PROPN
brj-23253	221	155	-	-	PUNCT
brj-23253	221	156	cars	car	NOUN
brj-23253	221	157	-	-	PUNCT
brj-23253	221	158	plsr	plsr	NOUN
brj-23253	221	159	model	model	NOUN
brj-23253	221	160	with	with	ADP
brj-23253	221	161	the	the	DET
brj-23253	221	162	robust	robust	ADJ
brj-23253	221	163	regression	regression	NOUN
brj-23253	221	164	function	function	NOUN
brj-23253	221	165	of	of	ADP
brj-23253	221	166	svm	svm	PROPN
brj-23253	221	167	(	(	PUNCT
brj-23253	221	168	lee	lee	PROPN
brj-23253	221	169	et	et	PROPN
brj-23253	221	170	al	al	PROPN
brj-23253	221	171	.	.	PROPN
brj-23253	221	172	2023	2023	NUM
brj-23253	221	173	)	)	PUNCT
brj-23253	221	174	.	.	PUNCT
brj-23253	222	1	svm	svm	ADJ
brj-23253	222	2	excels	excel	NOUN
brj-23253	222	3	at	at	ADP
brj-23253	222	4	complex	complex	ADJ
brj-23253	222	5	,	,	PUNCT
brj-23253	222	6	high	high	ADJ
brj-23253	222	7	-	-	PUNCT
brj-23253	222	8	dimensional	dimensional	ADJ
brj-23253	222	9	datasets	dataset	NOUN
brj-23253	222	10	and	and	CCONJ
brj-23253	222	11	is	be	AUX
brj-23253	222	12	especially	especially	ADV
brj-23253	222	13	adept	adept	ADJ
brj-23253	222	14	at	at	ADP
brj-23253	222	15	handling	handle	VERB
brj-23253	222	16	small	small	ADJ
brj-23253	222	17	-	-	PUNCT
brj-23253	222	18	sample	sample	NOUN
brj-23253	222	19	datasets	dataset	NOUN
brj-23253	222	20	and	and	CCONJ
brj-23253	222	21	nonlinear	nonlinear	ADJ
brj-23253	222	22	problems	problem	NOUN
brj-23253	222	23	(	(	PUNCT
brj-23253	222	24	vabalas	vabalas	PROPN
brj-23253	222	25	et	et	PROPN
brj-23253	222	26	al	al	PROPN
brj-23253	222	27	.	.	PROPN
brj-23253	222	28	2019	2019	NUM
brj-23253	222	29	)	)	PUNCT
brj-23253	222	30	.	.	PUNCT
brj-23253	223	1	in	in	ADP
brj-23253	223	2	this	this	DET
brj-23253	223	3	model	model	NOUN
brj-23253	223	4	,	,	PUNCT
brj-23253	223	5	svm	svm	PROPN
brj-23253	223	6	served	serve	VERB
brj-23253	223	7	as	as	ADP
brj-23253	223	8	a	a	DET
brj-23253	223	9	secondary	secondary	ADJ
brj-23253	223	10	prediction	prediction	NOUN
brj-23253	223	11	model	model	NOUN
brj-23253	223	12	,	,	PUNCT
brj-23253	223	13	using	use	VERB
brj-23253	223	14	the	the	DET
brj-23253	223	15	outputs	output	NOUN
brj-23253	223	16	of	of	ADP
brj-23253	223	17	the	the	DET
brj-23253	223	18	plsr	plsr	PROPN
brj-23253	223	19	model	model	NOUN
brj-23253	223	20	as	as	ADP
brj-23253	223	21	its	its	PRON
brj-23253	223	22	inputs	input	NOUN
brj-23253	223	23	to	to	PART
brj-23253	223	24	refine	refine	VERB
brj-23253	223	25	and	and	CCONJ
brj-23253	223	26	optimize	optimize	VERB
brj-23253	223	27	the	the	DET
brj-23253	223	28	prediction	prediction	NOUN
brj-23253	223	29	results	result	VERB
brj-23253	223	30	further	far	ADV
brj-23253	223	31	.	.	PUNCT
brj-23253	224	1	the	the	DET
brj-23253	224	2	radial	radial	ADJ
brj-23253	224	3	basis	basis	NOUN
brj-23253	224	4	function	function	NOUN
brj-23253	224	5	(	(	PUNCT
brj-23253	224	6	rbf	rbf	PROPN
brj-23253	224	7	)	)	PUNCT
brj-23253	224	8	was	be	AUX
brj-23253	224	9	used	use	VERB
brj-23253	224	10	as	as	ADP
brj-23253	224	11	the	the	DET
brj-23253	224	12	kernel	kernel	PROPN
brj-23253	224	13	function	function	NOUN
brj-23253	224	14	in	in	ADP
brj-23253	224	15	the	the	DET
brj-23253	224	16	model	model	NOUN
brj-23253	224	17	establishment	establishment	NOUN
brj-23253	224	18	process	process	NOUN
brj-23253	224	19	,	,	PUNCT
brj-23253	224	20	with	with	ADP
brj-23253	224	21	the	the	DET
brj-23253	224	22	penalty	penalty	NOUN
brj-23253	224	23	factor	factor	NOUN
brj-23253	224	24	(	(	PUNCT
brj-23253	224	25	c	c	NOUN
brj-23253	224	26	)	)	PUNCT
brj-23253	224	27	and	and	CCONJ
brj-23253	224	28	rbf	rbf	PROPN
brj-23253	224	29	parameter	parameter	NOUN
brj-23253	224	30	(	(	PUNCT
brj-23253	224	31	g	g	NOUN
brj-23253	224	32	)	)	PUNCT
brj-23253	224	33	set	set	NOUN
brj-23253	224	34	.	.	PUNCT
brj-23253	225	1	the	the	DET
brj-23253	225	2	model	model	NOUN
brj-23253	225	3	was	be	AUX
brj-23253	225	4	built	build	VERB
brj-23253	225	5	with	with	ADP
brj-23253	225	6	a	a	DET
brj-23253	225	7	10	10	NUM
brj-23253	225	8	-	-	ADJ
brj-23253	225	9	fold	fold	ADJ
brj-23253	225	10	cross	cross	ADJ
brj-23253	225	11	-	-	ADJ
brj-23253	225	12	validation	validation	ADJ
brj-23253	225	13	method	method	NOUN
brj-23253	225	14	with	with	ADP
brj-23253	225	15	the	the	DET
brj-23253	225	16	svmtrain	svmtrain	ADJ
brj-23253	225	17	function	function	NOUN
brj-23253	225	18	.	.	PUNCT
brj-23253	226	1	the	the	DET
brj-23253	226	2	msc	msc	PROPN
brj-23253	226	3	-	-	PUNCT
brj-23253	226	4	cars	car	NOUN
brj-23253	226	5	-	-	PUNCT
brj-23253	226	6	plsr	plsr	NOUN
brj-23253	226	7	-	-	PUNCT
brj-23253	226	8	lstm	lstm	NOUN
brj-23253	226	9	model	model	NOUN
brj-23253	226	10	combines	combine	VERB
brj-23253	226	11	the	the	DET
brj-23253	226	12	feature	feature	NOUN
brj-23253	226	13	extraction	extraction	NOUN
brj-23253	226	14	ability	ability	NOUN
brj-23253	226	15	of	of	ADP
brj-23253	226	16	msc	msc	NOUN
brj-23253	226	17	-	-	PUNCT
brj-23253	226	18	cars	car	NOUN
brj-23253	226	19	-	-	PUNCT
brj-23253	226	20	plsr	plsr	NOUN
brj-23253	226	21	with	with	ADP
brj-23253	226	22	the	the	DET
brj-23253	226	23	time	time	NOUN
brj-23253	226	24	series	series	PROPN
brj-23253	226	25	data	data	PROPN
brj-23253	226	26	processing	processing	NOUN
brj-23253	226	27	advantage	advantage	NOUN
brj-23253	226	28	of	of	ADP
brj-23253	226	29	lstm	lstm	PROPN
brj-23253	226	30	.	.	PUNCT
brj-23253	227	1	lstm	lstm	ADJ
brj-23253	227	2	networks	network	NOUN
brj-23253	227	3	are	be	AUX
brj-23253	227	4	suitable	suitable	ADJ
brj-23253	227	5	for	for	ADP
brj-23253	227	6	processing	process	VERB
brj-23253	227	7	data	datum	NOUN
brj-23253	227	8	with	with	ADP
brj-23253	227	9	strong	strong	ADJ
brj-23253	227	10	time	time	NOUN
brj-23253	227	11	dependencies	dependency	NOUN
brj-23253	227	12	and	and	CCONJ
brj-23253	227	13	can	can	AUX
brj-23253	227	14	effectively	effectively	ADV
brj-23253	227	15	capture	capture	VERB
brj-23253	227	16	long	long	ADJ
brj-23253	227	17	-	-	PUNCT
brj-23253	227	18	term	term	NOUN
brj-23253	227	19	dependencies	dependency	NOUN
brj-23253	227	20	in	in	ADP
brj-23253	227	21	time	time	NOUN
brj-23253	227	22	series	series	NOUN
brj-23253	227	23	(	(	PUNCT
brj-23253	227	24	fagerström	fagerström	PROPN
brj-23253	227	25	et	et	PROPN
brj-23253	227	26	al	al	PROPN
brj-23253	227	27	.	.	PROPN
brj-23253	227	28	2019	2019	NUM
brj-23253	227	29	)	)	PUNCT
brj-23253	227	30	.	.	PUNCT
brj-23253	228	1	in	in	ADP
brj-23253	228	2	this	this	DET
brj-23253	228	3	model	model	NOUN
brj-23253	228	4	,	,	PUNCT
brj-23253	228	5	lstm	lstm	PROPN
brj-23253	228	6	was	be	AUX
brj-23253	228	7	used	use	VERB
brj-23253	228	8	to	to	PART
brj-23253	228	9	analyze	analyze	VERB
brj-23253	228	10	and	and	CCONJ
brj-23253	228	11	predict	predict	VERB
brj-23253	228	12	time	time	NOUN
brj-23253	228	13	-	-	PUNCT
brj-23253	228	14	varying	vary	VERB
brj-23253	228	15	alfalfa	alfalfa	NOUN
brj-23253	228	16	sample	sample	NOUN
brj-23253	228	17	data	datum	NOUN
brj-23253	228	18	further	far	ADV
brj-23253	228	19	to	to	PART
brj-23253	228	20	enhance	enhance	VERB
brj-23253	228	21	prediction	prediction	NOUN
brj-23253	228	22	accuracy	accuracy	NOUN
brj-23253	228	23	.	.	PUNCT
brj-23253	229	1	the	the	DET
brj-23253	229	2	model	model	NOUN
brj-23253	229	3	structure	structure	NOUN
brj-23253	229	4	included	include	VERB
brj-23253	229	5	a	a	DET
brj-23253	229	6	sequence	sequence	NOUN
brj-23253	229	7	input	input	NOUN
brj-23253	229	8	layer	layer	NOUN
brj-23253	229	9	,	,	PUNCT
brj-23253	229	10	an	an	DET
brj-23253	229	11	lstm	lstm	ADJ
brj-23253	229	12	layer	layer	NOUN
brj-23253	229	13	,	,	PUNCT
brj-23253	229	14	a	a	DET
brj-23253	229	15	relu	relu	NOUN
brj-23253	229	16	activation	activation	NOUN
brj-23253	229	17	layer	layer	NOUN
brj-23253	229	18	,	,	PUNCT
brj-23253	229	19	a	a	DET
brj-23253	229	20	fully	fully	ADV
brj-23253	229	21	connected	connect	VERB
brj-23253	229	22	layer	layer	NOUN
brj-23253	229	23	,	,	PUNCT
brj-23253	229	24	and	and	CCONJ
brj-23253	229	25	a	a	DET
brj-23253	229	26	regression	regression	NOUN
brj-23253	229	27	layer	layer	NOUN
brj-23253	229	28	.	.	PUNCT
brj-23253	230	1	the	the	DET
brj-23253	230	2	adam	adam	PROPN
brj-23253	230	3	optimizer	optimizer	NOUN
brj-23253	230	4	was	be	AUX
brj-23253	230	5	used	use	VERB
brj-23253	230	6	,	,	PUNCT
brj-23253	230	7	with	with	ADP
brj-23253	230	8	parameters	parameter	NOUN
brj-23253	230	9	set	set	VERB
brj-23253	230	10	for	for	ADP
brj-23253	230	11	mini	mini	NOUN
brj-23253	230	12	-	-	NOUN
brj-23253	230	13	batch	batch	ADJ
brj-23253	230	14	size	size	NOUN
brj-23253	230	15	,	,	PUNCT
brj-23253	230	16	maximum	maximum	ADJ
brj-23253	230	17	number	number	NOUN
brj-23253	230	18	of	of	ADP
brj-23253	230	19	iterations	iteration	NOUN
brj-23253	230	20	,	,	PUNCT
brj-23253	230	21	initial	initial	ADJ
brj-23253	230	22	learning	learning	NOUN
brj-23253	230	23	rate	rate	NOUN
brj-23253	230	24	,	,	PUNCT
brj-23253	230	25	and	and	CCONJ
brj-23253	230	26	learning	learn	VERB
brj-23253	230	27	rate	rate	NOUN
brj-23253	230	28	drop	drop	NOUN
brj-23253	230	29	strategy	strategy	NOUN
brj-23253	230	30	.	.	PUNCT
brj-23253	231	1	parameters	parameter	NOUN
brj-23253	231	2	were	be	AUX
brj-23253	231	3	adjusted	adjust	VERB
brj-23253	231	4	to	to	PART
brj-23253	231	5	enhance	enhance	VERB
brj-23253	231	6	prediction	prediction	NOUN
brj-23253	231	7	accuracy	accuracy	NOUN
brj-23253	231	8	.	.	PUNCT
brj-23253	232	1	these	these	DET
brj-23253	232	2	two	two	NUM
brj-23253	232	3	new	new	ADJ
brj-23253	232	4	model	model	NOUN
brj-23253	232	5	combinations	combination	NOUN
brj-23253	232	6	were	be	AUX
brj-23253	232	7	used	use	VERB
brj-23253	232	8	to	to	PART
brj-23253	232	9	process	process	VERB
brj-23253	232	10	the	the	DET
brj-23253	232	11	same	same	ADJ
brj-23253	232	12	dataset	dataset	NOUN
brj-23253	232	13	,	,	PUNCT
brj-23253	232	14	with	with	ADP
brj-23253	232	15	the	the	DET
brj-23253	232	16	calibration	calibration	NOUN
brj-23253	232	17	and	and	CCONJ
brj-23253	232	18	prediction	prediction	NOUN
brj-23253	232	19	sets	set	NOUN
brj-23253	232	20	comprising	comprise	VERB
brj-23253	232	21	70	70	NUM
brj-23253	232	22	%	%	NOUN
brj-23253	232	23	and	and	CCONJ
brj-23253	232	24	30	30	NUM
brj-23253	232	25	%	%	NOUN
brj-23253	232	26	of	of	ADP
brj-23253	232	27	the	the	DET
brj-23253	232	28	total	total	ADJ
brj-23253	232	29	samples	sample	NOUN
brj-23253	232	30	,	,	PUNCT
brj-23253	232	31	peer	peer	NOUN
brj-23253	232	32	-	-	PUNCT
brj-23253	232	33	reviewed	review	VERB
brj-23253	232	34	article	article	NOUN
brj-23253	232	35	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	232	36	li	li	PROPN
brj-23253	232	37	et	et	PROPN
brj-23253	232	38	al	al	PROPN
brj-23253	232	39	.	.	PROPN
brj-23253	233	1	(	(	PUNCT
brj-23253	233	2	2024	2024	NUM
brj-23253	233	3	)	)	PUNCT
brj-23253	233	4	.	.	PUNCT
brj-23253	234	1	“	"	PUNCT
brj-23253	234	2	alfalfa	alfalfa	X
brj-23253	234	3	protein	protein	NOUN
brj-23253	234	4	with	with	ADP
brj-23253	234	5	vis	vis	X
brj-23253	234	6	/	/	SYM
brj-23253	234	7	nir	nir	NOUN
brj-23253	234	8	,	,	PUNCT
brj-23253	234	9	”	"	PUNCT
brj-23253	234	10	bioresources	bioresource	NOUN
brj-23253	234	11	19(2	19(2	NUM
brj-23253	234	12	)	)	PUNCT
brj-23253	234	13	,	,	PUNCT
brj-23253	234	14	3808	3808	NUM
brj-23253	234	15	-	-	SYM
brj-23253	234	16	3825	3825	NUM
brj-23253	234	17	.	.	PUNCT
brj-23253	234	18	3820	3820	NUM
brj-23253	234	19	respectively	respectively	ADV
brj-23253	234	20	,	,	PUNCT
brj-23253	234	21	and	and	CCONJ
brj-23253	234	22	were	be	AUX
brj-23253	234	23	compared	compare	VERB
brj-23253	234	24	with	with	ADP
brj-23253	234	25	the	the	DET
brj-23253	234	26	original	original	ADJ
brj-23253	234	27	msc	msc	NOUN
brj-23253	234	28	-	-	PUNCT
brj-23253	234	29	cars	car	NOUN
brj-23253	234	30	-	-	PUNCT
brj-23253	234	31	plsr	plsr	NOUN
brj-23253	234	32	model	model	NOUN
brj-23253	234	33	.	.	PUNCT
brj-23253	235	1	comparing	compare	VERB
brj-23253	235	2	the	the	DET
brj-23253	235	3	prediction	prediction	NOUN
brj-23253	235	4	results	result	NOUN
brj-23253	235	5	of	of	ADP
brj-23253	235	6	the	the	DET
brj-23253	235	7	three	three	NUM
brj-23253	235	8	models	model	NOUN
brj-23253	235	9	,	,	PUNCT
brj-23253	235	10	the	the	DET
brj-23253	235	11	predictive	predictive	ADJ
brj-23253	235	12	capability	capability	NOUN
brj-23253	235	13	of	of	ADP
brj-23253	235	14	the	the	DET
brj-23253	235	15	msc	msc	PROPN
brj-23253	235	16	-	-	PUNCT
brj-23253	235	17	carsplsr	carsplsr	PROPN
brj-23253	235	18	-	-	PUNCT
brj-23253	235	19	lstm	lstm	NOUN
brj-23253	235	20	model	model	NOUN
brj-23253	235	21	was	be	AUX
brj-23253	235	22	similar	similar	ADJ
brj-23253	235	23	to	to	ADP
brj-23253	235	24	that	that	PRON
brj-23253	235	25	of	of	ADP
brj-23253	235	26	the	the	DET
brj-23253	235	27	msc	msc	PROPN
brj-23253	235	28	-	-	PUNCT
brj-23253	235	29	cars	car	NOUN
brj-23253	235	30	-	-	PUNCT
brj-23253	235	31	plsr	plsr	NOUN
brj-23253	235	32	model	model	NOUN
brj-23253	235	33	.	.	PUNCT
brj-23253	236	1	in	in	ADP
brj-23253	236	2	contrast	contrast	NOUN
brj-23253	236	3	,	,	PUNCT
brj-23253	236	4	the	the	DET
brj-23253	236	5	msc	msc	PROPN
brj-23253	236	6	-	-	PUNCT
brj-23253	236	7	cars	car	NOUN
brj-23253	236	8	-	-	PUNCT
brj-23253	236	9	plsr	plsr	NOUN
brj-23253	236	10	-	-	PUNCT
brj-23253	236	11	svm	svm	NOUN
brj-23253	236	12	model	model	NOUN
brj-23253	236	13	provided	provide	VERB
brj-23253	236	14	more	more	ADV
brj-23253	236	15	accurate	accurate	ADJ
brj-23253	236	16	and	and	CCONJ
brj-23253	236	17	stable	stable	ADJ
brj-23253	236	18	prediction	prediction	NOUN
brj-23253	236	19	results	result	NOUN
brj-23253	236	20	than	than	ADP
brj-23253	236	21	the	the	DET
brj-23253	236	22	msc	msc	PROPN
brj-23253	236	23	-	-	PUNCT
brj-23253	236	24	cars	car	NOUN
brj-23253	236	25	-	-	PUNCT
brj-23253	236	26	plsr	plsr	NOUN
brj-23253	236	27	model	model	NOUN
brj-23253	236	28	,	,	PUNCT
brj-23253	236	29	with	with	ADP
brj-23253	236	30	a	a	DET
brj-23253	236	31	calibration	calibration	NOUN
brj-23253	236	32	set	set	VERB
brj-23253	236	33	rmse	rmse	NOUN
brj-23253	236	34	of	of	ADP
brj-23253	236	35	0.1088	0.1088	NUM
brj-23253	236	36	,	,	PUNCT
brj-23253	236	37	a	a	DET
brj-23253	236	38	determination	determination	NOUN
brj-23253	236	39	coefficient	coefficient	NOUN
brj-23253	236	40	of	of	ADP
brj-23253	236	41	0.9982	0.9982	NUM
brj-23253	236	42	,	,	PUNCT
brj-23253	236	43	a	a	DET
brj-23253	236	44	prediction	prediction	NOUN
brj-23253	236	45	set	set	VERB
brj-23253	236	46	rmse	rmse	NOUN
brj-23253	236	47	of	of	ADP
brj-23253	236	48	0.5230	0.5230	NUM
brj-23253	236	49	,	,	PUNCT
brj-23253	236	50	and	and	CCONJ
brj-23253	236	51	a	a	DET
brj-23253	236	52	determination	determination	NOUN
brj-23253	236	53	coefficient	coefficient	NOUN
brj-23253	236	54	of	of	ADP
brj-23253	236	55	0.9645	0.9645	NUM
brj-23253	236	56	.	.	PUNCT
brj-23253	237	1	specific	specific	ADJ
brj-23253	237	2	experimental	experimental	ADJ
brj-23253	237	3	results	result	NOUN
brj-23253	237	4	are	be	AUX
brj-23253	237	5	presented	present	VERB
brj-23253	237	6	in	in	ADP
brj-23253	237	7	table	table	NOUN
brj-23253	237	8	4	4	NUM
brj-23253	237	9	.	.	PUNCT
brj-23253	238	1	the	the	DET
brj-23253	238	2	scatter	scatter	NOUN
brj-23253	238	3	plot	plot	NOUN
brj-23253	238	4	of	of	ADP
brj-23253	238	5	predicted	predict	VERB
brj-23253	238	6	values	value	NOUN
brj-23253	238	7	versus	versus	ADP
brj-23253	238	8	actual	actual	ADJ
brj-23253	238	9	values	value	NOUN
brj-23253	238	10	for	for	ADP
brj-23253	238	11	the	the	DET
brj-23253	238	12	msc	msc	PROPN
brj-23253	238	13	-	-	PUNCT
brj-23253	238	14	cars	car	NOUN
brj-23253	238	15	-	-	PUNCT
brj-23253	238	16	plsr	plsr	NOUN
brj-23253	238	17	-	-	PUNCT
brj-23253	238	18	svm	svm	NOUN
brj-23253	238	19	model	model	NOUN
brj-23253	238	20	is	be	AUX
brj-23253	238	21	shown	show	VERB
brj-23253	238	22	in	in	ADP
brj-23253	238	23	fig	fig	NOUN
brj-23253	238	24	.	.	PUNCT
brj-23253	239	1	5	5	X
brj-23253	239	2	.	.	X
brj-23253	239	3	table	table	NOUN
brj-23253	239	4	4	4	NUM
brj-23253	239	5	.	.	PUNCT
brj-23253	239	6	model	model	NOUN
brj-23253	239	7	prediction	prediction	NOUN
brj-23253	239	8	results	result	NOUN
brj-23253	239	9	after	after	ADP
brj-23253	239	10	model	model	NOUN
brj-23253	239	11	optimization	optimization	NOUN
brj-23253	239	12	algorithm	algorithm	NOUN
brj-23253	239	13	combinations	combination	NOUN
brj-23253	239	14	calibration	calibration	NOUN
brj-23253	239	15	set	set	VERB
brj-23253	239	16	prediction	prediction	NOUN
brj-23253	239	17	set	set	VERB
brj-23253	239	18	msc	msc	PROPN
brj-23253	239	19	-	-	PUNCT
brj-23253	239	20	cars	car	NOUN
brj-23253	239	21	-	-	PUNCT
brj-23253	239	22	plsr	plsr	NOUN
brj-23253	240	1	0.1922	0.1922	NUM
brj-23253	240	2	0.9972	0.9972	NUM
brj-23253	240	3	0.6581	0.6581	NUM
brj-23253	240	4	0.9446	0.9446	NUM
brj-23253	240	5	msc	msc	NOUN
brj-23253	240	6	-	-	PUNCT
brj-23253	240	7	cars	car	NOUN
brj-23253	240	8	-	-	PUNCT
brj-23253	240	9	plsr	plsr	NOUN
brj-23253	240	10	-	-	PUNCT
brj-23253	240	11	svm	svm	NOUN
brj-23253	241	1	0.1088	0.1088	NOUN
brj-23253	242	1	0.9982	0.9982	NUM
brj-23253	242	2	0.5230	0.5230	NUM
brj-23253	242	3	0.9645	0.9645	NUM
brj-23253	242	4	msc	msc	PROPN
brj-23253	242	5	-	-	PUNCT
brj-23253	242	6	cars	car	NOUN
brj-23253	242	7	-	-	PUNCT
brj-23253	242	8	plsr	plsr	NOUN
brj-23253	242	9	-	-	PUNCT
brj-23253	242	10	lstm	lstm	NOUN
brj-23253	242	11	0.2071	0.2071	NUM
brj-23253	242	12	0.9943	0.9943	NUM
brj-23253	242	13	0.6361	0.6361	NUM
brj-23253	242	14	0.9449	0.9449	NUM
brj-23253	242	15	fig	fig	NOUN
brj-23253	242	16	.	.	PUNCT
brj-23253	243	1	5	5	X
brj-23253	243	2	.	.	X
brj-23253	243	3	scatter	scatter	NOUN
brj-23253	243	4	plot	plot	NOUN
brj-23253	243	5	of	of	ADP
brj-23253	243	6	predicted	predict	VERB
brj-23253	243	7	vs.	vs.	ADP
brj-23253	243	8	actual	actual	ADJ
brj-23253	243	9	values	value	NOUN
brj-23253	243	10	for	for	ADP
brj-23253	243	11	the	the	DET
brj-23253	243	12	msc	msc	PROPN
brj-23253	243	13	-	-	PUNCT
brj-23253	243	14	cars	car	NOUN
brj-23253	243	15	-	-	PUNCT
brj-23253	243	16	plsr	plsr	NOUN
brj-23253	243	17	-	-	PUNCT
brj-23253	243	18	svm	svm	NOUN
brj-23253	243	19	model	model	NOUN
brj-23253	243	20	peer	peer	NOUN
brj-23253	243	21	-	-	PUNCT
brj-23253	243	22	reviewed	review	VERB
brj-23253	243	23	article	article	NOUN
brj-23253	243	24	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	243	25	li	li	PROPN
brj-23253	243	26	et	et	PROPN
brj-23253	243	27	al	al	PROPN
brj-23253	243	28	.	.	PROPN
brj-23253	244	1	(	(	PUNCT
brj-23253	244	2	2024	2024	NUM
brj-23253	244	3	)	)	PUNCT
brj-23253	244	4	.	.	PUNCT
brj-23253	245	1	“	"	PUNCT
brj-23253	245	2	alfalfa	alfalfa	X
brj-23253	245	3	protein	protein	NOUN
brj-23253	245	4	with	with	ADP
brj-23253	245	5	vis	vis	X
brj-23253	245	6	/	/	SYM
brj-23253	245	7	nir	nir	NOUN
brj-23253	245	8	,	,	PUNCT
brj-23253	245	9	”	"	PUNCT
brj-23253	245	10	bioresources	bioresource	NOUN
brj-23253	245	11	19(2	19(2	NUM
brj-23253	245	12	)	)	PUNCT
brj-23253	245	13	,	,	PUNCT
brj-23253	245	14	3808	3808	NUM
brj-23253	245	15	-	-	SYM
brj-23253	245	16	3825	3825	NUM
brj-23253	245	17	.	.	PUNCT
brj-23253	246	1	3821	3821	NUM
brj-23253	246	2	results	result	NOUN
brj-23253	246	3	and	and	CCONJ
brj-23253	246	4	discussion	discussion	NOUN
brj-23253	246	5	this	this	DET
brj-23253	246	6	study	study	NOUN
brj-23253	246	7	successfully	successfully	ADV
brj-23253	246	8	established	establish	VERB
brj-23253	246	9	predictive	predictive	ADJ
brj-23253	246	10	models	model	NOUN
brj-23253	246	11	for	for	ADP
brj-23253	246	12	the	the	DET
brj-23253	246	13	nutritional	nutritional	ADJ
brj-23253	246	14	substances	substance	NOUN
brj-23253	246	15	in	in	ADP
brj-23253	246	16	purple	purple	ADJ
brj-23253	246	17	alfalfa	alfalfa	NOUN
brj-23253	246	18	using	use	VERB
brj-23253	246	19	near	near	ADV
brj-23253	246	20	-	-	PUNCT
brj-23253	246	21	infrared	infrared	ADJ
brj-23253	246	22	spectroscopy	spectroscopy	NOUN
brj-23253	246	23	combined	combine	VERB
brj-23253	246	24	with	with	ADP
brj-23253	246	25	various	various	ADJ
brj-23253	246	26	preprocessing	preprocessing	NOUN
brj-23253	246	27	and	and	CCONJ
brj-23253	246	28	feature	feature	NOUN
brj-23253	246	29	extraction	extraction	NOUN
brj-23253	246	30	methods	method	NOUN
brj-23253	246	31	,	,	PUNCT
brj-23253	246	32	and	and	CCONJ
brj-23253	246	33	these	these	DET
brj-23253	246	34	models	model	NOUN
brj-23253	246	35	underwent	undergo	VERB
brj-23253	246	36	detailed	detailed	ADJ
brj-23253	246	37	performance	performance	NOUN
brj-23253	246	38	evaluations	evaluation	NOUN
brj-23253	246	39	.	.	PUNCT
brj-23253	247	1	initially	initially	ADV
brj-23253	247	2	,	,	PUNCT
brj-23253	247	3	in	in	ADP
brj-23253	247	4	the	the	DET
brj-23253	247	5	preprocessing	preprocessing	NOUN
brj-23253	247	6	phase	phase	NOUN
brj-23253	247	7	,	,	PUNCT
brj-23253	247	8	four	four	NUM
brj-23253	247	9	methods	method	NOUN
brj-23253	247	10	were	be	AUX
brj-23253	247	11	applied	apply	VERB
brj-23253	247	12	to	to	ADP
brj-23253	247	13	the	the	DET
brj-23253	247	14	average	average	ADJ
brj-23253	247	15	spectra	spectra	NOUN
brj-23253	247	16	of	of	ADP
brj-23253	247	17	purple	purple	ADJ
brj-23253	247	18	alfalfa	alfalfa	NOUN
brj-23253	247	19	samples	sample	NOUN
brj-23253	247	20	in	in	ADP
brj-23253	247	21	the	the	DET
brj-23253	247	22	450	450	NUM
brj-23253	247	23	to	to	PART
brj-23253	247	24	1830	1830	NUM
brj-23253	247	25	nm	nm	PRON
brj-23253	247	26	range	range	NOUN
brj-23253	247	27	:	:	PUNCT
brj-23253	247	28	standard	standard	ADJ
brj-23253	247	29	normal	normal	ADJ
brj-23253	247	30	variate	variate	NOUN
brj-23253	247	31	(	(	PUNCT
brj-23253	247	32	snv	snv	PROPN
brj-23253	247	33	)	)	PUNCT
brj-23253	247	34	,	,	PUNCT
brj-23253	247	35	multiplicative	multiplicative	ADJ
brj-23253	247	36	scatter	scatter	NOUN
brj-23253	247	37	correction	correction	NOUN
brj-23253	247	38	(	(	PUNCT
brj-23253	247	39	msc	msc	PROPN
brj-23253	247	40	)	)	PUNCT
brj-23253	247	41	,	,	PUNCT
brj-23253	247	42	savitzky	savitzky	NOUN
brj-23253	247	43	-	-	PUNCT
brj-23253	247	44	golay	golay	PROPN
brj-23253	247	45	(	(	PUNCT
brj-23253	247	46	sg	sg	NOUN
brj-23253	247	47	)	)	PUNCT
brj-23253	247	48	smoothing	smoothing	NOUN
brj-23253	247	49	,	,	PUNCT
brj-23253	247	50	and	and	CCONJ
brj-23253	247	51	first	first	ADJ
brj-23253	247	52	derivative	derivative	ADJ
brj-23253	247	53	(	(	PUNCT
brj-23253	247	54	fd	fd	PROPN
brj-23253	247	55	)	)	PUNCT
brj-23253	247	56	.	.	PUNCT
brj-23253	248	1	the	the	DET
brj-23253	248	2	snv	snv	PROPN
brj-23253	248	3	method	method	NOUN
brj-23253	248	4	effectively	effectively	ADV
brj-23253	248	5	reduced	reduce	VERB
brj-23253	248	6	the	the	DET
brj-23253	248	7	impact	impact	NOUN
brj-23253	248	8	of	of	ADP
brj-23253	248	9	surface	surface	NOUN
brj-23253	248	10	scattering	scattering	NOUN
brj-23253	248	11	and	and	CCONJ
brj-23253	248	12	intensity	intensity	NOUN
brj-23253	248	13	changes	change	NOUN
brj-23253	248	14	,	,	PUNCT
brj-23253	248	15	msc	msc	PROPN
brj-23253	248	16	addressed	address	VERB
brj-23253	248	17	issues	issue	NOUN
brj-23253	248	18	caused	cause	VERB
brj-23253	248	19	by	by	ADP
brj-23253	248	20	particle	particle	NOUN
brj-23253	248	21	inhomogeneity	inhomogeneity	NOUN
brj-23253	248	22	,	,	PUNCT
brj-23253	248	23	and	and	CCONJ
brj-23253	248	24	sg	sg	AUX
brj-23253	248	25	smoothing	smooth	VERB
brj-23253	248	26	significantly	significantly	ADV
brj-23253	248	27	enhanced	enhance	VERB
brj-23253	248	28	spectral	spectral	ADJ
brj-23253	248	29	smoothness	smoothness	NOUN
brj-23253	248	30	,	,	PUNCT
brj-23253	248	31	effectively	effectively	ADV
brj-23253	248	32	reducing	reduce	VERB
brj-23253	248	33	noise	noise	NOUN
brj-23253	248	34	interference	interference	NOUN
brj-23253	248	35	.	.	PUNCT
brj-23253	249	1	in	in	ADP
brj-23253	249	2	terms	term	NOUN
brj-23253	249	3	of	of	ADP
brj-23253	249	4	feature	feature	NOUN
brj-23253	249	5	extraction	extraction	NOUN
brj-23253	249	6	,	,	PUNCT
brj-23253	249	7	both	both	CCONJ
brj-23253	249	8	the	the	DET
brj-23253	249	9	competitive	competitive	ADJ
brj-23253	249	10	adaptive	adaptive	ADJ
brj-23253	249	11	reweighted	reweighte	VERB
brj-23253	249	12	sampling	sample	VERB
brj-23253	249	13	(	(	PUNCT
brj-23253	249	14	cars	car	NOUN
brj-23253	249	15	)	)	PUNCT
brj-23253	249	16	and	and	CCONJ
brj-23253	249	17	iteratively	iteratively	ADV
brj-23253	249	18	retains	retain	VERB
brj-23253	249	19	informative	informative	ADJ
brj-23253	249	20	variables	variable	NOUN
brj-23253	249	21	(	(	PUNCT
brj-23253	249	22	iriv	iriv	ADJ
brj-23253	249	23	)	)	PUNCT
brj-23253	249	24	algorithms	algorithm	NOUN
brj-23253	249	25	were	be	AUX
brj-23253	249	26	used	use	VERB
brj-23253	249	27	,	,	PUNCT
brj-23253	249	28	enabling	enable	VERB
brj-23253	249	29	more	more	ADV
brj-23253	249	30	effective	effective	ADJ
brj-23253	249	31	extraction	extraction	NOUN
brj-23253	249	32	of	of	ADP
brj-23253	249	33	crucial	crucial	ADJ
brj-23253	249	34	information	information	NOUN
brj-23253	249	35	related	relate	VERB
brj-23253	249	36	to	to	ADP
brj-23253	249	37	the	the	DET
brj-23253	249	38	protein	protein	NOUN
brj-23253	249	39	content	content	NOUN
brj-23253	249	40	in	in	ADP
brj-23253	249	41	purple	purple	ADJ
brj-23253	249	42	alfalfa	alfalfa	NOUN
brj-23253	249	43	from	from	ADP
brj-23253	249	44	complex	complex	ADJ
brj-23253	249	45	spectral	spectral	ADJ
brj-23253	249	46	data	datum	NOUN
brj-23253	249	47	.	.	PUNCT
brj-23253	250	1	additionally	additionally	ADV
brj-23253	250	2	,	,	PUNCT
brj-23253	250	3	the	the	DET
brj-23253	250	4	partial	partial	ADJ
brj-23253	250	5	least	least	ADJ
brj-23253	250	6	squares	square	NOUN
brj-23253	250	7	regression	regression	NOUN
brj-23253	250	8	(	(	PUNCT
brj-23253	250	9	plsr	plsr	PROPN
brj-23253	250	10	)	)	PUNCT
brj-23253	250	11	and	and	CCONJ
brj-23253	250	12	extreme	extreme	ADJ
brj-23253	250	13	learning	learning	NOUN
brj-23253	250	14	machine	machine	NOUN
brj-23253	250	15	(	(	PUNCT
brj-23253	250	16	elm	elm	NOUN
brj-23253	250	17	)	)	PUNCT
brj-23253	250	18	methods	method	NOUN
brj-23253	250	19	were	be	AUX
brj-23253	250	20	used	use	VERB
brj-23253	250	21	to	to	PART
brj-23253	250	22	establish	establish	VERB
brj-23253	250	23	protein	protein	NOUN
brj-23253	250	24	content	content	NOUN
brj-23253	250	25	prediction	prediction	NOUN
brj-23253	250	26	models	model	NOUN
brj-23253	250	27	for	for	ADP
brj-23253	250	28	both	both	CCONJ
brj-23253	250	29	the	the	DET
brj-23253	250	30	full	full	ADJ
brj-23253	250	31	spectrum	spectrum	NOUN
brj-23253	250	32	and	and	CCONJ
brj-23253	250	33	feature	feature	NOUN
brj-23253	250	34	wavelengths	wavelength	NOUN
brj-23253	250	35	of	of	ADP
brj-23253	250	36	purple	purple	ADJ
brj-23253	250	37	alfalfa	alfalfa	NOUN
brj-23253	250	38	.	.	PUNCT
brj-23253	251	1	after	after	ADP
brj-23253	251	2	evaluating	evaluate	VERB
brj-23253	251	3	the	the	DET
brj-23253	251	4	models	model	NOUN
brj-23253	251	5	on	on	ADP
brj-23253	251	6	calibration	calibration	NOUN
brj-23253	251	7	and	and	CCONJ
brj-23253	251	8	prediction	prediction	NOUN
brj-23253	251	9	sets	set	NOUN
brj-23253	251	10	,	,	PUNCT
brj-23253	251	11	it	it	PRON
brj-23253	251	12	was	be	AUX
brj-23253	251	13	found	find	VERB
brj-23253	251	14	that	that	SCONJ
brj-23253	251	15	the	the	DET
brj-23253	251	16	feature	feature	NOUN
brj-23253	251	17	wavelength	wavelength	NOUN
brj-23253	251	18	models	model	NOUN
brj-23253	251	19	demonstrated	demonstrate	VERB
brj-23253	251	20	superior	superior	ADJ
brj-23253	251	21	predictive	predictive	ADJ
brj-23253	251	22	performance	performance	NOUN
brj-23253	251	23	compared	compare	VERB
brj-23253	251	24	to	to	ADP
brj-23253	251	25	full	full	ADJ
brj-23253	251	26	-	-	PUNCT
brj-23253	251	27	spectrum	spectrum	NOUN
brj-23253	251	28	models	model	NOUN
brj-23253	251	29	,	,	PUNCT
brj-23253	251	30	as	as	SCONJ
brj-23253	251	31	indicated	indicate	VERB
brj-23253	251	32	by	by	ADP
brj-23253	251	33	higher	high	ADJ
brj-23253	251	34	determination	determination	NOUN
brj-23253	251	35	coefficients	coefficient	NOUN
brj-23253	251	36	and	and	CCONJ
brj-23253	251	37	lower	low	ADJ
brj-23253	251	38	root	root	NOUN
brj-23253	251	39	mean	mean	ADJ
brj-23253	251	40	square	square	ADJ
brj-23253	251	41	errors	error	NOUN
brj-23253	251	42	(	(	PUNCT
brj-23253	251	43	rmse	rmse	NOUN
brj-23253	251	44	)	)	PUNCT
brj-23253	251	45	.	.	PUNCT
brj-23253	252	1	notably	notably	ADV
brj-23253	252	2	,	,	PUNCT
brj-23253	252	3	the	the	DET
brj-23253	252	4	msc	msc	PROPN
brj-23253	252	5	-	-	PUNCT
brj-23253	252	6	cars	car	NOUN
brj-23253	252	7	-	-	PUNCT
brj-23253	252	8	plsr	plsr	NOUN
brj-23253	252	9	model	model	NOUN
brj-23253	252	10	showed	show	VERB
brj-23253	252	11	a	a	DET
brj-23253	252	12	determination	determination	NOUN
brj-23253	252	13	coefficient	coefficient	NOUN
brj-23253	252	14	of	of	ADP
brj-23253	252	15	0.9972	0.9972	NUM
brj-23253	252	16	and	and	CCONJ
brj-23253	252	17	an	an	DET
brj-23253	252	18	rmse	rmse	NOUN
brj-23253	252	19	of	of	ADP
brj-23253	252	20	0.1922	0.1922	NUM
brj-23253	252	21	on	on	ADP
brj-23253	252	22	the	the	DET
brj-23253	252	23	calibration	calibration	NOUN
brj-23253	252	24	set	set	VERB
brj-23253	252	25	and	and	CCONJ
brj-23253	252	26	a	a	DET
brj-23253	252	27	determination	determination	NOUN
brj-23253	252	28	coefficient	coefficient	NOUN
brj-23253	252	29	of	of	ADP
brj-23253	252	30	0.9446	0.9446	NUM
brj-23253	252	31	and	and	CCONJ
brj-23253	252	32	an	an	DET
brj-23253	252	33	rmse	rmse	NOUN
brj-23253	252	34	of	of	ADP
brj-23253	252	35	0.6581	0.6581	NUM
brj-23253	252	36	on	on	ADP
brj-23253	252	37	the	the	DET
brj-23253	252	38	prediction	prediction	NOUN
brj-23253	252	39	set	set	NOUN
brj-23253	252	40	,	,	PUNCT
brj-23253	252	41	indicating	indicate	VERB
brj-23253	252	42	that	that	SCONJ
brj-23253	252	43	this	this	DET
brj-23253	252	44	model	model	NOUN
brj-23253	252	45	can	can	AUX
brj-23253	252	46	accurately	accurately	ADV
brj-23253	252	47	and	and	CCONJ
brj-23253	252	48	reliably	reliably	ADV
brj-23253	252	49	predict	predict	VERB
brj-23253	252	50	the	the	DET
brj-23253	252	51	nutritional	nutritional	ADJ
brj-23253	252	52	content	content	NOUN
brj-23253	252	53	of	of	ADP
brj-23253	252	54	purple	purple	ADJ
brj-23253	252	55	alfalfa	alfalfa	NOUN
brj-23253	252	56	.	.	PUNCT
brj-23253	253	1	furthermore	furthermore	ADV
brj-23253	253	2	,	,	PUNCT
brj-23253	253	3	model	model	NOUN
brj-23253	253	4	accuracy	accuracy	NOUN
brj-23253	253	5	was	be	AUX
brj-23253	253	6	enhanced	enhance	VERB
brj-23253	253	7	by	by	ADP
brj-23253	253	8	introducing	introduce	VERB
brj-23253	253	9	a	a	DET
brj-23253	253	10	support	support	NOUN
brj-23253	253	11	vector	vector	NOUN
brj-23253	253	12	machine	machine	NOUN
brj-23253	253	13	(	(	PUNCT
brj-23253	253	14	svm	svm	PROPN
brj-23253	253	15	)	)	PUNCT
brj-23253	253	16	and	and	CCONJ
brj-23253	253	17	long	long	ADJ
brj-23253	253	18	short	short	ADJ
brj-23253	253	19	-	-	PUNCT
brj-23253	253	20	term	term	NOUN
brj-23253	253	21	memory	memory	NOUN
brj-23253	253	22	network	network	NOUN
brj-23253	253	23	(	(	PUNCT
brj-23253	253	24	lstm	lstm	PROPN
brj-23253	253	25	)	)	PUNCT
brj-23253	253	26	for	for	ADP
brj-23253	253	27	regression	regression	NOUN
brj-23253	253	28	prediction	prediction	NOUN
brj-23253	253	29	of	of	ADP
brj-23253	253	30	the	the	DET
brj-23253	253	31	principal	principal	ADJ
brj-23253	253	32	factors	factor	NOUN
brj-23253	253	33	derived	derive	VERB
brj-23253	253	34	from	from	ADP
brj-23253	253	35	plsr	plsr	NOUN
brj-23253	253	36	.	.	PUNCT
brj-23253	254	1	the	the	DET
brj-23253	254	2	msc	msc	PROPN
brj-23253	254	3	-	-	PUNCT
brj-23253	254	4	cars	car	NOUN
brj-23253	254	5	-	-	PUNCT
brj-23253	254	6	plsr	plsr	NOUN
brj-23253	254	7	-	-	PUNCT
brj-23253	254	8	svm	svm	NOUN
brj-23253	254	9	model	model	NOUN
brj-23253	254	10	,	,	PUNCT
brj-23253	254	11	in	in	ADP
brj-23253	254	12	particular	particular	ADJ
brj-23253	254	13	,	,	PUNCT
brj-23253	254	14	exhibited	exhibit	VERB
brj-23253	254	15	a	a	DET
brj-23253	254	16	determination	determination	NOUN
brj-23253	254	17	coefficient	coefficient	NOUN
brj-23253	254	18	of	of	ADP
brj-23253	254	19	0.9982	0.9982	NUM
brj-23253	254	20	and	and	CCONJ
brj-23253	254	21	an	an	DET
brj-23253	254	22	rmse	rmse	NOUN
brj-23253	254	23	of	of	ADP
brj-23253	254	24	0.1088	0.1088	NUM
brj-23253	254	25	on	on	ADP
brj-23253	254	26	the	the	DET
brj-23253	254	27	calibration	calibration	NOUN
brj-23253	254	28	set	set	NOUN
brj-23253	254	29	,	,	PUNCT
brj-23253	254	30	and	and	CCONJ
brj-23253	254	31	a	a	DET
brj-23253	254	32	determination	determination	NOUN
brj-23253	254	33	coefficient	coefficient	NOUN
brj-23253	254	34	of	of	ADP
brj-23253	254	35	0.9645	0.9645	NUM
brj-23253	254	36	and	and	CCONJ
brj-23253	254	37	an	an	DET
brj-23253	254	38	rmse	rmse	NOUN
brj-23253	254	39	of	of	ADP
brj-23253	254	40	0.5230	0.5230	NUM
brj-23253	254	41	on	on	ADP
brj-23253	254	42	the	the	DET
brj-23253	254	43	prediction	prediction	NOUN
brj-23253	254	44	set	set	NOUN
brj-23253	254	45	,	,	PUNCT
brj-23253	254	46	further	far	ADV
brj-23253	254	47	improving	improve	VERB
brj-23253	254	48	the	the	DET
brj-23253	254	49	predictive	predictive	ADJ
brj-23253	254	50	accuracy	accuracy	NOUN
brj-23253	254	51	of	of	ADP
brj-23253	254	52	the	the	DET
brj-23253	254	53	model	model	NOUN
brj-23253	254	54	.	.	PUNCT
brj-23253	255	1	overall	overall	ADV
brj-23253	255	2	,	,	PUNCT
brj-23253	255	3	this	this	DET
brj-23253	255	4	study	study	NOUN
brj-23253	255	5	not	not	PART
brj-23253	255	6	only	only	ADV
brj-23253	255	7	successfully	successfully	ADV
brj-23253	255	8	established	establish	VERB
brj-23253	255	9	predictive	predictive	ADJ
brj-23253	255	10	models	model	NOUN
brj-23253	255	11	for	for	ADP
brj-23253	255	12	the	the	DET
brj-23253	255	13	nutritional	nutritional	ADJ
brj-23253	255	14	substances	substance	NOUN
brj-23253	255	15	in	in	ADP
brj-23253	255	16	purple	purple	ADJ
brj-23253	255	17	alfalfa	alfalfa	NOUN
brj-23253	255	18	using	use	VERB
brj-23253	255	19	near	near	ADV
brj-23253	255	20	-	-	PUNCT
brj-23253	255	21	infrared	infrared	ADJ
brj-23253	255	22	spectroscopy	spectroscopy	NOUN
brj-23253	255	23	and	and	CCONJ
brj-23253	255	24	advanced	advanced	ADJ
brj-23253	255	25	algorithms	algorithm	NOUN
brj-23253	255	26	but	but	CCONJ
brj-23253	255	27	also	also	ADV
brj-23253	255	28	confirmed	confirm	VERB
brj-23253	255	29	the	the	DET
brj-23253	255	30	effectiveness	effectiveness	NOUN
brj-23253	255	31	and	and	CCONJ
brj-23253	255	32	reliability	reliability	NOUN
brj-23253	255	33	of	of	ADP
brj-23253	255	34	these	these	DET
brj-23253	255	35	models	model	NOUN
brj-23253	255	36	in	in	ADP
brj-23253	255	37	accurately	accurately	ADV
brj-23253	255	38	predicting	predict	VERB
brj-23253	255	39	the	the	DET
brj-23253	255	40	nutritional	nutritional	ADJ
brj-23253	255	41	content	content	NOUN
brj-23253	255	42	of	of	ADP
brj-23253	255	43	purple	purple	ADJ
brj-23253	255	44	alfalfa	alfalfa	NOUN
brj-23253	255	45	through	through	ADP
brj-23253	255	46	comprehensive	comprehensive	ADJ
brj-23253	255	47	performance	performance	NOUN
brj-23253	255	48	evaluations	evaluation	NOUN
brj-23253	255	49	.	.	PUNCT
brj-23253	256	1	these	these	DET
brj-23253	256	2	achievements	achievement	NOUN
brj-23253	256	3	provide	provide	VERB
brj-23253	256	4	new	new	ADJ
brj-23253	256	5	methods	method	NOUN
brj-23253	256	6	and	and	CCONJ
brj-23253	256	7	technical	technical	ADJ
brj-23253	256	8	support	support	NOUN
brj-23253	256	9	for	for	ADP
brj-23253	256	10	purple	purple	ADJ
brj-23253	256	11	alfalfa	alfalfa	NOUN
brj-23253	256	12	's	's	PART
brj-23253	256	13	quality	quality	NOUN
brj-23253	256	14	assessment	assessment	NOUN
brj-23253	256	15	and	and	CCONJ
brj-23253	256	16	nutritional	nutritional	ADJ
brj-23253	256	17	substance	substance	NOUN
brj-23253	256	18	monitoring	monitoring	NOUN
brj-23253	256	19	.	.	PUNCT
brj-23253	257	1	however	however	ADV
brj-23253	257	2	,	,	PUNCT
brj-23253	257	3	there	there	PRON
brj-23253	257	4	are	be	VERB
brj-23253	257	5	some	some	DET
brj-23253	257	6	limitations	limitation	NOUN
brj-23253	257	7	to	to	ADP
brj-23253	257	8	this	this	DET
brj-23253	257	9	study	study	NOUN
brj-23253	257	10	.	.	PUNCT
brj-23253	258	1	first	first	ADV
brj-23253	258	2	,	,	PUNCT
brj-23253	258	3	the	the	DET
brj-23253	258	4	relatively	relatively	ADV
brj-23253	258	5	small	small	ADJ
brj-23253	258	6	sample	sample	NOUN
brj-23253	258	7	size	size	NOUN
brj-23253	258	8	may	may	AUX
brj-23253	258	9	affect	affect	VERB
brj-23253	258	10	the	the	DET
brj-23253	258	11	generalizability	generalizability	NOUN
brj-23253	258	12	of	of	ADP
brj-23253	258	13	the	the	DET
brj-23253	258	14	models	model	NOUN
brj-23253	258	15	.	.	PUNCT
brj-23253	259	1	increasing	increase	VERB
brj-23253	259	2	the	the	DET
brj-23253	259	3	sample	sample	NOUN
brj-23253	259	4	size	size	NOUN
brj-23253	259	5	and	and	CCONJ
brj-23253	259	6	conducting	conduct	VERB
brj-23253	259	7	more	more	ADJ
brj-23253	259	8	validation	validation	NOUN
brj-23253	259	9	experiments	experiment	NOUN
brj-23253	259	10	could	could	AUX
brj-23253	259	11	enhance	enhance	VERB
brj-23253	259	12	model	model	NOUN
brj-23253	259	13	performance	performance	NOUN
brj-23253	259	14	further	far	ADV
brj-23253	259	15	.	.	PUNCT
brj-23253	260	1	second	second	ADV
brj-23253	260	2	,	,	PUNCT
brj-23253	260	3	this	this	DET
brj-23253	260	4	study	study	NOUN
brj-23253	260	5	focused	focus	VERB
brj-23253	260	6	solely	solely	ADV
brj-23253	260	7	on	on	ADP
brj-23253	260	8	predicting	predict	VERB
brj-23253	260	9	the	the	DET
brj-23253	260	10	protein	protein	NOUN
brj-23253	260	11	content	content	NOUN
brj-23253	260	12	of	of	ADP
brj-23253	260	13	purple	purple	ADJ
brj-23253	260	14	alfalfa	alfalfa	NOUN
brj-23253	260	15	and	and	CCONJ
brj-23253	260	16	did	do	AUX
brj-23253	260	17	not	not	PART
brj-23253	260	18	consider	consider	VERB
brj-23253	260	19	other	other	ADJ
brj-23253	260	20	essential	essential	ADJ
brj-23253	260	21	nutrients	nutrient	NOUN
brj-23253	260	22	.	.	PUNCT
brj-23253	261	1	future	future	ADJ
brj-23253	261	2	research	research	NOUN
brj-23253	261	3	could	could	AUX
brj-23253	261	4	expand	expand	VERB
brj-23253	261	5	the	the	DET
brj-23253	261	6	scope	scope	NOUN
brj-23253	261	7	of	of	ADP
brj-23253	261	8	the	the	DET
brj-23253	261	9	models	model	NOUN
brj-23253	261	10	to	to	PART
brj-23253	261	11	predict	predict	VERB
brj-23253	261	12	more	more	ADJ
brj-23253	261	13	nutritional	nutritional	ADJ
brj-23253	261	14	substances	substance	NOUN
brj-23253	261	15	in	in	ADP
brj-23253	261	16	purple	purple	ADJ
brj-23253	261	17	alfalfa	alfalfa	NOUN
brj-23253	261	18	.	.	PUNCT
brj-23253	262	1	additionally	additionally	ADV
brj-23253	262	2	,	,	PUNCT
brj-23253	262	3	the	the	DET
brj-23253	262	4	reliability	reliability	NOUN
brj-23253	262	5	and	and	CCONJ
brj-23253	262	6	stability	stability	NOUN
brj-23253	262	7	of	of	ADP
brj-23253	262	8	the	the	DET
brj-23253	262	9	models	model	NOUN
brj-23253	262	10	need	need	VERB
brj-23253	262	11	further	further	ADJ
brj-23253	262	12	verification	verification	NOUN
brj-23253	262	13	in	in	ADP
brj-23253	262	14	practical	practical	ADJ
brj-23253	262	15	applications	application	NOUN
brj-23253	262	16	to	to	PART
brj-23253	262	17	ensure	ensure	VERB
brj-23253	262	18	their	their	PRON
brj-23253	262	19	effective	effective	ADJ
brj-23253	262	20	use	use	NOUN
brj-23253	262	21	in	in	ADP
brj-23253	262	22	different	different	ADJ
brj-23253	262	23	scenarios	scenario	NOUN
brj-23253	262	24	.	.	PUNCT
brj-23253	263	1	peer	peer	NOUN
brj-23253	263	2	-	-	PUNCT
brj-23253	263	3	reviewed	review	VERB
brj-23253	263	4	article	article	NOUN
brj-23253	263	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	263	6	li	li	PROPN
brj-23253	263	7	et	et	PROPN
brj-23253	263	8	al	al	PROPN
brj-23253	263	9	.	.	PROPN
brj-23253	264	1	(	(	PUNCT
brj-23253	264	2	2024	2024	NUM
brj-23253	264	3	)	)	PUNCT
brj-23253	264	4	.	.	PUNCT
brj-23253	265	1	“	"	PUNCT
brj-23253	265	2	alfalfa	alfalfa	X
brj-23253	265	3	protein	protein	NOUN
brj-23253	265	4	with	with	ADP
brj-23253	265	5	vis	vis	X
brj-23253	265	6	/	/	SYM
brj-23253	265	7	nir	nir	NOUN
brj-23253	265	8	,	,	PUNCT
brj-23253	265	9	”	"	PUNCT
brj-23253	265	10	bioresources	bioresource	NOUN
brj-23253	265	11	19(2	19(2	NUM
brj-23253	265	12	)	)	PUNCT
brj-23253	265	13	,	,	PUNCT
brj-23253	265	14	3808	3808	NUM
brj-23253	265	15	-	-	SYM
brj-23253	265	16	3825	3825	NUM
brj-23253	265	17	.	.	PUNCT
brj-23253	266	1	3822	3822	NUM
brj-23253	266	2	conclusions	conclusion	NOUN
brj-23253	266	3	1	1	NUM
brj-23253	266	4	.	.	PUNCT
brj-23253	267	1	this	this	DET
brj-23253	267	2	study	study	NOUN
brj-23253	267	3	successfully	successfully	ADV
brj-23253	267	4	developed	develop	VERB
brj-23253	267	5	an	an	DET
brj-23253	267	6	accurate	accurate	ADJ
brj-23253	267	7	and	and	CCONJ
brj-23253	267	8	non	non	ADJ
brj-23253	267	9	-	-	ADJ
brj-23253	267	10	destructive	destructive	ADJ
brj-23253	267	11	method	method	NOUN
brj-23253	267	12	to	to	PART
brj-23253	267	13	predict	predict	VERB
brj-23253	267	14	the	the	DET
brj-23253	267	15	protein	protein	NOUN
brj-23253	267	16	content	content	NOUN
brj-23253	267	17	in	in	ADP
brj-23253	267	18	purple	purple	ADJ
brj-23253	267	19	alfalfa	alfalfa	NOUN
brj-23253	267	20	by	by	ADP
brj-23253	267	21	integrating	integrate	VERB
brj-23253	267	22	near	near	ADV
brj-23253	267	23	-	-	PUNCT
brj-23253	267	24	infrared	infrared	ADJ
brj-23253	267	25	spectroscopy	spectroscopy	NOUN
brj-23253	267	26	with	with	ADP
brj-23253	267	27	machine	machine	NOUN
brj-23253	267	28	learning	learn	VERB
brj-23253	267	29	algorithms	algorithm	NOUN
brj-23253	267	30	.	.	PUNCT
brj-23253	268	1	the	the	DET
brj-23253	268	2	application	application	NOUN
brj-23253	268	3	of	of	ADP
brj-23253	268	4	various	various	ADJ
brj-23253	268	5	preprocessing	preprocessing	NOUN
brj-23253	268	6	and	and	CCONJ
brj-23253	268	7	feature	feature	NOUN
brj-23253	268	8	extraction	extraction	NOUN
brj-23253	268	9	techniques	technique	NOUN
brj-23253	268	10	led	lead	VERB
brj-23253	268	11	to	to	ADP
brj-23253	268	12	the	the	DET
brj-23253	268	13	msc	msc	NOUN
brj-23253	268	14	-	-	PUNCT
brj-23253	268	15	cars	car	NOUN
brj-23253	268	16	-	-	PUNCT
brj-23253	268	17	plsr	plsr	NOUN
brj-23253	268	18	model	model	NOUN
brj-23253	268	19	exhibiting	exhibit	VERB
brj-23253	268	20	the	the	DET
brj-23253	268	21	best	good	ADJ
brj-23253	268	22	performance	performance	NOUN
brj-23253	268	23	among	among	ADP
brj-23253	268	24	all	all	DET
brj-23253	268	25	tested	test	VERB
brj-23253	268	26	models	model	NOUN
brj-23253	268	27	,	,	PUNCT
brj-23253	268	28	offering	offer	VERB
brj-23253	268	29	high	high	ADJ
brj-23253	268	30	precision	precision	NOUN
brj-23253	268	31	and	and	CCONJ
brj-23253	268	32	reliability	reliability	NOUN
brj-23253	268	33	.	.	PUNCT
brj-23253	269	1	2	2	X
brj-23253	269	2	.	.	X
brj-23253	269	3	the	the	DET
brj-23253	269	4	results	result	NOUN
brj-23253	269	5	demonstrate	demonstrate	VERB
brj-23253	269	6	that	that	SCONJ
brj-23253	269	7	the	the	DET
brj-23253	269	8	predictive	predictive	ADJ
brj-23253	269	9	accuracy	accuracy	NOUN
brj-23253	269	10	of	of	ADP
brj-23253	269	11	the	the	DET
brj-23253	269	12	model	model	NOUN
brj-23253	269	13	can	can	AUX
brj-23253	269	14	be	be	AUX
brj-23253	269	15	further	far	ADV
brj-23253	269	16	enhanced	enhance	VERB
brj-23253	269	17	by	by	ADP
brj-23253	269	18	employing	employ	VERB
brj-23253	269	19	regression	regression	NOUN
brj-23253	269	20	predictions	prediction	NOUN
brj-23253	269	21	with	with	ADP
brj-23253	269	22	support	support	NOUN
brj-23253	269	23	vector	vector	NOUN
brj-23253	269	24	machines	machine	NOUN
brj-23253	269	25	(	(	PUNCT
brj-23253	269	26	svm	svm	PROPN
brj-23253	269	27	)	)	PUNCT
brj-23253	269	28	and	and	CCONJ
brj-23253	269	29	long	long	ADJ
brj-23253	269	30	short	short	ADJ
brj-23253	269	31	-	-	PUNCT
brj-23253	269	32	term	term	NOUN
brj-23253	269	33	memory	memory	NOUN
brj-23253	269	34	networks	network	NOUN
brj-23253	269	35	(	(	PUNCT
brj-23253	269	36	lstm	lstm	NOUN
brj-23253	269	37	)	)	PUNCT
brj-23253	269	38	.	.	PUNCT
brj-23253	270	1	specifically	specifically	ADV
brj-23253	270	2	,	,	PUNCT
brj-23253	270	3	the	the	DET
brj-23253	270	4	msc	msc	PROPN
brj-23253	270	5	-	-	PUNCT
brj-23253	270	6	carsplsr	carsplsr	NOUN
brj-23253	270	7	-	-	PUNCT
brj-23253	270	8	svm	svm	NOUN
brj-23253	270	9	model	model	NOUN
brj-23253	270	10	showed	show	VERB
brj-23253	270	11	superior	superior	ADJ
brj-23253	270	12	predictive	predictive	ADJ
brj-23253	270	13	performance	performance	NOUN
brj-23253	270	14	,	,	PUNCT
brj-23253	270	15	reflected	reflect	VERB
brj-23253	270	16	in	in	ADP
brj-23253	270	17	higher	high	ADJ
brj-23253	270	18	determination	determination	NOUN
brj-23253	270	19	coefficients	coefficient	NOUN
brj-23253	270	20	and	and	CCONJ
brj-23253	270	21	lower	low	ADJ
brj-23253	270	22	root	root	NOUN
brj-23253	270	23	mean	mean	ADJ
brj-23253	270	24	square	square	ADJ
brj-23253	270	25	errors	error	NOUN
brj-23253	270	26	in	in	ADP
brj-23253	270	27	both	both	CCONJ
brj-23253	270	28	the	the	DET
brj-23253	270	29	calibration	calibration	NOUN
brj-23253	270	30	and	and	CCONJ
brj-23253	270	31	prediction	prediction	NOUN
brj-23253	270	32	sets	set	NOUN
brj-23253	270	33	.	.	PUNCT
brj-23253	271	1	3	3	X
brj-23253	271	2	.	.	X
brj-23253	271	3	the	the	DET
brj-23253	271	4	methodologies	methodology	NOUN
brj-23253	271	5	and	and	CCONJ
brj-23253	271	6	findings	finding	NOUN
brj-23253	271	7	of	of	ADP
brj-23253	271	8	this	this	DET
brj-23253	271	9	study	study	NOUN
brj-23253	271	10	provide	provide	VERB
brj-23253	271	11	new	new	ADJ
brj-23253	271	12	perspectives	perspective	NOUN
brj-23253	271	13	and	and	CCONJ
brj-23253	271	14	technical	technical	ADJ
brj-23253	271	15	support	support	NOUN
brj-23253	271	16	for	for	ADP
brj-23253	271	17	quality	quality	NOUN
brj-23253	271	18	assessment	assessment	NOUN
brj-23253	271	19	and	and	CCONJ
brj-23253	271	20	nutritional	nutritional	ADJ
brj-23253	271	21	substance	substance	NOUN
brj-23253	271	22	monitoring	monitoring	NOUN
brj-23253	271	23	of	of	ADP
brj-23253	271	24	purple	purple	ADJ
brj-23253	271	25	alfalfa	alfalfa	NOUN
brj-23253	271	26	,	,	PUNCT
brj-23253	271	27	laying	lay	VERB
brj-23253	271	28	a	a	DET
brj-23253	271	29	foundation	foundation	NOUN
brj-23253	271	30	for	for	ADP
brj-23253	271	31	future	future	ADJ
brj-23253	271	32	research	research	NOUN
brj-23253	271	33	and	and	CCONJ
brj-23253	271	34	practical	practical	ADJ
brj-23253	271	35	applications	application	NOUN
brj-23253	271	36	in	in	ADP
brj-23253	271	37	related	related	ADJ
brj-23253	271	38	fields	field	NOUN
brj-23253	271	39	.	.	PUNCT
brj-23253	272	1	additionally	additionally	ADV
brj-23253	272	2	,	,	PUNCT
brj-23253	272	3	the	the	DET
brj-23253	272	4	study	study	NOUN
brj-23253	272	5	emphasizes	emphasize	VERB
brj-23253	272	6	the	the	DET
brj-23253	272	7	importance	importance	NOUN
brj-23253	272	8	of	of	ADP
brj-23253	272	9	increasing	increase	VERB
brj-23253	272	10	the	the	DET
brj-23253	272	11	sample	sample	NOUN
brj-23253	272	12	size	size	NOUN
brj-23253	272	13	and	and	CCONJ
brj-23253	272	14	further	far	ADV
brj-23253	272	15	verifying	verify	VERB
brj-23253	272	16	the	the	DET
brj-23253	272	17	stability	stability	NOUN
brj-23253	272	18	of	of	ADP
brj-23253	272	19	the	the	DET
brj-23253	272	20	models	model	NOUN
brj-23253	272	21	to	to	PART
brj-23253	272	22	enhance	enhance	VERB
brj-23253	272	23	the	the	DET
brj-23253	272	24	performance	performance	NOUN
brj-23253	272	25	of	of	ADP
brj-23253	272	26	the	the	DET
brj-23253	272	27	models	model	NOUN
brj-23253	272	28	.	.	PUNCT
brj-23253	273	1	acknowledgments	acknowledgment	NOUN
brj-23253	273	2	this	this	DET
brj-23253	273	3	project	project	NOUN
brj-23253	273	4	is	be	AUX
brj-23253	273	5	supported	support	VERB
brj-23253	273	6	by	by	ADP
brj-23253	273	7	the	the	DET
brj-23253	273	8	national	national	ADJ
brj-23253	273	9	natural	natural	PROPN
brj-23253	273	10	science	science	PROPN
brj-23253	273	11	foundation	foundation	PROPN
brj-23253	273	12	of	of	ADP
brj-23253	273	13	china	china	PROPN
brj-23253	273	14	(	(	PUNCT
brj-23253	273	15	grant	grant	VERB
brj-23253	273	16	no	no	NOUN
brj-23253	273	17	.	.	NOUN
brj-23253	273	18	32060414	32060414	NUM
brj-23253	273	19	)	)	PUNCT
brj-23253	273	20	;	;	PUNCT
brj-23253	273	21	natural	natural	ADJ
brj-23253	273	22	science	science	NOUN
brj-23253	273	23	foundation	foundation	NOUN
brj-23253	273	24	of	of	ADP
brj-23253	273	25	inner	inner	PROPN
brj-23253	273	26	mongolia	mongolia	PROPN
brj-23253	273	27	,	,	PUNCT
brj-23253	273	28	china	china	PROPN
brj-23253	273	29	(	(	PUNCT
brj-23253	273	30	2022ms06023,2023qn05034	2022ms06023,2023qn05034	NUM
brj-23253	273	31	)	)	PUNCT
brj-23253	273	32	;	;	PUNCT
brj-23253	273	33	natural	natural	ADJ
brj-23253	273	34	science	science	NOUN
brj-23253	273	35	foundation	foundation	NOUN
brj-23253	273	36	of	of	ADP
brj-23253	273	37	the	the	DET
brj-23253	273	38	autonomous	autonomous	ADJ
brj-23253	273	39	region	region	NOUN
brj-23253	273	40	military	military	ADJ
brj-23253	273	41	-	-	PUNCT
brj-23253	273	42	civilian	civilian	ADJ
brj-23253	273	43	integration	integration	NOUN
brj-23253	273	44	key	key	ADJ
brj-23253	273	45	research	research	NOUN
brj-23253	273	46	&	&	CCONJ
brj-23253	273	47	soft	soft	ADJ
brj-23253	273	48	science	science	NOUN
brj-23253	273	49	research	research	NOUN
brj-23253	273	50	projects	project	NOUN
brj-23253	273	51	of	of	ADP
brj-23253	273	52	inner	inner	ADJ
brj-23253	273	53	mongolia	mongolia	PROPN
brj-23253	273	54	,	,	PUNCT
brj-23253	273	55	china	china	PROPN
brj-23253	273	56	(	(	PUNCT
brj-23253	273	57	jmzd202201	jmzd202201	PROPN
brj-23253	273	58	)	)	PUNCT
brj-23253	273	59	;	;	PUNCT
brj-23253	273	60	scientific	scientific	ADJ
brj-23253	273	61	research	research	NOUN
brj-23253	273	62	project	project	NOUN
brj-23253	273	63	of	of	ADP
brj-23253	273	64	universities	university	NOUN
brj-23253	273	65	in	in	ADP
brj-23253	273	66	inner	inner	ADJ
brj-23253	273	67	mongolia	mongolia	PROPN
brj-23253	273	68	,	,	PUNCT
brj-23253	273	69	china	china	PROPN
brj-23253	273	70	(	(	PUNCT
brj-23253	273	71	njzy21461	njzy21461	PROPN
brj-23253	273	72	)	)	PUNCT
brj-23253	273	73	.	.	PUNCT
brj-23253	274	1	and	and	CCONJ
brj-23253	274	2	the	the	DET
brj-23253	274	3	inner	inner	PROPN
brj-23253	274	4	mongolia	mongolia	PROPN
brj-23253	274	5	engineering	engineering	PROPN
brj-23253	274	6	research	research	NOUN
brj-23253	274	7	center	center	NOUN
brj-23253	274	8	of	of	ADP
brj-23253	274	9	intelligent	intelligent	ADJ
brj-23253	274	10	equipment	equipment	NOUN
brj-23253	274	11	for	for	ADP
brj-23253	274	12	the	the	DET
brj-23253	274	13	entire	entire	ADJ
brj-23253	274	14	process	process	NOUN
brj-23253	274	15	of	of	ADP
brj-23253	274	16	forage	forage	NOUN
brj-23253	274	17	and	and	CCONJ
brj-23253	274	18	feed	feed	NOUN
brj-23253	274	19	production	production	NOUN
brj-23253	274	20	.	.	PUNCT
brj-23253	275	1	references	reference	NOUN
brj-23253	275	2	cited	cite	VERB
brj-23253	275	3	acuna	acuna	PROPN
brj-23253	275	4	-	-	PUNCT
brj-23253	275	5	gutierrez	gutierrez	PROPN
brj-23253	275	6	,	,	PUNCT
brj-23253	275	7	c.	c.	PROPN
brj-23253	275	8	,	,	PUNCT
brj-23253	275	9	schock	schock	NOUN
brj-23253	275	10	,	,	PUNCT
brj-23253	275	11	s.	s.	PROPN
brj-23253	275	12	,	,	PUNCT
brj-23253	275	13	jimenez	jimenez	PROPN
brj-23253	275	14	,	,	PUNCT
brj-23253	275	15	v.	v.	ADP
brj-23253	275	16	m.	m.	NOUN
brj-23253	275	17	,	,	PUNCT
brj-23253	275	18	and	and	CCONJ
brj-23253	275	19	mueller	mueller	PROPN
brj-23253	275	20	,	,	PUNCT
brj-23253	275	21	j.	j.	PROPN
brj-23253	275	22	(	(	PUNCT
brj-23253	275	23	2021	2021	NUM
brj-23253	275	24	)	)	PUNCT
brj-23253	275	25	.	.	PUNCT
brj-23253	276	1	“	"	PUNCT
brj-23253	276	2	detecting	detect	VERB
brj-23253	276	3	fumonisin	fumonisin	NOUN
brj-23253	276	4	b1	b1	NOUN
brj-23253	276	5	in	in	ADP
brj-23253	276	6	black	black	ADJ
brj-23253	276	7	beans	bean	NOUN
brj-23253	276	8	(	(	PUNCT
brj-23253	276	9	phaseolus	phaseolus	X
brj-23253	276	10	vulgaris	vulgaris	X
brj-23253	276	11	l.	l.	PROPN
brj-23253	276	12	)	)	PUNCT
brj-23253	276	13	by	by	ADP
brj-23253	276	14	near	near	ADV
brj-23253	276	15	-	-	PUNCT
brj-23253	276	16	infrared	infrared	ADJ
brj-23253	276	17	spectroscopy	spectroscopy	NOUN
brj-23253	276	18	(	(	PUNCT
brj-23253	276	19	nirs	nirs	PROPN
brj-23253	276	20	)	)	PUNCT
brj-23253	276	21	,	,	PUNCT
brj-23253	276	22	”	"	PUNCT
brj-23253	276	23	food	food	NOUN
brj-23253	276	24	control	control	NOUN
brj-23253	276	25	2021(130	2021(130	NUM
brj-23253	276	26	)	)	PUNCT
brj-23253	276	27	,	,	PUNCT
brj-23253	276	28	130	130	NUM
brj-23253	276	29	.	.	PUNCT
brj-23253	276	30	doi	doi	NOUN
brj-23253	276	31	:	:	PUNCT
brj-23253	276	32	10.1016	10.1016	NUM
brj-23253	276	33	/	/	SYM
brj-23253	276	34	j.foodcont.2021.108335	j.foodcont.2021.108335	PROPN
brj-23253	276	35	bedaf	bedaf	NOUN
brj-23253	276	36	,	,	PUNCT
brj-23253	276	37	m.	m.	NOUN
brj-23253	276	38	t.	t.	PROPN
brj-23253	276	39	,	,	PUNCT
brj-23253	276	40	masoud	masoud	PROPN
brj-23253	276	41	,	,	PUNCT
brj-23253	276	42	b.	b.	PROPN
brj-23253	276	43	,	,	PUNCT
brj-23253	276	44	ghodratollah	ghodratollah	PROPN
brj-23253	276	45	,	,	PUNCT
brj-23253	276	46	s.	s.	PROPN
brj-23253	276	47	,	,	PUNCT
brj-23253	276	48	mengoni	mengoni	NOUN
brj-23253	276	49	,	,	PUNCT
brj-23253	276	50	a.	a.	NOUN
brj-23253	276	51	,	,	PUNCT
brj-23253	276	52	and	and	CCONJ
brj-23253	276	53	bazzicalupo	bazzicalupo	NOUN
brj-23253	276	54	,	,	PUNCT
brj-23253	276	55	m.	m.	NOUN
brj-23253	276	56	(	(	PUNCT
brj-23253	276	57	2008	2008	NUM
brj-23253	276	58	)	)	PUNCT
brj-23253	276	59	.	.	PUNCT
brj-23253	277	1	“	"	PUNCT
brj-23253	277	2	diversity	diversity	NOUN
brj-23253	277	3	of	of	ADP
brj-23253	277	4	sinorhizobium	sinorhizobium	NOUN
brj-23253	277	5	strains	strain	NOUN
brj-23253	277	6	nodulating	nodulate	VERB
brj-23253	277	7	medicago	medicago	PROPN
brj-23253	277	8	sativa	sativa	NOUN
brj-23253	277	9	from	from	ADP
brj-23253	277	10	different	different	ADJ
brj-23253	277	11	iranian	iranian	ADJ
brj-23253	277	12	regions	region	NOUN
brj-23253	277	13	,	,	PUNCT
brj-23253	277	14	”	"	PUNCT
brj-23253	277	15	fems	fems	PROPN
brj-23253	277	16	microbiology	microbiology	NOUN
brj-23253	277	17	letters	letter	NOUN
brj-23253	277	18	288(1	288(1	NUM
brj-23253	277	19	)	)	PUNCT
brj-23253	277	20	,	,	PUNCT
brj-23253	277	21	40	40	NUM
brj-23253	277	22	-	-	SYM
brj-23253	277	23	46	46	NUM
brj-23253	277	24	.	.	PUNCT
brj-23253	278	1	doi	doi	NOUN
brj-23253	278	2	:	:	PUNCT
brj-23253	278	3	10.1111	10.1111	NUM
brj-23253	278	4	/	/	SYM
brj-23253	278	5	j.15746968.2008.01329.x	j.15746968.2008.01329.x	PROPN
brj-23253	278	6	beć	beć	PROPN
brj-23253	278	7	,	,	PUNCT
brj-23253	278	8	k.	k.	PROPN
brj-23253	278	9	b.	b.	PROPN
brj-23253	278	10	,	,	PUNCT
brj-23253	278	11	grabska	grabska	NOUN
brj-23253	278	12	,	,	PUNCT
brj-23253	278	13	j.	j.	PROPN
brj-23253	278	14	,	,	PUNCT
brj-23253	278	15	and	and	CCONJ
brj-23253	278	16	huck	huck	PROPN
brj-23253	278	17	,	,	PUNCT
brj-23253	278	18	c.	c.	PROPN
brj-23253	278	19	w.	w.	PROPN
brj-23253	278	20	(	(	PUNCT
brj-23253	278	21	2020	2020	NUM
brj-23253	278	22	)	)	PUNCT
brj-23253	278	23	.	.	PUNCT
brj-23253	279	1	“	"	PUNCT
brj-23253	279	2	near	near	ADV
brj-23253	279	3	-	-	PUNCT
brj-23253	279	4	infrared	infrared	ADJ
brj-23253	279	5	spectroscopy	spectroscopy	NOUN
brj-23253	279	6	in	in	ADP
brj-23253	279	7	bioapplications	bioapplication	NOUN
brj-23253	279	8	,	,	PUNCT
brj-23253	279	9	”	"	PUNCT
brj-23253	279	10	molecules	molecule	NOUN
brj-23253	279	11	25(12	25(12	NUM
brj-23253	279	12	)	)	PUNCT
brj-23253	279	13	,	,	PUNCT
brj-23253	279	14	article	article	NOUN
brj-23253	279	15	2948	2948	NUM
brj-23253	279	16	.	.	PUNCT
brj-23253	280	1	doi	doi	NOUN
brj-23253	280	2	:	:	PUNCT
brj-23253	280	3	10.3390	10.3390	NUM
brj-23253	280	4	/	/	SYM
brj-23253	280	5	molecules25122948	molecules25122948	NOUN
brj-23253	280	6	belini	belini	NOUN
brj-23253	280	7	,	,	PUNCT
brj-23253	280	8	u.	u.	PROPN
brj-23253	280	9	l.	l.	PROPN
brj-23253	280	10	,	,	PUNCT
brj-23253	280	11	hein	hein	PROPN
brj-23253	280	12	,	,	PUNCT
brj-23253	280	13	p.	p.	PROPN
brj-23253	280	14	r.	r.	PROPN
brj-23253	280	15	g.	g.	PROPN
brj-23253	280	16	,	,	PUNCT
brj-23253	280	17	tomazello	tomazello	PROPN
brj-23253	280	18	filho	filho	PROPN
brj-23253	280	19	,	,	PUNCT
brj-23253	280	20	m.	m.	NOUN
brj-23253	280	21	,	,	PUNCT
brj-23253	280	22	rodrigues	rodrigues	PROPN
brj-23253	280	23	,	,	PUNCT
brj-23253	280	24	j.	j.	PROPN
brj-23253	280	25	c.	c.	PROPN
brj-23253	280	26	,	,	PUNCT
brj-23253	280	27	and	and	CCONJ
brj-23253	280	28	chaix	chaix	NOUN
brj-23253	280	29	,	,	PUNCT
brj-23253	280	30	g.	g.	PROPN
brj-23253	280	31	(	(	PUNCT
brj-23253	280	32	2011	2011	NUM
brj-23253	280	33	)	)	PUNCT
brj-23253	280	34	.	.	PUNCT
brj-23253	281	1	“	"	PUNCT
brj-23253	281	2	near	near	ADP
brj-23253	281	3	infrared	infrared	ADJ
brj-23253	281	4	spectroscopy	spectroscopy	NOUN
brj-23253	281	5	for	for	ADP
brj-23253	281	6	estimating	estimate	VERB
brj-23253	281	7	sugarcane	sugarcane	NOUN
brj-23253	281	8	bagasse	bagasse	NOUN
brj-23253	281	9	content	content	NOUN
brj-23253	281	10	in	in	ADP
brj-23253	281	11	medium	medium	ADJ
brj-23253	281	12	density	density	NOUN
brj-23253	281	13	fiberboard	fiberboard	NOUN
brj-23253	281	14	,	,	PUNCT
brj-23253	281	15	”	"	PUNCT
brj-23253	281	16	bioresources	bioresource	NOUN
brj-23253	281	17	6(2	6(2	NUM
brj-23253	281	18	)	)	PUNCT
brj-23253	281	19	,	,	PUNCT
brj-23253	281	20	1816	1816	NUM
brj-23253	281	21	-	-	SYM
brj-23253	281	22	1829	1829	NUM
brj-23253	281	23	.	.	PUNCT
brj-23253	282	1	doi	doi	NOUN
brj-23253	282	2	:	:	PUNCT
brj-23253	282	3	10.15376	10.15376	NUM
brj-23253	282	4	/	/	SYM
brj-23253	282	5	biores.6.2.1816	biores.6.2.1816	ADJ
brj-23253	282	6	-	-	PUNCT
brj-23253	282	7	1829	1829	NUM
brj-23253	282	8	bilal	bilal	PROPN
brj-23253	282	9	,	,	PUNCT
brj-23253	282	10	m.	m.	NOUN
brj-23253	282	11	,	,	PUNCT
brj-23253	282	12	zou	zou	PROPN
brj-23253	282	13	,	,	PUNCT
brj-23253	282	14	x.	x.	PROPN
brj-23253	282	15	,	,	PUNCT
brj-23253	282	16	arslan	arslan	PROPN
brj-23253	282	17	,	,	PUNCT
brj-23253	282	18	m.	m.	NOUN
brj-23253	282	19	,	,	PUNCT
brj-23253	282	20	tahir	tahir	PROPN
brj-23253	282	21	,	,	PUNCT
brj-23253	282	22	h.	h.	PROPN
brj-23253	282	23	e.	e.	PROPN
brj-23253	282	24	,	,	PUNCT
brj-23253	282	25	azam	azam	PROPN
brj-23253	282	26	,	,	PUNCT
brj-23253	282	27	m.	m.	NOUN
brj-23253	282	28	,	,	PUNCT
brj-23253	282	29	junjun	junjun	PROPN
brj-23253	282	30	,	,	PUNCT
brj-23253	282	31	z.	z.	PROPN
brj-23253	282	32	,	,	PUNCT
brj-23253	282	33	basheer	basheer	PROPN
brj-23253	282	34	,	,	PUNCT
brj-23253	282	35	s.	s.	PROPN
brj-23253	282	36	,	,	PUNCT
brj-23253	282	37	and	and	CCONJ
brj-23253	282	38	peer	peer	NOUN
brj-23253	282	39	-	-	PUNCT
brj-23253	282	40	reviewed	review	VERB
brj-23253	282	41	article	article	NOUN
brj-23253	282	42	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	282	43	li	li	PROPN
brj-23253	282	44	et	et	PROPN
brj-23253	282	45	al	al	PROPN
brj-23253	282	46	.	.	PROPN
brj-23253	283	1	(	(	PUNCT
brj-23253	283	2	2024	2024	NUM
brj-23253	283	3	)	)	PUNCT
brj-23253	283	4	.	.	PUNCT
brj-23253	284	1	“	"	PUNCT
brj-23253	284	2	alfalfa	alfalfa	X
brj-23253	284	3	protein	protein	NOUN
brj-23253	284	4	with	with	ADP
brj-23253	284	5	vis	vis	X
brj-23253	284	6	/	/	SYM
brj-23253	284	7	nir	nir	NOUN
brj-23253	284	8	,	,	PUNCT
brj-23253	284	9	”	"	PUNCT
brj-23253	284	10	bioresources	bioresource	NOUN
brj-23253	284	11	19(2	19(2	NUM
brj-23253	284	12	)	)	PUNCT
brj-23253	284	13	,	,	PUNCT
brj-23253	284	14	3808	3808	NUM
brj-23253	284	15	-	-	SYM
brj-23253	284	16	3825	3825	NUM
brj-23253	284	17	.	.	PUNCT
brj-23253	285	1	3823	3823	NUM
brj-23253	285	2	abdullah	abdullah	PROPN
brj-23253	285	3	.	.	PUNCT
brj-23253	286	1	(	(	PUNCT
brj-23253	286	2	2020	2020	NUM
brj-23253	286	3	)	)	PUNCT
brj-23253	286	4	.	.	PUNCT
brj-23253	287	1	“	"	PUNCT
brj-23253	287	2	rapid	rapid	ADJ
brj-23253	287	3	determination	determination	NOUN
brj-23253	287	4	of	of	ADP
brj-23253	287	5	the	the	DET
brj-23253	287	6	chemical	chemical	NOUN
brj-23253	287	7	compositions	composition	NOUN
brj-23253	287	8	of	of	ADP
brj-23253	287	9	peanut	peanut	NOUN
brj-23253	287	10	seed	seed	NOUN
brj-23253	287	11	(	(	PUNCT
brj-23253	287	12	arachis	arachis	DET
brj-23253	287	13	hypogaea	hypogaea	NOUN
brj-23253	287	14	)	)	PUNCT
brj-23253	287	15	using	use	VERB
brj-23253	287	16	portable	portable	ADJ
brj-23253	287	17	near	near	ADV
brj-23253	287	18	-	-	PUNCT
brj-23253	287	19	infrared	infrared	ADJ
brj-23253	287	20	spectroscopy	spectroscopy	NOUN
brj-23253	287	21	,	,	PUNCT
brj-23253	287	22	”	"	PUNCT
brj-23253	287	23	vibrational	vibrational	ADJ
brj-23253	287	24	spectroscopy	spectroscopy	NOUN
brj-23253	287	25	110	110	NUM
brj-23253	287	26	,	,	PUNCT
brj-23253	287	27	103138	103138	NUM
brj-23253	287	28	.	.	PUNCT
brj-23253	288	1	doi	doi	NOUN
brj-23253	288	2	:	:	PUNCT
brj-23253	288	3	10.1016	10.1016	NUM
brj-23253	288	4	/	/	SYM
brj-23253	288	5	j.vibspec.2020.103138	j.vibspec.2020.103138	PROPN
brj-23253	288	6	cao	cao	PROPN
brj-23253	288	7	,	,	PUNCT
brj-23253	288	8	h.	h.	PROPN
brj-23253	288	9	,	,	PUNCT
brj-23253	288	10	zhang	zhang	PROPN
brj-23253	288	11	,	,	PUNCT
brj-23253	288	12	h.	h.	PROPN
brj-23253	288	13	l.	l.	PROPN
brj-23253	288	14	,	,	PUNCT
brj-23253	288	15	gai	gai	PROPN
brj-23253	288	16	,	,	PUNCT
brj-23253	288	17	q.	q.	PROPN
brj-23253	288	18	h.	h.	PROPN
brj-23253	288	19	,	,	PUNCT
brj-23253	288	20	chen	chen	PROPN
brj-23253	288	21	,	,	PUNCT
brj-23253	288	22	h.	h.	PROPN
brj-23253	288	23	,	,	PUNCT
brj-23253	288	24	and	and	CCONJ
brj-23253	288	25	zhao	zhao	NOUN
brj-23253	288	26	,	,	PUNCT
brj-23253	288	27	m.	m.	NOUN
brj-23253	288	28	l.	l.	PROPN
brj-23253	288	29	(	(	PUNCT
brj-23253	288	30	2011	2011	NUM
brj-23253	288	31	)	)	PUNCT
brj-23253	288	32	.	.	PUNCT
brj-23253	289	1	“	"	PUNCT
brj-23253	289	2	introduction	introduction	NOUN
brj-23253	289	3	test	test	NOUN
brj-23253	289	4	and	and	CCONJ
brj-23253	289	5	comprehensive	comprehensive	ADJ
brj-23253	289	6	evaluation	evaluation	NOUN
brj-23253	289	7	of	of	ADP
brj-23253	289	8	production	production	NOUN
brj-23253	289	9	performance	performance	NOUN
brj-23253	289	10	of	of	ADP
brj-23253	289	11	22	22	NUM
brj-23253	289	12	alfalfa	alfalfa	NOUN
brj-23253	289	13	varieties	variety	NOUN
brj-23253	289	14	,	,	PUNCT
brj-23253	289	15	”	"	PUNCT
brj-23253	289	16	journal	journal	NOUN
brj-23253	289	17	of	of	ADP
brj-23253	289	18	grass	grass	NOUN
brj-23253	289	19	industry	industry	NOUN
brj-23253	289	20	20(6	20(6	NOUN
brj-23253	289	21	)	)	PUNCT
brj-23253	289	22	,	,	PUNCT
brj-23253	289	23	219	219	NUM
brj-23253	289	24	-	-	SYM
brj-23253	289	25	229	229	NUM
brj-23253	289	26	.	.	PUNCT
brj-23253	290	1	chen	chen	PROPN
brj-23253	290	2	,	,	PUNCT
brj-23253	290	3	h.	h.	PROPN
brj-23253	290	4	,	,	PUNCT
brj-23253	290	5	tan	tan	PROPN
brj-23253	290	6	,	,	PUNCT
brj-23253	290	7	c.	c.	PROPN
brj-23253	290	8	,	,	PUNCT
brj-23253	290	9	and	and	CCONJ
brj-23253	290	10	lin	lin	PROPN
brj-23253	290	11	,	,	PUNCT
brj-23253	290	12	z.	z.	PROPN
brj-23253	290	13	(	(	PUNCT
brj-23253	290	14	2020	2020	NUM
brj-23253	290	15	)	)	PUNCT
brj-23253	290	16	.	.	PUNCT
brj-23253	291	1	“	"	PUNCT
brj-23253	291	2	quantitative	quantitative	ADJ
brj-23253	291	3	determination	determination	NOUN
brj-23253	291	4	of	of	ADP
brj-23253	291	5	the	the	DET
brj-23253	291	6	fiber	fiber	NOUN
brj-23253	291	7	components	component	NOUN
brj-23253	291	8	in	in	ADP
brj-23253	291	9	textiles	textile	NOUN
brj-23253	291	10	by	by	ADP
brj-23253	291	11	near	near	ADV
brj-23253	291	12	-	-	PUNCT
brj-23253	291	13	infrared	infrared	ADJ
brj-23253	291	14	spectroscopy	spectroscopy	NOUN
brj-23253	291	15	and	and	CCONJ
brj-23253	291	16	extreme	extreme	ADJ
brj-23253	291	17	learning	learning	NOUN
brj-23253	291	18	machine	machine	NOUN
brj-23253	291	19	,	,	PUNCT
brj-23253	291	20	”	"	PUNCT
brj-23253	291	21	analytical	analytical	ADJ
brj-23253	291	22	letters	letter	NOUN
brj-23253	291	23	53(6	53(6	PROPN
brj-23253	291	24	)	)	PUNCT
brj-23253	291	25	.	.	PUNCT
brj-23253	292	1	doi	doi	NOUN
brj-23253	292	2	:	:	PUNCT
brj-23253	292	3	10.1080/00032719.2019.1683742	10.1080/00032719.2019.1683742	NUM
brj-23253	292	4	chen	chen	PROPN
brj-23253	292	5	,	,	PUNCT
brj-23253	292	6	x.	x.	PROPN
brj-23253	292	7	,	,	PUNCT
brj-23253	292	8	yang	yang	PROPN
brj-23253	292	9	,	,	PUNCT
brj-23253	292	10	q.	q.	PROPN
brj-23253	292	11	,	,	PUNCT
brj-23253	292	12	han	han	PROPN
brj-23253	292	13	,	,	PUNCT
brj-23253	292	14	j.	j.	PROPN
brj-23253	292	15	,	,	PUNCT
brj-23253	292	16	lin	lin	PROPN
brj-23253	292	17	,	,	PUNCT
brj-23253	292	18	l.	l.	PROPN
brj-23253	292	19	,	,	PUNCT
brj-23253	292	20	and	and	CCONJ
brj-23253	292	21	shi	shi	PROPN
brj-23253	292	22	,	,	PUNCT
brj-23253	292	23	l.	l.	PROPN
brj-23253	292	24	(	(	PUNCT
brj-23253	292	25	2020	2020	NUM
brj-23253	292	26	)	)	PUNCT
brj-23253	292	27	.	.	PUNCT
brj-23253	293	1	“	"	PUNCT
brj-23253	293	2	estimation	estimation	NOUN
brj-23253	293	3	of	of	ADP
brj-23253	293	4	leaf	leaf	NOUN
brj-23253	293	5	water	water	NOUN
brj-23253	293	6	content	content	NOUN
brj-23253	293	7	in	in	ADP
brj-23253	293	8	winter	winter	NOUN
brj-23253	293	9	wheat	wheat	NOUN
brj-23253	293	10	leaves	leave	NOUN
brj-23253	293	11	based	base	VERB
brj-23253	293	12	on	on	ADP
brj-23253	293	13	leaf	leaf	NOUN
brj-23253	293	14	-	-	PUNCT
brj-23253	293	15	scale	scale	NOUN
brj-23253	293	16	hyperspectral	hyperspectral	ADJ
brj-23253	293	17	data	datum	NOUN
brj-23253	293	18	,	,	PUNCT
brj-23253	293	19	”	"	PUNCT
brj-23253	293	20	spectroscopy	spectroscopy	NOUN
brj-23253	293	21	and	and	CCONJ
brj-23253	293	22	spectral	spectral	ADJ
brj-23253	293	23	analysis	analysis	NOUN
brj-23253	293	24	40(3	40(3	NOUN
brj-23253	293	25	)	)	PUNCT
brj-23253	293	26	,	,	PUNCT
brj-23253	293	27	7	7	X
brj-23253	293	28	.	.	X
brj-23253	293	29	doi	doi	NOUN
brj-23253	293	30	:	:	PUNCT
brj-23253	293	31	cnki	cnki	ADJ
brj-23253	293	32	:	:	PUNCT
brj-23253	293	33	sun	sun	NOUN
brj-23253	293	34	:	:	PUNCT
brj-23253	293	35	guan.0.2020	guan.0.2020	NOUN
brj-23253	293	36	-	-	PUNCT
brj-23253	293	37	03	03	NUM
brj-23253	293	38	-	-	PUNCT
brj-23253	293	39	047	047	NUM
brj-23253	293	40	ciza	ciza	NOUN
brj-23253	293	41	,	,	PUNCT
brj-23253	293	42	p.	p.	PROPN
brj-23253	293	43	h.	h.	PROPN
brj-23253	293	44	,	,	PUNCT
brj-23253	293	45	sacre	sacre	PROPN
brj-23253	293	46	,	,	PUNCT
brj-23253	293	47	p	p	PROPN
brj-23253	293	48	-	-	PUNCT
brj-23253	293	49	y	y	NOUN
brj-23253	293	50	,	,	PUNCT
brj-23253	293	51	waffo	waffo	PROPN
brj-23253	293	52	,	,	PUNCT
brj-23253	293	53	c.	c.	NOUN
brj-23253	293	54	,	,	PUNCT
brj-23253	293	55	coic	coic	ADJ
brj-23253	293	56	,	,	PUNCT
brj-23253	293	57	l.	l.	PROPN
brj-23253	293	58	,	,	PUNCT
brj-23253	293	59	avohou	avohou	PROPN
brj-23253	293	60	,	,	PUNCT
brj-23253	293	61	h.	h.	PROPN
brj-23253	293	62	,	,	PUNCT
brj-23253	293	63	mbinze	mbinze	PROPN
brj-23253	293	64	,	,	PUNCT
brj-23253	293	65	j.	j.	PROPN
brj-23253	293	66	k.	k.	PROPN
brj-23253	293	67	,	,	PUNCT
brj-23253	293	68	ngono	ngono	PROPN
brj-23253	293	69	,	,	PUNCT
brj-23253	293	70	r.	r.	PROPN
brj-23253	293	71	,	,	PUNCT
brj-23253	293	72	marini	marini	PROPN
brj-23253	293	73	,	,	PUNCT
brj-23253	293	74	r.	r.	PROPN
brj-23253	293	75	d.	d.	PROPN
brj-23253	293	76	,	,	PUNCT
brj-23253	293	77	hubert	hubert	PROPN
brj-23253	293	78	,	,	PUNCT
brj-23253	293	79	ph	ph	PROPN
brj-23253	293	80	.	.	PROPN
brj-23253	293	81	,	,	PUNCT
brj-23253	293	82	and	and	CCONJ
brj-23253	293	83	ziemons	ziemon	NOUN
brj-23253	293	84	,	,	PUNCT
brj-23253	293	85	e.	e.	PROPN
brj-23253	293	86	(	(	PUNCT
brj-23253	293	87	2019	2019	NUM
brj-23253	293	88	)	)	PUNCT
brj-23253	293	89	.	.	PUNCT
brj-23253	294	1	“	"	PUNCT
brj-23253	294	2	comparing	compare	VERB
brj-23253	294	3	the	the	DET
brj-23253	294	4	qualitative	qualitative	ADJ
brj-23253	294	5	performances	performance	NOUN
brj-23253	294	6	of	of	ADP
brj-23253	294	7	handheld	handheld	ADJ
brj-23253	294	8	nir	nir	ADJ
brj-23253	294	9	and	and	CCONJ
brj-23253	294	10	raman	raman	ADJ
brj-23253	294	11	spectrophotometers	spectrophotometer	NOUN
brj-23253	294	12	for	for	ADP
brj-23253	294	13	the	the	DET
brj-23253	294	14	detection	detection	NOUN
brj-23253	294	15	of	of	ADP
brj-23253	294	16	falsified	falsified	ADJ
brj-23253	294	17	pharmaceutical	pharmaceutical	NOUN
brj-23253	294	18	products	product	NOUN
brj-23253	294	19	,	,	PUNCT
brj-23253	294	20	”	"	PUNCT
brj-23253	294	21	talanta	talanta	PROPN
brj-23253	294	22	202	202	NUM
brj-23253	294	23	,	,	PUNCT
brj-23253	294	24	469	469	NUM
brj-23253	294	25	-	-	SYM
brj-23253	294	26	478	478	NUM
brj-23253	294	27	.	.	PUNCT
brj-23253	295	1	doi	doi	NOUN
brj-23253	295	2	:	:	PUNCT
brj-23253	295	3	10.1016	10.1016	NUM
brj-23253	295	4	/	/	SYM
brj-23253	295	5	j.talanta.2019.04.049	j.talanta.2019.04.049	PROPN
brj-23253	295	6	cortés	cortés	NOUN
brj-23253	295	7	,	,	PUNCT
brj-23253	295	8	v.	v.	PROPN
brj-23253	295	9	,	,	PUNCT
brj-23253	295	10	blasco	blasco	PROPN
brj-23253	295	11	,	,	PUNCT
brj-23253	295	12	j.	j.	PROPN
brj-23253	295	13	,	,	PUNCT
brj-23253	295	14	aleixos	aleixos	PROPN
brj-23253	295	15	,	,	PUNCT
brj-23253	295	16	n.	n.	NOUN
brj-23253	295	17	,	,	PUNCT
brj-23253	295	18	cubero	cubero	NOUN
brj-23253	295	19	,	,	PUNCT
brj-23253	295	20	s.	s.	PROPN
brj-23253	295	21	,	,	PUNCT
brj-23253	295	22	and	and	CCONJ
brj-23253	295	23	talens	talen	NOUN
brj-23253	295	24	,	,	PUNCT
brj-23253	295	25	p.	p.	NOUN
brj-23253	295	26	(	(	PUNCT
brj-23253	295	27	2019	2019	NUM
brj-23253	295	28	)	)	PUNCT
brj-23253	295	29	.	.	PUNCT
brj-23253	296	1	“	"	PUNCT
brj-23253	296	2	monitoring	monitor	VERB
brj-23253	296	3	strategies	strategy	NOUN
brj-23253	296	4	for	for	ADP
brj-23253	296	5	quality	quality	NOUN
brj-23253	296	6	control	control	NOUN
brj-23253	296	7	of	of	ADP
brj-23253	296	8	agricultural	agricultural	ADJ
brj-23253	296	9	products	product	NOUN
brj-23253	296	10	using	use	VERB
brj-23253	296	11	visible	visible	ADJ
brj-23253	296	12	and	and	CCONJ
brj-23253	296	13	near	near	ADV
brj-23253	296	14	-	-	PUNCT
brj-23253	296	15	infrared	infrared	ADJ
brj-23253	296	16	spectroscopy	spectroscopy	NOUN
brj-23253	296	17	:	:	PUNCT
brj-23253	296	18	a	a	DET
brj-23253	296	19	review	review	NOUN
brj-23253	296	20	,	,	PUNCT
brj-23253	296	21	”	"	PUNCT
brj-23253	296	22	trends	trend	NOUN
brj-23253	296	23	in	in	ADP
brj-23253	296	24	food	food	NOUN
brj-23253	296	25	science	science	NOUN
brj-23253	296	26	&	&	CCONJ
brj-23253	296	27	technology	technology	PROPN
brj-23253	296	28	85	85	NUM
brj-23253	296	29	,	,	PUNCT
brj-23253	296	30	138	138	NUM
brj-23253	296	31	-	-	SYM
brj-23253	296	32	148	148	NUM
brj-23253	296	33	.	.	PUNCT
brj-23253	297	1	doi	doi	NOUN
brj-23253	297	2	:	:	PUNCT
brj-23253	297	3	10.1016	10.1016	NUM
brj-23253	297	4	/	/	SYM
brj-23253	297	5	j.tifs.2019.01.015	j.tifs.2019.01.015	PROPN
brj-23253	297	6	fagerström	fagerström	PROPN
brj-23253	297	7	,	,	PUNCT
brj-23253	297	8	j.	j.	PROPN
brj-23253	297	9	,	,	PUNCT
brj-23253	297	10	bång	bång	NOUN
brj-23253	297	11	,	,	PUNCT
brj-23253	297	12	m.	m.	NOUN
brj-23253	297	13	,	,	PUNCT
brj-23253	297	14	wilhelms	wilhelms	PROPN
brj-23253	297	15	,	,	PUNCT
brj-23253	297	16	d.	d.	PROPN
brj-23253	297	17	,	,	PUNCT
brj-23253	297	18	and	and	CCONJ
brj-23253	297	19	chew	chew	VERB
brj-23253	297	20	,	,	PUNCT
brj-23253	297	21	m.	m.	NOUN
brj-23253	297	22	s.	s.	PROPN
brj-23253	297	23	(	(	PUNCT
brj-23253	297	24	2019	2019	NUM
brj-23253	297	25	)	)	PUNCT
brj-23253	297	26	.	.	PUNCT
brj-23253	298	1	“	"	PUNCT
brj-23253	298	2	lisep	lisep	VERB
brj-23253	298	3	lstm	lstm	ADJ
brj-23253	298	4	:	:	PUNCT
brj-23253	298	5	a	a	DET
brj-23253	298	6	machine	machine	NOUN
brj-23253	298	7	learning	learn	VERB
brj-23253	298	8	algorithm	algorithm	NOUN
brj-23253	298	9	for	for	ADP
brj-23253	298	10	early	early	ADJ
brj-23253	298	11	detection	detection	NOUN
brj-23253	298	12	of	of	ADP
brj-23253	298	13	septic	septic	ADJ
brj-23253	298	14	shock	shock	NOUN
brj-23253	298	15	,	,	PUNCT
brj-23253	298	16	”	"	PUNCT
brj-23253	298	17	scientific	scientific	ADJ
brj-23253	298	18	reports	report	NOUN
brj-23253	298	19	2019(1	2019(1	NUM
brj-23253	298	20	)	)	PUNCT
brj-23253	298	21	,	,	PUNCT
brj-23253	298	22	article	article	NOUN
brj-23253	298	23	15132	15132	NUM
brj-23253	298	24	.	.	PUNCT
brj-23253	299	1	doi	doi	NOUN
brj-23253	299	2	:	:	PUNCT
brj-23253	299	3	10.1038	10.1038	NUM
brj-23253	299	4	/	/	SYM
brj-23253	299	5	s41598	s41598	NOUN
brj-23253	299	6	-	-	PUNCT
brj-23253	299	7	019	019	NUM
brj-23253	299	8	-	-	PUNCT
brj-23253	299	9	51219	51219	NUM
brj-23253	299	10	-	-	SYM
brj-23253	299	11	4	4	NUM
brj-23253	299	12	fustini	fustini	NOUN
brj-23253	299	13	,	,	PUNCT
brj-23253	299	14	m.	m.	NOUN
brj-23253	299	15	,	,	PUNCT
brj-23253	299	16	palmonari	palmonari	PROPN
brj-23253	299	17	,	,	PUNCT
brj-23253	299	18	a.	a.	NOUN
brj-23253	299	19	,	,	PUNCT
brj-23253	299	20	canestrari	canestrari	PROPN
brj-23253	299	21	,	,	PUNCT
brj-23253	299	22	g.	g.	PROPN
brj-23253	299	23	,	,	PUNCT
brj-23253	299	24	bonfante	bonfante	ADV
brj-23253	299	25	,	,	PUNCT
brj-23253	299	26	e.	e.	PROPN
brj-23253	299	27	,	,	PUNCT
brj-23253	299	28	mammi	mammi	PROPN
brj-23253	299	29	,	,	PUNCT
brj-23253	299	30	l.	l.	PROPN
brj-23253	299	31	,	,	PUNCT
brj-23253	299	32	pacchioli	pacchioli	NOUN
brj-23253	299	33	,	,	PUNCT
brj-23253	299	34	m.	m.	NOUN
brj-23253	299	35	t.	t.	PROPN
brj-23253	299	36	,	,	PUNCT
brj-23253	299	37	sniffen	sniffen	NOUN
brj-23253	299	38	,	,	PUNCT
brj-23253	299	39	g.	g.	PROPN
brj-23253	299	40	c.	c.	PROPN
brj-23253	299	41	j.	j.	PROPN
brj-23253	299	42	,	,	PUNCT
brj-23253	299	43	grant	grant	PROPN
brj-23253	299	44	,	,	PUNCT
brj-23253	299	45	r.	r.	PROPN
brj-23253	299	46	j.	j.	PROPN
brj-23253	299	47	,	,	PUNCT
brj-23253	299	48	cotanch	cotanch	PROPN
brj-23253	299	49	,	,	PUNCT
brj-23253	299	50	k.	k.	PROPN
brj-23253	299	51	w.	w.	PROPN
brj-23253	299	52	,	,	PUNCT
brj-23253	299	53	and	and	CCONJ
brj-23253	299	54	formigoni	formigoni	PROPN
brj-23253	299	55	,	,	PUNCT
brj-23253	299	56	a.	a.	NOUN
brj-23253	299	57	(	(	PUNCT
brj-23253	299	58	2017	2017	NUM
brj-23253	299	59	)	)	PUNCT
brj-23253	299	60	.	.	PUNCT
brj-23253	300	1	“	"	PUNCT
brj-23253	300	2	effect	effect	NOUN
brj-23253	300	3	of	of	ADP
brj-23253	300	4	undigested	undigested	ADJ
brj-23253	300	5	neutral	neutral	ADJ
brj-23253	300	6	detergent	detergent	NOUN
brj-23253	300	7	fiber	fiber	NOUN
brj-23253	300	8	content	content	NOUN
brj-23253	300	9	of	of	ADP
brj-23253	300	10	alfalfa	alfalfa	PROPN
brj-23253	300	11	hay	hay	NOUN
brj-23253	300	12	on	on	ADP
brj-23253	300	13	lactating	lactate	VERB
brj-23253	300	14	dairy	dairy	NOUN
brj-23253	300	15	cows	cow	NOUN
brj-23253	300	16	:	:	PUNCT
brj-23253	300	17	feeding	feeding	NOUN
brj-23253	300	18	behavior	behavior	NOUN
brj-23253	300	19	,	,	PUNCT
brj-23253	300	20	fiber	fiber	NOUN
brj-23253	300	21	digestibility	digestibility	NOUN
brj-23253	300	22	,	,	PUNCT
brj-23253	300	23	and	and	CCONJ
brj-23253	300	24	lactation	lactation	NOUN
brj-23253	300	25	performance	performance	NOUN
brj-23253	300	26	,	,	PUNCT
brj-23253	300	27	”	"	PUNCT
brj-23253	300	28	journal	journal	NOUN
brj-23253	300	29	of	of	ADP
brj-23253	300	30	dairy	dairy	NOUN
brj-23253	300	31	science	science	NOUN
brj-23253	300	32	100(6	100(6	NUM
brj-23253	300	33	)	)	PUNCT
brj-23253	300	34	,	,	PUNCT
brj-23253	300	35	article	article	NOUN
brj-23253	300	36	4475	4475	NUM
brj-23253	300	37	.	.	PUNCT
brj-23253	301	1	doi	doi	NOUN
brj-23253	301	2	:	:	PUNCT
brj-23253	301	3	10.3168	10.3168	NUM
brj-23253	301	4	/	/	SYM
brj-23253	301	5	jds.2016	jds.2016	PROPN
brj-23253	301	6	-	-	PUNCT
brj-23253	301	7	12266	12266	NUM
brj-23253	301	8	gao	gao	PROPN
brj-23253	301	9	,	,	PUNCT
brj-23253	301	10	j.	j.	PROPN
brj-23253	301	11	,	,	PUNCT
brj-23253	301	12	nuyttens	nuyttens	PROPN
brj-23253	301	13	,	,	PUNCT
brj-23253	301	14	d.	d.	PROPN
brj-23253	301	15	,	,	PUNCT
brj-23253	301	16	lootens	looten	VERB
brj-23253	301	17	,	,	PUNCT
brj-23253	301	18	p.	p.	NOUN
brj-23253	301	19	,	,	PUNCT
brj-23253	301	20	he	he	PRON
brj-23253	301	21	,	,	PUNCT
brj-23253	301	22	y.	y.	PROPN
brj-23253	301	23	,	,	PUNCT
brj-23253	301	24	and	and	CCONJ
brj-23253	301	25	pieters	pieter	NOUN
brj-23253	301	26	,	,	PUNCT
brj-23253	301	27	j.	j.	PROPN
brj-23253	301	28	g.	g.	PROPN
brj-23253	301	29	(	(	PUNCT
brj-23253	301	30	2018	2018	NUM
brj-23253	301	31	)	)	PUNCT
brj-23253	301	32	.	.	PUNCT
brj-23253	302	1	“	"	PUNCT
brj-23253	302	2	recognising	recognise	VERB
brj-23253	302	3	weeds	weed	NOUN
brj-23253	302	4	in	in	ADP
brj-23253	302	5	a	a	DET
brj-23253	302	6	maize	maize	NOUN
brj-23253	302	7	crop	crop	NOUN
brj-23253	302	8	using	use	VERB
brj-23253	302	9	a	a	DET
brj-23253	302	10	random	random	ADJ
brj-23253	302	11	forest	forest	NOUN
brj-23253	302	12	machine	machine	NOUN
brj-23253	302	13	-	-	PUNCT
brj-23253	302	14	learning	learn	VERB
brj-23253	302	15	algorithm	algorithm	NOUN
brj-23253	302	16	and	and	CCONJ
brj-23253	302	17	near	near	ADV
brj-23253	302	18	-	-	PUNCT
brj-23253	302	19	infrared	infrared	ADJ
brj-23253	302	20	snapshot	snapshot	ADJ
brj-23253	302	21	mosaic	mosaic	ADJ
brj-23253	302	22	hyperspectral	hyperspectral	ADJ
brj-23253	302	23	imagery	imagery	NOUN
brj-23253	302	24	,	,	PUNCT
brj-23253	302	25	”	"	PUNCT
brj-23253	302	26	biosystems	biosystem	NOUN
brj-23253	302	27	engineering	engineer	VERB
brj-23253	302	28	170	170	NUM
brj-23253	302	29	,	,	PUNCT
brj-23253	302	30	39	39	NUM
brj-23253	302	31	-	-	SYM
brj-23253	302	32	50	50	NUM
brj-23253	302	33	.	.	PUNCT
brj-23253	303	1	doi	doi	NOUN
brj-23253	303	2	:	:	PUNCT
brj-23253	303	3	10.1016	10.1016	NUM
brj-23253	303	4	/	/	SYM
brj-23253	303	5	j.biosystemseng.2018.03.006	j.biosystemseng.2018.03.006	PROPN
brj-23253	303	6	.	.	PUNCT
brj-23253	304	1	hell	hell	PROPN
brj-23253	304	2	,	,	PUNCT
brj-23253	304	3	j.	j.	PROPN
brj-23253	304	4	,	,	PUNCT
brj-23253	304	5	prückler	prückler	PROPN
brj-23253	304	6	,	,	PUNCT
brj-23253	304	7	m.	m.	NOUN
brj-23253	304	8	,	,	PUNCT
brj-23253	304	9	danner	danner	NOUN
brj-23253	304	10	,	,	PUNCT
brj-23253	304	11	l.	l.	PROPN
brj-23253	304	12	,	,	PUNCT
brj-23253	304	13	henniges	hennige	NOUN
brj-23253	304	14	,	,	PUNCT
brj-23253	304	15	u.	u.	PROPN
brj-23253	304	16	,	,	PUNCT
brj-23253	304	17	apprich	apprich	PROPN
brj-23253	304	18	,	,	PUNCT
brj-23253	304	19	s.	s.	PROPN
brj-23253	304	20	,	,	PUNCT
brj-23253	304	21	rosenau	rosenau	PROPN
brj-23253	304	22	,	,	PUNCT
brj-23253	304	23	t.	t.	PROPN
brj-23253	304	24	,	,	PUNCT
brj-23253	304	25	kneifel	kneifel	PROPN
brj-23253	304	26	,	,	PUNCT
brj-23253	304	27	w.	w.	NOUN
brj-23253	304	28	,	,	PUNCT
brj-23253	304	29	and	and	CCONJ
brj-23253	304	30	böhmdorfer	böhmdorfer	NOUN
brj-23253	304	31	,	,	PUNCT
brj-23253	304	32	s.	s.	PROPN
brj-23253	304	33	(	(	PUNCT
brj-23253	304	34	2016	2016	NUM
brj-23253	304	35	)	)	PUNCT
brj-23253	304	36	.	.	PUNCT
brj-23253	305	1	“	"	PUNCT
brj-23253	305	2	a	a	DET
brj-23253	305	3	comparison	comparison	NOUN
brj-23253	305	4	between	between	ADP
brj-23253	305	5	near	near	ADV
brj-23253	305	6	-	-	PUNCT
brj-23253	305	7	infrared	infrared	ADJ
brj-23253	305	8	(	(	PUNCT
brj-23253	305	9	nir	nir	NOUN
brj-23253	305	10	)	)	PUNCT
brj-23253	305	11	and	and	CCONJ
brj-23253	305	12	midinfrared	midinfrared	ADJ
brj-23253	305	13	(	(	PUNCT
brj-23253	305	14	atr	atr	NOUN
brj-23253	305	15	-	-	PUNCT
brj-23253	305	16	ftir	ftir	NOUN
brj-23253	305	17	)	)	PUNCT
brj-23253	305	18	spectroscopy	spectroscopy	VERB
brj-23253	305	19	for	for	ADP
brj-23253	305	20	the	the	DET
brj-23253	305	21	multivariate	multivariate	NOUN
brj-23253	305	22	determination	determination	NOUN
brj-23253	305	23	of	of	ADP
brj-23253	305	24	compositional	compositional	ADJ
brj-23253	305	25	properties	property	NOUN
brj-23253	305	26	in	in	ADP
brj-23253	305	27	wheat	wheat	NOUN
brj-23253	305	28	bran	bran	NOUN
brj-23253	305	29	samples	sample	NOUN
brj-23253	305	30	,	,	PUNCT
brj-23253	305	31	”	"	PUNCT
brj-23253	305	32	food	food	NOUN
brj-23253	305	33	control	control	NOUN
brj-23253	305	34	365	365	NUM
brj-23253	305	35	-	-	SYM
brj-23253	305	36	369	369	NUM
brj-23253	305	37	.	.	PUNCT
brj-23253	306	1	doi	doi	NOUN
brj-23253	306	2	:	:	PUNCT
brj-23253	306	3	10.1016	10.1016	NUM
brj-23253	306	4	/	/	SYM
brj-23253	306	5	j.foodcont.2015.08.003	j.foodcont.2015.08.003	PROPN
brj-23253	306	6	jiang	jiang	PROPN
brj-23253	306	7	,	,	PUNCT
brj-23253	306	8	h.	h.	PROPN
brj-23253	306	9	,	,	PUNCT
brj-23253	306	10	liu	liu	PROPN
brj-23253	306	11	,	,	PUNCT
brj-23253	306	12	t.	t.	PROPN
brj-23253	306	13	,	,	PUNCT
brj-23253	306	14	and	and	CCONJ
brj-23253	306	15	chen	chen	PROPN
brj-23253	306	16	,	,	PUNCT
brj-23253	306	17	q.	q.	PROPN
brj-23253	306	18	(	(	PUNCT
brj-23253	306	19	2020	2020	NUM
brj-23253	306	20	)	)	PUNCT
brj-23253	306	21	.	.	PUNCT
brj-23253	307	1	“	"	PUNCT
brj-23253	307	2	quantitative	quantitative	ADJ
brj-23253	307	3	detection	detection	NOUN
brj-23253	307	4	of	of	ADP
brj-23253	307	5	fatty	fatty	NOUN
brj-23253	307	6	acid	acid	NOUN
brj-23253	307	7	value	value	NOUN
brj-23253	307	8	during	during	ADP
brj-23253	307	9	storage	storage	NOUN
brj-23253	307	10	of	of	ADP
brj-23253	307	11	wheat	wheat	NOUN
brj-23253	307	12	flour	flour	NOUN
brj-23253	307	13	based	base	VERB
brj-23253	307	14	on	on	ADP
brj-23253	307	15	a	a	DET
brj-23253	307	16	portable	portable	ADJ
brj-23253	307	17	near	near	ADV
brj-23253	307	18	-	-	PUNCT
brj-23253	307	19	infrared	infrared	ADJ
brj-23253	307	20	(	(	PUNCT
brj-23253	307	21	nir	nir	NOUN
brj-23253	307	22	)	)	PUNCT
brj-23253	307	23	spectroscopy	spectroscopy	NOUN
brj-23253	307	24	system	system	NOUN
brj-23253	307	25	,	,	PUNCT
brj-23253	307	26	”	"	PUNCT
brj-23253	307	27	infrared	infrared	PROPN
brj-23253	307	28	physics	physics	PROPN
brj-23253	307	29	&	&	CCONJ
brj-23253	307	30	technology	technology	PROPN
brj-23253	307	31	109	109	NUM
brj-23253	307	32	,	,	PUNCT
brj-23253	307	33	article	article	NOUN
brj-23253	307	34	103423	103423	NUM
brj-23253	307	35	.	.	PUNCT
brj-23253	308	1	doi	doi	NOUN
brj-23253	308	2	:	:	PUNCT
brj-23253	308	3	10.1016	10.1016	NUM
brj-23253	308	4	/	/	SYM
brj-23253	308	5	j.infrared.2020.103423	j.infrared.2020.103423	PROPN
brj-23253	308	6	jiao	jiao	PROPN
brj-23253	308	7	,	,	PUNCT
brj-23253	308	8	y.	y.	PROPN
brj-23253	308	9	,	,	PUNCT
brj-23253	308	10	li	li	PROPN
brj-23253	308	11	,	,	PUNCT
brj-23253	308	12	z.	z.	PROPN
brj-23253	308	13	,	,	PUNCT
brj-23253	308	14	chen	chen	PROPN
brj-23253	308	15	,	,	PUNCT
brj-23253	308	16	x.	x.	NOUN
brj-23253	308	17	,	,	PUNCT
brj-23253	308	18	and	and	CCONJ
brj-23253	308	19	fei	fei	PROPN
brj-23253	308	20	,	,	PUNCT
brj-23253	308	21	s.	s.	PROPN
brj-23253	308	22	(	(	PUNCT
brj-23253	308	23	2020	2020	NUM
brj-23253	308	24	)	)	PUNCT
brj-23253	308	25	.	.	PUNCT
brj-23253	309	1	“	"	PUNCT
brj-23253	309	2	preprocessing	preprocesse	VERB
brj-23253	309	3	methods	method	NOUN
brj-23253	309	4	for	for	ADP
brj-23253	309	5	near	near	ADV
brj-23253	309	6	-	-	PUNCT
brj-23253	309	7	infrared	infrare	VERB
brj-23253	309	8	spectrum	spectrum	NOUN
brj-23253	309	9	calibration	calibration	NOUN
brj-23253	309	10	,	,	PUNCT
brj-23253	309	11	”	"	PUNCT
brj-23253	309	12	journal	journal	NOUN
brj-23253	309	13	of	of	ADP
brj-23253	309	14	chemometrics	chemometric	NOUN
brj-23253	309	15	29(3	29(3	NUM
brj-23253	309	16	)	)	PUNCT
brj-23253	309	17	,	,	PUNCT
brj-23253	309	18	article	article	NOUN
brj-23253	309	19	682	682	NUM
brj-23253	309	20	.	.	PUNCT
brj-23253	310	1	doi	doi	NOUN
brj-23253	310	2	:	:	PUNCT
brj-23253	310	3	10.1002	10.1002	NUM
brj-23253	310	4	/	/	SYM
brj-23253	310	5	cem.3306	cem.3306	PROPN
brj-23253	310	6	jo	jo	PROPN
brj-23253	310	7	,	,	PUNCT
brj-23253	310	8	s.	s.	PROPN
brj-23253	310	9	,	,	PUNCT
brj-23253	310	10	sohng	sohng	PROPN
brj-23253	310	11	,	,	PUNCT
brj-23253	310	12	w.	w.	PROPN
brj-23253	310	13	,	,	PUNCT
brj-23253	310	14	lee	lee	PROPN
brj-23253	310	15	,	,	PUNCT
brj-23253	310	16	h.	h.	PROPN
brj-23253	310	17	,	,	PUNCT
brj-23253	310	18	and	and	CCONJ
brj-23253	310	19	chung	chung	PROPN
brj-23253	310	20	,	,	PUNCT
brj-23253	310	21	h.	h.	PROPN
brj-23253	310	22	(	(	PUNCT
brj-23253	310	23	2020	2020	NUM
brj-23253	310	24	)	)	PUNCT
brj-23253	310	25	.	.	PUNCT
brj-23253	311	1	“	"	PUNCT
brj-23253	311	2	evaluation	evaluation	NOUN
brj-23253	311	3	of	of	ADP
brj-23253	311	4	an	an	DET
brj-23253	311	5	autoencoder	autoencoder	NOUN
brj-23253	311	6	as	as	ADP
brj-23253	311	7	a	a	DET
brj-23253	311	8	feature	feature	NOUN
brj-23253	311	9	extraction	extraction	NOUN
brj-23253	311	10	tool	tool	NOUN
brj-23253	311	11	for	for	ADP
brj-23253	311	12	near	near	ADV
brj-23253	311	13	-	-	PUNCT
brj-23253	311	14	infrared	infrared	ADJ
brj-23253	311	15	spectroscopic	spectroscopic	ADJ
brj-23253	311	16	discriminant	discriminant	NOUN
brj-23253	311	17	analysis	analysis	NOUN
brj-23253	311	18	,	,	PUNCT
brj-23253	311	19	”	"	PUNCT
brj-23253	311	20	food	food	NOUN
brj-23253	311	21	chemistry	chemistry	NOUN
brj-23253	311	22	331	331	NUM
brj-23253	311	23	,	,	PUNCT
brj-23253	311	24	article	article	NOUN
brj-23253	311	25	127332	127332	NUM
brj-23253	311	26	.	.	PUNCT
brj-23253	312	1	doi	doi	NOUN
brj-23253	312	2	:	:	PUNCT
brj-23253	312	3	10.1016	10.1016	NUM
brj-23253	312	4	/	/	SYM
brj-23253	312	5	j.foodchem.2020.127332	j.foodchem.2020.127332	PROPN
brj-23253	312	6	peer	peer	NOUN
brj-23253	312	7	-	-	PUNCT
brj-23253	312	8	reviewed	review	VERB
brj-23253	312	9	article	article	NOUN
brj-23253	312	10	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	312	11	li	li	PROPN
brj-23253	312	12	et	et	PROPN
brj-23253	312	13	al	al	PROPN
brj-23253	312	14	.	.	PROPN
brj-23253	313	1	(	(	PUNCT
brj-23253	313	2	2024	2024	NUM
brj-23253	313	3	)	)	PUNCT
brj-23253	313	4	.	.	PUNCT
brj-23253	314	1	“	"	PUNCT
brj-23253	314	2	alfalfa	alfalfa	X
brj-23253	314	3	protein	protein	NOUN
brj-23253	314	4	with	with	ADP
brj-23253	314	5	vis	vis	X
brj-23253	314	6	/	/	SYM
brj-23253	314	7	nir	nir	NOUN
brj-23253	314	8	,	,	PUNCT
brj-23253	314	9	”	"	PUNCT
brj-23253	314	10	bioresources	bioresource	NOUN
brj-23253	314	11	19(2	19(2	NUM
brj-23253	314	12	)	)	PUNCT
brj-23253	314	13	,	,	PUNCT
brj-23253	314	14	3808	3808	NUM
brj-23253	314	15	-	-	SYM
brj-23253	314	16	3825	3825	NUM
brj-23253	314	17	.	.	PUNCT
brj-23253	315	1	3824	3824	NUM
brj-23253	315	2	lee	lee	PROPN
brj-23253	315	3	,	,	PUNCT
brj-23253	315	4	y.-j	y.-j	PROPN
brj-23253	315	5	.	.	PROPN
brj-23253	315	6	,	,	PUNCT
brj-23253	315	7	lee	lee	PROPN
brj-23253	315	8	,	,	PUNCT
brj-23253	315	9	t.-j	t.-j	PROPN
brj-23253	315	10	.	.	PROPN
brj-23253	315	11	,	,	PUNCT
brj-23253	315	12	and	and	CCONJ
brj-23253	315	13	kim	kim	PROPN
brj-23253	315	14	,	,	PUNCT
brj-23253	315	15	h.	h.	PROPN
brj-23253	315	16	j.	j.	PROPN
brj-23253	315	17	(	(	PUNCT
brj-23253	315	18	2023	2023	NUM
brj-23253	315	19	)	)	PUNCT
brj-23253	315	20	.	.	PUNCT
brj-23253	316	1	“	"	PUNCT
brj-23253	316	2	classification	classification	NOUN
brj-23253	316	3	analysis	analysis	NOUN
brj-23253	316	4	of	of	ADP
brj-23253	316	5	copy	copy	NOUN
brj-23253	316	6	papers	paper	NOUN
brj-23253	316	7	using	use	VERB
brj-23253	316	8	infrared	infrared	ADJ
brj-23253	316	9	spectroscopy	spectroscopy	NOUN
brj-23253	316	10	and	and	CCONJ
brj-23253	316	11	machine	machine	NOUN
brj-23253	316	12	learning	learn	VERB
brj-23253	316	13	modeling	modeling	NOUN
brj-23253	316	14	,	,	PUNCT
brj-23253	316	15	”	"	PUNCT
brj-23253	316	16	bioresources	bioresource	NOUN
brj-23253	316	17	19(1	19(1	NUM
brj-23253	316	18	)	)	PUNCT
brj-23253	316	19	,	,	PUNCT
brj-23253	316	20	160	160	NUM
brj-23253	316	21	-	-	SYM
brj-23253	316	22	182	182	NUM
brj-23253	316	23	.	.	PUNCT
brj-23253	317	1	doi	doi	NOUN
brj-23253	317	2	:	:	PUNCT
brj-23253	317	3	10.15376	10.15376	NUM
brj-23253	317	4	/	/	SYM
brj-23253	317	5	biores.19.1.160	biores.19.1.160	NOUN
brj-23253	317	6	-	-	SYM
brj-23253	317	7	182	182	NUM
brj-23253	317	8	leuenberger	leuenberg	ADJ
brj-23253	317	9	,	,	PUNCT
brj-23253	317	10	m.	m.	NOUN
brj-23253	317	11	,	,	PUNCT
brj-23253	317	12	and	and	CCONJ
brj-23253	317	13	kanevski	kanevski	PROPN
brj-23253	317	14	,	,	PUNCT
brj-23253	317	15	m.	m.	NOUN
brj-23253	317	16	(	(	PUNCT
brj-23253	317	17	2015	2015	NUM
brj-23253	317	18	)	)	PUNCT
brj-23253	317	19	.	.	PUNCT
brj-23253	318	1	“	"	PUNCT
brj-23253	318	2	extreme	extreme	ADJ
brj-23253	318	3	learning	learning	NOUN
brj-23253	318	4	machines	machine	NOUN
brj-23253	318	5	for	for	ADP
brj-23253	318	6	spatial	spatial	ADJ
brj-23253	318	7	environmental	environmental	ADJ
brj-23253	318	8	data	datum	NOUN
brj-23253	318	9	,	,	PUNCT
brj-23253	318	10	”	"	PUNCT
brj-23253	318	11	computers	computer	NOUN
brj-23253	318	12	&	&	CCONJ
brj-23253	318	13	geosciences	geoscience	NOUN
brj-23253	318	14	85(dec.pt.b	85(dec.pt.b	NOUN
brj-23253	318	15	)	)	PUNCT
brj-23253	318	16	,	,	PUNCT
brj-23253	318	17	64	64	NUM
brj-23253	318	18	-	-	SYM
brj-23253	318	19	73	73	NUM
brj-23253	318	20	.	.	PUNCT
brj-23253	319	1	doi	doi	NOUN
brj-23253	319	2	:	:	PUNCT
brj-23253	319	3	10.1016	10.1016	NUM
brj-23253	319	4	/	/	SYM
brj-23253	319	5	j.cageo.2015.06.020	j.cageo.2015.06.020	X
brj-23253	319	6	li	li	PROPN
brj-23253	319	7	,	,	PUNCT
brj-23253	319	8	l.	l.	PROPN
brj-23253	319	9	,	,	PUNCT
brj-23253	319	10	zhang	zhang	PROPN
brj-23253	319	11	,	,	PUNCT
brj-23253	319	12	s.	s.	PROPN
brj-23253	319	13	,	,	PUNCT
brj-23253	319	14	zuo	zuo	PROPN
brj-23253	319	15	,	,	PUNCT
brj-23253	319	16	z.	z.	PROPN
brj-23253	319	17	,	,	PUNCT
brj-23253	319	18	and	and	CCONJ
brj-23253	319	19	wang	wang	PROPN
brj-23253	319	20	,	,	PUNCT
brj-23253	319	21	y.	y.	PROPN
brj-23253	319	22	(	(	PUNCT
brj-23253	319	23	2022	2022	NUM
brj-23253	319	24	)	)	PUNCT
brj-23253	319	25	.	.	PUNCT
brj-23253	320	1	“	"	PUNCT
brj-23253	320	2	data	data	NOUN
brj-23253	320	3	fusion	fusion	NOUN
brj-23253	320	4	of	of	ADP
brj-23253	320	5	multiple	multiple	ADJ
brj-23253	320	6	-	-	PUNCT
brj-23253	320	7	information	information	NOUN
brj-23253	320	8	strategy	strategy	NOUN
brj-23253	320	9	based	base	VERB
brj-23253	320	10	on	on	ADP
brj-23253	320	11	fourier	fourier	NOUN
brj-23253	320	12	transform	transform	NOUN
brj-23253	320	13	near	near	ADV
brj-23253	320	14	-	-	PUNCT
brj-23253	320	15	infrared	infrared	ADJ
brj-23253	320	16	spectroscopy	spectroscopy	NOUN
brj-23253	320	17	and	and	CCONJ
brj-23253	320	18	fourier	fourier	NOUN
brj-23253	320	19	-	-	PUNCT
brj-23253	320	20	transform	transform	VERB
brj-23253	320	21	mid	mid	NOUN
brj-23253	320	22	-	-	ADJ
brj-23253	320	23	infrared	infrared	ADJ
brj-23253	320	24	for	for	ADP
brj-23253	320	25	geographical	geographical	ADJ
brj-23253	320	26	traceability	traceability	NOUN
brj-23253	320	27	of	of	ADP
brj-23253	320	28	wolfiporia	wolfiporia	PROPN
brj-23253	320	29	cocos	cocos	PROPN
brj-23253	320	30	combined	combine	VERB
brj-23253	320	31	with	with	ADP
brj-23253	320	32	chemometrics	chemometric	NOUN
brj-23253	320	33	,	,	PUNCT
brj-23253	320	34	”	"	PUNCT
brj-23253	320	35	j.	j.	PROPN
brj-23253	320	36	chemometrics	chemometrics	PROPN
brj-23253	320	37	36(9	36(9	PROPN
brj-23253	320	38	)	)	PUNCT
brj-23253	320	39	,	,	PUNCT
brj-23253	320	40	article	article	NOUN
brj-23253	320	41	e3436	e3436	PROPN
brj-23253	320	42	.	.	PROPN
brj-23253	320	43	doi	doi	PROPN
brj-23253	320	44	:	:	PUNCT
brj-23253	320	45	10.1002	10.1002	NUM
brj-23253	320	46	/	/	SYM
brj-23253	320	47	cem.3436	cem.3436	PROPN
brj-23253	320	48	li	li	PROPN
brj-23253	320	49	,	,	PUNCT
brj-23253	320	50	x.	x.	PROPN
brj-23253	320	51	,	,	PUNCT
brj-23253	320	52	wei	wei	PROPN
brj-23253	320	53	,	,	PUNCT
brj-23253	320	54	z.	z.	PROPN
brj-23253	320	55	,	,	PUNCT
brj-23253	320	56	peng	peng	PROPN
brj-23253	320	57	,	,	PUNCT
brj-23253	320	58	f.	f.	PROPN
brj-23253	320	59	,	,	PUNCT
brj-23253	320	60	liu	liu	PROPN
brj-23253	320	61	,	,	PUNCT
brj-23253	320	62	j.	j.	PROPN
brj-23253	320	63	,	,	PUNCT
brj-23253	320	64	and	and	CCONJ
brj-23253	320	65	han	han	PROPN
brj-23253	320	66	,	,	PUNCT
brj-23253	320	67	g.	g.	PROPN
brj-23253	320	68	(	(	PUNCT
brj-23253	320	69	2023	2023	NUM
brj-23253	320	70	)	)	PUNCT
brj-23253	320	71	.	.	PUNCT
brj-23253	321	1	“	"	PUNCT
brj-23253	321	2	non	non	ADJ
brj-23253	321	3	-	-	ADJ
brj-23253	321	4	destructive	destructive	ADJ
brj-23253	321	5	prediction	prediction	NOUN
brj-23253	321	6	and	and	CCONJ
brj-23253	321	7	visualization	visualization	NOUN
brj-23253	321	8	of	of	ADP
brj-23253	321	9	anthocyanin	anthocyanin	ADJ
brj-23253	321	10	content	content	NOUN
brj-23253	321	11	in	in	ADP
brj-23253	321	12	mulberry	mulberry	NOUN
brj-23253	321	13	fruits	fruit	NOUN
brj-23253	321	14	using	use	VERB
brj-23253	321	15	hyperspectral	hyperspectral	ADJ
brj-23253	321	16	imaging	imaging	NOUN
brj-23253	321	17	,	,	PUNCT
brj-23253	321	18	”	"	PUNCT
brj-23253	321	19	frontiers	frontier	NOUN
brj-23253	321	20	in	in	ADP
brj-23253	321	21	plant	plant	NOUN
brj-23253	321	22	science	science	NOUN
brj-23253	321	23	,	,	PUNCT
brj-23253	321	24	14	14	NUM
brj-23253	321	25	.	.	PUNCT
brj-23253	322	1	doi	doi	NOUN
brj-23253	322	2	:	:	PUNCT
brj-23253	322	3	10.3389	10.3389	NUM
brj-23253	322	4	/	/	SYM
brj-23253	322	5	fpls.2023.1137198	fpls.2023.1137198	PROPN
brj-23253	322	6	lopes	lopes	PROPN
brj-23253	322	7	,	,	PUNCT
brj-23253	322	8	j.	j.	PROPN
brj-23253	322	9	a.	a.	PROPN
brj-23253	322	10	,	,	PUNCT
brj-23253	322	11	sousa	sousa	PROPN
brj-23253	322	12	,	,	PUNCT
brj-23253	322	13	c.	c.	PROPN
brj-23253	322	14	,	,	PUNCT
brj-23253	322	15	ferreira	ferreira	PROPN
brj-23253	322	16	,	,	PUNCT
brj-23253	322	17	e.	e.	PROPN
brj-23253	322	18	c.	c.	PROPN
brj-23253	322	19	,	,	PUNCT
brj-23253	322	20	mesquita	mesquita	PROPN
brj-23253	322	21	,	,	PUNCT
brj-23253	322	22	d.	d.	PROPN
brj-23253	322	23	p.	p.	PROPN
brj-23253	322	24	,	,	PUNCT
brj-23253	322	25	and	and	CCONJ
brj-23253	322	26	quintelas	quintelas	ADV
brj-23253	322	27	,	,	PUNCT
brj-23253	322	28	c.	c.	NOUN
brj-23253	322	29	(	(	PUNCT
brj-23253	322	30	2015	2015	NUM
brj-23253	322	31	)	)	PUNCT
brj-23253	322	32	.	.	PUNCT
brj-23253	323	1	“	"	PUNCT
brj-23253	323	2	nearinfrared	nearinfrare	VERB
brj-23253	323	3	spectroscopy	spectroscopy	NOUN
brj-23253	323	4	for	for	ADP
brj-23253	323	5	the	the	DET
brj-23253	323	6	detection	detection	NOUN
brj-23253	323	7	and	and	CCONJ
brj-23253	323	8	quantification	quantification	NOUN
brj-23253	323	9	of	of	ADP
brj-23253	323	10	bacterial	bacterial	ADJ
brj-23253	323	11	contaminations	contamination	NOUN
brj-23253	323	12	in	in	ADP
brj-23253	323	13	pharmaceutical	pharmaceutical	ADJ
brj-23253	323	14	products	product	NOUN
brj-23253	323	15	,	,	PUNCT
brj-23253	323	16	”	"	PUNCT
brj-23253	323	17	international	international	ADJ
brj-23253	323	18	journal	journal	NOUN
brj-23253	323	19	of	of	ADP
brj-23253	323	20	pharmaceutics	pharmaceutic	NOUN
brj-23253	323	21	492(1	492(1	NUM
brj-23253	323	22	-	-	SYM
brj-23253	323	23	2	2	NUM
brj-23253	323	24	)	)	PUNCT
brj-23253	323	25	,	,	PUNCT
brj-23253	323	26	199206	199206	NUM
brj-23253	323	27	.	.	PUNCT
brj-23253	324	1	doi	doi	NOUN
brj-23253	324	2	:	:	PUNCT
brj-23253	324	3	10.1016	10.1016	NUM
brj-23253	324	4	/	/	SYM
brj-23253	324	5	j.ijpharm.2015.07.005	j.ijpharm.2015.07.005	PROPN
brj-23253	324	6	ma	ma	PROPN
brj-23253	324	7	,	,	PUNCT
brj-23253	324	8	t.	t.	PROPN
brj-23253	324	9	,	,	PUNCT
brj-23253	324	10	inagaki	inagaki	PROPN
brj-23253	324	11	,	,	PUNCT
brj-23253	324	12	t.	t.	PROPN
brj-23253	324	13	,	,	PUNCT
brj-23253	324	14	and	and	CCONJ
brj-23253	324	15	tsuchikawa	tsuchikawa	PROPN
brj-23253	324	16	,	,	PUNCT
brj-23253	324	17	s.	s.	PROPN
brj-23253	324	18	(	(	PUNCT
brj-23253	324	19	2023	2023	NUM
brj-23253	324	20	)	)	PUNCT
brj-23253	324	21	.	.	PUNCT
brj-23253	325	1	“	"	PUNCT
brj-23253	325	2	demonstration	demonstration	NOUN
brj-23253	325	3	of	of	ADP
brj-23253	325	4	the	the	DET
brj-23253	325	5	applicability	applicability	NOUN
brj-23253	325	6	of	of	ADP
brj-23253	325	7	visible	visible	ADJ
brj-23253	325	8	and	and	CCONJ
brj-23253	325	9	near	near	ADV
brj-23253	325	10	-	-	PUNCT
brj-23253	325	11	infrared	infrare	VERB
brj-23253	325	12	spatially	spatially	ADV
brj-23253	325	13	resolved	resolve	VERB
brj-23253	325	14	spectroscopy	spectroscopy	NOUN
brj-23253	325	15	for	for	ADP
brj-23253	325	16	rapid	rapid	ADJ
brj-23253	325	17	and	and	CCONJ
brj-23253	325	18	nondestructive	nondestructive	ADJ
brj-23253	325	19	wood	wood	NOUN
brj-23253	325	20	classification	classification	NOUN
brj-23253	325	21	,	,	PUNCT
brj-23253	325	22	”	"	PUNCT
brj-23253	325	23	holzforschung	holzforschung	PROPN
brj-23253	325	24	12(2	12(2	NUM
brj-23253	325	25	)	)	PUNCT
brj-23253	325	26	,	,	PUNCT
brj-23253	325	27	153	153	NUM
brj-23253	325	28	-	-	SYM
brj-23253	325	29	162	162	NUM
brj-23253	325	30	.	.	PUNCT
brj-23253	326	1	doi	doi	NOUN
brj-23253	326	2	:	:	PUNCT
brj-23253	326	3	10.1515	10.1515	NUM
brj-23253	326	4	/	/	SYM
brj-23253	326	5	hf-2020	hf-2020	PROPN
brj-23253	326	6	-	-	PUNCT
brj-23253	326	7	0074	0074	NUM
brj-23253	326	8	makino	makino	PROPN
brj-23253	326	9	,	,	PUNCT
brj-23253	326	10	y.	y.	PROPN
brj-23253	326	11	,	,	PUNCT
brj-23253	326	12	oshita	oshita	PROPN
brj-23253	326	13	,	,	PUNCT
brj-23253	326	14	s.	s.	PROPN
brj-23253	326	15	,	,	PUNCT
brj-23253	326	16	and	and	CCONJ
brj-23253	326	17	kamruzzaman	kamruzzaman	NOUN
brj-23253	326	18	,	,	PUNCT
brj-23253	326	19	m.	m.	NOUN
brj-23253	326	20	(	(	PUNCT
brj-23253	326	21	2016	2016	NUM
brj-23253	326	22	)	)	PUNCT
brj-23253	326	23	.	.	PUNCT
brj-23253	327	1	“	"	PUNCT
brj-23253	327	2	hyperspectral	hyperspectral	ADJ
brj-23253	327	3	imaging	imaging	NOUN
brj-23253	327	4	for	for	ADP
brj-23253	327	5	realtime	realtime	ADJ
brj-23253	327	6	monitoring	monitoring	NOUN
brj-23253	327	7	of	of	ADP
brj-23253	327	8	water	water	NOUN
brj-23253	327	9	holding	hold	VERB
brj-23253	327	10	capacity	capacity	NOUN
brj-23253	327	11	in	in	ADP
brj-23253	327	12	red	red	ADJ
brj-23253	327	13	meat	meat	NOUN
brj-23253	327	14	,	,	PUNCT
brj-23253	327	15	”	"	PUNCT
brj-23253	327	16	lwt	lwt	NOUN
brj-23253	327	17	-	-	PUNCT
brj-23253	327	18	food	food	NOUN
brj-23253	327	19	science	science	NOUN
brj-23253	327	20	&	&	CCONJ
brj-23253	327	21	technology	technology	PROPN
brj-23253	327	22	66	66	NUM
brj-23253	327	23	(	(	PUNCT
brj-23253	327	24	2016	2016	NUM
brj-23253	327	25	)	)	PUNCT
brj-23253	327	26	,	,	PUNCT
brj-23253	327	27	685	685	NUM
brj-23253	327	28	-	-	SYM
brj-23253	327	29	691	691	NUM
brj-23253	327	30	.	.	PUNCT
brj-23253	327	31	doi:10.1016	doi:10.1016	PROPN
brj-23253	327	32	/	/	SYM
brj-23253	327	33	j.lwt.2015.11.021	j.lwt.2015.11.021	PROPN
brj-23253	327	34	masithoh	masithoh	PROPN
brj-23253	327	35	,	,	PUNCT
brj-23253	327	36	r.	r.	PROPN
brj-23253	327	37	e.	e.	PROPN
brj-23253	327	38	,	,	PUNCT
brj-23253	327	39	amanah	amanah	PROPN
brj-23253	327	40	,	,	PUNCT
brj-23253	327	41	h.	h.	PROPN
brj-23253	327	42	z.	z.	PROPN
brj-23253	327	43	,	,	PUNCT
brj-23253	327	44	yoon	yoon	PROPN
brj-23253	327	45	,	,	PUNCT
brj-23253	327	46	w.	w.	PROPN
brj-23253	327	47	s.	s.	PROPN
brj-23253	327	48	,	,	PUNCT
brj-23253	327	49	et	et	PROPN
brj-23253	327	50	al	al	PROPN
brj-23253	327	51	.	.	PROPN
brj-23253	327	52	(	(	PUNCT
brj-23253	327	53	2020	2020	NUM
brj-23253	327	54	)	)	PUNCT
brj-23253	327	55	.	.	PUNCT
brj-23253	328	1	“	"	PUNCT
brj-23253	328	2	determination	determination	NOUN
brj-23253	328	3	of	of	ADP
brj-23253	328	4	protein	protein	NOUN
brj-23253	328	5	and	and	CCONJ
brj-23253	328	6	glucose	glucose	NOUN
brj-23253	328	7	of	of	ADP
brj-23253	328	8	tuber	tuber	NOUN
brj-23253	328	9	and	and	CCONJ
brj-23253	328	10	root	root	NOUN
brj-23253	328	11	flours	flour	NOUN
brj-23253	328	12	using	use	VERB
brj-23253	328	13	nir	nir	ADJ
brj-23253	328	14	and	and	CCONJ
brj-23253	328	15	mir	mir	PROPN
brj-23253	328	16	spectroscopy	spectroscopy	NOUN
brj-23253	328	17	,	,	PUNCT
brj-23253	328	18	”	"	PUNCT
brj-23253	328	19	infrared	infrared	PROPN
brj-23253	328	20	physics	physics	PROPN
brj-23253	328	21	&	&	CCONJ
brj-23253	328	22	technology	technology	PROPN
brj-23253	328	23	.	.	PUNCT
brj-23253	329	1	doi	doi	NOUN
brj-23253	329	2	:	:	PUNCT
brj-23253	329	3	10.1016	10.1016	NUM
brj-23253	329	4	/	/	SYM
brj-23253	329	5	j.infrared.2020.103577	j.infrared.2020.103577	PROPN
brj-23253	329	6	mei	mei	PROPN
brj-23253	329	7	,	,	PUNCT
brj-23253	329	8	q.-p	q.-p	PROPN
brj-23253	329	9	.	.	PUNCT
brj-23253	329	10	,	,	PUNCT
brj-23253	329	11	li	li	PROPN
brj-23253	329	12	,	,	PUNCT
brj-23253	329	13	t.-f	t.-f	PROPN
brj-23253	329	14	.	.	PUNCT
brj-23253	329	15	,	,	PUNCT
brj-23253	329	16	yao	yao	PROPN
brj-23253	329	17	,	,	PUNCT
brj-23253	329	18	l.-z	l.-z	PROPN
brj-23253	329	19	.	.	PUNCT
brj-23253	329	20	,	,	PUNCT
brj-23253	329	21	liu	liu	PROPN
brj-23253	329	22	,	,	PUNCT
brj-23253	329	23	x.-h	x.-h	PROPN
brj-23253	329	24	.	.	PROPN
brj-23253	329	25	,	,	PUNCT
brj-23253	329	26	hu	hu	PROPN
brj-23253	329	27	,	,	PUNCT
brj-23253	329	28	y.-l	y.-l	NOUN
brj-23253	329	29	.	.	PUNCT
brj-23253	329	30	,	,	PUNCT
brj-23253	329	31	and	and	CCONJ
brj-23253	329	32	hu	hu	PROPN
brj-23253	329	33	,	,	PUNCT
brj-23253	329	34	l.	l.	PROPN
brj-23253	329	35	(	(	PUNCT
brj-23253	329	36	2019	2019	NUM
brj-23253	329	37	)	)	PUNCT
brj-23253	329	38	.	.	PUNCT
brj-23253	330	1	"	"	PUNCT
brj-23253	330	2	characterization	characterization	NOUN
brj-23253	330	3	of	of	ADP
brj-23253	330	4	a	a	DET
brj-23253	330	5	wavelength	wavelength	NOUN
brj-23253	330	6	selection	selection	NOUN
brj-23253	330	7	method	method	NOUN
brj-23253	330	8	using	use	VERB
brj-23253	330	9	near	near	ADV
brj-23253	330	10	-	-	PUNCT
brj-23253	330	11	infrared	infrared	ADJ
brj-23253	330	12	spectroscopy	spectroscopy	NOUN
brj-23253	330	13	and	and	CCONJ
brj-23253	330	14	partial	partial	ADJ
brj-23253	330	15	least	least	ADJ
brj-23253	330	16	squares	square	NOUN
brj-23253	330	17	with	with	ADP
brj-23253	330	18	false	false	ADJ
brj-23253	330	19	nearest	near	ADJ
brj-23253	330	20	neighbors	neighbor	NOUN
brj-23253	330	21	and	and	CCONJ
brj-23253	330	22	its	its	PRON
brj-23253	330	23	application	application	NOUN
brj-23253	330	24	in	in	ADP
brj-23253	330	25	the	the	DET
brj-23253	330	26	detection	detection	NOUN
brj-23253	330	27	of	of	ADP
brj-23253	330	28	the	the	DET
brj-23253	330	29	chemical	chemical	NOUN
brj-23253	330	30	oxygen	oxygen	NOUN
brj-23253	330	31	demand	demand	NOUN
brj-23253	330	32	of	of	ADP
brj-23253	330	33	waste	waste	NOUN
brj-23253	330	34	liquid	liquid	NOUN
brj-23253	330	35	,	,	PUNCT
brj-23253	330	36	”	"	PUNCT
brj-23253	330	37	spectroscopy	spectroscopy	NOUN
brj-23253	330	38	letters	letter	NOUN
brj-23253	330	39	52(9	52(9	NUM
brj-23253	330	40	)	)	PUNCT
brj-23253	330	41	,	,	PUNCT
brj-23253	330	42	553	553	NUM
brj-23253	330	43	-	-	SYM
brj-23253	330	44	562	562	NUM
brj-23253	330	45	.	.	PUNCT
brj-23253	331	1	doi	doi	NOUN
brj-23253	331	2	:	:	PUNCT
brj-23253	331	3	10.1080/00387010.2019.1676261	10.1080/00387010.2019.1676261	NUM
brj-23253	331	4	mishra	mishra	PROPN
brj-23253	331	5	,	,	PUNCT
brj-23253	331	6	p.	p.	PROPN
brj-23253	331	7	,	,	PUNCT
brj-23253	331	8	nordon	nordon	PROPN
brj-23253	331	9	,	,	PUNCT
brj-23253	331	10	a.	a.	NOUN
brj-23253	331	11	,	,	PUNCT
brj-23253	331	12	mohd	mohd	PROPN
brj-23253	331	13	asaari	asaari	PROPN
brj-23253	331	14	,	,	PUNCT
brj-23253	331	15	m.	m.	PROPN
brj-23253	331	16	s.	s.	PROPN
brj-23253	331	17	,	,	PUNCT
brj-23253	331	18	lian	lian	PROPN
brj-23253	331	19	,	,	PUNCT
brj-23253	331	20	g.	g.	PROPN
brj-23253	331	21	,	,	PUNCT
brj-23253	331	22	and	and	CCONJ
brj-23253	331	23	redfern	redfern	PROPN
brj-23253	331	24	,	,	PUNCT
brj-23253	331	25	s.	s.	PROPN
brj-23253	331	26	(	(	PUNCT
brj-23253	331	27	2019	2019	NUM
brj-23253	331	28	)	)	PUNCT
brj-23253	331	29	.	.	PUNCT
brj-23253	332	1	“	"	PUNCT
brj-23253	332	2	fusing	fuse	VERB
brj-23253	332	3	spectral	spectral	ADJ
brj-23253	332	4	and	and	CCONJ
brj-23253	332	5	textural	textural	ADJ
brj-23253	332	6	information	information	NOUN
brj-23253	332	7	in	in	ADP
brj-23253	332	8	near	near	ADV
brj-23253	332	9	-	-	PUNCT
brj-23253	332	10	infrared	infrared	ADJ
brj-23253	332	11	hyperspectral	hyperspectral	ADJ
brj-23253	332	12	imaging	imaging	NOUN
brj-23253	332	13	to	to	PART
brj-23253	332	14	improve	improve	VERB
brj-23253	332	15	green	green	ADJ
brj-23253	332	16	tea	tea	NOUN
brj-23253	332	17	classification	classification	NOUN
brj-23253	332	18	modeling	modeling	NOUN
brj-23253	332	19	,	,	PUNCT
brj-23253	332	20	”	"	PUNCT
brj-23253	332	21	journal	journal	NOUN
brj-23253	332	22	of	of	ADP
brj-23253	332	23	food	food	NOUN
brj-23253	332	24	engineering	engineering	NOUN
brj-23253	332	25	249	249	NUM
brj-23253	332	26	,	,	PUNCT
brj-23253	332	27	40	40	NUM
brj-23253	332	28	-	-	SYM
brj-23253	332	29	47	47	NUM
brj-23253	332	30	.	.	PUNCT
brj-23253	333	1	doi	doi	NOUN
brj-23253	333	2	:	:	PUNCT
brj-23253	333	3	10.1016	10.1016	NUM
brj-23253	333	4	/	/	SYM
brj-23253	333	5	j.jfoodeng.2019.01.009	j.jfoodeng.2019.01.009	PROPN
brj-23253	333	6	niu	niu	PROPN
brj-23253	333	7	,	,	PUNCT
brj-23253	333	8	c.	c.	PROPN
brj-23253	333	9	,	,	PUNCT
brj-23253	333	10	tan	tan	PROPN
brj-23253	333	11	,	,	PUNCT
brj-23253	333	12	k.	k.	PROPN
brj-23253	333	13	,	,	PUNCT
brj-23253	333	14	jia	jia	PROPN
brj-23253	333	15	,	,	PUNCT
brj-23253	333	16	x.	x.	PROPN
brj-23253	333	17	,	,	PUNCT
brj-23253	333	18	and	and	CCONJ
brj-23253	333	19	wang	wang	PROPN
brj-23253	333	20	,	,	PUNCT
brj-23253	333	21	x.	x.	NOUN
brj-23253	333	22	(	(	PUNCT
brj-23253	333	23	2021	2021	NUM
brj-23253	333	24	)	)	PUNCT
brj-23253	333	25	.	.	PUNCT
brj-23253	334	1	“	"	PUNCT
brj-23253	334	2	deep	deep	ADJ
brj-23253	334	3	learning	learning	NOUN
brj-23253	334	4	based	base	VERB
brj-23253	334	5	regression	regression	NOUN
brj-23253	334	6	for	for	ADP
brj-23253	334	7	optically	optically	ADV
brj-23253	334	8	inactive	inactive	ADJ
brj-23253	334	9	inland	inland	ADJ
brj-23253	334	10	water	water	NOUN
brj-23253	334	11	quality	quality	NOUN
brj-23253	334	12	parameter	parameter	NOUN
brj-23253	334	13	estimation	estimation	NOUN
brj-23253	334	14	using	use	VERB
brj-23253	334	15	airborne	airborne	ADJ
brj-23253	334	16	hyperspectral	hyperspectral	ADJ
brj-23253	334	17	imagery	imagery	NOUN
brj-23253	334	18	,	,	PUNCT
brj-23253	334	19	”	"	PUNCT
brj-23253	334	20	environmental	environmental	ADJ
brj-23253	334	21	pollution	pollution	NOUN
brj-23253	334	22	286	286	NUM
brj-23253	334	23	,	,	PUNCT
brj-23253	334	24	article	article	NOUN
brj-23253	334	25	117534	117534	NUM
brj-23253	334	26	.	.	PUNCT
brj-23253	335	1	doi	doi	NOUN
brj-23253	335	2	:	:	PUNCT
brj-23253	335	3	10.1016	10.1016	NUM
brj-23253	335	4	/	/	SYM
brj-23253	335	5	j.envpol.2021.117534	j.envpol.2021.117534	PROPN
brj-23253	335	6	oliveri	oliveri	PROPN
brj-23253	335	7	,	,	PUNCT
brj-23253	335	8	p.	p.	PROPN
brj-23253	335	9	,	,	PUNCT
brj-23253	335	10	malegori	malegori	PROPN
brj-23253	335	11	,	,	PUNCT
brj-23253	335	12	c.	c.	PROPN
brj-23253	335	13	,	,	PUNCT
brj-23253	335	14	simonetti	simonetti	PROPN
brj-23253	335	15	,	,	PUNCT
brj-23253	335	16	r.	r.	PROPN
brj-23253	335	17	,	,	PUNCT
brj-23253	335	18	and	and	CCONJ
brj-23253	335	19	casale	casale	NOUN
brj-23253	335	20	,	,	PUNCT
brj-23253	335	21	m.	m.	NOUN
brj-23253	335	22	(	(	PUNCT
brj-23253	335	23	2019	2019	NUM
brj-23253	335	24	)	)	PUNCT
brj-23253	335	25	.	.	PUNCT
brj-23253	336	1	“	"	PUNCT
brj-23253	336	2	the	the	DET
brj-23253	336	3	impact	impact	NOUN
brj-23253	336	4	of	of	ADP
brj-23253	336	5	signal	signal	ADJ
brj-23253	336	6	preprocessing	preprocessing	NOUN
brj-23253	336	7	on	on	ADP
brj-23253	336	8	the	the	DET
brj-23253	336	9	final	final	ADJ
brj-23253	336	10	interpretation	interpretation	NOUN
brj-23253	336	11	of	of	ADP
brj-23253	336	12	analytical	analytical	ADJ
brj-23253	336	13	outcomes	outcome	NOUN
brj-23253	336	14	–	–	PUNCT
brj-23253	336	15	a	a	DET
brj-23253	336	16	tutorial	tutorial	NOUN
brj-23253	336	17	,	,	PUNCT
brj-23253	336	18	”	"	PUNCT
brj-23253	336	19	analytica	analytica	PROPN
brj-23253	336	20	chimica	chimica	PROPN
brj-23253	336	21	acta	acta	PROPN
brj-23253	336	22	1058	1058	NUM
brj-23253	336	23	,	,	PUNCT
brj-23253	336	24	9	9	NUM
brj-23253	336	25	-	-	SYM
brj-23253	336	26	17	17	NUM
brj-23253	336	27	.	.	PUNCT
brj-23253	337	1	doi	doi	NOUN
brj-23253	337	2	:	:	PUNCT
brj-23253	337	3	10.1016	10.1016	NUM
brj-23253	337	4	/	/	SYM
brj-23253	337	5	j.aca.2018.10.055	j.aca.2018.10.055	NOUN
brj-23253	337	6	phetpan	phetpan	NOUN
brj-23253	337	7	,	,	PUNCT
brj-23253	337	8	v.	v.	ADP
brj-23253	337	9	s.	s.	PROPN
brj-23253	337	10	p.	p.	PROPN
brj-23253	337	11	(	(	PUNCT
brj-23253	337	12	2019	2019	NUM
brj-23253	337	13	)	)	PUNCT
brj-23253	337	14	.	.	PUNCT
brj-23253	338	1	“	"	PUNCT
brj-23253	338	2	in	in	ADP
brj-23253	338	3	-	-	PUNCT
brj-23253	338	4	line	line	NOUN
brj-23253	338	5	near	near	ADP
brj-23253	338	6	infrared	infrared	ADJ
brj-23253	338	7	spectroscopy	spectroscopy	NOUN
brj-23253	338	8	for	for	ADP
brj-23253	338	9	the	the	DET
brj-23253	338	10	prediction	prediction	NOUN
brj-23253	338	11	of	of	ADP
brj-23253	338	12	moisture	moisture	NOUN
brj-23253	338	13	content	content	NOUN
brj-23253	338	14	in	in	ADP
brj-23253	338	15	the	the	DET
brj-23253	338	16	tapioca	tapioca	NOUN
brj-23253	338	17	starch	starch	NOUN
brj-23253	338	18	drying	dry	VERB
brj-23253	338	19	process	process	NOUN
brj-23253	338	20	,	,	PUNCT
brj-23253	338	21	”	"	PUNCT
brj-23253	338	22	powder	powder	NOUN
brj-23253	338	23	technology	technology	NOUN
brj-23253	338	24	345	345	NUM
brj-23253	338	25	,	,	PUNCT
brj-23253	338	26	608615	608615	NUM
brj-23253	338	27	.	.	PUNCT
brj-23253	339	1	doi	doi	NOUN
brj-23253	339	2	:	:	PUNCT
brj-23253	339	3	10.1016	10.1016	NUM
brj-23253	339	4	/	/	SYM
brj-23253	339	5	j.powtec.2019.01.050	j.powtec.2019.01.050	PROPN
brj-23253	339	6	pradhan	pradhan	PROPN
brj-23253	339	7	,	,	PUNCT
brj-23253	339	8	k.	k.	PROPN
brj-23253	339	9	,	,	PUNCT
brj-23253	339	10	monoj	monoj	NOUN
brj-23253	339	11	,	,	PUNCT
brj-23253	339	12	k.	k.	PROPN
brj-23253	339	13	,	,	PUNCT
brj-23253	339	14	minz	minz	PROPN
brj-23253	339	15	,	,	PUNCT
brj-23253	339	16	s.	s.	PROPN
brj-23253	339	17	,	,	PUNCT
brj-23253	339	18	and	and	CCONJ
brj-23253	339	19	shrivastava	shrivastava	PROPN
brj-23253	339	20	,	,	PUNCT
brj-23253	339	21	v.	v.	PROPN
brj-23253	339	22	k.	k.	PROPN
brj-23253	339	23	(	(	PUNCT
brj-23253	339	24	2019	2019	NUM
brj-23253	339	25	)	)	PUNCT
brj-23253	339	26	.	.	PUNCT
brj-23253	340	1	“	"	PUNCT
brj-23253	340	2	fast	fast	VERB
brj-23253	340	3	active	active	ADJ
brj-23253	340	4	learning	learning	NOUN
brj-23253	340	5	for	for	ADP
brj-23253	340	6	hyperspectral	hyperspectral	ADJ
brj-23253	340	7	image	image	NOUN
brj-23253	340	8	classification	classification	NOUN
brj-23253	340	9	using	use	VERB
brj-23253	340	10	extreme	extreme	ADJ
brj-23253	340	11	learning	learn	VERB
brj-23253	340	12	machine	machine	NOUN
brj-23253	340	13	,	,	PUNCT
brj-23253	340	14	”	"	PUNCT
brj-23253	340	15	iet	iet	PROPN
brj-23253	340	16	image	image	NOUN
brj-23253	340	17	processing	processing	NOUN
brj-23253	340	18	13(4	13(4	NOUN
brj-23253	340	19	)	)	PUNCT
brj-23253	340	20	,	,	PUNCT
brj-23253	340	21	549	549	NUM
brj-23253	340	22	-	-	SYM
brj-23253	340	23	555	555	NUM
brj-23253	340	24	.	.	PUNCT
brj-23253	341	1	doi	doi	NOUN
brj-23253	341	2	:	:	PUNCT
brj-23253	341	3	10.1049	10.1049	NUM
brj-23253	341	4	/	/	SYM
brj-23253	341	5	iet	iet	PROPN
brj-23253	341	6	-	-	PUNCT
brj-23253	341	7	ipr.2018.5104	ipr.2018.5104	PROPN
brj-23253	341	8	https://doi.org/10.1016/j.lwt.2015.11.021	https://doi.org/10.1016/j.lwt.2015.11.021	PROPN
brj-23253	341	9	peer	peer	NOUN
brj-23253	341	10	-	-	PUNCT
brj-23253	341	11	reviewed	review	VERB
brj-23253	341	12	article	article	NOUN
brj-23253	341	13	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23253	341	14	li	li	PROPN
brj-23253	341	15	et	et	PROPN
brj-23253	341	16	al	al	PROPN
brj-23253	341	17	.	.	PROPN
brj-23253	342	1	(	(	PUNCT
brj-23253	342	2	2024	2024	NUM
brj-23253	342	3	)	)	PUNCT
brj-23253	342	4	.	.	PUNCT
brj-23253	343	1	“	"	PUNCT
brj-23253	343	2	alfalfa	alfalfa	X
brj-23253	343	3	protein	protein	NOUN
brj-23253	343	4	with	with	ADP
brj-23253	343	5	vis	vis	X
brj-23253	343	6	/	/	SYM
brj-23253	343	7	nir	nir	NOUN
brj-23253	343	8	,	,	PUNCT
brj-23253	343	9	”	"	PUNCT
brj-23253	343	10	bioresources	bioresource	NOUN
brj-23253	343	11	19(2	19(2	NUM
brj-23253	343	12	)	)	PUNCT
brj-23253	343	13	,	,	PUNCT
brj-23253	343	14	3808	3808	NUM
brj-23253	343	15	-	-	SYM
brj-23253	343	16	3825	3825	NUM
brj-23253	343	17	.	.	PUNCT
brj-23253	344	1	3825	3825	NUM
brj-23253	344	2	rego	rego	NOUN
brj-23253	344	3	,	,	PUNCT
brj-23253	344	4	g.	g.	PROPN
brj-23253	344	5	,	,	PUNCT
brj-23253	344	6	ferrero	ferrero	PROPN
brj-23253	344	7	,	,	PUNCT
brj-23253	344	8	f.	f.	PROPN
brj-23253	344	9	,	,	PUNCT
brj-23253	344	10	valledor	valledor	PROPN
brj-23253	344	11	,	,	PUNCT
brj-23253	344	12	m.	m.	NOUN
brj-23253	344	13	,	,	PUNCT
brj-23253	344	14	campo	campo	PROPN
brj-23253	344	15	,	,	PUNCT
brj-23253	344	16	j.	j.	PROPN
brj-23253	344	17	carlos	carlos	PROPN
brj-23253	344	18	,	,	PUNCT
brj-23253	344	19	forcada	forcada	PROPN
brj-23253	344	20	,	,	PUNCT
brj-23253	344	21	s.	s.	PROPN
brj-23253	344	22	,	,	PUNCT
brj-23253	344	23	royo	royo	PROPN
brj-23253	344	24	,	,	PUNCT
brj-23253	344	25	l.	l.	PROPN
brj-23253	344	26	j.	j.	PROPN
brj-23253	344	27	,	,	PUNCT
brj-23253	344	28	and	and	CCONJ
brj-23253	344	29	soldado	soldado	ADJ
brj-23253	344	30	,	,	PUNCT
brj-23253	344	31	a.	a.	NOUN
brj-23253	344	32	(	(	PUNCT
brj-23253	344	33	2020	2020	NUM
brj-23253	344	34	)	)	PUNCT
brj-23253	344	35	.	.	PUNCT
brj-23253	345	1	“	"	PUNCT
brj-23253	345	2	a	a	DET
brj-23253	345	3	portable	portable	ADJ
brj-23253	345	4	iot	iot	ADJ
brj-23253	345	5	nir	nir	ADJ
brj-23253	345	6	spectroscopic	spectroscopic	NOUN
brj-23253	345	7	system	system	NOUN
brj-23253	345	8	to	to	PART
brj-23253	345	9	analyze	analyze	VERB
brj-23253	345	10	the	the	DET
brj-23253	345	11	quality	quality	NOUN
brj-23253	345	12	of	of	ADP
brj-23253	345	13	dairy	dairy	NOUN
brj-23253	345	14	farm	farm	NOUN
brj-23253	345	15	forage	forage	NOUN
brj-23253	345	16	,	,	PUNCT
brj-23253	345	17	”	"	PUNCT
brj-23253	345	18	computers	computer	NOUN
brj-23253	345	19	and	and	CCONJ
brj-23253	345	20	electronics	electronic	NOUN
brj-23253	345	21	in	in	ADP
brj-23253	345	22	agriculture	agriculture	NOUN
brj-23253	345	23	175	175	NUM
brj-23253	345	24	,	,	PUNCT
brj-23253	345	25	article	article	NOUN
brj-23253	345	26	105578	105578	NUM
brj-23253	345	27	.	.	PUNCT
brj-23253	346	1	doi	doi	NOUN
brj-23253	346	2	:	:	PUNCT
brj-23253	346	3	10.1016	10.1016	NUM
brj-23253	346	4	/	/	SYM
brj-23253	346	5	j.compag.2020.105578	j.compag.2020.105578	PROPN
brj-23253	346	6	saly	saly	PROPN
brj-23253	346	7	,	,	PUNCT
brj-23253	346	8	r.	r.	PROPN
brj-23253	346	9	,	,	PUNCT
brj-23253	346	10	romero	romero	PROPN
brj-23253	346	11	,	,	PUNCT
brj-23253	346	12	r.	r.	PROPN
brj-23253	346	13	,	,	PUNCT
brj-23253	346	14	torres	torre	NOUN
brj-23253	346	15	,	,	PUNCT
brj-23253	346	16	t.	t.	PROPN
brj-23253	346	17	,	,	PUNCT
brj-23253	346	18	mojgan	mojgan	NOUN
brj-23253	346	19	,	,	PUNCT
brj-23253	346	20	m.	m.	NOUN
brj-23253	346	21	,	,	PUNCT
brj-23253	346	22	moshgbar	moshgbar	PROPN
brj-23253	346	23	,	,	PUNCT
brj-23253	346	24	m.	m.	NOUN
brj-23253	346	25	,	,	PUNCT
brj-23253	346	26	jun	jun	PROPN
brj-23253	346	27	,	,	PUNCT
brj-23253	346	28	j.	j.	PROPN
brj-23253	346	29	,	,	PUNCT
brj-23253	346	30	and	and	CCONJ
brj-23253	346	31	huang	huang	PROPN
brj-23253	346	32	,	,	PUNCT
brj-23253	346	33	h.	h.	PROPN
brj-23253	346	34	(	(	PUNCT
brj-23253	346	35	2010	2010	NUM
brj-23253	346	36	)	)	PUNCT
brj-23253	346	37	.	.	PUNCT
brj-23253	347	1	“	"	PUNCT
brj-23253	347	2	practical	practical	ADJ
brj-23253	347	3	considerations	consideration	NOUN
brj-23253	347	4	in	in	ADP
brj-23253	347	5	data	datum	NOUN
brj-23253	347	6	pre	pre	NOUN
brj-23253	347	7	-	-	NOUN
brj-23253	347	8	treatment	treatment	NOUN
brj-23253	347	9	for	for	ADP
brj-23253	347	10	nir	nir	ADJ
brj-23253	347	11	and	and	CCONJ
brj-23253	347	12	raman	raman	NOUN
brj-23253	347	13	spectroscopy	spectroscopy	NOUN
brj-23253	347	14	,	,	PUNCT
brj-23253	347	15	”	"	PUNCT
brj-23253	347	16	american	american	PROPN
brj-23253	347	17	pharmaceutical	pharmaceutical	PROPN
brj-23253	347	18	review	review	PROPN
brj-23253	347	19	13(6	13(6	PROPN
brj-23253	347	20	)	)	PUNCT
brj-23253	347	21	,	,	PUNCT
brj-23253	347	22	116	116	NUM
brj-23253	347	23	-	-	SYM
brj-23253	347	24	127	127	NUM
brj-23253	347	25	.	.	PUNCT
brj-23253	348	1	shen	shen	PROPN
brj-23253	348	2	,	,	PUNCT
brj-23253	348	3	s.	s.	PROPN
brj-23253	348	4	,	,	PUNCT
brj-23253	348	5	hua	hua	PROPN
brj-23253	348	6	,	,	PUNCT
brj-23253	348	7	j.	j.	PROPN
brj-23253	348	8	,	,	PUNCT
brj-23253	348	9	zhu	zhu	PROPN
brj-23253	348	10	,	,	PUNCT
brj-23253	348	11	h.	h.	PROPN
brj-23253	348	12	,	,	PUNCT
brj-23253	348	13	yang	yang	PROPN
brj-23253	348	14	,	,	PUNCT
brj-23253	348	15	y.	y.	PROPN
brj-23253	348	16	,	,	PUNCT
brj-23253	348	17	deng	deng	PROPN
brj-23253	348	18	,	,	PUNCT
brj-23253	348	19	y.	y.	PROPN
brj-23253	348	20	,	,	PUNCT
brj-23253	348	21	li	li	PROPN
brj-23253	348	22	,	,	PUNCT
brj-23253	348	23	j.	j.	PROPN
brj-23253	348	24	,	,	PUNCT
brj-23253	348	25	yuan	yuan	PROPN
brj-23253	348	26	,	,	PUNCT
brj-23253	348	27	h.	h.	PROPN
brj-23253	348	28	,	,	PUNCT
brj-23253	348	29	wang	wang	PROPN
brj-23253	348	30	,	,	PUNCT
brj-23253	348	31	j.	j.	PROPN
brj-23253	348	32	,	,	PUNCT
brj-23253	348	33	zhu	zhu	PROPN
brj-23253	348	34	,	,	PUNCT
brj-23253	348	35	j.	j.	PROPN
brj-23253	348	36	,	,	PUNCT
brj-23253	348	37	and	and	CCONJ
brj-23253	348	38	jiang	jiang	PROPN
brj-23253	348	39	,	,	PUNCT
brj-23253	348	40	y.	y.	PROPN
brj-23253	348	41	(	(	PUNCT
brj-23253	348	42	2022	2022	NUM
brj-23253	348	43	)	)	PUNCT
brj-23253	348	44	.	.	PUNCT
brj-23253	349	1	“	"	PUNCT
brj-23253	349	2	rapid	rapid	ADJ
brj-23253	349	3	and	and	CCONJ
brj-23253	349	4	real	real	ADJ
brj-23253	349	5	-	-	PUNCT
brj-23253	349	6	time	time	NOUN
brj-23253	349	7	detection	detection	NOUN
brj-23253	349	8	of	of	ADP
brj-23253	349	9	moisture	moisture	NOUN
brj-23253	349	10	in	in	ADP
brj-23253	349	11	black	black	ADJ
brj-23253	349	12	tea	tea	NOUN
brj-23253	349	13	during	during	ADP
brj-23253	349	14	withering	wither	VERB
brj-23253	349	15	using	use	VERB
brj-23253	349	16	micro	micro	NOUN
brj-23253	349	17	-	-	ADJ
brj-23253	349	18	near	near	ADV
brj-23253	349	19	-	-	PUNCT
brj-23253	349	20	infrared	infrared	ADJ
brj-23253	349	21	spectroscopy	spectroscopy	NOUN
brj-23253	349	22	,	,	PUNCT
brj-23253	349	23	”	"	PUNCT
brj-23253	349	24	lwt	lwt	PROPN
brj-23253	349	25	155(112970	155(112970	NUM
brj-23253	349	26	)	)	PUNCT
brj-23253	349	27	.	.	PUNCT
brj-23253	350	1	doi	doi	NOUN
brj-23253	350	2	:	:	PUNCT
brj-23253	350	3	10.1016	10.1016	NUM
brj-23253	350	4	/	/	SYM
brj-23253	350	5	j.lwt.2021.112970	j.lwt.2021.112970	PROPN
brj-23253	350	6	vabalas	vabalas	PROPN
brj-23253	350	7	,	,	PUNCT
brj-23253	350	8	a.	a.	PROPN
brj-23253	350	9	,	,	PUNCT
brj-23253	350	10	gowen	gowen	PROPN
brj-23253	350	11	,	,	PUNCT
brj-23253	350	12	e.	e.	PROPN
brj-23253	350	13	,	,	PUNCT
brj-23253	350	14	poliakoff	poliakoff	PROPN
brj-23253	350	15	,	,	PUNCT
brj-23253	350	16	e.	e.	PROPN
brj-23253	350	17	,	,	PUNCT
brj-23253	350	18	and	and	CCONJ
brj-23253	350	19	casson	casson	PROPN
brj-23253	350	20	,	,	PUNCT
brj-23253	350	21	a.	a.	PROPN
brj-23253	350	22	j.	j.	PROPN
brj-23253	350	23	(	(	PUNCT
brj-23253	350	24	2019	2019	NUM
brj-23253	350	25	)	)	PUNCT
brj-23253	350	26	.	.	PUNCT
brj-23253	351	1	“	"	PUNCT
brj-23253	351	2	machine	machine	NOUN
brj-23253	351	3	learning	learn	VERB
brj-23253	351	4	algorithm	algorithm	NOUN
brj-23253	351	5	validation	validation	NOUN
brj-23253	351	6	with	with	ADP
brj-23253	351	7	a	a	DET
brj-23253	351	8	limited	limited	ADJ
brj-23253	351	9	sample	sample	NOUN
brj-23253	351	10	size	size	NOUN
brj-23253	351	11	,	,	PUNCT
brj-23253	351	12	”	"	PUNCT
brj-23253	351	13	plos	plos	PROPN
brj-23253	351	14	one	one	NUM
brj-23253	351	15	14(11	14(11	NUM
brj-23253	351	16	)	)	PUNCT
brj-23253	351	17	,	,	PUNCT
brj-23253	351	18	article	article	NOUN
brj-23253	351	19	e0224365	e0224365	PROPN
brj-23253	351	20	.	.	PUNCT
brj-23253	352	1	doi	doi	NOUN
brj-23253	352	2	:	:	PUNCT
brj-23253	352	3	10.1371	10.1371	NUM
brj-23253	352	4	/	/	SYM
brj-23253	352	5	journal.pone.0224365	journal.pone.0224365	PROPN
brj-23253	352	6	.	.	PUNCT
brj-23253	353	1	xie	xie	PROPN
brj-23253	353	2	,	,	PUNCT
brj-23253	353	3	l.	l.	PROPN
brj-23253	353	4	,	,	PUNCT
brj-23253	353	5	hong	hong	PROPN
brj-23253	353	6	,	,	PUNCT
brj-23253	353	7	m.	m.	NOUN
brj-23253	353	8	,	,	PUNCT
brj-23253	353	9	and	and	CCONJ
brj-23253	353	10	yu	yu	PROPN
brj-23253	353	11	,	,	PUNCT
brj-23253	353	12	z.	z.	PROPN
brj-23253	353	13	(	(	PUNCT
brj-23253	353	14	2022	2022	NUM
brj-23253	353	15	)	)	PUNCT
brj-23253	353	16	.	.	PUNCT
brj-23253	354	1	“	"	PUNCT
brj-23253	354	2	a	a	DET
brj-23253	354	3	wavelength	wavelength	NOUN
brj-23253	354	4	selection	selection	NOUN
brj-23253	354	5	method	method	NOUN
brj-23253	354	6	combining	combine	VERB
brj-23253	354	7	direct	direct	ADJ
brj-23253	354	8	orthogonal	orthogonal	ADJ
brj-23253	354	9	signal	signal	NOUN
brj-23253	354	10	correction	correction	NOUN
brj-23253	354	11	and	and	CCONJ
brj-23253	354	12	monte	monte	PROPN
brj-23253	354	13	carlo	carlo	PROPN
brj-23253	354	14	,	,	PUNCT
brj-23253	354	15	”	"	PUNCT
brj-23253	354	16	spectroscopy	spectroscopy	NOUN
brj-23253	354	17	and	and	CCONJ
brj-23253	354	18	spectral	spectral	ADJ
brj-23253	354	19	analysis	analysis	NOUN
brj-23253	354	20	42(2	42(2	NUM
brj-23253	354	21	)	)	PUNCT
brj-23253	354	22	,	,	PUNCT
brj-23253	354	23	article	article	NOUN
brj-23253	354	24	6	6	NUM
brj-23253	354	25	.	.	PUNCT
brj-23253	354	26	doi	doi	NOUN
brj-23253	354	27	:	:	PUNCT
brj-23253	354	28	10.3964	10.3964	NUM
brj-23253	354	29	/	/	SYM
brj-23253	354	30	j.issn.1000	j.issn.1000	NOUN
brj-23253	354	31	-	-	PUNCT
brj-23253	354	32	0593(2022)02	0593(2022)02	PROPN
brj-23253	354	33	-	-	PUNCT
brj-23253	354	34	0440	0440	NUM
brj-23253	354	35	-	-	SYM
brj-23253	354	36	06	06	NUM
brj-23253	354	37	xu	xu	PROPN
brj-23253	354	38	,	,	PUNCT
brj-23253	354	39	y.	y.	PROPN
brj-23253	354	40	,	,	PUNCT
brj-23253	354	41	zhang	zhang	PROPN
brj-23253	354	42	,	,	PUNCT
brj-23253	354	43	h.	h.	PROPN
brj-23253	354	44	,	,	PUNCT
brj-23253	354	45	zhang	zhang	PROPN
brj-23253	354	46	,	,	PUNCT
brj-23253	354	47	c.	c.	PROPN
brj-23253	354	48	,	,	PUNCT
brj-23253	354	49	wu	wu	PROPN
brj-23253	354	50	,	,	PUNCT
brj-23253	354	51	p.	p.	PROPN
brj-23253	354	52	,	,	PUNCT
brj-23253	354	53	li	li	PROPN
brj-23253	354	54	,	,	PUNCT
brj-23253	354	55	j.	j.	PROPN
brj-23253	354	56	,	,	PUNCT
brj-23253	354	57	xia	xia	PROPN
brj-23253	354	58	,	,	PUNCT
brj-23253	354	59	y.	y.	NOUN
brj-23253	354	60	,	,	PUNCT
brj-23253	354	61	and	and	CCONJ
brj-23253	354	62	fan	fan	PROPN
brj-23253	354	63	,	,	PUNCT
brj-23253	354	64	s.	s.	PROPN
brj-23253	354	65	(	(	PUNCT
brj-23253	354	66	2019	2019	NUM
brj-23253	354	67	)	)	PUNCT
brj-23253	354	68	.	.	PUNCT
brj-23253	355	1	“	"	PUNCT
brj-23253	355	2	rapid	rapid	ADJ
brj-23253	355	3	prediction	prediction	NOUN
brj-23253	355	4	and	and	CCONJ
brj-23253	355	5	visualization	visualization	NOUN
brj-23253	355	6	of	of	ADP
brj-23253	355	7	moisture	moisture	NOUN
brj-23253	355	8	content	content	NOUN
brj-23253	355	9	in	in	ADP
brj-23253	355	10	single	single	ADJ
brj-23253	355	11	cucumber	cucumber	NOUN
brj-23253	355	12	(	(	PUNCT
brj-23253	355	13	cucumis	cucumis	PROPN
brj-23253	355	14	sativus	sativus	PROPN
brj-23253	355	15	l.	l.	PROPN
brj-23253	355	16	)	)	PUNCT
brj-23253	355	17	seed	seed	NOUN
brj-23253	355	18	using	use	VERB
brj-23253	355	19	hyperspectral	hyperspectral	ADJ
brj-23253	355	20	imaging	imaging	NOUN
brj-23253	355	21	technology	technology	NOUN
brj-23253	355	22	,	,	PUNCT
brj-23253	355	23	”	"	PUNCT
brj-23253	355	24	infrared	infrared	PROPN
brj-23253	355	25	physics	physics	PROPN
brj-23253	355	26	technol	technol	NOUN
brj-23253	355	27	.	.	PROPN
brj-23253	355	28	102	102	NUM
brj-23253	355	29	,	,	PUNCT
brj-23253	355	30	103034	103034	NUM
brj-23253	355	31	.	.	PUNCT
brj-23253	356	1	doi	doi	NOUN
brj-23253	356	2	:	:	PUNCT
brj-23253	356	3	10.1016	10.1016	NUM
brj-23253	356	4	/	/	SYM
brj-23253	356	5	j.infrared.2019.103034	j.infrared.2019.103034	PROPN
brj-23253	356	6	yang	yang	PROPN
brj-23253	356	7	,	,	PUNCT
brj-23253	356	8	j.	j.	PROPN
brj-23253	356	9	,	,	PUNCT
brj-23253	356	10	du	du	PROPN
brj-23253	356	11	,	,	PUNCT
brj-23253	356	12	l.	l.	PROPN
brj-23253	356	13	,	,	PUNCT
brj-23253	356	14	gong	gong	PROPN
brj-23253	356	15	,	,	PUNCT
brj-23253	356	16	w.	w.	PROPN
brj-23253	356	17	,	,	PUNCT
brj-23253	356	18	shi	shi	PROPN
brj-23253	356	19	,	,	PUNCT
brj-23253	356	20	s.	s.	PROPN
brj-23253	356	21	,	,	PUNCT
brj-23253	356	22	and	and	CCONJ
brj-23253	356	23	chen	chen	PROPN
brj-23253	356	24	,	,	PUNCT
brj-23253	356	25	b.	b.	PROPN
brj-23253	356	26	(	(	PUNCT
brj-23253	356	27	2019	2019	NUM
brj-23253	356	28	)	)	PUNCT
brj-23253	356	29	.	.	PUNCT
brj-23253	357	1	“	"	PUNCT
brj-23253	357	2	analyzing	analyze	VERB
brj-23253	357	3	the	the	DET
brj-23253	357	4	performance	performance	NOUN
brj-23253	357	5	of	of	ADP
brj-23253	357	6	the	the	DET
brj-23253	357	7	first	first	ADJ
brj-23253	357	8	-	-	PUNCT
brj-23253	357	9	derivative	derivative	ADJ
brj-23253	357	10	fluorescence	fluorescence	NOUN
brj-23253	357	11	spectrum	spectrum	NOUN
brj-23253	357	12	for	for	ADP
brj-23253	357	13	estimating	estimate	VERB
brj-23253	357	14	leaf	leaf	NOUN
brj-23253	357	15	nitrogen	nitrogen	NOUN
brj-23253	357	16	concentration	concentration	NOUN
brj-23253	357	17	,	,	PUNCT
brj-23253	357	18	”	"	PUNCT
brj-23253	357	19	optics	optic	NOUN
brj-23253	357	20	express	express	VERB
brj-23253	357	21	27(4	27(4	PROPN
brj-23253	357	22	)	)	PUNCT
brj-23253	357	23	,	,	PUNCT
brj-23253	357	24	article	article	NOUN
brj-23253	357	25	3978	3978	NUM
brj-23253	357	26	.	.	PUNCT
brj-23253	358	1	doi	doi	NOUN
brj-23253	358	2	:	:	PUNCT
brj-23253	358	3	10.1364	10.1364	NUM
brj-23253	358	4	/	/	SYM
brj-23253	358	5	oe.27.003978	oe.27.003978	NOUN
brj-23253	358	6	ye	ye	NOUN
brj-23253	358	7	,	,	PUNCT
brj-23253	358	8	q.	q.	PROPN
brj-23253	358	9	,	,	PUNCT
brj-23253	358	10	zhu	zhu	PROPN
brj-23253	358	11	,	,	PUNCT
brj-23253	358	12	f.	f.	PROPN
brj-23253	358	13	,	,	PUNCT
brj-23253	358	14	sun	sun	PROPN
brj-23253	358	15	,	,	PUNCT
brj-23253	358	16	f.	f.	PROPN
brj-23253	358	17	,	,	PUNCT
brj-23253	358	18	wang	wang	PROPN
brj-23253	358	19	,	,	PUNCT
brj-23253	358	20	t.	t.	PROPN
brj-23253	358	21	c.	c.	PROPN
brj-23253	358	22	,	,	PUNCT
brj-23253	358	23	wu	wu	PROPN
brj-23253	358	24	,	,	PUNCT
brj-23253	358	25	j.	j.	PROPN
brj-23253	358	26	,	,	PUNCT
brj-23253	358	27	liu	liu	PROPN
brj-23253	358	28	,	,	PUNCT
brj-23253	358	29	p.	p.	PROPN
brj-23253	358	30	,	,	PUNCT
brj-23253	358	31	shen	shen	PROPN
brj-23253	358	32	,	,	PUNCT
brj-23253	358	33	c.	c.	PROPN
brj-23253	358	34	,	,	PUNCT
brj-23253	358	35	dong	dong	PROPN
brj-23253	358	36	,	,	PUNCT
brj-23253	358	37	j.	j.	PROPN
brj-23253	358	38	,	,	PUNCT
brj-23253	358	39	and	and	CCONJ
brj-23253	358	40	wang	wang	PROPN
brj-23253	358	41	.	.	PUNCT
brj-23253	359	1	t.	t.	PROPN
brj-23253	359	2	(	(	PUNCT
brj-23253	359	3	2022	2022	NUM
brj-23253	359	4	)	)	PUNCT
brj-23253	359	5	.	.	PUNCT
brj-23253	360	1	“	"	PUNCT
brj-23253	360	2	differentiation	differentiation	NOUN
brj-23253	360	3	trajectories	trajectory	NOUN
brj-23253	360	4	and	and	CCONJ
brj-23253	360	5	biofunctions	biofunction	NOUN
brj-23253	360	6	of	of	ADP
brj-23253	360	7	symbiotic	symbiotic	ADJ
brj-23253	360	8	and	and	CCONJ
brj-23253	360	9	un	un	ADJ
brj-23253	360	10	-	-	ADJ
brj-23253	360	11	symbiotic	symbiotic	ADJ
brj-23253	360	12	fate	fate	NOUN
brj-23253	360	13	cells	cell	NOUN
brj-23253	360	14	in	in	ADP
brj-23253	360	15	root	root	NOUN
brj-23253	360	16	nodules	nodule	NOUN
brj-23253	360	17	of	of	ADP
brj-23253	360	18	medicago	medicago	PROPN
brj-23253	360	19	truncatula	truncatula	NOUN
brj-23253	360	20	,	,	PUNCT
brj-23253	360	21	”	"	PUNCT
brj-23253	360	22	molecular	molecular	ADJ
brj-23253	360	23	plant	plant	NOUN
brj-23253	360	24	.	.	PUNCT
brj-23253	361	1	15(12	15(12	NUM
brj-23253	361	2	)	)	PUNCT
brj-23253	361	3	,	,	PUNCT
brj-23253	361	4	18521867	18521867	NUM
brj-23253	361	5	.	.	PUNCT
brj-23253	362	1	doi	doi	NOUN
brj-23253	362	2	:	:	PUNCT
brj-23253	362	3	10.1016	10.1016	NUM
brj-23253	362	4	/	/	SYM
brj-23253	362	5	j.molp.2022.10.019	j.molp.2022.10.019	PROPN
brj-23253	362	6	yu	yu	PROPN
brj-23253	362	7	,	,	PUNCT
brj-23253	362	8	l.	l.	PROPN
brj-23253	362	9	,	,	PUNCT
brj-23253	362	10	zhang	zhang	PROPN
brj-23253	362	11	,	,	PUNCT
brj-23253	362	12	t.	t.	PROPN
brj-23253	362	13	,	,	PUNCT
brj-23253	362	14	zhu	zhu	PROPN
brj-23253	362	15	,	,	PUNCT
brj-23253	362	16	y.	y.	PROPN
brj-23253	362	17	,	,	PUNCT
brj-23253	362	18	zhou	zhou	PROPN
brj-23253	362	19	,	,	PUNCT
brj-23253	362	20	y.	y.	PROPN
brj-23253	362	21	,	,	PUNCT
brj-23253	362	22	xia	xia	PROPN
brj-23253	362	23	,	,	PUNCT
brj-23253	362	24	t.	t.	PROPN
brj-23253	362	25	,	,	PUNCT
brj-23253	362	26	and	and	CCONJ
brj-23253	362	27	nie	nie	PROPN
brj-23253	362	28	,	,	PUNCT
brj-23253	362	29	y.	y.	PROPN
brj-23253	362	30	(	(	PUNCT
brj-23253	362	31	2018	2018	NUM
brj-23253	362	32	)	)	PUNCT
brj-23253	362	33	.	.	PUNCT
brj-23253	363	1	“	"	PUNCT
brj-23253	363	2	estimation	estimation	NOUN
brj-23253	363	3	of	of	ADP
brj-23253	363	4	spad	spad	NOUN
brj-23253	363	5	values	value	NOUN
brj-23253	363	6	in	in	ADP
brj-23253	363	7	soybean	soybean	NOUN
brj-23253	363	8	leaves	leave	NOUN
brj-23253	363	9	using	use	VERB
brj-23253	363	10	iriv	iriv	ADJ
brj-23253	363	11	algorithm	algorithm	NOUN
brj-23253	363	12	for	for	ADP
brj-23253	363	13	wavelength	wavelength	NOUN
brj-23253	363	14	variable	variable	ADJ
brj-23253	363	15	selection	selection	NOUN
brj-23253	363	16	based	base	VERB
brj-23253	363	17	on	on	ADP
brj-23253	363	18	hyperspectral	hyperspectral	ADJ
brj-23253	363	19	data	datum	NOUN
brj-23253	363	20	,	,	PUNCT
brj-23253	363	21	”	"	PUNCT
brj-23253	363	22	transactions	transaction	NOUN
brj-23253	363	23	of	of	ADP
brj-23253	363	24	the	the	DET
brj-23253	363	25	chinese	chinese	ADJ
brj-23253	363	26	society	society	NOUN
brj-23253	363	27	of	of	ADP
brj-23253	363	28	agricultural	agricultural	ADJ
brj-23253	363	29	engineering	engineering	NOUN
brj-23253	363	30	34(16	34(16	NUM
brj-23253	363	31	)	)	PUNCT
brj-23253	363	32	,	,	PUNCT
brj-23253	363	33	7	7	X
brj-23253	363	34	.	.	X
brj-23253	363	35	doi	doi	NOUN
brj-23253	363	36	:	:	PUNCT
brj-23253	363	37	10.11975	10.11975	NUM
brj-23253	363	38	/	/	SYM
brj-23253	363	39	j.issn.1002	j.issn.1002	ADV
brj-23253	363	40	-	-	PUNCT
brj-23253	363	41	6819.2018.16.019	6819.2018.16.019	NUM
brj-23253	363	42	rego	rego	NOUN
brj-23253	363	43	,	,	PUNCT
brj-23253	363	44	g.	g.	PROPN
brj-23253	363	45	,	,	PUNCT
brj-23253	363	46	ferrero	ferrero	PROPN
brj-23253	363	47	,	,	PUNCT
brj-23253	363	48	f.	f.	PROPN
brj-23253	363	49	,	,	PUNCT
brj-23253	363	50	valledor	valledor	PROPN
brj-23253	363	51	,	,	PUNCT
brj-23253	363	52	m.	m.	NOUN
brj-23253	363	53	,	,	PUNCT
brj-23253	363	54	campo	campo	PROPN
brj-23253	363	55	,	,	PUNCT
brj-23253	363	56	j.	j.	PROPN
brj-23253	363	57	c.	c.	PROPN
brj-23253	363	58	,	,	PUNCT
brj-23253	363	59	forcada	forcada	PROPN
brj-23253	363	60	,	,	PUNCT
brj-23253	363	61	s.	s.	PROPN
brj-23253	363	62	,	,	PUNCT
brj-23253	363	63	royo	royo	PROPN
brj-23253	363	64	,	,	PUNCT
brj-23253	363	65	l.	l.	PROPN
brj-23253	363	66	j.	j.	PROPN
brj-23253	363	67	,	,	PUNCT
brj-23253	363	68	and	and	CCONJ
brj-23253	363	69	soldado	soldado	ADJ
brj-23253	363	70	,	,	PUNCT
brj-23253	363	71	a.	a.	NOUN
brj-23253	363	72	(	(	PUNCT
brj-23253	363	73	2020	2020	NUM
brj-23253	363	74	)	)	PUNCT
brj-23253	363	75	.	.	PUNCT
brj-23253	364	1	“	"	PUNCT
brj-23253	364	2	a	a	DET
brj-23253	364	3	portable	portable	ADJ
brj-23253	364	4	iot	iot	ADJ
brj-23253	364	5	nir	nir	ADJ
brj-23253	364	6	spectroscopic	spectroscopic	NOUN
brj-23253	364	7	system	system	NOUN
brj-23253	364	8	to	to	PART
brj-23253	364	9	analyze	analyze	VERB
brj-23253	364	10	the	the	DET
brj-23253	364	11	quality	quality	NOUN
brj-23253	364	12	of	of	ADP
brj-23253	364	13	dairy	dairy	NOUN
brj-23253	364	14	farm	farm	NOUN
brj-23253	364	15	forage	forage	NOUN
brj-23253	364	16	,	,	PUNCT
brj-23253	364	17	”	"	PUNCT
brj-23253	364	18	computers	computer	NOUN
brj-23253	364	19	and	and	CCONJ
brj-23253	364	20	electronics	electronic	NOUN
brj-23253	364	21	in	in	ADP
brj-23253	364	22	agriculture	agriculture	NOUN
brj-23253	364	23	,	,	PUNCT
brj-23253	364	24	175	175	NUM
brj-23253	364	25	,	,	PUNCT
brj-23253	364	26	105578	105578	NUM
brj-23253	364	27	.	.	PUNCT
brj-23253	365	1	doi	doi	NOUN
brj-23253	365	2	:	:	PUNCT
brj-23253	365	3	10.1016	10.1016	NUM
brj-23253	365	4	/	/	SYM
brj-23253	365	5	j.compag.2020.105578	j.compag.2020.105578	PROPN
brj-23253	365	6	zhang	zhang	PROPN
brj-23253	365	7	,	,	PUNCT
brj-23253	365	8	j.	j.	PROPN
brj-23253	365	9	,	,	PUNCT
brj-23253	365	10	guo	guo	PROPN
brj-23253	365	11	,	,	PUNCT
brj-23253	365	12	z.	z.	PROPN
brj-23253	365	13	,	,	PUNCT
brj-23253	365	14	ren	ren	PROPN
brj-23253	365	15	,	,	PUNCT
brj-23253	365	16	z.	z.	PROPN
brj-23253	365	17	,	,	PUNCT
brj-23253	365	18	wang	wang	PROPN
brj-23253	365	19	,	,	PUNCT
brj-23253	365	20	s.	s.	PROPN
brj-23253	365	21	,	,	PUNCT
brj-23253	365	22	yue	yue	PROPN
brj-23253	365	23	,	,	PUNCT
brj-23253	365	24	m.	m.	NOUN
brj-23253	365	25	,	,	PUNCT
brj-23253	365	26	zhang	zhang	PROPN
brj-23253	365	27	,	,	PUNCT
brj-23253	365	28	s.	s.	PROPN
brj-23253	365	29	,	,	PUNCT
brj-23253	365	30	yin	yin	PROPN
brj-23253	365	31	,	,	PUNCT
brj-23253	365	32	x.	x.	PROPN
brj-23253	365	33	,	,	PUNCT
brj-23253	365	34	du	du	PROPN
brj-23253	365	35	,	,	PUNCT
brj-23253	365	36	j.	j.	PROPN
brj-23253	365	37	,	,	PUNCT
brj-23253	365	38	and	and	CCONJ
brj-23253	365	39	ma	ma	PROPN
brj-23253	365	40	,	,	PUNCT
brj-23253	365	41	c.	c.	PROPN
brj-23253	365	42	(	(	PUNCT
brj-23253	365	43	2023	2023	NUM
brj-23253	365	44	)	)	PUNCT
brj-23253	365	45	.	.	PUNCT
brj-23253	366	1	“	"	PUNCT
brj-23253	366	2	variable	variable	ADJ
brj-23253	366	3	selection	selection	NOUN
brj-23253	366	4	methods	method	NOUN
brj-23253	366	5	to	to	PART
brj-23253	366	6	determine	determine	VERB
brj-23253	366	7	protein	protein	NOUN
brj-23253	366	8	content	content	NOUN
brj-23253	366	9	in	in	ADP
brj-23253	366	10	paddy	paddy	NOUN
brj-23253	366	11	using	use	VERB
brj-23253	366	12	nearinfrared	nearinfrare	VERB
brj-23253	366	13	hyperspectral	hyperspectral	ADJ
brj-23253	366	14	imaging	imaging	NOUN
brj-23253	366	15	,	,	PUNCT
brj-23253	366	16	”	"	PUNCT
brj-23253	366	17	journal	journal	NOUN
brj-23253	366	18	of	of	ADP
brj-23253	366	19	food	food	NOUN
brj-23253	366	20	measurement	measurement	NOUN
brj-23253	366	21	and	and	CCONJ
brj-23253	366	22	characterization	characterization	NOUN
brj-23253	366	23	17	17	NUM
brj-23253	366	24	,	,	PUNCT
brj-23253	366	25	4506	4506	NUM
brj-23253	366	26	-	-	SYM
brj-23253	366	27	4519	4519	NUM
brj-23253	366	28	.	.	PUNCT
brj-23253	367	1	doi	doi	NOUN
brj-23253	367	2	:	:	PUNCT
brj-23253	367	3	10.1007	10.1007	NUM
brj-23253	367	4	/	/	SYM
brj-23253	367	5	s11694	s11694	PROPN
brj-23253	367	6	-	-	PUNCT
brj-23253	367	7	023	023	NUM
brj-23253	367	8	-	-	PUNCT
brj-23253	367	9	01964	01964	NUM
brj-23253	367	10	-	-	PUNCT
brj-23253	367	11	y	y	PROPN
brj-23253	367	12	zhang	zhang	PROPN
brj-23253	367	13	,	,	PUNCT
brj-23253	367	14	m.	m.	PROPN
brj-23253	367	15	s.	s.	PROPN
brj-23253	367	16	,	,	PUNCT
brj-23253	367	17	zhang	zhang	PROPN
brj-23253	367	18	,	,	PUNCT
brj-23253	367	19	b.	b.	PROPN
brj-23253	367	20	,	,	PUNCT
brj-23253	367	21	li	li	PROPN
brj-23253	367	22	,	,	PUNCT
brj-23253	367	23	h.	h.	PROPN
brj-23253	367	24	,	,	PUNCT
brj-23253	367	25	shen	shen	PROPN
brj-23253	367	26	,	,	PUNCT
brj-23253	367	27	m.	m.	PROPN
brj-23253	367	28	s.	s.	PROPN
brj-23253	367	29	,	,	PUNCT
brj-23253	367	30	tian	tian	PROPN
brj-23253	367	31	,	,	PUNCT
brj-23253	367	32	s.	s.	PROPN
brj-23253	367	33	j.	j.	PROPN
brj-23253	367	34	,	,	PUNCT
brj-23253	367	35	zhang	zhang	PROPN
brj-23253	367	36	,	,	PUNCT
brj-23253	367	37	h.	h.	PROPN
brj-23253	367	38	h.	h.	PROPN
brj-23253	367	39	,	,	PUNCT
brj-23253	367	40	ren	ren	PROPN
brj-23253	367	41	,	,	PUNCT
brj-23253	367	42	x.	x.	PROPN
brj-23253	367	43	l.	l.	PROPN
brj-23253	367	44	,	,	PUNCT
brj-23253	367	45	xing	xing	PROPN
brj-23253	367	46	,	,	PUNCT
brj-23253	367	47	l.	l.	PROPN
brj-23253	367	48	b.	b.	PROPN
brj-23253	367	49	,	,	PUNCT
brj-23253	367	50	and	and	CCONJ
brj-23253	367	51	zhao	zhao	PROPN
brj-23253	367	52	,	,	PUNCT
brj-23253	367	53	j.	j.	PROPN
brj-23253	367	54	(	(	PUNCT
brj-23253	367	55	2020	2020	NUM
brj-23253	367	56	)	)	PUNCT
brj-23253	367	57	.	.	PUNCT
brj-23253	368	1	“	"	PUNCT
brj-23253	368	2	determination	determination	NOUN
brj-23253	368	3	of	of	ADP
brj-23253	368	4	bagged	bagged	ADJ
brj-23253	368	5	'	'	PUNCT
brj-23253	368	6	fuji	fuji	PROPN
brj-23253	368	7	'	'	PUNCT
brj-23253	368	8	apple	apple	NOUN
brj-23253	368	9	maturity	maturity	NOUN
brj-23253	368	10	by	by	ADP
brj-23253	368	11	visible	visible	ADJ
brj-23253	368	12	and	and	CCONJ
brj-23253	368	13	near	near	ADV
brj-23253	368	14	-	-	PUNCT
brj-23253	368	15	infrared	infrared	ADJ
brj-23253	368	16	spectroscopy	spectroscopy	NOUN
brj-23253	368	17	combined	combine	VERB
brj-23253	368	18	with	with	ADP
brj-23253	368	19	a	a	DET
brj-23253	368	20	machine	machine	NOUN
brj-23253	368	21	learning	learn	VERB
brj-23253	368	22	algorithm	algorithm	NOUN
brj-23253	368	23	,	,	PUNCT
brj-23253	368	24	”	"	PUNCT
brj-23253	368	25	infrared	infrared	PROPN
brj-23253	368	26	physics	physics	PROPN
brj-23253	368	27	&	&	CCONJ
brj-23253	368	28	technology	technology	PROPN
brj-23253	368	29	111	111	NUM
brj-23253	368	30	.	.	PUNCT
brj-23253	369	1	doi	doi	NOUN
brj-23253	369	2	:	:	PUNCT
brj-23253	369	3	10.1016	10.1016	NUM
brj-23253	369	4	/	/	SYM
brj-23253	369	5	j.infrared.2020.103529	j.infrared.2020.103529	NOUN
brj-23253	369	6	article	article	NOUN
brj-23253	369	7	submitted	submit	VERB
brj-23253	369	8	:	:	PUNCT
brj-23253	369	9	january	january	PROPN
brj-23253	369	10	8	8	NUM
brj-23253	369	11	,	,	PUNCT
brj-23253	369	12	2024	2024	NUM
brj-23253	369	13	;	;	PUNCT
brj-23253	369	14	peer	peer	NOUN
brj-23253	369	15	review	review	NOUN
brj-23253	369	16	completed	complete	VERB
brj-23253	369	17	:	:	PUNCT
brj-23253	369	18	march	march	PROPN
brj-23253	369	19	9	9	NUM
brj-23253	369	20	,	,	PUNCT
brj-23253	369	21	2024	2024	NUM
brj-23253	369	22	;	;	PUNCT
brj-23253	369	23	revised	revise	VERB
brj-23253	369	24	version	version	NOUN
brj-23253	369	25	received	receive	VERB
brj-23253	369	26	:	:	PUNCT
brj-23253	369	27	march	march	PROPN
brj-23253	369	28	24	24	NUM
brj-23253	369	29	,	,	PUNCT
brj-23253	369	30	2024	2024	NUM
brj-23253	369	31	;	;	PUNCT
brj-23253	369	32	accepted	accept	VERB
brj-23253	369	33	:	:	PUNCT
brj-23253	369	34	march	march	PROPN
brj-23253	369	35	30	30	NUM
brj-23253	369	36	,	,	PUNCT
brj-23253	369	37	2024	2024	NUM
brj-23253	369	38	;	;	PUNCT
brj-23253	369	39	published	publish	VERB
brj-23253	369	40	:	:	PUNCT
brj-23253	369	41	april	april	PROPN
brj-23253	369	42	26	26	NUM
brj-23253	369	43	,	,	PUNCT
brj-23253	369	44	2024	2024	NUM
brj-23253	369	45	.	.	PUNCT
brj-23253	370	1	doi	doi	NOUN
brj-23253	370	2	:	:	PUNCT
brj-23253	370	3	10.15376	10.15376	NUM
brj-23253	370	4	/	/	SYM
brj-23253	370	5	biores.19.2.3808	biores.19.2.3808	NOUN
brj-23253	370	6	-	-	PUNCT
brj-23253	370	7	3825	3825	NUM