id	sid	tid	token	lemma	pos
brj-23498	1	1	peer	peer	NOUN
brj-23498	1	2	-	-	PUNCT
brj-23498	1	3	review	review	NOUN
brj-23498	1	4	article	article	NOUN
brj-23498	1	5	peer	peer	NOUN
brj-23498	1	6	-	-	PUNCT
brj-23498	1	7	reviewed	review	VERB
brj-23498	1	8	article	article	NOUN
brj-23498	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	1	10	yang	yang	PROPN
brj-23498	1	11	et	et	PROPN
brj-23498	1	12	al	al	PROPN
brj-23498	1	13	.	.	PROPN
brj-23498	2	1	(	(	PUNCT
brj-23498	2	2	2024	2024	NUM
brj-23498	2	3	)	)	PUNCT
brj-23498	2	4	.	.	PUNCT
brj-23498	3	1	“	"	PUNCT
brj-23498	3	2	alfalfa	alfalfa	NOUN
brj-23498	3	3	quality	quality	NOUN
brj-23498	3	4	assessment	assessment	NOUN
brj-23498	3	5	,	,	PUNCT
brj-23498	3	6	”	"	PUNCT
brj-23498	3	7	bioresources	bioresource	NOUN
brj-23498	3	8	19(3	19(3	NUM
brj-23498	3	9	)	)	PUNCT
brj-23498	3	10	,	,	PUNCT
brj-23498	3	11	4531	4531	NUM
brj-23498	3	12	-	-	SYM
brj-23498	3	13	4546	4546	NUM
brj-23498	3	14	.	.	PUNCT
brj-23498	4	1	4531	4531	NUM
brj-23498	4	2	quality	quality	NOUN
brj-23498	4	3	detection	detection	NOUN
brj-23498	4	4	of	of	ADP
brj-23498	4	5	alfalfa	alfalfa	PROPN
brj-23498	4	6	hay	hay	NOUN
brj-23498	4	7	based	base	VERB
brj-23498	4	8	on	on	ADP
brj-23498	4	9	multisource	multisource	ADJ
brj-23498	4	10	information	information	NOUN
brj-23498	4	11	fusion	fusion	NOUN
brj-23498	4	12	:	:	PUNCT
brj-23498	4	13	a	a	DET
brj-23498	4	14	preliminary	preliminary	ADJ
brj-23498	4	15	study	study	NOUN
brj-23498	4	16	huihe	huihe	PROPN
brj-23498	4	17	yang	yang	PROPN
brj-23498	4	18	,	,	PUNCT
brj-23498	4	19	a	a	PRON
brj-23498	4	20	,	,	PUNCT
brj-23498	4	21	b	b	NOUN
brj-23498	4	22	,	,	PUNCT
brj-23498	4	23	#	#	PROPN
brj-23498	4	24	jie	jie	PROPN
brj-23498	4	25	li	li	PROPN
brj-23498	4	26	,	,	PUNCT
brj-23498	4	27	a	a	PRON
brj-23498	4	28	,	,	PUNCT
brj-23498	4	29	b	b	NOUN
brj-23498	4	30	,	,	PUNCT
brj-23498	4	31	#	#	PROPN
brj-23498	4	32	guifang	guifang	PROPN
brj-23498	4	33	wu	wu	PROPN
brj-23498	4	34	,	,	PUNCT
brj-23498	4	35	a	a	DET
brj-23498	4	36	,	,	PUNCT
brj-23498	4	37	b	b	NOUN
brj-23498	4	38	,	,	PUNCT
brj-23498	4	39	*	*	PUNCT
brj-23498	4	40	xuehong	xuehong	PROPN
brj-23498	4	41	de	de	PROPN
brj-23498	4	42	,	,	PUNCT
brj-23498	4	43	a	a	DET
brj-23498	4	44	,	,	PUNCT
brj-23498	4	45	b	b	NOUN
brj-23498	4	46	,	,	PUNCT
brj-23498	4	47	*	*	PROPN
brj-23498	4	48	yong	yong	PROPN
brj-23498	4	49	zhang	zhang	PROPN
brj-23498	4	50	,	,	PUNCT
brj-23498	4	51	a	a	PRON
brj-23498	4	52	,	,	PUNCT
brj-23498	4	53	b	b	PROPN
brj-23498	4	54	fang	fang	X
brj-23498	4	55	guo	guo	PROPN
brj-23498	4	56	,	,	PUNCT
brj-23498	4	57	a	a	PRON
brj-23498	4	58	,	,	PUNCT
brj-23498	4	59	b	b	PROPN
brj-23498	4	60	shubin	shubin	PROPN
brj-23498	4	61	yan	yan	PROPN
brj-23498	4	62	,	,	PUNCT
brj-23498	4	63	a	a	DET
brj-23498	4	64	,	,	PUNCT
brj-23498	4	65	b	b	PROPN
brj-23498	4	66	xiangping	xiangping	NOUN
brj-23498	4	67	bai	bai	PROPN
brj-23498	4	68	,	,	PUNCT
brj-23498	4	69	c	c	PROPN
brj-23498	4	70	haowen	haowen	PROPN
brj-23498	4	71	xiao	xiao	PROPN
brj-23498	4	72	,	,	PUNCT
brj-23498	4	73	c	c	PROPN
brj-23498	4	74	and	and	CCONJ
brj-23498	4	75	yang	yang	PROPN
brj-23498	4	76	cao	cao	PROPN
brj-23498	5	1	c	c	PROPN
brj-23498	5	2	the	the	DET
brj-23498	5	3	quality	quality	NOUN
brj-23498	5	4	detection	detection	NOUN
brj-23498	5	5	of	of	ADP
brj-23498	5	6	alfalfa	alfalfa	NOUN
brj-23498	5	7	hay	hay	NOUN
brj-23498	5	8	is	be	AUX
brj-23498	5	9	crucial	crucial	ADJ
brj-23498	5	10	for	for	ADP
brj-23498	5	11	the	the	DET
brj-23498	5	12	development	development	NOUN
brj-23498	5	13	of	of	ADP
brj-23498	5	14	animal	animal	NOUN
brj-23498	5	15	husbandry	husbandry	NOUN
brj-23498	5	16	.	.	PUNCT
brj-23498	6	1	in	in	ADP
brj-23498	6	2	this	this	DET
brj-23498	6	3	study	study	NOUN
brj-23498	6	4	,	,	PUNCT
brj-23498	6	5	a	a	DET
brj-23498	6	6	method	method	NOUN
brj-23498	6	7	for	for	ADP
brj-23498	6	8	quality	quality	NOUN
brj-23498	6	9	detection	detection	NOUN
brj-23498	6	10	of	of	ADP
brj-23498	6	11	alfalfa	alfalfa	PROPN
brj-23498	6	12	hay	hay	NOUN
brj-23498	6	13	based	base	VERB
brj-23498	6	14	on	on	ADP
brj-23498	6	15	the	the	DET
brj-23498	6	16	fusion	fusion	NOUN
brj-23498	6	17	of	of	ADP
brj-23498	6	18	multisource	multisource	ADJ
brj-23498	6	19	information	information	NOUN
brj-23498	6	20	including	include	VERB
brj-23498	6	21	near	near	ADV
brj-23498	6	22	-	-	PUNCT
brj-23498	6	23	infrared	infrared	ADJ
brj-23498	6	24	spectroscopy	spectroscopy	NOUN
brj-23498	6	25	,	,	PUNCT
brj-23498	6	26	image	image	NOUN
brj-23498	6	27	processing	processing	NOUN
brj-23498	6	28	techniques	technique	NOUN
brj-23498	6	29	,	,	PUNCT
brj-23498	6	30	and	and	CCONJ
brj-23498	6	31	electronic	electronic	ADJ
brj-23498	6	32	nose	nose	NOUN
brj-23498	6	33	is	be	AUX
brj-23498	6	34	proposed	propose	VERB
brj-23498	6	35	.	.	PUNCT
brj-23498	7	1	after	after	ADP
brj-23498	7	2	sg	sg	ADP
brj-23498	7	3	convolution	convolution	NOUN
brj-23498	7	4	smoothing	smoothing	NOUN
brj-23498	7	5	,	,	PUNCT
brj-23498	7	6	feature	feature	NOUN
brj-23498	7	7	wavelengths	wavelength	NOUN
brj-23498	7	8	were	be	AUX
brj-23498	7	9	extracted	extract	VERB
brj-23498	7	10	using	use	VERB
brj-23498	7	11	competitive	competitive	ADJ
brj-23498	7	12	adaptive	adaptive	ADJ
brj-23498	7	13	re	re	ADJ
brj-23498	7	14	-	-	ADJ
brj-23498	7	15	weighting	weight	VERB
brj-23498	7	16	scheme	scheme	NOUN
brj-23498	7	17	and	and	CCONJ
brj-23498	7	18	successive	successive	ADJ
brj-23498	7	19	projections	projection	NOUN
brj-23498	7	20	algorithm	algorithm	NOUN
brj-23498	7	21	from	from	ADP
brj-23498	7	22	the	the	DET
brj-23498	7	23	spectral	spectral	ADJ
brj-23498	7	24	data	datum	NOUN
brj-23498	7	25	.	.	PUNCT
brj-23498	8	1	the	the	DET
brj-23498	8	2	image	image	NOUN
brj-23498	8	3	data	datum	NOUN
brj-23498	8	4	were	be	AUX
brj-23498	8	5	denoised	denoise	VERB
brj-23498	8	6	using	use	VERB
brj-23498	8	7	adaptive	adaptive	ADJ
brj-23498	8	8	wavelet	wavelet	NOUN
brj-23498	8	9	thresholding	thresholding	NOUN
brj-23498	8	10	,	,	PUNCT
brj-23498	8	11	and	and	CCONJ
brj-23498	8	12	color	color	NOUN
brj-23498	8	13	and	and	CCONJ
brj-23498	8	14	texture	texture	ADJ
brj-23498	8	15	features	feature	NOUN
brj-23498	8	16	were	be	AUX
brj-23498	8	17	extracted	extract	VERB
brj-23498	8	18	using	use	VERB
brj-23498	8	19	color	color	NOUN
brj-23498	8	20	histograms	histogram	NOUN
brj-23498	8	21	and	and	CCONJ
brj-23498	8	22	random	random	ADJ
brj-23498	8	23	forest	forest	NOUN
brj-23498	8	24	algorithms	algorithm	NOUN
brj-23498	8	25	,	,	PUNCT
brj-23498	8	26	respectively	respectively	ADV
brj-23498	8	27	.	.	PUNCT
brj-23498	9	1	electronic	electronic	ADJ
brj-23498	9	2	nose	nose	NOUN
brj-23498	9	3	data	datum	NOUN
brj-23498	9	4	using	use	VERB
brj-23498	9	5	principal	principal	ADJ
brj-23498	9	6	component	component	NOUN
brj-23498	9	7	analysis	analysis	NOUN
brj-23498	9	8	was	be	AUX
brj-23498	9	9	used	use	VERB
brj-23498	9	10	for	for	ADP
brj-23498	9	11	data	data	NOUN
brj-23498	9	12	dimensionality	dimensionality	NOUN
brj-23498	9	13	reduction	reduction	NOUN
brj-23498	9	14	.	.	PUNCT
brj-23498	10	1	support	support	NOUN
brj-23498	10	2	vector	vector	NOUN
brj-23498	10	3	machine	machine	NOUN
brj-23498	10	4	,	,	PUNCT
brj-23498	10	5	extreme	extreme	ADJ
brj-23498	10	6	learning	learning	NOUN
brj-23498	10	7	machine	machine	NOUN
brj-23498	10	8	,	,	PUNCT
brj-23498	10	9	and	and	CCONJ
brj-23498	10	10	multi	multi	ADJ
brj-23498	10	11	-	-	ADJ
brj-23498	10	12	layer	layer	ADJ
brj-23498	10	13	perceptron	perceptron	NOUN
brj-23498	10	14	were	be	AUX
brj-23498	10	15	employed	employ	VERB
brj-23498	10	16	to	to	PART
brj-23498	10	17	establish	establish	VERB
brj-23498	10	18	quality	quality	NOUN
brj-23498	10	19	detection	detection	NOUN
brj-23498	10	20	models	model	NOUN
brj-23498	10	21	of	of	ADP
brj-23498	10	22	alfalfa	alfalfa	NOUN
brj-23498	10	23	hay	hay	NOUN
brj-23498	10	24	based	base	VERB
brj-23498	10	25	on	on	ADP
brj-23498	10	26	spectroscopy	spectroscopy	NOUN
brj-23498	10	27	,	,	PUNCT
brj-23498	10	28	image	image	NOUN
brj-23498	10	29	,	,	PUNCT
brj-23498	10	30	gas	gas	NOUN
brj-23498	10	31	information	information	NOUN
brj-23498	10	32	,	,	PUNCT
brj-23498	10	33	and	and	CCONJ
brj-23498	10	34	their	their	PRON
brj-23498	10	35	combination	combination	NOUN
brj-23498	10	36	,	,	PUNCT
brj-23498	10	37	respectively	respectively	ADV
brj-23498	10	38	.	.	PUNCT
brj-23498	11	1	experimental	experimental	ADJ
brj-23498	11	2	results	result	NOUN
brj-23498	11	3	demonstrate	demonstrate	VERB
brj-23498	11	4	that	that	SCONJ
brj-23498	11	5	the	the	DET
brj-23498	11	6	fusion	fusion	NOUN
brj-23498	11	7	of	of	ADP
brj-23498	11	8	near	near	ADV
brj-23498	11	9	-	-	PUNCT
brj-23498	11	10	infrared	infrared	ADJ
brj-23498	11	11	spectroscopy	spectroscopy	NOUN
brj-23498	11	12	,	,	PUNCT
brj-23498	11	13	image	image	NOUN
brj-23498	11	14	data	datum	NOUN
brj-23498	11	15	,	,	PUNCT
brj-23498	11	16	and	and	CCONJ
brj-23498	11	17	gas	gas	NOUN
brj-23498	11	18	information	information	NOUN
brj-23498	11	19	effectively	effectively	ADV
brj-23498	11	20	enhances	enhance	VERB
brj-23498	11	21	the	the	DET
brj-23498	11	22	classification	classification	NOUN
brj-23498	11	23	accuracy	accuracy	NOUN
brj-23498	11	24	of	of	ADP
brj-23498	11	25	the	the	DET
brj-23498	11	26	model	model	NOUN
brj-23498	11	27	.	.	PUNCT
brj-23498	12	1	the	the	DET
brj-23498	12	2	accuracy	accuracy	NOUN
brj-23498	12	3	of	of	ADP
brj-23498	12	4	the	the	DET
brj-23498	12	5	test	test	NOUN
brj-23498	12	6	set	set	NOUN
brj-23498	12	7	reaches	reach	VERB
brj-23498	12	8	100	100	NUM
brj-23498	12	9	%	%	NOUN
brj-23498	12	10	,	,	PUNCT
brj-23498	12	11	with	with	ADP
brj-23498	12	12	root	root	NOUN
brj-23498	12	13	mean	mean	VERB
brj-23498	12	14	square	square	ADJ
brj-23498	12	15	error	error	NOUN
brj-23498	12	16	and	and	CCONJ
brj-23498	12	17	determination	determination	NOUN
brj-23498	12	18	coefficient	coefficient	NOUN
brj-23498	12	19	values	value	NOUN
brj-23498	12	20	of	of	ADP
brj-23498	12	21	0.1728	0.1728	NUM
brj-23498	12	22	and	and	CCONJ
brj-23498	12	23	0.9239	0.9239	NUM
brj-23498	12	24	,	,	PUNCT
brj-23498	12	25	respectively	respectively	ADV
brj-23498	12	26	,	,	PUNCT
brj-23498	12	27	surpassing	surpass	VERB
brj-23498	12	28	prediction	prediction	NOUN
brj-23498	12	29	models	model	NOUN
brj-23498	12	30	established	establish	VERB
brj-23498	12	31	solely	solely	ADV
brj-23498	12	32	on	on	ADP
brj-23498	12	33	individual	individual	ADJ
brj-23498	12	34	information	information	NOUN
brj-23498	12	35	.	.	PUNCT
brj-23498	13	1	this	this	DET
brj-23498	13	2	study	study	NOUN
brj-23498	13	3	provides	provide	VERB
brj-23498	13	4	new	new	ADJ
brj-23498	13	5	insights	insight	NOUN
brj-23498	13	6	into	into	ADP
brj-23498	13	7	alfalfa	alfalfa	NOUN
brj-23498	13	8	hay	hay	NOUN
brj-23498	13	9	quality	quality	NOUN
brj-23498	13	10	detection	detection	NOUN
brj-23498	13	11	.	.	PUNCT
brj-23498	14	1	doi	doi	NOUN
brj-23498	14	2	:	:	PUNCT
brj-23498	14	3	10.15376	10.15376	NUM
brj-23498	14	4	/	/	SYM
brj-23498	14	5	biores.19.3.4531	biores.19.3.4531	PROPN
brj-23498	14	6	-	-	PUNCT
brj-23498	14	7	4546	4546	NUM
brj-23498	14	8	keywords	keyword	NOUN
brj-23498	14	9	:	:	PUNCT
brj-23498	14	10	alfalfa	alfalfa	PROPN
brj-23498	14	11	hay	hay	PROPN
brj-23498	14	12	;	;	PUNCT
brj-23498	14	13	image	image	NOUN
brj-23498	14	14	processing	processing	NOUN
brj-23498	14	15	;	;	PUNCT
brj-23498	14	16	near	near	ADV
brj-23498	14	17	-	-	PUNCT
brj-23498	14	18	infrared	infrared	ADJ
brj-23498	14	19	spectroscopy	spectroscopy	NOUN
brj-23498	14	20	;	;	PUNCT
brj-23498	14	21	electronic	electronic	ADJ
brj-23498	14	22	nose	nose	NOUN
brj-23498	14	23	;	;	PUNCT
brj-23498	14	24	machine	machine	NOUN
brj-23498	14	25	learning	learn	VERB
brj-23498	14	26	contact	contact	NOUN
brj-23498	14	27	information	information	NOUN
brj-23498	14	28	:	:	PUNCT
brj-23498	14	29	a	a	X
brj-23498	14	30	:	:	PUNCT
brj-23498	14	31	college	college	NOUN
brj-23498	14	32	of	of	ADP
brj-23498	14	33	mechanical	mechanical	ADJ
brj-23498	14	34	&	&	CCONJ
brj-23498	14	35	electrical	electrical	ADJ
brj-23498	14	36	engineering	engineering	NOUN
brj-23498	14	37	,	,	PUNCT
brj-23498	14	38	inner	inner	PROPN
brj-23498	14	39	mongolia	mongolia	PROPN
brj-23498	14	40	agricultural	agricultural	PROPN
brj-23498	14	41	university	university	PROPN
brj-23498	14	42	,	,	PUNCT
brj-23498	14	43	hohhot	hohhot	ADJ
brj-23498	14	44	,	,	PUNCT
brj-23498	14	45	010018	010018	NUM
brj-23498	14	46	,	,	PUNCT
brj-23498	14	47	p.r	p.r	PROPN
brj-23498	14	48	.	.	PROPN
brj-23498	14	49	china	china	PROPN
brj-23498	14	50	;	;	PUNCT
brj-23498	14	51	b	b	X
brj-23498	14	52	:	:	PUNCT
brj-23498	14	53	inner	inner	PROPN
brj-23498	14	54	mongolia	mongolia	PROPN
brj-23498	14	55	engineering	engineering	PROPN
brj-23498	14	56	research	research	NOUN
brj-23498	14	57	center	center	NOUN
brj-23498	14	58	of	of	ADP
brj-23498	14	59	intelligent	intelligent	ADJ
brj-23498	14	60	equipment	equipment	NOUN
brj-23498	14	61	for	for	ADP
brj-23498	14	62	the	the	DET
brj-23498	14	63	entire	entire	ADJ
brj-23498	14	64	process	process	NOUN
brj-23498	14	65	of	of	ADP
brj-23498	14	66	forage	forage	NOUN
brj-23498	14	67	and	and	CCONJ
brj-23498	14	68	feed	feed	NOUN
brj-23498	14	69	production	production	NOUN
brj-23498	14	70	,	,	PUNCT
brj-23498	14	71	hohhot	hohhot	ADJ
brj-23498	14	72	,	,	PUNCT
brj-23498	14	73	010018	010018	NUM
brj-23498	14	74	,	,	PUNCT
brj-23498	14	75	p.r	p.r	PROPN
brj-23498	14	76	.	.	PROPN
brj-23498	14	77	china	china	PROPN
brj-23498	14	78	;	;	PUNCT
brj-23498	15	1	c	c	X
brj-23498	15	2	:	:	PUNCT
brj-23498	15	3	inner	inner	PROPN
brj-23498	15	4	mongolia	mongolia	PROPN
brj-23498	15	5	autonomous	autonomous	PROPN
brj-23498	15	6	region	region	PROPN
brj-23498	15	7	agricultural	agricultural	ADJ
brj-23498	15	8	and	and	CCONJ
brj-23498	15	9	pastoral	pastoral	ADJ
brj-23498	15	10	technology	technology	NOUN
brj-23498	15	11	extension	extension	NOUN
brj-23498	15	12	center	center	NOUN
brj-23498	15	13	,	,	PUNCT
brj-23498	15	14	hohhot	hohhot	ADJ
brj-23498	15	15	,	,	PUNCT
brj-23498	15	16	010010	010010	NUM
brj-23498	15	17	,	,	PUNCT
brj-23498	15	18	p.r	p.r	PROPN
brj-23498	15	19	.	.	PROPN
brj-23498	15	20	china	china	PROPN
brj-23498	15	21	;	;	PUNCT
brj-23498	15	22	huihe	huihe	PROPN
brj-23498	15	23	yang	yang	PROPN
brj-23498	15	24	and	and	CCONJ
brj-23498	15	25	jie	jie	PROPN
brj-23498	15	26	li	li	PROPN
brj-23498	15	27	contributed	contribute	VERB
brj-23498	15	28	to	to	ADP
brj-23498	15	29	the	the	DET
brj-23498	15	30	work	work	NOUN
brj-23498	15	31	equally	equally	ADV
brj-23498	15	32	and	and	CCONJ
brj-23498	15	33	should	should	AUX
brj-23498	15	34	be	be	AUX
brj-23498	15	35	regarded	regard	VERB
brj-23498	15	36	as	as	ADP
brj-23498	15	37	co	co	ADJ
brj-23498	15	38	-	-	ADJ
brj-23498	15	39	first	first	ADJ
brj-23498	15	40	authors	author	NOUN
brj-23498	15	41	;	;	PUNCT
brj-23498	15	42	*	*	PUNCT
brj-23498	15	43	corresponding	correspond	VERB
brj-23498	15	44	authors	author	NOUN
brj-23498	15	45	:	:	PUNCT
brj-23498	15	46	wgfsara@126.com	wgfsara@126.com	PROPN
brj-23498	15	47	and	and	CCONJ
brj-23498	15	48	dexuehong@126.com	dexuehong@126.com	PROPN
brj-23498	15	49	introduction	introduction	NOUN
brj-23498	15	50	purple	purple	ADJ
brj-23498	15	51	alfalfa	alfalfa	NOUN
brj-23498	15	52	,	,	PUNCT
brj-23498	15	53	originating	originate	VERB
brj-23498	15	54	from	from	ADP
brj-23498	15	55	persia	persia	PROPN
brj-23498	15	56	,	,	PUNCT
brj-23498	15	57	is	be	AUX
brj-23498	15	58	the	the	DET
brj-23498	15	59	most	most	ADV
brj-23498	15	60	widely	widely	ADV
brj-23498	15	61	distributed	distribute	VERB
brj-23498	15	62	and	and	CCONJ
brj-23498	15	63	oldest	old	ADJ
brj-23498	15	64	cultivated	cultivate	VERB
brj-23498	15	65	leguminous	leguminous	ADJ
brj-23498	15	66	forage	forage	NOUN
brj-23498	15	67	grass	grass	NOUN
brj-23498	15	68	in	in	ADP
brj-23498	15	69	the	the	DET
brj-23498	15	70	world	world	NOUN
brj-23498	15	71	,	,	PUNCT
brj-23498	15	72	often	often	ADV
brj-23498	15	73	referred	refer	VERB
brj-23498	15	74	to	to	ADP
brj-23498	15	75	as	as	ADP
brj-23498	15	76	the	the	DET
brj-23498	15	77	“	"	PUNCT
brj-23498	15	78	king	king	NOUN
brj-23498	15	79	of	of	ADP
brj-23498	15	80	forage	forage	NOUN
brj-23498	15	81	grass	grass	NOUN
brj-23498	15	82	”	"	PUNCT
brj-23498	15	83	.	.	PUNCT
brj-23498	16	1	it	it	PRON
brj-23498	16	2	contains	contain	VERB
brj-23498	16	3	not	not	PART
brj-23498	16	4	only	only	ADV
brj-23498	16	5	many	many	ADJ
brj-23498	16	6	important	important	ADJ
brj-23498	16	7	nutrients	nutrient	NOUN
brj-23498	16	8	such	such	ADJ
brj-23498	16	9	as	as	ADP
brj-23498	16	10	proteins	protein	NOUN
brj-23498	16	11	,	,	PUNCT
brj-23498	16	12	minerals	mineral	NOUN
brj-23498	16	13	,	,	PUNCT
brj-23498	16	14	and	and	CCONJ
brj-23498	16	15	vitamins	vitamin	NOUN
brj-23498	16	16	but	but	CCONJ
brj-23498	16	17	also	also	ADV
brj-23498	16	18	essential	essential	ADJ
brj-23498	16	19	amino	amino	ADJ
brj-23498	16	20	acids	acid	NOUN
brj-23498	16	21	,	,	PUNCT
brj-23498	16	22	trace	trace	NOUN
brj-23498	16	23	elements	element	NOUN
brj-23498	16	24	,	,	PUNCT
brj-23498	16	25	and	and	CCONJ
brj-23498	16	26	unidentified	unidentified	ADJ
brj-23498	16	27	growth	growth	NOUN
brj-23498	16	28	factors	factor	NOUN
brj-23498	16	29	required	require	VERB
brj-23498	16	30	by	by	ADP
brj-23498	16	31	animals	animal	NOUN
brj-23498	16	32	.	.	PUNCT
brj-23498	17	1	therefore	therefore	ADV
brj-23498	17	2	,	,	PUNCT
brj-23498	17	3	it	it	PRON
brj-23498	17	4	is	be	AUX
brj-23498	17	5	used	use	VERB
brj-23498	17	6	as	as	ADP
brj-23498	17	7	the	the	DET
brj-23498	17	8	main	main	ADJ
brj-23498	17	9	raw	raw	ADJ
brj-23498	17	10	material	material	NOUN
brj-23498	17	11	for	for	ADP
brj-23498	17	12	protein	protein	NOUN
brj-23498	17	13	extraction	extraction	NOUN
brj-23498	17	14	and	and	CCONJ
brj-23498	17	15	the	the	DET
brj-23498	17	16	primary	primary	ADJ
brj-23498	17	17	high	high	ADJ
brj-23498	17	18	-	-	PUNCT
brj-23498	17	19	quality	quality	NOUN
brj-23498	17	20	feed	feed	NOUN
brj-23498	17	21	for	for	ADP
brj-23498	17	22	animals	animal	NOUN
brj-23498	17	23	(	(	PUNCT
brj-23498	17	24	li	li	PROPN
brj-23498	17	25	et	et	PROPN
brj-23498	17	26	al	al	PROPN
brj-23498	17	27	.	.	PROPN
brj-23498	17	28	2023	2023	NUM
brj-23498	17	29	)	)	PUNCT
brj-23498	17	30	.	.	PUNCT
brj-23498	18	1	thus	thus	ADV
brj-23498	18	2	,	,	PUNCT
brj-23498	18	3	classifying	classify	VERB
brj-23498	18	4	alfalfa	alfalfa	NOUN
brj-23498	18	5	hay	hay	NOUN
brj-23498	18	6	of	of	ADP
brj-23498	18	7	different	different	ADJ
brj-23498	18	8	qualities	quality	NOUN
brj-23498	18	9	is	be	AUX
brj-23498	18	10	a	a	DET
brj-23498	18	11	very	very	ADV
brj-23498	18	12	important	important	ADJ
brj-23498	18	13	research	research	NOUN
brj-23498	18	14	task	task	NOUN
brj-23498	18	15	.	.	PUNCT
brj-23498	19	1	traditional	traditional	ADJ
brj-23498	19	2	evaluation	evaluation	NOUN
brj-23498	19	3	methods	method	NOUN
brj-23498	19	4	include	include	VERB
brj-23498	19	5	sensory	sensory	ADJ
brj-23498	19	6	evaluation	evaluation	NOUN
brj-23498	19	7	and	and	CCONJ
brj-23498	19	8	objective	objective	ADJ
brj-23498	19	9	measurements	measurement	NOUN
brj-23498	19	10	(	(	PUNCT
brj-23498	19	11	li	li	PROPN
brj-23498	19	12	et	et	PROPN
brj-23498	19	13	al	al	PROPN
brj-23498	19	14	.	.	PROPN
brj-23498	19	15	2020	2020	NUM
brj-23498	19	16	)	)	PUNCT
brj-23498	19	17	.	.	PUNCT
brj-23498	20	1	the	the	DET
brj-23498	20	2	former	former	ADJ
brj-23498	20	3	is	be	AUX
brj-23498	20	4	mainly	mainly	ADV
brj-23498	20	5	judged	judge	VERB
brj-23498	20	6	by	by	ADP
brj-23498	20	7	experienced	experienced	ADJ
brj-23498	20	8	personnel	personnel	NOUN
brj-23498	20	9	based	base	VERB
brj-23498	20	10	on	on	ADP
brj-23498	20	11	morphological	morphological	ADJ
brj-23498	20	12	appearance	appearance	NOUN
brj-23498	20	13	,	,	PUNCT
brj-23498	20	14	while	while	SCONJ
brj-23498	20	15	the	the	DET
brj-23498	20	16	latter	latter	NOUN
brj-23498	20	17	usually	usually	ADV
brj-23498	20	18	employs	employ	VERB
brj-23498	20	19	chemical	chemical	ADJ
brj-23498	20	20	and	and	CCONJ
brj-23498	20	21	biological	biological	ADJ
brj-23498	20	22	methods	method	NOUN
brj-23498	20	23	such	such	ADJ
brj-23498	20	24	as	as	ADP
brj-23498	20	25	protein	protein	NOUN
brj-23498	20	26	electrophoresis	electrophoresis	NOUN
brj-23498	20	27	,	,	PUNCT
brj-23498	20	28	mailto:wgfsara@126.com	mailto:wgfsara@126.com	X
brj-23498	20	29	peer	peer	NOUN
brj-23498	20	30	-	-	PUNCT
brj-23498	20	31	reviewed	review	VERB
brj-23498	20	32	article	article	NOUN
brj-23498	20	33	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	20	34	yang	yang	PROPN
brj-23498	20	35	et	et	PROPN
brj-23498	20	36	al	al	PROPN
brj-23498	20	37	.	.	PROPN
brj-23498	21	1	(	(	PUNCT
brj-23498	21	2	2024	2024	NUM
brj-23498	21	3	)	)	PUNCT
brj-23498	21	4	.	.	PUNCT
brj-23498	22	1	“	"	PUNCT
brj-23498	22	2	alfalfa	alfalfa	NOUN
brj-23498	22	3	quality	quality	NOUN
brj-23498	22	4	assessment	assessment	NOUN
brj-23498	22	5	,	,	PUNCT
brj-23498	22	6	”	"	PUNCT
brj-23498	22	7	bioresources	bioresource	NOUN
brj-23498	22	8	19(3	19(3	NUM
brj-23498	22	9	)	)	PUNCT
brj-23498	22	10	,	,	PUNCT
brj-23498	22	11	4531	4531	NUM
brj-23498	22	12	-	-	SYM
brj-23498	22	13	4546	4546	NUM
brj-23498	22	14	.	.	PUNCT
brj-23498	22	15	4532	4532	NUM
brj-23498	22	16	gas	gas	NOUN
brj-23498	22	17	chromatography	chromatography	NOUN
brj-23498	22	18	,	,	PUNCT
brj-23498	22	19	spectrophotometry	spectrophotometry	NOUN
brj-23498	22	20	,	,	PUNCT
brj-23498	22	21	and	and	CCONJ
brj-23498	22	22	high	high	ADJ
brj-23498	22	23	-	-	PUNCT
brj-23498	22	24	performance	performance	NOUN
brj-23498	22	25	liquid	liquid	ADJ
brj-23498	22	26	chromatography	chromatography	NOUN
brj-23498	22	27	to	to	PART
brj-23498	22	28	detect	detect	VERB
brj-23498	22	29	relevant	relevant	ADJ
brj-23498	22	30	compounds	compound	NOUN
brj-23498	22	31	or	or	CCONJ
brj-23498	22	32	specific	specific	ADJ
brj-23498	22	33	spoilage	spoilage	NOUN
brj-23498	22	34	quantities	quantity	NOUN
brj-23498	22	35	through	through	ADP
brj-23498	22	36	microbial	microbial	ADJ
brj-23498	22	37	analysis	analysis	NOUN
brj-23498	22	38	.	.	PUNCT
brj-23498	23	1	the	the	DET
brj-23498	23	2	former	former	ADJ
brj-23498	23	3	is	be	AUX
brj-23498	23	4	time	time	NOUN
brj-23498	23	5	-	-	PUNCT
brj-23498	23	6	consuming	consume	VERB
brj-23498	23	7	,	,	PUNCT
brj-23498	23	8	labor	labor	NOUN
brj-23498	23	9	-	-	PUNCT
brj-23498	23	10	intensive	intensive	ADJ
brj-23498	23	11	,	,	PUNCT
brj-23498	23	12	and	and	CCONJ
brj-23498	23	13	susceptible	susceptible	ADJ
brj-23498	23	14	to	to	ADP
brj-23498	23	15	subjective	subjective	ADJ
brj-23498	23	16	factors	factor	NOUN
brj-23498	23	17	,	,	PUNCT
brj-23498	23	18	while	while	SCONJ
brj-23498	23	19	the	the	DET
brj-23498	23	20	latter	latter	ADJ
brj-23498	23	21	methods	method	NOUN
brj-23498	23	22	are	be	AUX
brj-23498	23	23	more	more	ADV
brj-23498	23	24	accurate	accurate	ADJ
brj-23498	23	25	but	but	CCONJ
brj-23498	23	26	always	always	ADV
brj-23498	23	27	require	require	VERB
brj-23498	23	28	corresponding	corresponding	ADJ
brj-23498	23	29	professional	professional	ADJ
brj-23498	23	30	personnel	personnel	NOUN
brj-23498	23	31	for	for	ADP
brj-23498	23	32	operation	operation	NOUN
brj-23498	23	33	and	and	CCONJ
brj-23498	23	34	can	can	AUX
brj-23498	23	35	not	not	PART
brj-23498	23	36	be	be	AUX
brj-23498	23	37	achieved	achieve	VERB
brj-23498	23	38	online	online	ADV
brj-23498	23	39	.	.	PUNCT
brj-23498	24	1	from	from	ADP
brj-23498	24	2	economic	economic	ADJ
brj-23498	24	3	and	and	CCONJ
brj-23498	24	4	technological	technological	ADJ
brj-23498	24	5	perspectives	perspective	NOUN
brj-23498	24	6	,	,	PUNCT
brj-23498	24	7	addressing	address	VERB
brj-23498	24	8	these	these	DET
brj-23498	24	9	limitations	limitation	NOUN
brj-23498	24	10	is	be	AUX
brj-23498	24	11	necessary	necessary	ADJ
brj-23498	24	12	and	and	CCONJ
brj-23498	24	13	beneficial	beneficial	ADJ
brj-23498	24	14	.	.	PUNCT
brj-23498	25	1	therefore	therefore	ADV
brj-23498	25	2	,	,	PUNCT
brj-23498	25	3	it	it	PRON
brj-23498	25	4	is	be	AUX
brj-23498	25	5	necessary	necessary	ADJ
brj-23498	25	6	to	to	PART
brj-23498	25	7	develop	develop	VERB
brj-23498	25	8	a	a	DET
brj-23498	25	9	new	new	ADJ
brj-23498	25	10	method	method	NOUN
brj-23498	25	11	to	to	PART
brj-23498	25	12	overcome	overcome	VERB
brj-23498	25	13	the	the	DET
brj-23498	25	14	limitations	limitation	NOUN
brj-23498	25	15	of	of	ADP
brj-23498	25	16	traditional	traditional	ADJ
brj-23498	25	17	methods	method	NOUN
brj-23498	25	18	.	.	PUNCT
brj-23498	26	1	near	near	ADP
brj-23498	26	2	-	-	PUNCT
brj-23498	26	3	infrared	infrared	ADJ
brj-23498	26	4	spectroscopy	spectroscopy	NOUN
brj-23498	26	5	technology	technology	NOUN
brj-23498	26	6	is	be	AUX
brj-23498	26	7	a	a	DET
brj-23498	26	8	rapid	rapid	ADJ
brj-23498	26	9	and	and	CCONJ
brj-23498	26	10	non	non	ADJ
brj-23498	26	11	-	-	ADJ
brj-23498	26	12	destructive	destructive	ADJ
brj-23498	26	13	analysis	analysis	NOUN
brj-23498	26	14	method	method	NOUN
brj-23498	26	15	capable	capable	ADJ
brj-23498	26	16	of	of	ADP
brj-23498	26	17	detecting	detect	VERB
brj-23498	26	18	the	the	DET
brj-23498	26	19	different	different	ADJ
brj-23498	26	20	absorption	absorption	NOUN
brj-23498	26	21	frequencies	frequency	NOUN
brj-23498	26	22	of	of	ADP
brj-23498	26	23	specific	specific	ADJ
brj-23498	26	24	molecules	molecule	NOUN
brj-23498	26	25	in	in	ADP
brj-23498	26	26	substances	substance	NOUN
brj-23498	26	27	.	.	PUNCT
brj-23498	27	1	since	since	SCONJ
brj-23498	27	2	different	different	ADJ
brj-23498	27	3	chemical	chemical	NOUN
brj-23498	27	4	components	component	NOUN
brj-23498	27	5	contain	contain	VERB
brj-23498	27	6	different	different	ADJ
brj-23498	27	7	chemical	chemical	NOUN
brj-23498	27	8	groups	group	NOUN
brj-23498	27	9	corresponding	correspond	VERB
brj-23498	27	10	to	to	ADP
brj-23498	27	11	different	different	ADJ
brj-23498	27	12	group	group	NOUN
brj-23498	27	13	frequencies	frequency	NOUN
brj-23498	27	14	,	,	PUNCT
brj-23498	27	15	the	the	DET
brj-23498	27	16	positions	position	NOUN
brj-23498	27	17	of	of	ADP
brj-23498	27	18	characteristic	characteristic	ADJ
brj-23498	27	19	absorbance	absorbance	NOUN
brj-23498	27	20	peaks	peak	NOUN
brj-23498	27	21	generated	generate	VERB
brj-23498	27	22	are	be	AUX
brj-23498	27	23	also	also	ADV
brj-23498	27	24	different	different	ADJ
brj-23498	27	25	,	,	PUNCT
brj-23498	27	26	and	and	CCONJ
brj-23498	27	27	the	the	DET
brj-23498	27	28	characteristic	characteristic	ADJ
brj-23498	27	29	absorbance	absorbance	NOUN
brj-23498	27	30	peaks	peak	NOUN
brj-23498	27	31	reflected	reflect	VERB
brj-23498	27	32	by	by	ADP
brj-23498	27	33	different	different	ADJ
brj-23498	27	34	contents	content	NOUN
brj-23498	27	35	of	of	ADP
brj-23498	27	36	the	the	DET
brj-23498	27	37	same	same	ADJ
brj-23498	27	38	chemical	chemical	NOUN
brj-23498	27	39	component	component	NOUN
brj-23498	27	40	vary	vary	NOUN
brj-23498	27	41	(	(	PUNCT
brj-23498	27	42	tang	tang	NOUN
brj-23498	27	43	et	et	PROPN
brj-23498	27	44	al	al	PROPN
brj-23498	27	45	.	.	PROPN
brj-23498	27	46	2023	2023	NUM
brj-23498	27	47	)	)	PUNCT
brj-23498	27	48	.	.	PUNCT
brj-23498	28	1	therefore	therefore	ADV
brj-23498	28	2	,	,	PUNCT
brj-23498	28	3	both	both	CCONJ
brj-23498	28	4	quantitative	quantitative	ADJ
brj-23498	28	5	and	and	CCONJ
brj-23498	28	6	qualitative	qualitative	ADJ
brj-23498	28	7	analyses	analysis	NOUN
brj-23498	28	8	of	of	ADP
brj-23498	28	9	substances	substance	NOUN
brj-23498	28	10	can	can	AUX
brj-23498	28	11	be	be	AUX
brj-23498	28	12	conducted	conduct	VERB
brj-23498	28	13	using	use	VERB
brj-23498	28	14	infrared	infrared	ADJ
brj-23498	28	15	spectroscopy	spectroscopy	NOUN
brj-23498	28	16	technology	technology	NOUN
brj-23498	28	17	.	.	PUNCT
brj-23498	29	1	in	in	ADP
brj-23498	29	2	this	this	DET
brj-23498	29	3	experiment	experiment	NOUN
brj-23498	29	4	,	,	PUNCT
brj-23498	29	5	a	a	DET
brj-23498	29	6	quantitative	quantitative	ADJ
brj-23498	29	7	prediction	prediction	NOUN
brj-23498	29	8	model	model	NOUN
brj-23498	29	9	for	for	ADP
brj-23498	29	10	crude	crude	ADJ
brj-23498	29	11	fat	fat	NOUN
brj-23498	29	12	in	in	ADP
brj-23498	29	13	alfalfa	alfalfa	PROPN
brj-23498	29	14	hay	hay	NOUN
brj-23498	29	15	was	be	AUX
brj-23498	29	16	established	establish	VERB
brj-23498	29	17	based	base	VERB
brj-23498	29	18	on	on	ADP
brj-23498	29	19	near	near	ADV
brj-23498	29	20	-	-	PUNCT
brj-23498	29	21	infrared	infrared	ADJ
brj-23498	29	22	spectroscopy	spectroscopy	NOUN
brj-23498	29	23	to	to	PART
brj-23498	29	24	predict	predict	VERB
brj-23498	29	25	the	the	DET
brj-23498	29	26	content	content	NOUN
brj-23498	29	27	of	of	ADP
brj-23498	29	28	crude	crude	ADJ
brj-23498	29	29	fat	fat	NOUN
brj-23498	29	30	in	in	ADP
brj-23498	29	31	alfalfa	alfalfa	NOUN
brj-23498	29	32	hay	hay	NOUN
brj-23498	29	33	through	through	ADP
brj-23498	29	34	spectral	spectral	ADJ
brj-23498	29	35	data	datum	NOUN
brj-23498	29	36	.	.	PUNCT
brj-23498	30	1	image	image	NOUN
brj-23498	30	2	processing	processing	NOUN
brj-23498	30	3	technology	technology	NOUN
brj-23498	30	4	refers	refer	VERB
brj-23498	30	5	to	to	ADP
brj-23498	30	6	the	the	DET
brj-23498	30	7	analysis	analysis	NOUN
brj-23498	30	8	of	of	ADP
brj-23498	30	9	images	image	NOUN
brj-23498	30	10	to	to	PART
brj-23498	30	11	extract	extract	VERB
brj-23498	30	12	various	various	ADJ
brj-23498	30	13	information	information	NOUN
brj-23498	30	14	from	from	ADP
brj-23498	30	15	them	they	PRON
brj-23498	30	16	using	use	VERB
brj-23498	30	17	computers	computer	NOUN
brj-23498	30	18	.	.	PUNCT
brj-23498	31	1	in	in	ADP
brj-23498	31	2	recent	recent	ADJ
brj-23498	31	3	years	year	NOUN
brj-23498	31	4	,	,	PUNCT
brj-23498	31	5	image	image	NOUN
brj-23498	31	6	processing	processing	NOUN
brj-23498	31	7	technology	technology	NOUN
brj-23498	31	8	has	have	AUX
brj-23498	31	9	been	be	AUX
brj-23498	31	10	widely	widely	ADV
brj-23498	31	11	used	use	VERB
brj-23498	31	12	in	in	ADP
brj-23498	31	13	agricultural	agricultural	ADJ
brj-23498	31	14	production	production	NOUN
brj-23498	31	15	for	for	ADP
brj-23498	31	16	crop	crop	NOUN
brj-23498	31	17	detection	detection	NOUN
brj-23498	31	18	(	(	PUNCT
brj-23498	31	19	song	song	NOUN
brj-23498	31	20	2022	2022	NUM
brj-23498	31	21	)	)	PUNCT
brj-23498	31	22	,	,	PUNCT
brj-23498	31	23	pest	pest	VERB
brj-23498	31	24	and	and	CCONJ
brj-23498	31	25	disease	disease	NOUN
brj-23498	31	26	identification	identification	NOUN
brj-23498	31	27	(	(	PUNCT
brj-23498	31	28	kemal	kemal	NOUN
brj-23498	31	29	et	et	PROPN
brj-23498	31	30	al	al	PROPN
brj-23498	31	31	.	.	PROPN
brj-23498	31	32	2022	2022	NUM
brj-23498	31	33	)	)	PUNCT
brj-23498	31	34	,	,	PUNCT
brj-23498	31	35	soil	soil	NOUN
brj-23498	31	36	analysis	analysis	NOUN
brj-23498	31	37	,	,	PUNCT
brj-23498	31	38	etc	etc	X
brj-23498	31	39	.	.	X
brj-23498	31	40	,	,	PUNCT
brj-23498	31	41	thereby	thereby	ADV
brj-23498	31	42	helping	help	VERB
brj-23498	31	43	to	to	PART
brj-23498	31	44	improve	improve	VERB
brj-23498	31	45	agricultural	agricultural	ADJ
brj-23498	31	46	production	production	NOUN
brj-23498	31	47	efficiency	efficiency	NOUN
brj-23498	31	48	and	and	CCONJ
brj-23498	31	49	quality	quality	NOUN
brj-23498	31	50	.	.	PUNCT
brj-23498	32	1	alfalfa	alfalfa	PROPN
brj-23498	32	2	hay	hay	PROPN
brj-23498	32	3	is	be	AUX
brj-23498	32	4	prone	prone	ADJ
brj-23498	32	5	to	to	PART
brj-23498	32	6	moisture	moisture	VERB
brj-23498	32	7	absorption	absorption	NOUN
brj-23498	32	8	and	and	CCONJ
brj-23498	32	9	mold	mold	NOUN
brj-23498	32	10	,	,	PUNCT
brj-23498	32	11	and	and	CCONJ
brj-23498	32	12	if	if	SCONJ
brj-23498	32	13	alfalfa	alfalfa	NOUN
brj-23498	32	14	with	with	ADP
brj-23498	32	15	moldy	moldy	ADJ
brj-23498	32	16	properties	property	NOUN
brj-23498	32	17	is	be	AUX
brj-23498	32	18	mixed	mix	VERB
brj-23498	32	19	into	into	ADP
brj-23498	32	20	feed	feed	NOUN
brj-23498	32	21	for	for	ADP
brj-23498	32	22	feeding	feeding	NOUN
brj-23498	32	23	cows	cow	NOUN
brj-23498	32	24	,	,	PUNCT
brj-23498	32	25	the	the	DET
brj-23498	32	26	cows	cow	NOUN
brj-23498	32	27	’	'	PUNCT
brj-23498	32	28	intake	intake	NOUN
brj-23498	32	29	will	will	AUX
brj-23498	32	30	decrease	decrease	VERB
brj-23498	32	31	due	due	ADP
brj-23498	32	32	to	to	ADP
brj-23498	32	33	the	the	DET
brj-23498	32	34	loss	loss	NOUN
brj-23498	32	35	of	of	ADP
brj-23498	32	36	the	the	DET
brj-23498	32	37	original	original	ADJ
brj-23498	32	38	taste	taste	NOUN
brj-23498	32	39	of	of	ADP
brj-23498	32	40	alfalfa	alfalfa	NOUN
brj-23498	32	41	and	and	CCONJ
brj-23498	32	42	the	the	DET
brj-23498	32	43	presence	presence	NOUN
brj-23498	32	44	of	of	ADP
brj-23498	32	45	peculiar	peculiar	ADJ
brj-23498	32	46	smells	smell	NOUN
brj-23498	32	47	,	,	PUNCT
brj-23498	32	48	leading	lead	VERB
brj-23498	32	49	to	to	ADP
brj-23498	32	50	digestive	digestive	ADJ
brj-23498	32	51	disorders	disorder	NOUN
brj-23498	32	52	such	such	ADJ
brj-23498	32	53	as	as	ADP
brj-23498	32	54	rumen	ruman	NOUN
brj-23498	32	55	stasis	stasis	NOUN
brj-23498	32	56	and	and	CCONJ
brj-23498	32	57	reduced	reduced	ADJ
brj-23498	32	58	rumination	rumination	NOUN
brj-23498	32	59	,	,	PUNCT
brj-23498	32	60	as	as	ADV
brj-23498	32	61	well	well	ADV
brj-23498	32	62	as	as	ADP
brj-23498	32	63	symptoms	symptom	NOUN
brj-23498	32	64	of	of	ADP
brj-23498	32	65	poisoning	poisoning	NOUN
brj-23498	32	66	such	such	ADJ
brj-23498	32	67	as	as	ADP
brj-23498	32	68	drooling	drool	VERB
brj-23498	32	69	;	;	PUNCT
brj-23498	32	70	milk	milk	NOUN
brj-23498	32	71	production	production	NOUN
brj-23498	32	72	will	will	AUX
brj-23498	32	73	decrease	decrease	VERB
brj-23498	32	74	sharply	sharply	ADV
brj-23498	32	75	,	,	PUNCT
brj-23498	32	76	affecting	affect	VERB
brj-23498	32	77	the	the	DET
brj-23498	32	78	quality	quality	NOUN
brj-23498	32	79	of	of	ADP
brj-23498	32	80	dairy	dairy	NOUN
brj-23498	32	81	products	product	NOUN
brj-23498	32	82	,	,	PUNCT
brj-23498	32	83	with	with	ADP
brj-23498	32	84	protein	protein	NOUN
brj-23498	32	85	,	,	PUNCT
brj-23498	32	86	fat	fat	ADJ
brj-23498	32	87	,	,	PUNCT
brj-23498	32	88	and	and	CCONJ
brj-23498	32	89	lactose	lactose	NOUN
brj-23498	32	90	all	all	PRON
brj-23498	32	91	failing	fail	VERB
brj-23498	32	92	to	to	PART
brj-23498	32	93	meet	meet	VERB
brj-23498	32	94	requirements	requirement	NOUN
brj-23498	32	95	(	(	PUNCT
brj-23498	32	96	xue	xue	PROPN
brj-23498	32	97	2006	2006	NUM
brj-23498	32	98	)	)	PUNCT
brj-23498	32	99	.	.	PUNCT
brj-23498	33	1	therefore	therefore	ADV
brj-23498	33	2	,	,	PUNCT
brj-23498	33	3	detecting	detect	VERB
brj-23498	33	4	the	the	DET
brj-23498	33	5	degree	degree	NOUN
brj-23498	33	6	of	of	ADP
brj-23498	33	7	moldiness	moldiness	NOUN
brj-23498	33	8	in	in	ADP
brj-23498	33	9	alfalfa	alfalfa	NOUN
brj-23498	33	10	hay	hay	NOUN
brj-23498	33	11	is	be	AUX
brj-23498	33	12	crucial	crucial	ADJ
brj-23498	33	13	.	.	PUNCT
brj-23498	34	1	after	after	SCONJ
brj-23498	34	2	alfalfa	alfalfa	NOUN
brj-23498	34	3	grass	grass	NOUN
brj-23498	34	4	becomes	become	VERB
brj-23498	34	5	moldy	moldy	ADJ
brj-23498	34	6	,	,	PUNCT
brj-23498	34	7	its	its	PRON
brj-23498	34	8	color	color	NOUN
brj-23498	34	9	and	and	CCONJ
brj-23498	34	10	texture	texture	ADJ
brj-23498	34	11	characteristics	characteristic	NOUN
brj-23498	34	12	change	change	VERB
brj-23498	34	13	significantly	significantly	ADV
brj-23498	34	14	,	,	PUNCT
brj-23498	34	15	so	so	ADV
brj-23498	34	16	establishing	establish	VERB
brj-23498	34	17	a	a	DET
brj-23498	34	18	qualitative	qualitative	ADJ
brj-23498	34	19	discrimination	discrimination	NOUN
brj-23498	34	20	model	model	NOUN
brj-23498	34	21	by	by	ADP
brj-23498	34	22	collecting	collect	VERB
brj-23498	34	23	images	image	NOUN
brj-23498	34	24	of	of	ADP
brj-23498	34	25	alfalfa	alfalfa	NOUN
brj-23498	34	26	hay	hay	NOUN
brj-23498	34	27	and	and	CCONJ
brj-23498	34	28	using	use	VERB
brj-23498	34	29	image	image	NOUN
brj-23498	34	30	processing	processing	NOUN
brj-23498	34	31	technology	technology	NOUN
brj-23498	34	32	achieves	achieve	VERB
brj-23498	34	33	the	the	DET
brj-23498	34	34	effect	effect	NOUN
brj-23498	34	35	of	of	ADP
brj-23498	34	36	detecting	detect	VERB
brj-23498	34	37	whether	whether	SCONJ
brj-23498	34	38	alfalfa	alfalfa	NOUN
brj-23498	34	39	hay	hay	NOUN
brj-23498	34	40	is	be	AUX
brj-23498	34	41	moldy	moldy	ADJ
brj-23498	34	42	.	.	PUNCT
brj-23498	35	1	the	the	DET
brj-23498	35	2	electronic	electronic	ADJ
brj-23498	35	3	nose	nose	NOUN
brj-23498	35	4	is	be	AUX
brj-23498	35	5	a	a	DET
brj-23498	35	6	biomimetic	biomimetic	ADJ
brj-23498	35	7	detection	detection	NOUN
brj-23498	35	8	system	system	NOUN
brj-23498	35	9	developed	develop	VERB
brj-23498	35	10	to	to	PART
brj-23498	35	11	mimic	mimic	VERB
brj-23498	35	12	the	the	DET
brj-23498	35	13	human	human	ADJ
brj-23498	35	14	olfactory	olfactory	NOUN
brj-23498	35	15	system	system	NOUN
brj-23498	35	16	.	.	PUNCT
brj-23498	36	1	it	it	PRON
brj-23498	36	2	captures	capture	VERB
brj-23498	36	3	the	the	DET
brj-23498	36	4	odor	odor	NOUN
brj-23498	36	5	information	information	NOUN
brj-23498	36	6	of	of	ADP
brj-23498	36	7	samples	sample	NOUN
brj-23498	36	8	through	through	ADP
brj-23498	36	9	gas	gas	NOUN
brj-23498	36	10	-	-	PUNCT
brj-23498	36	11	sensitive	sensitive	ADJ
brj-23498	36	12	sensors	sensor	NOUN
brj-23498	36	13	,	,	PUNCT
brj-23498	36	14	converts	convert	VERB
brj-23498	36	15	it	it	PRON
brj-23498	36	16	into	into	ADP
brj-23498	36	17	electrical	electrical	ADJ
brj-23498	36	18	signals	signal	NOUN
brj-23498	36	19	,	,	PUNCT
brj-23498	36	20	and	and	CCONJ
brj-23498	36	21	uses	use	VERB
brj-23498	36	22	this	this	DET
brj-23498	36	23	information	information	NOUN
brj-23498	36	24	for	for	ADP
brj-23498	36	25	quality	quality	NOUN
brj-23498	36	26	detection	detection	NOUN
brj-23498	36	27	of	of	ADP
brj-23498	36	28	samples	sample	NOUN
brj-23498	36	29	(	(	PUNCT
brj-23498	36	30	shi	shi	PROPN
brj-23498	36	31	et	et	PROPN
brj-23498	36	32	al	al	PROPN
brj-23498	36	33	.	.	PROPN
brj-23498	36	34	2024	2024	NUM
brj-23498	36	35	)	)	PUNCT
brj-23498	36	36	.	.	PUNCT
brj-23498	37	1	grass	grass	NOUN
brj-23498	37	2	undergoes	undergo	VERB
brj-23498	37	3	significant	significant	ADJ
brj-23498	37	4	changes	change	NOUN
brj-23498	37	5	in	in	ADP
brj-23498	37	6	odor	odor	NOUN
brj-23498	37	7	before	before	ADV
brj-23498	37	8	and	and	CCONJ
brj-23498	37	9	after	after	ADP
brj-23498	37	10	molding	mold	VERB
brj-23498	37	11	.	.	PUNCT
brj-23498	38	1	by	by	ADP
brj-23498	38	2	collecting	collect	VERB
brj-23498	38	3	its	its	PRON
brj-23498	38	4	odor	odor	NOUN
brj-23498	38	5	information	information	NOUN
brj-23498	38	6	and	and	CCONJ
brj-23498	38	7	combining	combine	VERB
brj-23498	38	8	it	it	PRON
brj-23498	38	9	with	with	ADP
brj-23498	38	10	sensory	sensory	ADJ
brj-23498	38	11	evaluation	evaluation	NOUN
brj-23498	38	12	,	,	PUNCT
brj-23498	38	13	a	a	DET
brj-23498	38	14	model	model	NOUN
brj-23498	38	15	can	can	AUX
brj-23498	38	16	be	be	AUX
brj-23498	38	17	established	establish	VERB
brj-23498	38	18	to	to	PART
brj-23498	38	19	discriminate	discriminate	VERB
brj-23498	38	20	whether	whether	SCONJ
brj-23498	38	21	alfalfa	alfalfa	NOUN
brj-23498	38	22	is	be	AUX
brj-23498	38	23	moldy	moldy	ADJ
brj-23498	38	24	.	.	PUNCT
brj-23498	39	1	multi	multi	ADJ
brj-23498	39	2	-	-	ADJ
brj-23498	39	3	source	source	ADJ
brj-23498	39	4	information	information	NOUN
brj-23498	39	5	fusion	fusion	NOUN
brj-23498	39	6	,	,	PUNCT
brj-23498	39	7	also	also	ADV
brj-23498	39	8	known	know	VERB
brj-23498	39	9	as	as	ADP
brj-23498	39	10	multi	multi	ADJ
brj-23498	39	11	-	-	ADJ
brj-23498	39	12	sensor	sensor	ADJ
brj-23498	39	13	information	information	NOUN
brj-23498	39	14	fusion	fusion	NOUN
brj-23498	39	15	,	,	PUNCT
brj-23498	39	16	refers	refer	VERB
brj-23498	39	17	to	to	ADP
brj-23498	39	18	the	the	DET
brj-23498	39	19	integration	integration	NOUN
brj-23498	39	20	and	and	CCONJ
brj-23498	39	21	processing	processing	NOUN
brj-23498	39	22	of	of	ADP
brj-23498	39	23	multiple	multiple	ADJ
brj-23498	39	24	information	information	NOUN
brj-23498	39	25	sources	source	NOUN
brj-23498	39	26	from	from	ADP
brj-23498	39	27	different	different	ADJ
brj-23498	39	28	origins	origin	NOUN
brj-23498	39	29	,	,	PUNCT
brj-23498	39	30	types	type	NOUN
brj-23498	39	31	,	,	PUNCT
brj-23498	39	32	and	and	CCONJ
brj-23498	39	33	spatial	spatial	ADJ
brj-23498	39	34	resolutions	resolution	NOUN
brj-23498	39	35	.	.	PUNCT
brj-23498	40	1	it	it	PRON
brj-23498	40	2	can	can	AUX
brj-23498	40	3	provide	provide	VERB
brj-23498	40	4	richer	rich	ADJ
brj-23498	40	5	information	information	NOUN
brj-23498	40	6	than	than	ADP
brj-23498	40	7	a	a	DET
brj-23498	40	8	single	single	ADJ
brj-23498	40	9	information	information	NOUN
brj-23498	40	10	source	source	NOUN
brj-23498	40	11	,	,	PUNCT
brj-23498	40	12	thereby	thereby	ADV
brj-23498	40	13	enhancing	enhance	VERB
brj-23498	40	14	the	the	DET
brj-23498	40	15	cognitive	cognitive	ADJ
brj-23498	40	16	ability	ability	NOUN
brj-23498	40	17	and	and	CCONJ
brj-23498	40	18	decision	decision	NOUN
brj-23498	40	19	-	-	PUNCT
brj-23498	40	20	making	make	VERB
brj-23498	40	21	level	level	NOUN
brj-23498	40	22	towards	towards	ADP
brj-23498	40	23	the	the	DET
brj-23498	40	24	target	target	NOUN
brj-23498	40	25	(	(	PUNCT
brj-23498	40	26	jiang	jiang	PROPN
brj-23498	40	27	et	et	NOUN
brj-23498	40	28	al.2023	al.2023	PROPN
brj-23498	40	29	)	)	PUNCT
brj-23498	40	30	.	.	PUNCT
brj-23498	41	1	in	in	ADP
brj-23498	41	2	recent	recent	ADJ
brj-23498	41	3	years	year	NOUN
brj-23498	41	4	,	,	PUNCT
brj-23498	41	5	multi	multi	ADJ
brj-23498	41	6	-	-	ADJ
brj-23498	41	7	source	source	ADJ
brj-23498	41	8	information	information	NOUN
brj-23498	41	9	fusion	fusion	NOUN
brj-23498	41	10	has	have	AUX
brj-23498	41	11	been	be	AUX
brj-23498	41	12	extensively	extensively	ADV
brj-23498	41	13	researched	research	VERB
brj-23498	41	14	and	and	CCONJ
brj-23498	41	15	advanced	advanced	ADJ
brj-23498	41	16	,	,	PUNCT
brj-23498	41	17	spanning	span	VERB
brj-23498	41	18	various	various	ADJ
brj-23498	41	19	fields	field	NOUN
brj-23498	41	20	such	such	ADJ
brj-23498	41	21	as	as	ADP
brj-23498	41	22	military	military	NOUN
brj-23498	41	23	(	(	PUNCT
brj-23498	41	24	zhou	zhou	PROPN
brj-23498	41	25	et	et	PROPN
brj-23498	41	26	al	al	PROPN
brj-23498	41	27	.	.	PROPN
brj-23498	41	28	2024	2024	NUM
brj-23498	41	29	)	)	PUNCT
brj-23498	41	30	,	,	PUNCT
brj-23498	41	31	medical	medical	NOUN
brj-23498	41	32	(	(	PUNCT
brj-23498	41	33	li	li	PROPN
brj-23498	41	34	et	et	PROPN
brj-23498	41	35	al	al	PROPN
brj-23498	41	36	.	.	PROPN
brj-23498	41	37	2023	2023	NUM
brj-23498	41	38	)	)	PUNCT
brj-23498	41	39	,	,	PUNCT
brj-23498	41	40	unmanned	unmanned	ADJ
brj-23498	41	41	driving	driving	NOUN
brj-23498	41	42	(	(	PUNCT
brj-23498	41	43	ding	ding	NOUN
brj-23498	41	44	et	et	PROPN
brj-23498	41	45	al	al	PROPN
brj-23498	41	46	.	.	PROPN
brj-23498	41	47	2023	2023	NUM
brj-23498	41	48	)	)	PUNCT
brj-23498	41	49	,	,	PUNCT
brj-23498	41	50	geology	geology	NOUN
brj-23498	41	51	(	(	PUNCT
brj-23498	41	52	kong	kong	PROPN
brj-23498	41	53	et	et	PROPN
brj-23498	41	54	al	al	PROPN
brj-23498	41	55	.	.	PROPN
brj-23498	41	56	2022	2022	NUM
brj-23498	41	57	)	)	PUNCT
brj-23498	41	58	,	,	PUNCT
brj-23498	41	59	among	among	ADP
brj-23498	41	60	others	other	NOUN
brj-23498	41	61	.	.	PUNCT
brj-23498	42	1	however	however	ADV
brj-23498	42	2	,	,	PUNCT
brj-23498	42	3	there	there	PRON
brj-23498	42	4	are	be	VERB
brj-23498	42	5	few	few	ADJ
brj-23498	42	6	cases	case	NOUN
brj-23498	42	7	applying	apply	VERB
brj-23498	42	8	multi	multi	ADJ
brj-23498	42	9	-	-	ADJ
brj-23498	42	10	source	source	ADJ
brj-23498	42	11	information	information	NOUN
brj-23498	42	12	fusion	fusion	NOUN
brj-23498	42	13	to	to	ADP
brj-23498	42	14	agricultural	agricultural	ADJ
brj-23498	42	15	production	production	NOUN
brj-23498	42	16	.	.	PUNCT
brj-23498	43	1	this	this	DET
brj-23498	43	2	paper	paper	NOUN
brj-23498	43	3	proposes	propose	VERB
brj-23498	43	4	a	a	DET
brj-23498	43	5	new	new	ADJ
brj-23498	43	6	approach	approach	NOUN
brj-23498	43	7	to	to	PART
brj-23498	43	8	apply	apply	VERB
brj-23498	43	9	multi	multi	ADJ
brj-23498	43	10	-	-	ADJ
brj-23498	43	11	source	source	ADJ
brj-23498	43	12	information	information	NOUN
brj-23498	43	13	fusion	fusion	NOUN
brj-23498	43	14	to	to	ADP
brj-23498	43	15	alfalfa	alfalfa	NOUN
brj-23498	43	16	detection	detection	NOUN
brj-23498	43	17	,	,	PUNCT
brj-23498	43	18	achieving	achieve	VERB
brj-23498	43	19	more	more	ADV
brj-23498	43	20	accurate	accurate	ADJ
brj-23498	43	21	control	control	NOUN
brj-23498	43	22	over	over	ADP
brj-23498	43	23	peer	peer	NOUN
brj-23498	43	24	-	-	PUNCT
brj-23498	43	25	reviewed	review	VERB
brj-23498	43	26	article	article	NOUN
brj-23498	43	27	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	43	28	yang	yang	PROPN
brj-23498	43	29	et	et	PROPN
brj-23498	43	30	al	al	PROPN
brj-23498	43	31	.	.	PROPN
brj-23498	44	1	(	(	PUNCT
brj-23498	44	2	2024	2024	NUM
brj-23498	44	3	)	)	PUNCT
brj-23498	44	4	.	.	PUNCT
brj-23498	45	1	“	"	PUNCT
brj-23498	45	2	alfalfa	alfalfa	NOUN
brj-23498	45	3	quality	quality	NOUN
brj-23498	45	4	assessment	assessment	NOUN
brj-23498	45	5	,	,	PUNCT
brj-23498	45	6	”	"	PUNCT
brj-23498	45	7	bioresources	bioresource	NOUN
brj-23498	45	8	19(3	19(3	NUM
brj-23498	45	9	)	)	PUNCT
brj-23498	45	10	,	,	PUNCT
brj-23498	45	11	4531	4531	NUM
brj-23498	45	12	-	-	SYM
brj-23498	45	13	4546	4546	NUM
brj-23498	45	14	.	.	PUNCT
brj-23498	46	1	4533	4533	NUM
brj-23498	46	2	the	the	DET
brj-23498	46	3	quality	quality	NOUN
brj-23498	46	4	of	of	ADP
brj-23498	46	5	alfalfa	alfalfa	NOUN
brj-23498	46	6	.	.	PUNCT
brj-23498	47	1	machine	machine	NOUN
brj-23498	47	2	learning	learning	NOUN
brj-23498	47	3	is	be	AUX
brj-23498	47	4	a	a	DET
brj-23498	47	5	field	field	NOUN
brj-23498	47	6	of	of	ADP
brj-23498	47	7	study	study	NOUN
brj-23498	47	8	that	that	PRON
brj-23498	47	9	automatically	automatically	ADV
brj-23498	47	10	detects	detect	VERB
brj-23498	47	11	patterns	pattern	NOUN
brj-23498	47	12	and	and	CCONJ
brj-23498	47	13	rules	rule	NOUN
brj-23498	47	14	from	from	ADP
brj-23498	47	15	a	a	DET
brj-23498	47	16	given	give	VERB
brj-23498	47	17	database	database	NOUN
brj-23498	47	18	and	and	CCONJ
brj-23498	47	19	uses	use	VERB
brj-23498	47	20	the	the	DET
brj-23498	47	21	detected	detect	VERB
brj-23498	47	22	patterns	pattern	NOUN
brj-23498	47	23	to	to	PART
brj-23498	47	24	predict	predict	VERB
brj-23498	47	25	unknown	unknown	ADJ
brj-23498	47	26	data	datum	NOUN
brj-23498	47	27	(	(	PUNCT
brj-23498	47	28	quintero	quintero	PROPN
brj-23498	47	29	et	et	PROPN
brj-23498	47	30	al	al	PROPN
brj-23498	47	31	.	.	PROPN
brj-23498	47	32	2023	2023	NUM
brj-23498	47	33	)	)	PUNCT
brj-23498	47	34	.	.	PUNCT
brj-23498	48	1	therefore	therefore	ADV
brj-23498	48	2	,	,	PUNCT
brj-23498	48	3	combining	combine	VERB
brj-23498	48	4	digital	digital	ADJ
brj-23498	48	5	images	image	NOUN
brj-23498	48	6	,	,	PUNCT
brj-23498	48	7	near	near	ADV
brj-23498	48	8	-	-	PUNCT
brj-23498	48	9	infrared	infrared	ADJ
brj-23498	48	10	spectroscopy	spectroscopy	NOUN
brj-23498	48	11	,	,	PUNCT
brj-23498	48	12	electronic	electronic	ADJ
brj-23498	48	13	nose	nose	NOUN
brj-23498	48	14	,	,	PUNCT
brj-23498	48	15	and	and	CCONJ
brj-23498	48	16	machine	machine	NOUN
brj-23498	48	17	learning	learning	NOUN
brj-23498	48	18	may	may	AUX
brj-23498	48	19	be	be	AUX
brj-23498	48	20	a	a	DET
brj-23498	48	21	potential	potential	ADJ
brj-23498	48	22	solution	solution	NOUN
brj-23498	48	23	for	for	ADP
brj-23498	48	24	identifying	identify	VERB
brj-23498	48	25	the	the	DET
brj-23498	48	26	quality	quality	NOUN
brj-23498	48	27	of	of	ADP
brj-23498	48	28	alfalfa	alfalfa	PROPN
brj-23498	48	29	hay	hay	PROPN
brj-23498	48	30	,	,	PUNCT
brj-23498	48	31	thereby	thereby	ADV
brj-23498	48	32	achieving	achieve	VERB
brj-23498	48	33	rapid	rapid	ADJ
brj-23498	48	34	and	and	CCONJ
brj-23498	48	35	non	non	ADJ
brj-23498	48	36	-	-	ADJ
brj-23498	48	37	destructive	destructive	ADJ
brj-23498	48	38	detection	detection	NOUN
brj-23498	48	39	of	of	ADP
brj-23498	48	40	alfalfa	alfalfa	NOUN
brj-23498	48	41	hay	hay	PROPN
brj-23498	48	42	quality	quality	NOUN
brj-23498	48	43	.	.	PUNCT
brj-23498	49	1	experimental	experimental	ADJ
brj-23498	49	2	samples	sample	NOUN
brj-23498	49	3	and	and	CCONJ
brj-23498	49	4	equipment	equipment	NOUN
brj-23498	49	5	following	follow	VERB
brj-23498	49	6	the	the	DET
brj-23498	49	7	basic	basic	ADJ
brj-23498	49	8	requirements	requirement	NOUN
brj-23498	49	9	of	of	ADP
brj-23498	49	10	plant	plant	NOUN
brj-23498	49	11	biology	biology	NOUN
brj-23498	49	12	experiments	experiment	NOUN
brj-23498	49	13	,	,	PUNCT
brj-23498	49	14	three	three	NUM
brj-23498	49	15	representative	representative	ADJ
brj-23498	49	16	samples	sample	NOUN
brj-23498	49	17	of	of	ADP
brj-23498	49	18	alfalfa	alfalfa	NOUN
brj-23498	49	19	from	from	ADP
brj-23498	49	20	inner	inner	ADJ
brj-23498	49	21	mongolia	mongolia	PROPN
brj-23498	49	22	were	be	AUX
brj-23498	49	23	selected	select	VERB
brj-23498	49	24	as	as	ADP
brj-23498	49	25	experimental	experimental	ADJ
brj-23498	49	26	objects	object	NOUN
brj-23498	49	27	.	.	PUNCT
brj-23498	50	1	after	after	ADP
brj-23498	50	2	removing	remove	VERB
brj-23498	50	3	impurities	impurity	NOUN
brj-23498	50	4	such	such	ADJ
brj-23498	50	5	as	as	ADP
brj-23498	50	6	weeds	weed	NOUN
brj-23498	50	7	and	and	CCONJ
brj-23498	50	8	sand	sand	NOUN
brj-23498	50	9	,	,	PUNCT
brj-23498	50	10	each	each	DET
brj-23498	50	11	sample	sample	NOUN
brj-23498	50	12	was	be	AUX
brj-23498	50	13	subjected	subject	VERB
brj-23498	50	14	to	to	ADP
brj-23498	50	15	sun	sun	NOUN
brj-23498	50	16	drying	drying	NOUN
brj-23498	50	17	,	,	PUNCT
brj-23498	50	18	and	and	CCONJ
brj-23498	50	19	moldy	moldy	ADJ
brj-23498	50	20	treatment	treatment	NOUN
brj-23498	50	21	.	.	PUNCT
brj-23498	51	1	sixty	sixty	NUM
brj-23498	51	2	samples	sample	NOUN
brj-23498	51	3	were	be	AUX
brj-23498	51	4	taken	take	VERB
brj-23498	51	5	for	for	ADP
brj-23498	51	6	each	each	DET
brj-23498	51	7	treatment	treatment	NOUN
brj-23498	51	8	method	method	NOUN
brj-23498	51	9	,	,	PUNCT
brj-23498	51	10	totaling	total	VERB
brj-23498	51	11	120	120	NUM
brj-23498	51	12	samples	sample	NOUN
brj-23498	51	13	.	.	PUNCT
brj-23498	52	1	after	after	ADP
brj-23498	52	2	treatment	treatment	NOUN
brj-23498	52	3	,	,	PUNCT
brj-23498	52	4	image	image	NOUN
brj-23498	52	5	collection	collection	NOUN
brj-23498	52	6	was	be	AUX
brj-23498	52	7	carried	carry	VERB
brj-23498	52	8	out	out	ADP
brj-23498	52	9	,	,	PUNCT
brj-23498	52	10	followed	follow	VERB
brj-23498	52	11	by	by	ADP
brj-23498	52	12	grinding	grind	VERB
brj-23498	52	13	the	the	DET
brj-23498	52	14	samples	sample	NOUN
brj-23498	52	15	into	into	ADP
brj-23498	52	16	powder	powder	NOUN
brj-23498	52	17	and	and	CCONJ
brj-23498	52	18	sieving	sieve	VERB
brj-23498	52	19	through	through	ADP
brj-23498	52	20	a	a	DET
brj-23498	52	21	100	100	NUM
brj-23498	52	22	-	-	PUNCT
brj-23498	52	23	mesh	mesh	NOUN
brj-23498	52	24	sieve	sieve	NOUN
brj-23498	52	25	,	,	PUNCT
brj-23498	52	26	and	and	CCONJ
brj-23498	52	27	then	then	ADV
brj-23498	52	28	collecting	collect	VERB
brj-23498	52	29	nearinfrared	nearinfrared	ADJ
brj-23498	52	30	spectroscopy	spectroscopy	NOUN
brj-23498	52	31	data	datum	NOUN
brj-23498	52	32	and	and	CCONJ
brj-23498	52	33	electronic	electronic	ADJ
brj-23498	52	34	nose	nose	NOUN
brj-23498	52	35	data	datum	NOUN
brj-23498	52	36	.	.	PUNCT
brj-23498	53	1	the	the	DET
brj-23498	53	2	fat	fat	ADJ
brj-23498	53	3	content	content	NOUN
brj-23498	53	4	of	of	ADP
brj-23498	53	5	the	the	DET
brj-23498	53	6	samples	sample	NOUN
brj-23498	53	7	was	be	AUX
brj-23498	53	8	determined	determine	VERB
brj-23498	53	9	using	use	VERB
brj-23498	53	10	an	an	DET
brj-23498	53	11	ankom	ankom	NOUN
brj-23498	53	12	xt15i	xt15i	PUNCT
brj-23498	53	13	fat	fat	ADJ
brj-23498	53	14	analyzer	analyzer	NOUN
brj-23498	53	15	.	.	PUNCT
brj-23498	54	1	representative	representative	ADJ
brj-23498	54	2	samples	sample	NOUN
brj-23498	54	3	of	of	ADP
brj-23498	54	4	alfalfa	alfalfa	NOUN
brj-23498	54	5	hay	hay	NOUN
brj-23498	54	6	were	be	AUX
brj-23498	54	7	placed	place	VERB
brj-23498	54	8	on	on	ADP
brj-23498	54	9	a	a	DET
brj-23498	54	10	black	black	ADJ
brj-23498	54	11	background	background	NOUN
brj-23498	54	12	and	and	CCONJ
brj-23498	54	13	photographed	photograph	VERB
brj-23498	54	14	using	use	VERB
brj-23498	54	15	a	a	DET
brj-23498	54	16	digital	digital	ADJ
brj-23498	54	17	camera	camera	NOUN
brj-23498	54	18	.	.	PUNCT
brj-23498	55	1	during	during	ADP
brj-23498	55	2	photography	photography	NOUN
brj-23498	55	3	,	,	PUNCT
brj-23498	55	4	the	the	DET
brj-23498	55	5	camera	camera	NOUN
brj-23498	55	6	lens	lens	NOUN
brj-23498	55	7	was	be	AUX
brj-23498	55	8	kept	keep	VERB
brj-23498	55	9	parallel	parallel	ADJ
brj-23498	55	10	to	to	ADP
brj-23498	55	11	the	the	DET
brj-23498	55	12	plane	plane	NOUN
brj-23498	55	13	of	of	ADP
brj-23498	55	14	the	the	DET
brj-23498	55	15	sample	sample	NOUN
brj-23498	55	16	,	,	PUNCT
brj-23498	55	17	with	with	SCONJ
brj-23498	55	18	the	the	DET
brj-23498	55	19	camera	camera	NOUN
brj-23498	55	20	positioned	position	VERB
brj-23498	55	21	20	20	NUM
brj-23498	55	22	cm	cm	NOUN
brj-23498	55	23	above	above	ADP
brj-23498	55	24	the	the	DET
brj-23498	55	25	sample	sample	NOUN
brj-23498	55	26	.	.	PUNCT
brj-23498	56	1	the	the	DET
brj-23498	56	2	obtained	obtain	VERB
brj-23498	56	3	images	image	NOUN
brj-23498	56	4	had	have	VERB
brj-23498	56	5	a	a	DET
brj-23498	56	6	resolution	resolution	NOUN
brj-23498	56	7	of	of	ADP
brj-23498	56	8	3072×4096	3072×4096	NUM
brj-23498	56	9	pixels	pixel	NOUN
brj-23498	56	10	.	.	PUNCT
brj-23498	57	1	one	one	NUM
brj-23498	57	2	or	or	CCONJ
brj-23498	57	3	more	more	ADJ
brj-23498	57	4	sub	sub	NOUN
brj-23498	57	5	-	-	NOUN
brj-23498	57	6	images	image	NOUN
brj-23498	57	7	were	be	AUX
brj-23498	57	8	cropped	crop	VERB
brj-23498	57	9	manually	manually	ADV
brj-23498	57	10	from	from	ADP
brj-23498	57	11	each	each	DET
brj-23498	57	12	image	image	NOUN
brj-23498	57	13	to	to	PART
brj-23498	57	14	construct	construct	VERB
brj-23498	57	15	the	the	DET
brj-23498	57	16	image	image	NOUN
brj-23498	57	17	library	library	NOUN
brj-23498	57	18	.	.	PUNCT
brj-23498	58	1	fig	fig	NOUN
brj-23498	58	2	.	.	PUNCT
brj-23498	59	1	1	1	X
brj-23498	59	2	.	.	X
brj-23498	59	3	information	information	NOUN
brj-23498	59	4	collection	collection	NOUN
brj-23498	59	5	process	process	NOUN
brj-23498	59	6	peer	peer	NOUN
brj-23498	59	7	-	-	PUNCT
brj-23498	59	8	reviewed	review	VERB
brj-23498	59	9	article	article	NOUN
brj-23498	59	10	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	59	11	yang	yang	PROPN
brj-23498	59	12	et	et	PROPN
brj-23498	59	13	al	al	PROPN
brj-23498	59	14	.	.	PROPN
brj-23498	60	1	(	(	PUNCT
brj-23498	60	2	2024	2024	NUM
brj-23498	60	3	)	)	PUNCT
brj-23498	60	4	.	.	PUNCT
brj-23498	61	1	“	"	PUNCT
brj-23498	61	2	alfalfa	alfalfa	NOUN
brj-23498	61	3	quality	quality	NOUN
brj-23498	61	4	assessment	assessment	NOUN
brj-23498	61	5	,	,	PUNCT
brj-23498	61	6	”	"	PUNCT
brj-23498	61	7	bioresources	bioresource	NOUN
brj-23498	61	8	19(3	19(3	NUM
brj-23498	61	9	)	)	PUNCT
brj-23498	61	10	,	,	PUNCT
brj-23498	61	11	4531	4531	NUM
brj-23498	61	12	-	-	SYM
brj-23498	61	13	4546	4546	NUM
brj-23498	61	14	.	.	PUNCT
brj-23498	62	1	4534	4534	NUM
brj-23498	62	2	the	the	DET
brj-23498	62	3	near	near	ADV
brj-23498	62	4	-	-	PUNCT
brj-23498	62	5	infrared	infrared	ADJ
brj-23498	62	6	spectroscopy	spectroscopy	NOUN
brj-23498	62	7	instrument	instrument	NOUN
brj-23498	62	8	used	use	VERB
brj-23498	62	9	for	for	ADP
brj-23498	62	10	data	datum	NOUN
brj-23498	62	11	collection	collection	NOUN
brj-23498	62	12	was	be	AUX
brj-23498	62	13	the	the	DET
brj-23498	62	14	quality	quality	NOUN
brj-23498	62	15	spec	spec	PROPN
brj-23498	62	16	pro	pro	ADJ
brj-23498	62	17	spectrometer	spectrometer	NOUN
brj-23498	62	18	manufactured	manufacture	VERB
brj-23498	62	19	by	by	ADP
brj-23498	62	20	analytical	analytical	ADJ
brj-23498	62	21	spectral	spectral	ADJ
brj-23498	62	22	devices	device	NOUN
brj-23498	62	23	(	(	PUNCT
brj-23498	62	24	asd	asd	NOUN
brj-23498	62	25	,	,	PUNCT
brj-23498	62	26	incl	incl	NOUN
brj-23498	62	27	,	,	PUNCT
brj-23498	62	28	usa	usa	PROPN
brj-23498	62	29	)	)	PUNCT
brj-23498	62	30	.	.	PUNCT
brj-23498	63	1	the	the	DET
brj-23498	63	2	wavelength	wavelength	NOUN
brj-23498	63	3	range	range	NOUN
brj-23498	63	4	of	of	ADP
brj-23498	63	5	the	the	DET
brj-23498	63	6	spectrometer	spectrometer	NOUN
brj-23498	63	7	was	be	AUX
brj-23498	63	8	350	350	NUM
brj-23498	63	9	to	to	ADP
brj-23498	63	10	1830	1830	NUM
brj-23498	63	11	nm	nm	NOUN
brj-23498	63	12	,	,	PUNCT
brj-23498	63	13	with	with	ADP
brj-23498	63	14	a	a	DET
brj-23498	63	15	spectral	spectral	ADJ
brj-23498	63	16	sampling	sampling	NOUN
brj-23498	63	17	interval	interval	NOUN
brj-23498	63	18	of	of	ADP
brj-23498	63	19	1	1	NUM
brj-23498	63	20	nm	nm	NOUN
brj-23498	63	21	.	.	PUNCT
brj-23498	64	1	after	after	ADP
brj-23498	64	2	preheating	preheat	VERB
brj-23498	64	3	the	the	DET
brj-23498	64	4	spectrometer	spectrometer	NOUN
brj-23498	64	5	for	for	ADP
brj-23498	64	6	20	20	NUM
brj-23498	64	7	min	min	NOUN
brj-23498	64	8	,	,	PUNCT
brj-23498	64	9	the	the	DET
brj-23498	64	10	sample	sample	NOUN
brj-23498	64	11	was	be	AUX
brj-23498	64	12	placed	place	VERB
brj-23498	64	13	in	in	ADP
brj-23498	64	14	the	the	DET
brj-23498	64	15	measurement	measurement	NOUN
brj-23498	64	16	chamber	chamber	NOUN
brj-23498	64	17	of	of	ADP
brj-23498	64	18	the	the	DET
brj-23498	64	19	spectrometer	spectrometer	NOUN
brj-23498	64	20	.	.	PUNCT
brj-23498	65	1	external	external	ADJ
brj-23498	65	2	interference	interference	NOUN
brj-23498	65	3	such	such	ADJ
brj-23498	65	4	as	as	ADP
brj-23498	65	5	light	light	NOUN
brj-23498	65	6	and	and	CCONJ
brj-23498	65	7	sound	sound	NOUN
brj-23498	65	8	was	be	AUX
brj-23498	65	9	eliminated	eliminate	VERB
brj-23498	65	10	,	,	PUNCT
brj-23498	65	11	and	and	CCONJ
brj-23498	65	12	near	near	ADV
brj-23498	65	13	-	-	PUNCT
brj-23498	65	14	infrared	infrared	ADJ
brj-23498	65	15	light	light	NOUN
brj-23498	65	16	was	be	AUX
brj-23498	65	17	then	then	ADV
brj-23498	65	18	irradiated	irradiate	VERB
brj-23498	65	19	onto	onto	ADP
brj-23498	65	20	the	the	DET
brj-23498	65	21	sample	sample	NOUN
brj-23498	65	22	,	,	PUNCT
brj-23498	65	23	with	with	ADP
brj-23498	65	24	the	the	DET
brj-23498	65	25	reflected	reflect	VERB
brj-23498	65	26	or	or	CCONJ
brj-23498	65	27	transmitted	transmit	VERB
brj-23498	65	28	spectral	spectral	ADJ
brj-23498	65	29	data	datum	NOUN
brj-23498	65	30	recorded	record	VERB
brj-23498	65	31	.	.	PUNCT
brj-23498	66	1	multiple	multiple	ADJ
brj-23498	66	2	measurements	measurement	NOUN
brj-23498	66	3	were	be	AUX
brj-23498	66	4	taken	take	VERB
brj-23498	66	5	on	on	ADP
brj-23498	66	6	the	the	DET
brj-23498	66	7	sample	sample	NOUN
brj-23498	66	8	to	to	PART
brj-23498	66	9	obtain	obtain	VERB
brj-23498	66	10	the	the	DET
brj-23498	66	11	average	average	ADJ
brj-23498	66	12	spectrum	spectrum	NOUN
brj-23498	66	13	and	and	CCONJ
brj-23498	66	14	check	check	VERB
brj-23498	66	15	the	the	DET
brj-23498	66	16	measurement	measurement	NOUN
brj-23498	66	17	repeatability	repeatability	NOUN
brj-23498	66	18	.	.	PUNCT
brj-23498	67	1	the	the	DET
brj-23498	67	2	electronic	electronic	ADJ
brj-23498	67	3	nose	nose	NOUN
brj-23498	67	4	detection	detection	NOUN
brj-23498	67	5	device	device	NOUN
brj-23498	67	6	used	use	VERB
brj-23498	67	7	was	be	AUX
brj-23498	67	8	the	the	DET
brj-23498	67	9	pen3	pen3	PROPN
brj-23498	67	10	model	model	PROPN
brj-23498	67	11	electronic	electronic	PROPN
brj-23498	67	12	nose	nose	NOUN
brj-23498	67	13	manufactured	manufacture	VERB
brj-23498	67	14	by	by	ADP
brj-23498	67	15	airsense	airsense	NOUN
brj-23498	67	16	,	,	PUNCT
brj-23498	67	17	germany	germany	PROPN
brj-23498	67	18	,	,	PUNCT
brj-23498	67	19	equipped	equip	VERB
brj-23498	67	20	with	with	ADP
brj-23498	67	21	ten	ten	NUM
brj-23498	67	22	gas	gas	NOUN
brj-23498	67	23	-	-	PUNCT
brj-23498	67	24	sensitive	sensitive	ADJ
brj-23498	67	25	sensors	sensor	NOUN
brj-23498	67	26	.	.	PUNCT
brj-23498	68	1	for	for	ADP
brj-23498	68	2	detection	detection	NOUN
brj-23498	68	3	,	,	PUNCT
brj-23498	68	4	15	15	NUM
brj-23498	68	5	to	to	PART
brj-23498	68	6	18	18	NUM
brj-23498	68	7	g	g	NOUN
brj-23498	68	8	of	of	ADP
brj-23498	68	9	dried	dry	VERB
brj-23498	68	10	purple	purple	ADJ
brj-23498	68	11	alfalfa	alfalfa	NOUN
brj-23498	68	12	sample	sample	NOUN
brj-23498	68	13	was	be	AUX
brj-23498	68	14	placed	place	VERB
brj-23498	68	15	in	in	ADP
brj-23498	68	16	a	a	DET
brj-23498	68	17	centrifuge	centrifuge	NOUN
brj-23498	68	18	tube	tube	NOUN
brj-23498	68	19	,	,	PUNCT
brj-23498	68	20	heated	heat	VERB
brj-23498	68	21	in	in	ADP
brj-23498	68	22	a	a	DET
brj-23498	68	23	50	50	NUM
brj-23498	68	24	°	°	NOUN
brj-23498	68	25	c	c	NOUN
brj-23498	68	26	water	water	NOUN
brj-23498	68	27	bath	bath	NOUN
brj-23498	68	28	for	for	ADP
brj-23498	68	29	30	30	NUM
brj-23498	68	30	min	min	NOUN
brj-23498	68	31	,	,	PUNCT
brj-23498	68	32	washed	wash	VERB
brj-23498	68	33	for	for	ADP
brj-23498	68	34	12	12	NUM
brj-23498	68	35	seconds	second	NOUN
brj-23498	68	36	,	,	PUNCT
brj-23498	68	37	and	and	CCONJ
brj-23498	68	38	the	the	DET
brj-23498	68	39	measurement	measurement	NOUN
brj-23498	68	40	was	be	AUX
brj-23498	68	41	carried	carry	VERB
brj-23498	68	42	out	out	ADP
brj-23498	68	43	for	for	ADP
brj-23498	68	44	24	24	NUM
brj-23498	68	45	seconds	second	NOUN
brj-23498	68	46	.	.	PUNCT
brj-23498	69	1	the	the	DET
brj-23498	69	2	software	software	NOUN
brj-23498	69	3	used	use	VERB
brj-23498	69	4	for	for	ADP
brj-23498	69	5	spectral	spectral	ADJ
brj-23498	69	6	analysis	analysis	NOUN
brj-23498	69	7	,	,	PUNCT
brj-23498	69	8	image	image	NOUN
brj-23498	69	9	processing	processing	NOUN
brj-23498	69	10	,	,	PUNCT
brj-23498	69	11	and	and	CCONJ
brj-23498	69	12	modeling	modeling	NOUN
brj-23498	69	13	was	be	AUX
brj-23498	69	14	unscrambler	unscrambl	ADJ
brj-23498	69	15	x	x	PUNCT
brj-23498	69	16	10.4	10.4	NUM
brj-23498	69	17	from	from	ADP
brj-23498	69	18	camo	camo	NOUN
brj-23498	69	19	,	,	PUNCT
brj-23498	69	20	norway	norway	NOUN
brj-23498	69	21	,	,	PUNCT
brj-23498	69	22	and	and	CCONJ
brj-23498	69	23	matlab	matlab	PROPN
brj-23498	69	24	r2022b	r2022b	PROPN
brj-23498	69	25	from	from	ADP
brj-23498	69	26	mathworks	mathworks	PROPN
brj-23498	69	27	,	,	PUNCT
brj-23498	69	28	usa	usa	PROPN
brj-23498	69	29	.	.	PUNCT
brj-23498	70	1	the	the	DET
brj-23498	70	2	odor	odor	NOUN
brj-23498	70	3	information	information	NOUN
brj-23498	70	4	was	be	AUX
brj-23498	70	5	analyzed	analyze	VERB
brj-23498	70	6	using	use	VERB
brj-23498	70	7	the	the	DET
brj-23498	70	8	win	win	NOUN
brj-23498	70	9	muster	muster	NOUN
brj-23498	70	10	software	software	NOUN
brj-23498	70	11	,	,	PUNCT
brj-23498	70	12	which	which	PRON
brj-23498	70	13	is	be	AUX
brj-23498	70	14	built	build	VERB
brj-23498	70	15	into	into	ADP
brj-23498	70	16	the	the	DET
brj-23498	70	17	pen3	pen3	PROPN
brj-23498	70	18	instrument	instrument	NOUN
brj-23498	70	19	.	.	PUNCT
brj-23498	71	1	the	the	DET
brj-23498	71	2	information	information	NOUN
brj-23498	71	3	collection	collection	NOUN
brj-23498	71	4	process	process	NOUN
brj-23498	71	5	is	be	AUX
brj-23498	71	6	shown	show	VERB
brj-23498	71	7	in	in	ADP
brj-23498	71	8	fig	fig	NOUN
brj-23498	71	9	.	.	PUNCT
brj-23498	72	1	1	1	X
brj-23498	72	2	.	.	X
brj-23498	72	3	spectral	spectral	ADJ
brj-23498	72	4	data	datum	NOUN
brj-23498	72	5	preprocessing	preprocesse	VERB
brj-23498	72	6	due	due	ADP
brj-23498	72	7	to	to	ADP
brj-23498	72	8	the	the	DET
brj-23498	72	9	interference	interference	NOUN
brj-23498	72	10	of	of	ADP
brj-23498	72	11	unrelated	unrelated	ADJ
brj-23498	72	12	information	information	NOUN
brj-23498	72	13	such	such	ADJ
brj-23498	72	14	as	as	ADP
brj-23498	72	15	stray	stray	ADJ
brj-23498	72	16	light	light	NOUN
brj-23498	72	17	,	,	PUNCT
brj-23498	72	18	baseline	baseline	ADJ
brj-23498	72	19	drift	drift	NOUN
brj-23498	72	20	(	(	PUNCT
brj-23498	72	21	sun	sun	NOUN
brj-23498	72	22	et	et	PROPN
brj-23498	72	23	al	al	PROPN
brj-23498	72	24	.	.	PROPN
brj-23498	72	25	2023	2023	NUM
brj-23498	72	26	)	)	PUNCT
brj-23498	72	27	,	,	PUNCT
brj-23498	72	28	noise	noise	NOUN
brj-23498	72	29	,	,	PUNCT
brj-23498	72	30	and	and	CCONJ
brj-23498	72	31	sample	sample	NOUN
brj-23498	72	32	background	background	NOUN
brj-23498	72	33	,	,	PUNCT
brj-23498	72	34	the	the	DET
brj-23498	72	35	spectral	spectral	ADJ
brj-23498	72	36	data	datum	NOUN
brj-23498	72	37	obtained	obtain	VERB
brj-23498	72	38	by	by	ADP
brj-23498	72	39	the	the	DET
brj-23498	72	40	spectrometer	spectrometer	NOUN
brj-23498	72	41	are	be	AUX
brj-23498	72	42	susceptible	susceptible	ADJ
brj-23498	72	43	to	to	ADP
brj-23498	72	44	disturbance	disturbance	NOUN
brj-23498	72	45	,	,	PUNCT
brj-23498	72	46	which	which	PRON
brj-23498	72	47	affects	affect	VERB
brj-23498	72	48	the	the	DET
brj-23498	72	49	modeling	modeling	NOUN
brj-23498	72	50	effect	effect	NOUN
brj-23498	72	51	.	.	PUNCT
brj-23498	73	1	to	to	PART
brj-23498	73	2	improve	improve	VERB
brj-23498	73	3	the	the	DET
brj-23498	73	4	accuracy	accuracy	NOUN
brj-23498	73	5	of	of	ADP
brj-23498	73	6	near	near	ADV
brj-23498	73	7	-	-	PUNCT
brj-23498	73	8	infrared	infrared	ADJ
brj-23498	73	9	spectroscopy	spectroscopy	NOUN
brj-23498	73	10	measurements	measurement	NOUN
brj-23498	73	11	and	and	CCONJ
brj-23498	73	12	enhance	enhance	VERB
brj-23498	73	13	the	the	DET
brj-23498	73	14	signal	signal	NOUN
brj-23498	73	15	-	-	PUNCT
brj-23498	73	16	to	to	ADP
brj-23498	73	17	-	-	PUNCT
brj-23498	73	18	noise	noise	NOUN
brj-23498	73	19	ratio	ratio	NOUN
brj-23498	73	20	of	of	ADP
brj-23498	73	21	the	the	DET
brj-23498	73	22	spectra	spectra	NOUN
brj-23498	73	23	,	,	PUNCT
brj-23498	73	24	noise	noise	NOUN
brj-23498	73	25	spectra	spectra	NOUN
brj-23498	73	26	in	in	ADP
brj-23498	73	27	the	the	DET
brj-23498	73	28	range	range	NOUN
brj-23498	73	29	of	of	ADP
brj-23498	73	30	350	350	NUM
brj-23498	73	31	to	to	PART
brj-23498	73	32	429	429	NUM
brj-23498	73	33	nm	nm	NOUN
brj-23498	73	34	were	be	AUX
brj-23498	73	35	excluded	exclude	VERB
brj-23498	73	36	,	,	PUNCT
brj-23498	73	37	and	and	CCONJ
brj-23498	73	38	models	model	NOUN
brj-23498	73	39	were	be	AUX
brj-23498	73	40	built	build	VERB
brj-23498	73	41	using	use	VERB
brj-23498	73	42	spectral	spectral	ADJ
brj-23498	73	43	bands	band	NOUN
brj-23498	73	44	between	between	ADP
brj-23498	73	45	430	430	NUM
brj-23498	73	46	to	to	ADP
brj-23498	73	47	1830	1830	NUM
brj-23498	73	48	nm	nm	NOUN
brj-23498	73	49	.	.	PUNCT
brj-23498	74	1	before	before	ADP
brj-23498	74	2	building	build	VERB
brj-23498	74	3	the	the	DET
brj-23498	74	4	model	model	NOUN
brj-23498	74	5	,	,	PUNCT
brj-23498	74	6	the	the	DET
brj-23498	74	7	spectral	spectral	ADJ
brj-23498	74	8	data	datum	NOUN
brj-23498	74	9	were	be	AUX
brj-23498	74	10	preprocessed	preprocesse	VERB
brj-23498	74	11	using	use	VERB
brj-23498	74	12	the	the	DET
brj-23498	74	13	s	s	PROPN
brj-23498	74	14	-	-	PUNCT
brj-23498	74	15	g	g	NOUN
brj-23498	74	16	smoothing	smoothing	NOUN
brj-23498	74	17	method	method	NOUN
brj-23498	74	18	.	.	PUNCT
brj-23498	75	1	the	the	DET
brj-23498	75	2	derivative	derivative	ADJ
brj-23498	75	3	order	order	NOUN
brj-23498	75	4	was	be	AUX
brj-23498	75	5	set	set	VERB
brj-23498	75	6	to	to	ADP
brj-23498	75	7	0	0	NUM
brj-23498	75	8	,	,	PUNCT
brj-23498	75	9	the	the	DET
brj-23498	75	10	window	window	NOUN
brj-23498	75	11	number	number	NOUN
brj-23498	75	12	was	be	AUX
brj-23498	75	13	set	set	VERB
brj-23498	75	14	to	to	ADP
brj-23498	75	15	7	7	NUM
brj-23498	75	16	,	,	PUNCT
brj-23498	75	17	and	and	CCONJ
brj-23498	75	18	the	the	DET
brj-23498	75	19	smoothing	smooth	VERB
brj-23498	75	20	order	order	NOUN
brj-23498	75	21	was	be	AUX
brj-23498	75	22	set	set	VERB
brj-23498	75	23	to	to	ADP
brj-23498	75	24	3	3	NUM
brj-23498	75	25	.	.	PUNCT
brj-23498	76	1	the	the	DET
brj-23498	76	2	original	original	ADJ
brj-23498	76	3	spectral	spectral	ADJ
brj-23498	76	4	graph	graph	NOUN
brj-23498	76	5	and	and	CCONJ
brj-23498	76	6	the	the	DET
brj-23498	76	7	graph	graph	NOUN
brj-23498	76	8	after	after	ADP
brj-23498	76	9	s	s	NOUN
brj-23498	76	10	-	-	PUNCT
brj-23498	76	11	g	g	NOUN
brj-23498	76	12	convolution	convolution	NOUN
brj-23498	76	13	smoothing	smoothing	NOUN
brj-23498	76	14	are	be	AUX
brj-23498	76	15	shown	show	VERB
brj-23498	76	16	in	in	ADP
brj-23498	76	17	fig	fig	NOUN
brj-23498	76	18	.	.	PUNCT
brj-23498	77	1	2	2	X
brj-23498	77	2	.	.	X
brj-23498	77	3	(	(	PUNCT
brj-23498	77	4	a	a	X
brj-23498	77	5	)	)	PUNCT
brj-23498	77	6	(	(	PUNCT
brj-23498	77	7	b	b	X
brj-23498	77	8	)	)	PUNCT
brj-23498	77	9	fig	fig	NOUN
brj-23498	77	10	.	.	PUNCT
brj-23498	78	1	2	2	X
brj-23498	78	2	.	.	X
brj-23498	78	3	(	(	PUNCT
brj-23498	78	4	a	a	X
brj-23498	78	5	)	)	PUNCT
brj-23498	78	6	reflectivity	reflectivity	NOUN
brj-23498	78	7	curve	curve	NOUN
brj-23498	78	8	of	of	ADP
brj-23498	78	9	alfalfa	alfalfa	PROPN
brj-23498	78	10	hay	hay	NOUN
brj-23498	78	11	samples	sample	NOUN
brj-23498	78	12	,	,	PUNCT
brj-23498	78	13	(	(	PUNCT
brj-23498	78	14	b	b	X
brj-23498	78	15	)	)	PUNCT
brj-23498	78	16	spectral	spectral	ADJ
brj-23498	78	17	curve	curve	NOUN
brj-23498	78	18	after	after	ADP
brj-23498	78	19	sg	sg	ADP
brj-23498	78	20	preprocessing	preprocesse	VERB
brj-23498	78	21	preprocessing	preprocessing	NOUN
brj-23498	78	22	of	of	ADP
brj-23498	78	23	image	image	NOUN
brj-23498	78	24	data	datum	NOUN
brj-23498	78	25	to	to	PART
brj-23498	78	26	ensure	ensure	VERB
brj-23498	78	27	the	the	DET
brj-23498	78	28	accuracy	accuracy	NOUN
brj-23498	78	29	and	and	CCONJ
brj-23498	78	30	stability	stability	NOUN
brj-23498	78	31	of	of	ADP
brj-23498	78	32	the	the	DET
brj-23498	78	33	experimental	experimental	ADJ
brj-23498	78	34	results	result	NOUN
brj-23498	78	35	,	,	PUNCT
brj-23498	78	36	preprocessing	preprocessing	NOUN
brj-23498	78	37	was	be	AUX
brj-23498	78	38	performed	perform	VERB
brj-23498	78	39	on	on	ADP
brj-23498	78	40	the	the	DET
brj-23498	78	41	collected	collect	VERB
brj-23498	78	42	images	image	NOUN
brj-23498	78	43	.	.	PUNCT
brj-23498	79	1	after	after	ADP
brj-23498	79	2	removing	remove	VERB
brj-23498	79	3	irrelevant	irrelevant	ADJ
brj-23498	79	4	backgrounds	background	NOUN
brj-23498	79	5	,	,	PUNCT
brj-23498	79	6	images	image	NOUN
brj-23498	79	7	of	of	ADP
brj-23498	79	8	different	different	ADJ
brj-23498	79	9	sizes	size	NOUN
brj-23498	79	10	were	be	AUX
brj-23498	79	11	adjusted	adjust	VERB
brj-23498	79	12	to	to	ADP
brj-23498	79	13	the	the	DET
brj-23498	79	14	same	same	ADJ
brj-23498	79	15	pixel	pixel	NOUN
brj-23498	79	16	size	size	NOUN
brj-23498	79	17	to	to	PART
brj-23498	79	18	eliminate	eliminate	VERB
brj-23498	79	19	noise	noise	NOUN
brj-23498	79	20	interference	interference	NOUN
brj-23498	79	21	introduced	introduce	VERB
brj-23498	79	22	during	during	ADP
brj-23498	79	23	collection	collection	NOUN
brj-23498	79	24	and	and	CCONJ
brj-23498	79	25	transmission	transmission	NOUN
brj-23498	79	26	.	.	PUNCT
brj-23498	80	1	this	this	DET
brj-23498	80	2	process	process	NOUN
brj-23498	80	3	separates	separate	VERB
brj-23498	80	4	images	image	NOUN
brj-23498	80	5	of	of	ADP
brj-23498	80	6	moldy	moldy	ADJ
brj-23498	80	7	alfalfa	alfalfa	NOUN
brj-23498	80	8	for	for	ADP
brj-23498	80	9	identification	identification	NOUN
brj-23498	80	10	.	.	PUNCT
brj-23498	81	1	peer	peer	NOUN
brj-23498	81	2	-	-	PUNCT
brj-23498	81	3	reviewed	review	VERB
brj-23498	81	4	article	article	NOUN
brj-23498	81	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	81	6	yang	yang	PROPN
brj-23498	81	7	et	et	PROPN
brj-23498	81	8	al	al	PROPN
brj-23498	81	9	.	.	PROPN
brj-23498	82	1	(	(	PUNCT
brj-23498	82	2	2024	2024	NUM
brj-23498	82	3	)	)	PUNCT
brj-23498	82	4	.	.	PUNCT
brj-23498	83	1	“	"	PUNCT
brj-23498	83	2	alfalfa	alfalfa	NOUN
brj-23498	83	3	quality	quality	NOUN
brj-23498	83	4	assessment	assessment	NOUN
brj-23498	83	5	,	,	PUNCT
brj-23498	83	6	”	"	PUNCT
brj-23498	83	7	bioresources	bioresource	NOUN
brj-23498	83	8	19(3	19(3	NUM
brj-23498	83	9	)	)	PUNCT
brj-23498	83	10	,	,	PUNCT
brj-23498	83	11	4531	4531	NUM
brj-23498	83	12	-	-	SYM
brj-23498	83	13	4546	4546	NUM
brj-23498	83	14	.	.	PUNCT
brj-23498	84	1	4535	4535	NUM
brj-23498	84	2	during	during	ADP
brj-23498	84	3	image	image	NOUN
brj-23498	84	4	collection	collection	NOUN
brj-23498	84	5	and	and	CCONJ
brj-23498	84	6	transmission	transmission	NOUN
brj-23498	85	1	,	,	PUNCT
brj-23498	85	2	the	the	DET
brj-23498	85	3	presence	presence	NOUN
brj-23498	85	4	of	of	ADP
brj-23498	85	5	noise	noise	NOUN
brj-23498	85	6	can	can	AUX
brj-23498	85	7	degrade	degrade	VERB
brj-23498	85	8	the	the	DET
brj-23498	85	9	quality	quality	NOUN
brj-23498	85	10	of	of	ADP
brj-23498	85	11	the	the	DET
brj-23498	85	12	images	image	NOUN
brj-23498	85	13	.	.	PUNCT
brj-23498	86	1	noise	noise	NOUN
brj-23498	86	2	interference	interference	NOUN
brj-23498	86	3	affects	affect	VERB
brj-23498	86	4	the	the	DET
brj-23498	86	5	segmentation	segmentation	NOUN
brj-23498	86	6	effect	effect	NOUN
brj-23498	86	7	,	,	PUNCT
brj-23498	86	8	feature	feature	NOUN
brj-23498	86	9	extraction	extraction	NOUN
brj-23498	86	10	parameters	parameter	NOUN
brj-23498	86	11	,	,	PUNCT
brj-23498	86	12	and	and	CCONJ
brj-23498	86	13	mold	mold	NOUN
brj-23498	86	14	recognition	recognition	NOUN
brj-23498	86	15	accuracy	accuracy	NOUN
brj-23498	86	16	of	of	ADP
brj-23498	86	17	leaf	leaf	NOUN
brj-23498	86	18	images	image	NOUN
brj-23498	86	19	,	,	PUNCT
brj-23498	86	20	leading	lead	VERB
brj-23498	86	21	to	to	ADP
brj-23498	86	22	serious	serious	ADJ
brj-23498	86	23	impacts	impact	NOUN
brj-23498	86	24	on	on	ADP
brj-23498	86	25	image	image	NOUN
brj-23498	86	26	processing	processing	NOUN
brj-23498	86	27	algorithms	algorithm	NOUN
brj-23498	86	28	.	.	PUNCT
brj-23498	87	1	therefore	therefore	ADV
brj-23498	87	2	,	,	PUNCT
brj-23498	87	3	noise	noise	NOUN
brj-23498	87	4	removal	removal	NOUN
brj-23498	87	5	is	be	AUX
brj-23498	87	6	an	an	DET
brj-23498	87	7	essential	essential	ADJ
brj-23498	87	8	step	step	NOUN
brj-23498	87	9	in	in	ADP
brj-23498	87	10	image	image	NOUN
brj-23498	87	11	processing	processing	NOUN
brj-23498	87	12	.	.	PUNCT
brj-23498	88	1	adaptive	adaptive	ADJ
brj-23498	88	2	wavelet	wavelet	NOUN
brj-23498	88	3	thresholding	thresholding	NOUN
brj-23498	88	4	was	be	AUX
brj-23498	88	5	selected	select	VERB
brj-23498	88	6	to	to	PART
brj-23498	88	7	denoise	denoise	VERB
brj-23498	88	8	the	the	DET
brj-23498	88	9	collected	collect	VERB
brj-23498	88	10	images	image	NOUN
brj-23498	88	11	(	(	PUNCT
brj-23498	88	12	liu	liu	PROPN
brj-23498	88	13	et	et	PROPN
brj-23498	88	14	al	al	PROPN
brj-23498	88	15	.	.	PROPN
brj-23498	88	16	2022	2022	NUM
brj-23498	88	17	)	)	PUNCT
brj-23498	88	18	,	,	PUNCT
brj-23498	88	19	which	which	PRON
brj-23498	88	20	eliminates	eliminate	VERB
brj-23498	88	21	noise	noise	NOUN
brj-23498	88	22	while	while	SCONJ
brj-23498	88	23	preserving	preserve	VERB
brj-23498	88	24	details	detail	NOUN
brj-23498	88	25	and	and	CCONJ
brj-23498	88	26	edge	edge	NOUN
brj-23498	88	27	information	information	NOUN
brj-23498	88	28	in	in	ADP
brj-23498	88	29	moldy	moldy	ADJ
brj-23498	88	30	alfalfa	alfalfa	NOUN
brj-23498	88	31	images	image	NOUN
brj-23498	88	32	.	.	PUNCT
brj-23498	89	1	the	the	DET
brj-23498	89	2	process	process	NOUN
brj-23498	89	3	of	of	ADP
brj-23498	89	4	wavelet	wavelet	NOUN
brj-23498	89	5	threshold	threshold	NOUN
brj-23498	89	6	denoising	denoising	NOUN
brj-23498	89	7	can	can	AUX
brj-23498	89	8	be	be	AUX
brj-23498	89	9	divided	divide	VERB
brj-23498	89	10	into	into	ADP
brj-23498	89	11	three	three	NUM
brj-23498	89	12	steps	step	NOUN
brj-23498	89	13	:	:	PUNCT
brj-23498	89	14	transforming	transform	VERB
brj-23498	89	15	real	real	ADJ
brj-23498	89	16	natural	natural	ADJ
brj-23498	89	17	images	image	NOUN
brj-23498	89	18	into	into	ADP
brj-23498	89	19	the	the	DET
brj-23498	89	20	wavelet	wavelet	NOUN
brj-23498	89	21	domain	domain	NOUN
brj-23498	89	22	using	use	VERB
brj-23498	89	23	wavelet	wavelet	NOUN
brj-23498	89	24	transformation	transformation	NOUN
brj-23498	89	25	,	,	PUNCT
brj-23498	89	26	applying	apply	VERB
brj-23498	89	27	nonlinear	nonlinear	ADJ
brj-23498	89	28	shrinkage	shrinkage	NOUN
brj-23498	89	29	rules	rule	NOUN
brj-23498	89	30	to	to	ADP
brj-23498	89	31	wavelet	wavelet	NOUN
brj-23498	89	32	coefficients	coefficient	NOUN
brj-23498	89	33	,	,	PUNCT
brj-23498	89	34	and	and	CCONJ
brj-23498	89	35	performing	perform	VERB
brj-23498	89	36	wavelet	wavelet	NOUN
brj-23498	89	37	inverse	inverse	NOUN
brj-23498	89	38	transformation	transformation	NOUN
brj-23498	89	39	on	on	ADP
brj-23498	89	40	thresholded	thresholde	VERB
brj-23498	89	41	wavelet	wavelet	NOUN
brj-23498	89	42	coefficients	coefficient	NOUN
brj-23498	89	43	to	to	PART
brj-23498	89	44	obtain	obtain	VERB
brj-23498	89	45	denoised	denoise	VERB
brj-23498	89	46	images	image	NOUN
brj-23498	89	47	.	.	PUNCT
brj-23498	90	1	the	the	DET
brj-23498	90	2	effectiveness	effectiveness	NOUN
brj-23498	90	3	of	of	ADP
brj-23498	90	4	denoising	denoising	NOUN
brj-23498	90	5	depends	depend	VERB
brj-23498	90	6	on	on	ADP
brj-23498	90	7	several	several	ADJ
brj-23498	90	8	factors	factor	NOUN
brj-23498	90	9	:	:	PUNCT
brj-23498	90	10	the	the	DET
brj-23498	90	11	choice	choice	NOUN
brj-23498	90	12	of	of	ADP
brj-23498	90	13	wavelet	wavelet	NOUN
brj-23498	90	14	basis	basis	NOUN
brj-23498	90	15	,	,	PUNCT
brj-23498	90	16	determination	determination	NOUN
brj-23498	90	17	of	of	ADP
brj-23498	90	18	wavelet	wavelet	NOUN
brj-23498	90	19	decomposition	decomposition	NOUN
brj-23498	90	20	levels	level	NOUN
brj-23498	90	21	,	,	PUNCT
brj-23498	90	22	selection	selection	NOUN
brj-23498	90	23	of	of	ADP
brj-23498	90	24	threshold	threshold	NOUN
brj-23498	90	25	functions	function	NOUN
brj-23498	90	26	and	and	CCONJ
brj-23498	90	27	threshold	threshold	NOUN
brj-23498	90	28	estimation	estimation	NOUN
brj-23498	90	29	methods	method	NOUN
brj-23498	90	30	.	.	PUNCT
brj-23498	91	1	the	the	DET
brj-23498	91	2	adaptive	adaptive	ADJ
brj-23498	91	3	wavelet	wavelet	NOUN
brj-23498	91	4	thresholding	thresholde	VERB
brj-23498	91	5	algorithm	algorithm	NOUN
brj-23498	91	6	dynamically	dynamically	ADV
brj-23498	91	7	selects	select	VERB
brj-23498	91	8	thresholds	threshold	NOUN
brj-23498	91	9	based	base	VERB
brj-23498	91	10	on	on	ADP
brj-23498	91	11	local	local	ADJ
brj-23498	91	12	characteristics	characteristic	NOUN
brj-23498	91	13	of	of	ADP
brj-23498	91	14	the	the	DET
brj-23498	91	15	image	image	NOUN
brj-23498	91	16	,	,	PUNCT
brj-23498	91	17	resulting	result	VERB
brj-23498	91	18	in	in	ADP
brj-23498	91	19	the	the	DET
brj-23498	91	20	removal	removal	NOUN
brj-23498	91	21	of	of	ADP
brj-23498	91	22	irrelevant	irrelevant	ADJ
brj-23498	91	23	backgrounds	background	NOUN
brj-23498	91	24	.	.	PUNCT
brj-23498	92	1	examples	example	NOUN
brj-23498	92	2	of	of	ADP
brj-23498	92	3	images	image	NOUN
brj-23498	92	4	before	before	ADV
brj-23498	92	5	and	and	CCONJ
brj-23498	92	6	after	after	ADP
brj-23498	92	7	denoising	denoising	NOUN
brj-23498	92	8	are	be	AUX
brj-23498	92	9	shown	show	VERB
brj-23498	92	10	in	in	ADP
brj-23498	92	11	fig	fig	NOUN
brj-23498	92	12	.	.	PUNCT
brj-23498	93	1	3	3	X
brj-23498	93	2	.	.	X
brj-23498	93	3	(	(	PUNCT
brj-23498	93	4	a	a	X
brj-23498	93	5	)	)	PUNCT
brj-23498	93	6	(	(	PUNCT
brj-23498	93	7	b	b	X
brj-23498	93	8	)	)	PUNCT
brj-23498	93	9	(	(	PUNCT
brj-23498	93	10	c	c	X
brj-23498	93	11	)	)	PUNCT
brj-23498	93	12	(	(	PUNCT
brj-23498	93	13	d	d	X
brj-23498	93	14	)	)	PUNCT
brj-23498	93	15	fig	fig	NOUN
brj-23498	93	16	.	.	PUNCT
brj-23498	94	1	3	3	X
brj-23498	94	2	.	.	X
brj-23498	94	3	(	(	PUNCT
brj-23498	94	4	a	a	X
brj-23498	94	5	)	)	PUNCT
brj-23498	94	6	normal	normal	ADJ
brj-23498	94	7	alfalfa	alfalfa	NOUN
brj-23498	94	8	(	(	PUNCT
brj-23498	94	9	b	b	NOUN
brj-23498	94	10	)	)	PUNCT
brj-23498	94	11	denoised	denoise	VERB
brj-23498	94	12	normal	normal	ADJ
brj-23498	94	13	alfalfa	alfalfa	NOUN
brj-23498	94	14	(	(	PUNCT
brj-23498	94	15	c	c	NOUN
brj-23498	94	16	)	)	PUNCT
brj-23498	94	17	moldy	moldy	ADJ
brj-23498	94	18	alfalfa	alfalfa	NOUN
brj-23498	94	19	(	(	PUNCT
brj-23498	94	20	d	d	NOUN
brj-23498	94	21	)	)	PUNCT
brj-23498	94	22	denoised	denoise	VERB
brj-23498	94	23	moldy	moldy	PROPN
brj-23498	94	24	alfalfa	alfalfa	PROPN
brj-23498	94	25	peer	peer	NOUN
brj-23498	94	26	-	-	PUNCT
brj-23498	94	27	reviewed	review	VERB
brj-23498	94	28	article	article	NOUN
brj-23498	94	29	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	94	30	yang	yang	PROPN
brj-23498	94	31	et	et	PROPN
brj-23498	94	32	al	al	PROPN
brj-23498	94	33	.	.	PROPN
brj-23498	95	1	(	(	PUNCT
brj-23498	95	2	2024	2024	NUM
brj-23498	95	3	)	)	PUNCT
brj-23498	95	4	.	.	PUNCT
brj-23498	96	1	“	"	PUNCT
brj-23498	96	2	alfalfa	alfalfa	NOUN
brj-23498	96	3	quality	quality	NOUN
brj-23498	96	4	assessment	assessment	NOUN
brj-23498	96	5	,	,	PUNCT
brj-23498	96	6	”	"	PUNCT
brj-23498	96	7	bioresources	bioresource	NOUN
brj-23498	96	8	19(3	19(3	NUM
brj-23498	96	9	)	)	PUNCT
brj-23498	96	10	,	,	PUNCT
brj-23498	96	11	4531	4531	NUM
brj-23498	96	12	-	-	SYM
brj-23498	96	13	4546	4546	NUM
brj-23498	96	14	.	.	PUNCT
brj-23498	97	1	4536	4536	NUM
brj-23498	97	2	electronic	electronic	ADJ
brj-23498	97	3	nose	nose	NOUN
brj-23498	97	4	data	datum	NOUN
brj-23498	97	5	preprocessing	preprocesse	VERB
brj-23498	97	6	the	the	DET
brj-23498	97	7	odors	odor	NOUN
brj-23498	97	8	released	release	VERB
brj-23498	97	9	when	when	SCONJ
brj-23498	97	10	alfalfa	alfalfa	PROPN
brj-23498	97	11	hay	hay	PROPN
brj-23498	97	12	molds	mold	NOUN
brj-23498	97	13	are	be	AUX
brj-23498	97	14	usually	usually	ADV
brj-23498	97	15	associated	associate	VERB
brj-23498	97	16	with	with	ADP
brj-23498	97	17	volatile	volatile	ADJ
brj-23498	97	18	organic	organic	ADJ
brj-23498	97	19	compounds	compound	NOUN
brj-23498	97	20	,	,	PUNCT
brj-23498	97	21	including	include	VERB
brj-23498	97	22	but	but	CCONJ
brj-23498	97	23	not	not	PART
brj-23498	97	24	limited	limit	VERB
brj-23498	97	25	to	to	ADP
brj-23498	97	26	ketones	ketone	NOUN
brj-23498	97	27	,	,	PUNCT
brj-23498	97	28	alcohols	alcohol	NOUN
brj-23498	97	29	,	,	PUNCT
brj-23498	97	30	aldehydes	aldehyde	NOUN
brj-23498	97	31	,	,	PUNCT
brj-23498	97	32	acids	acid	NOUN
brj-23498	97	33	,	,	PUNCT
brj-23498	97	34	and	and	CCONJ
brj-23498	97	35	other	other	ADJ
brj-23498	97	36	volatile	volatile	ADJ
brj-23498	97	37	organic	organic	ADJ
brj-23498	97	38	compounds	compound	NOUN
brj-23498	97	39	(	(	PUNCT
brj-23498	97	40	tian	tian	PROPN
brj-23498	97	41	et	et	PROPN
brj-23498	97	42	al	al	PROPN
brj-23498	97	43	.	.	PROPN
brj-23498	97	44	2021	2021	NUM
brj-23498	97	45	)	)	PUNCT
brj-23498	97	46	.	.	PUNCT
brj-23498	98	1	electronic	electronic	ADJ
brj-23498	98	2	noses	nose	NOUN
brj-23498	98	3	can	can	AUX
brj-23498	98	4	effectively	effectively	ADV
brj-23498	98	5	capture	capture	VERB
brj-23498	98	6	such	such	ADJ
brj-23498	98	7	signals	signal	NOUN
brj-23498	98	8	and	and	CCONJ
brj-23498	98	9	convert	convert	VERB
brj-23498	98	10	them	they	PRON
brj-23498	98	11	into	into	ADP
brj-23498	98	12	digital	digital	ADJ
brj-23498	98	13	information	information	NOUN
brj-23498	98	14	.	.	PUNCT
brj-23498	99	1	the	the	DET
brj-23498	99	2	data	datum	NOUN
brj-23498	99	3	collected	collect	VERB
brj-23498	99	4	by	by	ADP
brj-23498	99	5	the	the	DET
brj-23498	99	6	electronic	electronic	ADJ
brj-23498	99	7	nose	nose	NOUN
brj-23498	99	8	often	often	ADV
brj-23498	99	9	exhibit	exhibit	VERB
brj-23498	99	10	a	a	DET
brj-23498	99	11	waveform	waveform	NOUN
brj-23498	99	12	that	that	PRON
brj-23498	99	13	rises	rise	VERB
brj-23498	99	14	first	first	ADV
brj-23498	99	15	,	,	PUNCT
brj-23498	99	16	then	then	ADV
brj-23498	99	17	decreases	decrease	VERB
brj-23498	99	18	,	,	PUNCT
brj-23498	99	19	and	and	CCONJ
brj-23498	99	20	finally	finally	ADV
brj-23498	99	21	stabilizes	stabilize	VERB
brj-23498	99	22	.	.	PUNCT
brj-23498	100	1	the	the	DET
brj-23498	100	2	odors	odor	NOUN
brj-23498	100	3	or	or	CCONJ
brj-23498	100	4	volatile	volatile	ADJ
brj-23498	100	5	compounds	compound	NOUN
brj-23498	100	6	produced	produce	VERB
brj-23498	100	7	by	by	ADP
brj-23498	100	8	moldy	moldy	ADJ
brj-23498	100	9	grass	grass	NOUN
brj-23498	100	10	usually	usually	ADV
brj-23498	100	11	reach	reach	VERB
brj-23498	100	12	a	a	DET
brj-23498	100	13	stable	stable	ADJ
brj-23498	100	14	state	state	NOUN
brj-23498	100	15	after	after	ADP
brj-23498	100	16	the	the	DET
brj-23498	100	17	grass	grass	NOUN
brj-23498	100	18	molds	mold	NOUN
brj-23498	100	19	and	and	CCONJ
brj-23498	100	20	may	may	AUX
brj-23498	100	21	continue	continue	VERB
brj-23498	100	22	to	to	PART
brj-23498	100	23	be	be	AUX
brj-23498	100	24	released	release	VERB
brj-23498	100	25	for	for	ADP
brj-23498	100	26	a	a	DET
brj-23498	100	27	period	period	NOUN
brj-23498	100	28	of	of	ADP
brj-23498	100	29	time	time	NOUN
brj-23498	100	30	.	.	PUNCT
brj-23498	101	1	therefore	therefore	ADV
brj-23498	101	2	,	,	PUNCT
brj-23498	101	3	analyzing	analyze	VERB
brj-23498	101	4	the	the	DET
brj-23498	101	5	data	datum	NOUN
brj-23498	101	6	in	in	ADP
brj-23498	101	7	the	the	DET
brj-23498	101	8	final	final	ADJ
brj-23498	101	9	stable	stable	ADJ
brj-23498	101	10	stage	stage	NOUN
brj-23498	101	11	can	can	AUX
brj-23498	101	12	better	well	ADV
brj-23498	101	13	capture	capture	VERB
brj-23498	101	14	the	the	DET
brj-23498	101	15	odor	odor	NOUN
brj-23498	101	16	characteristics	characteristic	NOUN
brj-23498	101	17	related	relate	VERB
brj-23498	101	18	to	to	ADP
brj-23498	101	19	molding	molding	NOUN
brj-23498	101	20	,	,	PUNCT
brj-23498	101	21	while	while	SCONJ
brj-23498	101	22	eliminating	eliminate	VERB
brj-23498	101	23	possible	possible	ADJ
brj-23498	101	24	interference	interference	NOUN
brj-23498	101	25	at	at	ADP
brj-23498	101	26	the	the	DET
brj-23498	101	27	beginning	beginning	NOUN
brj-23498	101	28	of	of	ADP
brj-23498	101	29	the	the	DET
brj-23498	101	30	collection	collection	NOUN
brj-23498	101	31	,	,	PUNCT
brj-23498	101	32	such	such	ADJ
brj-23498	101	33	as	as	ADP
brj-23498	101	34	environmental	environmental	ADJ
brj-23498	101	35	odors	odor	NOUN
brj-23498	101	36	or	or	CCONJ
brj-23498	101	37	other	other	ADJ
brj-23498	101	38	noises	noise	NOUN
brj-23498	101	39	.	.	PUNCT
brj-23498	102	1	therefore	therefore	ADV
brj-23498	102	2	,	,	PUNCT
brj-23498	102	3	data	datum	NOUN
brj-23498	102	4	collected	collect	VERB
brj-23498	102	5	from	from	ADP
brj-23498	102	6	the	the	DET
brj-23498	102	7	stable	stable	ADJ
brj-23498	102	8	20	20	NUM
brj-23498	102	9	seconds	second	NOUN
brj-23498	102	10	are	be	AUX
brj-23498	102	11	analyzed	analyze	VERB
brj-23498	102	12	.	.	PUNCT
brj-23498	103	1	feature	feature	NOUN
brj-23498	103	2	extraction	extraction	NOUN
brj-23498	103	3	and	and	CCONJ
brj-23498	103	4	dimensionality	dimensionality	NOUN
brj-23498	103	5	reduction	reduction	NOUN
brj-23498	103	6	of	of	ADP
brj-23498	103	7	near	near	ADV
brj-23498	103	8	-	-	PUNCT
brj-23498	103	9	infrared	infrared	ADJ
brj-23498	103	10	spectroscopy	spectroscopy	VERB
brj-23498	103	11	the	the	DET
brj-23498	103	12	preprocessed	preprocesse	VERB
brj-23498	103	13	spectral	spectral	ADJ
brj-23498	103	14	data	datum	NOUN
brj-23498	103	15	contain	contain	VERB
brj-23498	103	16	a	a	DET
brj-23498	103	17	large	large	ADJ
brj-23498	103	18	number	number	NOUN
brj-23498	103	19	of	of	ADP
brj-23498	103	20	wavelength	wavelength	NOUN
brj-23498	103	21	variables	variable	NOUN
brj-23498	103	22	,	,	PUNCT
brj-23498	103	23	resulting	result	VERB
brj-23498	103	24	in	in	ADP
brj-23498	103	25	high	high	ADJ
brj-23498	103	26	data	datum	NOUN
brj-23498	103	27	dimensionality	dimensionality	NOUN
brj-23498	103	28	,	,	PUNCT
brj-23498	103	29	excessive	excessive	ADJ
brj-23498	103	30	redundancy	redundancy	NOUN
brj-23498	103	31	,	,	PUNCT
brj-23498	103	32	prolonged	prolonged	ADJ
brj-23498	103	33	processing	processing	NOUN
brj-23498	103	34	time	time	NOUN
brj-23498	103	35	,	,	PUNCT
brj-23498	103	36	and	and	CCONJ
brj-23498	103	37	potential	potential	ADJ
brj-23498	103	38	degradation	degradation	NOUN
brj-23498	103	39	in	in	ADP
brj-23498	103	40	classification	classification	NOUN
brj-23498	103	41	results	result	NOUN
brj-23498	103	42	if	if	SCONJ
brj-23498	103	43	models	model	NOUN
brj-23498	103	44	are	be	AUX
brj-23498	103	45	directly	directly	ADV
brj-23498	103	46	built	build	VERB
brj-23498	103	47	(	(	PUNCT
brj-23498	103	48	gibertoni	gibertoni	PROPN
brj-23498	103	49	et	et	PROPN
brj-23498	103	50	al	al	PROPN
brj-23498	103	51	.	.	PROPN
brj-23498	103	52	2022	2022	NUM
brj-23498	103	53	)	)	PUNCT
brj-23498	103	54	.	.	PUNCT
brj-23498	104	1	fig	fig	NOUN
brj-23498	104	2	.	.	PUNCT
brj-23498	105	1	4	4	X
brj-23498	105	2	.	.	X
brj-23498	105	3	feature	feature	NOUN
brj-23498	105	4	wavelength	wavelength	NOUN
brj-23498	105	5	extraction	extraction	NOUN
brj-23498	105	6	map	map	NOUN
brj-23498	105	7	peer	peer	NOUN
brj-23498	105	8	-	-	PUNCT
brj-23498	105	9	reviewed	review	VERB
brj-23498	105	10	article	article	NOUN
brj-23498	105	11	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	105	12	yang	yang	PROPN
brj-23498	105	13	et	et	PROPN
brj-23498	105	14	al	al	PROPN
brj-23498	105	15	.	.	PROPN
brj-23498	106	1	(	(	PUNCT
brj-23498	106	2	2024	2024	NUM
brj-23498	106	3	)	)	PUNCT
brj-23498	106	4	.	.	PUNCT
brj-23498	107	1	“	"	PUNCT
brj-23498	107	2	alfalfa	alfalfa	NOUN
brj-23498	107	3	quality	quality	NOUN
brj-23498	107	4	assessment	assessment	NOUN
brj-23498	107	5	,	,	PUNCT
brj-23498	107	6	”	"	PUNCT
brj-23498	107	7	bioresources	bioresource	NOUN
brj-23498	107	8	19(3	19(3	NUM
brj-23498	107	9	)	)	PUNCT
brj-23498	107	10	,	,	PUNCT
brj-23498	107	11	4531	4531	NUM
brj-23498	107	12	-	-	SYM
brj-23498	107	13	4546	4546	NUM
brj-23498	107	14	.	.	PUNCT
brj-23498	108	1	4537	4537	NUM
brj-23498	108	2	hence	hence	ADV
brj-23498	108	3	,	,	PUNCT
brj-23498	108	4	the	the	DET
brj-23498	108	5	competitive	competitive	ADJ
brj-23498	108	6	adaptive	adaptive	ADJ
brj-23498	108	7	re	re	ADJ
brj-23498	108	8	-	-	ADJ
brj-23498	108	9	weighting	weighting	ADJ
brj-23498	108	10	scheme	scheme	NOUN
brj-23498	108	11	(	(	PUNCT
brj-23498	108	12	cars	car	NOUN
brj-23498	108	13	)	)	PUNCT
brj-23498	108	14	algorithm	algorithm	NOUN
brj-23498	108	15	and	and	CCONJ
brj-23498	108	16	successive	successive	ADJ
brj-23498	108	17	projections	projection	NOUN
brj-23498	108	18	algorithm	algorithm	NOUN
brj-23498	108	19	(	(	PUNCT
brj-23498	108	20	spa	spa	NOUN
brj-23498	108	21	)	)	PUNCT
brj-23498	108	22	are	be	AUX
brj-23498	108	23	employed	employ	VERB
brj-23498	108	24	to	to	PART
brj-23498	108	25	extract	extract	VERB
brj-23498	108	26	feature	feature	NOUN
brj-23498	108	27	wavelengths	wavelength	NOUN
brj-23498	108	28	,	,	PUNCT
brj-23498	108	29	selecting	select	VERB
brj-23498	108	30	fewer	few	ADJ
brj-23498	108	31	wavelength	wavelength	NOUN
brj-23498	108	32	variables	variable	NOUN
brj-23498	108	33	based	base	VERB
brj-23498	108	34	on	on	ADP
brj-23498	108	35	the	the	DET
brj-23498	108	36	principle	principle	NOUN
brj-23498	108	37	of	of	ADP
brj-23498	108	38	minimizing	minimize	VERB
brj-23498	108	39	the	the	DET
brj-23498	108	40	root	root	NOUN
brj-23498	108	41	mean	mean	ADJ
brj-23498	108	42	square	square	ADJ
brj-23498	108	43	error	error	NOUN
brj-23498	108	44	(	(	PUNCT
brj-23498	108	45	rmse	rmse	NOUN
brj-23498	108	46	)	)	PUNCT
brj-23498	108	47	to	to	PART
brj-23498	108	48	establish	establish	VERB
brj-23498	108	49	a	a	DET
brj-23498	108	50	predictive	predictive	ADJ
brj-23498	108	51	model	model	NOUN
brj-23498	108	52	for	for	ADP
brj-23498	108	53	the	the	DET
brj-23498	108	54	nutritional	nutritional	ADJ
brj-23498	108	55	quality	quality	NOUN
brj-23498	108	56	of	of	ADP
brj-23498	108	57	alfalfa	alfalfa	PROPN
brj-23498	108	58	hay	hay	PROPN
brj-23498	108	59	.	.	PUNCT
brj-23498	109	1	the	the	DET
brj-23498	109	2	competitive	competitive	ADJ
brj-23498	109	3	adaptive	adaptive	ADJ
brj-23498	109	4	re	re	ADJ
brj-23498	109	5	-	-	ADJ
brj-23498	109	6	weighting	weighting	ADJ
brj-23498	109	7	scheme	scheme	NOUN
brj-23498	109	8	(	(	PUNCT
brj-23498	109	9	cars	car	NOUN
brj-23498	109	10	)	)	PUNCT
brj-23498	109	11	algorithm	algorithm	NOUN
brj-23498	109	12	determines	determine	VERB
brj-23498	109	13	the	the	DET
brj-23498	109	14	importance	importance	NOUN
brj-23498	109	15	weights	weight	NOUN
brj-23498	109	16	of	of	ADP
brj-23498	109	17	each	each	DET
brj-23498	109	18	sample	sample	NOUN
brj-23498	109	19	during	during	ADP
brj-23498	109	20	the	the	DET
brj-23498	109	21	training	training	NOUN
brj-23498	109	22	process	process	NOUN
brj-23498	109	23	through	through	ADP
brj-23498	109	24	a	a	DET
brj-23498	109	25	competitive	competitive	ADJ
brj-23498	109	26	mechanism	mechanism	NOUN
brj-23498	109	27	,	,	PUNCT
brj-23498	109	28	giving	give	VERB
brj-23498	109	29	more	more	ADJ
brj-23498	109	30	weight	weight	NOUN
brj-23498	109	31	to	to	ADP
brj-23498	109	32	samples	sample	NOUN
brj-23498	109	33	that	that	PRON
brj-23498	109	34	are	be	AUX
brj-23498	109	35	more	more	ADV
brj-23498	109	36	difficult	difficult	ADJ
brj-23498	109	37	to	to	PART
brj-23498	109	38	classify	classify	VERB
brj-23498	109	39	.	.	PUNCT
brj-23498	110	1	typically	typically	ADV
brj-23498	110	2	,	,	PUNCT
brj-23498	110	3	this	this	DET
brj-23498	110	4	competition	competition	NOUN
brj-23498	110	5	can	can	AUX
brj-23498	110	6	be	be	AUX
brj-23498	110	7	based	base	VERB
brj-23498	110	8	on	on	ADP
brj-23498	110	9	sample	sample	NOUN
brj-23498	110	10	difficulty	difficulty	NOUN
brj-23498	110	11	,	,	PUNCT
brj-23498	110	12	error	error	NOUN
brj-23498	110	13	rate	rate	NOUN
brj-23498	110	14	,	,	PUNCT
brj-23498	110	15	or	or	CCONJ
brj-23498	110	16	potential	potential	ADJ
brj-23498	110	17	impact	impact	NOUN
brj-23498	110	18	metrics	metric	NOUN
brj-23498	110	19	.	.	PUNCT
brj-23498	111	1	the	the	DET
brj-23498	111	2	algorithm	algorithm	NOUN
brj-23498	111	3	performs	perform	VERB
brj-23498	111	4	re	re	VERB
brj-23498	111	5	-	-	NOUN
brj-23498	111	6	weighting	weighting	NOUN
brj-23498	111	7	of	of	ADP
brj-23498	111	8	each	each	DET
brj-23498	111	9	sample	sample	NOUN
brj-23498	111	10	based	base	VERB
brj-23498	111	11	on	on	ADP
brj-23498	111	12	its	its	PRON
brj-23498	111	13	importance	importance	NOUN
brj-23498	111	14	weight	weight	NOUN
brj-23498	111	15	iteratively	iteratively	ADV
brj-23498	111	16	during	during	ADP
brj-23498	111	17	training	training	NOUN
brj-23498	111	18	.	.	PUNCT
brj-23498	112	1	for	for	ADP
brj-23498	112	2	classifier	classifier	NOUN
brj-23498	112	3	training	training	NOUN
brj-23498	112	4	,	,	PUNCT
brj-23498	112	5	samples	sample	NOUN
brj-23498	112	6	with	with	ADP
brj-23498	112	7	higher	high	ADJ
brj-23498	112	8	importance	importance	NOUN
brj-23498	112	9	weights	weight	NOUN
brj-23498	112	10	are	be	AUX
brj-23498	112	11	given	give	VERB
brj-23498	112	12	greater	great	ADJ
brj-23498	112	13	weight	weight	NOUN
brj-23498	112	14	,	,	PUNCT
brj-23498	112	15	thereby	thereby	ADV
brj-23498	112	16	enhancing	enhance	VERB
brj-23498	112	17	the	the	DET
brj-23498	112	18	learning	learning	NOUN
brj-23498	112	19	effect	effect	NOUN
brj-23498	112	20	on	on	ADP
brj-23498	112	21	minority	minority	NOUN
brj-23498	112	22	class	class	NOUN
brj-23498	112	23	samples	sample	NOUN
brj-23498	112	24	and	and	CCONJ
brj-23498	112	25	improving	improve	VERB
brj-23498	112	26	the	the	DET
brj-23498	112	27	model	model	NOUN
brj-23498	112	28	’s	’s	PART
brj-23498	112	29	ability	ability	NOUN
brj-23498	112	30	to	to	PART
brj-23498	112	31	identify	identify	VERB
brj-23498	112	32	samples	sample	NOUN
brj-23498	112	33	(	(	PUNCT
brj-23498	112	34	zhang	zhang	X
brj-23498	112	35	et	et	PROPN
brj-23498	112	36	al	al	PROPN
brj-23498	112	37	.	.	PROPN
brj-23498	112	38	2023	2023	NUM
brj-23498	112	39	)	)	PUNCT
brj-23498	112	40	.	.	PUNCT
brj-23498	113	1	the	the	DET
brj-23498	113	2	competitive	competitive	ADJ
brj-23498	113	3	adaptive	adaptive	ADJ
brj-23498	113	4	algorithm	algorithm	NOUN
brj-23498	113	5	is	be	AUX
brj-23498	113	6	used	use	VERB
brj-23498	113	7	to	to	PART
brj-23498	113	8	extract	extract	VERB
brj-23498	113	9	feature	feature	NOUN
brj-23498	113	10	wavelengths	wavelength	NOUN
brj-23498	113	11	from	from	ADP
brj-23498	113	12	the	the	DET
brj-23498	113	13	full	full	ADJ
brj-23498	113	14	spectral	spectral	ADJ
brj-23498	113	15	range	range	NOUN
brj-23498	113	16	,	,	PUNCT
brj-23498	113	17	with	with	ADP
brj-23498	113	18	100	100	NUM
brj-23498	113	19	iterations	iteration	NOUN
brj-23498	113	20	and	and	CCONJ
brj-23498	113	21	10	10	NUM
brj-23498	113	22	cross	cross	ADJ
brj-23498	113	23	-	-	ADJ
brj-23498	113	24	validation	validation	ADJ
brj-23498	113	25	folds	fold	NOUN
brj-23498	113	26	.	.	PUNCT
brj-23498	114	1	the	the	DET
brj-23498	114	2	results	result	NOUN
brj-23498	114	3	show	show	VERB
brj-23498	114	4	that	that	SCONJ
brj-23498	114	5	the	the	DET
brj-23498	114	6	minimum	minimum	ADJ
brj-23498	114	7	rmsecv	rmsecv	NOUN
brj-23498	114	8	is	be	AUX
brj-23498	114	9	achieved	achieve	VERB
brj-23498	114	10	with	with	ADP
brj-23498	114	11	47	47	NUM
brj-23498	114	12	iterations	iteration	NOUN
brj-23498	114	13	,	,	PUNCT
brj-23498	114	14	extracting	extract	VERB
brj-23498	114	15	a	a	DET
brj-23498	114	16	total	total	NOUN
brj-23498	114	17	of	of	ADP
brj-23498	114	18	67	67	NUM
brj-23498	114	19	feature	feature	NOUN
brj-23498	114	20	wavelengths	wavelength	NOUN
brj-23498	114	21	.	.	PUNCT
brj-23498	115	1	the	the	DET
brj-23498	115	2	results	result	NOUN
brj-23498	115	3	are	be	AUX
brj-23498	115	4	shown	show	VERB
brj-23498	115	5	in	in	ADP
brj-23498	115	6	fig	fig	NOUN
brj-23498	115	7	.	.	PUNCT
brj-23498	116	1	4	4	X
brj-23498	116	2	.	.	X
brj-23498	116	3	the	the	DET
brj-23498	116	4	successive	successive	ADJ
brj-23498	116	5	projections	projection	NOUN
brj-23498	116	6	algorithm	algorithm	NOUN
brj-23498	116	7	is	be	AUX
brj-23498	116	8	a	a	DET
brj-23498	116	9	forward	forward	ADJ
brj-23498	116	10	variable	variable	ADJ
brj-23498	116	11	selection	selection	NOUN
brj-23498	116	12	algorithm	algorithm	NOUN
brj-23498	116	13	that	that	PRON
brj-23498	116	14	minimizes	minimize	VERB
brj-23498	116	15	collinearity	collinearity	NOUN
brj-23498	116	16	in	in	ADP
brj-23498	116	17	vector	vector	NOUN
brj-23498	116	18	space	space	NOUN
brj-23498	116	19	.	.	PUNCT
brj-23498	117	1	it	it	PRON
brj-23498	117	2	eliminates	eliminate	VERB
brj-23498	117	3	redundant	redundant	ADJ
brj-23498	117	4	information	information	NOUN
brj-23498	117	5	in	in	ADP
brj-23498	117	6	the	the	DET
brj-23498	117	7	original	original	ADJ
brj-23498	117	8	spectral	spectral	ADJ
brj-23498	117	9	matrix	matrix	NOUN
brj-23498	117	10	,	,	PUNCT
brj-23498	117	11	selecting	select	VERB
brj-23498	117	12	fewer	few	ADJ
brj-23498	117	13	feature	feature	NOUN
brj-23498	117	14	wavelengths	wavelength	NOUN
brj-23498	117	15	.	.	PUNCT
brj-23498	118	1	the	the	DET
brj-23498	118	2	wavelengths	wavelength	NOUN
brj-23498	118	3	selected	select	VERB
brj-23498	118	4	through	through	ADP
brj-23498	118	5	this	this	DET
brj-23498	118	6	algorithm	algorithm	NOUN
brj-23498	118	7	demonstrate	demonstrate	VERB
brj-23498	118	8	better	well	ADJ
brj-23498	118	9	predictive	predictive	ADJ
brj-23498	118	10	performance	performance	NOUN
brj-23498	118	11	when	when	SCONJ
brj-23498	118	12	used	use	VERB
brj-23498	118	13	to	to	PART
brj-23498	118	14	build	build	VERB
brj-23498	118	15	models	model	NOUN
brj-23498	118	16	.	.	PUNCT
brj-23498	119	1	the	the	DET
brj-23498	119	2	successive	successive	ADJ
brj-23498	119	3	projections	projection	NOUN
brj-23498	119	4	algorithm	algorithm	NOUN
brj-23498	119	5	extracts	extract	VERB
brj-23498	119	6	51	51	NUM
brj-23498	119	7	feature	feature	NOUN
brj-23498	119	8	wavelengths	wavelength	NOUN
brj-23498	119	9	,	,	PUNCT
brj-23498	119	10	as	as	SCONJ
brj-23498	119	11	shown	show	VERB
brj-23498	119	12	in	in	ADP
brj-23498	119	13	fig	fig	NOUN
brj-23498	119	14	.	.	PUNCT
brj-23498	120	1	4	4	X
brj-23498	120	2	.	.	NOUN
brj-23498	120	3	feature	feature	NOUN
brj-23498	120	4	extraction	extraction	NOUN
brj-23498	120	5	and	and	CCONJ
brj-23498	120	6	dimensionality	dimensionality	NOUN
brj-23498	120	7	reduction	reduction	NOUN
brj-23498	120	8	of	of	ADP
brj-23498	120	9	image	image	NOUN
brj-23498	120	10	data	data	NOUN
brj-23498	120	11	image	image	NOUN
brj-23498	120	12	preprocessing	preprocesse	VERB
brj-23498	120	13	only	only	ADV
brj-23498	120	14	removes	remove	VERB
brj-23498	120	15	irrelevant	irrelevant	ADJ
brj-23498	120	16	information	information	NOUN
brj-23498	120	17	from	from	ADP
brj-23498	120	18	moldy	moldy	ADJ
brj-23498	120	19	alfalfa	alfalfa	NOUN
brj-23498	120	20	images	image	NOUN
brj-23498	120	21	.	.	PUNCT
brj-23498	121	1	to	to	PART
brj-23498	121	2	achieve	achieve	VERB
brj-23498	121	3	the	the	DET
brj-23498	121	4	automatic	automatic	ADJ
brj-23498	121	5	recognition	recognition	NOUN
brj-23498	121	6	function	function	NOUN
brj-23498	121	7	of	of	ADP
brj-23498	121	8	the	the	DET
brj-23498	121	9	mold	mold	NOUN
brj-23498	121	10	recognition	recognition	NOUN
brj-23498	121	11	system	system	NOUN
brj-23498	121	12	,	,	PUNCT
brj-23498	121	13	feature	feature	NOUN
brj-23498	121	14	extraction	extraction	NOUN
brj-23498	121	15	of	of	ADP
brj-23498	121	16	images	image	NOUN
brj-23498	121	17	is	be	AUX
brj-23498	121	18	also	also	ADV
brj-23498	121	19	required	require	VERB
brj-23498	121	20	.	.	PUNCT
brj-23498	122	1	selecting	select	VERB
brj-23498	122	2	appropriate	appropriate	ADJ
brj-23498	122	3	feature	feature	NOUN
brj-23498	122	4	extraction	extraction	NOUN
brj-23498	122	5	methods	method	NOUN
brj-23498	122	6	and	and	CCONJ
brj-23498	122	7	categories	category	NOUN
brj-23498	122	8	is	be	AUX
brj-23498	122	9	a	a	DET
brj-23498	122	10	key	key	ADJ
brj-23498	122	11	factor	factor	NOUN
brj-23498	122	12	in	in	ADP
brj-23498	122	13	ensuring	ensure	VERB
brj-23498	122	14	recognition	recognition	NOUN
brj-23498	122	15	accuracy	accuracy	NOUN
brj-23498	122	16	.	.	PUNCT
brj-23498	123	1	since	since	SCONJ
brj-23498	123	2	the	the	DET
brj-23498	123	3	moldy	moldy	ADJ
brj-23498	123	4	parts	part	NOUN
brj-23498	123	5	of	of	ADP
brj-23498	123	6	the	the	DET
brj-23498	123	7	image	image	NOUN
brj-23498	123	8	have	have	VERB
brj-23498	123	9	significant	significant	ADJ
brj-23498	123	10	differences	difference	NOUN
brj-23498	123	11	in	in	ADP
brj-23498	123	12	color	color	NOUN
brj-23498	123	13	and	and	CCONJ
brj-23498	123	14	texture	texture	NOUN
brj-23498	123	15	compared	compare	VERB
brj-23498	123	16	to	to	ADP
brj-23498	123	17	normal	normal	ADJ
brj-23498	123	18	leaves	leave	NOUN
brj-23498	123	19	,	,	PUNCT
brj-23498	123	20	color	color	NOUN
brj-23498	123	21	features	feature	NOUN
brj-23498	123	22	and	and	CCONJ
brj-23498	123	23	texture	texture	NOUN
brj-23498	123	24	features	feature	NOUN
brj-23498	123	25	of	of	ADP
brj-23498	123	26	the	the	DET
brj-23498	123	27	moldy	moldy	ADJ
brj-23498	123	28	images	image	NOUN
brj-23498	123	29	are	be	AUX
brj-23498	123	30	extracted	extract	VERB
brj-23498	123	31	for	for	ADP
brj-23498	123	32	model	model	NOUN
brj-23498	123	33	building	building	NOUN
brj-23498	123	34	.	.	PUNCT
brj-23498	124	1	color	color	NOUN
brj-23498	124	2	features	feature	NOUN
brj-23498	124	3	are	be	AUX
brj-23498	124	4	extracted	extract	VERB
brj-23498	124	5	using	use	VERB
brj-23498	124	6	color	color	NOUN
brj-23498	124	7	histograms	histogram	NOUN
brj-23498	124	8	,	,	PUNCT
brj-23498	124	9	capturing	capture	VERB
brj-23498	124	10	nine	nine	NUM
brj-23498	124	11	features	feature	NOUN
brj-23498	124	12	including	include	VERB
brj-23498	124	13	rgb	rgb	PROPN
brj-23498	124	14	,	,	PUNCT
brj-23498	124	15	h	h	NOUN
brj-23498	124	16	(	(	PUNCT
brj-23498	124	17	hue	hue	NOUN
brj-23498	124	18	)	)	PUNCT
brj-23498	124	19	,	,	PUNCT
brj-23498	124	20	s	s	X
brj-23498	124	21	(	(	PUNCT
brj-23498	124	22	saturation	saturation	NOUN
brj-23498	124	23	)	)	PUNCT
brj-23498	124	24	,	,	PUNCT
brj-23498	124	25	v	v	NOUN
brj-23498	124	26	(	(	PUNCT
brj-23498	124	27	brightness	brightness	NOUN
brj-23498	124	28	)	)	PUNCT
brj-23498	124	29	,	,	PUNCT
brj-23498	124	30	l	l	NOUN
brj-23498	124	31	(	(	PUNCT
brj-23498	124	32	lightness	lightness	NOUN
brj-23498	124	33	)	)	PUNCT
brj-23498	124	34	,	,	PUNCT
brj-23498	124	35	a	a	PRON
brj-23498	124	36	(	(	PUNCT
brj-23498	124	37	from	from	ADP
brj-23498	124	38	red	red	ADJ
brj-23498	124	39	to	to	ADP
brj-23498	124	40	green	green	ADJ
brj-23498	124	41	range	range	NOUN
brj-23498	124	42	)	)	PUNCT
brj-23498	124	43	,	,	PUNCT
brj-23498	124	44	and	and	CCONJ
brj-23498	124	45	b	b	X
brj-23498	124	46	(	(	PUNCT
brj-23498	124	47	from	from	ADP
brj-23498	124	48	yellow	yellow	ADJ
brj-23498	124	49	to	to	ADP
brj-23498	124	50	blue	blue	ADJ
brj-23498	124	51	range	range	NOUN
brj-23498	124	52	)	)	PUNCT
brj-23498	124	53	.	.	PUNCT
brj-23498	125	1	texture	texture	NOUN
brj-23498	125	2	features	feature	NOUN
brj-23498	125	3	are	be	AUX
brj-23498	125	4	extracted	extract	VERB
brj-23498	125	5	using	use	VERB
brj-23498	125	6	the	the	DET
brj-23498	125	7	tamura	tamura	ADJ
brj-23498	125	8	algorithm	algorithm	NOUN
brj-23498	125	9	,	,	PUNCT
brj-23498	125	10	extracting	extract	VERB
brj-23498	125	11	22	22	NUM
brj-23498	125	12	texture	texture	NOUN
brj-23498	125	13	features	feature	NOUN
brj-23498	125	14	such	such	ADJ
brj-23498	125	15	as	as	ADP
brj-23498	125	16	autocorrelation	autocorrelation	NOUN
brj-23498	125	17	,	,	PUNCT
brj-23498	125	18	entropy	entropy	NOUN
brj-23498	125	19	,	,	PUNCT
brj-23498	125	20	and	and	CCONJ
brj-23498	125	21	contrast	contrast	NOUN
brj-23498	125	22	.	.	PUNCT
brj-23498	126	1	from	from	ADP
brj-23498	126	2	these	these	DET
brj-23498	126	3	22	22	NUM
brj-23498	126	4	texture	texture	NOUN
brj-23498	126	5	features	feature	NOUN
brj-23498	126	6	,	,	PUNCT
brj-23498	126	7	the	the	DET
brj-23498	126	8	random	random	ADJ
brj-23498	126	9	forest	forest	NOUN
brj-23498	126	10	(	(	PUNCT
brj-23498	126	11	rf	rf	NOUN
brj-23498	126	12	)	)	PUNCT
brj-23498	126	13	algorithm	algorithm	NOUN
brj-23498	126	14	is	be	AUX
brj-23498	126	15	used	use	VERB
brj-23498	126	16	to	to	PART
brj-23498	126	17	extract	extract	VERB
brj-23498	126	18	10	10	NUM
brj-23498	126	19	texture	texture	ADJ
brj-23498	126	20	features	feature	NOUN
brj-23498	126	21	including	include	VERB
brj-23498	126	22	energy	energy	NOUN
brj-23498	126	23	,	,	PUNCT
brj-23498	126	24	entropy	entropy	PROPN
brj-23498	126	25	,	,	PUNCT
brj-23498	126	26	contrast	contrast	NOUN
brj-23498	126	27	,	,	PUNCT
brj-23498	126	28	and	and	CCONJ
brj-23498	126	29	variance	variance	NOUN
brj-23498	126	30	for	for	ADP
brj-23498	126	31	texture	texture	ADJ
brj-23498	126	32	feature	feature	NOUN
brj-23498	126	33	representation	representation	NOUN
brj-23498	126	34	.	.	PUNCT
brj-23498	127	1	color	color	NOUN
brj-23498	127	2	histograms	histogram	NOUN
brj-23498	127	3	are	be	AUX
brj-23498	127	4	widely	widely	ADV
brj-23498	127	5	used	use	VERB
brj-23498	127	6	color	color	NOUN
brj-23498	127	7	features	feature	NOUN
brj-23498	127	8	in	in	ADP
brj-23498	127	9	many	many	ADJ
brj-23498	127	10	image	image	NOUN
brj-23498	127	11	retrieval	retrieval	NOUN
brj-23498	127	12	systems	system	NOUN
brj-23498	127	13	(	(	PUNCT
brj-23498	127	14	zhangzhong	zhangzhong	PROPN
brj-23498	127	15	et	et	PROPN
brj-23498	127	16	al	al	PROPN
brj-23498	127	17	.	.	PROPN
brj-23498	127	18	2023	2023	NUM
brj-23498	127	19	)	)	PUNCT
brj-23498	127	20	.	.	PUNCT
brj-23498	128	1	they	they	PRON
brj-23498	128	2	describe	describe	VERB
brj-23498	128	3	the	the	DET
brj-23498	128	4	proportion	proportion	NOUN
brj-23498	128	5	of	of	ADP
brj-23498	128	6	different	different	ADJ
brj-23498	128	7	colors	color	NOUN
brj-23498	128	8	in	in	ADP
brj-23498	128	9	the	the	DET
brj-23498	128	10	entire	entire	ADJ
brj-23498	128	11	image	image	NOUN
brj-23498	128	12	,	,	PUNCT
brj-23498	128	13	without	without	ADP
brj-23498	128	14	considering	consider	VERB
brj-23498	128	15	the	the	DET
brj-23498	128	16	spatial	spatial	ADJ
brj-23498	128	17	position	position	NOUN
brj-23498	128	18	of	of	ADP
brj-23498	128	19	each	each	DET
brj-23498	128	20	color	color	NOUN
brj-23498	128	21	,	,	PUNCT
brj-23498	128	22	thus	thus	ADV
brj-23498	128	23	unable	unable	ADJ
brj-23498	128	24	to	to	PART
brj-23498	128	25	describe	describe	VERB
brj-23498	128	26	objects	object	NOUN
brj-23498	128	27	or	or	CCONJ
brj-23498	128	28	entities	entity	NOUN
brj-23498	128	29	in	in	ADP
brj-23498	128	30	the	the	DET
brj-23498	128	31	image	image	NOUN
brj-23498	128	32	.	.	PUNCT
brj-23498	129	1	color	color	NOUN
brj-23498	129	2	histograms	histogram	NOUN
brj-23498	129	3	are	be	AUX
brj-23498	129	4	particularly	particularly	ADV
brj-23498	129	5	suitable	suitable	ADJ
brj-23498	129	6	for	for	ADP
brj-23498	129	7	describing	describe	VERB
brj-23498	129	8	images	image	NOUN
brj-23498	129	9	that	that	PRON
brj-23498	129	10	are	be	AUX
brj-23498	129	11	difficult	difficult	ADJ
brj-23498	129	12	to	to	PART
brj-23498	129	13	automatically	automatically	ADV
brj-23498	129	14	segment	segment	VERB
brj-23498	129	15	.	.	PUNCT
brj-23498	130	1	the	the	DET
brj-23498	130	2	color	color	NOUN
brj-23498	130	3	histogram	histogram	NOUN
brj-23498	130	4	of	of	ADP
brj-23498	130	5	alfalfa	alfalfa	NOUN
brj-23498	130	6	hay	hay	NOUN
brj-23498	130	7	is	be	AUX
brj-23498	130	8	shown	show	VERB
brj-23498	130	9	in	in	ADP
brj-23498	130	10	fig	fig	NOUN
brj-23498	130	11	.	.	PUNCT
brj-23498	131	1	5	5	X
brj-23498	131	2	.	.	X
brj-23498	131	3	in	in	ADP
brj-23498	131	4	the	the	DET
brj-23498	131	5	rgb	rgb	PROPN
brj-23498	131	6	histogram	histogram	NOUN
brj-23498	131	7	,	,	PUNCT
brj-23498	131	8	the	the	DET
brj-23498	131	9	horizontal	horizontal	ADJ
brj-23498	131	10	axis	axis	NOUN
brj-23498	131	11	represents	represent	VERB
brj-23498	131	12	pixel	pixel	ADJ
brj-23498	131	13	values	value	NOUN
brj-23498	131	14	(	(	PUNCT
brj-23498	131	15	0	0	NUM
brj-23498	131	16	to	to	ADP
brj-23498	131	17	255	255	NUM
brj-23498	131	18	)	)	PUNCT
brj-23498	131	19	,	,	PUNCT
brj-23498	131	20	representing	represent	VERB
brj-23498	131	21	the	the	DET
brj-23498	131	22	brightness	brightness	NOUN
brj-23498	131	23	or	or	CCONJ
brj-23498	131	24	color	color	NOUN
brj-23498	131	25	component	component	NOUN
brj-23498	131	26	values	value	NOUN
brj-23498	131	27	of	of	ADP
brj-23498	131	28	the	the	DET
brj-23498	131	29	image	image	NOUN
brj-23498	131	30	.	.	PUNCT
brj-23498	132	1	the	the	DET
brj-23498	132	2	vertical	vertical	ADJ
brj-23498	132	3	axis	axis	NOUN
brj-23498	132	4	represents	represent	VERB
brj-23498	132	5	the	the	DET
brj-23498	132	6	frequency	frequency	NOUN
brj-23498	132	7	at	at	ADP
brj-23498	132	8	which	which	PRON
brj-23498	132	9	the	the	DET
brj-23498	132	10	pixel	pixel	PROPN
brj-23498	132	11	value	value	NOUN
brj-23498	132	12	appears	appear	VERB
brj-23498	132	13	in	in	ADP
brj-23498	132	14	the	the	DET
brj-23498	132	15	image	image	NOUN
brj-23498	132	16	,	,	PUNCT
brj-23498	132	17	i.e.	i.e.	X
brj-23498	132	18	,	,	PUNCT
brj-23498	132	19	the	the	DET
brj-23498	132	20	number	number	NOUN
brj-23498	132	21	of	of	ADP
brj-23498	132	22	pixels	pixel	NOUN
brj-23498	132	23	with	with	ADP
brj-23498	132	24	a	a	DET
brj-23498	132	25	specific	specific	ADJ
brj-23498	132	26	pixel	pixel	NOUN
brj-23498	132	27	value	value	NOUN
brj-23498	132	28	in	in	ADP
brj-23498	132	29	the	the	DET
brj-23498	132	30	image	image	NOUN
brj-23498	132	31	.	.	PUNCT
brj-23498	133	1	in	in	ADP
brj-23498	133	2	the	the	DET
brj-23498	133	3	hsv	hsv	PROPN
brj-23498	133	4	histogram	histogram	NOUN
brj-23498	133	5	,	,	PUNCT
brj-23498	133	6	for	for	ADP
brj-23498	133	7	the	the	DET
brj-23498	133	8	h	h	PROPN
brj-23498	133	9	channel	channel	NOUN
brj-23498	133	10	,	,	PUNCT
brj-23498	133	11	the	the	DET
brj-23498	133	12	horizontal	horizontal	ADJ
brj-23498	133	13	axis	axis	NOUN
brj-23498	133	14	represents	represent	VERB
brj-23498	133	15	the	the	DET
brj-23498	133	16	range	range	NOUN
brj-23498	133	17	of	of	ADP
brj-23498	133	18	hue	hue	NOUN
brj-23498	133	19	values	value	NOUN
brj-23498	133	20	(	(	PUNCT
brj-23498	133	21	0	0	NUM
brj-23498	133	22	to	to	PART
brj-23498	133	23	1	1	NUM
brj-23498	133	24	)	)	PUNCT
brj-23498	133	25	,	,	PUNCT
brj-23498	133	26	representing	represent	VERB
brj-23498	133	27	the	the	DET
brj-23498	133	28	color	color	NOUN
brj-23498	133	29	in	in	ADP
brj-23498	133	30	the	the	DET
brj-23498	133	31	peer	peer	NOUN
brj-23498	133	32	-	-	PUNCT
brj-23498	133	33	reviewed	review	VERB
brj-23498	133	34	article	article	NOUN
brj-23498	133	35	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	133	36	yang	yang	PROPN
brj-23498	133	37	et	et	PROPN
brj-23498	133	38	al	al	PROPN
brj-23498	133	39	.	.	PROPN
brj-23498	134	1	(	(	PUNCT
brj-23498	134	2	2024	2024	NUM
brj-23498	134	3	)	)	PUNCT
brj-23498	134	4	.	.	PUNCT
brj-23498	135	1	“	"	PUNCT
brj-23498	135	2	alfalfa	alfalfa	NOUN
brj-23498	135	3	quality	quality	NOUN
brj-23498	135	4	assessment	assessment	NOUN
brj-23498	135	5	,	,	PUNCT
brj-23498	135	6	”	"	PUNCT
brj-23498	135	7	bioresources	bioresource	NOUN
brj-23498	135	8	19(3	19(3	NUM
brj-23498	135	9	)	)	PUNCT
brj-23498	135	10	,	,	PUNCT
brj-23498	135	11	4531	4531	NUM
brj-23498	135	12	-	-	SYM
brj-23498	135	13	4546	4546	NUM
brj-23498	135	14	.	.	PUNCT
brj-23498	135	15	4538	4538	NUM
brj-23498	135	16	image	image	NOUN
brj-23498	135	17	.	.	PUNCT
brj-23498	136	1	for	for	ADP
brj-23498	136	2	the	the	DET
brj-23498	136	3	s	s	X
brj-23498	136	4	and	and	CCONJ
brj-23498	136	5	v	v	NOUN
brj-23498	136	6	channels	channel	NOUN
brj-23498	136	7	,	,	PUNCT
brj-23498	136	8	the	the	DET
brj-23498	136	9	horizontal	horizontal	ADJ
brj-23498	136	10	axis	axis	NOUN
brj-23498	136	11	also	also	ADV
brj-23498	136	12	represents	represent	VERB
brj-23498	136	13	the	the	DET
brj-23498	136	14	range	range	NOUN
brj-23498	136	15	(	(	PUNCT
brj-23498	136	16	0	0	NUM
brj-23498	136	17	to	to	PART
brj-23498	136	18	1	1	NUM
brj-23498	136	19	)	)	PUNCT
brj-23498	136	20	,	,	PUNCT
brj-23498	136	21	representing	represent	VERB
brj-23498	136	22	the	the	DET
brj-23498	136	23	saturation	saturation	NOUN
brj-23498	136	24	and	and	CCONJ
brj-23498	136	25	brightness	brightness	NOUN
brj-23498	136	26	in	in	ADP
brj-23498	136	27	the	the	DET
brj-23498	136	28	image	image	NOUN
brj-23498	136	29	.	.	PUNCT
brj-23498	137	1	the	the	DET
brj-23498	137	2	vertical	vertical	ADJ
brj-23498	137	3	axis	axis	NOUN
brj-23498	137	4	represents	represent	VERB
brj-23498	137	5	the	the	DET
brj-23498	137	6	frequency	frequency	NOUN
brj-23498	137	7	or	or	CCONJ
brj-23498	137	8	count	count	NOUN
brj-23498	137	9	of	of	ADP
brj-23498	137	10	data	datum	NOUN
brj-23498	137	11	within	within	ADP
brj-23498	137	12	the	the	DET
brj-23498	137	13	corresponding	corresponding	ADJ
brj-23498	137	14	range	range	NOUN
brj-23498	137	15	.	.	PUNCT
brj-23498	138	1	(	(	PUNCT
brj-23498	138	2	a	a	X
brj-23498	138	3	)	)	PUNCT
brj-23498	138	4	(	(	PUNCT
brj-23498	138	5	b	b	X
brj-23498	138	6	)	)	PUNCT
brj-23498	138	7	fig	fig	NOUN
brj-23498	138	8	.	.	PUNCT
brj-23498	139	1	5	5	X
brj-23498	139	2	.	.	X
brj-23498	139	3	color	color	NOUN
brj-23498	139	4	histograms	histogram	NOUN
brj-23498	139	5	(	(	PUNCT
brj-23498	139	6	a	a	X
brj-23498	139	7	)	)	PUNCT
brj-23498	139	8	rgb	rgb	PROPN
brj-23498	139	9	histogram	histogram	NOUN
brj-23498	139	10	(	(	PUNCT
brj-23498	139	11	b	b	NOUN
brj-23498	139	12	)	)	PUNCT
brj-23498	139	13	hsv	hsv	PROPN
brj-23498	139	14	histogram	histogram	NOUN
brj-23498	139	15	random	random	ADJ
brj-23498	139	16	forest	forest	NOUN
brj-23498	139	17	(	(	PUNCT
brj-23498	139	18	rf	rf	NOUN
brj-23498	139	19	)	)	PUNCT
brj-23498	139	20	exhibits	exhibit	VERB
brj-23498	139	21	high	high	ADJ
brj-23498	139	22	prediction	prediction	NOUN
brj-23498	139	23	accuracy	accuracy	NOUN
brj-23498	139	24	,	,	PUNCT
brj-23498	139	25	good	good	ADJ
brj-23498	139	26	robustness	robustness	NOUN
brj-23498	139	27	,	,	PUNCT
brj-23498	139	28	and	and	CCONJ
brj-23498	139	29	strong	strong	ADJ
brj-23498	139	30	resistance	resistance	NOUN
brj-23498	139	31	to	to	ADP
brj-23498	139	32	overfitting	overfitte	VERB
brj-23498	139	33	.	.	PUNCT
brj-23498	140	1	additionally	additionally	ADV
brj-23498	140	2	,	,	PUNCT
brj-23498	140	3	it	it	PRON
brj-23498	140	4	can	can	AUX
brj-23498	140	5	handle	handle	VERB
brj-23498	140	6	high	high	ADJ
brj-23498	140	7	-	-	PUNCT
brj-23498	140	8	dimensional	dimensional	ADJ
brj-23498	140	9	data	datum	NOUN
brj-23498	140	10	and	and	CCONJ
brj-23498	140	11	is	be	AUX
brj-23498	140	12	relatively	relatively	ADV
brj-23498	140	13	robust	robust	ADJ
brj-23498	140	14	to	to	ADP
brj-23498	140	15	missing	missing	ADJ
brj-23498	140	16	and	and	CCONJ
brj-23498	140	17	outlier	outlier	NOUN
brj-23498	140	18	values	value	NOUN
brj-23498	140	19	.	.	PUNCT
brj-23498	141	1	the	the	DET
brj-23498	141	2	calculation	calculation	NOUN
brj-23498	141	3	method	method	NOUN
brj-23498	141	4	for	for	ADP
brj-23498	141	5	extracting	extract	VERB
brj-23498	141	6	texture	texture	NOUN
brj-23498	141	7	features	feature	NOUN
brj-23498	141	8	using	use	VERB
brj-23498	141	9	random	random	ADJ
brj-23498	141	10	forest	forest	NOUN
brj-23498	141	11	is	be	AUX
brj-23498	141	12	as	as	SCONJ
brj-23498	141	13	follows	follow	VERB
brj-23498	141	14	(	(	PUNCT
brj-23498	141	15	ye	ye	INTJ
brj-23498	141	16	et	et	NOUN
brj-23498	141	17	al	al	PROPN
brj-23498	141	18	.	.	PROPN
brj-23498	141	19	2023	2023	NUM
brj-23498	141	20	):	):	PUNCT
brj-23498	141	21	suppose	suppose	VERB
brj-23498	141	22	there	there	PRON
brj-23498	141	23	are	be	VERB
brj-23498	141	24	m	m	PROPN
brj-23498	141	25	features	feature	NOUN
brj-23498	141	26	x1	x1	PROPN
brj-23498	141	27	,	,	PUNCT
brj-23498	141	28	x2	x2	PROPN
brj-23498	141	29	,	,	PUNCT
brj-23498	141	30	x3	x3	ADJ
brj-23498	141	31	,	,	PUNCT
brj-23498	141	32	…	…	PUNCT
brj-23498	141	33	,	,	PUNCT
brj-23498	141	34	xm	xm	PROPN
brj-23498	141	35	,	,	PUNCT
brj-23498	141	36	collected	collect	VERB
brj-23498	141	37	from	from	ADP
brj-23498	141	38	image	image	NOUN
brj-23498	141	39	samples	sample	NOUN
brj-23498	141	40	.	.	PUNCT
brj-23498	142	1	first	first	ADV
brj-23498	142	2	,	,	PUNCT
brj-23498	142	3	calculate	calculate	VERB
brj-23498	142	4	the	the	DET
brj-23498	142	5	gini	gini	PROPN
brj-23498	142	6	index	index	NOUN
brj-23498	142	7	for	for	ADP
brj-23498	142	8	each	each	DET
brj-23498	142	9	feature	feature	NOUN
brj-23498	142	10	,	,	PUNCT
brj-23498	142	11	and	and	CCONJ
brj-23498	142	12	then	then	ADV
brj-23498	142	13	compute	compute	VERB
brj-23498	142	14	the	the	DET
brj-23498	142	15	importance	importance	NOUN
brj-23498	142	16	score	score	NOUN
brj-23498	142	17	of	of	ADP
brj-23498	142	18	each	each	DET
brj-23498	142	19	texture	texture	ADJ
brj-23498	142	20	feature	feature	NOUN
brj-23498	142	21	using	use	VERB
brj-23498	142	22	the	the	DET
brj-23498	142	23	𝑉𝐼𝑀𝑗	𝑉𝐼𝑀𝑗	PROPN
brj-23498	142	24	(	(	PUNCT
brj-23498	142	25	𝐺𝑖𝑛𝑖	𝐺𝑖𝑛𝑖	VERB
brj-23498	142	26	)	)	PUNCT
brj-23498	142	27	formula	formula	NOUN
brj-23498	142	28	.	.	PUNCT
brj-23498	143	1	the	the	DET
brj-23498	143	2	gini	gini	PROPN
brj-23498	143	3	index	index	NOUN
brj-23498	143	4	is	be	AUX
brj-23498	143	5	calculated	calculate	VERB
brj-23498	143	6	using	use	VERB
brj-23498	143	7	eq	eq	ADP
brj-23498	143	8	.	.	PROPN
brj-23498	143	9	1	1	NUM
brj-23498	143	10	,	,	PUNCT
brj-23498	143	11	𝑮𝒊𝒏𝒊𝒎	𝑮𝒊𝒏𝒊𝒎	PROPN
brj-23498	143	12	=	=	SYM
brj-23498	143	13	∑	∑	PROPN
brj-23498	143	14	∑	∑	PROPN
brj-23498	143	15	𝒑𝒎𝒌𝒌′≠𝒌	𝒑𝒎𝒌𝒌′≠𝒌	NOUN
brj-23498	143	16	|𝒌|	|𝒌|	ADJ
brj-23498	143	17	𝒌=𝟏	𝒌=𝟏	PUNCT
brj-23498	144	1	𝒑𝒎𝒌′	𝒑𝒎𝒌′	NOUN
brj-23498	144	2	=	=	SYM
brj-23498	144	3	1	1	NUM
brj-23498	144	4	−	−	PROPN
brj-23498	144	5	∑	∑	PROPN
brj-23498	144	6	𝒑𝒎𝒌	𝒑𝒎𝒌	PROPN
brj-23498	144	7	𝟐|𝑘|	𝟐|𝑘|	PROPN
brj-23498	144	8	𝑘=1	𝑘=1	X
brj-23498	144	9	(	(	PUNCT
brj-23498	144	10	1	1	X
brj-23498	144	11	)	)	PUNCT
brj-23498	144	12	where	where	SCONJ
brj-23498	144	13	k	k	PROPN
brj-23498	144	14	represents	represent	VERB
brj-23498	144	15	the	the	DET
brj-23498	144	16	category	category	NOUN
brj-23498	144	17	,	,	PUNCT
brj-23498	144	18	𝑝𝑚𝑘	𝑝𝑚𝑘	DET
brj-23498	144	19	denotes	denote	NOUN
brj-23498	144	20	the	the	DET
brj-23498	144	21	proportion	proportion	NOUN
brj-23498	144	22	of	of	ADP
brj-23498	144	23	category	category	NOUN
brj-23498	144	24	k	k	PROPN
brj-23498	144	25	in	in	ADP
brj-23498	144	26	node	node	PROPN
brj-23498	144	27	m	m	PROPN
brj-23498	144	28	,	,	PUNCT
brj-23498	144	29	which	which	PRON
brj-23498	144	30	can	can	AUX
brj-23498	144	31	also	also	ADV
brj-23498	144	32	be	be	AUX
brj-23498	144	33	seen	see	VERB
brj-23498	144	34	as	as	ADP
brj-23498	144	35	the	the	DET
brj-23498	144	36	probability	probability	NOUN
brj-23498	144	37	of	of	ADP
brj-23498	144	38	two	two	NUM
brj-23498	144	39	randomly	randomly	ADV
brj-23498	144	40	sampled	sample	VERB
brj-23498	144	41	samples	sample	NOUN
brj-23498	144	42	from	from	ADP
brj-23498	144	43	node	node	NOUN
brj-23498	144	44	m	m	AUX
brj-23498	144	45	having	have	VERB
brj-23498	144	46	different	different	ADJ
brj-23498	144	47	category	category	NOUN
brj-23498	144	48	labels	label	NOUN
brj-23498	144	49	.	.	PUNCT
brj-23498	145	1	the	the	DET
brj-23498	145	2	importance	importance	NOUN
brj-23498	145	3	of	of	ADP
brj-23498	145	4	feature	feature	NOUN
brj-23498	145	5	x	x	PUNCT
brj-23498	145	6	in	in	ADP
brj-23498	145	7	node	node	PROPN
brj-23498	145	8	m	m	PROPN
brj-23498	145	9	,	,	PUNCT
brj-23498	145	10	denoted	denote	VERB
brj-23498	145	11	as	as	ADP
brj-23498	145	12	𝑉𝐼𝑀𝑗𝑚	𝑉𝐼𝑀𝑗𝑚	PROPN
brj-23498	145	13	(	(	PUNCT
brj-23498	145	14	𝐺𝑖𝑛𝑖	𝐺𝑖𝑛𝑖	PROPN
brj-23498	145	15	)	)	PUNCT
brj-23498	145	16	,	,	PUNCT
brj-23498	145	17	which	which	PRON
brj-23498	145	18	is	be	AUX
brj-23498	145	19	the	the	DET
brj-23498	145	20	change	change	NOUN
brj-23498	145	21	in	in	ADP
brj-23498	145	22	gini	gini	PROPN
brj-23498	145	23	index	index	NOUN
brj-23498	145	24	before	before	ADP
brj-23498	145	25	and	and	CCONJ
brj-23498	145	26	after	after	SCONJ
brj-23498	145	27	node	node	NOUN
brj-23498	145	28	m	m	VERB
brj-23498	145	29	splits	split	VERB
brj-23498	145	30	,	,	PUNCT
brj-23498	145	31	is	be	AUX
brj-23498	145	32	calculated	calculate	VERB
brj-23498	145	33	using	use	VERB
brj-23498	145	34	eq	eq	ADP
brj-23498	145	35	.	.	PROPN
brj-23498	145	36	2	2	NUM
brj-23498	145	37	,	,	PUNCT
brj-23498	145	38	𝑽𝑰𝑴𝒋𝒎	𝑽𝑰𝑴𝒋𝒎	X
brj-23498	145	39	(	(	PUNCT
brj-23498	145	40	𝑮𝒊𝒏𝒊	𝑮𝒊𝒏𝒊	PROPN
brj-23498	145	41	)	)	PUNCT
brj-23498	146	1	=	=	PUNCT
brj-23498	146	2	𝑮𝒊𝒏𝒊𝒎	𝑮𝒊𝒏𝒊𝒎	PROPN
brj-23498	146	3	−	−	PROPN
brj-23498	146	4	𝑮𝒊𝒏𝒊𝒍	𝑮𝒊𝒏𝒊𝒍	PROPN
brj-23498	146	5	−	−	PROPN
brj-23498	146	6	𝑮𝒊𝒏𝒊𝒓	𝑮𝒊𝒏𝒊𝒓	PROPN
brj-23498	146	7	(	(	PUNCT
brj-23498	146	8	2	2	NUM
brj-23498	146	9	)	)	PUNCT
brj-23498	146	10	where	where	SCONJ
brj-23498	146	11	𝑮𝒊𝒏𝒊𝒍and	𝑮𝒊𝒏𝒊𝒍and	PROPN
brj-23498	146	12	𝑮𝒊𝒏𝒊𝒓	𝑮𝒊𝒏𝒊𝒓	PROPN
brj-23498	146	13	represent	represent	VERB
brj-23498	146	14	the	the	DET
brj-23498	146	15	gini	gini	PROPN
brj-23498	146	16	index	index	NOUN
brj-23498	146	17	of	of	ADP
brj-23498	146	18	the	the	DET
brj-23498	146	19	two	two	NUM
brj-23498	146	20	new	new	ADJ
brj-23498	146	21	nodes	node	NOUN
brj-23498	146	22	after	after	ADP
brj-23498	146	23	the	the	DET
brj-23498	146	24	split	split	NOUN
brj-23498	146	25	of	of	ADP
brj-23498	146	26	node	node	NOUN
brj-23498	146	27	m	m	NOUN
brj-23498	146	28	in	in	ADP
brj-23498	146	29	the	the	DET
brj-23498	146	30	random	random	ADJ
brj-23498	146	31	forest	forest	NOUN
brj-23498	146	32	.	.	PUNCT
brj-23498	147	1	if	if	SCONJ
brj-23498	147	2	feature	feature	NOUN
brj-23498	147	3	xj	xj	PROPN
brj-23498	147	4	appears	appear	VERB
brj-23498	147	5	in	in	ADP
brj-23498	147	6	node	node	PROPN
brj-23498	147	7	i	i	PRON
brj-23498	147	8	of	of	ADP
brj-23498	147	9	decision	decision	NOUN
brj-23498	147	10	tree	tree	NOUN
brj-23498	147	11	i	i	PRON
brj-23498	147	12	and	and	CCONJ
brj-23498	147	13	is	be	AUX
brj-23498	147	14	in	in	ADP
brj-23498	147	15	the	the	DET
brj-23498	147	16	set	set	NOUN
brj-23498	147	17	m	m	PROPN
brj-23498	147	18	,	,	PUNCT
brj-23498	147	19	then	then	ADV
brj-23498	147	20	the	the	DET
brj-23498	147	21	importance	importance	NOUN
brj-23498	147	22	of	of	ADP
brj-23498	147	23	xj	xj	PROPN
brj-23498	147	24	in	in	ADP
brj-23498	147	25	tree	tree	NOUN
brj-23498	147	26	i	i	PRON
brj-23498	147	27	,	,	PUNCT
brj-23498	147	28	denoted	denote	VERB
brj-23498	147	29	as	as	ADP
brj-23498	147	30	𝑽𝑰𝑴𝒊𝒋	𝑽𝑰𝑴𝒊𝒋	PROPN
brj-23498	147	31	(	(	PUNCT
brj-23498	147	32	𝑮𝒊𝒏𝒊	𝑮𝒊𝒏𝒊	PROPN
brj-23498	147	33	)	)	PUNCT
brj-23498	147	34	,	,	PUNCT
brj-23498	147	35	is	be	AUX
brj-23498	147	36	calculated	calculate	VERB
brj-23498	147	37	as	as	SCONJ
brj-23498	147	38	follows	follow	VERB
brj-23498	147	39	eq	eq	ADP
brj-23498	147	40	.	.	PROPN
brj-23498	147	41	3	3	NUM
brj-23498	147	42	,	,	PUNCT
brj-23498	147	43	peer	peer	NOUN
brj-23498	147	44	-	-	PUNCT
brj-23498	147	45	reviewed	review	VERB
brj-23498	147	46	article	article	NOUN
brj-23498	147	47	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	148	1	yang	yang	PROPN
brj-23498	148	2	et	et	PROPN
brj-23498	148	3	al	al	PROPN
brj-23498	148	4	.	.	PROPN
brj-23498	148	5	(	(	PUNCT
brj-23498	148	6	2024	2024	NUM
brj-23498	148	7	)	)	PUNCT
brj-23498	148	8	.	.	PUNCT
brj-23498	149	1	“	"	PUNCT
brj-23498	149	2	alfalfa	alfalfa	NOUN
brj-23498	149	3	quality	quality	NOUN
brj-23498	149	4	assessment	assessment	NOUN
brj-23498	149	5	,	,	PUNCT
brj-23498	149	6	”	"	PUNCT
brj-23498	149	7	bioresources	bioresource	NOUN
brj-23498	149	8	19(3	19(3	NUM
brj-23498	149	9	)	)	PUNCT
brj-23498	149	10	,	,	PUNCT
brj-23498	149	11	4531	4531	NUM
brj-23498	149	12	-	-	SYM
brj-23498	149	13	4546	4546	NUM
brj-23498	149	14	.	.	PUNCT
brj-23498	150	1	4539	4539	NUM
brj-23498	150	2	𝑽𝑰𝑴𝒊𝒋	𝑽𝑰𝑴𝒊𝒋	PROPN
brj-23498	150	3	(	(	PUNCT
brj-23498	150	4	𝑮𝒊𝒏𝒊	𝑮𝒊𝒏𝒊	PROPN
brj-23498	150	5	)	)	PUNCT
brj-23498	150	6	=	=	PUNCT
brj-23498	150	7	∑	∑	PROPN
brj-23498	150	8	𝑉𝐼𝑀𝑗𝑚	𝑉𝐼𝑀𝑗𝑚	X
brj-23498	150	9	(	(	PUNCT
brj-23498	150	10	𝑮𝒊𝒏𝒊	𝑮𝒊𝒏𝒊	PROPN
brj-23498	150	11	)	)	PUNCT
brj-23498	150	12	𝒎∈𝑴	𝒎∈𝑴	PROPN
brj-23498	150	13	(	(	PUNCT
brj-23498	150	14	3	3	X
brj-23498	150	15	)	)	PUNCT
brj-23498	150	16	assuming	assume	VERB
brj-23498	150	17	that	that	SCONJ
brj-23498	150	18	the	the	DET
brj-23498	150	19	texture	texture	ADJ
brj-23498	150	20	feature	feature	NOUN
brj-23498	150	21	xj	xj	PROPN
brj-23498	150	22	appears	appear	VERB
brj-23498	150	23	in	in	ADP
brj-23498	150	24	a	a	DET
brj-23498	150	25	total	total	NOUN
brj-23498	150	26	of	of	ADP
brj-23498	150	27	n	n	DET
brj-23498	150	28	trees	tree	NOUN
brj-23498	150	29	in	in	ADP
brj-23498	150	30	the	the	DET
brj-23498	150	31	random	random	ADJ
brj-23498	150	32	forest	forest	NOUN
brj-23498	150	33	,	,	PUNCT
brj-23498	150	34	the	the	DET
brj-23498	150	35	evaluation	evaluation	NOUN
brj-23498	150	36	formula	formula	NOUN
brj-23498	150	37	for	for	ADP
brj-23498	150	38	its	its	PRON
brj-23498	150	39	final	final	ADJ
brj-23498	150	40	importance	importance	NOUN
brj-23498	150	41	is	be	AUX
brj-23498	150	42	eq	eq	ADJ
brj-23498	150	43	.	.	PROPN
brj-23498	150	44	4	4	NUM
brj-23498	150	45	,	,	PUNCT
brj-23498	150	46	𝑽𝑰𝑴𝒋	𝑽𝑰𝑴𝒋	PROPN
brj-23498	150	47	(	(	PUNCT
brj-23498	150	48	𝑮𝒊𝒏𝒊	𝑮𝒊𝒏𝒊	PROPN
brj-23498	150	49	)	)	PUNCT
brj-23498	150	50	=	=	PUNCT
brj-23498	150	51	∑	∑	PUNCT
brj-23498	150	52	𝑽𝑰𝑴𝒊𝒋	𝑽𝑰𝑴𝒊𝒋	PROPN
brj-23498	150	53	(	(	PUNCT
brj-23498	150	54	𝑮𝒊𝒏𝒊)𝒏	𝑮𝒊𝒏𝒊)𝒏	ADJ
brj-23498	150	55	𝒊=𝟏	𝒊=𝟏	PROPN
brj-23498	150	56	(	(	PUNCT
brj-23498	150	57	4	4	NUM
brj-23498	150	58	)	)	PUNCT
brj-23498	150	59	finally	finally	ADV
brj-23498	150	60	,	,	PUNCT
brj-23498	150	61	the	the	DET
brj-23498	150	62	calculated	calculated	ADJ
brj-23498	150	63	importance	importance	NOUN
brj-23498	150	64	of	of	ADP
brj-23498	150	65	texture	texture	ADJ
brj-23498	150	66	features	feature	NOUN
brj-23498	150	67	is	be	AUX
brj-23498	150	68	sorted	sort	VERB
brj-23498	150	69	to	to	PART
brj-23498	150	70	obtain	obtain	VERB
brj-23498	150	71	the	the	DET
brj-23498	150	72	required	require	VERB
brj-23498	150	73	texture	texture	NOUN
brj-23498	150	74	features	feature	NOUN
brj-23498	150	75	.	.	PUNCT
brj-23498	151	1	dimensionality	dimensionality	NOUN
brj-23498	151	2	reduction	reduction	NOUN
brj-23498	151	3	of	of	ADP
brj-23498	151	4	electronic	electronic	ADJ
brj-23498	151	5	nose	nose	NOUN
brj-23498	151	6	data	data	PROPN
brj-23498	151	7	original	original	ADJ
brj-23498	151	8	electronic	electronic	ADJ
brj-23498	151	9	nose	nose	NOUN
brj-23498	151	10	data	datum	NOUN
brj-23498	151	11	may	may	AUX
brj-23498	151	12	contain	contain	VERB
brj-23498	151	13	a	a	DET
brj-23498	151	14	large	large	ADJ
brj-23498	151	15	number	number	NOUN
brj-23498	151	16	of	of	ADP
brj-23498	151	17	features	feature	NOUN
brj-23498	151	18	,	,	PUNCT
brj-23498	151	19	which	which	PRON
brj-23498	151	20	can	can	AUX
brj-23498	151	21	lead	lead	VERB
brj-23498	151	22	to	to	ADP
brj-23498	151	23	very	very	ADV
brj-23498	151	24	high	high	ADJ
brj-23498	151	25	computational	computational	ADJ
brj-23498	151	26	complexity	complexity	NOUN
brj-23498	151	27	during	during	ADP
brj-23498	151	28	model	model	NOUN
brj-23498	151	29	training	training	NOUN
brj-23498	151	30	and	and	CCONJ
brj-23498	151	31	inference	inference	NOUN
brj-23498	151	32	.	.	PUNCT
brj-23498	152	1	dimensionality	dimensionality	NOUN
brj-23498	152	2	reduction	reduction	NOUN
brj-23498	152	3	can	can	AUX
brj-23498	152	4	reduce	reduce	VERB
brj-23498	152	5	the	the	DET
brj-23498	152	6	number	number	NOUN
brj-23498	152	7	of	of	ADP
brj-23498	152	8	features	feature	NOUN
brj-23498	152	9	,	,	PUNCT
brj-23498	152	10	thus	thus	ADV
brj-23498	152	11	reducing	reduce	VERB
brj-23498	152	12	computational	computational	ADJ
brj-23498	152	13	complexity	complexity	NOUN
brj-23498	152	14	.	.	PUNCT
brj-23498	153	1	additionally	additionally	ADV
brj-23498	153	2	,	,	PUNCT
brj-23498	153	3	electronic	electronic	ADJ
brj-23498	153	4	nose	nose	NOUN
brj-23498	153	5	data	datum	NOUN
brj-23498	153	6	may	may	AUX
brj-23498	153	7	contain	contain	VERB
brj-23498	153	8	some	some	DET
brj-23498	153	9	redundant	redundant	ADJ
brj-23498	153	10	information	information	NOUN
brj-23498	153	11	or	or	CCONJ
brj-23498	153	12	noise	noise	NOUN
brj-23498	153	13	,	,	PUNCT
brj-23498	153	14	which	which	PRON
brj-23498	153	15	can	can	AUX
brj-23498	153	16	affect	affect	VERB
brj-23498	153	17	the	the	DET
brj-23498	153	18	model	model	NOUN
brj-23498	153	19	’s	’s	PART
brj-23498	153	20	performance	performance	NOUN
brj-23498	153	21	.	.	PUNCT
brj-23498	154	1	dimensionality	dimensionality	NOUN
brj-23498	154	2	reduction	reduction	NOUN
brj-23498	154	3	helps	help	VERB
brj-23498	154	4	remove	remove	VERB
brj-23498	154	5	this	this	DET
brj-23498	154	6	redundant	redundant	ADJ
brj-23498	154	7	information	information	NOUN
brj-23498	154	8	,	,	PUNCT
brj-23498	154	9	improving	improve	VERB
brj-23498	154	10	the	the	DET
brj-23498	154	11	model	model	NOUN
brj-23498	154	12	’s	’s	PART
brj-23498	154	13	generalization	generalization	NOUN
brj-23498	154	14	ability	ability	NOUN
brj-23498	154	15	and	and	CCONJ
brj-23498	154	16	efficiency	efficiency	NOUN
brj-23498	154	17	.	.	PUNCT
brj-23498	155	1	principal	principal	ADJ
brj-23498	155	2	component	component	NOUN
brj-23498	155	3	analysis	analysis	NOUN
brj-23498	155	4	(	(	PUNCT
brj-23498	155	5	pca	pca	NOUN
brj-23498	155	6	)	)	PUNCT
brj-23498	155	7	was	be	AUX
brj-23498	155	8	chosen	choose	VERB
brj-23498	155	9	in	in	ADP
brj-23498	155	10	this	this	DET
brj-23498	155	11	study	study	NOUN
brj-23498	155	12	to	to	PART
brj-23498	155	13	reduce	reduce	VERB
brj-23498	155	14	the	the	DET
brj-23498	155	15	dimensionality	dimensionality	NOUN
brj-23498	155	16	of	of	ADP
brj-23498	155	17	electronic	electronic	ADJ
brj-23498	155	18	nose	nose	NOUN
brj-23498	155	19	data	datum	NOUN
brj-23498	155	20	(	(	PUNCT
brj-23498	155	21	tian	tian	PROPN
brj-23498	155	22	et	et	PROPN
brj-23498	155	23	al	al	PROPN
brj-23498	155	24	.	.	PROPN
brj-23498	155	25	2023	2023	NUM
brj-23498	155	26	)	)	PUNCT
brj-23498	155	27	.	.	PUNCT
brj-23498	156	1	to	to	PART
brj-23498	156	2	verify	verify	VERB
brj-23498	156	3	whether	whether	SCONJ
brj-23498	156	4	the	the	DET
brj-23498	156	5	data	datum	NOUN
brj-23498	156	6	is	be	AUX
brj-23498	156	7	suitable	suitable	ADJ
brj-23498	156	8	for	for	SCONJ
brj-23498	156	9	pca	pca	PROPN
brj-23498	156	10	,	,	PUNCT
brj-23498	156	11	the	the	DET
brj-23498	156	12	kaiser	kaiser	PROPN
brj-23498	156	13	-	-	PUNCT
brj-23498	156	14	meyer	meyer	PROPN
brj-23498	156	15	-	-	PUNCT
brj-23498	156	16	olkin	olkin	PROPN
brj-23498	156	17	(	(	PUNCT
brj-23498	156	18	kmo	kmo	NOUN
brj-23498	156	19	)	)	PUNCT
brj-23498	156	20	test	test	NOUN
brj-23498	156	21	and	and	CCONJ
brj-23498	156	22	bartlett	bartlett	PROPN
brj-23498	156	23	's	's	PART
brj-23498	156	24	sphericity	sphericity	NOUN
brj-23498	156	25	test	test	NOUN
brj-23498	156	26	were	be	AUX
brj-23498	156	27	conducted	conduct	VERB
brj-23498	156	28	.	.	PUNCT
brj-23498	157	1	the	the	DET
brj-23498	157	2	results	result	NOUN
brj-23498	157	3	indicate	indicate	VERB
brj-23498	157	4	that	that	SCONJ
brj-23498	157	5	the	the	DET
brj-23498	157	6	kmo	kmo	NOUN
brj-23498	157	7	sampling	sample	VERB
brj-23498	157	8	adequacy	adequacy	NOUN
brj-23498	157	9	measure	measure	NOUN
brj-23498	157	10	is	be	AUX
brj-23498	157	11	0.656	0.656	NUM
brj-23498	157	12	,	,	PUNCT
brj-23498	157	13	greater	great	ADJ
brj-23498	157	14	than	than	ADP
brj-23498	157	15	0.6	0.6	NUM
brj-23498	157	16	,	,	PUNCT
brj-23498	157	17	and	and	CCONJ
brj-23498	157	18	the	the	DET
brj-23498	157	19	significance	significance	NOUN
brj-23498	157	20	(	(	PUNCT
brj-23498	157	21	sig	sig	NOUN
brj-23498	157	22	)	)	PUNCT
brj-23498	157	23	is	be	AUX
brj-23498	157	24	less	less	ADJ
brj-23498	157	25	than	than	ADP
brj-23498	157	26	0.05	0.05	NUM
brj-23498	157	27	,	,	PUNCT
brj-23498	157	28	indicating	indicate	VERB
brj-23498	157	29	that	that	SCONJ
brj-23498	157	30	the	the	DET
brj-23498	157	31	data	datum	NOUN
brj-23498	157	32	supports	support	VERB
brj-23498	157	33	pca	pca	PROPN
brj-23498	157	34	.	.	PUNCT
brj-23498	158	1	following	follow	VERB
brj-23498	158	2	the	the	DET
brj-23498	158	3	criterion	criterion	NOUN
brj-23498	158	4	of	of	ADP
brj-23498	158	5	eigenvalues	eigenvalue	NOUN
brj-23498	158	6	greater	great	ADJ
brj-23498	158	7	than	than	ADP
brj-23498	158	8	1	1	NUM
brj-23498	158	9	,	,	PUNCT
brj-23498	158	10	two	two	NUM
brj-23498	158	11	common	common	ADJ
brj-23498	158	12	factors	factor	NOUN
brj-23498	158	13	are	be	AUX
brj-23498	158	14	extracted	extract	VERB
brj-23498	158	15	,	,	PUNCT
brj-23498	158	16	contributing	contribute	VERB
brj-23498	158	17	to	to	ADP
brj-23498	158	18	a	a	DET
brj-23498	158	19	cumulative	cumulative	ADJ
brj-23498	158	20	variance	variance	NOUN
brj-23498	158	21	of	of	ADP
brj-23498	158	22	87.2	87.2	NUM
brj-23498	158	23	%	%	NOUN
brj-23498	158	24	.	.	PUNCT
brj-23498	159	1	hence	hence	ADV
brj-23498	159	2	,	,	PUNCT
brj-23498	159	3	extracting	extract	VERB
brj-23498	159	4	two	two	NUM
brj-23498	159	5	common	common	ADJ
brj-23498	159	6	factors	factor	NOUN
brj-23498	159	7	can	can	AUX
brj-23498	159	8	reflect	reflect	VERB
brj-23498	159	9	87.2	87.2	NUM
brj-23498	159	10	%	%	NOUN
brj-23498	159	11	of	of	ADP
brj-23498	159	12	the	the	DET
brj-23498	159	13	variance	variance	NOUN
brj-23498	159	14	in	in	ADP
brj-23498	159	15	the	the	DET
brj-23498	159	16	original	original	ADJ
brj-23498	159	17	data	datum	NOUN
brj-23498	159	18	,	,	PUNCT
brj-23498	159	19	achieving	achieve	VERB
brj-23498	159	20	dimensionality	dimensionality	NOUN
brj-23498	159	21	reduction	reduction	NOUN
brj-23498	159	22	of	of	ADP
brj-23498	159	23	electronic	electronic	ADJ
brj-23498	159	24	nose	nose	NOUN
brj-23498	159	25	data	datum	NOUN
brj-23498	159	26	.	.	PUNCT
brj-23498	160	1	information	information	NOUN
brj-23498	160	2	fusion	fusion	NOUN
brj-23498	160	3	method	method	NOUN
brj-23498	160	4	the	the	DET
brj-23498	160	5	spectrometer	spectrometer	NOUN
brj-23498	160	6	provides	provide	VERB
brj-23498	160	7	spectral	spectral	ADJ
brj-23498	160	8	absorption	absorption	NOUN
brj-23498	160	9	information	information	NOUN
brj-23498	160	10	containing	contain	VERB
brj-23498	160	11	chemical	chemical	ADJ
brj-23498	160	12	bonds	bond	NOUN
brj-23498	160	13	and	and	CCONJ
brj-23498	160	14	functional	functional	ADJ
brj-23498	160	15	groups	group	NOUN
brj-23498	160	16	,	,	PUNCT
brj-23498	160	17	while	while	SCONJ
brj-23498	160	18	the	the	DET
brj-23498	160	19	electronic	electronic	ADJ
brj-23498	160	20	nose	nose	NOUN
brj-23498	160	21	provides	provide	VERB
brj-23498	160	22	comprehensive	comprehensive	ADJ
brj-23498	160	23	gas	gas	NOUN
brj-23498	160	24	information	information	NOUN
brj-23498	160	25	obtained	obtain	VERB
brj-23498	160	26	using	use	VERB
brj-23498	160	27	a	a	DET
brj-23498	160	28	cross	cross	ADJ
brj-23498	160	29	-	-	ADJ
brj-23498	160	30	sensitive	sensitive	ADJ
brj-23498	160	31	gas	gas	NOUN
brj-23498	160	32	sensor	sensor	NOUN
brj-23498	160	33	array	array	NOUN
brj-23498	160	34	.	.	PUNCT
brj-23498	161	1	images	image	NOUN
brj-23498	161	2	capture	capture	VERB
brj-23498	161	3	color	color	NOUN
brj-23498	161	4	and	and	CCONJ
brj-23498	161	5	texture	texture	ADJ
brj-23498	161	6	information	information	NOUN
brj-23498	161	7	from	from	ADP
brj-23498	161	8	the	the	DET
brj-23498	161	9	material	material	NOUN
brj-23498	161	10	surface	surface	NOUN
brj-23498	161	11	.	.	PUNCT
brj-23498	162	1	these	these	DET
brj-23498	162	2	three	three	NUM
brj-23498	162	3	types	type	NOUN
brj-23498	162	4	of	of	ADP
brj-23498	162	5	information	information	NOUN
brj-23498	162	6	are	be	AUX
brj-23498	162	7	fused	fuse	VERB
brj-23498	162	8	using	use	VERB
brj-23498	162	9	a	a	DET
brj-23498	162	10	feature	feature	NOUN
brj-23498	162	11	set	set	VERB
brj-23498	162	12	fusion	fusion	NOUN
brj-23498	162	13	method	method	NOUN
brj-23498	162	14	(	(	PUNCT
brj-23498	162	15	jia	jia	PROPN
brj-23498	162	16	et	et	PROPN
brj-23498	162	17	al	al	PROPN
brj-23498	162	18	.	.	PROPN
brj-23498	162	19	2021	2021	NUM
brj-23498	162	20	)	)	PUNCT
brj-23498	162	21	.	.	PUNCT
brj-23498	163	1	features	feature	NOUN
brj-23498	163	2	are	be	AUX
brj-23498	163	3	extracted	extract	VERB
brj-23498	163	4	from	from	ADP
brj-23498	163	5	each	each	DET
brj-23498	163	6	data	datum	NOUN
brj-23498	163	7	source	source	NOUN
brj-23498	163	8	,	,	PUNCT
brj-23498	163	9	and	and	CCONJ
brj-23498	163	10	they	they	PRON
brj-23498	163	11	are	be	AUX
brj-23498	163	12	combined	combine	VERB
brj-23498	163	13	to	to	PART
brj-23498	163	14	form	form	VERB
brj-23498	163	15	larger	large	ADJ
brj-23498	163	16	feature	feature	NOUN
brj-23498	163	17	vectors	vector	NOUN
brj-23498	163	18	.	.	PUNCT
brj-23498	164	1	here	here	ADV
brj-23498	164	2	,	,	PUNCT
brj-23498	164	3	the	the	DET
brj-23498	164	4	weight	weight	NOUN
brj-23498	164	5	of	of	ADP
brj-23498	164	6	each	each	DET
brj-23498	164	7	information	information	NOUN
brj-23498	164	8	type	type	NOUN
brj-23498	164	9	is	be	AUX
brj-23498	164	10	set	set	VERB
brj-23498	164	11	to	to	ADP
brj-23498	164	12	1	1	NUM
brj-23498	164	13	.	.	PUNCT
brj-23498	165	1	the	the	DET
brj-23498	165	2	fused	fuse	VERB
brj-23498	165	3	information	information	NOUN
brj-23498	165	4	obtained	obtain	VERB
brj-23498	165	5	is	be	AUX
brj-23498	165	6	used	use	VERB
brj-23498	165	7	as	as	ADP
brj-23498	165	8	input	input	NOUN
brj-23498	165	9	to	to	PART
brj-23498	165	10	evaluate	evaluate	VERB
brj-23498	165	11	the	the	DET
brj-23498	165	12	performance	performance	NOUN
brj-23498	165	13	of	of	ADP
brj-23498	165	14	the	the	DET
brj-23498	165	15	alfalfa	alfalfa	NOUN
brj-23498	165	16	hay	hay	PROPN
brj-23498	165	17	quality	quality	NOUN
brj-23498	165	18	detection	detection	NOUN
brj-23498	165	19	model	model	NOUN
brj-23498	165	20	based	base	VERB
brj-23498	165	21	on	on	ADP
brj-23498	165	22	multi	multi	ADJ
brj-23498	165	23	-	-	ADJ
brj-23498	165	24	source	source	ADJ
brj-23498	165	25	information	information	NOUN
brj-23498	165	26	fusion	fusion	NOUN
brj-23498	165	27	.	.	PUNCT
brj-23498	166	1	alfalfa	alfalfa	PROPN
brj-23498	166	2	hay	hay	PROPN
brj-23498	166	3	quality	quality	NOUN
brj-23498	166	4	model	model	NOUN
brj-23498	166	5	construction	construction	NOUN
brj-23498	166	6	and	and	CCONJ
brj-23498	166	7	optimization	optimization	NOUN
brj-23498	166	8	in	in	ADP
brj-23498	166	9	this	this	DET
brj-23498	166	10	study	study	NOUN
brj-23498	166	11	,	,	PUNCT
brj-23498	166	12	three	three	NUM
brj-23498	166	13	algorithms	algorithm	NOUN
brj-23498	166	14	,	,	PUNCT
brj-23498	166	15	namely	namely	ADV
brj-23498	166	16	support	support	VERB
brj-23498	166	17	vector	vector	NOUN
brj-23498	166	18	machine	machine	NOUN
brj-23498	166	19	(	(	PUNCT
brj-23498	166	20	svm	svm	PROPN
brj-23498	166	21	)	)	PUNCT
brj-23498	166	22	,	,	PUNCT
brj-23498	166	23	extreme	extreme	ADJ
brj-23498	166	24	learning	learning	NOUN
brj-23498	166	25	machine	machine	NOUN
brj-23498	166	26	(	(	PUNCT
brj-23498	166	27	elm	elm	PROPN
brj-23498	166	28	)	)	PUNCT
brj-23498	166	29	,	,	PUNCT
brj-23498	166	30	and	and	CCONJ
brj-23498	166	31	multi	multi	ADJ
brj-23498	166	32	-	-	ADJ
brj-23498	166	33	layer	layer	ADJ
brj-23498	166	34	perceptron	perceptron	NOUN
brj-23498	166	35	(	(	PUNCT
brj-23498	166	36	mlp	mlp	PROPN
brj-23498	166	37	)	)	PUNCT
brj-23498	166	38	,	,	PUNCT
brj-23498	166	39	were	be	AUX
brj-23498	166	40	employed	employ	VERB
brj-23498	166	41	to	to	PART
brj-23498	166	42	explore	explore	VERB
brj-23498	166	43	the	the	DET
brj-23498	166	44	optimal	optimal	ADJ
brj-23498	166	45	model	model	NOUN
brj-23498	166	46	for	for	ADP
brj-23498	166	47	identifying	identify	VERB
brj-23498	166	48	the	the	DET
brj-23498	166	49	quality	quality	NOUN
brj-23498	166	50	of	of	ADP
brj-23498	166	51	alfalfa	alfalfa	NOUN
brj-23498	166	52	hay	hay	PROPN
brj-23498	166	53	.	.	PUNCT
brj-23498	167	1	the	the	DET
brj-23498	167	2	model	model	NOUN
brj-23498	167	3	evaluation	evaluation	NOUN
brj-23498	167	4	criterion	criterion	NOUN
brj-23498	167	5	was	be	AUX
brj-23498	167	6	the	the	DET
brj-23498	167	7	confusion	confusion	NOUN
brj-23498	167	8	matrix	matrix	NOUN
brj-23498	167	9	method	method	NOUN
brj-23498	167	10	,	,	PUNCT
brj-23498	167	11	which	which	PRON
brj-23498	167	12	assesses	assess	VERB
brj-23498	167	13	the	the	DET
brj-23498	167	14	model	model	NOUN
brj-23498	167	15	's	's	PART
brj-23498	167	16	performance	performance	NOUN
brj-23498	167	17	based	base	VERB
brj-23498	167	18	on	on	ADP
brj-23498	167	19	the	the	DET
brj-23498	167	20	accuracy	accuracy	NOUN
brj-23498	167	21	of	of	ADP
brj-23498	167	22	identification	identification	NOUN
brj-23498	167	23	.	.	PUNCT
brj-23498	168	1	in	in	ADP
brj-23498	168	2	this	this	DET
brj-23498	168	3	experiment	experiment	NOUN
brj-23498	168	4	,	,	PUNCT
brj-23498	168	5	the	the	DET
brj-23498	168	6	preprocessed	preprocesse	VERB
brj-23498	168	7	dataset	dataset	NOUN
brj-23498	168	8	was	be	AUX
brj-23498	168	9	subjected	subject	VERB
brj-23498	168	10	to	to	ADP
brj-23498	168	11	10	10	NUM
brj-23498	168	12	-	-	ADJ
brj-23498	168	13	fold	fold	ADJ
brj-23498	168	14	cross	cross	NOUN
brj-23498	168	15	validation	validation	NOUN
brj-23498	168	16	,	,	PUNCT
brj-23498	168	17	dividing	divide	VERB
brj-23498	168	18	it	it	PRON
brj-23498	168	19	into	into	ADP
brj-23498	168	20	10	10	NUM
brj-23498	168	21	equally	equally	ADV
brj-23498	168	22	sized	sized	ADJ
brj-23498	168	23	subsets	subset	NOUN
brj-23498	168	24	.	.	PUNCT
brj-23498	169	1	each	each	DET
brj-23498	169	2	time	time	NOUN
brj-23498	169	3	,	,	PUNCT
brj-23498	169	4	9	9	NUM
brj-23498	169	5	subsets	subset	NOUN
brj-23498	169	6	were	be	AUX
brj-23498	169	7	used	use	VERB
brj-23498	169	8	as	as	ADP
brj-23498	169	9	the	the	DET
brj-23498	169	10	training	training	NOUN
brj-23498	169	11	set	set	NOUN
brj-23498	169	12	,	,	PUNCT
brj-23498	169	13	and	and	CCONJ
brj-23498	169	14	the	the	DET
brj-23498	169	15	remaining	remain	VERB
brj-23498	169	16	1	1	NUM
brj-23498	169	17	subset	subset	NOUN
brj-23498	169	18	was	be	AUX
brj-23498	169	19	used	use	VERB
brj-23498	169	20	as	as	ADP
brj-23498	169	21	the	the	DET
brj-23498	169	22	testing	testing	NOUN
brj-23498	169	23	set	set	NOUN
brj-23498	169	24	.	.	PUNCT
brj-23498	170	1	this	this	DET
brj-23498	170	2	process	process	NOUN
brj-23498	170	3	was	be	AUX
brj-23498	170	4	repeated	repeat	VERB
brj-23498	170	5	10	10	NUM
brj-23498	170	6	times	time	NOUN
brj-23498	170	7	,	,	PUNCT
brj-23498	170	8	selecting	select	VERB
brj-23498	170	9	different	different	ADJ
brj-23498	170	10	subsets	subset	NOUN
brj-23498	170	11	as	as	ADP
brj-23498	170	12	the	the	DET
brj-23498	170	13	testing	testing	NOUN
brj-23498	170	14	set	set	NOUN
brj-23498	170	15	.	.	PUNCT
brj-23498	171	1	finally	finally	ADV
brj-23498	171	2	,	,	PUNCT
brj-23498	171	3	take	take	VERB
brj-23498	171	4	the	the	DET
brj-23498	171	5	average	average	NOUN
brj-23498	171	6	of	of	ADP
brj-23498	171	7	these	these	DET
brj-23498	171	8	10	10	NUM
brj-23498	171	9	evaluation	evaluation	NOUN
brj-23498	171	10	results	result	NOUN
brj-23498	171	11	as	as	ADP
brj-23498	171	12	the	the	DET
brj-23498	171	13	performance	performance	NOUN
brj-23498	171	14	indicator	indicator	NOUN
brj-23498	171	15	of	of	ADP
brj-23498	171	16	the	the	DET
brj-23498	171	17	model	model	NOUN
brj-23498	171	18	.	.	PUNCT
brj-23498	172	1	regression	regression	NOUN
brj-23498	172	2	models	model	NOUN
brj-23498	172	3	were	be	AUX
brj-23498	172	4	established	establish	VERB
brj-23498	172	5	for	for	ADP
brj-23498	172	6	spectral	spectral	ADJ
brj-23498	172	7	bands	band	NOUN
brj-23498	172	8	,	,	PUNCT
brj-23498	172	9	and	and	CCONJ
brj-23498	172	10	the	the	DET
brj-23498	172	11	predictive	predictive	ADJ
brj-23498	172	12	results	result	NOUN
brj-23498	172	13	are	be	AUX
brj-23498	172	14	shown	show	VERB
brj-23498	172	15	peer	peer	NOUN
brj-23498	172	16	-	-	PUNCT
brj-23498	172	17	reviewed	review	VERB
brj-23498	172	18	article	article	NOUN
brj-23498	172	19	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	172	20	yang	yang	PROPN
brj-23498	172	21	et	et	PROPN
brj-23498	172	22	al	al	PROPN
brj-23498	172	23	.	.	PROPN
brj-23498	173	1	(	(	PUNCT
brj-23498	173	2	2024	2024	NUM
brj-23498	173	3	)	)	PUNCT
brj-23498	173	4	.	.	PUNCT
brj-23498	174	1	“	"	PUNCT
brj-23498	174	2	alfalfa	alfalfa	NOUN
brj-23498	174	3	quality	quality	NOUN
brj-23498	174	4	assessment	assessment	NOUN
brj-23498	174	5	,	,	PUNCT
brj-23498	174	6	”	"	PUNCT
brj-23498	174	7	bioresources	bioresource	NOUN
brj-23498	174	8	19(3	19(3	NUM
brj-23498	174	9	)	)	PUNCT
brj-23498	174	10	,	,	PUNCT
brj-23498	174	11	4531	4531	NUM
brj-23498	174	12	-	-	SYM
brj-23498	174	13	4546	4546	NUM
brj-23498	174	14	.	.	PUNCT
brj-23498	175	1	4540	4540	NUM
brj-23498	175	2	in	in	ADP
brj-23498	175	3	table	table	NOUN
brj-23498	175	4	1	1	NUM
brj-23498	175	5	.	.	PUNCT
brj-23498	175	6	classification	classification	NOUN
brj-23498	175	7	models	model	NOUN
brj-23498	175	8	were	be	AUX
brj-23498	175	9	built	build	VERB
brj-23498	175	10	for	for	ADP
brj-23498	175	11	image	image	NOUN
brj-23498	175	12	and	and	CCONJ
brj-23498	175	13	electronic	electronic	ADJ
brj-23498	175	14	nose	nose	NOUN
brj-23498	175	15	data	datum	NOUN
brj-23498	175	16	,	,	PUNCT
brj-23498	175	17	with	with	ADP
brj-23498	175	18	results	result	NOUN
brj-23498	175	19	presented	present	VERB
brj-23498	175	20	in	in	ADP
brj-23498	175	21	tables	table	NOUN
brj-23498	175	22	2	2	NUM
brj-23498	175	23	and	and	CCONJ
brj-23498	175	24	3	3	NUM
brj-23498	175	25	,	,	PUNCT
brj-23498	175	26	respectively	respectively	ADV
brj-23498	175	27	.	.	PUNCT
brj-23498	176	1	the	the	DET
brj-23498	176	2	model	model	NOUN
brj-23498	176	3	results	result	VERB
brj-23498	176	4	for	for	ADP
brj-23498	176	5	fused	fused	ADJ
brj-23498	176	6	information	information	NOUN
brj-23498	176	7	are	be	AUX
brj-23498	176	8	shown	show	VERB
brj-23498	176	9	in	in	ADP
brj-23498	176	10	table	table	NOUN
brj-23498	176	11	4	4	NUM
brj-23498	176	12	.	.	PUNCT
brj-23498	177	1	support	support	NOUN
brj-23498	177	2	vector	vector	NOUN
brj-23498	177	3	machine	machine	NOUN
brj-23498	177	4	(	(	PUNCT
brj-23498	177	5	svm	svm	PROPN
brj-23498	177	6	)	)	PUNCT
brj-23498	177	7	is	be	AUX
brj-23498	177	8	a	a	DET
brj-23498	177	9	common	common	ADJ
brj-23498	177	10	machine	machine	NOUN
brj-23498	177	11	learning	learn	VERB
brj-23498	177	12	algorithm	algorithm	NOUN
brj-23498	177	13	used	use	VERB
brj-23498	177	14	for	for	ADP
brj-23498	177	15	classification	classification	NOUN
brj-23498	177	16	and	and	CCONJ
brj-23498	177	17	regression	regression	NOUN
brj-23498	177	18	problems	problem	NOUN
brj-23498	177	19	(	(	PUNCT
brj-23498	177	20	shen	shen	PROPN
brj-23498	177	21	et	et	PROPN
brj-23498	177	22	al	al	PROPN
brj-23498	177	23	.	.	PROPN
brj-23498	177	24	2023	2023	NUM
brj-23498	177	25	)	)	PUNCT
brj-23498	177	26	.	.	PUNCT
brj-23498	178	1	in	in	ADP
brj-23498	178	2	classification	classification	NOUN
brj-23498	178	3	tasks	task	NOUN
brj-23498	178	4	,	,	PUNCT
brj-23498	178	5	the	the	DET
brj-23498	178	6	working	work	VERB
brj-23498	178	7	principle	principle	NOUN
brj-23498	178	8	of	of	ADP
brj-23498	178	9	svm	svm	PROPN
brj-23498	178	10	is	be	AUX
brj-23498	178	11	as	as	SCONJ
brj-23498	178	12	follows	follow	VERB
brj-23498	178	13	:	:	PUNCT
brj-23498	178	14	the	the	DET
brj-23498	178	15	first	first	ADJ
brj-23498	178	16	step	step	NOUN
brj-23498	178	17	is	be	AUX
brj-23498	178	18	to	to	PART
brj-23498	178	19	map	map	VERB
brj-23498	178	20	data	datum	NOUN
brj-23498	178	21	to	to	ADP
brj-23498	178	22	a	a	DET
brj-23498	178	23	high	high	ADJ
brj-23498	178	24	-	-	PUNCT
brj-23498	178	25	dimensional	dimensional	ADJ
brj-23498	178	26	feature	feature	NOUN
brj-23498	178	27	space	space	NOUN
brj-23498	178	28	.	.	PUNCT
brj-23498	179	1	then	then	ADV
brj-23498	179	2	one	one	NUM
brj-23498	179	3	finds	find	VERB
brj-23498	179	4	a	a	DET
brj-23498	179	5	hyperplane	hyperplane	NOUN
brj-23498	179	6	in	in	ADP
brj-23498	179	7	the	the	DET
brj-23498	179	8	feature	feature	NOUN
brj-23498	179	9	space	space	NOUN
brj-23498	179	10	that	that	PRON
brj-23498	179	11	maximizes	maximize	VERB
brj-23498	179	12	the	the	DET
brj-23498	179	13	margin	margin	NOUN
brj-23498	179	14	between	between	ADP
brj-23498	179	15	different	different	ADJ
brj-23498	179	16	classes	class	NOUN
brj-23498	179	17	of	of	ADP
brj-23498	179	18	samples	sample	NOUN
brj-23498	179	19	.	.	PUNCT
brj-23498	180	1	the	the	DET
brj-23498	180	2	distance	distance	NOUN
brj-23498	180	3	from	from	ADP
brj-23498	180	4	samples	sample	NOUN
brj-23498	180	5	to	to	ADP
brj-23498	180	6	the	the	DET
brj-23498	180	7	hyperplane	hyperplane	NOUN
brj-23498	180	8	is	be	AUX
brj-23498	180	9	called	call	VERB
brj-23498	180	10	support	support	NOUN
brj-23498	180	11	vectors	vector	NOUN
brj-23498	180	12	,	,	PUNCT
brj-23498	180	13	which	which	PRON
brj-23498	180	14	determine	determine	VERB
brj-23498	180	15	the	the	DET
brj-23498	180	16	position	position	NOUN
brj-23498	180	17	of	of	ADP
brj-23498	180	18	the	the	DET
brj-23498	180	19	hyperplane	hyperplane	NOUN
brj-23498	180	20	.	.	PUNCT
brj-23498	181	1	new	new	ADJ
brj-23498	181	2	samples	sample	NOUN
brj-23498	181	3	can	can	AUX
brj-23498	181	4	be	be	AUX
brj-23498	181	5	mapped	map	VERB
brj-23498	181	6	to	to	ADP
brj-23498	181	7	the	the	DET
brj-23498	181	8	feature	feature	NOUN
brj-23498	181	9	space	space	NOUN
brj-23498	181	10	and	and	CCONJ
brj-23498	181	11	classified	classify	VERB
brj-23498	181	12	through	through	ADP
brj-23498	181	13	the	the	DET
brj-23498	181	14	hyperplane	hyperplane	NOUN
brj-23498	181	15	.	.	PUNCT
brj-23498	182	1	the	the	DET
brj-23498	182	2	advantages	advantage	NOUN
brj-23498	182	3	of	of	ADP
brj-23498	182	4	svm	svm	PROPN
brj-23498	182	5	include	include	VERB
brj-23498	182	6	:	:	PUNCT
brj-23498	182	7	applicable	applicable	ADJ
brj-23498	182	8	to	to	PART
brj-23498	182	9	linearly	linearly	ADV
brj-23498	182	10	separable	separable	VERB
brj-23498	182	11	and	and	CCONJ
brj-23498	182	12	non	non	ADJ
brj-23498	182	13	-	-	ADJ
brj-23498	182	14	linearly	linearly	ADV
brj-23498	182	15	separable	separable	ADJ
brj-23498	182	16	data	datum	NOUN
brj-23498	182	17	;	;	PUNCT
brj-23498	182	18	capable	capable	ADJ
brj-23498	182	19	of	of	ADP
brj-23498	182	20	handling	handle	VERB
brj-23498	182	21	high	high	ADJ
brj-23498	182	22	-	-	PUNCT
brj-23498	182	23	dimensional	dimensional	ADJ
brj-23498	182	24	data	datum	NOUN
brj-23498	182	25	,	,	PUNCT
brj-23498	182	26	which	which	PRON
brj-23498	182	27	is	be	AUX
brj-23498	182	28	effective	effective	ADJ
brj-23498	182	29	for	for	ADP
brj-23498	182	30	problems	problem	NOUN
brj-23498	182	31	with	with	ADP
brj-23498	182	32	many	many	ADJ
brj-23498	182	33	features	feature	NOUN
brj-23498	182	34	;	;	PUNCT
brj-23498	182	35	providing	provide	VERB
brj-23498	182	36	good	good	ADJ
brj-23498	182	37	results	result	NOUN
brj-23498	182	38	even	even	ADV
brj-23498	182	39	with	with	ADP
brj-23498	182	40	a	a	DET
brj-23498	182	41	small	small	ADJ
brj-23498	182	42	number	number	NOUN
brj-23498	182	43	of	of	ADP
brj-23498	182	44	samples	sample	NOUN
brj-23498	182	45	;	;	PUNCT
brj-23498	182	46	and	and	CCONJ
brj-23498	182	47	an	an	DET
brj-23498	182	48	ability	ability	NOUN
brj-23498	182	49	to	to	PART
brj-23498	182	50	handle	handle	VERB
brj-23498	182	51	nonlinear	nonlinear	ADJ
brj-23498	182	52	problems	problem	NOUN
brj-23498	182	53	by	by	ADP
brj-23498	182	54	selecting	select	VERB
brj-23498	182	55	different	different	ADJ
brj-23498	182	56	kernel	kernel	NOUN
brj-23498	182	57	functions	function	NOUN
brj-23498	182	58	(	(	PUNCT
brj-23498	182	59	qin	qin	X
brj-23498	182	60	et	et	PROPN
brj-23498	182	61	al	al	PROPN
brj-23498	182	62	.	.	PROPN
brj-23498	182	63	2017	2017	NUM
brj-23498	182	64	)	)	PUNCT
brj-23498	182	65	.	.	PUNCT
brj-23498	183	1	using	use	VERB
brj-23498	183	2	crossvalidation	crossvalidation	NOUN
brj-23498	183	3	techniques	technique	NOUN
brj-23498	183	4	to	to	PART
brj-23498	183	5	search	search	VERB
brj-23498	183	6	for	for	ADP
brj-23498	183	7	the	the	DET
brj-23498	183	8	best	good	ADJ
brj-23498	183	9	penalty	penalty	NOUN
brj-23498	183	10	factor	factor	NOUN
brj-23498	183	11	(	(	PUNCT
brj-23498	183	12	c	c	NOUN
brj-23498	183	13	)	)	PUNCT
brj-23498	183	14	and	and	CCONJ
brj-23498	183	15	radial	radial	ADJ
brj-23498	183	16	basis	basis	NOUN
brj-23498	183	17	function	function	NOUN
brj-23498	183	18	parameters	parameter	NOUN
brj-23498	183	19	(	(	PUNCT
brj-23498	183	20	γ	γ	NOUN
brj-23498	183	21	)	)	PUNCT
brj-23498	183	22	,	,	PUNCT
brj-23498	183	23	the	the	DET
brj-23498	183	24	values	value	NOUN
brj-23498	183	25	of	of	ADP
brj-23498	183	26	c	c	PROPN
brj-23498	183	27	and	and	CCONJ
brj-23498	183	28	γ	γ	PROPN
brj-23498	183	29	selected	select	VERB
brj-23498	183	30	for	for	ADP
brj-23498	183	31	the	the	DET
brj-23498	183	32	feature	feature	NOUN
brj-23498	183	33	wavelengths	wavelength	NOUN
brj-23498	183	34	extracted	extract	VERB
brj-23498	183	35	from	from	ADP
brj-23498	183	36	the	the	DET
brj-23498	183	37	full	full	ADJ
brj-23498	183	38	spectrum	spectrum	NOUN
brj-23498	183	39	,	,	PUNCT
brj-23498	183	40	cars	car	NOUN
brj-23498	183	41	,	,	PUNCT
brj-23498	183	42	and	and	CCONJ
brj-23498	183	43	sap	sap	PROPN
brj-23498	183	44	are	be	AUX
brj-23498	183	45	respectively	respectively	ADV
brj-23498	183	46	1	1	NUM
brj-23498	183	47	1000	1000	NUM
brj-23498	183	48	100	100	NUM
brj-23498	183	49	and	and	CCONJ
brj-23498	183	50	0.1	0.1	NUM
brj-23498	183	51	0.01	0.01	NUM
brj-23498	183	52	0.01	0.01	NUM
brj-23498	183	53	.	.	PUNCT
brj-23498	184	1	from	from	ADP
brj-23498	184	2	the	the	DET
brj-23498	184	3	results	result	NOUN
brj-23498	184	4	,	,	PUNCT
brj-23498	184	5	it	it	PRON
brj-23498	184	6	can	can	AUX
brj-23498	184	7	be	be	AUX
brj-23498	184	8	observed	observe	VERB
brj-23498	184	9	that	that	SCONJ
brj-23498	184	10	using	use	VERB
brj-23498	184	11	the	the	DET
brj-23498	184	12	full	full	ADJ
brj-23498	184	13	spectrum	spectrum	NOUN
brj-23498	184	14	as	as	ADP
brj-23498	184	15	input	input	NOUN
brj-23498	184	16	led	lead	VERB
brj-23498	184	17	to	to	ADP
brj-23498	184	18	overfitting	overfitte	VERB
brj-23498	184	19	due	due	ADP
brj-23498	184	20	to	to	ADP
brj-23498	184	21	the	the	DET
brj-23498	184	22	large	large	ADJ
brj-23498	184	23	data	datum	NOUN
brj-23498	184	24	dimensionality	dimensionality	NOUN
brj-23498	184	25	and	and	CCONJ
brj-23498	184	26	high	high	ADJ
brj-23498	184	27	model	model	NOUN
brj-23498	184	28	complexity	complexity	NOUN
brj-23498	184	29	.	.	PUNCT
brj-23498	185	1	the	the	DET
brj-23498	185	2	model	model	NOUN
brj-23498	185	3	established	establish	VERB
brj-23498	185	4	using	use	VERB
brj-23498	185	5	feature	feature	NOUN
brj-23498	185	6	wavelengths	wavelength	NOUN
brj-23498	185	7	extracted	extract	VERB
brj-23498	185	8	from	from	ADP
brj-23498	185	9	cars	car	NOUN
brj-23498	185	10	performed	perform	VERB
brj-23498	185	11	better	well	ADV
brj-23498	185	12	than	than	ADP
brj-23498	185	13	sap	sap	PROPN
brj-23498	185	14	,	,	PUNCT
brj-23498	185	15	hence	hence	ADV
brj-23498	185	16	carsextracted	carsextracte	VERB
brj-23498	185	17	feature	feature	NOUN
brj-23498	185	18	wavelengths	wavelength	NOUN
brj-23498	185	19	were	be	AUX
brj-23498	185	20	chosen	choose	VERB
brj-23498	185	21	to	to	PART
brj-23498	185	22	build	build	VERB
brj-23498	185	23	the	the	DET
brj-23498	185	24	fusion	fusion	NOUN
brj-23498	185	25	model	model	NOUN
brj-23498	185	26	.	.	PUNCT
brj-23498	186	1	extreme	extreme	ADJ
brj-23498	186	2	learning	learning	NOUN
brj-23498	186	3	machine	machine	NOUN
brj-23498	186	4	(	(	PUNCT
brj-23498	186	5	elm	elm	PROPN
brj-23498	186	6	)	)	PUNCT
brj-23498	186	7	is	be	AUX
brj-23498	186	8	a	a	DET
brj-23498	186	9	fast	fast	ADJ
brj-23498	186	10	and	and	CCONJ
brj-23498	186	11	effective	effective	ADJ
brj-23498	186	12	machine	machine	NOUN
brj-23498	186	13	learning	learn	VERB
brj-23498	186	14	algorithm	algorithm	NOUN
brj-23498	186	15	used	use	VERB
brj-23498	186	16	for	for	ADP
brj-23498	186	17	solving	solve	VERB
brj-23498	186	18	classification	classification	NOUN
brj-23498	186	19	and	and	CCONJ
brj-23498	186	20	regression	regression	NOUN
brj-23498	186	21	problems	problem	NOUN
brj-23498	186	22	.	.	PUNCT
brj-23498	187	1	compared	compare	VERB
brj-23498	187	2	to	to	ADP
brj-23498	187	3	traditional	traditional	ADJ
brj-23498	187	4	neural	neural	ADJ
brj-23498	187	5	network	network	NOUN
brj-23498	187	6	algorithms	algorithm	NOUN
brj-23498	187	7	,	,	PUNCT
brj-23498	187	8	elm	elm	NOUN
brj-23498	187	9	has	have	VERB
brj-23498	187	10	faster	fast	ADJ
brj-23498	187	11	training	training	NOUN
brj-23498	187	12	speed	speed	NOUN
brj-23498	187	13	and	and	CCONJ
brj-23498	187	14	better	well	ADJ
brj-23498	187	15	generalization	generalization	NOUN
brj-23498	187	16	capabilities	capability	NOUN
brj-23498	187	17	(	(	PUNCT
brj-23498	187	18	swati	swati	PROPN
brj-23498	187	19	et	et	PROPN
brj-23498	187	20	al	al	PROPN
brj-23498	187	21	.	.	PROPN
brj-23498	187	22	2023	2023	NUM
brj-23498	187	23	)	)	PUNCT
brj-23498	187	24	.	.	PUNCT
brj-23498	188	1	the	the	DET
brj-23498	188	2	training	training	NOUN
brj-23498	188	3	process	process	NOUN
brj-23498	188	4	of	of	ADP
brj-23498	188	5	elm	elm	NOUN
brj-23498	188	6	is	be	AUX
brj-23498	188	7	very	very	ADV
brj-23498	188	8	simple	simple	ADJ
brj-23498	188	9	and	and	CCONJ
brj-23498	188	10	efficient	efficient	ADJ
brj-23498	188	11	:	:	PUNCT
brj-23498	188	12	it	it	PRON
brj-23498	188	13	randomly	randomly	ADV
brj-23498	188	14	initializes	initialize	VERB
brj-23498	188	15	the	the	DET
brj-23498	188	16	weight	weight	NOUN
brj-23498	188	17	matrix	matrix	NOUN
brj-23498	188	18	and	and	CCONJ
brj-23498	188	19	threshold	threshold	NOUN
brj-23498	188	20	vector	vector	NOUN
brj-23498	188	21	of	of	ADP
brj-23498	188	22	the	the	DET
brj-23498	188	23	hidden	hide	VERB
brj-23498	188	24	layer	layer	NOUN
brj-23498	188	25	,	,	PUNCT
brj-23498	188	26	maps	map	VERB
brj-23498	188	27	the	the	DET
brj-23498	188	28	input	input	NOUN
brj-23498	188	29	data	datum	NOUN
brj-23498	188	30	through	through	ADP
brj-23498	188	31	the	the	DET
brj-23498	188	32	nonlinear	nonlinear	ADJ
brj-23498	188	33	mapping	mapping	NOUN
brj-23498	188	34	of	of	ADP
brj-23498	188	35	the	the	DET
brj-23498	188	36	hidden	hide	VERB
brj-23498	188	37	layer	layer	NOUN
brj-23498	188	38	to	to	PART
brj-23498	188	39	obtain	obtain	VERB
brj-23498	188	40	the	the	DET
brj-23498	188	41	output	output	NOUN
brj-23498	188	42	of	of	ADP
brj-23498	188	43	the	the	DET
brj-23498	188	44	hidden	hide	VERB
brj-23498	188	45	layer	layer	NOUN
brj-23498	188	46	,	,	PUNCT
brj-23498	188	47	uses	use	VERB
brj-23498	188	48	least	least	ADJ
brj-23498	188	49	squares	square	NOUN
brj-23498	188	50	method	method	NOUN
brj-23498	188	51	or	or	CCONJ
brj-23498	188	52	other	other	ADJ
brj-23498	188	53	methods	method	NOUN
brj-23498	188	54	to	to	PART
brj-23498	188	55	perform	perform	VERB
brj-23498	188	56	linear	linear	ADJ
brj-23498	188	57	regression	regression	NOUN
brj-23498	188	58	between	between	ADP
brj-23498	188	59	the	the	DET
brj-23498	188	60	output	output	NOUN
brj-23498	188	61	of	of	ADP
brj-23498	188	62	the	the	DET
brj-23498	188	63	hidden	hide	VERB
brj-23498	188	64	layer	layer	NOUN
brj-23498	188	65	and	and	CCONJ
brj-23498	188	66	the	the	DET
brj-23498	188	67	target	target	NOUN
brj-23498	188	68	,	,	PUNCT
brj-23498	188	69	and	and	CCONJ
brj-23498	188	70	obtains	obtain	VERB
brj-23498	188	71	the	the	DET
brj-23498	188	72	weight	weight	NOUN
brj-23498	188	73	matrix	matrix	NOUN
brj-23498	188	74	of	of	ADP
brj-23498	188	75	the	the	DET
brj-23498	188	76	output	output	NOUN
brj-23498	188	77	layer	layer	NOUN
brj-23498	188	78	to	to	PART
brj-23498	188	79	complete	complete	VERB
brj-23498	188	80	model	model	NOUN
brj-23498	188	81	training	training	NOUN
brj-23498	188	82	.	.	PUNCT
brj-23498	189	1	the	the	DET
brj-23498	189	2	advantages	advantage	NOUN
brj-23498	189	3	of	of	ADP
brj-23498	189	4	elm	elm	NOUN
brj-23498	189	5	include	include	VERB
brj-23498	189	6	.	.	PUNCT
brj-23498	190	1	fast	fast	ADJ
brj-23498	190	2	training	training	NOUN
brj-23498	190	3	speed	speed	NOUN
brj-23498	190	4	.	.	PUNCT
brj-23498	191	1	unlike	unlike	ADP
brj-23498	191	2	traditional	traditional	ADJ
brj-23498	191	3	neural	neural	ADJ
brj-23498	191	4	network	network	NOUN
brj-23498	191	5	algorithms	algorithm	NOUN
brj-23498	191	6	,	,	PUNCT
brj-23498	191	7	elm	elm	PROPN
brj-23498	191	8	does	do	AUX
brj-23498	191	9	not	not	PART
brj-23498	191	10	require	require	VERB
brj-23498	191	11	an	an	DET
brj-23498	191	12	iterative	iterative	ADJ
brj-23498	191	13	optimization	optimization	NOUN
brj-23498	191	14	process	process	NOUN
brj-23498	191	15	;	;	PUNCT
brj-23498	191	16	it	it	PRON
brj-23498	191	17	can	can	AUX
brj-23498	191	18	quickly	quickly	ADV
brj-23498	191	19	train	train	VERB
brj-23498	191	20	models	model	NOUN
brj-23498	191	21	by	by	ADP
brj-23498	191	22	randomly	randomly	ADV
brj-23498	191	23	selecting	select	VERB
brj-23498	191	24	weight	weight	NOUN
brj-23498	191	25	matrices	matrix	NOUN
brj-23498	191	26	and	and	CCONJ
brj-23498	191	27	threshold	threshold	NOUN
brj-23498	191	28	vectors	vector	NOUN
brj-23498	191	29	,	,	PUNCT
brj-23498	191	30	and	and	CCONJ
brj-23498	191	31	it	it	PRON
brj-23498	191	32	has	have	VERB
brj-23498	191	33	strong	strong	ADJ
brj-23498	191	34	generalization	generalization	NOUN
brj-23498	191	35	capabilities	capability	NOUN
brj-23498	191	36	(	(	PUNCT
brj-23498	191	37	mumtaz	mumtaz	NOUN
brj-23498	191	38	et	et	PROPN
brj-23498	191	39	al	al	PROPN
brj-23498	191	40	.	.	PROPN
brj-23498	191	41	2022	2022	NUM
brj-23498	191	42	)	)	PUNCT
brj-23498	191	43	.	.	PUNCT
brj-23498	192	1	the	the	DET
brj-23498	192	2	weights	weight	NOUN
brj-23498	192	3	in	in	ADP
brj-23498	192	4	elm	elm	NOUN
brj-23498	192	5	are	be	AUX
brj-23498	192	6	fixed	fix	VERB
brj-23498	192	7	during	during	ADP
brj-23498	192	8	training	training	NOUN
brj-23498	192	9	,	,	PUNCT
brj-23498	192	10	allowing	allow	VERB
brj-23498	192	11	the	the	DET
brj-23498	192	12	generalization	generalization	NOUN
brj-23498	192	13	capability	capability	NOUN
brj-23498	192	14	of	of	ADP
brj-23498	192	15	the	the	DET
brj-23498	192	16	model	model	NOUN
brj-23498	192	17	to	to	PART
brj-23498	192	18	be	be	AUX
brj-23498	192	19	transferred	transfer	VERB
brj-23498	192	20	to	to	ADP
brj-23498	192	21	the	the	DET
brj-23498	192	22	linear	linear	ADJ
brj-23498	192	23	regression	regression	NOUN
brj-23498	192	24	in	in	ADP
brj-23498	192	25	the	the	DET
brj-23498	192	26	output	output	NOUN
brj-23498	192	27	layer	layer	NOUN
brj-23498	192	28	,	,	PUNCT
brj-23498	192	29	thereby	thereby	ADV
brj-23498	192	30	avoiding	avoid	VERB
brj-23498	192	31	overfitting	overfitte	VERB
brj-23498	192	32	problems	problem	NOUN
brj-23498	192	33	in	in	ADP
brj-23498	192	34	traditional	traditional	ADJ
brj-23498	192	35	neural	neural	ADJ
brj-23498	192	36	networks	network	NOUN
brj-23498	192	37	.	.	PUNCT
brj-23498	193	1	however	however	ADV
brj-23498	193	2	,	,	PUNCT
brj-23498	193	3	elm	elm	PROPN
brj-23498	193	4	also	also	ADV
brj-23498	193	5	has	have	VERB
brj-23498	193	6	some	some	DET
brj-23498	193	7	limitations	limitation	NOUN
brj-23498	193	8	:	:	PUNCT
brj-23498	193	9	it	it	PRON
brj-23498	193	10	may	may	AUX
brj-23498	193	11	be	be	AUX
brj-23498	193	12	affected	affect	VERB
brj-23498	193	13	by	by	ADP
brj-23498	193	14	data	datum	NOUN
brj-23498	193	15	containing	contain	VERB
brj-23498	193	16	a	a	DET
brj-23498	193	17	large	large	ADJ
brj-23498	193	18	amount	amount	NOUN
brj-23498	193	19	of	of	ADP
brj-23498	193	20	noise	noise	NOUN
brj-23498	193	21	;	;	PUNCT
brj-23498	193	22	random	random	ADJ
brj-23498	193	23	initialization	initialization	NOUN
brj-23498	193	24	of	of	ADP
brj-23498	193	25	weight	weight	NOUN
brj-23498	193	26	matrices	matrix	NOUN
brj-23498	193	27	and	and	CCONJ
brj-23498	193	28	threshold	threshold	NOUN
brj-23498	193	29	vectors	vector	NOUN
brj-23498	193	30	may	may	AUX
brj-23498	193	31	lead	lead	VERB
brj-23498	193	32	to	to	ADP
brj-23498	193	33	different	different	ADJ
brj-23498	193	34	model	model	NOUN
brj-23498	193	35	performances	performance	NOUN
brj-23498	193	36	for	for	ADP
brj-23498	193	37	different	different	ADJ
brj-23498	193	38	random	random	ADJ
brj-23498	193	39	initialization	initialization	NOUN
brj-23498	193	40	results	result	NOUN
brj-23498	193	41	.	.	PUNCT
brj-23498	194	1	in	in	ADP
brj-23498	194	2	this	this	DET
brj-23498	194	3	experiment	experiment	NOUN
brj-23498	194	4	,	,	PUNCT
brj-23498	194	5	the	the	DET
brj-23498	194	6	number	number	NOUN
brj-23498	194	7	of	of	ADP
brj-23498	194	8	hidden	hidden	ADJ
brj-23498	194	9	layers	layer	NOUN
brj-23498	194	10	was	be	AUX
brj-23498	194	11	set	set	VERB
brj-23498	194	12	to	to	ADP
brj-23498	194	13	10	10	NUM
brj-23498	194	14	for	for	ADP
brj-23498	194	15	building	building	NOUN
brj-23498	194	16	models	model	NOUN
brj-23498	194	17	using	use	VERB
brj-23498	194	18	spectral	spectral	ADJ
brj-23498	194	19	and	and	CCONJ
brj-23498	194	20	fusion	fusion	NOUN
brj-23498	194	21	information	information	NOUN
brj-23498	194	22	,	,	PUNCT
brj-23498	194	23	and	and	CCONJ
brj-23498	194	24	set	set	VERB
brj-23498	194	25	to	to	ADP
brj-23498	194	26	100	100	NUM
brj-23498	194	27	for	for	ADP
brj-23498	194	28	building	building	NOUN
brj-23498	194	29	models	model	NOUN
brj-23498	194	30	using	use	VERB
brj-23498	194	31	image	image	NOUN
brj-23498	194	32	and	and	CCONJ
brj-23498	194	33	electronic	electronic	ADJ
brj-23498	194	34	nose	nose	NOUN
brj-23498	194	35	data	datum	NOUN
brj-23498	194	36	.	.	PUNCT
brj-23498	195	1	multi	multi	ADJ
brj-23498	195	2	-	-	ADJ
brj-23498	195	3	layer	layer	ADJ
brj-23498	195	4	perceptron	perceptron	NOUN
brj-23498	195	5	(	(	PUNCT
brj-23498	195	6	mlp	mlp	PROPN
brj-23498	195	7	)	)	PUNCT
brj-23498	195	8	is	be	AUX
brj-23498	195	9	a	a	DET
brj-23498	195	10	common	common	ADJ
brj-23498	195	11	type	type	NOUN
brj-23498	195	12	of	of	ADP
brj-23498	195	13	artificial	artificial	ADJ
brj-23498	195	14	neural	neural	ADJ
brj-23498	195	15	network	network	NOUN
brj-23498	195	16	(	(	PUNCT
brj-23498	195	17	ann	ann	PROPN
brj-23498	195	18	)	)	PUNCT
brj-23498	195	19	model	model	NOUN
brj-23498	195	20	used	use	VERB
brj-23498	195	21	to	to	PART
brj-23498	195	22	solve	solve	VERB
brj-23498	195	23	supervised	supervised	ADJ
brj-23498	195	24	learning	learning	NOUN
brj-23498	195	25	problems	problem	NOUN
brj-23498	195	26	,	,	PUNCT
brj-23498	195	27	including	include	VERB
brj-23498	195	28	classification	classification	NOUN
brj-23498	195	29	and	and	CCONJ
brj-23498	195	30	regression	regression	NOUN
brj-23498	195	31	tasks	task	NOUN
brj-23498	195	32	(	(	PUNCT
brj-23498	195	33	ma	ma	PROPN
brj-23498	195	34	et	et	PROPN
brj-23498	195	35	al	al	PROPN
brj-23498	195	36	.	.	PROPN
brj-23498	195	37	2023	2023	NUM
brj-23498	195	38	)	)	PUNCT
brj-23498	195	39	.	.	PUNCT
brj-23498	196	1	mlp	mlp	NOUN
brj-23498	196	2	consists	consist	VERB
brj-23498	196	3	of	of	ADP
brj-23498	196	4	multiple	multiple	ADJ
brj-23498	196	5	layers	layer	NOUN
brj-23498	196	6	of	of	ADP
brj-23498	196	7	neurons	neuron	NOUN
brj-23498	196	8	,	,	PUNCT
brj-23498	196	9	each	each	DET
brj-23498	196	10	layer	layer	NOUN
brj-23498	196	11	connected	connect	VERB
brj-23498	196	12	to	to	ADP
brj-23498	196	13	all	all	DET
brj-23498	196	14	neurons	neuron	NOUN
brj-23498	196	15	in	in	ADP
brj-23498	196	16	the	the	DET
brj-23498	196	17	previous	previous	ADJ
brj-23498	196	18	layer	layer	NOUN
brj-23498	196	19	,	,	PUNCT
brj-23498	196	20	with	with	ADP
brj-23498	196	21	one	one	NUM
brj-23498	196	22	or	or	CCONJ
brj-23498	196	23	more	more	ADV
brj-23498	196	24	hidden	hidden	ADJ
brj-23498	196	25	layers	layer	NOUN
brj-23498	196	26	except	except	SCONJ
brj-23498	196	27	for	for	ADP
brj-23498	196	28	the	the	DET
brj-23498	196	29	input	input	NOUN
brj-23498	196	30	layer	layer	NOUN
brj-23498	196	31	,	,	PUNCT
brj-23498	196	32	and	and	CCONJ
brj-23498	196	33	the	the	DET
brj-23498	196	34	last	last	ADJ
brj-23498	196	35	layer	layer	NOUN
brj-23498	196	36	being	be	AUX
brj-23498	196	37	the	the	DET
brj-23498	196	38	output	output	NOUN
brj-23498	196	39	layer	layer	NOUN
brj-23498	196	40	.	.	PUNCT
brj-23498	197	1	the	the	DET
brj-23498	197	2	working	work	VERB
brj-23498	197	3	principle	principle	NOUN
brj-23498	197	4	of	of	ADP
brj-23498	197	5	mlp	mlp	NOUN
brj-23498	197	6	involves	involve	VERB
brj-23498	197	7	training	train	VERB
brj-23498	197	8	the	the	DET
brj-23498	197	9	network	network	NOUN
brj-23498	197	10	using	use	VERB
brj-23498	197	11	the	the	DET
brj-23498	197	12	backpropagation	backpropagation	NOUN
brj-23498	197	13	algorithm	algorithm	NOUN
brj-23498	197	14	to	to	PART
brj-23498	197	15	minimize	minimize	VERB
brj-23498	197	16	the	the	DET
brj-23498	197	17	error	error	NOUN
brj-23498	197	18	between	between	ADP
brj-23498	197	19	the	the	DET
brj-23498	197	20	predicted	predict	VERB
brj-23498	197	21	output	output	NOUN
brj-23498	197	22	and	and	CCONJ
brj-23498	197	23	the	the	DET
brj-23498	197	24	actual	actual	ADJ
brj-23498	197	25	labels	label	NOUN
brj-23498	197	26	.	.	PUNCT
brj-23498	198	1	during	during	ADP
brj-23498	198	2	training	training	NOUN
brj-23498	198	3	,	,	PUNCT
brj-23498	198	4	the	the	DET
brj-23498	198	5	forward	forward	ADJ
brj-23498	198	6	peer	peer	NOUN
brj-23498	198	7	-	-	PUNCT
brj-23498	198	8	reviewed	review	VERB
brj-23498	198	9	article	article	NOUN
brj-23498	198	10	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	198	11	yang	yang	PROPN
brj-23498	198	12	et	et	PROPN
brj-23498	198	13	al	al	PROPN
brj-23498	198	14	.	.	PROPN
brj-23498	199	1	(	(	PUNCT
brj-23498	199	2	2024	2024	NUM
brj-23498	199	3	)	)	PUNCT
brj-23498	199	4	.	.	PUNCT
brj-23498	200	1	“	"	PUNCT
brj-23498	200	2	alfalfa	alfalfa	NOUN
brj-23498	200	3	quality	quality	NOUN
brj-23498	200	4	assessment	assessment	NOUN
brj-23498	200	5	,	,	PUNCT
brj-23498	200	6	”	"	PUNCT
brj-23498	200	7	bioresources	bioresource	NOUN
brj-23498	200	8	19(3	19(3	NUM
brj-23498	200	9	)	)	PUNCT
brj-23498	200	10	,	,	PUNCT
brj-23498	200	11	4531	4531	NUM
brj-23498	200	12	-	-	SYM
brj-23498	200	13	4546	4546	NUM
brj-23498	200	14	.	.	PUNCT
brj-23498	201	1	4541	4541	NUM
brj-23498	201	2	propagation	propagation	NOUN
brj-23498	201	3	calculates	calculate	VERB
brj-23498	201	4	the	the	DET
brj-23498	201	5	output	output	NOUN
brj-23498	201	6	for	for	ADP
brj-23498	201	7	each	each	DET
brj-23498	201	8	sample	sample	NOUN
brj-23498	201	9	,	,	PUNCT
brj-23498	201	10	then	then	ADV
brj-23498	201	11	the	the	DET
brj-23498	201	12	error	error	NOUN
brj-23498	201	13	is	be	AUX
brj-23498	201	14	computed	compute	VERB
brj-23498	201	15	and	and	CCONJ
brj-23498	201	16	the	the	DET
brj-23498	201	17	weights	weight	NOUN
brj-23498	201	18	in	in	ADP
brj-23498	201	19	the	the	DET
brj-23498	201	20	network	network	NOUN
brj-23498	201	21	are	be	AUX
brj-23498	201	22	adjusted	adjust	VERB
brj-23498	201	23	through	through	ADP
brj-23498	201	24	backpropagation	backpropagation	NOUN
brj-23498	201	25	to	to	PART
brj-23498	201	26	minimize	minimize	VERB
brj-23498	201	27	the	the	DET
brj-23498	201	28	error	error	NOUN
brj-23498	201	29	.	.	PUNCT
brj-23498	202	1	each	each	DET
brj-23498	202	2	neuron	neuron	PROPN
brj-23498	202	3	in	in	ADP
brj-23498	202	4	mlp	mlp	PROPN
brj-23498	202	5	has	have	VERB
brj-23498	202	6	an	an	DET
brj-23498	202	7	activation	activation	NOUN
brj-23498	202	8	function	function	NOUN
brj-23498	202	9	to	to	PART
brj-23498	202	10	introduce	introduce	VERB
brj-23498	202	11	nonlinearity	nonlinearity	NOUN
brj-23498	202	12	.	.	PUNCT
brj-23498	203	1	common	common	ADJ
brj-23498	203	2	activation	activation	NOUN
brj-23498	203	3	functions	function	NOUN
brj-23498	203	4	include	include	VERB
brj-23498	203	5	sigmoid	sigmoid	NOUN
brj-23498	203	6	,	,	PUNCT
brj-23498	203	7	relu	relu	NOUN
brj-23498	203	8	(	(	PUNCT
brj-23498	203	9	rectified	rectify	VERB
brj-23498	203	10	linear	linear	NOUN
brj-23498	203	11	unit	unit	NOUN
brj-23498	203	12	)	)	PUNCT
brj-23498	203	13	,	,	PUNCT
brj-23498	203	14	and	and	CCONJ
brj-23498	203	15	tanh	tanh	NOUN
brj-23498	203	16	,	,	PUNCT
brj-23498	203	17	among	among	ADP
brj-23498	203	18	others	other	NOUN
brj-23498	203	19	.	.	PUNCT
brj-23498	204	1	nonlinear	nonlinear	ADJ
brj-23498	204	2	activation	activation	NOUN
brj-23498	204	3	functions	function	NOUN
brj-23498	204	4	enable	enable	VERB
brj-23498	204	5	mlp	mlp	PROPN
brj-23498	204	6	to	to	PART
brj-23498	204	7	learn	learn	VERB
brj-23498	204	8	and	and	CCONJ
brj-23498	204	9	represent	represent	VERB
brj-23498	204	10	complex	complex	ADJ
brj-23498	204	11	nonlinear	nonlinear	ADJ
brj-23498	204	12	relationships	relationship	NOUN
brj-23498	204	13	,	,	PUNCT
brj-23498	204	14	resulting	result	VERB
brj-23498	204	15	in	in	ADP
brj-23498	204	16	better	well	ADV
brj-23498	204	17	fitting	fitting	ADJ
brj-23498	204	18	of	of	ADP
brj-23498	204	19	data	datum	NOUN
brj-23498	204	20	(	(	PUNCT
brj-23498	204	21	najmeh	najmeh	NOUN
brj-23498	204	22	et	et	PROPN
brj-23498	204	23	al	al	PROPN
brj-23498	204	24	.	.	PROPN
brj-23498	204	25	2022	2022	NUM
brj-23498	204	26	)	)	PUNCT
brj-23498	204	27	.	.	PUNCT
brj-23498	205	1	the	the	DET
brj-23498	205	2	advantage	advantage	NOUN
brj-23498	205	3	of	of	ADP
brj-23498	205	4	mlp	mlp	NOUN
brj-23498	205	5	is	be	AUX
brj-23498	205	6	that	that	SCONJ
brj-23498	205	7	it	it	PRON
brj-23498	205	8	can	can	AUX
brj-23498	205	9	automatically	automatically	ADV
brj-23498	205	10	learn	learn	VERB
brj-23498	205	11	effective	effective	ADJ
brj-23498	205	12	feature	feature	NOUN
brj-23498	205	13	representations	representation	NOUN
brj-23498	205	14	from	from	ADP
brj-23498	205	15	raw	raw	ADJ
brj-23498	205	16	data	datum	NOUN
brj-23498	205	17	,	,	PUNCT
brj-23498	205	18	has	have	VERB
brj-23498	205	19	good	good	ADJ
brj-23498	205	20	generalization	generalization	NOUN
brj-23498	205	21	ability	ability	NOUN
brj-23498	205	22	,	,	PUNCT
brj-23498	205	23	and	and	CCONJ
brj-23498	205	24	the	the	DET
brj-23498	205	25	calculation	calculation	NOUN
brj-23498	205	26	process	process	NOUN
brj-23498	205	27	can	can	AUX
brj-23498	205	28	be	be	AUX
brj-23498	205	29	highly	highly	ADV
brj-23498	205	30	parallelized	parallelize	VERB
brj-23498	205	31	,	,	PUNCT
brj-23498	205	32	which	which	PRON
brj-23498	205	33	helps	help	VERB
brj-23498	205	34	to	to	PART
brj-23498	205	35	improve	improve	VERB
brj-23498	205	36	training	training	NOUN
brj-23498	205	37	speed	speed	NOUN
brj-23498	205	38	and	and	CCONJ
brj-23498	205	39	performance	performance	NOUN
brj-23498	205	40	.	.	PUNCT
brj-23498	206	1	mlp	mlp	NOUN
brj-23498	206	2	has	have	VERB
brj-23498	206	3	important	important	ADJ
brj-23498	206	4	hyperparameters	hyperparameter	NOUN
brj-23498	206	5	including	include	VERB
brj-23498	206	6	the	the	DET
brj-23498	206	7	number	number	NOUN
brj-23498	206	8	of	of	ADP
brj-23498	206	9	hidden	hidden	ADJ
brj-23498	206	10	layers	layer	NOUN
brj-23498	206	11	,	,	PUNCT
brj-23498	206	12	the	the	DET
brj-23498	206	13	number	number	NOUN
brj-23498	206	14	of	of	ADP
brj-23498	206	15	neurons	neuron	NOUN
brj-23498	206	16	in	in	ADP
brj-23498	206	17	each	each	DET
brj-23498	206	18	hidden	hide	VERB
brj-23498	206	19	layer	layer	NOUN
brj-23498	206	20	,	,	PUNCT
brj-23498	206	21	learning	learn	VERB
brj-23498	206	22	rate	rate	NOUN
brj-23498	206	23	,	,	PUNCT
brj-23498	206	24	regularization	regularization	NOUN
brj-23498	206	25	parameter	parameter	NOUN
brj-23498	206	26	,	,	PUNCT
brj-23498	206	27	etc	etc	X
brj-23498	206	28	.	.	X
brj-23498	206	29	the	the	DET
brj-23498	206	30	selection	selection	NOUN
brj-23498	206	31	of	of	ADP
brj-23498	206	32	these	these	DET
brj-23498	206	33	hyperparameters	hyperparameter	NOUN
brj-23498	206	34	is	be	AUX
brj-23498	206	35	crucial	crucial	ADJ
brj-23498	206	36	for	for	ADP
brj-23498	206	37	the	the	DET
brj-23498	206	38	performance	performance	NOUN
brj-23498	206	39	and	and	CCONJ
brj-23498	206	40	generalization	generalization	NOUN
brj-23498	206	41	capability	capability	NOUN
brj-23498	206	42	of	of	ADP
brj-23498	206	43	the	the	DET
brj-23498	206	44	model	model	NOUN
brj-23498	206	45	,	,	PUNCT
brj-23498	206	46	and	and	CCONJ
brj-23498	206	47	usually	usually	ADV
brj-23498	206	48	requires	require	VERB
brj-23498	206	49	tuning	tune	VERB
brj-23498	206	50	through	through	ADP
brj-23498	206	51	techniques	technique	NOUN
brj-23498	206	52	such	such	ADJ
brj-23498	206	53	as	as	ADP
brj-23498	206	54	cross	cross	NOUN
brj-23498	206	55	-	-	NOUN
brj-23498	206	56	validation	validation	NOUN
brj-23498	206	57	.	.	PUNCT
brj-23498	207	1	in	in	ADP
brj-23498	207	2	this	this	DET
brj-23498	207	3	experiment	experiment	NOUN
brj-23498	207	4	,	,	PUNCT
brj-23498	207	5	the	the	DET
brj-23498	207	6	number	number	NOUN
brj-23498	207	7	of	of	ADP
brj-23498	207	8	hidden	hidden	ADJ
brj-23498	207	9	layers	layer	NOUN
brj-23498	207	10	was	be	AUX
brj-23498	207	11	set	set	VERB
brj-23498	207	12	to	to	ADP
brj-23498	207	13	100	100	NUM
brj-23498	207	14	,	,	PUNCT
brj-23498	207	15	learning	learn	VERB
brj-23498	207	16	rate	rate	NOUN
brj-23498	207	17	was	be	AUX
brj-23498	207	18	set	set	VERB
brj-23498	207	19	to	to	ADP
brj-23498	207	20	0.01	0.01	NUM
brj-23498	207	21	,	,	PUNCT
brj-23498	207	22	and	and	CCONJ
brj-23498	207	23	the	the	DET
brj-23498	207	24	random	random	ADJ
brj-23498	207	25	seed	seed	NOUN
brj-23498	207	26	(	(	PUNCT
brj-23498	207	27	rng	rng	PROPN
brj-23498	207	28	)	)	PUNCT
brj-23498	207	29	was	be	AUX
brj-23498	207	30	set	set	VERB
brj-23498	207	31	to	to	ADP
brj-23498	207	32	0	0	NUM
brj-23498	207	33	.	.	PUNCT
brj-23498	208	1	table	table	NOUN
brj-23498	208	2	1	1	NUM
brj-23498	208	3	.	.	PUNCT
brj-23498	208	4	prediction	prediction	NOUN
brj-23498	208	5	results	result	NOUN
brj-23498	208	6	of	of	ADP
brj-23498	208	7	regression	regression	NOUN
brj-23498	208	8	models	model	NOUN
brj-23498	208	9	based	base	VERB
brj-23498	208	10	on	on	ADP
brj-23498	208	11	near	near	ADV
brj-23498	208	12	-	-	PUNCT
brj-23498	208	13	infrared	infrare	VERB
brj-23498	208	14	spectroscopy	spectroscopy	NOUN
brj-23498	208	15	model	model	NOUN
brj-23498	208	16	pretreatment	pretreatment	NOUN
brj-23498	208	17	bands	band	NOUN
brj-23498	208	18	used	use	VERB
brj-23498	208	19	for	for	ADP
brj-23498	208	20	modeling	model	VERB
brj-23498	208	21	feature	feature	NOUN
brj-23498	208	22	extraction	extraction	NOUN
brj-23498	208	23	method	method	NOUN
brj-23498	208	24	training	training	NOUN
brj-23498	208	25	set	set	NOUN
brj-23498	208	26	test	test	NOUN
brj-23498	208	27	set	set	VERB
brj-23498	208	28	rmse	rmse	PROPN
brj-23498	208	29	r2	r2	PROPN
brj-23498	208	30	rmse	rmse	PROPN
brj-23498	208	31	r2	r2	PROPN
brj-23498	208	32	svm	svm	PROPN
brj-23498	208	33	sg	sg	ADP
brj-23498	208	34	full	full	ADJ
brj-23498	208	35	spectrum	spectrum	NOUN
brj-23498	208	36	0.0209	0.0209	NUM
brj-23498	208	37	0.9978	0.9978	NUM
brj-23498	208	38	0.3195	0.3195	NUM
brj-23498	208	39	0.1894	0.1894	NUM
brj-23498	208	40	feature	feature	NOUN
brj-23498	208	41	wavelength	wavelength	NOUN
brj-23498	208	42	cars	car	NOUN
brj-23498	209	1	0.1385	0.1385	NUM
brj-23498	209	2	0.9079	0.9079	NUM
brj-23498	210	1	0.2161	0.2161	NUM
brj-23498	210	2	0.8217	0.8217	NUM
brj-23498	210	3	spa	spa	NOUN
brj-23498	210	4	0.2937	0.2937	NUM
brj-23498	210	5	0.8031	0.8031	NUM
brj-23498	210	6	0.2438	0.2438	NUM
brj-23498	210	7	0.6565	0.6565	NUM
brj-23498	210	8	elm	elm	NOUN
brj-23498	210	9	sg	sg	ADP
brj-23498	210	10	full	full	ADJ
brj-23498	210	11	spectrum	spectrum	NOUN
brj-23498	210	12	0.0986	0.0986	NUM
brj-23498	210	13	0.9628	0.9628	NUM
brj-23498	210	14	0.2645	0.2645	NUM
brj-23498	210	15	0.3820	0.3820	NUM
brj-23498	210	16	feature	feature	NOUN
brj-23498	210	17	wavelength	wavelength	NOUN
brj-23498	210	18	cars	car	NOUN
brj-23498	210	19	0.2275	0.2275	NUM
brj-23498	210	20	0.8726	0.8726	NUM
brj-23498	210	21	0.4017	0.4017	NUM
brj-23498	210	22	0.7158	0.7158	NUM
brj-23498	210	23	spa	spa	NOUN
brj-23498	210	24	0.2438	0.2438	NUM
brj-23498	210	25	0.8138	0.8138	NUM
brj-23498	210	26	0.5671	0.5671	NUM
brj-23498	210	27	0.6932	0.6932	NUM
brj-23498	210	28	mlp	mlp	NOUN
brj-23498	210	29	sg	sg	ADP
brj-23498	210	30	full	full	ADJ
brj-23498	210	31	spectrum	spectrum	NOUN
brj-23498	210	32	0.0567	0.0567	NUM
brj-23498	210	33	0.9723	0.9723	NUM
brj-23498	210	34	0.3523	0.3523	NUM
brj-23498	210	35	0.2627	0.2627	NUM
brj-23498	210	36	feature	feature	NOUN
brj-23498	210	37	wavelength	wavelength	NOUN
brj-23498	210	38	cars	car	NOUN
brj-23498	210	39	0.3257	0.3257	NUM
brj-23498	210	40	0.8231	0.8231	NUM
brj-23498	210	41	0.4096	0.4096	NUM
brj-23498	210	42	0.7139	0.7139	NUM
brj-23498	210	43	spa	spa	NOUN
brj-23498	210	44	0.4587	0.4587	NUM
brj-23498	210	45	0.7524	0.7524	NUM
brj-23498	210	46	0.3199	0.3199	NUM
brj-23498	211	1	0.6395	0.6395	NUM
brj-23498	211	2	table	table	NOUN
brj-23498	211	3	2	2	NUM
brj-23498	211	4	.	.	PUNCT
brj-23498	211	5	prediction	prediction	NOUN
brj-23498	211	6	results	result	NOUN
brj-23498	211	7	of	of	ADP
brj-23498	211	8	classification	classification	NOUN
brj-23498	211	9	model	model	NOUN
brj-23498	211	10	based	base	VERB
brj-23498	211	11	on	on	ADP
brj-23498	211	12	images	image	NOUN
brj-23498	211	13	model	model	NOUN
brj-23498	211	14	pretreatment	pretreatment	NOUN
brj-23498	211	15	bands	band	NOUN
brj-23498	211	16	used	use	VERB
brj-23498	211	17	for	for	ADP
brj-23498	211	18	modeling	model	VERB
brj-23498	211	19	feature	feature	NOUN
brj-23498	211	20	extraction	extraction	NOUN
brj-23498	211	21	method	method	NOUN
brj-23498	211	22	accuracy	accuracy	NOUN
brj-23498	211	23	training	training	NOUN
brj-23498	211	24	set	set	NOUN
brj-23498	211	25	test	test	NOUN
brj-23498	211	26	set	set	VERB
brj-23498	211	27	svm	svm	ADJ
brj-23498	211	28	awtd	awtd	NOUN
brj-23498	211	29	all	all	PRON
brj-23498	211	30	features	feature	VERB
brj-23498	211	31	93.518	93.518	NUM
brj-23498	211	32	%	%	NOUN
brj-23498	211	33	88.356	88.356	NUM
brj-23498	211	34	%	%	NOUN
brj-23498	211	35	extracted	extract	VERB
brj-23498	211	36	features	feature	NOUN
brj-23498	211	37	color	color	NOUN
brj-23498	211	38	histogram	histogram	NOUN
brj-23498	211	39	rf	rf	PROPN
brj-23498	211	40	95.074	95.074	NUM
brj-23498	211	41	%	%	NOUN
brj-23498	211	42	91	91	NUM
brj-23498	211	43	..	..	SYM
brj-23498	211	44	297	297	NUM
brj-23498	211	45	%	%	NOUN
brj-23498	211	46	elm	elm	NOUN
brj-23498	211	47	awtd	awtd	NOUN
brj-23498	211	48	all	all	PRON
brj-23498	211	49	features	feature	VERB
brj-23498	211	50	90.740	90.740	NUM
brj-23498	211	51	%	%	NOUN
brj-23498	211	52	85.458	85.458	NUM
brj-23498	211	53	%	%	NOUN
brj-23498	211	54	extracted	extract	VERB
brj-23498	211	55	features	feature	NOUN
brj-23498	211	56	color	color	NOUN
brj-23498	211	57	histogram	histogram	NOUN
brj-23498	211	58	rf	rf	VERB
brj-23498	211	59	95.370	95.370	NUM
brj-23498	211	60	%	%	NOUN
brj-23498	211	61	91.562	91.562	NUM
brj-23498	211	62	%	%	NOUN
brj-23498	211	63	mlp	mlp	NOUN
brj-23498	211	64	awtd	awtd	NOUN
brj-23498	211	65	all	all	PRON
brj-23498	211	66	features	feature	VERB
brj-23498	211	67	90.148	90.148	NUM
brj-23498	211	68	%	%	NOUN
brj-23498	211	69	77.851	77.851	NUM
brj-23498	211	70	%	%	NOUN
brj-23498	211	71	extracted	extract	VERB
brj-23498	211	72	features	feature	NOUN
brj-23498	211	73	color	color	NOUN
brj-23498	211	74	histogram	histogram	NOUN
brj-23498	211	75	rf	rf	NOUN
brj-23498	211	76	91.259	91.259	NUM
brj-23498	211	77	%	%	NOUN
brj-23498	211	78	80.754	80.754	NUM
brj-23498	211	79	%	%	NOUN
brj-23498	211	80	peer	peer	NOUN
brj-23498	211	81	-	-	PUNCT
brj-23498	211	82	reviewed	review	VERB
brj-23498	211	83	article	article	NOUN
brj-23498	211	84	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	211	85	yang	yang	PROPN
brj-23498	211	86	et	et	PROPN
brj-23498	211	87	al	al	PROPN
brj-23498	211	88	.	.	PROPN
brj-23498	212	1	(	(	PUNCT
brj-23498	212	2	2024	2024	NUM
brj-23498	212	3	)	)	PUNCT
brj-23498	212	4	.	.	PUNCT
brj-23498	213	1	“	"	PUNCT
brj-23498	213	2	alfalfa	alfalfa	NOUN
brj-23498	213	3	quality	quality	NOUN
brj-23498	213	4	assessment	assessment	NOUN
brj-23498	213	5	,	,	PUNCT
brj-23498	213	6	”	"	PUNCT
brj-23498	213	7	bioresources	bioresource	NOUN
brj-23498	213	8	19(3	19(3	NUM
brj-23498	213	9	)	)	PUNCT
brj-23498	213	10	,	,	PUNCT
brj-23498	213	11	4531	4531	NUM
brj-23498	213	12	-	-	SYM
brj-23498	213	13	4546	4546	NUM
brj-23498	213	14	.	.	PUNCT
brj-23498	214	1	4542	4542	NUM
brj-23498	214	2	table	table	NOUN
brj-23498	214	3	3	3	NUM
brj-23498	214	4	.	.	PUNCT
brj-23498	214	5	prediction	prediction	NOUN
brj-23498	214	6	results	result	NOUN
brj-23498	214	7	of	of	ADP
brj-23498	214	8	classification	classification	NOUN
brj-23498	214	9	model	model	NOUN
brj-23498	214	10	based	base	VERB
brj-23498	214	11	on	on	ADP
brj-23498	214	12	electronic	electronic	ADJ
brj-23498	214	13	nose	nose	NOUN
brj-23498	214	14	model	model	NOUN
brj-23498	214	15	bands	band	NOUN
brj-23498	214	16	used	use	VERB
brj-23498	214	17	for	for	ADP
brj-23498	214	18	modeling	model	VERB
brj-23498	214	19	accuracy	accuracy	NOUN
brj-23498	214	20	training	training	NOUN
brj-23498	214	21	set	set	NOUN
brj-23498	214	22	test	test	NOUN
brj-23498	214	23	set	set	VERB
brj-23498	214	24	svm	svm	PROPN
brj-23498	214	25	all	all	PRON
brj-23498	214	26	features	feature	VERB
brj-23498	214	27	75.037	75.037	NUM
brj-23498	214	28	%	%	NOUN
brj-23498	214	29	78.333	78.333	NUM
brj-23498	214	30	%	%	NOUN
brj-23498	214	31	pca	pca	NOUN
brj-23498	214	32	extracted	extract	VERB
brj-23498	214	33	features	feature	NOUN
brj-23498	214	34	78.518	78.518	NUM
brj-23498	214	35	%	%	NOUN
brj-23498	214	36	72.259	72.259	NUM
brj-23498	214	37	%	%	NOUN
brj-23498	214	38	elm	elm	NOUN
brj-23498	214	39	all	all	PRON
brj-23498	214	40	features	feature	VERB
brj-23498	214	41	67.592	67.592	NUM
brj-23498	214	42	%	%	NOUN
brj-23498	214	43	75.253	75.253	NUM
brj-23498	214	44	%	%	NOUN
brj-23498	214	45	pca	pca	NOUN
brj-23498	214	46	extracted	extract	VERB
brj-23498	214	47	features	feature	NOUN
brj-23498	214	48	73.254	73.254	NUM
brj-23498	214	49	%	%	NOUN
brj-23498	214	50	71.586	71.586	NUM
brj-23498	214	51	%	%	NOUN
brj-23498	214	52	mlp	mlp	NOUN
brj-23498	214	53	all	all	PRON
brj-23498	214	54	features	feature	VERB
brj-23498	214	55	85.351	85.351	NUM
brj-23498	214	56	%	%	NOUN
brj-23498	214	57	80.549	80.549	NUM
brj-23498	214	58	%	%	NOUN
brj-23498	214	59	pca	pca	NOUN
brj-23498	214	60	extracted	extract	VERB
brj-23498	214	61	features	feature	NOUN
brj-23498	214	62	80.569	80.569	NUM
brj-23498	214	63	%	%	NOUN
brj-23498	214	64	74.317	74.317	NUM
brj-23498	214	65	%	%	NOUN
brj-23498	214	66	table	table	NOUN
brj-23498	214	67	4	4	NUM
brj-23498	214	68	.	.	PUNCT
brj-23498	214	69	model	model	NOUN
brj-23498	214	70	prediction	prediction	NOUN
brj-23498	214	71	results	result	NOUN
brj-23498	214	72	based	base	VERB
brj-23498	214	73	on	on	ADP
brj-23498	214	74	fusion	fusion	NOUN
brj-23498	214	75	of	of	ADP
brj-23498	214	76	near	near	ADP
brj-23498	214	77	infrared	infrared	ADJ
brj-23498	214	78	spectroscopy	spectroscopy	NOUN
brj-23498	214	79	,	,	PUNCT
brj-23498	214	80	imaging	imaging	NOUN
brj-23498	214	81	,	,	PUNCT
brj-23498	214	82	and	and	CCONJ
brj-23498	214	83	electronic	electronic	ADJ
brj-23498	214	84	nose	nose	NOUN
brj-23498	214	85	information	information	NOUN
brj-23498	214	86	model	model	NOUN
brj-23498	214	87	training	training	NOUN
brj-23498	214	88	set	set	NOUN
brj-23498	214	89	test	test	NOUN
brj-23498	214	90	set	set	VERB
brj-23498	214	91	rmse	rmse	ADJ
brj-23498	214	92	r2	r2	PROPN
brj-23498	214	93	accuracy	accuracy	NOUN
brj-23498	214	94	rmse	rmse	PROPN
brj-23498	214	95	r2	r2	PROPN
brj-23498	214	96	accuracy	accuracy	NOUN
brj-23498	214	97	svm	svm	VERB
brj-23498	214	98	0.1379	0.1379	NUM
brj-23498	214	99	0.9486	0.9486	NUM
brj-23498	214	100	99.074	99.074	NUM
brj-23498	214	101	%	%	NOUN
brj-23498	214	102	0.1728	0.1728	NUM
brj-23498	214	103	0.9239	0.9239	NUM
brj-23498	214	104	100	100	NUM
brj-23498	214	105	%	%	NOUN
brj-23498	214	106	elm	elm	NOUN
brj-23498	215	1	0.2134	0.2134	NUM
brj-23498	215	2	0.9145	0.9145	NUM
brj-23498	215	3	93.452	93.452	NUM
brj-23498	215	4	%	%	NOUN
brj-23498	215	5	0.3384	0.3384	NUM
brj-23498	215	6	0.8036	0.8036	NUM
brj-23498	215	7	91.522	91.522	NUM
brj-23498	215	8	%	%	NOUN
brj-23498	215	9	mlp	mlp	NOUN
brj-23498	215	10	0.2585	0.2585	NUM
brj-23498	215	11	0.8624	0.8624	NUM
brj-23498	215	12	90.218	90.218	NUM
brj-23498	215	13	%	%	NOUN
brj-23498	215	14	0.3058	0.3058	NUM
brj-23498	215	15	0.8159	0.8159	NUM
brj-23498	215	16	87.884	87.884	NUM
brj-23498	215	17	%	%	NOUN
brj-23498	215	18	fig	fig	NOUN
brj-23498	215	19	.	.	PUNCT
brj-23498	216	1	6	6	NUM
brj-23498	216	2	.	.	X
brj-23498	216	3	line	line	NOUN
brj-23498	216	4	chart	chart	NOUN
brj-23498	216	5	of	of	ADP
brj-23498	216	6	estimated	estimate	VERB
brj-23498	216	7	vs	vs	ADP
brj-23498	216	8	actual	actual	ADJ
brj-23498	216	9	values	value	NOUN
brj-23498	216	10	for	for	ADP
brj-23498	216	11	the	the	DET
brj-23498	216	12	sg	sg	NOUN
brj-23498	216	13	-	-	PUNCT
brj-23498	216	14	cars	car	NOUN
brj-23498	216	15	-svm	-svm	NOUN
brj-23498	216	16	model	model	NOUN
brj-23498	216	17	based	base	VERB
brj-23498	216	18	on	on	ADP
brj-23498	216	19	fused	fuse	VERB
brj-23498	216	20	information	information	NOUN
brj-23498	216	21	fig	fig	NOUN
brj-23498	216	22	.	.	PUNCT
brj-23498	217	1	7	7	X
brj-23498	217	2	.	.	NOUN
brj-23498	217	3	prediction	prediction	NOUN
brj-23498	217	4	accuracy	accuracy	NOUN
brj-23498	217	5	for	for	ADP
brj-23498	217	6	the	the	DET
brj-23498	217	7	sg	sg	NOUN
brj-23498	217	8	-	-	PUNCT
brj-23498	217	9	cars	car	NOUN
brj-23498	217	10	-svm	-svm	NOUN
brj-23498	217	11	model	model	NOUN
brj-23498	217	12	based	base	VERB
brj-23498	217	13	on	on	ADP
brj-23498	217	14	fused	fuse	VERB
brj-23498	217	15	information	information	NOUN
brj-23498	217	16	peer	peer	NOUN
brj-23498	217	17	-	-	PUNCT
brj-23498	217	18	reviewed	review	VERB
brj-23498	217	19	article	article	NOUN
brj-23498	217	20	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	217	21	yang	yang	PROPN
brj-23498	217	22	et	et	PROPN
brj-23498	217	23	al	al	PROPN
brj-23498	217	24	.	.	PROPN
brj-23498	218	1	(	(	PUNCT
brj-23498	218	2	2024	2024	NUM
brj-23498	218	3	)	)	PUNCT
brj-23498	218	4	.	.	PUNCT
brj-23498	219	1	“	"	PUNCT
brj-23498	219	2	alfalfa	alfalfa	NOUN
brj-23498	219	3	quality	quality	NOUN
brj-23498	219	4	assessment	assessment	NOUN
brj-23498	219	5	,	,	PUNCT
brj-23498	219	6	”	"	PUNCT
brj-23498	219	7	bioresources	bioresource	NOUN
brj-23498	219	8	19(3	19(3	NUM
brj-23498	219	9	)	)	PUNCT
brj-23498	219	10	,	,	PUNCT
brj-23498	219	11	4531	4531	NUM
brj-23498	219	12	-	-	SYM
brj-23498	219	13	4546	4546	NUM
brj-23498	219	14	.	.	PUNCT
brj-23498	220	1	4543	4543	NUM
brj-23498	220	2	results	result	NOUN
brj-23498	220	3	and	and	CCONJ
brj-23498	220	4	discussion	discussion	NOUN
brj-23498	220	5	this	this	DET
brj-23498	220	6	study	study	NOUN
brj-23498	220	7	utilized	utilize	VERB
brj-23498	220	8	near	near	ADP
brj-23498	220	9	-	-	PUNCT
brj-23498	220	10	infrared	infrared	ADJ
brj-23498	220	11	spectroscopy	spectroscopy	NOUN
brj-23498	220	12	,	,	PUNCT
brj-23498	220	13	image	image	NOUN
brj-23498	220	14	processing	processing	NOUN
brj-23498	220	15	,	,	PUNCT
brj-23498	220	16	electronic	electronic	ADJ
brj-23498	220	17	nose	nose	NOUN
brj-23498	220	18	technology	technology	NOUN
brj-23498	220	19	,	,	PUNCT
brj-23498	220	20	and	and	CCONJ
brj-23498	220	21	various	various	ADJ
brj-23498	220	22	preprocessing	preprocessing	NOUN
brj-23498	220	23	and	and	CCONJ
brj-23498	220	24	feature	feature	NOUN
brj-23498	220	25	extraction	extraction	NOUN
brj-23498	220	26	methods	method	NOUN
brj-23498	220	27	to	to	PART
brj-23498	220	28	successfully	successfully	ADV
brj-23498	220	29	establish	establish	VERB
brj-23498	220	30	a	a	DET
brj-23498	220	31	predictive	predictive	ADJ
brj-23498	220	32	model	model	NOUN
brj-23498	220	33	for	for	ADP
brj-23498	220	34	the	the	DET
brj-23498	220	35	quality	quality	NOUN
brj-23498	220	36	of	of	ADP
brj-23498	220	37	alfalfa	alfalfa	NOUN
brj-23498	220	38	hay	hay	NOUN
brj-23498	220	39	based	base	VERB
brj-23498	220	40	on	on	ADP
brj-23498	220	41	the	the	DET
brj-23498	220	42	fusion	fusion	NOUN
brj-23498	220	43	of	of	ADP
brj-23498	220	44	multiple	multiple	ADJ
brj-23498	220	45	sources	source	NOUN
brj-23498	220	46	of	of	ADP
brj-23498	220	47	information	information	NOUN
brj-23498	220	48	.	.	PUNCT
brj-23498	221	1	for	for	ADP
brj-23498	221	2	spectroscopic	spectroscopic	ADJ
brj-23498	221	3	data	datum	NOUN
brj-23498	221	4	,	,	PUNCT
brj-23498	221	5	sg	sg	SCONJ
brj-23498	221	6	convolution	convolution	NOUN
brj-23498	221	7	smoothing	smoothing	NOUN
brj-23498	221	8	was	be	AUX
brj-23498	221	9	used	use	VERB
brj-23498	221	10	to	to	PART
brj-23498	221	11	process	process	VERB
brj-23498	221	12	the	the	DET
brj-23498	221	13	alfalfa	alfalfa	NOUN
brj-23498	221	14	hay	hay	NOUN
brj-23498	221	15	spectroscopic	spectroscopic	NOUN
brj-23498	221	16	data	datum	NOUN
brj-23498	221	17	in	in	ADP
brj-23498	221	18	the	the	DET
brj-23498	221	19	range	range	NOUN
brj-23498	221	20	of	of	ADP
brj-23498	221	21	430	430	NUM
brj-23498	221	22	to	to	ADP
brj-23498	221	23	1830	1830	NUM
brj-23498	221	24	nm	nm	NOUN
brj-23498	221	25	to	to	PART
brj-23498	221	26	reduce	reduce	VERB
brj-23498	221	27	noise	noise	NOUN
brj-23498	221	28	interference	interference	NOUN
brj-23498	221	29	.	.	PUNCT
brj-23498	222	1	then	then	ADV
brj-23498	222	2	the	the	DET
brj-23498	222	3	competitive	competitive	ADJ
brj-23498	222	4	adaptive	adaptive	ADJ
brj-23498	222	5	reweighted	reweighte	VERB
brj-23498	222	6	sampling	sample	VERB
brj-23498	222	7	(	(	PUNCT
brj-23498	222	8	cars	car	NOUN
brj-23498	222	9	)	)	PUNCT
brj-23498	222	10	algorithm	algorithm	NOUN
brj-23498	222	11	and	and	CCONJ
brj-23498	222	12	successive	successive	ADJ
brj-23498	222	13	projections	projection	NOUN
brj-23498	222	14	algorithm	algorithm	NOUN
brj-23498	222	15	(	(	PUNCT
brj-23498	222	16	spa	spa	NOUN
brj-23498	222	17	)	)	PUNCT
brj-23498	222	18	were	be	AUX
brj-23498	222	19	used	use	VERB
brj-23498	222	20	to	to	PART
brj-23498	222	21	extract	extract	VERB
brj-23498	222	22	feature	feature	NOUN
brj-23498	222	23	wavelengths	wavelength	NOUN
brj-23498	222	24	from	from	ADP
brj-23498	222	25	the	the	DET
brj-23498	222	26	spectral	spectral	ADJ
brj-23498	222	27	bands	band	NOUN
brj-23498	222	28	,	,	PUNCT
brj-23498	222	29	effectively	effectively	ADV
brj-23498	222	30	extracting	extract	VERB
brj-23498	222	31	key	key	ADJ
brj-23498	222	32	information	information	NOUN
brj-23498	222	33	related	relate	VERB
brj-23498	222	34	to	to	ADP
brj-23498	222	35	crude	crude	ADJ
brj-23498	222	36	fat	fat	ADJ
brj-23498	222	37	content	content	NOUN
brj-23498	222	38	in	in	ADP
brj-23498	222	39	alfalfa	alfalfa	PROPN
brj-23498	222	40	hay	hay	PROPN
brj-23498	222	41	from	from	ADP
brj-23498	222	42	complex	complex	ADJ
brj-23498	222	43	spectroscopic	spectroscopic	ADJ
brj-23498	222	44	data	datum	NOUN
brj-23498	222	45	.	.	PUNCT
brj-23498	223	1	for	for	ADP
brj-23498	223	2	image	image	NOUN
brj-23498	223	3	data	datum	NOUN
brj-23498	223	4	,	,	PUNCT
brj-23498	223	5	adaptive	adaptive	ADJ
brj-23498	223	6	wavelet	wavelet	NOUN
brj-23498	223	7	thresholding	thresholding	NOUN
brj-23498	223	8	was	be	AUX
brj-23498	223	9	selected	select	VERB
brj-23498	223	10	to	to	PART
brj-23498	223	11	denoise	denoise	VERB
brj-23498	223	12	images	image	NOUN
brj-23498	223	13	and	and	CCONJ
brj-23498	223	14	extracted	extract	VERB
brj-23498	223	15	color	color	NOUN
brj-23498	223	16	and	and	CCONJ
brj-23498	223	17	texture	texture	NOUN
brj-23498	223	18	features	feature	NOUN
brj-23498	223	19	since	since	SCONJ
brj-23498	223	20	alfalfa	alfalfa	PROPN
brj-23498	223	21	hay	hay	PROPN
brj-23498	223	22	undergoes	undergo	VERB
brj-23498	223	23	significant	significant	ADJ
brj-23498	223	24	changes	change	NOUN
brj-23498	223	25	in	in	ADP
brj-23498	223	26	color	color	NOUN
brj-23498	223	27	and	and	CCONJ
brj-23498	223	28	texture	texture	NOUN
brj-23498	223	29	after	after	ADP
brj-23498	223	30	mold	mold	NOUN
brj-23498	223	31	growth	growth	NOUN
brj-23498	223	32	.	.	PUNCT
brj-23498	224	1	nine	nine	NUM
brj-23498	224	2	color	color	NOUN
brj-23498	224	3	features	feature	NOUN
brj-23498	224	4	were	be	AUX
brj-23498	224	5	extracted	extract	VERB
brj-23498	224	6	,	,	PUNCT
brj-23498	224	7	including	include	VERB
brj-23498	224	8	rgb	rgb	PROPN
brj-23498	224	9	,	,	PUNCT
brj-23498	224	10	and	and	CCONJ
brj-23498	224	11	the	the	DET
brj-23498	224	12	random	random	ADJ
brj-23498	224	13	forests	forest	NOUN
brj-23498	224	14	(	(	PUNCT
brj-23498	224	15	rf	rf	NOUN
brj-23498	224	16	)	)	PUNCT
brj-23498	224	17	algorithm	algorithm	NOUN
brj-23498	224	18	was	be	AUX
brj-23498	224	19	used	use	VERB
brj-23498	224	20	to	to	PART
brj-23498	224	21	extract	extract	VERB
brj-23498	224	22	10	10	NUM
brj-23498	224	23	texture	texture	ADJ
brj-23498	224	24	features	feature	NOUN
brj-23498	224	25	including	include	VERB
brj-23498	224	26	energy	energy	NOUN
brj-23498	224	27	,	,	PUNCT
brj-23498	224	28	entropy	entropy	PROPN
brj-23498	224	29	,	,	PUNCT
brj-23498	224	30	contrast	contrast	NOUN
brj-23498	224	31	,	,	PUNCT
brj-23498	224	32	and	and	CCONJ
brj-23498	224	33	variance	variance	NOUN
brj-23498	224	34	from	from	ADP
brj-23498	224	35	22	22	NUM
brj-23498	224	36	texture	texture	NOUN
brj-23498	224	37	features	feature	NOUN
brj-23498	224	38	based	base	VERB
brj-23498	224	39	on	on	ADP
brj-23498	224	40	importance	importance	NOUN
brj-23498	224	41	ranking	ranking	NOUN
brj-23498	224	42	.	.	PUNCT
brj-23498	225	1	for	for	ADP
brj-23498	225	2	electronic	electronic	ADJ
brj-23498	225	3	nose	nose	NOUN
brj-23498	225	4	data	datum	NOUN
brj-23498	225	5	,	,	PUNCT
brj-23498	225	6	unstable	unstable	ADJ
brj-23498	225	7	data	datum	NOUN
brj-23498	225	8	were	be	AUX
brj-23498	225	9	excluded	exclude	VERB
brj-23498	225	10	to	to	PART
brj-23498	225	11	eliminate	eliminate	VERB
brj-23498	225	12	external	external	ADJ
brj-23498	225	13	interference	interference	NOUN
brj-23498	225	14	and	and	CCONJ
brj-23498	225	15	then	then	ADV
brj-23498	225	16	used	use	VERB
brj-23498	225	17	principal	principal	ADJ
brj-23498	225	18	component	component	NOUN
brj-23498	225	19	analysis	analysis	NOUN
brj-23498	225	20	was	be	AUX
brj-23498	225	21	applied	apply	VERB
brj-23498	225	22	to	to	PART
brj-23498	225	23	reduce	reduce	VERB
brj-23498	225	24	data	data	NOUN
brj-23498	225	25	dimensionality	dimensionality	NOUN
brj-23498	225	26	,	,	PUNCT
brj-23498	225	27	decrease	decrease	VERB
brj-23498	225	28	computational	computational	ADJ
brj-23498	225	29	complexity	complexity	NOUN
brj-23498	225	30	,	,	PUNCT
brj-23498	225	31	and	and	CCONJ
brj-23498	225	32	improve	improve	VERB
brj-23498	225	33	the	the	DET
brj-23498	225	34	model	model	NOUN
brj-23498	225	35	’s	’s	PART
brj-23498	225	36	generalization	generalization	NOUN
brj-23498	225	37	ability	ability	NOUN
brj-23498	225	38	and	and	CCONJ
brj-23498	225	39	efficiency	efficiency	NOUN
brj-23498	225	40	.	.	PUNCT
brj-23498	226	1	when	when	SCONJ
brj-23498	226	2	objects	object	NOUN
brj-23498	226	3	are	be	AUX
brj-23498	226	4	illuminated	illuminate	VERB
brj-23498	226	5	by	by	ADP
brj-23498	226	6	near	near	ADV
brj-23498	226	7	-	-	PUNCT
brj-23498	226	8	infrared	infrared	ADJ
brj-23498	226	9	light	light	NOUN
brj-23498	226	10	,	,	PUNCT
brj-23498	226	11	different	different	ADJ
brj-23498	226	12	chemical	chemical	NOUN
brj-23498	226	13	compositions	composition	NOUN
brj-23498	226	14	with	with	ADP
brj-23498	226	15	different	different	ADJ
brj-23498	226	16	chemical	chemical	NOUN
brj-23498	226	17	groups	group	NOUN
brj-23498	226	18	inside	inside	ADP
brj-23498	226	19	them	they	PRON
brj-23498	226	20	correspond	correspond	VERB
brj-23498	226	21	to	to	ADP
brj-23498	226	22	different	different	ADJ
brj-23498	226	23	group	group	NOUN
brj-23498	226	24	frequencies	frequency	NOUN
brj-23498	226	25	and	and	CCONJ
brj-23498	226	26	generate	generate	VERB
brj-23498	226	27	spectral	spectral	ADJ
brj-23498	226	28	feature	feature	NOUN
brj-23498	226	29	absorption	absorption	NOUN
brj-23498	226	30	peaks	peak	NOUN
brj-23498	226	31	at	at	ADP
brj-23498	226	32	different	different	ADJ
brj-23498	226	33	positions	position	NOUN
brj-23498	226	34	.	.	PUNCT
brj-23498	227	1	therefore	therefore	ADV
brj-23498	227	2	,	,	PUNCT
brj-23498	227	3	near	near	ADV
brj-23498	227	4	-	-	PUNCT
brj-23498	227	5	infrared	infrared	ADJ
brj-23498	227	6	spectroscopy	spectroscopy	NOUN
brj-23498	227	7	can	can	AUX
brj-23498	227	8	be	be	AUX
brj-23498	227	9	used	use	VERB
brj-23498	227	10	for	for	ADP
brj-23498	227	11	qualitative	qualitative	ADJ
brj-23498	227	12	analysis	analysis	NOUN
brj-23498	227	13	of	of	ADP
brj-23498	227	14	substances	substance	NOUN
brj-23498	227	15	.	.	PUNCT
brj-23498	228	1	in	in	ADP
brj-23498	228	2	this	this	DET
brj-23498	228	3	experiment	experiment	NOUN
brj-23498	228	4	,	,	PUNCT
brj-23498	228	5	a	a	DET
brj-23498	228	6	quantitative	quantitative	ADJ
brj-23498	228	7	prediction	prediction	NOUN
brj-23498	228	8	model	model	NOUN
brj-23498	228	9	was	be	AUX
brj-23498	228	10	established	establish	VERB
brj-23498	228	11	for	for	ADP
brj-23498	228	12	crude	crude	ADJ
brj-23498	228	13	fat	fat	NOUN
brj-23498	228	14	in	in	ADP
brj-23498	228	15	alfalfa	alfalfa	PROPN
brj-23498	228	16	hay	hay	NOUN
brj-23498	228	17	based	base	VERB
brj-23498	228	18	on	on	ADP
brj-23498	228	19	near	near	ADV
brj-23498	228	20	-	-	PUNCT
brj-23498	228	21	infrared	infrared	ADJ
brj-23498	228	22	spectroscopy	spectroscopy	NOUN
brj-23498	228	23	using	use	VERB
brj-23498	228	24	svm	svm	PROPN
brj-23498	228	25	,	,	PUNCT
brj-23498	228	26	elm	elm	PROPN
brj-23498	228	27	,	,	PUNCT
brj-23498	228	28	and	and	CCONJ
brj-23498	228	29	mlp	mlp	PROPN
brj-23498	228	30	algorithms	algorithm	NOUN
brj-23498	228	31	.	.	PUNCT
brj-23498	229	1	it	it	PRON
brj-23498	229	2	was	be	AUX
brj-23498	229	3	found	find	VERB
brj-23498	229	4	that	that	SCONJ
brj-23498	229	5	using	use	VERB
brj-23498	229	6	the	the	DET
brj-23498	229	7	full	full	ADJ
brj-23498	229	8	spectrum	spectrum	NOUN
brj-23498	229	9	as	as	ADP
brj-23498	229	10	input	input	NOUN
brj-23498	229	11	led	lead	VERB
brj-23498	229	12	to	to	ADP
brj-23498	229	13	overfitting	overfitte	VERB
brj-23498	229	14	due	due	ADP
brj-23498	229	15	to	to	ADP
brj-23498	229	16	high	high	ADJ
brj-23498	229	17	data	datum	NOUN
brj-23498	229	18	dimensionality	dimensionality	NOUN
brj-23498	229	19	and	and	CCONJ
brj-23498	229	20	model	model	NOUN
brj-23498	229	21	complexity	complexity	NOUN
brj-23498	229	22	.	.	PUNCT
brj-23498	230	1	the	the	DET
brj-23498	230	2	model	model	NOUN
brj-23498	230	3	established	establish	VERB
brj-23498	230	4	based	base	VERB
brj-23498	230	5	on	on	ADP
brj-23498	230	6	feature	feature	NOUN
brj-23498	230	7	wavelengths	wavelength	NOUN
brj-23498	230	8	extracted	extract	VERB
brj-23498	230	9	by	by	ADP
brj-23498	230	10	cars	car	NOUN
brj-23498	230	11	outperformed	outperform	VERB
brj-23498	230	12	sap	sap	PROPN
brj-23498	230	13	,	,	PUNCT
brj-23498	230	14	hence	hence	ADV
brj-23498	230	15	the	the	DET
brj-23498	230	16	cars	car	NOUN
brj-23498	230	17	-	-	PUNCT
brj-23498	230	18	extracted	extract	VERB
brj-23498	230	19	feature	feature	NOUN
brj-23498	230	20	wavelengths	wavelength	NOUN
brj-23498	230	21	were	be	AUX
brj-23498	230	22	selected	select	VERB
brj-23498	230	23	to	to	PART
brj-23498	230	24	establish	establish	VERB
brj-23498	230	25	the	the	DET
brj-23498	230	26	fusion	fusion	NOUN
brj-23498	230	27	model	model	NOUN
brj-23498	230	28	.	.	PUNCT
brj-23498	231	1	subsequently	subsequently	ADV
brj-23498	231	2	,	,	PUNCT
brj-23498	231	3	by	by	ADP
brj-23498	231	4	combining	combine	VERB
brj-23498	231	5	image	image	NOUN
brj-23498	231	6	and	and	CCONJ
brj-23498	231	7	electronic	electronic	ADJ
brj-23498	231	8	nose	nose	NOUN
brj-23498	231	9	data	datum	NOUN
brj-23498	231	10	with	with	ADP
brj-23498	231	11	machine	machine	NOUN
brj-23498	231	12	learning	learning	NOUN
brj-23498	231	13	algorithms	algorithm	NOUN
brj-23498	231	14	,	,	PUNCT
brj-23498	231	15	a	a	DET
brj-23498	231	16	qualitative	qualitative	ADJ
brj-23498	231	17	discrimination	discrimination	NOUN
brj-23498	231	18	model	model	NOUN
brj-23498	231	19	was	be	AUX
brj-23498	231	20	built	build	VERB
brj-23498	231	21	to	to	PART
brj-23498	231	22	detect	detect	VERB
brj-23498	231	23	mold	mold	NOUN
brj-23498	231	24	growth	growth	NOUN
brj-23498	231	25	in	in	ADP
brj-23498	231	26	alfalfa	alfalfa	NOUN
brj-23498	231	27	hay	hay	PROPN
brj-23498	231	28	effectively	effectively	ADV
brj-23498	231	29	.	.	PUNCT
brj-23498	232	1	finally	finally	ADV
brj-23498	232	2	,	,	PUNCT
brj-23498	232	3	using	use	VERB
brj-23498	232	4	feature	feature	NOUN
brj-23498	232	5	set	set	VERB
brj-23498	232	6	fusion	fusion	NOUN
brj-23498	232	7	method	method	NOUN
brj-23498	232	8	,	,	PUNCT
brj-23498	232	9	there	there	PRON
brj-23498	232	10	was	be	VERB
brj-23498	232	11	a	a	DET
brj-23498	232	12	merging	merging	NOUN
brj-23498	232	13	of	of	ADP
brj-23498	232	14	spectroscopic	spectroscopic	NOUN
brj-23498	232	15	,	,	PUNCT
brj-23498	232	16	image	image	NOUN
brj-23498	232	17	,	,	PUNCT
brj-23498	232	18	and	and	CCONJ
brj-23498	232	19	electronic	electronic	ADJ
brj-23498	232	20	nose	nose	NOUN
brj-23498	232	21	data	datum	NOUN
brj-23498	232	22	to	to	PART
brj-23498	232	23	separately	separately	ADV
brj-23498	232	24	establish	establish	VERB
brj-23498	232	25	quantitative	quantitative	ADJ
brj-23498	232	26	detection	detection	NOUN
brj-23498	232	27	models	model	NOUN
brj-23498	232	28	and	and	CCONJ
brj-23498	232	29	qualitative	qualitative	ADJ
brj-23498	232	30	discrimination	discrimination	NOUN
brj-23498	232	31	models	model	NOUN
brj-23498	232	32	for	for	ADP
brj-23498	232	33	alfalfa	alfalfa	NOUN
brj-23498	232	34	hay	hay	PROPN
brj-23498	232	35	.	.	PUNCT
brj-23498	233	1	the	the	DET
brj-23498	233	2	results	result	NOUN
brj-23498	233	3	demonstrated	demonstrate	VERB
brj-23498	233	4	that	that	SCONJ
brj-23498	233	5	the	the	DET
brj-23498	233	6	fusion	fusion	NOUN
brj-23498	233	7	information	information	NOUN
brj-23498	233	8	-	-	PUNCT
brj-23498	233	9	based	base	VERB
brj-23498	233	10	models	model	NOUN
brj-23498	233	11	achieved	achieve	VERB
brj-23498	233	12	higher	high	ADJ
brj-23498	233	13	accuracy	accuracy	NOUN
brj-23498	233	14	than	than	ADP
brj-23498	233	15	models	model	NOUN
brj-23498	233	16	based	base	VERB
brj-23498	233	17	on	on	ADP
brj-23498	233	18	single	single	ADJ
brj-23498	233	19	information	information	NOUN
brj-23498	233	20	.	.	PUNCT
brj-23498	234	1	the	the	DET
brj-23498	234	2	fusion	fusion	NOUN
brj-23498	234	3	information	information	NOUN
brj-23498	234	4	model	model	NOUN
brj-23498	234	5	performed	perform	VERB
brj-23498	234	6	best	well	ADV
brj-23498	234	7	under	under	ADP
brj-23498	234	8	the	the	DET
brj-23498	234	9	sg	sg	NOUN
brj-23498	234	10	-	-	PUNCT
brj-23498	234	11	cars	car	NOUN
brj-23498	234	12	-	-	PUNCT
brj-23498	234	13	svm	svm	ADJ
brj-23498	234	14	algorithm	algorithm	NOUN
brj-23498	234	15	combination	combination	NOUN
brj-23498	234	16	,	,	PUNCT
brj-23498	234	17	with	with	ADP
brj-23498	234	18	testing	testing	NOUN
brj-23498	234	19	set	set	VERB
brj-23498	235	1	root	root	NOUN
brj-23498	235	2	mean	mean	VERB
brj-23498	235	3	square	square	ADJ
brj-23498	235	4	error	error	NOUN
brj-23498	235	5	and	and	CCONJ
brj-23498	235	6	determination	determination	NOUN
brj-23498	235	7	coefficient	coefficient	NOUN
brj-23498	235	8	values	value	NOUN
brj-23498	235	9	of	of	ADP
brj-23498	235	10	0.1728	0.1728	NUM
brj-23498	235	11	and	and	CCONJ
brj-23498	235	12	0.9239	0.9239	NUM
brj-23498	235	13	,	,	PUNCT
brj-23498	235	14	respectively	respectively	ADV
brj-23498	235	15	higher	high	ADJ
brj-23498	235	16	than	than	ADP
brj-23498	235	17	the	the	DET
brj-23498	235	18	best	good	ADJ
brj-23498	235	19	results	result	NOUN
brj-23498	235	20	of	of	ADP
brj-23498	235	21	0.2161	0.2161	NUM
brj-23498	235	22	and	and	CCONJ
brj-23498	235	23	0.8217	0.8217	NUM
brj-23498	235	24	achieved	achieve	VERB
brj-23498	235	25	by	by	ADP
brj-23498	235	26	the	the	DET
brj-23498	235	27	prediction	prediction	NOUN
brj-23498	235	28	model	model	NOUN
brj-23498	235	29	based	base	VERB
brj-23498	235	30	on	on	ADP
brj-23498	235	31	near	near	ADV
brj-23498	235	32	-	-	PUNCT
brj-23498	235	33	infrared	infrared	ADJ
brj-23498	235	34	spectroscopy	spectroscopy	NOUN
brj-23498	235	35	.	.	PUNCT
brj-23498	236	1	the	the	DET
brj-23498	236	2	fusion	fusion	NOUN
brj-23498	236	3	information	information	NOUN
brj-23498	236	4	model	model	NOUN
brj-23498	236	5	achieved	achieve	VERB
brj-23498	236	6	100	100	NUM
brj-23498	236	7	%	%	NOUN
brj-23498	236	8	accuracy	accuracy	NOUN
brj-23498	236	9	in	in	ADP
brj-23498	236	10	discriminating	discriminate	VERB
brj-23498	236	11	mold	mold	NOUN
brj-23498	236	12	growth	growth	NOUN
brj-23498	236	13	,	,	PUNCT
brj-23498	236	14	surpassing	surpass	VERB
brj-23498	236	15	the	the	DET
brj-23498	236	16	best	good	ADJ
brj-23498	236	17	results	result	NOUN
brj-23498	236	18	of	of	ADP
brj-23498	236	19	91.562	91.562	NUM
brj-23498	236	20	%	%	NOUN
brj-23498	236	21	and	and	CCONJ
brj-23498	236	22	80.549	80.549	NUM
brj-23498	236	23	%	%	NOUN
brj-23498	236	24	from	from	ADP
brj-23498	236	25	models	model	NOUN
brj-23498	236	26	based	base	VERB
brj-23498	236	27	on	on	ADP
brj-23498	236	28	image	image	NOUN
brj-23498	236	29	and	and	CCONJ
brj-23498	236	30	electronic	electronic	ADJ
brj-23498	236	31	nose	nose	NOUN
brj-23498	236	32	data	datum	NOUN
brj-23498	236	33	.	.	PUNCT
brj-23498	237	1	in	in	ADP
brj-23498	237	2	summary	summary	NOUN
brj-23498	237	3	,	,	PUNCT
brj-23498	237	4	this	this	DET
brj-23498	237	5	study	study	NOUN
brj-23498	237	6	not	not	PART
brj-23498	237	7	only	only	ADV
brj-23498	237	8	successfully	successfully	ADV
brj-23498	237	9	established	establish	VERB
brj-23498	237	10	predictive	predictive	ADJ
brj-23498	237	11	models	model	NOUN
brj-23498	237	12	for	for	ADP
brj-23498	237	13	alfalfa	alfalfa	NOUN
brj-23498	237	14	quality	quality	NOUN
brj-23498	237	15	using	use	VERB
brj-23498	237	16	near	near	ADV
brj-23498	237	17	-	-	PUNCT
brj-23498	237	18	infrared	infrared	ADJ
brj-23498	237	19	spectroscopy	spectroscopy	NOUN
brj-23498	237	20	,	,	PUNCT
brj-23498	237	21	image	image	NOUN
brj-23498	237	22	processing	processing	NOUN
brj-23498	237	23	,	,	PUNCT
brj-23498	237	24	electronic	electronic	ADJ
brj-23498	237	25	nose	nose	NOUN
brj-23498	237	26	technology	technology	NOUN
brj-23498	237	27	,	,	PUNCT
brj-23498	237	28	and	and	CCONJ
brj-23498	237	29	machine	machine	NOUN
brj-23498	237	30	learning	learning	NOUN
brj-23498	237	31	algorithms	algorithm	NOUN
brj-23498	237	32	but	but	CCONJ
brj-23498	237	33	also	also	ADV
brj-23498	237	34	confirmed	confirm	VERB
brj-23498	237	35	the	the	DET
brj-23498	237	36	effectiveness	effectiveness	NOUN
brj-23498	237	37	and	and	CCONJ
brj-23498	237	38	reliability	reliability	NOUN
brj-23498	237	39	of	of	ADP
brj-23498	237	40	these	these	DET
brj-23498	237	41	models	model	NOUN
brj-23498	237	42	in	in	ADP
brj-23498	237	43	accurately	accurately	ADV
brj-23498	237	44	predicting	predict	VERB
brj-23498	237	45	the	the	DET
brj-23498	237	46	nutritional	nutritional	ADJ
brj-23498	237	47	content	content	NOUN
brj-23498	237	48	of	of	ADP
brj-23498	237	49	alfalfa	alfalfa	NOUN
brj-23498	237	50	.	.	PUNCT
brj-23498	238	1	this	this	DET
brj-23498	238	2	innovative	innovative	ADJ
brj-23498	238	3	approach	approach	NOUN
brj-23498	238	4	will	will	AUX
brj-23498	238	5	provide	provide	VERB
brj-23498	238	6	more	more	ADV
brj-23498	238	7	effective	effective	ADJ
brj-23498	238	8	tools	tool	NOUN
brj-23498	238	9	and	and	CCONJ
brj-23498	238	10	methods	method	NOUN
brj-23498	238	11	for	for	ADP
brj-23498	238	12	agricultural	agricultural	ADJ
brj-23498	238	13	production	production	NOUN
brj-23498	238	14	.	.	PUNCT
brj-23498	239	1	peer	peer	NOUN
brj-23498	239	2	-	-	PUNCT
brj-23498	239	3	reviewed	review	VERB
brj-23498	239	4	article	article	NOUN
brj-23498	239	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	239	6	yang	yang	PROPN
brj-23498	239	7	et	et	PROPN
brj-23498	239	8	al	al	PROPN
brj-23498	239	9	.	.	PROPN
brj-23498	240	1	(	(	PUNCT
brj-23498	240	2	2024	2024	NUM
brj-23498	240	3	)	)	PUNCT
brj-23498	240	4	.	.	PUNCT
brj-23498	241	1	“	"	PUNCT
brj-23498	241	2	alfalfa	alfalfa	NOUN
brj-23498	241	3	quality	quality	NOUN
brj-23498	241	4	assessment	assessment	NOUN
brj-23498	241	5	,	,	PUNCT
brj-23498	241	6	”	"	PUNCT
brj-23498	241	7	bioresources	bioresource	NOUN
brj-23498	241	8	19(3	19(3	NUM
brj-23498	241	9	)	)	PUNCT
brj-23498	241	10	,	,	PUNCT
brj-23498	241	11	4531	4531	NUM
brj-23498	241	12	-	-	SYM
brj-23498	241	13	4546	4546	NUM
brj-23498	241	14	.	.	PUNCT
brj-23498	242	1	4544	4544	NUM
brj-23498	242	2	however	however	ADV
brj-23498	242	3	,	,	PUNCT
brj-23498	242	4	this	this	DET
brj-23498	242	5	study	study	NOUN
brj-23498	242	6	also	also	ADV
brj-23498	242	7	has	have	VERB
brj-23498	242	8	certain	certain	ADJ
brj-23498	242	9	limitations	limitation	NOUN
brj-23498	242	10	and	and	CCONJ
brj-23498	242	11	areas	area	NOUN
brj-23498	242	12	for	for	ADP
brj-23498	242	13	improvement	improvement	NOUN
brj-23498	242	14	.	.	PUNCT
brj-23498	243	1	noise	noise	NOUN
brj-23498	243	2	and	and	CCONJ
brj-23498	243	3	lighting	lighting	NOUN
brj-23498	243	4	changes	change	NOUN
brj-23498	243	5	during	during	ADP
brj-23498	243	6	the	the	DET
brj-23498	243	7	image	image	NOUN
brj-23498	243	8	processing	processing	NOUN
brj-23498	243	9	may	may	AUX
brj-23498	243	10	affect	affect	VERB
brj-23498	243	11	the	the	DET
brj-23498	243	12	results	result	NOUN
brj-23498	243	13	,	,	PUNCT
brj-23498	243	14	requiring	require	VERB
brj-23498	243	15	further	further	ADJ
brj-23498	243	16	optimization	optimization	NOUN
brj-23498	243	17	of	of	ADP
brj-23498	243	18	algorithms	algorithm	NOUN
brj-23498	243	19	and	and	CCONJ
brj-23498	243	20	technologies	technology	NOUN
brj-23498	243	21	.	.	PUNCT
brj-23498	244	1	additionally	additionally	ADV
brj-23498	244	2	,	,	PUNCT
brj-23498	244	3	the	the	DET
brj-23498	244	4	limited	limited	ADJ
brj-23498	244	5	sample	sample	NOUN
brj-23498	244	6	size	size	NOUN
brj-23498	244	7	may	may	AUX
brj-23498	244	8	lead	lead	VERB
brj-23498	244	9	to	to	ADP
brj-23498	244	10	results	result	NOUN
brj-23498	244	11	lacking	lack	VERB
brj-23498	244	12	universality	universality	NOUN
brj-23498	244	13	.	.	PUNCT
brj-23498	245	1	subsequent	subsequent	ADJ
brj-23498	245	2	experiments	experiment	NOUN
brj-23498	245	3	will	will	AUX
brj-23498	245	4	need	need	VERB
brj-23498	245	5	to	to	PART
brj-23498	245	6	be	be	AUX
brj-23498	245	7	conducted	conduct	VERB
brj-23498	245	8	on	on	ADP
brj-23498	245	9	a	a	DET
brj-23498	245	10	large	large	ADJ
brj-23498	245	11	sample	sample	NOUN
brj-23498	245	12	basis	basis	NOUN
brj-23498	245	13	to	to	PART
brj-23498	245	14	enhance	enhance	VERB
brj-23498	245	15	the	the	DET
brj-23498	245	16	reliability	reliability	NOUN
brj-23498	245	17	and	and	CCONJ
brj-23498	245	18	stability	stability	NOUN
brj-23498	245	19	of	of	ADP
brj-23498	245	20	the	the	DET
brj-23498	245	21	models	model	NOUN
brj-23498	245	22	.	.	PUNCT
brj-23498	246	1	conclusions	conclusion	NOUN
brj-23498	246	2	1	1	X
brj-23498	246	3	.	.	PUNCT
brj-23498	247	1	this	this	DET
brj-23498	247	2	study	study	NOUN
brj-23498	247	3	explored	explore	VERB
brj-23498	247	4	the	the	DET
brj-23498	247	5	integration	integration	NOUN
brj-23498	247	6	of	of	ADP
brj-23498	247	7	image	image	NOUN
brj-23498	247	8	processing	processing	NOUN
brj-23498	247	9	techniques	technique	NOUN
brj-23498	247	10	,	,	PUNCT
brj-23498	247	11	electronic	electronic	ADJ
brj-23498	247	12	nose	nose	NOUN
brj-23498	247	13	technology	technology	NOUN
brj-23498	247	14	,	,	PUNCT
brj-23498	247	15	and	and	CCONJ
brj-23498	247	16	spectroscopy	spectroscopy	VERB
brj-23498	247	17	for	for	ADP
brj-23498	247	18	evaluating	evaluate	VERB
brj-23498	247	19	the	the	DET
brj-23498	247	20	quality	quality	NOUN
brj-23498	247	21	of	of	ADP
brj-23498	247	22	alfalfa	alfalfa	PROPN
brj-23498	247	23	hay	hay	PROPN
brj-23498	247	24	.	.	PUNCT
brj-23498	248	1	by	by	ADP
brj-23498	248	2	harnessing	harness	VERB
brj-23498	248	3	high	high	ADJ
brj-23498	248	4	-	-	PUNCT
brj-23498	248	5	resolution	resolution	NOUN
brj-23498	248	6	imaging	imaging	NOUN
brj-23498	248	7	,	,	PUNCT
brj-23498	248	8	electronic	electronic	ADJ
brj-23498	248	9	nose	nose	NOUN
brj-23498	248	10	outputs	output	NOUN
brj-23498	248	11	,	,	PUNCT
brj-23498	248	12	and	and	CCONJ
brj-23498	248	13	spectral	spectral	ADJ
brj-23498	248	14	data	datum	NOUN
brj-23498	248	15	from	from	ADP
brj-23498	248	16	alfalfa	alfalfa	PROPN
brj-23498	248	17	hay	hay	PROPN
brj-23498	248	18	,	,	PUNCT
brj-23498	248	19	coupled	couple	VERB
brj-23498	248	20	with	with	ADP
brj-23498	248	21	machine	machine	NOUN
brj-23498	248	22	learning	learning	NOUN
brj-23498	248	23	algorithms	algorithm	NOUN
brj-23498	248	24	,	,	PUNCT
brj-23498	248	25	it	it	PRON
brj-23498	248	26	was	be	AUX
brj-23498	248	27	possible	possible	ADJ
brj-23498	248	28	to	to	PART
brj-23498	248	29	present	present	VERB
brj-23498	248	30	a	a	DET
brj-23498	248	31	novel	novel	NOUN
brj-23498	248	32	,	,	PUNCT
brj-23498	248	33	noncontact	noncontact	ADJ
brj-23498	248	34	,	,	PUNCT
brj-23498	248	35	efficient	efficient	ADJ
brj-23498	248	36	,	,	PUNCT
brj-23498	248	37	and	and	CCONJ
brj-23498	248	38	non	non	ADJ
brj-23498	248	39	-	-	ADJ
brj-23498	248	40	destructive	destructive	ADJ
brj-23498	248	41	method	method	NOUN
brj-23498	248	42	for	for	ADP
brj-23498	248	43	assessing	assess	VERB
brj-23498	248	44	forage	forage	NOUN
brj-23498	248	45	quality	quality	NOUN
brj-23498	248	46	.	.	PUNCT
brj-23498	249	1	2	2	X
brj-23498	249	2	.	.	X
brj-23498	249	3	the	the	DET
brj-23498	249	4	experimental	experimental	ADJ
brj-23498	249	5	results	result	NOUN
brj-23498	249	6	demonstrated	demonstrate	VERB
brj-23498	249	7	that	that	SCONJ
brj-23498	249	8	the	the	DET
brj-23498	249	9	sg	sg	PROPN
brj-23498	249	10	-	-	PUNCT
brj-23498	249	11	cars	car	NOUN
brj-23498	249	12	-	-	PUNCT
brj-23498	249	13	svm	svm	NOUN
brj-23498	249	14	based	base	VERB
brj-23498	249	15	fusion	fusion	NOUN
brj-23498	249	16	information	information	NOUN
brj-23498	249	17	model	model	NOUN
brj-23498	249	18	delivered	deliver	VERB
brj-23498	249	19	exceptional	exceptional	ADJ
brj-23498	249	20	accuracy	accuracy	NOUN
brj-23498	249	21	and	and	CCONJ
brj-23498	249	22	reliability	reliability	NOUN
brj-23498	249	23	in	in	ADP
brj-23498	249	24	determining	determine	VERB
brj-23498	249	25	forage	forage	NOUN
brj-23498	249	26	quality	quality	NOUN
brj-23498	249	27	,	,	PUNCT
brj-23498	249	28	achieving	achieve	VERB
brj-23498	249	29	a	a	DET
brj-23498	249	30	100	100	NUM
brj-23498	249	31	%	%	NOUN
brj-23498	249	32	accuracy	accuracy	NOUN
brj-23498	249	33	rate	rate	NOUN
brj-23498	249	34	on	on	ADP
brj-23498	249	35	the	the	DET
brj-23498	249	36	test	test	NOUN
brj-23498	249	37	set	set	NOUN
brj-23498	249	38	.	.	PUNCT
brj-23498	250	1	surpassing	surpass	VERB
brj-23498	250	2	the	the	DET
brj-23498	250	3	best	good	ADJ
brj-23498	250	4	results	result	NOUN
brj-23498	250	5	of	of	ADP
brj-23498	250	6	91.562	91.562	NUM
brj-23498	250	7	%	%	NOUN
brj-23498	250	8	and	and	CCONJ
brj-23498	250	9	80.549	80.549	NUM
brj-23498	250	10	%	%	NOUN
brj-23498	250	11	from	from	ADP
brj-23498	250	12	models	model	NOUN
brj-23498	250	13	based	base	VERB
brj-23498	250	14	on	on	ADP
brj-23498	250	15	image	image	NOUN
brj-23498	250	16	and	and	CCONJ
brj-23498	250	17	electronic	electronic	ADJ
brj-23498	250	18	nose	nose	NOUN
brj-23498	250	19	data	datum	NOUN
brj-23498	250	20	.	.	PUNCT
brj-23498	251	1	furthermore	furthermore	ADV
brj-23498	251	2	,	,	PUNCT
brj-23498	251	3	it	it	PRON
brj-23498	251	4	reported	report	VERB
brj-23498	251	5	a	a	DET
brj-23498	251	6	root	root	NOUN
brj-23498	251	7	mean	mean	NOUN
brj-23498	251	8	square	square	ADJ
brj-23498	251	9	error	error	NOUN
brj-23498	251	10	of	of	ADP
brj-23498	251	11	0.15018	0.15018	NUM
brj-23498	251	12	and	and	CCONJ
brj-23498	251	13	a	a	DET
brj-23498	251	14	coefficient	coefficient	NOUN
brj-23498	251	15	of	of	ADP
brj-23498	251	16	determination	determination	NOUN
brj-23498	251	17	(	(	PUNCT
brj-23498	251	18	r^2	r^2	PROPN
brj-23498	251	19	)	)	PUNCT
brj-23498	251	20	of	of	ADP
brj-23498	251	21	0.92151	0.92151	NUM
brj-23498	251	22	,	,	PUNCT
brj-23498	251	23	respectively	respectively	ADV
brj-23498	251	24	higher	high	ADJ
brj-23498	251	25	than	than	ADP
brj-23498	251	26	the	the	DET
brj-23498	251	27	best	good	ADJ
brj-23498	251	28	results	result	NOUN
brj-23498	251	29	of	of	ADP
brj-23498	251	30	0.2161	0.2161	NUM
brj-23498	251	31	and	and	CCONJ
brj-23498	251	32	0.8217	0.8217	NUM
brj-23498	251	33	achieved	achieve	VERB
brj-23498	251	34	by	by	ADP
brj-23498	251	35	the	the	DET
brj-23498	251	36	prediction	prediction	NOUN
brj-23498	251	37	model	model	NOUN
brj-23498	251	38	based	base	VERB
brj-23498	251	39	on	on	ADP
brj-23498	251	40	near	near	ADV
brj-23498	251	41	-	-	PUNCT
brj-23498	251	42	infrared	infrared	ADJ
brj-23498	251	43	spectroscopy	spectroscopy	NOUN
brj-23498	251	44	,	,	PUNCT
brj-23498	251	45	underscoring	underscore	VERB
brj-23498	251	46	its	its	PRON
brj-23498	251	47	effectiveness	effectiveness	NOUN
brj-23498	251	48	.	.	PUNCT
brj-23498	252	1	3	3	X
brj-23498	252	2	.	.	PUNCT
brj-23498	252	3	by	by	ADP
brj-23498	252	4	amalgamating	amalgamate	VERB
brj-23498	252	5	visual	visual	ADJ
brj-23498	252	6	,	,	PUNCT
brj-23498	252	7	olfactory	olfactory	ADJ
brj-23498	252	8	,	,	PUNCT
brj-23498	252	9	and	and	CCONJ
brj-23498	252	10	spectral	spectral	ADJ
brj-23498	252	11	analyses	analysis	NOUN
brj-23498	252	12	,	,	PUNCT
brj-23498	252	13	this	this	DET
brj-23498	252	14	research	research	NOUN
brj-23498	252	15	offers	offer	VERB
brj-23498	252	16	a	a	DET
brj-23498	252	17	holistic	holistic	ADJ
brj-23498	252	18	approach	approach	NOUN
brj-23498	252	19	to	to	ADP
brj-23498	252	20	forage	forage	VERB
brj-23498	252	21	quality	quality	NOUN
brj-23498	252	22	assessment	assessment	NOUN
brj-23498	252	23	.	.	PUNCT
brj-23498	253	1	this	this	DET
brj-23498	253	2	groundbreaking	groundbreake	VERB
brj-23498	253	3	method	method	NOUN
brj-23498	253	4	stands	stand	VERB
brj-23498	253	5	to	to	PART
brj-23498	253	6	benefit	benefit	VERB
brj-23498	253	7	agricultural	agricultural	ADJ
brj-23498	253	8	and	and	CCONJ
brj-23498	253	9	livestock	livestock	NOUN
brj-23498	253	10	management	management	NOUN
brj-23498	253	11	practices	practice	NOUN
brj-23498	253	12	significantly	significantly	ADV
brj-23498	253	13	,	,	PUNCT
brj-23498	253	14	paving	pave	VERB
brj-23498	253	15	the	the	DET
brj-23498	253	16	way	way	NOUN
brj-23498	253	17	for	for	ADP
brj-23498	253	18	more	more	ADV
brj-23498	253	19	sustainable	sustainable	ADJ
brj-23498	253	20	agricultural	agricultural	ADJ
brj-23498	253	21	advancements	advancement	NOUN
brj-23498	253	22	.	.	PUNCT
brj-23498	254	1	acknowledgments	acknowledgment	NOUN
brj-23498	254	2	this	this	DET
brj-23498	254	3	project	project	NOUN
brj-23498	254	4	is	be	AUX
brj-23498	254	5	supported	support	VERB
brj-23498	254	6	by	by	ADP
brj-23498	254	7	national	national	ADJ
brj-23498	254	8	natural	natural	PROPN
brj-23498	254	9	science	science	PROPN
brj-23498	254	10	foundation	foundation	PROPN
brj-23498	254	11	of	of	ADP
brj-23498	254	12	china	china	PROPN
brj-23498	254	13	(	(	PUNCT
brj-23498	254	14	grant	grant	PROPN
brj-23498	254	15	nos	nos	PROPN
brj-23498	254	16	.	.	PROPN
brj-23498	254	17	32060414	32060414	NUM
brj-23498	254	18	,	,	PUNCT
brj-23498	254	19	51766016	51766016	NUM
brj-23498	254	20	)	)	PUNCT
brj-23498	254	21	;	;	PUNCT
brj-23498	254	22	natural	natural	ADJ
brj-23498	254	23	science	science	NOUN
brj-23498	254	24	foundation	foundation	NOUN
brj-23498	254	25	of	of	ADP
brj-23498	254	26	inner	inner	PROPN
brj-23498	254	27	mongolia	mongolia	PROPN
brj-23498	254	28	,	,	PUNCT
brj-23498	254	29	china	china	PROPN
brj-23498	254	30	(	(	PUNCT
brj-23498	254	31	2022ms06023	2022ms06023	NUM
brj-23498	254	32	,	,	PUNCT
brj-23498	254	33	2023qn05034	2023qn05034	NOUN
brj-23498	254	34	)	)	PUNCT
brj-23498	254	35	;	;	PUNCT
brj-23498	254	36	natural	natural	ADJ
brj-23498	254	37	science	science	NOUN
brj-23498	254	38	foundation	foundation	NOUN
brj-23498	254	39	of	of	ADP
brj-23498	254	40	the	the	DET
brj-23498	254	41	autonomous	autonomous	ADJ
brj-23498	254	42	region	region	NOUN
brj-23498	254	43	military	military	ADJ
brj-23498	254	44	-	-	PUNCT
brj-23498	254	45	civilian	civilian	ADJ
brj-23498	254	46	integration	integration	NOUN
brj-23498	254	47	key	key	ADJ
brj-23498	254	48	research	research	NOUN
brj-23498	254	49	&	&	CCONJ
brj-23498	254	50	soft	soft	ADJ
brj-23498	254	51	science	science	NOUN
brj-23498	254	52	research	research	NOUN
brj-23498	254	53	projects	project	NOUN
brj-23498	254	54	of	of	ADP
brj-23498	254	55	inner	inner	ADJ
brj-23498	254	56	mongolia	mongolia	PROPN
brj-23498	254	57	,	,	PUNCT
brj-23498	254	58	china	china	PROPN
brj-23498	254	59	(	(	PUNCT
brj-23498	254	60	jmzd202201	jmzd202201	PROPN
brj-23498	254	61	)	)	PUNCT
brj-23498	254	62	;	;	PUNCT
brj-23498	254	63	scientific	scientific	ADJ
brj-23498	254	64	research	research	NOUN
brj-23498	254	65	project	project	NOUN
brj-23498	254	66	of	of	ADP
brj-23498	254	67	universities	university	NOUN
brj-23498	254	68	in	in	ADP
brj-23498	254	69	inner	inner	ADJ
brj-23498	254	70	mongolia	mongolia	PROPN
brj-23498	254	71	,	,	PUNCT
brj-23498	254	72	china	china	PROPN
brj-23498	254	73	(	(	PUNCT
brj-23498	254	74	njzy21461	njzy21461	PROPN
brj-23498	254	75	)	)	PUNCT
brj-23498	254	76	;	;	PUNCT
brj-23498	254	77	references	reference	NOUN
brj-23498	254	78	cited	cite	VERB
brj-23498	254	79	ding	ding	NOUN
brj-23498	254	80	,	,	PUNCT
brj-23498	254	81	p.	p.	PROPN
brj-23498	254	82	,	,	PUNCT
brj-23498	254	83	zou	zou	PROPN
brj-23498	254	84	,	,	PUNCT
brj-23498	254	85	y.	y.	PROPN
brj-23498	254	86	,	,	PUNCT
brj-23498	254	87	gou	gou	PROPN
brj-23498	254	88	,	,	PUNCT
brj-23498	254	89	x.l	x.l	PROPN
brj-23498	254	90	.	.	PROPN
brj-23498	254	91	,	,	PUNCT
brj-23498	254	92	chen	chen	PROPN
brj-23498	254	93	.	.	PUNCT
brj-23498	255	1	x.	x.	PROPN
brj-23498	255	2	,	,	PUNCT
brj-23498	255	3	and	and	CCONJ
brj-23498	255	4	lu	lu	PROPN
brj-23498	255	5	,	,	PUNCT
brj-23498	255	6	f.	f.	PROPN
brj-23498	255	7	s.	s.	PROPN
brj-23498	255	8	(	(	PUNCT
brj-23498	255	9	2023	2023	NUM
brj-23498	255	10	)	)	PUNCT
brj-23498	255	11	.	.	PUNCT
brj-23498	256	1	“	"	PUNCT
brj-23498	256	2	an	an	DET
brj-23498	256	3	automatic	automatic	ADJ
brj-23498	256	4	control	control	NOUN
brj-23498	256	5	method	method	NOUN
brj-23498	256	6	for	for	ADP
brj-23498	256	7	semi	semi	ADJ
brj-23498	256	8	-	-	ADJ
brj-23498	256	9	active	active	ADJ
brj-23498	256	10	suspension	suspension	NOUN
brj-23498	256	11	of	of	ADP
brj-23498	256	12	driverless	driverless	NOUN
brj-23498	256	13	vehicle	vehicle	NOUN
brj-23498	256	14	based	base	VERB
brj-23498	256	15	on	on	ADP
brj-23498	256	16	multisensor	multisensor	NOUN
brj-23498	256	17	information	information	NOUN
brj-23498	256	18	fusion	fusion	NOUN
brj-23498	256	19	in	in	ADP
brj-23498	256	20	complex	complex	ADJ
brj-23498	256	21	environment	environment	NOUN
brj-23498	256	22	,	,	PUNCT
brj-23498	256	23	”	"	PUNCT
brj-23498	256	24	journal	journal	NOUN
brj-23498	256	25	of	of	ADP
brj-23498	256	26	automotive	automotive	ADJ
brj-23498	256	27	safety	safety	NOUN
brj-23498	256	28	and	and	CCONJ
brj-23498	256	29	energy	energy	NOUN
brj-23498	256	30	14(03	14(03	PROPN
brj-23498	256	31	)	)	PUNCT
brj-23498	256	32	,	,	PUNCT
brj-23498	256	33	355	355	NUM
brj-23498	256	34	-	-	SYM
brj-23498	256	35	364	364	NUM
brj-23498	256	36	.	.	PUNCT
brj-23498	257	1	doi	doi	NOUN
brj-23498	257	2	:	:	PUNCT
brj-23498	257	3	10.3969	10.3969	NUM
brj-23498	257	4	/	/	SYM
brj-23498	257	5	j.issn.1674	j.issn.1674	NOUN
brj-23498	257	6	-	-	PUNCT
brj-23498	257	7	8484.2023.03	8484.2023.03	NOUN
brj-23498	257	8	.	.	PUNCT
brj-23498	258	1	011	011	NUM
brj-23498	258	2	gibertoni	gibertoni	PROPN
brj-23498	258	3	,	,	PUNCT
brj-23498	258	4	g.	g.	PROPN
brj-23498	258	5	,	,	PUNCT
brj-23498	258	6	lenzini	lenzini	PROPN
brj-23498	258	7	,	,	PUNCT
brj-23498	258	8	n.	n.	NOUN
brj-23498	258	9	,	,	PUNCT
brj-23498	258	10	ferrari	ferrari	PROPN
brj-23498	258	11	,	,	PUNCT
brj-23498	258	12	l.	l.	PROPN
brj-23498	258	13	,	,	PUNCT
brj-23498	258	14	and	and	CCONJ
brj-23498	258	15	rovati	rovati	PROPN
brj-23498	258	16	,	,	PUNCT
brj-23498	258	17	l.	l.	PROPN
brj-23498	258	18	(	(	PUNCT
brj-23498	258	19	2022	2022	NUM
brj-23498	258	20	)	)	PUNCT
brj-23498	258	21	.	.	PUNCT
brj-23498	259	1	“	"	PUNCT
brj-23498	259	2	design	design	NOUN
brj-23498	259	3	and	and	CCONJ
brj-23498	259	4	performance	performance	NOUN
brj-23498	259	5	of	of	ADP
brj-23498	259	6	a	a	DET
brj-23498	259	7	near	near	ADV
brj-23498	259	8	-	-	PUNCT
brj-23498	259	9	infrared	infrared	ADJ
brj-23498	259	10	spectroscopy	spectroscopy	NOUN
brj-23498	259	11	measurement	measurement	NOUN
brj-23498	259	12	system	system	NOUN
brj-23498	259	13	for	for	ADP
brj-23498	259	14	in	in	ADP
brj-23498	259	15	-	-	PUNCT
brj-23498	259	16	field	field	NOUN
brj-23498	259	17	alfalfa	alfalfa	NOUN
brj-23498	259	18	moisture	moisture	PROPN
brj-23498	259	19	peer	peer	NOUN
brj-23498	259	20	-	-	PUNCT
brj-23498	259	21	reviewed	review	VERB
brj-23498	259	22	article	article	NOUN
brj-23498	259	23	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	259	24	yang	yang	PROPN
brj-23498	259	25	et	et	PROPN
brj-23498	259	26	al	al	PROPN
brj-23498	259	27	.	.	PROPN
brj-23498	260	1	(	(	PUNCT
brj-23498	260	2	2024	2024	NUM
brj-23498	260	3	)	)	PUNCT
brj-23498	260	4	.	.	PUNCT
brj-23498	261	1	“	"	PUNCT
brj-23498	261	2	alfalfa	alfalfa	NOUN
brj-23498	261	3	quality	quality	NOUN
brj-23498	261	4	assessment	assessment	NOUN
brj-23498	261	5	,	,	PUNCT
brj-23498	261	6	”	"	PUNCT
brj-23498	261	7	bioresources	bioresource	NOUN
brj-23498	261	8	19(3	19(3	NUM
brj-23498	261	9	)	)	PUNCT
brj-23498	261	10	,	,	PUNCT
brj-23498	261	11	4531	4531	NUM
brj-23498	261	12	-	-	SYM
brj-23498	261	13	4546	4546	NUM
brj-23498	261	14	.	.	PUNCT
brj-23498	262	1	4545	4545	NUM
brj-23498	262	2	measurement	measurement	NOUN
brj-23498	262	3	photonics	photonic	NOUN
brj-23498	262	4	,	,	PUNCT
brj-23498	262	5	”	"	PUNCT
brj-23498	262	6	photonics	photonic	NOUN
brj-23498	262	7	9(3	9(3	NUM
brj-23498	262	8	)	)	PUNCT
brj-23498	262	9	,	,	PUNCT
brj-23498	262	10	178	178	NUM
brj-23498	262	11	-	-	SYM
brj-23498	262	12	178	178	NUM
brj-23498	262	13	.	.	PUNCT
brj-23498	263	1	doi	doi	NOUN
brj-23498	263	2	:	:	PUNCT
brj-23498	263	3	10.3390	10.3390	NUM
brj-23498	263	4	/	/	SYM
brj-23498	263	5	photonics9030178	photonics9030178	PROPN
brj-23498	263	6	jia	jia	PROPN
brj-23498	263	7	,	,	PUNCT
brj-23498	263	8	y.	y.	PROPN
brj-23498	263	9	r.	r.	PROPN
brj-23498	263	10	,	,	PUNCT
brj-23498	263	11	huang	huang	PROPN
brj-23498	263	12	,	,	PUNCT
brj-23498	263	13	s.	s.	PROPN
brj-23498	263	14	,	,	PUNCT
brj-23498	263	15	and	and	CCONJ
brj-23498	263	16	zhang	zhang	PROPN
brj-23498	263	17	,	,	PUNCT
brj-23498	263	18	t.	t.	PROPN
brj-23498	263	19	j.	j.	PROPN
brj-23498	263	20	(	(	PUNCT
brj-23498	263	21	2021	2021	NUM
brj-23498	263	22	)	)	PUNCT
brj-23498	263	23	.	.	PUNCT
brj-23498	264	1	“	"	PUNCT
brj-23498	264	2	kk	kk	PROPN
brj-23498	264	3	-	-	PUNCT
brj-23498	264	4	dbp	dbp	PROPN
brj-23498	264	5	:	:	PUNCT
brj-23498	264	6	a	a	DET
brj-23498	264	7	multi	multi	ADJ
brj-23498	264	8	-	-	ADJ
brj-23498	264	9	feature	feature	ADJ
brj-23498	264	10	fusion	fusion	NOUN
brj-23498	264	11	method	method	NOUN
brj-23498	264	12	for	for	ADP
brj-23498	264	13	dna	dna	NOUN
brj-23498	264	14	-	-	PUNCT
brj-23498	264	15	binding	bind	VERB
brj-23498	264	16	protein	protein	NOUN
brj-23498	264	17	identification	identification	NOUN
brj-23498	264	18	based	base	VERB
brj-23498	264	19	on	on	ADP
brj-23498	264	20	random	random	ADJ
brj-23498	264	21	forest	forest	NOUN
brj-23498	264	22	,	,	PUNCT
brj-23498	264	23	”	"	PUNCT
brj-23498	264	24	frontiers	frontier	NOUN
brj-23498	264	25	in	in	ADP
brj-23498	264	26	genetics	genetic	NOUN
brj-23498	264	27	12(13	12(13	NUM
brj-23498	264	28	)	)	PUNCT
brj-23498	264	29	,	,	PUNCT
brj-23498	264	30	12811158	12811158	NUM
brj-23498	264	31	-	-	SYM
brj-23498	264	32	811158	811158	NUM
brj-23498	264	33	.	.	PUNCT
brj-23498	265	1	doi:10.3389	doi:10.3389	PROPN
brj-23498	265	2	/	/	SYM
brj-23498	265	3	fgene.2021.811158	fgene.2021.811158	PROPN
brj-23498	265	4	jiang	jiang	PROPN
brj-23498	265	5	,	,	PUNCT
brj-23498	265	6	c.	c.	PROPN
brj-23498	265	7	s.	s.	PROPN
brj-23498	265	8	,	,	PUNCT
brj-23498	265	9	zeng	zeng	PROPN
brj-23498	265	10	,	,	PUNCT
brj-23498	265	11	z.	z.	PROPN
brj-23498	265	12	,	,	PUNCT
brj-23498	265	13	and	and	CCONJ
brj-23498	265	14	wang	wang	PROPN
brj-23498	265	15	,	,	PUNCT
brj-23498	265	16	j.	j.	PROPN
brj-23498	265	17	(	(	PUNCT
brj-23498	265	18	2023	2023	NUM
brj-23498	265	19	)	)	PUNCT
brj-23498	265	20	.	.	PUNCT
brj-23498	266	1	“	"	PUNCT
brj-23498	266	2	a	a	DET
brj-23498	266	3	review	review	NOUN
brj-23498	266	4	of	of	ADP
brj-23498	266	5	research	research	NOUN
brj-23498	266	6	advances	advance	NOUN
brj-23498	266	7	in	in	ADP
brj-23498	266	8	multisource	multisource	ADJ
brj-23498	266	9	information	information	NOUN
brj-23498	266	10	fusion	fusion	NOUN
brj-23498	266	11	,	,	PUNCT
brj-23498	266	12	”	"	PUNCT
brj-23498	266	13	modern	modern	ADJ
brj-23498	266	14	computer	computer	NOUN
brj-23498	266	15	29(18),1	29(18),1	NUM
brj-23498	266	16	-	-	SYM
brj-23498	266	17	9	9	NUM
brj-23498	266	18	.	.	PUNCT
brj-23498	266	19	doi	doi	NOUN
brj-23498	266	20	:	:	PUNCT
brj-23498	266	21	10.3969	10.3969	NUM
brj-23498	266	22	/	/	SYM
brj-23498	266	23	j.issn.10071423.2023.18.001	j.issn.10071423.2023.18.001	NOUN
brj-23498	266	24	kemal	kemal	PROPN
brj-23498	266	25	,	,	PUNCT
brj-23498	266	26	a.	a.	PROPN
brj-23498	266	27	,	,	PUNCT
brj-23498	266	28	metin	metin	PROPN
brj-23498	266	29	,	,	PUNCT
brj-23498	266	30	m.	m.	NOUN
brj-23498	266	31	o.	o.	PROPN
brj-23498	266	32	,	,	PUNCT
brj-23498	266	33	and	and	CCONJ
brj-23498	266	34	ziya	ziya	PROPN
brj-23498	266	35	,	,	PUNCT
brj-23498	266	36	a.	a.	NOUN
brj-23498	266	37	(	(	PUNCT
brj-23498	266	38	2022	2022	NUM
brj-23498	266	39	)	)	PUNCT
brj-23498	266	40	.	.	PUNCT
brj-23498	267	1	“	"	PUNCT
brj-23498	267	2	a	a	DET
brj-23498	267	3	sugar	sugar	NOUN
brj-23498	267	4	beet	beet	NOUN
brj-23498	267	5	leaf	leaf	NOUN
brj-23498	267	6	disease	disease	NOUN
brj-23498	267	7	classification	classification	NOUN
brj-23498	267	8	method	method	NOUN
brj-23498	267	9	based	base	VERB
brj-23498	267	10	on	on	ADP
brj-23498	267	11	image	image	NOUN
brj-23498	267	12	processing	processing	NOUN
brj-23498	267	13	and	and	CCONJ
brj-23498	267	14	deep	deep	ADJ
brj-23498	267	15	learning	learning	NOUN
brj-23498	267	16	,	,	PUNCT
brj-23498	267	17	”	"	PUNCT
brj-23498	267	18	multimedia	multimedia	NOUN
brj-23498	267	19	tools	tool	NOUN
brj-23498	267	20	and	and	CCONJ
brj-23498	267	21	application	application	NOUN
brj-23498	267	22	82(8	82(8	NUM
brj-23498	267	23	)	)	PUNCT
brj-23498	267	24	,	,	PUNCT
brj-23498	267	25	12577	12577	NUM
brj-23498	267	26	-	-	SYM
brj-23498	267	27	12594	12594	NUM
brj-23498	267	28	.	.	PUNCT
brj-23498	268	1	doi	doi	NOUN
brj-23498	268	2	:	:	PUNCT
brj-23498	268	3	10.1007	10.1007	NUM
brj-23498	268	4	/	/	SYM
brj-23498	268	5	s11042	s11042	PROPN
brj-23498	268	6	-	-	PUNCT
brj-23498	268	7	022	022	NUM
brj-23498	268	8	-	-	PUNCT
brj-23498	268	9	13925	13925	NUM
brj-23498	268	10	-	-	SYM
brj-23498	268	11	6	6	NUM
brj-23498	268	12	kong	kong	PROPN
brj-23498	268	13	,	,	PUNCT
brj-23498	268	14	x.	x.	PROPN
brj-23498	268	15	l.	l.	PROPN
brj-23498	268	16	,	,	PUNCT
brj-23498	268	17	xia	xia	PROPN
brj-23498	268	18	.	.	PUNCT
brj-23498	269	1	y.	y.	PROPN
brj-23498	269	2	h.	h.	PROPN
brj-23498	269	3	,	,	PUNCT
brj-23498	269	4	and	and	CCONJ
brj-23498	269	5	wu	wu	PROPN
brj-23498	269	6	,	,	PUNCT
brj-23498	269	7	x.	x.	PROPN
brj-23498	269	8	q.	q.	PROPN
brj-23498	269	9	(	(	PUNCT
brj-23498	269	10	2022	2022	NUM
brj-23498	269	11	)	)	PUNCT
brj-23498	269	12	.	.	PUNCT
brj-23498	270	1	“	"	PUNCT
brj-23498	270	2	discontinuity	discontinuity	NOUN
brj-23498	270	3	recognition	recognition	NOUN
brj-23498	270	4	and	and	CCONJ
brj-23498	270	5	information	information	NOUN
brj-23498	270	6	extraction	extraction	NOUN
brj-23498	270	7	of	of	ADP
brj-23498	270	8	high	high	ADJ
brj-23498	270	9	and	and	CCONJ
brj-23498	270	10	steep	steep	ADJ
brj-23498	270	11	cliff	cliff	NOUN
brj-23498	270	12	rock	rock	NOUN
brj-23498	270	13	mass	mass	PROPN
brj-23498	270	14	based	base	VERB
brj-23498	270	15	on	on	ADP
brj-23498	270	16	multi	multi	ADJ
brj-23498	270	17	-	-	ADJ
brj-23498	270	18	source	source	NOUN
brj-23498	270	19	data	datum	NOUN
brj-23498	270	20	fusion	fusion	NOUN
brj-23498	270	21	,	,	PUNCT
brj-23498	270	22	”	"	PUNCT
brj-23498	270	23	applied	apply	VERB
brj-23498	270	24	sciences	science	NOUN
brj-23498	270	25	12(21	12(21	NUM
brj-23498	270	26	)	)	PUNCT
brj-23498	270	27	,	,	PUNCT
brj-23498	270	28	11258	11258	NUM
brj-23498	270	29	-	-	SYM
brj-23498	270	30	11258	11258	NUM
brj-23498	270	31	.	.	PUNCT
brj-23498	271	1	doi	doi	NOUN
brj-23498	271	2	:	:	PUNCT
brj-23498	271	3	10.3390	10.3390	NUM
brj-23498	271	4	/	/	SYM
brj-23498	271	5	app122111258	app122111258	PROPN
brj-23498	271	6	li	li	PROPN
brj-23498	271	7	,	,	PUNCT
brj-23498	271	8	z.	z.	PROPN
brj-23498	271	9	m.	m.	PROPN
brj-23498	271	10	,	,	PUNCT
brj-23498	271	11	zhang	zhang	PROPN
brj-23498	271	12	,	,	PUNCT
brj-23498	271	13	c.	c.	PROPN
brj-23498	271	14	,	,	PUNCT
brj-23498	271	15	zhang	zhang	PROPN
brj-23498	271	16	,	,	PUNCT
brj-23498	271	17	c.	c.	PROPN
brj-23498	271	18	y.	y.	PROPN
brj-23498	271	19	,	,	PUNCT
brj-23498	271	20	and	and	CCONJ
brj-23498	271	21	zhang	zhang	PROPN
brj-23498	271	22	,	,	PUNCT
brj-23498	271	23	g.	g.	PROPN
brj-23498	271	24	g.	g.	PROPN
brj-23498	271	25	(	(	PUNCT
brj-23498	271	26	2020	2020	NUM
brj-23498	271	27	)	)	PUNCT
brj-23498	271	28	.	.	PUNCT
brj-23498	272	1	“	"	PUNCT
brj-23498	272	2	the	the	DET
brj-23498	272	3	relationship	relationship	NOUN
brj-23498	272	4	between	between	ADP
brj-23498	272	5	nutrients	nutrient	NOUN
brj-23498	272	6	and	and	CCONJ
brj-23498	272	7	biological	biological	ADJ
brj-23498	272	8	yield	yield	NOUN
brj-23498	272	9	of	of	ADP
brj-23498	272	10	different	different	ADJ
brj-23498	272	11	varieties	variety	NOUN
brj-23498	272	12	of	of	ADP
brj-23498	272	13	alfalfa	alfalfa	NOUN
brj-23498	272	14	,	,	PUNCT
brj-23498	272	15	”	"	PUNCT
brj-23498	272	16	scientia	scientia	PROPN
brj-23498	272	17	agircultura	agircultura	PROPN
brj-23498	272	18	sinica	sinica	PROPN
brj-23498	272	19	53(6	53(6	PROPN
brj-23498	272	20	)	)	PUNCT
brj-23498	272	21	,	,	PUNCT
brj-23498	272	22	1269	1269	NUM
brj-23498	272	23	-	-	SYM
brj-23498	272	24	1277	1277	NUM
brj-23498	272	25	.	.	PUNCT
brj-23498	273	1	doi	doi	NOUN
brj-23498	273	2	:	:	PUNCT
brj-23498	273	3	10.3864	10.3864	NUM
brj-23498	273	4	/	/	SYM
brj-23498	273	5	j.issn.0578	j.issn.0578	PROPN
brj-23498	273	6	-	-	PUNCT
brj-23498	273	7	1752,2020.06.018	1752,2020.06.018	NUM
brj-23498	273	8	li	li	PROPN
brj-23498	273	9	,	,	PUNCT
brj-23498	273	10	z.w	z.w	PROPN
brj-23498	273	11	.	.	PROPN
brj-23498	273	12	,	,	PUNCT
brj-23498	273	13	wang	wang	PROPN
brj-23498	273	14	,	,	PUNCT
brj-23498	273	15	y.w	y.w	PROPN
brj-23498	273	16	.	.	PROPN
brj-23498	273	17	,	,	PUNCT
brj-23498	273	18	liu	liu	PROPN
brj-23498	273	19	,	,	PUNCT
brj-23498	273	20	j.	j.	PROPN
brj-23498	273	21	,	,	PUNCT
brj-23498	273	22	chen	chen	PROPN
brj-23498	273	23	,	,	PUNCT
brj-23498	273	24	d.	d.	PROPN
brj-23498	273	25	,	,	PUNCT
brj-23498	273	26	feng	feng	PROPN
brj-23498	273	27	,	,	PUNCT
brj-23498	273	28	g.	g.	PROPN
brj-23498	273	29	,	,	PUNCT
brj-23498	273	30	chen	chen	PROPN
brj-23498	273	31	,	,	PUNCT
brj-23498	273	32	m.	m.	NOUN
brj-23498	273	33	,	,	PUNCT
brj-23498	273	34	feng	feng	PROPN
brj-23498	273	35	,	,	PUNCT
brj-23498	273	36	y.	y.	PROPN
brj-23498	273	37	,	,	PUNCT
brj-23498	273	38	zhanga	zhanga	PROPN
brj-23498	273	39	,	,	PUNCT
brj-23498	273	40	r.	r.	PROPN
brj-23498	273	41	,	,	PUNCT
brj-23498	273	42	and	and	CCONJ
brj-23498	273	43	yan	yan	PROPN
brj-23498	273	44	,	,	PUNCT
brj-23498	273	45	x.	x.	NOUN
brj-23498	273	46	(	(	PUNCT
brj-23498	273	47	2023	2023	NUM
brj-23498	273	48	)	)	PUNCT
brj-23498	273	49	.	.	PUNCT
brj-23498	274	1	“	"	PUNCT
brj-23498	274	2	the	the	DET
brj-23498	274	3	potential	potential	ADJ
brj-23498	274	4	role	role	NOUN
brj-23498	274	5	of	of	ADP
brj-23498	274	6	alfalfa	alfalfa	NOUN
brj-23498	274	7	polysaccharides	polysaccharide	NOUN
brj-23498	274	8	and	and	CCONJ
brj-23498	274	9	their	their	PRON
brj-23498	274	10	sulphated	sulphate	VERB
brj-23498	274	11	derivatives	derivative	NOUN
brj-23498	274	12	in	in	ADP
brj-23498	274	13	the	the	DET
brj-23498	274	14	alleviation	alleviation	NOUN
brj-23498	274	15	of	of	ADP
brj-23498	274	16	obesity	obesity	NOUN
brj-23498	274	17	,	,	PUNCT
brj-23498	274	18	”	"	PUNCT
brj-23498	274	19	food	food	NOUN
brj-23498	274	20	&	&	CCONJ
brj-23498	274	21	function	function	PROPN
brj-23498	274	22	14(16	14(16	NUM
brj-23498	274	23	)	)	PUNCT
brj-23498	274	24	,	,	PUNCT
brj-23498	274	25	7586	7586	NUM
brj-23498	274	26	-	-	SYM
brj-23498	274	27	7602	7602	NUM
brj-23498	274	28	.	.	PUNCT
brj-23498	275	1	doi	doi	NOUN
brj-23498	275	2	:	:	PUNCT
brj-23498	275	3	10.1039	10.1039	NUM
brj-23498	275	4	/	/	SYM
brj-23498	275	5	d3fo01390a	d3fo01390a	PROPN
brj-23498	275	6	liu	liu	PROPN
brj-23498	275	7	,	,	PUNCT
brj-23498	275	8	z.	z.	PROPN
brj-23498	275	9	c.	c.	PROPN
brj-23498	275	10	,	,	PUNCT
brj-23498	275	11	yuan	yuan	PROPN
brj-23498	275	12	,	,	PUNCT
brj-23498	275	13	l.	l.	PROPN
brj-23498	275	14	y.	y.	PROPN
brj-23498	275	15	,	,	PUNCT
brj-23498	275	16	and	and	CCONJ
brj-23498	275	17	gai	gai	NOUN
brj-23498	275	18	,	,	PUNCT
brj-23498	275	19	x.	x.	PROPN
brj-23498	275	20	h.	h.	PROPN
brj-23498	275	21	(	(	PUNCT
brj-23498	275	22	2022	2022	NUM
brj-23498	275	23	)	)	PUNCT
brj-23498	275	24	.	.	PUNCT
brj-23498	276	1	“	"	PUNCT
brj-23498	276	2	cow	cow	NOUN
brj-23498	276	3	monitoring	monitor	VERB
brj-23498	276	4	image	image	NOUN
brj-23498	276	5	enhancement	enhancement	NOUN
brj-23498	276	6	algorithm	algorithm	NOUN
brj-23498	276	7	in	in	ADP
brj-23498	276	8	complex	complex	ADJ
brj-23498	276	9	environment	environment	NOUN
brj-23498	276	10	based	base	VERB
brj-23498	276	11	on	on	ADP
brj-23498	276	12	dual	dual	ADJ
brj-23498	276	13	domain	domain	NOUN
brj-23498	276	14	decomposition	decomposition	NOUN
brj-23498	276	15	,	,	PUNCT
brj-23498	276	16	”	"	PUNCT
brj-23498	276	17	jiangsu	jiangsu	PROPN
brj-23498	276	18	agricultural	agricultural	PROPN
brj-23498	276	19	sciences	sciences	PROPN
brj-23498	276	20	50(09	50(09	NUM
brj-23498	276	21	)	)	PUNCT
brj-23498	276	22	,	,	PUNCT
brj-23498	276	23	203	203	NUM
brj-23498	276	24	-	-	SYM
brj-23498	276	25	210	210	NUM
brj-23498	276	26	.	.	PUNCT
brj-23498	277	1	doi	doi	NOUN
brj-23498	277	2	:	:	PUNCT
brj-23498	277	3	10.15889	10.15889	NUM
brj-23498	277	4	/	/	SYM
brj-23498	277	5	j.issn.1002	j.issn.1002	ADV
brj-23498	277	6	-	-	PUNCT
brj-23498	277	7	1302.2022.09.033	1302.2022.09.033	NUM
brj-23498	277	8	li	li	PROPN
brj-23498	277	9	,	,	PUNCT
brj-23498	277	10	h.	h.	PROPN
brj-23498	277	11	,	,	PUNCT
brj-23498	277	12	xie	xie	PROPN
brj-23498	277	13	,	,	PUNCT
brj-23498	277	14	m.	m.	PROPN
brj-23498	277	15	d.	d.	PROPN
brj-23498	277	16	,	,	PUNCT
brj-23498	277	17	and	and	CCONJ
brj-23498	277	18	gui	gui	NOUN
brj-23498	277	19	,	,	PUNCT
brj-23498	277	20	x.	x.	PROPN
brj-23498	277	21	j.	j.	PROPN
brj-23498	277	22	(	(	PUNCT
brj-23498	277	23	2023	2023	NUM
brj-23498	277	24	)	)	PUNCT
brj-23498	277	25	.	.	PUNCT
brj-23498	278	1	“	"	PUNCT
brj-23498	278	2	research	research	NOUN
brj-23498	278	3	progress	progress	NOUN
brj-23498	278	4	of	of	ADP
brj-23498	278	5	multi	multi	ADJ
brj-23498	278	6	-	-	ADJ
brj-23498	278	7	source	source	ADJ
brj-23498	278	8	information	information	NOUN
brj-23498	278	9	fusion	fusion	NOUN
brj-23498	278	10	technology	technology	NOUN
brj-23498	278	11	in	in	ADP
brj-23498	278	12	quality	quality	NOUN
brj-23498	278	13	evaluation	evaluation	NOUN
brj-23498	278	14	of	of	ADP
brj-23498	278	15	traditional	traditional	ADJ
brj-23498	278	16	chinese	chinese	ADJ
brj-23498	278	17	medicine	medicine	NOUN
brj-23498	278	18	,	,	PUNCT
brj-23498	278	19	”	"	PUNCT
brj-23498	278	20	acta	acta	PROPN
brj-23498	278	21	pharmaceutica	pharmaceutica	PROPN
brj-23498	278	22	sinica	sinica	PROPN
brj-23498	278	23	58(10	58(10	PROPN
brj-23498	278	24	)	)	PUNCT
brj-23498	278	25	,	,	PUNCT
brj-23498	278	26	2835	2835	NUM
brj-23498	278	27	-	-	SYM
brj-23498	278	28	2852	2852	NUM
brj-23498	278	29	.	.	PUNCT
brj-23498	279	1	doi:10.16438	doi:10.16438	PROPN
brj-23498	279	2	/	/	SYM
brj-23498	279	3	j.0513	j.0513	PROPN
brj-23498	279	4	-	-	PUNCT
brj-23498	279	5	4870.2023	4870.2023	NUM
brj-23498	279	6	-	-	PUNCT
brj-23498	279	7	0195	0195	NUM
brj-23498	279	8	ma	ma	PROPN
brj-23498	279	9	,	,	PUNCT
brj-23498	279	10	l.	l.	PROPN
brj-23498	279	11	,	,	PUNCT
brj-23498	279	12	zhou	zhou	PROPN
brj-23498	279	13	,	,	PUNCT
brj-23498	279	14	q.	q.	PROPN
brj-23498	279	15	l.	l.	PROPN
brj-23498	279	16	,	,	PUNCT
brj-23498	279	17	and	and	CCONJ
brj-23498	279	18	zhao	zhao	PROPN
brj-23498	279	19	,	,	PUNCT
brj-23498	279	20	l.	l.	PROPN
brj-23498	279	21	y.	y.	PROPN
brj-23498	279	22	(	(	PUNCT
brj-23498	279	23	2023	2023	NUM
brj-23498	279	24	)	)	PUNCT
brj-23498	279	25	.	.	PUNCT
brj-23498	280	1	“	"	PUNCT
brj-23498	280	2	classification	classification	NOUN
brj-23498	280	3	and	and	CCONJ
brj-23498	280	4	recognition	recognition	NOUN
brj-23498	280	5	of	of	ADP
brj-23498	280	6	tomato	tomato	NOUN
brj-23498	280	7	leaf	leaf	NOUN
brj-23498	280	8	diseases	disease	NOUN
brj-23498	280	9	based	base	VERB
brj-23498	280	10	on	on	ADP
brj-23498	280	11	deep	deep	ADJ
brj-23498	280	12	learning	learning	NOUN
brj-23498	280	13	,	,	PUNCT
brj-23498	280	14	”	"	PUNCT
brj-23498	280	15	journal	journal	NOUN
brj-23498	280	16	of	of	ADP
brj-23498	280	17	chinese	chinese	ADJ
brj-23498	280	18	agricultural	agricultural	ADJ
brj-23498	280	19	mechanization	mechanization	NOUN
brj-23498	280	20	44(07	44(07	NUM
brj-23498	280	21	)	)	PUNCT
brj-23498	280	22	,	,	PUNCT
brj-23498	280	23	187	187	NUM
brj-23498	280	24	-	-	SYM
brj-23498	280	25	193	193	NUM
brj-23498	280	26	+	+	NOUN
brj-23498	280	27	206	206	NUM
brj-23498	280	28	.	.	PUNCT
brj-23498	281	1	doi:10.13733	doi:10.13733	NOUN
brj-23498	281	2	/	/	SYM
brj-23498	281	3	j.jcam.issn.2095	j.jcam.issn.2095	PROPN
brj-23498	281	4	-	-	ADJ
brj-23498	281	5	5553,2023.07.025	5553,2023.07.025	NUM
brj-23498	281	6	mumtaz	mumtaz	NOUN
brj-23498	281	7	.	.	PUNCT
brj-23498	282	1	a.	a.	PROPN
brj-23498	282	2	,	,	PUNCT
brj-23498	282	3	and	and	CCONJ
brj-23498	282	4	xiang	xiang	PROPN
brj-23498	282	5	,	,	PUNCT
brj-23498	282	6	y.	y.	PROPN
brj-23498	282	7	(	(	PUNCT
brj-23498	282	8	2022	2022	NUM
brj-23498	282	9	)	)	PUNCT
brj-23498	282	10	.	.	PUNCT
brj-23498	283	1	“	"	PUNCT
brj-23498	283	2	coupled	couple	VERB
brj-23498	283	3	online	online	ADJ
brj-23498	283	4	sequential	sequential	ADJ
brj-23498	283	5	extreme	extreme	ADJ
brj-23498	283	6	learning	learning	NOUN
brj-23498	283	7	machine	machine	NOUN
brj-23498	283	8	model	model	NOUN
brj-23498	283	9	with	with	ADP
brj-23498	283	10	ant	ant	ADJ
brj-23498	283	11	colony	colony	NOUN
brj-23498	283	12	optimization	optimization	NOUN
brj-23498	283	13	algorithm	algorithm	NOUN
brj-23498	283	14	for	for	ADP
brj-23498	283	15	wheat	wheat	NOUN
brj-23498	283	16	yield	yield	NOUN
brj-23498	283	17	prediction	prediction	NOUN
brj-23498	283	18	,	,	PUNCT
brj-23498	283	19	”	"	PUNCT
brj-23498	283	20	scientific	scientific	ADJ
brj-23498	283	21	reports	report	NOUN
brj-23498	283	22	12(1	12(1	NUM
brj-23498	283	23	)	)	PUNCT
brj-23498	283	24	,	,	PUNCT
brj-23498	283	25	5488	5488	NUM
brj-23498	283	26	-	-	SYM
brj-23498	283	27	5488	5488	NUM
brj-23498	283	28	.	.	PUNCT
brj-23498	284	1	doi	doi	NOUN
brj-23498	284	2	:	:	PUNCT
brj-23498	284	3	10.1038	10.1038	NUM
brj-23498	284	4	/	/	SYM
brj-23498	284	5	s41598	s41598	NOUN
brj-23498	284	6	-	-	PUNCT
brj-23498	284	7	022	022	NUM
brj-23498	284	8	-	-	PUNCT
brj-23498	284	9	09482	09482	NUM
brj-23498	284	10	-	-	SYM
brj-23498	284	11	5	5	NUM
brj-23498	284	12	najmeh	najmeh	NOUN
brj-23498	284	13	,	,	PUNCT
brj-23498	284	14	h.	h.	PROPN
brj-23498	284	15	,	,	PUNCT
brj-23498	284	16	adel	adel	PROPN
brj-23498	284	17	,	,	PUNCT
brj-23498	284	18	b.	b.	PROPN
brj-23498	284	19	,	,	PUNCT
brj-23498	284	20	and	and	CCONJ
brj-23498	284	21	sedigheh	sedigheh	ADJ
brj-23498	284	22	,	,	PUNCT
brj-23498	284	23	m.	m.	NOUN
brj-23498	284	24	(	(	PUNCT
brj-23498	284	25	2022	2022	NUM
brj-23498	284	26	)	)	PUNCT
brj-23498	284	27	.	.	PUNCT
brj-23498	285	1	“	"	PUNCT
brj-23498	285	2	monitoring	monitor	VERB
brj-23498	285	3	botrytis	botrytis	NOUN
brj-23498	285	4	cinerea	cinerea	NOUN
brj-23498	285	5	infection	infection	NOUN
brj-23498	285	6	in	in	ADP
brj-23498	285	7	kiwifruit	kiwifruit	NOUN
brj-23498	285	8	using	use	VERB
brj-23498	285	9	electronic	electronic	ADJ
brj-23498	285	10	nose	nose	NOUN
brj-23498	285	11	and	and	CCONJ
brj-23498	285	12	machine	machine	NOUN
brj-23498	285	13	learning	learn	VERB
brj-23498	285	14	techniques	technique	NOUN
brj-23498	285	15	,	,	PUNCT
brj-23498	285	16	”	"	PUNCT
brj-23498	285	17	food	food	NOUN
brj-23498	285	18	and	and	CCONJ
brj-23498	285	19	bioprocess	bioprocess	NOUN
brj-23498	285	20	technology	technology	NOUN
brj-23498	285	21	16(4	16(4	NUM
brj-23498	285	22	)	)	PUNCT
brj-23498	285	23	,	,	PUNCT
brj-23498	285	24	749	749	NUM
brj-23498	285	25	-	-	SYM
brj-23498	285	26	767	767	NUM
brj-23498	285	27	.	.	PUNCT
brj-23498	286	1	doi	doi	NOUN
brj-23498	286	2	:	:	PUNCT
brj-23498	286	3	10.1007	10.1007	NUM
brj-23498	286	4	/	/	SYM
brj-23498	286	5	s11947	s11947	PROPN
brj-23498	286	6	-	-	PUNCT
brj-23498	286	7	022	022	NUM
brj-23498	286	8	-	-	PUNCT
brj-23498	286	9	02967	02967	NUM
brj-23498	286	10	-	-	SYM
brj-23498	286	11	1	1	NUM
brj-23498	286	12	qin	qin	NOUN
brj-23498	286	13	,	,	PUNCT
brj-23498	286	14	f.	f.	PROPN
brj-23498	286	15	,	,	PUNCT
brj-23498	286	16	liu	liu	PROPN
brj-23498	286	17	,	,	PUNCT
brj-23498	286	18	d.	d.	PROPN
brj-23498	286	19	x.	x.	PROPN
brj-23498	286	20	,	,	PUNCT
brj-23498	286	21	and	and	CCONJ
brj-23498	286	22	sun	sun	PROPN
brj-23498	286	23	,	,	PUNCT
brj-23498	286	24	b.	b.	PROPN
brj-23498	286	25	d.	d.	PROPN
brj-23498	286	26	(	(	PUNCT
brj-23498	286	27	2017	2017	NUM
brj-23498	286	28	)	)	PUNCT
brj-23498	286	29	.	.	PUNCT
brj-23498	287	1	“	"	PUNCT
brj-23498	287	2	image	image	NOUN
brj-23498	287	3	recognition	recognition	NOUN
brj-23498	287	4	of	of	ADP
brj-23498	287	5	four	four	NUM
brj-23498	287	6	different	different	ADJ
brj-23498	287	7	alfalfa	alfalfa	NOUN
brj-23498	287	8	leaf	leaf	NOUN
brj-23498	287	9	diseases	disease	NOUN
brj-23498	287	10	based	base	VERB
brj-23498	287	11	on	on	ADP
brj-23498	287	12	deep	deep	ADJ
brj-23498	287	13	learning	learning	NOUN
brj-23498	287	14	and	and	CCONJ
brj-23498	287	15	support	support	VERB
brj-23498	287	16	vector	vector	NOUN
brj-23498	287	17	machine	machine	NOUN
brj-23498	287	18	,	,	PUNCT
brj-23498	287	19	”	"	PUNCT
brj-23498	287	20	journal	journal	NOUN
brj-23498	287	21	of	of	ADP
brj-23498	287	22	china	china	PROPN
brj-23498	287	23	agricultural	agricultural	PROPN
brj-23498	287	24	university	university	PROPN
brj-23498	287	25	22(07	22(07	PROPN
brj-23498	287	26	)	)	PUNCT
brj-23498	287	27	,	,	PUNCT
brj-23498	287	28	123	123	NUM
brj-23498	287	29	-	-	SYM
brj-23498	287	30	133	133	NUM
brj-23498	287	31	.	.	PUNCT
brj-23498	288	1	doi:10.11841	doi:10.11841	NOUN
brj-23498	288	2	/	/	SYM
brj-23498	288	3	j.issn.1007	j.issn.1007	PROPN
brj-23498	288	4	-	-	PUNCT
brj-23498	288	5	4333,2017.07.15	4333,2017.07.15	NUM
brj-23498	288	6	quintero	quintero	PROPN
brj-23498	288	7	,	,	PUNCT
brj-23498	288	8	d.	d.	PROPN
brj-23498	288	9	,	,	PUNCT
brj-23498	288	10	andrade	andrade	PROPN
brj-23498	288	11	,	,	PUNCT
brj-23498	288	12	a.	a.	NOUN
brj-23498	288	13	m.	m.	NOUN
brj-23498	288	14	,	,	PUNCT
brj-23498	288	15	cholula	cholula	PROPN
brj-23498	288	16	,	,	PUNCT
brj-23498	288	17	u.	u.	PROPN
brj-23498	288	18	,	,	PUNCT
brj-23498	288	19	and	and	CCONJ
brj-23498	288	20	solomon	solomon	PROPN
brj-23498	288	21	,	,	PUNCT
brj-23498	288	22	j.	j.	PROPN
brj-23498	288	23	k.	k.	PROPN
brj-23498	288	24	q.	q.	PROPN
brj-23498	288	25	(	(	PUNCT
brj-23498	288	26	2023	2023	NUM
brj-23498	288	27	)	)	PUNCT
brj-23498	288	28	.	.	PUNCT
brj-23498	289	1	“	"	PUNCT
brj-23498	289	2	a	a	DET
brj-23498	289	3	machine	machine	NOUN
brj-23498	289	4	learning	learn	VERB
brj-23498	289	5	approach	approach	NOUN
brj-23498	289	6	for	for	ADP
brj-23498	289	7	the	the	DET
brj-23498	289	8	estimation	estimation	NOUN
brj-23498	289	9	of	of	ADP
brj-23498	289	10	alfalfa	alfalfa	NOUN
brj-23498	289	11	hay	hay	NOUN
brj-23498	289	12	crop	crop	NOUN
brj-23498	289	13	yield	yield	NOUN
brj-23498	289	14	in	in	ADP
brj-23498	289	15	northern	northern	PROPN
brj-23498	289	16	nevada	nevada	PROPN
brj-23498	289	17	,	,	PUNCT
brj-23498	289	18	”	"	PUNCT
brj-23498	289	19	agriengineering	agriengineere	VERB
brj-23498	289	20	5(4	5(4	NUM
brj-23498	289	21	)	)	PUNCT
brj-23498	289	22	,	,	PUNCT
brj-23498	289	23	1943	1943	NUM
brj-23498	289	24	-	-	SYM
brj-23498	289	25	1954	1954	NUM
brj-23498	289	26	.	.	PUNCT
brj-23498	290	1	doi	doi	NOUN
brj-23498	290	2	:	:	PUNCT
brj-23498	290	3	10.3390	10.3390	NUM
brj-23498	290	4	/	/	SYM
brj-23498	290	5	agriengineering5040119	agriengineering5040119	PROPN
brj-23498	290	6	shen	shen	NOUN
brj-23498	290	7	,	,	PUNCT
brj-23498	290	8	s.	s.	PROPN
brj-23498	290	9	c.	c.	PROPN
brj-23498	290	10	,	,	PUNCT
brj-23498	290	11	zhang	zhang	PROPN
brj-23498	290	12	,	,	PUNCT
brj-23498	290	13	j.	j.	PROPN
brj-23498	290	14	x.	x.	PROPN
brj-23498	290	15	,	,	PUNCT
brj-23498	290	16	and	and	CCONJ
brj-23498	290	17	chen	chen	PROPN
brj-23498	290	18	,	,	PUNCT
brj-23498	290	19	n.	n.	PROPN
brj-23498	290	20	h.	h.	PROPN
brj-23498	290	21	(	(	PUNCT
brj-23498	290	22	2023	2023	NUM
brj-23498	290	23	)	)	PUNCT
brj-23498	290	24	.	.	PUNCT
brj-23498	291	1	“	"	PUNCT
brj-23498	291	2	estimation	estimation	NOUN
brj-23498	291	3	of	of	ADP
brj-23498	291	4	above	above	ADP
brj-23498	291	5	-	-	PUNCT
brj-23498	291	6	ground	ground	NOUN
brj-23498	291	7	biomass	biomass	NOUN
brj-23498	291	8	and	and	CCONJ
brj-23498	291	9	chlorophyll	chlorophyll	NOUN
brj-23498	291	10	content	content	NOUN
brj-23498	291	11	of	of	ADP
brj-23498	291	12	different	different	ADJ
brj-23498	291	13	alfalfa	alfalfa	NOUN
brj-23498	291	14	varieties	variety	NOUN
brj-23498	291	15	based	base	VERB
brj-23498	291	16	on	on	ADP
brj-23498	291	17	uav	uav	PROPN
brj-23498	291	18	multi	multi	PROPN
brj-23498	291	19	-	-	ADJ
brj-23498	291	20	spectrum	spectrum	ADJ
brj-23498	291	21	,	,	PUNCT
brj-23498	291	22	”	"	PUNCT
brj-23498	291	23	spectroscopy	spectroscopy	NOUN
brj-23498	291	24	and	and	CCONJ
brj-23498	291	25	spectral	spectral	ADJ
brj-23498	291	26	analysis	analysis	NOUN
brj-23498	291	27	43(12	43(12	NUM
brj-23498	291	28	)	)	PUNCT
brj-23498	291	29	,	,	PUNCT
brj-23498	291	30	3847	3847	NUM
brj-23498	291	31	-	-	SYM
brj-23498	291	32	3852	3852	NUM
brj-23498	291	33	.	.	PUNCT
brj-23498	292	1	doi	doi	NOUN
brj-23498	292	2	:	:	PUNCT
brj-23498	292	3	10.3964	10.3964	NUM
brj-23498	292	4	/	/	SYM
brj-23498	292	5	j.issn.10000593(2023	j.issn.10000593(2023	NOUN
brj-23498	292	6	)	)	PUNCT
brj-23498	292	7	12	12	NUM
brj-23498	292	8	-	-	PUNCT
brj-23498	292	9	3847	3847	NUM
brj-23498	292	10	-	-	SYM
brj-23498	292	11	06	06	NUM
brj-23498	292	12	shi	shi	PROPN
brj-23498	292	13	,	,	PUNCT
brj-23498	292	14	y.	y.	PROPN
brj-23498	292	15	,	,	PUNCT
brj-23498	292	16	ren	ren	PROPN
brj-23498	292	17	,	,	PUNCT
brj-23498	292	18	y.	y.	PROPN
brj-23498	292	19	q.	q.	PROPN
brj-23498	292	20	,	,	PUNCT
brj-23498	292	21	and	and	CCONJ
brj-23498	292	22	wang	wang	PROPN
brj-23498	292	23	,	,	PUNCT
brj-23498	292	24	s.	s.	PROPN
brj-23498	292	25	y.	y.	PROPN
brj-23498	292	26	(	(	PUNCT
brj-23498	292	27	2024	2024	NUM
brj-23498	292	28	)	)	PUNCT
brj-23498	292	29	.	.	PUNCT
brj-23498	293	1	“	"	PUNCT
brj-23498	293	2	adaptive	adaptive	ADJ
brj-23498	293	3	fusion	fusion	NOUN
brj-23498	293	4	of	of	ADP
brj-23498	293	5	gas	gas	NOUN
brj-23498	293	6	spectral	spectral	ADJ
brj-23498	293	7	bimodal	bimodal	NOUN
brj-23498	293	8	information	information	NOUN
brj-23498	293	9	for	for	ADP
brj-23498	293	10	peanut	peanut	NOUN
brj-23498	293	11	origin	origin	NOUN
brj-23498	293	12	traceability	traceability	NOUN
brj-23498	293	13	,	,	PUNCT
brj-23498	293	14	”	"	PUNCT
brj-23498	293	15	transactions	transaction	NOUN
brj-23498	293	16	of	of	ADP
brj-23498	293	17	the	the	DET
brj-23498	293	18	chinese	chinese	ADJ
brj-23498	293	19	society	society	NOUN
brj-23498	293	20	for	for	ADP
brj-23498	293	21	peer	peer	NOUN
brj-23498	293	22	-	-	PUNCT
brj-23498	293	23	reviewed	review	VERB
brj-23498	293	24	article	article	NOUN
brj-23498	293	25	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-23498	293	26	yang	yang	PROPN
brj-23498	293	27	et	et	PROPN
brj-23498	293	28	al	al	PROPN
brj-23498	293	29	.	.	PROPN
brj-23498	294	1	(	(	PUNCT
brj-23498	294	2	2024	2024	NUM
brj-23498	294	3	)	)	PUNCT
brj-23498	294	4	.	.	PUNCT
brj-23498	295	1	“	"	PUNCT
brj-23498	295	2	alfalfa	alfalfa	NOUN
brj-23498	295	3	quality	quality	NOUN
brj-23498	295	4	assessment	assessment	NOUN
brj-23498	295	5	,	,	PUNCT
brj-23498	295	6	”	"	PUNCT
brj-23498	295	7	bioresources	bioresource	NOUN
brj-23498	295	8	19(3	19(3	NUM
brj-23498	295	9	)	)	PUNCT
brj-23498	295	10	,	,	PUNCT
brj-23498	295	11	4531	4531	NUM
brj-23498	295	12	-	-	SYM
brj-23498	295	13	4546	4546	NUM
brj-23498	295	14	.	.	PUNCT
brj-23498	296	1	4546	4546	NUM
brj-23498	296	2	agricultural	agricultural	ADJ
brj-23498	296	3	machinery	machinery	NOUN
brj-23498	296	4	,	,	PUNCT
brj-23498	296	5	1	1	NUM
brj-23498	296	6	-	-	SYM
brj-23498	296	7	13	13	NUM
brj-23498	296	8	.	.	PUNCT
brj-23498	296	9	song	song	NOUN
brj-23498	296	10	,	,	PUNCT
brj-23498	296	11	y.	y.	PROPN
brj-23498	296	12	b.	b.	PROPN
brj-23498	296	13	(	(	PUNCT
brj-23498	296	14	2022	2022	NUM
brj-23498	296	15	)	)	PUNCT
brj-23498	296	16	.	.	PUNCT
brj-23498	297	1	“	"	PUNCT
brj-23498	297	2	leaf	leaf	NOUN
brj-23498	297	3	area	area	NOUN
brj-23498	297	4	measurement	measurement	NOUN
brj-23498	297	5	system	system	NOUN
brj-23498	297	6	based	base	VERB
brj-23498	297	7	on	on	ADP
brj-23498	297	8	digital	digital	ADJ
brj-23498	297	9	image	image	NOUN
brj-23498	297	10	processing	processing	NOUN
brj-23498	297	11	,	,	PUNCT
brj-23498	297	12	”	"	PUNCT
brj-23498	297	13	technology	technology	NOUN
brj-23498	297	14	journal	journal	NOUN
brj-23498	297	15	of	of	ADP
brj-23498	297	16	agriculture	agriculture	NOUN
brj-23498	297	17	12(02	12(02	PROPN
brj-23498	297	18	)	)	PUNCT
brj-23498	297	19	,	,	PUNCT
brj-23498	297	20	73	73	NUM
brj-23498	297	21	-	-	SYM
brj-23498	297	22	75	75	NUM
brj-23498	297	23	.	.	PUNCT
brj-23498	298	1	sun	sun	NOUN
brj-23498	298	2	,	,	PUNCT
brj-23498	298	3	p.	p.	NOUN
brj-23498	298	4	y.	y.	PROPN
brj-23498	298	5	(	(	PUNCT
brj-23498	298	6	2023	2023	NUM
brj-23498	298	7	)	)	PUNCT
brj-23498	298	8	.	.	PUNCT
brj-23498	299	1	research	research	NOUN
brj-23498	299	2	on	on	ADP
brj-23498	299	3	high	high	ADJ
brj-23498	299	4	-	-	PUNCT
brj-23498	299	5	precision	precision	NOUN
brj-23498	299	6	detection	detection	NOUN
brj-23498	299	7	technology	technology	NOUN
brj-23498	299	8	of	of	ADP
brj-23498	299	9	field	field	NOUN
brj-23498	299	10	nearinfrared	nearinfrare	VERB
brj-23498	299	11	spectrometer	spectrometer	NOUN
brj-23498	299	12	,	,	PUNCT
brj-23498	299	13	master	master	NOUN
brj-23498	299	14	’s	’s	PART
brj-23498	299	15	thesis	thesis	NOUN
brj-23498	299	16	,	,	PUNCT
brj-23498	299	17	jilin	jilin	PROPN
brj-23498	299	18	university	university	PROPN
brj-23498	299	19	,	,	PUNCT
brj-23498	299	20	jilin	jilin	PROPN
brj-23498	299	21	,	,	PUNCT
brj-23498	299	22	china	china	PROPN
brj-23498	299	23	.	.	PUNCT
brj-23498	300	1	swati	swati	PROPN
brj-23498	300	2	,	,	PUNCT
brj-23498	300	3	v.	v.	PROPN
brj-23498	300	4	,	,	PUNCT
brj-23498	300	5	praveen	praveen	PROPN
brj-23498	300	6	,	,	PUNCT
brj-23498	300	7	k.	k.	PROPN
brj-23498	300	8	,	,	PUNCT
brj-23498	300	9	and	and	CCONJ
brj-23498	300	10	chandra	chandra	PROPN
brj-23498	300	11	,	,	PUNCT
brj-23498	300	12	m.	m.	NOUN
brj-23498	300	13	t.	t.	PROPN
brj-23498	300	14	(	(	PUNCT
brj-23498	300	15	2023	2023	NUM
brj-23498	300	16	)	)	PUNCT
brj-23498	300	17	.	.	PUNCT
brj-23498	301	1	“	"	PUNCT
brj-23498	301	2	crop	crop	NOUN
brj-23498	301	3	yield	yield	NOUN
brj-23498	301	4	prediction	prediction	NOUN
brj-23498	301	5	using	use	VERB
brj-23498	301	6	improved	improve	VERB
brj-23498	301	7	extreme	extreme	ADJ
brj-23498	301	8	learning	learning	NOUN
brj-23498	301	9	machine	machine	NOUN
brj-23498	301	10	,	,	PUNCT
brj-23498	301	11	”	"	PUNCT
brj-23498	301	12	communications	communication	NOUN
brj-23498	301	13	in	in	ADP
brj-23498	301	14	soil	soil	NOUN
brj-23498	301	15	science	science	NOUN
brj-23498	301	16	and	and	CCONJ
brj-23498	301	17	plant	plant	NOUN
brj-23498	301	18	analysis	analysis	NOUN
brj-23498	301	19	54(1	54(1	NUM
brj-23498	301	20	)	)	PUNCT
brj-23498	301	21	,	,	PUNCT
brj-23498	301	22	1	1	NUM
brj-23498	301	23	-	-	SYM
brj-23498	301	24	21	21	NUM
brj-23498	301	25	.	.	PUNCT
brj-23498	302	1	doi	doi	NOUN
brj-23498	302	2	:	:	PUNCT
brj-23498	302	3	10.1080/00103624.2022.2108828	10.1080/00103624.2022.2108828	NUM
brj-23498	302	4	tang	tang	NOUN
brj-23498	302	5	,	,	PUNCT
brj-23498	302	6	y.	y.	PROPN
brj-23498	302	7	,	,	PUNCT
brj-23498	302	8	wang	wang	PROPN
brj-23498	302	9	,	,	PUNCT
brj-23498	302	10	x.	x.	PROPN
brj-23498	302	11	p.	p.	PROPN
brj-23498	302	12	,	,	PUNCT
brj-23498	302	13	and	and	CCONJ
brj-23498	302	14	lu	lu	PROPN
brj-23498	302	15	,	,	PUNCT
brj-23498	302	16	c.	c.	PROPN
brj-23498	302	17	c.	c.	PROPN
brj-23498	302	18	(	(	PUNCT
brj-23498	302	19	2023	2023	NUM
brj-23498	302	20	)	)	PUNCT
brj-23498	302	21	.	.	PUNCT
brj-23498	303	1	“	"	PUNCT
brj-23498	303	2	estimating	estimate	VERB
brj-23498	303	3	the	the	DET
brj-23498	303	4	canopy	canopy	NOUN
brj-23498	303	5	water	water	NOUN
brj-23498	303	6	content	content	NOUN
brj-23498	303	7	of	of	ADP
brj-23498	303	8	alfalfa	alfalfa	NOUN
brj-23498	303	9	based	base	VERB
brj-23498	303	10	on	on	ADP
brj-23498	303	11	the	the	DET
brj-23498	303	12	prosail	prosail	NOUN
brj-23498	303	13	model	model	NOUN
brj-23498	303	14	and	and	CCONJ
brj-23498	303	15	spectral	spectral	ADJ
brj-23498	303	16	index	index	NOUN
brj-23498	303	17	,	,	PUNCT
brj-23498	303	18	”	"	PUNCT
brj-23498	303	19	journal	journal	NOUN
brj-23498	303	20	of	of	ADP
brj-23498	303	21	lanzhou	lanzhou	PROPN
brj-23498	303	22	university	university	PROPN
brj-23498	303	23	(	(	PUNCT
brj-23498	303	24	natural	natural	ADJ
brj-23498	303	25	sciences	science	NOUN
brj-23498	303	26	)	)	PUNCT
brj-23498	303	27	59(01	59(01	NUM
brj-23498	303	28	)	)	PUNCT
brj-23498	303	29	,	,	PUNCT
brj-23498	303	30	55	55	NUM
brj-23498	303	31	-	-	SYM
brj-23498	303	32	62	62	NUM
brj-23498	303	33	.	.	PUNCT
brj-23498	304	1	doi	doi	NOUN
brj-23498	304	2	:	:	PUNCT
brj-23498	304	3	10.13885	10.13885	NUM
brj-23498	304	4	/	/	SYM
brj-23498	304	5	j.issn.04552059.2023.01.008	j.issn.04552059.2023.01.008	NOUN
brj-23498	304	6	tian	tian	PROPN
brj-23498	304	7	,	,	PUNCT
brj-23498	304	8	b.	b.	PROPN
brj-23498	304	9	,	,	PUNCT
brj-23498	304	10	ma	ma	PROPN
brj-23498	304	11	,	,	PUNCT
brj-23498	304	12	c.	c.	PROPN
brj-23498	304	13	,	,	PUNCT
brj-23498	304	14	and	and	CCONJ
brj-23498	304	15	di	di	NOUN
brj-23498	304	16	,	,	PUNCT
brj-23498	304	17	y.	y.	PROPN
brj-23498	304	18	w.	w.	PROPN
brj-23498	304	19	(	(	PUNCT
brj-23498	304	20	2023	2023	NUM
brj-23498	304	21	)	)	PUNCT
brj-23498	304	22	.	.	PUNCT
brj-23498	305	1	the	the	DET
brj-23498	305	2	feeding	feeding	NOUN
brj-23498	305	3	value	value	NOUN
brj-23498	305	4	of	of	ADP
brj-23498	305	5	different	different	ADJ
brj-23498	305	6	forage	forage	NOUN
brj-23498	305	7	nutrients	nutrient	NOUN
brj-23498	305	8	was	be	AUX
brj-23498	305	9	evaluated	evaluate	VERB
brj-23498	305	10	based	base	VERB
brj-23498	305	11	on	on	ADP
brj-23498	305	12	principal	principal	ADJ
brj-23498	305	13	component	component	NOUN
brj-23498	305	14	analysis	analysis	NOUN
brj-23498	305	15	.	.	PUNCT
brj-23498	306	1	feed	feed	NOUN
brj-23498	306	2	industry	industry	NOUN
brj-23498	306	3	,	,	PUNCT
brj-23498	306	4	1	1	NUM
brj-23498	306	5	-	-	SYM
brj-23498	306	6	5	5	NUM
brj-23498	306	7	tian	tian	ADJ
brj-23498	306	8	,	,	PUNCT
brj-23498	306	9	h.	h.	PROPN
brj-23498	306	10	x.	x.	PROPN
brj-23498	306	11	,	,	PUNCT
brj-23498	306	12	yang	yang	PROPN
brj-23498	306	13	,	,	PUNCT
brj-23498	306	14	r.	r.	PROPN
brj-23498	306	15	q.	q.	PROPN
brj-23498	306	16	,	,	PUNCT
brj-23498	306	17	zou	zou	PROPN
brj-23498	306	18	,	,	PUNCT
brj-23498	306	19	h.q	h.q	PROPN
brj-23498	306	20	.	.	PUNCT
brj-23498	306	21	guo	guo	PROPN
brj-23498	306	22	,	,	PUNCT
brj-23498	306	23	x.	x.	NOUN
brj-23498	306	24	y.	y.	PROPN
brj-23498	306	25	,	,	PUNCT
brj-23498	306	26	hong	hong	PROPN
brj-23498	306	27	,	,	PUNCT
brj-23498	306	28	w.	w.	PROPN
brj-23498	306	29	f.	f.	PROPN
brj-23498	306	30	,	,	PUNCT
brj-23498	306	31	yao	yao	PROPN
brj-23498	306	32	,	,	PUNCT
brj-23498	306	33	y.	y.	PROPN
brj-23498	306	34	b.	b.	PROPN
brj-23498	306	35	,	,	PUNCT
brj-23498	306	36	liu	liu	PROPN
brj-23498	306	37	,	,	PUNCT
brj-23498	306	38	y.	y.	PROPN
brj-23498	306	39	,	,	PUNCT
brj-23498	306	40	and	and	CCONJ
brj-23498	306	41	yan	yan	PROPN
brj-23498	306	42	,	,	PUNCT
brj-23498	306	43	y.	y.	PROPN
brj-23498	306	44	h.	h.	PROPN
brj-23498	306	45	(	(	PUNCT
brj-23498	306	46	2021	2021	NUM
brj-23498	306	47	)	)	PUNCT
brj-23498	306	48	.	.	PUNCT
brj-23498	307	1	“	"	PUNCT
brj-23498	307	2	high	high	ADJ
brj-23498	307	3	-	-	PUNCT
brj-23498	307	4	speed	speed	NOUN
brj-23498	307	5	identification	identification	NOUN
brj-23498	307	6	of	of	ADP
brj-23498	307	7	odor	odor	NOUN
brj-23498	307	8	changes	change	NOUN
brj-23498	307	9	and	and	CCONJ
brj-23498	307	10	substance	substance	NOUN
brj-23498	307	11	basis	basis	NOUN
brj-23498	307	12	of	of	ADP
brj-23498	307	13	myristicae	myristicae	PROPN
brj-23498	307	14	semen	semen	NOUN
brj-23498	307	15	mildew	mildew	NOUN
brj-23498	307	16	by	by	ADP
brj-23498	307	17	electronic	electronic	ADJ
brj-23498	307	18	nose	nose	NOUN
brj-23498	307	19	and	and	CCONJ
brj-23498	307	20	hs	hs	PROPN
brj-23498	307	21	-	-	PROPN
brj-23498	307	22	gc	gc	PROPN
brj-23498	307	23	-	-	PUNCT
brj-23498	307	24	ms	ms	NOUN
brj-23498	307	25	,	,	PUNCT
brj-23498	307	26	”	"	PUNCT
brj-23498	307	27	zhong	zhong	PROPN
brj-23498	307	28	guo	guo	PROPN
brj-23498	307	29	zhong	zhong	PROPN
brj-23498	307	30	yao	yao	PROPN
brj-23498	307	31	za	za	PROPN
brj-23498	307	32	zhi	zhi	PROPN
brj-23498	307	33	46(22	46(22	PROPN
brj-23498	307	34	)	)	PUNCT
brj-23498	307	35	,	,	PUNCT
brj-23498	307	36	5853	5853	NUM
brj-23498	307	37	-	-	SYM
brj-23498	307	38	5860	5860	NUM
brj-23498	307	39	.	.	PUNCT
brj-23498	308	1	doi	doi	NOUN
brj-23498	308	2	:	:	PUNCT
brj-23498	308	3	10.19540	10.19540	NUM
brj-23498	308	4	/	/	SYM
brj-23498	308	5	j.cnki.cjcmm.20210526.302	j.cnki.cjcmm.20210526.302	PROPN
brj-23498	308	6	xue	xue	PROPN
brj-23498	308	7	,	,	PUNCT
brj-23498	308	8	x.	x.	PROPN
brj-23498	308	9	y.	y.	PROPN
brj-23498	308	10	(	(	PUNCT
brj-23498	308	11	2006	2006	NUM
brj-23498	308	12	)	)	PUNCT
brj-23498	308	13	.	.	PUNCT
brj-23498	309	1	“	"	PUNCT
brj-23498	309	2	harm	harm	NOUN
brj-23498	309	3	and	and	CCONJ
brj-23498	309	4	prevention	prevention	NOUN
brj-23498	309	5	of	of	ADP
brj-23498	309	6	moldy	moldy	ADJ
brj-23498	309	7	alfalfa	alfalfa	NOUN
brj-23498	309	8	grass	grass	NOUN
brj-23498	309	9	on	on	ADP
brj-23498	309	10	cows	cow	NOUN
brj-23498	309	11	,	,	PUNCT
brj-23498	309	12	”	"	PUNCT
brj-23498	309	13	northern	northern	ADJ
brj-23498	309	14	animal	animal	NOUN
brj-23498	309	15	husbandry	husbandry	NOUN
brj-23498	309	16	12(20	12(20	NUM
brj-23498	309	17	)	)	PUNCT
brj-23498	309	18	,	,	PUNCT
brj-23498	309	19	20	20	NUM
brj-23498	309	20	.	.	PUNCT
brj-23498	310	1	ye	ye	PROPN
brj-23498	310	2	,	,	PUNCT
brj-23498	310	3	w.	w.	PROPN
brj-23498	310	4	c.	c.	PROPN
brj-23498	310	5	,	,	PUNCT
brj-23498	310	6	luo	luo	PROPN
brj-23498	310	7	,	,	PUNCT
brj-23498	310	8	s.	s.	PROPN
brj-23498	310	9	y.	y.	PROPN
brj-23498	310	10	,	,	PUNCT
brj-23498	310	11	and	and	CCONJ
brj-23498	310	12	li	li	PROPN
brj-23498	310	13	,	,	PUNCT
brj-23498	310	14	j.	j.	PROPN
brj-23498	310	15	h.	h.	PROPN
brj-23498	310	16	(	(	PUNCT
brj-23498	310	17	2023	2023	NUM
brj-23498	310	18	)	)	PUNCT
brj-23498	310	19	.	.	PUNCT
brj-23498	311	1	“	"	PUNCT
brj-23498	311	2	research	research	NOUN
brj-23498	311	3	on	on	ADP
brj-23498	311	4	classification	classification	NOUN
brj-23498	311	5	method	method	NOUN
brj-23498	311	6	of	of	ADP
brj-23498	311	7	hybrid	hybrid	NOUN
brj-23498	311	8	rice	rice	NOUN
brj-23498	311	9	seeds	seed	NOUN
brj-23498	311	10	based	base	VERB
brj-23498	311	11	on	on	ADP
brj-23498	311	12	the	the	DET
brj-23498	311	13	fusion	fusion	NOUN
brj-23498	311	14	of	of	ADP
brj-23498	311	15	near	near	ADV
brj-23498	311	16	-	-	PUNCT
brj-23498	311	17	infrared	infrared	ADJ
brj-23498	311	18	spectra	spectra	NOUN
brj-23498	311	19	and	and	CCONJ
brj-23498	311	20	images	image	NOUN
brj-23498	311	21	,	,	PUNCT
brj-23498	311	22	”	"	PUNCT
brj-23498	311	23	spectroscopy	spectroscopy	NOUN
brj-23498	311	24	and	and	CCONJ
brj-23498	311	25	spectral	spectral	ADJ
brj-23498	311	26	analysis	analysis	NOUN
brj-23498	311	27	43(09	43(09	NUM
brj-23498	311	28	)	)	PUNCT
brj-23498	311	29	,	,	PUNCT
brj-23498	311	30	2935	2935	NUM
brj-23498	311	31	-	-	SYM
brj-23498	311	32	2941	2941	NUM
brj-23498	311	33	.	.	PUNCT
brj-23498	312	1	doi	doi	NOUN
brj-23498	312	2	:	:	PUNCT
brj-23498	312	3	10.3964	10.3964	NUM
brj-23498	312	4	/	/	SYM
brj-23498	312	5	j.issn.1000	j.issn.1000	NOUN
brj-23498	312	6	-	-	PUNCT
brj-23498	312	7	0593(2023	0593(2023	NUM
brj-23498	312	8	)	)	PUNCT
brj-23498	312	9	09	09	NUM
brj-23498	312	10	-	-	SYM
brj-23498	312	11	293507	293507	NUM
brj-23498	312	12	zhang	zhang	PROPN
brj-23498	312	13	,	,	PUNCT
brj-23498	312	14	f.	f.	PROPN
brj-23498	312	15	,	,	PUNCT
brj-23498	312	16	cao	cao	PROPN
brj-23498	312	17	,	,	PUNCT
brj-23498	312	18	w.	w.	PROPN
brj-23498	312	19	y.	y.	PROPN
brj-23498	312	20	,	,	PUNCT
brj-23498	312	21	and	and	CCONJ
brj-23498	312	22	cui	cui	NOUN
brj-23498	312	23	,	,	PUNCT
brj-23498	312	24	x.	x.	PROPN
brj-23498	312	25	h.	h.	PROPN
brj-23498	312	26	(	(	PUNCT
brj-23498	312	27	2023	2023	NUM
brj-23498	312	28	)	)	PUNCT
brj-23498	312	29	.	.	PUNCT
brj-23498	313	1	“	"	PUNCT
brj-23498	313	2	non	non	ADJ
brj-23498	313	3	-	-	ADJ
brj-23498	313	4	destructive	destructive	ADJ
brj-23498	313	5	detection	detection	NOUN
brj-23498	313	6	of	of	ADP
brj-23498	313	7	soluble	soluble	ADJ
brj-23498	313	8	solids	solid	NOUN
brj-23498	313	9	in	in	ADP
brj-23498	313	10	cherry	cherry	NOUN
brj-23498	313	11	tomatoes	tomato	NOUN
brj-23498	313	12	by	by	ADP
brj-23498	313	13	visible	visible	ADJ
brj-23498	313	14	/	/	SYM
brj-23498	313	15	near	near	ADV
brj-23498	313	16	infrared	infrared	ADJ
brj-23498	313	17	spectroscopy	spectroscopy	NOUN
brj-23498	313	18	based	base	VERB
brj-23498	313	19	on	on	ADP
brj-23498	313	20	sg	sg	PROPN
brj-23498	313	21	-	-	PUNCT
brj-23498	313	22	carsibp	carsibp	NOUN
brj-23498	313	23	,	,	PUNCT
brj-23498	313	24	”	"	PUNCT
brj-23498	313	25	spectroscopy	spectroscopy	NOUN
brj-23498	313	26	and	and	CCONJ
brj-23498	313	27	spectral	spectral	ADJ
brj-23498	313	28	analysis	analysis	NOUN
brj-23498	313	29	43(03	43(03	PROPN
brj-23498	313	30	)	)	PUNCT
brj-23498	313	31	,	,	PUNCT
brj-23498	313	32	737	737	NUM
brj-23498	313	33	-	-	SYM
brj-23498	313	34	743	743	NUM
brj-23498	313	35	.	.	PUNCT
brj-23498	314	1	doi	doi	NOUN
brj-23498	314	2	:	:	PUNCT
brj-23498	314	3	10.3964	10.3964	NUM
brj-23498	314	4	/	/	SYM
brj-23498	314	5	j.issn.1000	j.issn.1000	PROPN
brj-23498	314	6	-	-	PUNCT
brj-23498	314	7	0593(2023)03	0593(2023)03	VERB
brj-23498	314	8	-	-	PUNCT
brj-23498	314	9	0737	0737	NUM
brj-23498	314	10	-	-	PUNCT
brj-23498	314	11	07	07	NUM
brj-23498	314	12	zhangzhong	zhangzhong	PROPN
brj-23498	314	13	,	,	PUNCT
brj-23498	314	14	l.	l.	PROPN
brj-23498	314	15	l.	l.	PROPN
brj-23498	314	16	,	,	PUNCT
brj-23498	314	17	he	he	PRON
brj-23498	314	18	,	,	PUNCT
brj-23498	314	19	t.	t.	PROPN
brj-23498	314	20	t.	t.	PROPN
brj-23498	314	21	,	,	PUNCT
brj-23498	314	22	and	and	CCONJ
brj-23498	314	23	li	li	PROPN
brj-23498	314	24	,	,	PUNCT
brj-23498	314	25	z.	z.	PROPN
brj-23498	314	26	w.	w.	PROPN
brj-23498	314	27	(	(	PUNCT
brj-23498	314	28	2023	2023	NUM
brj-23498	314	29	)	)	PUNCT
brj-23498	314	30	.	.	PUNCT
brj-23498	315	1	“	"	PUNCT
brj-23498	315	2	quantitative	quantitative	ADJ
brj-23498	315	3	grading	grading	NOUN
brj-23498	315	4	method	method	NOUN
brj-23498	315	5	for	for	ADP
brj-23498	315	6	tomato	tomato	NOUN
brj-23498	315	7	maturity	maturity	NOUN
brj-23498	315	8	using	use	VERB
brj-23498	315	9	regional	regional	ADJ
brj-23498	315	10	brightness	brightness	NOUN
brj-23498	315	11	correction	correction	NOUN
brj-23498	315	12	,	,	PUNCT
brj-23498	315	13	”	"	PUNCT
brj-23498	315	14	transactions	transaction	NOUN
brj-23498	315	15	of	of	ADP
brj-23498	315	16	the	the	DET
brj-23498	315	17	chinese	chinese	ADJ
brj-23498	315	18	society	society	NOUN
brj-23498	315	19	of	of	ADP
brj-23498	315	20	agricultural	agricultural	ADJ
brj-23498	315	21	engineering	engineering	NOUN
brj-23498	315	22	39(07	39(07	NUM
brj-23498	315	23	)	)	PUNCT
brj-23498	315	24	,	,	PUNCT
brj-23498	315	25	195	195	NUM
brj-23498	315	26	-	-	SYM
brj-23498	315	27	204	204	NUM
brj-23498	315	28	.	.	PUNCT
brj-23498	316	1	doi	doi	NOUN
brj-23498	316	2	:	:	PUNCT
brj-23498	316	3	10.11975	10.11975	NUM
brj-23498	316	4	/	/	SYM
brj-23498	316	5	j.issn.10026819.202211192	j.issn.10026819.202211192	PROPN
brj-23498	316	6	zhou	zhou	PROPN
brj-23498	316	7	,	,	PUNCT
brj-23498	316	8	y.	y.	PROPN
brj-23498	316	9	,	,	PUNCT
brj-23498	316	10	zhao	zhao	PROPN
brj-23498	316	11	,	,	PUNCT
brj-23498	316	12	l.	l.	PROPN
brj-23498	316	13	,	,	PUNCT
brj-23498	316	14	yang	yang	PROPN
brj-23498	316	15	,	,	PUNCT
brj-23498	316	16	h.	h.	PROPN
brj-23498	316	17	,	,	PUNCT
brj-23498	316	18	zhang	zhang	PROPN
brj-23498	316	19	,	,	PUNCT
brj-23498	316	20	y.	y.	PROPN
brj-23498	316	21	,	,	PUNCT
brj-23498	316	22	li	li	PROPN
brj-23498	316	23	,	,	PUNCT
brj-23498	316	24	g.	g.	PROPN
brj-23498	316	25	w.	w.	PROPN
brj-23498	316	26	,	,	PUNCT
brj-23498	316	27	and	and	CCONJ
brj-23498	316	28	liu	liu	PROPN
brj-23498	316	29	,	,	PUNCT
brj-23498	316	30	d.	d.	PROPN
brj-23498	316	31	(	(	PUNCT
brj-23498	316	32	2024	2024	NUM
brj-23498	316	33	)	)	PUNCT
brj-23498	316	34	.	.	PUNCT
brj-23498	317	1	“	"	PUNCT
brj-23498	317	2	application	application	NOUN
brj-23498	317	3	of	of	ADP
brj-23498	317	4	multi	multi	ADJ
brj-23498	317	5	-	-	ADJ
brj-23498	317	6	information	information	ADJ
brj-23498	317	7	intelligent	intelligent	ADJ
brj-23498	317	8	sensor	sensor	NOUN
brj-23498	317	9	fusion	fusion	NOUN
brj-23498	317	10	technology	technology	NOUN
brj-23498	317	11	in	in	ADP
brj-23498	317	12	joint	joint	ADJ
brj-23498	317	13	operations	operation	NOUN
brj-23498	317	14	,	,	PUNCT
brj-23498	317	15	”	"	PUNCT
brj-23498	317	16	national	national	ADJ
brj-23498	317	17	defense	defense	PROPN
brj-23498	317	18	technology	technology	PROPN
brj-23498	317	19	45(01	45(01	PROPN
brj-23498	317	20	)	)	PUNCT
brj-23498	317	21	,	,	PUNCT
brj-23498	317	22	22	22	NUM
brj-23498	317	23	-	-	SYM
brj-23498	317	24	29	29	NUM
brj-23498	317	25	.	.	PUNCT
brj-23498	317	26	doi:10.13943	doi:10.13943	PROPN
brj-23498	317	27	/	/	SYM
brj-23498	317	28	j.	j.	PROPN
brj-23498	317	29	issn1671	issn1671	PROPN
brj-23498	317	30	-	-	PUNCT
brj-23498	317	31	4547.2024.01.04	4547.2024.01.04	NOUN
brj-23498	317	32	article	article	NOUN
brj-23498	317	33	submitted	submit	VERB
brj-23498	317	34	:	:	PUNCT
brj-23498	317	35	april	april	PROPN
brj-23498	317	36	4	4	NUM
brj-23498	317	37	,	,	PUNCT
brj-23498	317	38	2024	2024	NUM
brj-23498	317	39	;	;	PUNCT
brj-23498	317	40	peer	peer	NOUN
brj-23498	317	41	review	review	NOUN
brj-23498	317	42	completed	complete	VERB
brj-23498	317	43	:	:	PUNCT
brj-23498	317	44	april	april	PROPN
brj-23498	317	45	24	24	NUM
brj-23498	317	46	,	,	PUNCT
brj-23498	317	47	2024	2024	NUM
brj-23498	317	48	;	;	PUNCT
brj-23498	317	49	revised	revise	VERB
brj-23498	317	50	version	version	NOUN
brj-23498	317	51	received	receive	VERB
brj-23498	317	52	and	and	CCONJ
brj-23498	317	53	accepted	accept	VERB
brj-23498	317	54	:	:	PUNCT
brj-23498	317	55	may	may	AUX
brj-23498	317	56	7	7	NUM
brj-23498	317	57	,	,	PUNCT
brj-23498	317	58	2024	2024	NUM
brj-23498	317	59	;	;	PUNCT
brj-23498	317	60	published	publish	VERB
brj-23498	317	61	:	:	PUNCT
brj-23498	317	62	may	may	AUX
brj-23498	317	63	20	20	NUM
brj-23498	317	64	,	,	PUNCT
brj-23498	317	65	2024	2024	NUM
brj-23498	317	66	.	.	PUNCT
brj-23498	318	1	doi	doi	NOUN
brj-23498	318	2	:	:	PUNCT
brj-23498	318	3	10.15376	10.15376	NUM
brj-23498	318	4	/	/	SYM
brj-23498	318	5	biores.19.3.4531	biores.19.3.4531	PROPN
brj-23498	318	6	-	-	PUNCT
brj-23498	318	7	4546	4546	NUM