id	sid	tid	token	lemma	pos
brj-23026	1	1	peer	peer	NOUN
brj-23026	1	2	-	-	PUNCT
brj-23026	1	3	review	review	NOUN
brj-23026	1	4	article	article	NOUN
brj-23026	1	5	peer	peer	NOUN
brj-23026	1	6	-	-	PUNCT
brj-23026	1	7	reviewed	review	VERB
brj-23026	1	8	article	article	NOUN
brj-23026	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	1	10	liu	liu	PROPN
brj-23026	1	11	et	et	PROPN
brj-23026	1	12	al	al	PROPN
brj-23026	1	13	.	.	PROPN
brj-23026	2	1	(	(	PUNCT
brj-23026	2	2	2024	2024	NUM
brj-23026	2	3	)	)	PUNCT
brj-23026	2	4	.	.	PUNCT
brj-23026	3	1	“	"	PUNCT
brj-23026	3	2	nir	nir	ADJ
brj-23026	3	3	lignin	lignin	NOUN
brj-23026	3	4	model	model	NOUN
brj-23026	3	5	transfer	transfer	NOUN
brj-23026	3	6	coupling	coupling	NOUN
brj-23026	3	7	,	,	PUNCT
brj-23026	3	8	”	"	PUNCT
brj-23026	3	9	bioresources	bioresource	NOUN
brj-23026	3	10	19(1	19(1	NUM
brj-23026	3	11	)	)	PUNCT
brj-23026	3	12	,	,	PUNCT
brj-23026	3	13	245	245	NUM
brj-23026	3	14	-	-	SYM
brj-23026	3	15	256	256	NUM
brj-23026	3	16	.	.	NUM
brj-23026	3	17	245	245	NUM
brj-23026	3	18	near	near	ADV
brj-23026	3	19	-	-	PUNCT
brj-23026	3	20	infrared	infrared	ADJ
brj-23026	3	21	lignin	lignin	NOUN
brj-23026	3	22	model	model	NOUN
brj-23026	3	23	transfer	transfer	NOUN
brj-23026	3	24	:	:	PUNCT
brj-23026	3	25	a	a	DET
brj-23026	3	26	study	study	NOUN
brj-23026	3	27	based	base	VERB
brj-23026	3	28	on	on	ADP
brj-23026	3	29	swcss	swcss	PROPN
brj-23026	3	30	-	-	PUNCT
brj-23026	3	31	cars	car	NOUN
brj-23026	3	32	coupling	couple	VERB
brj-23026	3	33	algorithm	algorithm	NOUN
brj-23026	3	34	zhijian	zhijian	PROPN
brj-23026	3	35	liu	liu	PROPN
brj-23026	3	36	,	,	PUNCT
brj-23026	3	37	honghong	honghong	PROPN
brj-23026	3	38	wang	wang	PROPN
brj-23026	3	39	,	,	PUNCT
brj-23026	3	40	zhixin	zhixin	PROPN
brj-23026	3	41	xiong	xiong	PROPN
brj-23026	3	42	,	,	PUNCT
brj-23026	3	43	*	*	PUNCT
brj-23026	3	44	yunchao	yunchao	PROPN
brj-23026	3	45	hu	hu	PROPN
brj-23026	3	46	,	,	PUNCT
brj-23026	3	47	haoran	haoran	PROPN
brj-23026	3	48	huang	huang	PROPN
brj-23026	3	49	,	,	PUNCT
brj-23026	3	50	ying	ying	PROPN
brj-23026	3	51	wang	wang	PROPN
brj-23026	3	52	,	,	PUNCT
brj-23026	3	53	xianzhi	xianzhi	PROPN
brj-23026	3	54	wu	wu	PROPN
brj-23026	3	55	,	,	PUNCT
brj-23026	3	56	and	and	CCONJ
brj-23026	3	57	long	long	ADJ
brj-23026	3	58	liang	liang	PROPN
brj-23026	3	59	in	in	ADP
brj-23026	3	60	nir	nir	ADJ
brj-23026	3	61	spectral	spectral	ADJ
brj-23026	3	62	modeling	modeling	NOUN
brj-23026	3	63	,	,	PUNCT
brj-23026	3	64	the	the	DET
brj-23026	3	65	method	method	NOUN
brj-23026	3	66	of	of	ADP
brj-23026	3	67	screening	screen	VERB
brj-23026	3	68	wavelengths	wavelength	NOUN
brj-23026	3	69	with	with	ADP
brj-23026	3	70	consistent	consistent	ADJ
brj-23026	3	71	stable	stable	ADJ
brj-23026	3	72	signals	signal	NOUN
brj-23026	3	73	(	(	PUNCT
brj-23026	3	74	swcss	swcss	PROPN
brj-23026	3	75	)	)	PUNCT
brj-23026	3	76	is	be	AUX
brj-23026	3	77	based	base	VERB
brj-23026	3	78	on	on	ADP
brj-23026	3	79	a	a	DET
brj-23026	3	80	standard	standard	ADJ
brj-23026	3	81	-	-	PUNCT
brj-23026	3	82	free	free	ADJ
brj-23026	3	83	algorithm	algorithm	NOUN
brj-23026	3	84	.	.	PUNCT
brj-23026	4	1	however	however	ADV
brj-23026	4	2	,	,	PUNCT
brj-23026	4	3	the	the	DET
brj-23026	4	4	wavelengths	wavelength	NOUN
brj-23026	4	5	selected	select	VERB
brj-23026	4	6	by	by	ADP
brj-23026	4	7	swcss	swcss	PROPN
brj-23026	4	8	may	may	AUX
brj-23026	4	9	contain	contain	VERB
brj-23026	4	10	invalid	invalid	ADJ
brj-23026	4	11	information	information	NOUN
brj-23026	4	12	.	.	PUNCT
brj-23026	5	1	in	in	ADP
brj-23026	5	2	this	this	DET
brj-23026	5	3	paper	paper	NOUN
brj-23026	5	4	,	,	PUNCT
brj-23026	5	5	the	the	DET
brj-23026	5	6	competitive	competitive	ADJ
brj-23026	5	7	adaptive	adaptive	ADJ
brj-23026	5	8	reweighted	reweighte	VERB
brj-23026	5	9	sampling	sample	VERB
brj-23026	5	10	(	(	PUNCT
brj-23026	5	11	cars	car	NOUN
brj-23026	5	12	)	)	PUNCT
brj-23026	5	13	wavelength	wavelength	NOUN
brj-23026	5	14	optimization	optimization	NOUN
brj-23026	5	15	algorithm	algorithm	NOUN
brj-23026	5	16	was	be	AUX
brj-23026	5	17	used	use	VERB
brj-23026	5	18	in	in	ADP
brj-23026	5	19	conjunction	conjunction	NOUN
brj-23026	5	20	with	with	ADP
brj-23026	5	21	swcss	swcss	PROPN
brj-23026	5	22	to	to	PART
brj-23026	5	23	eliminate	eliminate	VERB
brj-23026	5	24	the	the	DET
brj-23026	5	25	uninformative	uninformative	ADJ
brj-23026	5	26	variables	variable	NOUN
brj-23026	5	27	in	in	ADP
brj-23026	5	28	the	the	DET
brj-23026	5	29	wavelengths	wavelength	NOUN
brj-23026	5	30	selected	select	VERB
brj-23026	5	31	by	by	ADP
brj-23026	5	32	swcss	swcss	PROPN
brj-23026	5	33	.	.	PUNCT
brj-23026	6	1	the	the	DET
brj-23026	6	2	swcss	swcss	PROPN
brj-23026	6	3	-	-	PUNCT
brj-23026	6	4	cars	car	NOUN
brj-23026	6	5	method	method	NOUN
brj-23026	6	6	was	be	AUX
brj-23026	6	7	based	base	VERB
brj-23026	6	8	on	on	ADP
brj-23026	6	9	three	three	NUM
brj-23026	6	10	near	near	ADV
brj-23026	6	11	-	-	PUNCT
brj-23026	6	12	infrared	infrared	ADJ
brj-23026	6	13	spectrometers	spectrometer	NOUN
brj-23026	6	14	(	(	PUNCT
brj-23026	6	15	lengguang	lengguang	PROPN
brj-23026	6	16	1	1	NUM
brj-23026	6	17	,	,	PUNCT
brj-23026	6	18	lengguang	lengguang	NOUN
brj-23026	6	19	2	2	NUM
brj-23026	6	20	,	,	PUNCT
brj-23026	6	21	and	and	CCONJ
brj-23026	6	22	lengguang	lengguang	PROPN
brj-23026	6	23	3	3	NUM
brj-23026	6	24	)	)	PUNCT
brj-23026	6	25	,	,	PUNCT
brj-23026	6	26	with	with	ADP
brj-23026	6	27	lengguang	lengguang	PROPN
brj-23026	6	28	1	1	NUM
brj-23026	6	29	as	as	ADP
brj-23026	6	30	the	the	DET
brj-23026	6	31	master	master	NOUN
brj-23026	6	32	and	and	CCONJ
brj-23026	6	33	the	the	DET
brj-23026	6	34	other	other	ADJ
brj-23026	6	35	two	two	NUM
brj-23026	6	36	instruments	instrument	NOUN
brj-23026	6	37	as	as	ADP
brj-23026	6	38	the	the	DET
brj-23026	6	39	targets	target	NOUN
brj-23026	6	40	,	,	PUNCT
brj-23026	6	41	using	use	VERB
brj-23026	6	42	a	a	DET
brj-23026	6	43	total	total	NOUN
brj-23026	6	44	of	of	ADP
brj-23026	6	45	84	84	NUM
brj-23026	6	46	sample	sample	NOUN
brj-23026	6	47	spectra	spectra	NOUN
brj-23026	6	48	of	of	ADP
brj-23026	6	49	five	five	NUM
brj-23026	6	50	types	type	NOUN
brj-23026	6	51	of	of	ADP
brj-23026	6	52	pulpwood	pulpwood	NOUN
brj-23026	6	53	and	and	CCONJ
brj-23026	6	54	their	their	PRON
brj-23026	6	55	lignin	lignin	NOUN
brj-23026	6	56	contents	content	NOUN
brj-23026	6	57	as	as	ADP
brj-23026	6	58	the	the	DET
brj-23026	6	59	research	research	NOUN
brj-23026	6	60	objects	object	VERB
brj-23026	6	61	.	.	PUNCT
brj-23026	7	1	compared	compare	VERB
brj-23026	7	2	with	with	ADP
brj-23026	7	3	the	the	DET
brj-23026	7	4	full	full	ADJ
brj-23026	7	5	spectrum	spectrum	NOUN
brj-23026	7	6	,	,	PUNCT
brj-23026	7	7	the	the	DET
brj-23026	7	8	number	number	NOUN
brj-23026	7	9	of	of	ADP
brj-23026	7	10	wavelengths	wavelength	NOUN
brj-23026	7	11	was	be	AUX
brj-23026	7	12	reduced	reduce	VERB
brj-23026	7	13	from	from	ADP
brj-23026	7	14	1601	1601	NUM
brj-23026	7	15	to	to	ADP
brj-23026	7	16	24	24	NUM
brj-23026	7	17	in	in	ADP
brj-23026	7	18	the	the	DET
brj-23026	7	19	model	model	NOUN
brj-23026	7	20	built	build	VERB
brj-23026	7	21	using	use	VERB
brj-23026	7	22	the	the	DET
brj-23026	7	23	coupling	coupling	NOUN
brj-23026	7	24	algorithm	algorithm	NOUN
brj-23026	7	25	.	.	PUNCT
brj-23026	8	1	for	for	ADP
brj-23026	8	2	target	target	NOUN
brj-23026	8	3	1	1	NUM
brj-23026	8	4	,	,	PUNCT
brj-23026	8	5	the	the	DET
brj-23026	8	6	value	value	NOUN
brj-23026	8	7	of	of	ADP
brj-23026	8	8	rpd	rpd	PROPN
brj-23026	8	9	was	be	AUX
brj-23026	8	10	improved	improve	VERB
brj-23026	8	11	from	from	ADP
brj-23026	8	12	1.9247	1.9247	NUM
brj-23026	8	13	to	to	ADP
brj-23026	8	14	3.1880	3.1880	NUM
brj-23026	8	15	;	;	PUNCT
brj-23026	8	16	for	for	ADP
brj-23026	8	17	target	target	NOUN
brj-23026	8	18	2	2	NUM
brj-23026	8	19	,	,	PUNCT
brj-23026	8	20	t	t	VERB
brj-23026	8	21	the	the	DET
brj-23026	8	22	value	value	NOUN
brj-23026	8	23	of	of	ADP
brj-23026	8	24	rpd	rpd	PROPN
brj-23026	8	25	was	be	AUX
brj-23026	8	26	improved	improve	VERB
brj-23026	8	27	from	from	ADP
brj-23026	8	28	1.7415	1.7415	NUM
brj-23026	8	29	to	to	ADP
brj-23026	8	30	3.2508	3.2508	NUM
brj-23026	8	31	.	.	PUNCT
brj-23026	9	1	the	the	DET
brj-23026	9	2	wavelengths	wavelength	NOUN
brj-23026	9	3	selected	select	VERB
brj-23026	9	4	by	by	ADP
brj-23026	9	5	the	the	DET
brj-23026	9	6	swcss	swcss	PROPN
brj-23026	9	7	-	-	PUNCT
brj-23026	9	8	cars	car	NOUN
brj-23026	9	9	coupling	couple	VERB
brj-23026	9	10	algorithm	algorithm	NOUN
brj-23026	9	11	were	be	AUX
brj-23026	9	12	able	able	ADJ
brj-23026	9	13	to	to	PART
brj-23026	9	14	build	build	VERB
brj-23026	9	15	stable	stable	ADJ
brj-23026	9	16	,	,	PUNCT
brj-23026	9	17	robust	robust	ADJ
brj-23026	9	18	models	model	NOUN
brj-23026	9	19	.	.	PUNCT
brj-23026	10	1	doi	doi	NOUN
brj-23026	10	2	:	:	PUNCT
brj-23026	10	3	10.15376	10.15376	NUM
brj-23026	10	4	/	/	SYM
brj-23026	10	5	biores.19.1.245	biores.19.1.245	NOUN
brj-23026	10	6	-	-	SYM
brj-23026	10	7	256	256	NUM
brj-23026	10	8	keywords	keyword	NOUN
brj-23026	10	9	:	:	PUNCT
brj-23026	10	10	near	near	ADV
brj-23026	10	11	-	-	PUNCT
brj-23026	10	12	infrared	infrared	ADJ
brj-23026	10	13	;	;	PUNCT
brj-23026	10	14	mode	mode	NOUN
brj-23026	10	15	-	-	PUNCT
brj-23026	10	16	transfer	transfer	NOUN
brj-23026	10	17	;	;	PUNCT
brj-23026	10	18	lignin	lignin	NOUN
brj-23026	10	19	;	;	PUNCT
brj-23026	10	20	swcss	swcss	PROPN
brj-23026	10	21	-	-	PUNCT
brj-23026	10	22	cars	car	NOUN
brj-23026	10	23	contact	contact	NOUN
brj-23026	10	24	information	information	NOUN
brj-23026	10	25	:	:	PUNCT
brj-23026	10	26	college	college	NOUN
brj-23026	10	27	of	of	ADP
brj-23026	10	28	light	light	ADJ
brj-23026	10	29	industry	industry	NOUN
brj-23026	10	30	and	and	CCONJ
brj-23026	10	31	food	food	NOUN
brj-23026	10	32	engineering	engineering	NOUN
brj-23026	10	33	,	,	PUNCT
brj-23026	10	34	nanjing	nanjing	PROPN
brj-23026	10	35	forestry	forestry	PROPN
brj-23026	10	36	university	university	PROPN
brj-23026	10	37	,	,	PUNCT
brj-23026	10	38	longpan	longpan	ADJ
brj-23026	10	39	road	road	NOUN
brj-23026	10	40	159	159	NUM
brj-23026	10	41	,	,	PUNCT
brj-23026	10	42	nanjing	nanjing	PROPN
brj-23026	10	43	210037	210037	NUM
brj-23026	10	44	china	china	PROPN
brj-23026	10	45	;	;	PUNCT
brj-23026	10	46	*	*	PUNCT
brj-23026	10	47	corresponding	correspond	VERB
brj-23026	10	48	author	author	NOUN
brj-23026	10	49	:	:	PUNCT
brj-23026	10	50	leo_xzx@njfu.edu.cn	leo_xzx@njfu.edu.cn	NOUN
brj-23026	10	51	introduction	introduction	NOUN
brj-23026	10	52	wood	wood	NOUN
brj-23026	10	53	used	use	VERB
brj-23026	10	54	for	for	ADP
brj-23026	10	55	pulp	pulp	NOUN
brj-23026	10	56	should	should	AUX
brj-23026	10	57	have	have	VERB
brj-23026	10	58	a	a	DET
brj-23026	10	59	high	high	ADJ
brj-23026	10	60	cellulose	cellulose	NOUN
brj-23026	10	61	and	and	CCONJ
brj-23026	10	62	low	low	ADJ
brj-23026	10	63	lignin	lignin	NOUN
brj-23026	10	64	content	content	NOUN
brj-23026	10	65	(	(	PUNCT
brj-23026	10	66	liang	liang	PROPN
brj-23026	10	67	et	et	PROPN
brj-23026	10	68	al	al	PROPN
brj-23026	10	69	.	.	PROPN
brj-23026	10	70	2020	2020	NUM
brj-23026	10	71	)	)	PUNCT
brj-23026	10	72	.	.	PUNCT
brj-23026	11	1	the	the	DET
brj-23026	11	2	lignin	lignin	PROPN
brj-23026	11	3	content	content	NOUN
brj-23026	11	4	determines	determine	VERB
brj-23026	11	5	the	the	DET
brj-23026	11	6	amount	amount	NOUN
brj-23026	11	7	of	of	ADP
brj-23026	11	8	bleach	bleach	NOUN
brj-23026	11	9	,	,	PUNCT
brj-23026	11	10	so	so	CCONJ
brj-23026	11	11	the	the	DET
brj-23026	11	12	rapid	rapid	ADJ
brj-23026	11	13	detection	detection	NOUN
brj-23026	11	14	of	of	ADP
brj-23026	11	15	lignin	lignin	NOUN
brj-23026	11	16	in	in	ADP
brj-23026	11	17	the	the	DET
brj-23026	11	18	control	control	NOUN
brj-23026	11	19	of	of	ADP
brj-23026	11	20	the	the	DET
brj-23026	11	21	pulp	pulp	NOUN
brj-23026	11	22	production	production	NOUN
brj-23026	11	23	process	process	NOUN
brj-23026	11	24	is	be	AUX
brj-23026	11	25	important	important	ADJ
brj-23026	11	26	.	.	PUNCT
brj-23026	12	1	near	near	ADP
brj-23026	12	2	-	-	PUNCT
brj-23026	12	3	infrared	infrared	ADJ
brj-23026	12	4	spectroscopy	spectroscopy	NOUN
brj-23026	12	5	(	(	PUNCT
brj-23026	12	6	nir	nir	NOUN
brj-23026	12	7	)	)	PUNCT
brj-23026	12	8	analysis	analysis	NOUN
brj-23026	12	9	technology	technology	NOUN
brj-23026	12	10	has	have	VERB
brj-23026	12	11	the	the	DET
brj-23026	12	12	advantages	advantage	NOUN
brj-23026	12	13	of	of	ADP
brj-23026	12	14	being	be	AUX
brj-23026	12	15	fast	fast	ADJ
brj-23026	12	16	,	,	PUNCT
brj-23026	12	17	nondestructive	nondestructive	ADJ
brj-23026	12	18	,	,	PUNCT
brj-23026	12	19	and	and	CCONJ
brj-23026	12	20	green	green	ADJ
brj-23026	12	21	,	,	PUNCT
brj-23026	12	22	and	and	CCONJ
brj-23026	12	23	the	the	DET
brj-23026	12	24	method	method	NOUN
brj-23026	12	25	has	have	AUX
brj-23026	12	26	been	be	AUX
brj-23026	12	27	widely	widely	ADV
brj-23026	12	28	used	use	VERB
brj-23026	12	29	in	in	ADP
brj-23026	12	30	the	the	DET
brj-23026	12	31	fields	field	NOUN
brj-23026	12	32	of	of	ADP
brj-23026	12	33	food	food	NOUN
brj-23026	12	34	(	(	PUNCT
brj-23026	12	35	castro	castro	PROPN
brj-23026	12	36	et	et	PROPN
brj-23026	12	37	al	al	PROPN
brj-23026	12	38	.	.	PROPN
brj-23026	12	39	2023	2023	NUM
brj-23026	12	40	)	)	PUNCT
brj-23026	12	41	,	,	PUNCT
brj-23026	12	42	medicine	medicine	NOUN
brj-23026	12	43	(	(	PUNCT
brj-23026	12	44	yin	yin	PROPN
brj-23026	12	45	et	et	PROPN
brj-23026	12	46	al	al	PROPN
brj-23026	12	47	.	.	PROPN
brj-23026	12	48	2019	2019	NUM
brj-23026	12	49	)	)	PUNCT
brj-23026	12	50	and	and	CCONJ
brj-23026	12	51	agriculture	agriculture	NOUN
brj-23026	12	52	(	(	PUNCT
brj-23026	12	53	cortés	cortés	NOUN
brj-23026	12	54	et	et	PROPN
brj-23026	12	55	al	al	PROPN
brj-23026	12	56	.	.	PROPN
brj-23026	12	57	2019	2019	NUM
brj-23026	12	58	)	)	PUNCT
brj-23026	12	59	.	.	PUNCT
brj-23026	13	1	in	in	ADP
brj-23026	13	2	most	most	ADJ
brj-23026	13	3	cases	case	NOUN
brj-23026	13	4	,	,	PUNCT
brj-23026	13	5	it	it	PRON
brj-23026	13	6	is	be	AUX
brj-23026	13	7	timeconsuming	timeconsuming	ADJ
brj-23026	13	8	and	and	CCONJ
brj-23026	13	9	expensive	expensive	ADJ
brj-23026	13	10	to	to	PART
brj-23026	13	11	build	build	VERB
brj-23026	13	12	a	a	DET
brj-23026	13	13	good	good	ADJ
brj-23026	13	14	multivariate	multivariate	NOUN
brj-23026	13	15	calibration	calibration	NOUN
brj-23026	13	16	model	model	NOUN
brj-23026	13	17	,	,	PUNCT
brj-23026	13	18	so	so	SCONJ
brj-23026	13	19	it	it	PRON
brj-23026	13	20	is	be	AUX
brj-23026	13	21	desirable	desirable	ADJ
brj-23026	13	22	for	for	SCONJ
brj-23026	13	23	a	a	DET
brj-23026	13	24	model	model	NOUN
brj-23026	13	25	to	to	PART
brj-23026	13	26	be	be	AUX
brj-23026	13	27	stable	stable	ADJ
brj-23026	13	28	and	and	CCONJ
brj-23026	13	29	valid	valid	ADJ
brj-23026	13	30	for	for	ADP
brj-23026	13	31	a	a	DET
brj-23026	13	32	long	long	ADJ
brj-23026	13	33	time	time	NOUN
brj-23026	13	34	.	.	PUNCT
brj-23026	14	1	however	however	ADV
brj-23026	14	2	,	,	PUNCT
brj-23026	14	3	changes	change	NOUN
brj-23026	14	4	in	in	ADP
brj-23026	14	5	measurement	measurement	NOUN
brj-23026	14	6	conditions	condition	NOUN
brj-23026	14	7	,	,	PUNCT
brj-23026	14	8	aging	aging	NOUN
brj-23026	14	9	or	or	CCONJ
brj-23026	14	10	replacement	replacement	NOUN
brj-23026	14	11	of	of	ADP
brj-23026	14	12	instrument	instrument	NOUN
brj-23026	14	13	components	component	NOUN
brj-23026	14	14	,	,	PUNCT
brj-23026	14	15	and	and	CCONJ
brj-23026	14	16	changes	change	NOUN
brj-23026	14	17	from	from	ADP
brj-23026	14	18	external	external	ADJ
brj-23026	14	19	environments	environment	NOUN
brj-23026	14	20	and	and	CCONJ
brj-23026	14	21	samples	sample	NOUN
brj-23026	14	22	may	may	AUX
brj-23026	14	23	affect	affect	VERB
brj-23026	14	24	the	the	DET
brj-23026	14	25	accuracy	accuracy	NOUN
brj-23026	14	26	and	and	CCONJ
brj-23026	14	27	applicability	applicability	NOUN
brj-23026	14	28	of	of	ADP
brj-23026	14	29	the	the	DET
brj-23026	14	30	calibration	calibration	NOUN
brj-23026	14	31	model	model	NOUN
brj-23026	14	32	.	.	PUNCT
brj-23026	15	1	model	model	NOUN
brj-23026	15	2	transfer	transfer	NOUN
brj-23026	15	3	,	,	PUNCT
brj-23026	15	4	on	on	ADP
brj-23026	15	5	the	the	DET
brj-23026	15	6	other	other	ADJ
brj-23026	15	7	hand	hand	NOUN
brj-23026	15	8	,	,	PUNCT
brj-23026	15	9	can	can	AUX
brj-23026	15	10	adapt	adapt	VERB
brj-23026	15	11	the	the	DET
brj-23026	15	12	calibrated	calibrate	VERB
brj-23026	15	13	model	model	NOUN
brj-23026	15	14	to	to	ADP
brj-23026	15	15	a	a	DET
brj-23026	15	16	new	new	ADJ
brj-23026	15	17	instrument	instrument	NOUN
brj-23026	15	18	(	(	PUNCT
brj-23026	15	19	target	target	NOUN
brj-23026	15	20	)	)	PUNCT
brj-23026	15	21	or	or	CCONJ
brj-23026	15	22	testing	testing	NOUN
brj-23026	15	23	conditions	condition	NOUN
brj-23026	15	24	through	through	ADP
brj-23026	15	25	various	various	ADJ
brj-23026	15	26	chemometrics	chemometric	NOUN
brj-23026	15	27	methods	method	NOUN
brj-23026	15	28	.	.	PUNCT
brj-23026	16	1	although	although	SCONJ
brj-23026	16	2	the	the	DET
brj-23026	16	3	transferred	transfer	VERB
brj-23026	16	4	model	model	NOUN
brj-23026	16	5	is	be	AUX
brj-23026	16	6	usually	usually	ADV
brj-23026	16	7	not	not	PART
brj-23026	16	8	as	as	ADV
brj-23026	16	9	accurate	accurate	ADJ
brj-23026	16	10	as	as	ADP
brj-23026	16	11	the	the	DET
brj-23026	16	12	original	original	ADJ
brj-23026	16	13	model	model	NOUN
brj-23026	16	14	applied	apply	VERB
brj-23026	16	15	in	in	ADP
brj-23026	16	16	the	the	DET
brj-23026	16	17	master	master	NOUN
brj-23026	16	18	machine	machine	NOUN
brj-23026	16	19	,	,	PUNCT
brj-23026	16	20	its	its	PRON
brj-23026	16	21	accuracy	accuracy	NOUN
brj-23026	16	22	usually	usually	ADV
brj-23026	16	23	still	still	ADV
brj-23026	16	24	meets	meet	VERB
brj-23026	16	25	the	the	DET
brj-23026	16	26	requirements	requirement	NOUN
brj-23026	16	27	of	of	ADP
brj-23026	16	28	the	the	DET
brj-23026	16	29	application	application	NOUN
brj-23026	16	30	and	and	CCONJ
brj-23026	16	31	saves	save	VERB
brj-23026	16	32	a	a	DET
brj-23026	16	33	lot	lot	NOUN
brj-23026	16	34	of	of	ADP
brj-23026	16	35	time	time	NOUN
brj-23026	16	36	and	and	CCONJ
brj-23026	16	37	cost	cost	NOUN
brj-23026	16	38	required	require	VERB
brj-23026	16	39	for	for	ADP
brj-23026	16	40	modifying	modify	VERB
brj-23026	16	41	the	the	DET
brj-23026	16	42	instrument	instrument	NOUN
brj-23026	16	43	or	or	CCONJ
brj-23026	16	44	rebuilding	rebuild	VERB
brj-23026	16	45	the	the	DET
brj-23026	16	46	model	model	NOUN
brj-23026	16	47	(	(	PUNCT
brj-23026	16	48	dardenne	dardenne	NOUN
brj-23026	16	49	2002	2002	NUM
brj-23026	16	50	)	)	PUNCT
brj-23026	16	51	.	.	PUNCT
brj-23026	17	1	peer	peer	NOUN
brj-23026	17	2	-	-	PUNCT
brj-23026	17	3	reviewed	review	VERB
brj-23026	17	4	article	article	NOUN
brj-23026	17	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	17	6	liu	liu	PROPN
brj-23026	17	7	et	et	PROPN
brj-23026	17	8	al	al	PROPN
brj-23026	17	9	.	.	PROPN
brj-23026	18	1	(	(	PUNCT
brj-23026	18	2	2024	2024	NUM
brj-23026	18	3	)	)	PUNCT
brj-23026	18	4	.	.	PUNCT
brj-23026	19	1	“	"	PUNCT
brj-23026	19	2	nir	nir	ADJ
brj-23026	19	3	lignin	lignin	NOUN
brj-23026	19	4	model	model	NOUN
brj-23026	19	5	transfer	transfer	NOUN
brj-23026	19	6	coupling	coupling	NOUN
brj-23026	19	7	,	,	PUNCT
brj-23026	19	8	”	"	PUNCT
brj-23026	19	9	bioresources	bioresource	NOUN
brj-23026	19	10	19(1	19(1	NUM
brj-23026	19	11	)	)	PUNCT
brj-23026	19	12	,	,	PUNCT
brj-23026	19	13	245	245	NUM
brj-23026	19	14	-	-	SYM
brj-23026	19	15	256	256	NUM
brj-23026	19	16	.	.	PUNCT
brj-23026	20	1	246	246	NUM
brj-23026	20	2	currently	currently	ADV
brj-23026	20	3	,	,	PUNCT
brj-23026	20	4	model	model	NOUN
brj-23026	20	5	transfer	transfer	NOUN
brj-23026	20	6	research	research	NOUN
brj-23026	20	7	is	be	AUX
brj-23026	20	8	mostly	mostly	ADV
brj-23026	20	9	focused	focus	VERB
brj-23026	20	10	on	on	ADP
brj-23026	20	11	improving	improve	VERB
brj-23026	20	12	and	and	CCONJ
brj-23026	20	13	developing	develop	VERB
brj-23026	20	14	new	new	ADJ
brj-23026	20	15	algorithms	algorithm	NOUN
brj-23026	20	16	to	to	PART
brj-23026	20	17	realize	realize	VERB
brj-23026	20	18	model	model	NOUN
brj-23026	20	19	transfer	transfer	NOUN
brj-23026	20	20	sharing	sharing	NOUN
brj-23026	20	21	between	between	ADP
brj-23026	20	22	different	different	ADJ
brj-23026	20	23	models	model	NOUN
brj-23026	20	24	of	of	ADP
brj-23026	20	25	nir	nir	ADJ
brj-23026	20	26	spectrometers	spectrometer	NOUN
brj-23026	20	27	,	,	PUNCT
brj-23026	20	28	which	which	PRON
brj-23026	20	29	often	often	ADV
brj-23026	20	30	requires	require	VERB
brj-23026	20	31	a	a	DET
brj-23026	20	32	large	large	ADJ
brj-23026	20	33	number	number	NOUN
brj-23026	20	34	of	of	ADP
brj-23026	20	35	samples	sample	NOUN
brj-23026	20	36	to	to	PART
brj-23026	20	37	ensure	ensure	VERB
brj-23026	20	38	the	the	DET
brj-23026	20	39	reliability	reliability	NOUN
brj-23026	20	40	of	of	ADP
brj-23026	20	41	the	the	DET
brj-23026	20	42	model	model	NOUN
brj-23026	20	43	transfer	transfer	NOUN
brj-23026	20	44	,	,	PUNCT
brj-23026	20	45	and	and	CCONJ
brj-23026	20	46	this	this	DET
brj-23026	20	47	method	method	NOUN
brj-23026	20	48	is	be	AUX
brj-23026	20	49	called	call	VERB
brj-23026	20	50	scaled	scaled	ADJ
brj-23026	20	51	sample	sample	NOUN
brj-23026	20	52	model	model	NOUN
brj-23026	20	53	transfer	transfer	NOUN
brj-23026	20	54	.	.	PUNCT
brj-23026	21	1	in	in	ADP
brj-23026	21	2	order	order	NOUN
brj-23026	21	3	to	to	PART
brj-23026	21	4	solve	solve	VERB
brj-23026	21	5	the	the	DET
brj-23026	21	6	problem	problem	NOUN
brj-23026	21	7	of	of	ADP
brj-23026	21	8	requiring	require	VERB
brj-23026	21	9	a	a	DET
brj-23026	21	10	large	large	ADJ
brj-23026	21	11	number	number	NOUN
brj-23026	21	12	of	of	ADP
brj-23026	21	13	samples	sample	NOUN
brj-23026	21	14	for	for	ADP
brj-23026	21	15	the	the	DET
brj-23026	21	16	traditional	traditional	ADJ
brj-23026	21	17	model	model	NOUN
brj-23026	21	18	transfer	transfer	NOUN
brj-23026	21	19	method	method	NOUN
brj-23026	21	20	with	with	ADP
brj-23026	21	21	labeled	label	VERB
brj-23026	21	22	samples	sample	NOUN
brj-23026	21	23	,	,	PUNCT
brj-23026	21	24	model	model	NOUN
brj-23026	21	25	transfer	transfer	NOUN
brj-23026	21	26	using	use	VERB
brj-23026	21	27	the	the	DET
brj-23026	21	28	swcss	swcss	PROPN
brj-23026	21	29	method	method	NOUN
brj-23026	21	30	is	be	AUX
brj-23026	21	31	a	a	DET
brj-23026	21	32	new	new	ADJ
brj-23026	21	33	strategy	strategy	NOUN
brj-23026	21	34	worthy	worthy	ADJ
brj-23026	21	35	of	of	ADP
brj-23026	21	36	study	study	NOUN
brj-23026	21	37	(	(	PUNCT
brj-23026	21	38	ni	ni	PROPN
brj-23026	21	39	et	et	PROPN
brj-23026	21	40	al	al	PROPN
brj-23026	21	41	.	.	PROPN
brj-23026	21	42	2018	2018	NUM
brj-23026	21	43	;	;	PUNCT
brj-23026	21	44	zhang	zhang	PROPN
brj-23026	21	45	et	et	PROPN
brj-23026	21	46	al	al	PROPN
brj-23026	21	47	.	.	PROPN
brj-23026	21	48	2020	2020	NUM
brj-23026	21	49	;	;	PUNCT
brj-23026	21	50	wang	wang	PROPN
brj-23026	21	51	et	et	PROPN
brj-23026	21	52	al	al	PROPN
brj-23026	21	53	.	.	PROPN
brj-23026	21	54	2022	2022	NUM
brj-23026	21	55	)	)	PUNCT
brj-23026	21	56	,	,	PUNCT
brj-23026	21	57	whose	whose	DET
brj-23026	21	58	transfer	transfer	NOUN
brj-23026	21	59	process	process	NOUN
brj-23026	21	60	is	be	AUX
brj-23026	21	61	relatively	relatively	ADV
brj-23026	21	62	simple	simple	ADJ
brj-23026	21	63	and	and	CCONJ
brj-23026	21	64	only	only	ADV
brj-23026	21	65	requires	require	VERB
brj-23026	21	66	the	the	DET
brj-23026	21	67	selection	selection	NOUN
brj-23026	21	68	of	of	ADP
brj-23026	21	69	very	very	ADV
brj-23026	21	70	few	few	ADJ
brj-23026	21	71	representative	representative	ADJ
brj-23026	21	72	samples	sample	NOUN
brj-23026	21	73	for	for	ADP
brj-23026	21	74	screening	screen	VERB
brj-23026	21	75	the	the	DET
brj-23026	21	76	consistent	consistent	ADJ
brj-23026	21	77	wavelengths	wavelength	NOUN
brj-23026	21	78	,	,	PUNCT
brj-23026	21	79	which	which	PRON
brj-23026	21	80	makes	make	VERB
brj-23026	21	81	the	the	DET
brj-23026	21	82	model	model	NOUN
brj-23026	21	83	transfer	transfer	NOUN
brj-23026	21	84	easier	easy	ADJ
brj-23026	21	85	to	to	PART
brj-23026	21	86	realize	realize	VERB
brj-23026	21	87	.	.	PUNCT
brj-23026	22	1	however	however	ADV
brj-23026	22	2	,	,	PUNCT
brj-23026	22	3	the	the	DET
brj-23026	22	4	wavelengths	wavelength	NOUN
brj-23026	22	5	selected	select	VERB
brj-23026	22	6	by	by	ADP
brj-23026	22	7	the	the	DET
brj-23026	22	8	current	current	ADJ
brj-23026	22	9	swcss	swcss	PROPN
brj-23026	22	10	method	method	NOUN
brj-23026	22	11	for	for	ADP
brj-23026	22	12	consistency	consistency	NOUN
brj-23026	22	13	between	between	ADP
brj-23026	22	14	different	different	ADJ
brj-23026	22	15	spectroscopic	spectroscopic	NOUN
brj-23026	22	16	instruments	instrument	NOUN
brj-23026	22	17	may	may	AUX
brj-23026	22	18	contain	contain	VERB
brj-23026	22	19	no	no	DET
brj-23026	22	20	information	information	NOUN
brj-23026	22	21	as	as	ADV
brj-23026	22	22	well	well	ADV
brj-23026	22	23	as	as	ADP
brj-23026	22	24	wavelengths	wavelength	NOUN
brj-23026	22	25	with	with	ADP
brj-23026	22	26	very	very	ADV
brj-23026	22	27	little	little	ADJ
brj-23026	22	28	information	information	NOUN
brj-23026	22	29	,	,	PUNCT
brj-23026	22	30	which	which	PRON
brj-23026	22	31	leads	lead	VERB
brj-23026	22	32	to	to	ADP
brj-23026	22	33	poor	poor	ADJ
brj-23026	22	34	swcss	swcss	PROPN
brj-23026	22	35	-	-	PUNCT
brj-23026	22	36	plsr	plsr	NOUN
brj-23026	22	37	model	model	NOUN
brj-23026	22	38	transfer	transfer	NOUN
brj-23026	22	39	performance	performance	NOUN
brj-23026	22	40	.	.	PUNCT
brj-23026	23	1	if	if	SCONJ
brj-23026	23	2	the	the	DET
brj-23026	23	3	invalid	invalid	ADJ
brj-23026	23	4	wavelength	wavelength	NOUN
brj-23026	23	5	variables	variable	NOUN
brj-23026	23	6	in	in	ADP
brj-23026	23	7	the	the	DET
brj-23026	23	8	swcss	swcss	PROPN
brj-23026	23	9	results	result	NOUN
brj-23026	23	10	can	can	AUX
brj-23026	23	11	be	be	AUX
brj-23026	23	12	eliminated	eliminate	VERB
brj-23026	23	13	with	with	ADP
brj-23026	23	14	other	other	ADJ
brj-23026	23	15	preferred	preferred	ADJ
brj-23026	23	16	algorithms	algorithm	NOUN
brj-23026	23	17	,	,	PUNCT
brj-23026	23	18	and	and	CCONJ
brj-23026	23	19	then	then	ADV
brj-23026	23	20	the	the	DET
brj-23026	23	21	plsr	plsr	PROPN
brj-23026	23	22	model	model	NOUN
brj-23026	23	23	constructed	construct	VERB
brj-23026	23	24	with	with	ADP
brj-23026	23	25	the	the	DET
brj-23026	23	26	remaining	remain	VERB
brj-23026	23	27	wavelengths	wavelength	NOUN
brj-23026	23	28	can	can	AUX
brj-23026	23	29	be	be	AUX
brj-23026	23	30	transferred	transfer	VERB
brj-23026	23	31	,	,	PUNCT
brj-23026	23	32	it	it	PRON
brj-23026	23	33	will	will	AUX
brj-23026	23	34	have	have	VERB
brj-23026	23	35	stronger	strong	ADJ
brj-23026	23	36	robustness	robustness	NOUN
brj-23026	23	37	and	and	CCONJ
brj-23026	23	38	prediction	prediction	NOUN
brj-23026	23	39	accuracy	accuracy	NOUN
brj-23026	23	40	compared	compare	VERB
brj-23026	23	41	with	with	ADP
brj-23026	23	42	the	the	DET
brj-23026	23	43	model	model	NOUN
brj-23026	23	44	built	build	VERB
brj-23026	23	45	by	by	ADP
brj-23026	23	46	the	the	DET
brj-23026	23	47	original	original	ADJ
brj-23026	23	48	swcss	swcss	PROPN
brj-23026	23	49	method	method	NOUN
brj-23026	23	50	.	.	PUNCT
brj-23026	24	1	it	it	PRON
brj-23026	24	2	has	have	AUX
brj-23026	24	3	been	be	AUX
brj-23026	24	4	shown	show	VERB
brj-23026	24	5	that	that	SCONJ
brj-23026	24	6	the	the	DET
brj-23026	24	7	use	use	NOUN
brj-23026	24	8	of	of	ADP
brj-23026	24	9	cars	car	NOUN
brj-23026	24	10	and	and	CCONJ
brj-23026	24	11	uninformative	uninformative	ADJ
brj-23026	24	12	variables	variable	NOUN
brj-23026	24	13	elimination	elimination	NOUN
brj-23026	24	14	(	(	PUNCT
brj-23026	24	15	uve	uve	PROPN
brj-23026	24	16	)	)	PUNCT
brj-23026	24	17	(	(	PUNCT
brj-23026	24	18	cai	cai	X
brj-23026	24	19	et	et	PROPN
brj-23026	24	20	al	al	PROPN
brj-23026	24	21	.	.	PROPN
brj-23026	24	22	2008	2008	NUM
brj-23026	24	23	)	)	PUNCT
brj-23026	24	24	as	as	ADV
brj-23026	24	25	well	well	ADV
brj-23026	24	26	as	as	ADP
brj-23026	24	27	successive	successive	ADJ
brj-23026	24	28	projections	projection	NOUN
brj-23026	24	29	algorithm	algorithm	NOUN
brj-23026	24	30	(	(	PUNCT
brj-23026	24	31	spa	spa	NOUN
brj-23026	24	32	)	)	PUNCT
brj-23026	24	33	(	(	PUNCT
brj-23026	24	34	soares	soare	NOUN
brj-23026	24	35	et	et	PROPN
brj-23026	24	36	al	al	PROPN
brj-23026	24	37	.	.	PROPN
brj-23026	24	38	2013	2013	NUM
brj-23026	24	39	)	)	PUNCT
brj-23026	24	40	algorithms	algorithm	NOUN
brj-23026	24	41	are	be	AUX
brj-23026	24	42	able	able	ADJ
brj-23026	24	43	to	to	PART
brj-23026	24	44	effectively	effectively	ADV
brj-23026	24	45	remove	remove	VERB
brj-23026	24	46	the	the	DET
brj-23026	24	47	unimportant	unimportant	ADJ
brj-23026	24	48	wavelengths	wavelength	NOUN
brj-23026	24	49	from	from	ADP
brj-23026	24	50	the	the	DET
brj-23026	24	51	spectrum	spectrum	NOUN
brj-23026	24	52	.	.	PUNCT
brj-23026	25	1	therefore	therefore	ADV
brj-23026	25	2	,	,	PUNCT
brj-23026	25	3	based	base	VERB
brj-23026	25	4	on	on	ADP
brj-23026	25	5	the	the	DET
brj-23026	25	6	previous	previous	ADJ
brj-23026	25	7	studies	study	NOUN
brj-23026	25	8	,	,	PUNCT
brj-23026	25	9	this	this	DET
brj-23026	25	10	paper	paper	NOUN
brj-23026	25	11	continues	continue	VERB
brj-23026	25	12	to	to	PART
brj-23026	25	13	explore	explore	VERB
brj-23026	25	14	the	the	DET
brj-23026	25	15	transfer	transfer	NOUN
brj-23026	25	16	results	result	NOUN
brj-23026	25	17	of	of	ADP
brj-23026	25	18	the	the	DET
brj-23026	25	19	swcsscars	swcsscar	NOUN
brj-23026	25	20	method	method	NOUN
brj-23026	25	21	between	between	ADP
brj-23026	25	22	three	three	NUM
brj-23026	25	23	different	different	ADJ
brj-23026	25	24	batches	batch	NOUN
brj-23026	25	25	of	of	ADP
brj-23026	25	26	prismatic	prismatic	ADJ
brj-23026	25	27	near	near	ADV
brj-23026	25	28	-	-	PUNCT
brj-23026	25	29	infrared	infrared	ADJ
brj-23026	25	30	spectrometers	spectrometer	NOUN
brj-23026	25	31	and	and	CCONJ
brj-23026	25	32	compares	compare	VERB
brj-23026	25	33	them	they	PRON
brj-23026	25	34	with	with	ADP
brj-23026	25	35	the	the	DET
brj-23026	25	36	results	result	NOUN
brj-23026	25	37	of	of	ADP
brj-23026	25	38	analyzing	analyze	VERB
brj-23026	25	39	the	the	DET
brj-23026	25	40	target	target	NOUN
brj-23026	25	41	samples	sample	NOUN
brj-23026	25	42	by	by	ADP
brj-23026	25	43	the	the	DET
brj-23026	25	44	separate	separate	ADJ
brj-23026	25	45	swcss	swcss	PROPN
brj-23026	25	46	,	,	PUNCT
brj-23026	25	47	uve	uve	NOUN
brj-23026	25	48	,	,	PUNCT
brj-23026	25	49	cars	car	NOUN
brj-23026	25	50	,	,	PUNCT
brj-23026	25	51	and	and	CCONJ
brj-23026	25	52	spa	spa	NOUN
brj-23026	25	53	algorithms	algorithm	NOUN
brj-23026	25	54	,	,	PUNCT
brj-23026	25	55	to	to	PART
brj-23026	25	56	validate	validate	VERB
brj-23026	25	57	the	the	DET
brj-23026	25	58	feasibility	feasibility	NOUN
brj-23026	25	59	of	of	ADP
brj-23026	25	60	the	the	DET
brj-23026	25	61	concatenation	concatenation	NOUN
brj-23026	25	62	of	of	ADP
brj-23026	25	63	the	the	DET
brj-23026	25	64	swcss	swcss	PROPN
brj-23026	25	65	-	-	PUNCT
brj-23026	25	66	cars	car	NOUN
brj-23026	25	67	algorithms	algorithm	NOUN
brj-23026	25	68	.	.	PUNCT
brj-23026	26	1	experimental	experimental	ADJ
brj-23026	26	2	analysis	analysis	NOUN
brj-23026	26	3	of	of	ADP
brj-23026	26	4	samples	sample	NOUN
brj-23026	26	5	and	and	CCONJ
brj-23026	26	6	their	their	PRON
brj-23026	26	7	lignin	lignin	NOUN
brj-23026	26	8	content	content	NOUN
brj-23026	26	9	five	five	NUM
brj-23026	26	10	common	common	ADJ
brj-23026	26	11	woods	wood	NOUN
brj-23026	26	12	(	(	PUNCT
brj-23026	26	13	pinus	pinus	NOUN
brj-23026	26	14	massoniana	massoniana	PROPN
brj-23026	26	15	,	,	PUNCT
brj-23026	26	16	cunninghamia	cunninghamia	NOUN
brj-23026	26	17	lanceolata	lanceolata	NOUN
brj-23026	26	18	,	,	PUNCT
brj-23026	26	19	acacia	acacia	NOUN
brj-23026	26	20	,	,	PUNCT
brj-23026	26	21	eucalyptus	eucalyptus	NOUN
brj-23026	26	22	robusta	robusta	NOUN
brj-23026	26	23	,	,	PUNCT
brj-23026	26	24	and	and	CCONJ
brj-23026	26	25	populus	populus	NOUN
brj-23026	26	26	)	)	PUNCT
brj-23026	26	27	were	be	AUX
brj-23026	26	28	provided	provide	VERB
brj-23026	26	29	by	by	ADP
brj-23026	26	30	the	the	DET
brj-23026	26	31	institute	institute	PROPN
brj-23026	26	32	of	of	ADP
brj-23026	26	33	forestry	forestry	PROPN
brj-23026	26	34	and	and	CCONJ
brj-23026	26	35	chemical	chemical	NOUN
brj-23026	26	36	industry	industry	NOUN
brj-23026	26	37	of	of	ADP
brj-23026	26	38	the	the	DET
brj-23026	26	39	chinese	chinese	PROPN
brj-23026	26	40	academy	academy	PROPN
brj-23026	26	41	of	of	ADP
brj-23026	26	42	forestry	forestry	PROPN
brj-23026	26	43	,	,	PUNCT
brj-23026	26	44	totaling	total	VERB
brj-23026	26	45	84	84	NUM
brj-23026	26	46	log	log	NOUN
brj-23026	26	47	samples	sample	NOUN
brj-23026	26	48	.	.	PUNCT
brj-23026	27	1	the	the	DET
brj-23026	27	2	logs	log	NOUN
brj-23026	27	3	were	be	AUX
brj-23026	27	4	chipped	chip	VERB
brj-23026	27	5	into	into	ADP
brj-23026	27	6	wood	wood	NOUN
brj-23026	27	7	chips	chip	NOUN
brj-23026	27	8	and	and	CCONJ
brj-23026	27	9	ground	ground	NOUN
brj-23026	27	10	,	,	PUNCT
brj-23026	27	11	and	and	CCONJ
brj-23026	27	12	then	then	ADV
brj-23026	27	13	the	the	DET
brj-23026	27	14	wood	wood	NOUN
brj-23026	27	15	powder	powder	NOUN
brj-23026	27	16	samples	sample	NOUN
brj-23026	27	17	with	with	ADP
brj-23026	27	18	particle	particle	NOUN
brj-23026	27	19	size	size	NOUN
brj-23026	27	20	of	of	ADP
brj-23026	27	21	0.250	0.250	NUM
brj-23026	27	22	to	to	ADP
brj-23026	27	23	0.425	0.425	NUM
brj-23026	27	24	mm	mm	NOUN
brj-23026	27	25	(	(	PUNCT
brj-23026	27	26	40	40	NUM
brj-23026	27	27	to	to	PART
brj-23026	27	28	60	60	NUM
brj-23026	27	29	mesh	mesh	NOUN
brj-23026	27	30	)	)	PUNCT
brj-23026	27	31	were	be	AUX
brj-23026	27	32	selected	select	VERB
brj-23026	27	33	to	to	PART
brj-23026	27	34	determine	determine	VERB
brj-23026	27	35	the	the	DET
brj-23026	27	36	acid	acid	NOUN
brj-23026	27	37	-	-	PUNCT
brj-23026	27	38	insoluble	insoluble	ADJ
brj-23026	27	39	lignin	lignin	NOUN
brj-23026	27	40	content	content	NOUN
brj-23026	27	41	according	accord	VERB
brj-23026	27	42	to	to	ADP
brj-23026	27	43	gb	gb	ADP
brj-23026	27	44	/	/	SYM
brj-23026	27	45	t	t	NOUN
brj-23026	27	46	2677.8	2677.8	NUM
brj-23026	27	47	-	-	SYM
brj-23026	27	48	1994	1994	NUM
brj-23026	27	49	.	.	PUNCT
brj-23026	28	1	the	the	DET
brj-23026	28	2	statistical	statistical	ADJ
brj-23026	28	3	characteristics	characteristic	NOUN
brj-23026	28	4	of	of	ADP
brj-23026	28	5	the	the	DET
brj-23026	28	6	results	result	NOUN
brj-23026	28	7	are	be	AUX
brj-23026	28	8	shown	show	VERB
brj-23026	28	9	in	in	ADP
brj-23026	28	10	table	table	NOUN
brj-23026	28	11	1	1	NUM
brj-23026	28	12	.	.	PUNCT
brj-23026	28	13	table	table	NOUN
brj-23026	28	14	1	1	NUM
brj-23026	28	15	.	.	PUNCT
brj-23026	28	16	statistical	statistical	ADJ
brj-23026	28	17	table	table	NOUN
brj-23026	28	18	of	of	ADP
brj-23026	28	19	lignin	lignin	NOUN
brj-23026	28	20	content	content	NOUN
brj-23026	28	21	in	in	ADP
brj-23026	28	22	pulpwood	pulpwood	NOUN
brj-23026	28	23	types	type	NOUN
brj-23026	28	24	of	of	ADP
brj-23026	28	25	wood	wood	NOUN
brj-23026	28	26	flour	flour	NOUN
brj-23026	28	27	number	number	NOUN
brj-23026	28	28	min	min	NOUN
brj-23026	28	29	(	(	PUNCT
brj-23026	28	30	%	%	INTJ
brj-23026	28	31	)	)	PUNCT
brj-23026	28	32	max	max	PROPN
brj-23026	28	33	(	(	PUNCT
brj-23026	28	34	%	%	INTJ
brj-23026	28	35	)	)	PUNCT
brj-23026	29	1	mean	mean	NOUN
brj-23026	29	2	(	(	PUNCT
brj-23026	29	3	%	%	INTJ
brj-23026	29	4	)	)	PUNCT
brj-23026	29	5	eucalyptus	eucalyptus	NOUN
brj-23026	29	6	robusta	robusta	NOUN
brj-23026	29	7	24	24	NUM
brj-23026	29	8	21.49	21.49	NUM
brj-23026	29	9	27.56	27.56	NUM
brj-23026	29	10	23.74	23.74	NUM
brj-23026	29	11	cunninghamia	cunninghamia	NOUN
brj-23026	29	12	23	23	NUM
brj-23026	29	13	32.55	32.55	NUM
brj-23026	29	14	34.20	34.20	NUM
brj-23026	29	15	33.44	33.44	NUM
brj-23026	29	16	populus	populus	PROPN
brj-23026	29	17	15	15	NUM
brj-23026	29	18	14.82	14.82	NUM
brj-23026	29	19	20.51	20.51	NUM
brj-23026	29	20	18.00	18.00	NUM
brj-23026	29	21	acacia	acacia	NOUN
brj-23026	29	22	12	12	NUM
brj-23026	29	23	24.62	24.62	NUM
brj-23026	29	24	27.15	27.15	NUM
brj-23026	29	25	25.69	25.69	NUM
brj-23026	29	26	pinus	pinus	NOUN
brj-23026	29	27	massoniana	massoniana	PROPN
brj-23026	29	28	10	10	NUM
brj-23026	29	29	28.48	28.48	NUM
brj-23026	29	30	28.95	28.95	NUM
brj-23026	29	31	28.63	28.63	NUM
brj-23026	29	32	total	total	NOUN
brj-23026	29	33	84	84	NUM
brj-23026	29	34	14.82	14.82	NUM
brj-23026	29	35	34.20	34.20	NUM
brj-23026	29	36	26.43	26.43	NUM
brj-23026	29	37	peer	peer	NOUN
brj-23026	29	38	-	-	PUNCT
brj-23026	29	39	reviewed	review	VERB
brj-23026	29	40	article	article	NOUN
brj-23026	29	41	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	29	42	liu	liu	PROPN
brj-23026	29	43	et	et	PROPN
brj-23026	29	44	al	al	PROPN
brj-23026	29	45	.	.	PROPN
brj-23026	30	1	(	(	PUNCT
brj-23026	30	2	2024	2024	NUM
brj-23026	30	3	)	)	PUNCT
brj-23026	30	4	.	.	PUNCT
brj-23026	31	1	“	"	PUNCT
brj-23026	31	2	nir	nir	ADJ
brj-23026	31	3	lignin	lignin	NOUN
brj-23026	31	4	model	model	NOUN
brj-23026	31	5	transfer	transfer	NOUN
brj-23026	31	6	coupling	coupling	NOUN
brj-23026	31	7	,	,	PUNCT
brj-23026	31	8	”	"	PUNCT
brj-23026	31	9	bioresources	bioresource	NOUN
brj-23026	31	10	19(1	19(1	NUM
brj-23026	31	11	)	)	PUNCT
brj-23026	31	12	,	,	PUNCT
brj-23026	31	13	245	245	NUM
brj-23026	31	14	-	-	SYM
brj-23026	31	15	256	256	NUM
brj-23026	31	16	.	.	NOUN
brj-23026	31	17	247	247	NUM
brj-23026	31	18	near	near	ADV
brj-23026	31	19	-	-	PUNCT
brj-23026	31	20	infrared	infrared	ADJ
brj-23026	31	21	spectral	spectral	ADJ
brj-23026	31	22	acquisition	acquisition	NOUN
brj-23026	31	23	the	the	DET
brj-23026	31	24	experiment	experiment	NOUN
brj-23026	31	25	uses	use	VERB
brj-23026	31	26	three	three	NUM
brj-23026	31	27	near	near	ADV
brj-23026	31	28	-	-	PUNCT
brj-23026	31	29	infrared	infrared	ADJ
brj-23026	31	30	spectrometers	spectrometer	NOUN
brj-23026	31	31	(	(	PUNCT
brj-23026	31	32	three	three	NUM
brj-23026	31	33	lengguang	lengguang	NOUN
brj-23026	31	34	s450	s450	PROPN
brj-23026	31	35	raster	raster	NOUN
brj-23026	31	36	scanning	scanning	NOUN
brj-23026	31	37	near	near	ADP
brj-23026	31	38	-	-	PUNCT
brj-23026	31	39	infrared	infrared	ADJ
brj-23026	31	40	spectrometer	spectrometer	NOUN
brj-23026	31	41	of	of	ADP
brj-23026	31	42	the	the	DET
brj-23026	31	43	same	same	ADJ
brj-23026	31	44	type	type	NOUN
brj-23026	31	45	)	)	PUNCT
brj-23026	31	46	,	,	PUNCT
brj-23026	31	47	one	one	NUM
brj-23026	31	48	of	of	ADP
brj-23026	31	49	which	which	PRON
brj-23026	31	50	is	be	AUX
brj-23026	31	51	located	locate	VERB
brj-23026	31	52	in	in	ADP
brj-23026	31	53	zhenjiang	zhenjiang	PROPN
brj-23026	31	54	users	user	NOUN
brj-23026	31	55	of	of	ADP
brj-23026	31	56	the	the	DET
brj-23026	31	57	lengguang	lengguang	PROPN
brj-23026	31	58	s450	s450	PROPN
brj-23026	31	59	as	as	ADP
brj-23026	31	60	the	the	DET
brj-23026	31	61	master	master	NOUN
brj-23026	31	62	,	,	PUNCT
brj-23026	31	63	the	the	DET
brj-23026	31	64	other	other	ADJ
brj-23026	31	65	two	two	NUM
brj-23026	31	66	are	be	AUX
brj-23026	31	67	located	locate	VERB
brj-23026	31	68	in	in	ADP
brj-23026	31	69	shanghai	shanghai	PROPN
brj-23026	31	70	users	user	NOUN
brj-23026	31	71	of	of	ADP
brj-23026	31	72	the	the	DET
brj-23026	31	73	lengguang	lengguang	PROPN
brj-23026	31	74	s450	s450	PROPN
brj-23026	31	75	as	as	ADP
brj-23026	31	76	targets	target	NOUN
brj-23026	31	77	(	(	PUNCT
brj-23026	31	78	target	target	NOUN
brj-23026	31	79	1	1	NUM
brj-23026	31	80	and	and	CCONJ
brj-23026	31	81	target	target	VERB
brj-23026	31	82	2	2	NUM
brj-23026	31	83	)	)	PUNCT
brj-23026	31	84	.	.	PUNCT
brj-23026	32	1	the	the	DET
brj-23026	32	2	instrument	instrument	NOUN
brj-23026	32	3	is	be	AUX
brj-23026	32	4	a	a	DET
brj-23026	32	5	grating	grate	VERB
brj-23026	32	6	scanning	scanning	NOUN
brj-23026	32	7	near	near	ADP
brj-23026	32	8	-	-	PUNCT
brj-23026	32	9	infrared	infrared	ADJ
brj-23026	32	10	spectrometer	spectrometer	NOUN
brj-23026	32	11	with	with	ADP
brj-23026	32	12	an	an	DET
brj-23026	32	13	indium	indium	NOUN
brj-23026	32	14	gallium	gallium	NOUN
brj-23026	32	15	arsenide	arsenide	NOUN
brj-23026	32	16	(	(	PUNCT
brj-23026	32	17	ingaas	ingaas	NOUN
brj-23026	32	18	)	)	PUNCT
brj-23026	32	19	detector	detector	NOUN
brj-23026	32	20	,	,	PUNCT
brj-23026	32	21	wavelength	wavelength	NOUN
brj-23026	32	22	range	range	NOUN
brj-23026	32	23	of	of	ADP
brj-23026	32	24	900	900	NUM
brj-23026	32	25	to	to	PART
brj-23026	32	26	2500	2500	NUM
brj-23026	32	27	nm	nm	NOUN
brj-23026	32	28	,	,	PUNCT
brj-23026	32	29	resolution	resolution	NOUN
brj-23026	32	30	of	of	ADP
brj-23026	32	31	12	12	NUM
brj-23026	32	32	nm	nm	NOUN
brj-23026	32	33	,	,	PUNCT
brj-23026	32	34	sampling	sample	VERB
brj-23026	32	35	interval	interval	NOUN
brj-23026	32	36	of	of	ADP
brj-23026	32	37	1	1	NUM
brj-23026	32	38	nm	nm	NOUN
brj-23026	32	39	,	,	PUNCT
brj-23026	32	40	and	and	CCONJ
brj-23026	32	41	total	total	NOUN
brj-23026	32	42	of	of	ADP
brj-23026	32	43	1601	1601	NUM
brj-23026	32	44	wavelength	wavelength	NOUN
brj-23026	32	45	points	point	NOUN
brj-23026	32	46	.	.	PUNCT
brj-23026	33	1	the	the	DET
brj-23026	33	2	spectral	spectral	ADJ
brj-23026	33	3	data	datum	NOUN
brj-23026	33	4	of	of	ADP
brj-23026	33	5	84	84	NUM
brj-23026	33	6	wood	wood	NOUN
brj-23026	33	7	flour	flour	NOUN
brj-23026	33	8	samples	sample	NOUN
brj-23026	33	9	were	be	AUX
brj-23026	33	10	collected	collect	VERB
brj-23026	33	11	on	on	ADP
brj-23026	33	12	these	these	DET
brj-23026	33	13	three	three	NUM
brj-23026	33	14	instruments	instrument	NOUN
brj-23026	33	15	respectively	respectively	ADV
brj-23026	33	16	.	.	PUNCT
brj-23026	34	1	since	since	SCONJ
brj-23026	34	2	the	the	DET
brj-23026	34	3	s450	s450	PROPN
brj-23026	34	4	near	near	ADV
brj-23026	34	5	-	-	PUNCT
brj-23026	34	6	infrared	infrare	VERB
brj-23026	34	7	spectrometer	spectrometer	NOUN
brj-23026	34	8	is	be	AUX
brj-23026	34	9	equipped	equip	VERB
brj-23026	34	10	with	with	ADP
brj-23026	34	11	a	a	DET
brj-23026	34	12	rotary	rotary	ADJ
brj-23026	34	13	stage	stage	NOUN
brj-23026	34	14	,	,	PUNCT
brj-23026	34	15	the	the	DET
brj-23026	34	16	step	step	NOUN
brj-23026	34	17	of	of	ADP
brj-23026	34	18	repeated	repeat	VERB
brj-23026	34	19	sample	sample	NOUN
brj-23026	34	20	loading	loading	NOUN
brj-23026	34	21	can	can	AUX
brj-23026	34	22	be	be	AUX
brj-23026	34	23	omitted	omit	VERB
brj-23026	34	24	.	.	PUNCT
brj-23026	35	1	the	the	DET
brj-23026	35	2	measurement	measurement	NOUN
brj-23026	35	3	of	of	ADP
brj-23026	35	4	each	each	DET
brj-23026	35	5	sample	sample	NOUN
brj-23026	35	6	was	be	AUX
brj-23026	35	7	repeated	repeat	VERB
brj-23026	35	8	6	6	NUM
brj-23026	35	9	times	time	NOUN
brj-23026	35	10	,	,	PUNCT
brj-23026	35	11	and	and	CCONJ
brj-23026	35	12	the	the	DET
brj-23026	35	13	average	average	ADJ
brj-23026	35	14	spectrum	spectrum	NOUN
brj-23026	35	15	was	be	AUX
brj-23026	35	16	taken	take	VERB
brj-23026	35	17	as	as	ADP
brj-23026	35	18	the	the	DET
brj-23026	35	19	final	final	ADJ
brj-23026	35	20	sample	sample	NOUN
brj-23026	35	21	spectrum	spectrum	NOUN
brj-23026	35	22	.	.	PUNCT
brj-23026	36	1	the	the	DET
brj-23026	36	2	kennard	kennard	NOUN
brj-23026	36	3	-	-	PUNCT
brj-23026	36	4	stone	stone	NOUN
brj-23026	36	5	method	method	NOUN
brj-23026	36	6	was	be	AUX
brj-23026	36	7	used	use	VERB
brj-23026	36	8	to	to	PART
brj-23026	36	9	divide	divide	VERB
brj-23026	36	10	the	the	DET
brj-23026	36	11	84	84	NUM
brj-23026	36	12	specimens	specimen	NOUN
brj-23026	36	13	into	into	ADP
brj-23026	36	14	56	56	NUM
brj-23026	36	15	correction	correction	NOUN
brj-23026	36	16	set	set	VERB
brj-23026	36	17	and	and	CCONJ
brj-23026	36	18	28	28	NUM
brj-23026	36	19	prediction	prediction	NOUN
brj-23026	36	20	set	set	VERB
brj-23026	36	21	specimens	specimen	NOUN
brj-23026	36	22	.	.	PUNCT
brj-23026	37	1	wavelength	wavelength	NOUN
brj-23026	37	2	selection	selection	NOUN
brj-23026	37	3	method	method	NOUN
brj-23026	37	4	screening	screen	VERB
brj-23026	37	5	wavelengths	wavelength	NOUN
brj-23026	37	6	with	with	ADP
brj-23026	37	7	consistent	consistent	ADJ
brj-23026	37	8	and	and	CCONJ
brj-23026	37	9	stable	stable	ADJ
brj-23026	37	10	signals	signal	NOUN
brj-23026	37	11	(	(	PUNCT
brj-23026	37	12	ni	ni	PROPN
brj-23026	37	13	et	et	PROPN
brj-23026	37	14	al	al	PROPN
brj-23026	37	15	.	.	PROPN
brj-23026	37	16	2019	2019	NUM
brj-23026	37	17	)	)	PUNCT
brj-23026	38	1	the	the	DET
brj-23026	38	2	standard	standard	ADJ
brj-23026	38	3	deviation	deviation	NOUN
brj-23026	38	4	of	of	ADP
brj-23026	38	5	the	the	DET
brj-23026	38	6	two	two	NUM
brj-23026	38	7	spectra	spectra	NOUN
brj-23026	38	8	was	be	AUX
brj-23026	38	9	calculated	calculate	VERB
brj-23026	38	10	and	and	CCONJ
brj-23026	38	11	analyzed	analyze	VERB
brj-23026	38	12	using	use	VERB
brj-23026	38	13	the	the	DET
brj-23026	38	14	spectral	spectral	ADJ
brj-23026	38	15	response	response	NOUN
brj-23026	38	16	of	of	ADP
brj-23026	38	17	the	the	DET
brj-23026	38	18	samples	sample	NOUN
brj-23026	38	19	at	at	ADP
brj-23026	38	20	each	each	DET
brj-23026	38	21	wavelength	wavelength	NOUN
brj-23026	38	22	as	as	ADP
brj-23026	38	23	a	a	DET
brj-23026	38	24	variable	variable	NOUN
brj-23026	38	25	to	to	PART
brj-23026	38	26	find	find	VERB
brj-23026	38	27	out	out	ADP
brj-23026	38	28	the	the	DET
brj-23026	38	29	wavelengths	wavelength	NOUN
brj-23026	38	30	at	at	ADP
brj-23026	38	31	which	which	PRON
brj-23026	38	32	the	the	DET
brj-23026	38	33	spectral	spectral	ADJ
brj-23026	38	34	signals	signal	NOUN
brj-23026	38	35	of	of	ADP
brj-23026	38	36	different	different	ADJ
brj-23026	38	37	instruments	instrument	NOUN
brj-23026	38	38	are	be	AUX
brj-23026	38	39	consistent	consistent	ADJ
brj-23026	38	40	and	and	CCONJ
brj-23026	38	41	stable	stable	ADJ
brj-23026	38	42	.	.	PUNCT
brj-23026	39	1	the	the	DET
brj-23026	39	2	standard	standard	ADJ
brj-23026	39	3	deviation	deviation	NOUN
brj-23026	39	4	of	of	ADP
brj-23026	39	5	precision	precision	NOUN
brj-23026	39	6	detection	detection	NOUN
brj-23026	39	7	spectra	spectra	NOUN
brj-23026	39	8	(	(	PUNCT
brj-23026	39	9	sdpds	sdpds	NOUN
brj-23026	39	10	)	)	PUNCT
brj-23026	39	11	is	be	AUX
brj-23026	39	12	the	the	DET
brj-23026	39	13	standard	standard	ADJ
brj-23026	39	14	deviation	deviation	NOUN
brj-23026	39	15	of	of	ADP
brj-23026	39	16	the	the	DET
brj-23026	39	17	sample	sample	NOUN
brj-23026	39	18	spectra	spectra	NOUN
brj-23026	39	19	taken	take	VERB
brj-23026	39	20	on	on	ADP
brj-23026	39	21	several	several	ADJ
brj-23026	39	22	consecutive	consecutive	ADJ
brj-23026	39	23	occasions	occasion	NOUN
brj-23026	39	24	on	on	ADP
brj-23026	39	25	the	the	DET
brj-23026	39	26	main	main	ADJ
brj-23026	39	27	instrument	instrument	NOUN
brj-23026	39	28	.	.	PUNCT
brj-23026	40	1	it	it	PRON
brj-23026	40	2	is	be	AUX
brj-23026	40	3	calculated	calculate	VERB
brj-23026	40	4	as	as	SCONJ
brj-23026	40	5	follows	follow	VERB
brj-23026	40	6	.	.	PUNCT
brj-23026	41	1	(	(	PUNCT
brj-23026	41	2	1	1	X
brj-23026	41	3	)	)	PUNCT
brj-23026	41	4	where	where	SCONJ
brj-23026	41	5	xij	xij	PRON
brj-23026	41	6	is	be	AUX
brj-23026	41	7	the	the	DET
brj-23026	41	8	spectral	spectral	ADJ
brj-23026	41	9	signal	signal	NOUN
brj-23026	41	10	at	at	ADP
brj-23026	41	11	the	the	DET
brj-23026	41	12	jth	jth	PROPN
brj-23026	41	13	wavelength	wavelength	NOUN
brj-23026	41	14	of	of	ADP
brj-23026	41	15	the	the	DET
brj-23026	41	16	sample	sample	NOUN
brj-23026	41	17	under	under	ADP
brj-23026	41	18	test	test	NOUN
brj-23026	41	19	at	at	ADP
brj-23026	41	20	the	the	DET
brj-23026	41	21	ith	ith	PROPN
brj-23026	41	22	acquisition	acquisition	NOUN
brj-23026	41	23	,	,	PUNCT
brj-23026	41	24	and	and	CCONJ
brj-23026	41	25	n	n	PRON
brj-23026	41	26	is	be	AUX
brj-23026	41	27	the	the	DET
brj-23026	41	28	number	number	NOUN
brj-23026	41	29	of	of	ADP
brj-23026	41	30	acquisitions	acquisition	NOUN
brj-23026	41	31	.	.	PUNCT
brj-23026	42	1	�	�	NOUN
brj-23026	42	2	̅	̅	NOUN
brj-23026	42	3	�	�	NOUN
brj-23026	42	4	j	j	PROPN
brj-23026	42	5	is	be	AUX
brj-23026	42	6	the	the	DET
brj-23026	42	7	average	average	NOUN
brj-23026	42	8	of	of	ADP
brj-23026	42	9	the	the	DET
brj-23026	42	10	spectral	spectral	ADJ
brj-23026	42	11	signals	signal	NOUN
brj-23026	42	12	at	at	ADP
brj-23026	42	13	the	the	DET
brj-23026	42	14	jth	jth	PROPN
brj-23026	42	15	wavelength	wavelength	NOUN
brj-23026	42	16	.	.	PUNCT
brj-23026	43	1	sdpds	sdpds	PROPN
brj-23026	43	2	describes	describe	VERB
brj-23026	43	3	the	the	DET
brj-23026	43	4	level	level	NOUN
brj-23026	43	5	of	of	ADP
brj-23026	43	6	fluctuation	fluctuation	NOUN
brj-23026	43	7	in	in	ADP
brj-23026	43	8	the	the	DET
brj-23026	43	9	spectra	spectra	NOUN
brj-23026	43	10	of	of	ADP
brj-23026	43	11	the	the	DET
brj-23026	43	12	repeated	repeat	VERB
brj-23026	43	13	tests	test	NOUN
brj-23026	43	14	.	.	PUNCT
brj-23026	44	1	the	the	DET
brj-23026	44	2	fluctuation	fluctuation	NOUN
brj-23026	44	3	of	of	ADP
brj-23026	44	4	the	the	DET
brj-23026	44	5	spectrum	spectrum	NOUN
brj-23026	44	6	is	be	AUX
brj-23026	44	7	caused	cause	VERB
brj-23026	44	8	by	by	ADP
brj-23026	44	9	noise	noise	NOUN
brj-23026	44	10	and	and	CCONJ
brj-23026	44	11	measurement	measurement	NOUN
brj-23026	44	12	error	error	NOUN
brj-23026	44	13	of	of	ADP
brj-23026	44	14	the	the	DET
brj-23026	44	15	instrument	instrument	NOUN
brj-23026	44	16	in	in	ADP
brj-23026	44	17	a	a	DET
brj-23026	44	18	very	very	ADV
brj-23026	44	19	short	short	ADJ
brj-23026	44	20	period	period	NOUN
brj-23026	44	21	of	of	ADP
brj-23026	44	22	time	time	NOUN
brj-23026	44	23	.	.	PUNCT
brj-23026	45	1	the	the	DET
brj-23026	45	2	smaller	small	ADJ
brj-23026	45	3	the	the	DET
brj-23026	45	4	sdpds	sdpds	NOUN
brj-23026	45	5	is	be	AUX
brj-23026	45	6	,	,	PUNCT
brj-23026	45	7	the	the	PRON
brj-23026	45	8	more	more	ADV
brj-23026	45	9	stable	stable	ADJ
brj-23026	45	10	the	the	DET
brj-23026	45	11	spectral	spectral	ADJ
brj-23026	45	12	signal	signal	NOUN
brj-23026	45	13	is	be	AUX
brj-23026	45	14	at	at	ADP
brj-23026	45	15	this	this	DET
brj-23026	45	16	wavelength	wavelength	NOUN
brj-23026	45	17	.	.	PUNCT
brj-23026	46	1	the	the	DET
brj-23026	46	2	standard	standard	ADJ
brj-23026	46	3	deviation	deviation	NOUN
brj-23026	46	4	of	of	ADP
brj-23026	46	5	difference	difference	NOUN
brj-23026	46	6	spectra	spectra	NOUN
brj-23026	46	7	between	between	ADP
brj-23026	46	8	the	the	DET
brj-23026	46	9	instruments	instrument	NOUN
brj-23026	46	10	(	(	PUNCT
brj-23026	46	11	sddsi	sddsi	PROPN
brj-23026	46	12	)	)	PUNCT
brj-23026	46	13	reflects	reflect	VERB
brj-23026	46	14	the	the	DET
brj-23026	46	15	range	range	NOUN
brj-23026	46	16	of	of	ADP
brj-23026	46	17	fluctuations	fluctuation	NOUN
brj-23026	46	18	in	in	ADP
brj-23026	46	19	the	the	DET
brj-23026	46	20	difference	difference	NOUN
brj-23026	46	21	spectra	spectra	NOUN
brj-23026	46	22	of	of	ADP
brj-23026	46	23	the	the	DET
brj-23026	46	24	master	master	NOUN
brj-23026	46	25	and	and	CCONJ
brj-23026	46	26	target	target	NOUN
brj-23026	46	27	.	.	PUNCT
brj-23026	47	1	it	it	PRON
brj-23026	47	2	is	be	AUX
brj-23026	47	3	calculated	calculate	VERB
brj-23026	47	4	as	as	SCONJ
brj-23026	47	5	follows	follow	VERB
brj-23026	47	6	,	,	PUNCT
brj-23026	47	7	(	(	PUNCT
brj-23026	47	8	2	2	X
brj-23026	47	9	)	)	PUNCT
brj-23026	47	10	where	where	SCONJ
brj-23026	47	11	m	m	NOUN
brj-23026	47	12	is	be	AUX
brj-23026	47	13	the	the	DET
brj-23026	47	14	number	number	NOUN
brj-23026	47	15	of	of	ADP
brj-23026	47	16	samples	sample	NOUN
brj-23026	47	17	,	,	PUNCT
brj-23026	47	18	while	while	SCONJ
brj-23026	47	19	aij	aij	PROPN
brj-23026	47	20	denotes	denote	VERB
brj-23026	47	21	the	the	DET
brj-23026	47	22	difference	difference	NOUN
brj-23026	47	23	spectrum	spectrum	NOUN
brj-23026	47	24	between	between	ADP
brj-23026	47	25	the	the	DET
brj-23026	47	26	spectra	spectra	NOUN
brj-23026	47	27	of	of	ADP
brj-23026	47	28	the	the	DET
brj-23026	47	29	master	master	NOUN
brj-23026	47	30	and	and	CCONJ
brj-23026	47	31	the	the	DET
brj-23026	47	32	target	target	NOUN
brj-23026	47	33	(	(	PUNCT
brj-23026	47	34	aij	aij	PROPN
brj-23026	47	35	=	=	SYM
brj-23026	47	36	mij	mij	NOUN
brj-23026	47	37	-	-	NOUN
brj-23026	47	38	sij	sij	NOUN
brj-23026	47	39	)	)	PUNCT
brj-23026	47	40	,	,	PUNCT
brj-23026	47	41	and	and	CCONJ
brj-23026	47	42	mij	mij	NOUN
brj-23026	47	43	and	and	CCONJ
brj-23026	47	44	sij	sij	PROPN
brj-23026	47	45	denote	denote	VERB
brj-23026	47	46	the	the	DET
brj-23026	47	47	spectral	spectral	ADJ
brj-23026	47	48	response	response	NOUN
brj-23026	47	49	values	value	NOUN
brj-23026	47	50	of	of	ADP
brj-23026	47	51	the	the	DET
brj-23026	47	52	ith	ith	PROPN
brj-23026	47	53	sample	sample	NOUN
brj-23026	47	54	of	of	ADP
brj-23026	47	55	the	the	DET
brj-23026	47	56	master	master	NOUN
brj-23026	47	57	and	and	CCONJ
brj-23026	47	58	the	the	DET
brj-23026	47	59	target	target	NOUN
brj-23026	47	60	,	,	PUNCT
brj-23026	47	61	respectively	respectively	ADV
brj-23026	47	62	,	,	PUNCT
brj-23026	47	63	measured	measure	VERB
brj-23026	47	64	at	at	ADP
brj-23026	47	65	the	the	DET
brj-23026	47	66	wavelength	wavelength	NOUN
brj-23026	47	67	point	point	NOUN
brj-23026	47	68	j.	j.	PROPN
brj-23026	47	69	the	the	PRON
brj-23026	47	70	smaller	small	ADJ
brj-23026	47	71	the	the	DET
brj-23026	47	72	sddsij	sddsij	NOUN
brj-23026	47	73	is	be	AUX
brj-23026	47	74	,	,	PUNCT
brj-23026	47	75	the	the	PRON
brj-23026	47	76	better	well	ADJ
brj-23026	47	77	the	the	DET
brj-23026	47	78	consistency	consistency	NOUN
brj-23026	47	79	of	of	ADP
brj-23026	47	80	the	the	DET
brj-23026	47	81	spectral	spectral	ADJ
brj-23026	47	82	signal	signal	NOUN
brj-23026	47	83	of	of	ADP
brj-23026	47	84	the	the	DET
brj-23026	47	85	instrument	instrument	NOUN
brj-23026	47	86	at	at	ADP
brj-23026	47	87	the	the	DET
brj-23026	47	88	jth	jth	PROPN
brj-23026	47	89	wavelength	wavelength	NOUN
brj-23026	47	90	.	.	PUNCT
brj-23026	48	1	screening	screen	VERB
brj-23026	48	2	for	for	ADP
brj-23026	48	3	stable	stable	ADJ
brj-23026	48	4	and	and	CCONJ
brj-23026	48	5	consistent	consistent	ADJ
brj-23026	48	6	wavelengths	wavelength	NOUN
brj-23026	48	7	:	:	PUNCT
brj-23026	48	8	the	the	DET
brj-23026	48	9	k	k	PROPN
brj-23026	48	10	-	-	PUNCT
brj-23026	48	11	s	s	PART
brj-23026	48	12	algorithm	algorithm	NOUN
brj-23026	48	13	is	be	AUX
brj-23026	48	14	used	use	VERB
brj-23026	48	15	to	to	PART
brj-23026	48	16	select	select	VERB
brj-23026	48	17	a	a	DET
brj-23026	48	18	certain	certain	ADJ
brj-23026	48	19	number	number	NOUN
brj-23026	48	20	of	of	ADP
brj-23026	48	21	representative	representative	ADJ
brj-23026	48	22	samples	sample	NOUN
brj-23026	48	23	,	,	PUNCT
brj-23026	48	24	calculate	calculate	VERB
brj-23026	48	25	the	the	DET
brj-23026	48	26	standard	standard	ADJ
brj-23026	48	27	deviation	deviation	NOUN
brj-23026	48	28	of	of	ADP
brj-23026	48	29	the	the	DET
brj-23026	48	30	difference	difference	NOUN
brj-23026	48	31	spectra	spectra	NOUN
brj-23026	48	32	between	between	ADP
brj-23026	48	33	the	the	DET
brj-23026	48	34	master	master	NOUN
brj-23026	48	35	and	and	CCONJ
brj-23026	48	36	target	target	NOUN
brj-23026	48	37	of	of	ADP
brj-23026	48	38	these	these	DET
brj-23026	48	39	samples	sample	NOUN
brj-23026	48	40	at	at	ADP
brj-23026	48	41	wavelength	wavelength	NOUN
brj-23026	48	42	j	j	PROPN
brj-23026	48	43	,	,	PUNCT
brj-23026	48	44	sddsij	sddsij	ADJ
brj-23026	48	45	,	,	PUNCT
brj-23026	48	46	and	and	CCONJ
brj-23026	48	47	the	the	DET
brj-23026	48	48	standard	standard	ADJ
brj-23026	48	49	deviation	deviation	NOUN
brj-23026	48	50	of	of	ADP
brj-23026	48	51	the	the	DET
brj-23026	48	52	precision	precision	NOUN
brj-23026	48	53	test	test	NOUN
brj-23026	48	54	spectra	spectra	NOUN
brj-23026	48	55	of	of	ADP
brj-23026	48	56	the	the	DET
brj-23026	48	57	master	master	NOUN
brj-23026	48	58	,	,	PUNCT
brj-23026	48	59	sdpdsj	sdpdsj	PROPN
brj-23026	48	60	,	,	PUNCT
brj-23026	48	61	and	and	CCONJ
brj-23026	48	62	define	define	VERB
brj-23026	48	63	the	the	DET
brj-23026	48	64	ratio	ratio	NOUN
brj-23026	48	65	of	of	ADP
brj-23026	48	66	sddsij	sddsij	NOUN
brj-23026	48	67	and	and	CCONJ
brj-23026	48	68	sdpdsj	sdpdsj	NOUN
brj-23026	48	69	as	as	ADP
brj-23026	48	70	the	the	DET
brj-23026	48	71	consistency	consistency	NOUN
brj-23026	48	72	parameter	parameter	NOUN
brj-23026	48	73	,	,	PUNCT
brj-23026	48	74	(	(	PUNCT
brj-23026	48	75	)	)	PUNCT
brj-23026	48	76	2	2	NUM
brj-23026	48	77	1sdpds	1sdpds	NUM
brj-23026	48	78	(	(	PUNCT
brj-23026	48	79	)	)	PUNCT
brj-23026	49	1	1	1	NUM
brj-23026	49	2	n	n	NUM
brj-23026	49	3	ij	ij	INTJ
brj-23026	49	4	j	j	PROPN
brj-23026	50	1	i	i	INTJ
brj-23026	50	2	x	x	X
brj-23026	50	3	x	x	VERB
brj-23026	50	4	j	j	PROPN
brj-23026	50	5	n	n	NOUN
brj-23026	50	6	=	=	SYM
brj-23026	50	7	−	−	PROPN
brj-23026	50	8	=	=	SYM
brj-23026	50	9	−	−	NOUN
brj-23026	50	10			X
brj-23026	50	11	(	(	PUNCT
brj-23026	50	12	)	)	PUNCT
brj-23026	50	13	2	2	NUM
brj-23026	50	14	1sddsi	1sddsi	NUM
brj-23026	50	15	(	(	PUNCT
brj-23026	50	16	)	)	PUNCT
brj-23026	50	17	1	1	NUM
brj-23026	50	18	m	m	NOUN
brj-23026	51	1	ij	ij	INTJ
brj-23026	51	2	j	j	PROPN
brj-23026	52	1	i	i	PRON
brj-23026	52	2	a	a	PRON
brj-23026	52	3	a	a	DET
brj-23026	52	4	j	j	NOUN
brj-23026	52	5	m	m	NOUN
brj-23026	52	6	=	=	SYM
brj-23026	53	1	−	−	PROPN
brj-23026	53	2	=	=	SYM
brj-23026	54	1	−	−	PROPN
brj-23026	54	2			NUM
brj-23026	54	3	peer	peer	NOUN
brj-23026	54	4	-	-	PUNCT
brj-23026	54	5	reviewed	review	VERB
brj-23026	54	6	article	article	NOUN
brj-23026	54	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	54	8	liu	liu	PROPN
brj-23026	54	9	et	et	PROPN
brj-23026	54	10	al	al	PROPN
brj-23026	54	11	.	.	PROPN
brj-23026	55	1	(	(	PUNCT
brj-23026	55	2	2024	2024	NUM
brj-23026	55	3	)	)	PUNCT
brj-23026	55	4	.	.	PUNCT
brj-23026	56	1	“	"	PUNCT
brj-23026	56	2	nir	nir	ADJ
brj-23026	56	3	lignin	lignin	NOUN
brj-23026	56	4	model	model	NOUN
brj-23026	56	5	transfer	transfer	NOUN
brj-23026	56	6	coupling	coupling	NOUN
brj-23026	56	7	,	,	PUNCT
brj-23026	56	8	”	"	PUNCT
brj-23026	56	9	bioresources	bioresource	NOUN
brj-23026	56	10	19(1	19(1	NUM
brj-23026	56	11	)	)	PUNCT
brj-23026	56	12	,	,	PUNCT
brj-23026	56	13	245	245	NUM
brj-23026	56	14	-	-	SYM
brj-23026	56	15	256	256	NUM
brj-23026	56	16	.	.	NUM
brj-23026	57	1	248	248	NUM
brj-23026	57	2	(	(	PUNCT
brj-23026	57	3	3	3	NUM
brj-23026	57	4	)	)	PUNCT
brj-23026	57	5	where	where	SCONJ
brj-23026	57	6	n	n	PRON
brj-23026	57	7	is	be	AUX
brj-23026	57	8	the	the	DET
brj-23026	57	9	number	number	NOUN
brj-23026	57	10	of	of	ADP
brj-23026	57	11	wavelengths	wavelength	NOUN
brj-23026	57	12	.	.	PUNCT
brj-23026	58	1	usually	usually	ADV
brj-23026	58	2	,	,	PUNCT
brj-23026	58	3	sddsij	sddsij	PROPN
brj-23026	58	4	is	be	AUX
brj-23026	58	5	larger	large	ADJ
brj-23026	58	6	than	than	ADP
brj-23026	58	7	sdpdsj	sdpdsj	NOUN
brj-23026	58	8	.	.	PUNCT
brj-23026	59	1	the	the	DET
brj-23026	59	2	closer	close	ADJ
brj-23026	59	3	bj	bj	NOUN
brj-23026	59	4	is	be	AUX
brj-23026	59	5	to	to	ADP
brj-23026	59	6	1	1	NUM
brj-23026	59	7	,	,	PUNCT
brj-23026	59	8	the	the	DET
brj-23026	59	9	smaller	small	ADJ
brj-23026	59	10	is	be	AUX
brj-23026	59	11	the	the	DET
brj-23026	59	12	standard	standard	ADJ
brj-23026	59	13	deviation	deviation	NOUN
brj-23026	59	14	of	of	ADP
brj-23026	59	15	the	the	DET
brj-23026	59	16	difference	difference	NOUN
brj-23026	59	17	spectrum	spectrum	NOUN
brj-23026	59	18	between	between	ADP
brj-23026	59	19	the	the	DET
brj-23026	59	20	master	master	NOUN
brj-23026	59	21	and	and	CCONJ
brj-23026	59	22	target	target	NOUN
brj-23026	59	23	instruments	instrument	NOUN
brj-23026	59	24	.	.	PUNCT
brj-23026	60	1	the	the	DET
brj-23026	60	2	goal	goal	NOUN
brj-23026	60	3	is	be	AUX
brj-23026	60	4	to	to	PART
brj-23026	60	5	match	match	VERB
brj-23026	60	6	the	the	DET
brj-23026	60	7	standard	standard	ADJ
brj-23026	60	8	deviation	deviation	NOUN
brj-23026	60	9	of	of	ADP
brj-23026	60	10	the	the	DET
brj-23026	60	11	master	master	NOUN
brj-23026	60	12	instrument	instrument	NOUN
brj-23026	60	13	’s	’s	PART
brj-23026	60	14	accuracy	accuracy	NOUN
brj-23026	60	15	test	test	NOUN
brj-23026	60	16	,	,	PUNCT
brj-23026	60	17	so	so	CCONJ
brj-23026	60	18	the	the	DET
brj-23026	60	19	signals	signal	NOUN
brj-23026	60	20	of	of	ADP
brj-23026	60	21	the	the	DET
brj-23026	60	22	master	master	NOUN
brj-23026	60	23	and	and	CCONJ
brj-23026	60	24	the	the	DET
brj-23026	60	25	target	target	NOUN
brj-23026	60	26	instruments	instrument	NOUN
brj-23026	60	27	have	have	VERB
brj-23026	60	28	a	a	DET
brj-23026	60	29	very	very	ADV
brj-23026	60	30	good	good	ADJ
brj-23026	60	31	consistency	consistency	NOUN
brj-23026	60	32	at	at	ADP
brj-23026	60	33	that	that	DET
brj-23026	60	34	wavelength	wavelength	NOUN
brj-23026	60	35	.	.	PUNCT
brj-23026	61	1	based	base	VERB
brj-23026	61	2	on	on	ADP
brj-23026	61	3	previous	previous	ADJ
brj-23026	61	4	experiments	experiment	NOUN
brj-23026	61	5	,	,	PUNCT
brj-23026	61	6	when	when	SCONJ
brj-23026	61	7	bj	bj	NOUN
brj-23026	61	8	is	be	AUX
brj-23026	61	9	set	set	VERB
brj-23026	61	10	smaller	small	ADJ
brj-23026	61	11	,	,	PUNCT
brj-23026	61	12	the	the	DET
brj-23026	61	13	selected	select	VERB
brj-23026	61	14	wavelengths	wavelength	NOUN
brj-23026	61	15	will	will	AUX
brj-23026	61	16	be	be	AUX
brj-23026	61	17	very	very	ADV
brj-23026	61	18	few	few	ADJ
brj-23026	61	19	,	,	PUNCT
brj-23026	61	20	and	and	CCONJ
brj-23026	61	21	much	much	ADJ
brj-23026	61	22	important	important	ADJ
brj-23026	61	23	information	information	NOUN
brj-23026	61	24	will	will	AUX
brj-23026	61	25	be	be	AUX
brj-23026	61	26	lost	lose	VERB
brj-23026	61	27	.	.	PUNCT
brj-23026	62	1	however	however	ADV
brj-23026	62	2	,	,	PUNCT
brj-23026	62	3	when	when	SCONJ
brj-23026	62	4	bj	bj	NOUN
brj-23026	62	5	is	be	AUX
brj-23026	62	6	too	too	ADV
brj-23026	62	7	large	large	ADJ
brj-23026	62	8	,	,	PUNCT
brj-23026	62	9	wavelengths	wavelength	NOUN
brj-23026	62	10	with	with	ADP
brj-23026	62	11	large	large	ADJ
brj-23026	62	12	spectral	spectral	ADJ
brj-23026	62	13	differences	difference	NOUN
brj-23026	62	14	between	between	ADP
brj-23026	62	15	instruments	instrument	NOUN
brj-23026	62	16	will	will	AUX
brj-23026	62	17	be	be	AUX
brj-23026	62	18	included	include	VERB
brj-23026	62	19	.	.	PUNCT
brj-23026	63	1	nir	nir	ADJ
brj-23026	63	2	models	model	NOUN
brj-23026	63	3	that	that	PRON
brj-23026	63	4	include	include	VERB
brj-23026	63	5	information	information	NOUN
brj-23026	63	6	about	about	ADP
brj-23026	63	7	these	these	DET
brj-23026	63	8	wavelengths	wavelength	NOUN
brj-23026	63	9	have	have	VERB
brj-23026	63	10	poor	poor	ADJ
brj-23026	63	11	analytical	analytical	ADJ
brj-23026	63	12	performance	performance	NOUN
brj-23026	63	13	for	for	ADP
brj-23026	63	14	target	target	NOUN
brj-23026	63	15	samples	sample	NOUN
brj-23026	63	16	.	.	PUNCT
brj-23026	64	1	therefore	therefore	ADV
brj-23026	64	2	,	,	PUNCT
brj-23026	64	3	a	a	DET
brj-23026	64	4	reasonable	reasonable	ADJ
brj-23026	64	5	bj	bj	NOUN
brj-23026	64	6	needs	need	NOUN
brj-23026	64	7	to	to	PART
brj-23026	64	8	be	be	AUX
brj-23026	64	9	selected	select	VERB
brj-23026	64	10	based	base	VERB
brj-23026	64	11	on	on	ADP
brj-23026	64	12	the	the	DET
brj-23026	64	13	analytical	analytical	ADJ
brj-23026	64	14	effectiveness	effectiveness	NOUN
brj-23026	64	15	of	of	ADP
brj-23026	64	16	the	the	DET
brj-23026	64	17	model	model	NOUN
brj-23026	64	18	built	build	VERB
brj-23026	64	19	from	from	ADP
brj-23026	64	20	the	the	DET
brj-23026	64	21	selected	select	VERB
brj-23026	64	22	wavelengths	wavelength	NOUN
brj-23026	64	23	on	on	ADP
brj-23026	64	24	the	the	DET
brj-23026	64	25	target	target	NOUN
brj-23026	64	26	samples	sample	NOUN
brj-23026	64	27	.	.	PUNCT
brj-23026	65	1	after	after	SCONJ
brj-23026	65	2	the	the	DET
brj-23026	65	3	wavelengths	wavelength	NOUN
brj-23026	65	4	with	with	ADP
brj-23026	65	5	large	large	ADJ
brj-23026	65	6	sdpds	sdpds	NOUN
brj-23026	65	7	values	value	NOUN
brj-23026	65	8	are	be	AUX
brj-23026	65	9	excluded	exclude	VERB
brj-23026	65	10	from	from	ADP
brj-23026	65	11	the	the	DET
brj-23026	65	12	selected	select	VERB
brj-23026	65	13	wavelengths	wavelength	NOUN
brj-23026	65	14	,	,	PUNCT
brj-23026	65	15	the	the	DET
brj-23026	65	16	set	set	NOUN
brj-23026	65	17	of	of	ADP
brj-23026	65	18	wavelengths	wavelength	NOUN
brj-23026	65	19	for	for	ADP
brj-23026	65	20	which	which	PRON
brj-23026	65	21	the	the	DET
brj-23026	65	22	spectral	spectral	ADJ
brj-23026	65	23	signals	signal	NOUN
brj-23026	65	24	are	be	AUX
brj-23026	65	25	consistent	consistent	ADJ
brj-23026	65	26	between	between	ADP
brj-23026	65	27	the	the	DET
brj-23026	65	28	master	master	NOUN
brj-23026	65	29	instrument	instrument	NOUN
brj-23026	65	30	and	and	CCONJ
brj-23026	65	31	each	each	PRON
brj-23026	65	32	of	of	ADP
brj-23026	65	33	the	the	DET
brj-23026	65	34	target	target	NOUN
brj-23026	65	35	instruments	instrument	NOUN
brj-23026	65	36	is	be	AUX
brj-23026	65	37	recorded	record	VERB
brj-23026	65	38	as	as	ADP
brj-23026	65	39	u1	u1	NOUN
brj-23026	65	40	,	,	PUNCT
brj-23026	65	41	...	...	PUNCT
brj-23026	65	42	,	,	PUNCT
brj-23026	65	43	uk	uk	PROPN
brj-23026	65	44	.	.	PUNCT
brj-23026	66	1	the	the	DET
brj-23026	66	2	intersection	intersection	NOUN
brj-23026	66	3	of	of	ADP
brj-23026	66	4	these	these	DET
brj-23026	66	5	sets	set	NOUN
brj-23026	66	6	is	be	AUX
brj-23026	66	7	uc	uc	PROPN
brj-23026	66	8	.	.	PUNCT
brj-23026	67	1	competitive	competitive	ADJ
brj-23026	67	2	adaptive	adaptive	ADJ
brj-23026	67	3	reweighted	reweighte	VERB
brj-23026	67	4	sampling	sample	VERB
brj-23026	67	5	algorithm	algorithm	NOUN
brj-23026	67	6	using	use	VERB
brj-23026	67	7	the	the	DET
brj-23026	67	8	competitive	competitive	ADJ
brj-23026	67	9	adaptive	adaptive	ADJ
brj-23026	67	10	reweighted	reweighte	VERB
brj-23026	67	11	sampling	sample	VERB
brj-23026	67	12	(	(	PUNCT
brj-23026	67	13	cars	car	NOUN
brj-23026	67	14	)	)	PUNCT
brj-23026	67	15	algorithm	algorithm	NOUN
brj-23026	67	16	(	(	PUNCT
brj-23026	67	17	jiang	jiang	PROPN
brj-23026	67	18	et	et	PROPN
brj-23026	67	19	al	al	PROPN
brj-23026	67	20	.	.	PROPN
brj-23026	67	21	2015	2015	NUM
brj-23026	67	22	)	)	PUNCT
brj-23026	67	23	,	,	PUNCT
brj-23026	67	24	based	base	VERB
brj-23026	67	25	on	on	ADP
brj-23026	67	26	the	the	DET
brj-23026	67	27	monte	monte	PROPN
brj-23026	67	28	carlo	carlo	PROPN
brj-23026	67	29	resampling	resample	VERB
brj-23026	67	30	method	method	NOUN
brj-23026	67	31	,	,	PUNCT
brj-23026	67	32	a	a	DET
brj-23026	67	33	fixed	fix	VERB
brj-23026	67	34	proportion	proportion	NOUN
brj-23026	67	35	of	of	ADP
brj-23026	67	36	samples	sample	NOUN
brj-23026	67	37	were	be	AUX
brj-23026	67	38	randomly	randomly	ADV
brj-23026	67	39	selected	select	VERB
brj-23026	67	40	from	from	ADP
brj-23026	67	41	the	the	DET
brj-23026	67	42	sample	sample	NOUN
brj-23026	67	43	set	set	VERB
brj-23026	67	44	as	as	SCONJ
brj-23026	67	45	the	the	DET
brj-23026	67	46	correction	correction	NOUN
brj-23026	67	47	set	set	VERB
brj-23026	67	48	,	,	PUNCT
brj-23026	67	49	and	and	CCONJ
brj-23026	67	50	the	the	DET
brj-23026	67	51	importance	importance	NOUN
brj-23026	67	52	of	of	ADP
brj-23026	67	53	each	each	DET
brj-23026	67	54	variable	variable	NOUN
brj-23026	67	55	was	be	AUX
brj-23026	67	56	evaluated	evaluate	VERB
brj-23026	67	57	by	by	ADP
brj-23026	67	58	the	the	DET
brj-23026	67	59	absolute	absolute	ADJ
brj-23026	67	60	value	value	NOUN
brj-23026	67	61	of	of	ADP
brj-23026	67	62	the	the	DET
brj-23026	67	63	regression	regression	NOUN
brj-23026	67	64	coefficients	coefficient	NOUN
brj-23026	67	65	of	of	ADP
brj-23026	67	66	the	the	DET
brj-23026	67	67	established	establish	VERB
brj-23026	67	68	pls	pls	NOUN
brj-23026	67	69	model	model	NOUN
brj-23026	67	70	.	.	PUNCT
brj-23026	68	1	in	in	ADP
brj-23026	68	2	each	each	DET
brj-23026	68	3	resampling	resampling	NOUN
brj-23026	68	4	,	,	PUNCT
brj-23026	68	5	adaptive	adaptive	ADJ
brj-23026	68	6	reweighted	reweighted	ADJ
brj-23026	68	7	sampling	sampling	NOUN
brj-23026	68	8	was	be	AUX
brj-23026	68	9	used	use	VERB
brj-23026	68	10	to	to	PART
brj-23026	68	11	select	select	VERB
brj-23026	68	12	the	the	DET
brj-23026	68	13	important	important	ADJ
brj-23026	68	14	spectral	spectral	ADJ
brj-23026	68	15	variables	variable	NOUN
brj-23026	68	16	with	with	ADP
brj-23026	68	17	larger	large	ADJ
brj-23026	68	18	absolute	absolute	ADJ
brj-23026	68	19	regression	regression	NOUN
brj-23026	68	20	coefficients	coefficient	NOUN
brj-23026	68	21	in	in	ADP
brj-23026	68	22	the	the	DET
brj-23026	68	23	calibration	calibration	NOUN
brj-23026	68	24	model	model	NOUN
brj-23026	68	25	,	,	PUNCT
brj-23026	68	26	and	and	CCONJ
brj-23026	68	27	an	an	DET
brj-23026	68	28	exponential	exponential	NOUN
brj-23026	68	29	decreasing	decrease	VERB
brj-23026	68	30	function	function	NOUN
brj-23026	68	31	was	be	AUX
brj-23026	68	32	used	use	VERB
brj-23026	68	33	to	to	PART
brj-23026	68	34	determine	determine	VERB
brj-23026	68	35	the	the	DET
brj-23026	68	36	number	number	NOUN
brj-23026	68	37	of	of	ADP
brj-23026	68	38	variables	variable	NOUN
brj-23026	68	39	to	to	PART
brj-23026	68	40	be	be	AUX
brj-23026	68	41	selected	select	VERB
brj-23026	68	42	.	.	PUNCT
brj-23026	69	1	finally	finally	ADV
brj-23026	69	2	,	,	PUNCT
brj-23026	69	3	the	the	DET
brj-23026	69	4	cross	cross	ADJ
brj-23026	69	5	-	-	ADJ
brj-23026	69	6	validation	validation	ADJ
brj-23026	69	7	method	method	NOUN
brj-23026	69	8	was	be	AUX
brj-23026	69	9	applied	apply	VERB
brj-23026	69	10	to	to	PART
brj-23026	69	11	select	select	VERB
brj-23026	69	12	the	the	DET
brj-23026	69	13	optimal	optimal	ADJ
brj-23026	69	14	subset	subset	NOUN
brj-23026	69	15	of	of	ADP
brj-23026	69	16	variables	variable	NOUN
brj-23026	69	17	where	where	SCONJ
brj-23026	69	18	the	the	DET
brj-23026	69	19	root	root	NOUN
brj-23026	69	20	-	-	PUNCT
brj-23026	69	21	mean	mean	ADJ
brj-23026	69	22	-	-	PUNCT
brj-23026	69	23	square	square	NOUN
brj-23026	69	24	error	error	NOUN
brj-23026	69	25	allowed	allow	VERB
brj-23026	69	26	.	.	PUNCT
brj-23026	70	1	modeling	modeling	NOUN
brj-23026	70	2	and	and	CCONJ
brj-23026	70	3	model	model	NOUN
brj-23026	70	4	evaluation	evaluation	NOUN
brj-23026	70	5	methods	method	NOUN
brj-23026	70	6	the	the	DET
brj-23026	70	7	partial	partial	ADJ
brj-23026	70	8	least	least	ADJ
brj-23026	70	9	square	square	ADJ
brj-23026	70	10	regression	regression	NOUN
brj-23026	70	11	(	(	PUNCT
brj-23026	70	12	plsr	plsr	PROPN
brj-23026	70	13	)	)	PUNCT
brj-23026	70	14	algorithm	algorithm	NOUN
brj-23026	70	15	was	be	AUX
brj-23026	70	16	used	use	VERB
brj-23026	70	17	to	to	PART
brj-23026	70	18	establish	establish	VERB
brj-23026	70	19	the	the	DET
brj-23026	70	20	nir	nir	ADJ
brj-23026	70	21	correction	correction	NOUN
brj-23026	70	22	model	model	NOUN
brj-23026	70	23	for	for	ADP
brj-23026	70	24	lignin	lignin	PROPN
brj-23026	70	25	content	content	NOUN
brj-23026	70	26	.	.	PUNCT
brj-23026	71	1	the	the	DET
brj-23026	71	2	size	size	NOUN
brj-23026	71	3	of	of	ADP
brj-23026	71	4	the	the	DET
brj-23026	71	5	number	number	NOUN
brj-23026	71	6	of	of	ADP
brj-23026	71	7	principal	principal	ADJ
brj-23026	71	8	factors	factor	NOUN
brj-23026	71	9	in	in	ADP
brj-23026	71	10	the	the	DET
brj-23026	71	11	modeling	modeling	NOUN
brj-23026	71	12	process	process	NOUN
brj-23026	71	13	directly	directly	ADV
brj-23026	71	14	affects	affect	VERB
brj-23026	71	15	the	the	DET
brj-23026	71	16	effectiveness	effectiveness	NOUN
brj-23026	71	17	of	of	ADP
brj-23026	71	18	the	the	DET
brj-23026	71	19	model	model	NOUN
brj-23026	71	20	.	.	PUNCT
brj-23026	72	1	choosing	choose	VERB
brj-23026	72	2	too	too	ADV
brj-23026	72	3	small	small	ADJ
brj-23026	72	4	a	a	DET
brj-23026	72	5	number	number	NOUN
brj-23026	72	6	of	of	ADP
brj-23026	72	7	principal	principal	ADJ
brj-23026	72	8	factors	factor	NOUN
brj-23026	72	9	will	will	AUX
brj-23026	72	10	result	result	VERB
brj-23026	72	11	in	in	ADP
brj-23026	72	12	the	the	DET
brj-23026	72	13	loss	loss	NOUN
brj-23026	72	14	of	of	ADP
brj-23026	72	15	many	many	ADJ
brj-23026	72	16	important	important	ADJ
brj-23026	72	17	inter	inter	ADJ
brj-23026	72	18	-	-	ADJ
brj-23026	72	19	spectral	spectral	ADJ
brj-23026	72	20	information	information	NOUN
brj-23026	72	21	,	,	PUNCT
brj-23026	72	22	while	while	SCONJ
brj-23026	72	23	choosing	choose	VERB
brj-23026	72	24	too	too	ADV
brj-23026	72	25	large	large	ADJ
brj-23026	72	26	a	a	DET
brj-23026	72	27	number	number	NOUN
brj-23026	72	28	of	of	ADP
brj-23026	72	29	principal	principal	ADJ
brj-23026	72	30	factors	factor	NOUN
brj-23026	72	31	introduces	introduce	VERB
brj-23026	72	32	more	more	ADJ
brj-23026	72	33	redundancy	redundancy	NOUN
brj-23026	72	34	into	into	ADP
brj-23026	72	35	the	the	DET
brj-23026	72	36	model	model	NOUN
brj-23026	72	37	information	information	NOUN
brj-23026	72	38	,	,	PUNCT
brj-23026	72	39	leading	lead	VERB
brj-23026	72	40	to	to	ADP
brj-23026	72	41	overfitting	overfitte	VERB
brj-23026	72	42	(	(	PUNCT
brj-23026	72	43	son	son	NOUN
brj-23026	72	44	et	et	PROPN
brj-23026	72	45	al	al	PROPN
brj-23026	72	46	.	.	PROPN
brj-23026	72	47	2020	2020	NUM
brj-23026	72	48	)	)	PUNCT
brj-23026	72	49	.	.	PUNCT
brj-23026	73	1	in	in	ADP
brj-23026	73	2	this	this	DET
brj-23026	73	3	study	study	NOUN
brj-23026	73	4	,	,	PUNCT
brj-23026	73	5	the	the	DET
brj-23026	73	6	maximum	maximum	ADJ
brj-23026	73	7	number	number	NOUN
brj-23026	73	8	of	of	ADP
brj-23026	73	9	principal	principal	ADJ
brj-23026	73	10	factors	factor	NOUN
brj-23026	73	11	was	be	AUX
brj-23026	73	12	set	set	VERB
brj-23026	73	13	to	to	ADP
brj-23026	73	14	15	15	NUM
brj-23026	73	15	and	and	CCONJ
brj-23026	73	16	the	the	DET
brj-23026	73	17	minimum	minimum	ADJ
brj-23026	73	18	number	number	NOUN
brj-23026	73	19	of	of	ADP
brj-23026	73	20	principal	principal	ADJ
brj-23026	73	21	factors	factor	NOUN
brj-23026	73	22	was	be	AUX
brj-23026	73	23	set	set	VERB
brj-23026	73	24	to	to	ADP
brj-23026	73	25	2	2	NUM
brj-23026	73	26	during	during	ADP
brj-23026	73	27	the	the	DET
brj-23026	73	28	modeling	modeling	NOUN
brj-23026	73	29	process	process	NOUN
brj-23026	73	30	.	.	PUNCT
brj-23026	74	1	the	the	DET
brj-23026	74	2	pls	pls	PROPN
brj-23026	74	3	component	component	NOUN
brj-23026	74	4	with	with	ADP
brj-23026	74	5	the	the	DET
brj-23026	74	6	smallest	small	ADJ
brj-23026	74	7	sum	sum	NOUN
brj-23026	74	8	of	of	ADP
brj-23026	74	9	squared	square	VERB
brj-23026	74	10	prediction	prediction	NOUN
brj-23026	74	11	errors	error	NOUN
brj-23026	74	12	(	(	PUNCT
brj-23026	74	13	press	press	NOUN
brj-23026	74	14	)	)	PUNCT
brj-23026	74	15	was	be	AUX
brj-23026	74	16	selected	select	VERB
brj-23026	74	17	using	use	VERB
brj-23026	74	18	the	the	DET
brj-23026	74	19	leave	leave	VERB
brj-23026	74	20	-	-	PUNCT
brj-23026	74	21	one	one	NUM
brj-23026	74	22	-	-	PUNCT
brj-23026	74	23	out	out	NOUN
brj-23026	74	24	method	method	NOUN
brj-23026	74	25	for	for	ADP
brj-23026	74	26	cross	cross	ADJ
brj-23026	74	27	-	-	ADJ
brj-23026	74	28	valid	valid	ADJ
brj-23026	74	29	model	model	NOUN
brj-23026	74	30	building	building	NOUN
brj-23026	74	31	,	,	PUNCT
brj-23026	74	32	model	model	NOUN
brj-23026	74	33	prediction	prediction	NOUN
brj-23026	74	34	,	,	PUNCT
brj-23026	74	35	and	and	CCONJ
brj-23026	74	36	the	the	DET
brj-23026	74	37	effect	effect	NOUN
brj-23026	74	38	of	of	ADP
brj-23026	74	39	prediction	prediction	NOUN
brj-23026	74	40	after	after	SCONJ
brj-23026	74	41	model	model	NOUN
brj-23026	74	42	delivery	delivery	NOUN
brj-23026	74	43	are	be	AUX
brj-23026	74	44	evaluated	evaluate	VERB
brj-23026	74	45	,	,	PUNCT
brj-23026	74	46	and	and	CCONJ
brj-23026	74	47	the	the	DET
brj-23026	74	48	methods	method	NOUN
brj-23026	74	49	of	of	ADP
brj-23026	74	50	evaluating	evaluate	VERB
brj-23026	74	51	the	the	DET
brj-23026	74	52	constructed	construct	VERB
brj-23026	74	53	models	model	NOUN
brj-23026	74	54	in	in	ADP
brj-23026	74	55	this	this	DET
brj-23026	74	56	study	study	NOUN
brj-23026	74	57	used	use	VERB
brj-23026	74	58	the	the	DET
brj-23026	74	59	correlation	correlation	NOUN
brj-23026	74	60	coefficient	coefficient	NOUN
brj-23026	74	61	(	(	PUNCT
brj-23026	74	62	r	r	NOUN
brj-23026	74	63	)	)	PUNCT
brj-23026	74	64	(	(	PUNCT
brj-23026	74	65	wang	wang	PROPN
brj-23026	74	66	et	et	PROPN
brj-23026	74	67	al	al	PROPN
brj-23026	74	68	.	.	PROPN
brj-23026	74	69	2019	2019	NUM
brj-23026	74	70	)	)	PUNCT
brj-23026	74	71	,	,	PUNCT
brj-23026	74	72	determination	determination	NOUN
brj-23026	74	73	coefficient	coefficient	NOUN
brj-23026	74	74	(	(	PUNCT
brj-23026	74	75	r2	r2	PROPN
brj-23026	74	76	)	)	PUNCT
brj-23026	74	77	(	(	PUNCT
brj-23026	74	78	morellos	morello	NOUN
brj-23026	74	79	et	et	PROPN
brj-23026	74	80	al	al	PROPN
brj-23026	74	81	.	.	PROPN
brj-23026	74	82	2016	2016	NUM
brj-23026	74	83	)	)	PUNCT
brj-23026	74	84	,	,	PUNCT
brj-23026	74	85	and	and	CCONJ
brj-23026	74	86	root	root	NOUN
brj-23026	74	87	mean	mean	VERB
brj-23026	74	88	squared	square	VERB
brj-23026	74	89	error	error	NOUN
brj-23026	74	90	for	for	ADP
brj-23026	74	91	cross	cross	NOUN
brj-23026	74	92	validation	validation	NOUN
brj-23026	74	93	(	(	PUNCT
brj-23026	74	94	rmscv	rmscv	NOUN
brj-23026	74	95	)	)	PUNCT
brj-23026	74	96	(	(	PUNCT
brj-23026	74	97	morellos	morello	NOUN
brj-23026	74	98	et	et	PROPN
brj-23026	74	99	al	al	PROPN
brj-23026	74	100	.	.	PROPN
brj-23026	74	101	2016	2016	NUM
brj-23026	74	102	)	)	PUNCT
brj-23026	74	103	.	.	PUNCT
brj-23026	75	1	in	in	ADP
brj-23026	75	2	addition	addition	NOUN
brj-23026	75	3	,	,	PUNCT
brj-23026	75	4	the	the	DET
brj-23026	75	5	root	root	NOUN
brj-23026	75	6	mean	mean	VERB
brj-23026	75	7	squared	square	VERB
brj-23026	75	8	error	error	NOUN
brj-23026	75	9	for	for	ADP
brj-23026	75	10	prediction	prediction	NOUN
brj-23026	75	11	(	(	PUNCT
brj-23026	75	12	rmsep	rmsep	NOUN
brj-23026	75	13	)	)	PUNCT
brj-23026	75	14	and	and	CCONJ
brj-23026	75	15	ratio	ratio	NOUN
brj-23026	75	16	of	of	ADP
brj-23026	75	17	prediction	prediction	NOUN
brj-23026	75	18	to	to	ADP
brj-23026	75	19	deviation	deviation	NOUN
brj-23026	75	20	(	(	PUNCT
brj-23026	75	21	rpd	rpd	PROPN
brj-23026	75	22	)	)	PUNCT
brj-23026	75	23	were	be	AUX
brj-23026	75	24	used	use	VERB
brj-23026	75	25	(	(	PUNCT
brj-23026	75	26	rossel	rossel	NOUN
brj-23026	75	27	et	et	PROPN
brj-23026	75	28	al	al	PROPN
brj-23026	75	29	.	.	PROPN
brj-23026	75	30	2006	2006	NUM
brj-23026	75	31	)	)	PUNCT
brj-23026	75	32	.	.	PUNCT
brj-23026	76	1	among	among	ADP
brj-23026	76	2	them	they	PRON
brj-23026	76	3	,	,	PUNCT
brj-23026	76	4	the	the	DET
brj-23026	76	5	closer	close	ADJ
brj-23026	76	6	r	r	NOUN
brj-23026	76	7	and	and	CCONJ
brj-23026	76	8	r2	r2	PROPN
brj-23026	76	9	are	be	AUX
brj-23026	76	10	to	to	ADP
brj-23026	76	11	1	1	NUM
brj-23026	76	12	,	,	PUNCT
brj-23026	76	13	the	the	PRON
brj-23026	76	14	higher	high	ADJ
brj-23026	76	15	the	the	DET
brj-23026	76	16	rpd	rpd	PROPN
brj-23026	76	17	is	be	AUX
brj-23026	76	18	,	,	PUNCT
brj-23026	76	19	the	the	PRON
brj-23026	76	20	lower	low	ADJ
brj-23026	76	21	the	the	DET
brj-23026	76	22	rmse	rmse	NOUN
brj-23026	76	23	is	be	AUX
brj-23026	76	24	,	,	PUNCT
brj-23026	76	25	the	the	PRON
brj-23026	76	26	better	well	ADJ
brj-23026	76	27	the	the	DET
brj-23026	76	28	model	model	NOUN
brj-23026	76	29	is	be	AUX
brj-23026	76	30	.	.	PUNCT
brj-23026	77	1	sddsi	sddsi	PROPN
brj-23026	77	2	(	(	PUNCT
brj-23026	77	3	1,2,3	1,2,3	NUM
brj-23026	77	4	,	,	PUNCT
brj-23026	77	5	,	,	PUNCT
brj-23026	77	6	)	)	PUNCT
brj-23026	77	7	sdpds	sdpds	PROPN
brj-23026	77	8	j	j	PROPN
brj-23026	77	9	j	j	PROPN
brj-23026	77	10	j	j	PROPN
brj-23026	77	11	b	b	PROPN
brj-23026	77	12	j	j	PROPN
brj-23026	77	13	n=	n=	PROPN
brj-23026	77	14	=	=	SYM
brj-23026	77	15	peer	peer	NOUN
brj-23026	77	16	-	-	PUNCT
brj-23026	77	17	reviewed	review	VERB
brj-23026	77	18	article	article	NOUN
brj-23026	77	19	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	77	20	liu	liu	PROPN
brj-23026	77	21	et	et	PROPN
brj-23026	77	22	al	al	PROPN
brj-23026	77	23	.	.	PROPN
brj-23026	77	24	(	(	PUNCT
brj-23026	77	25	2024	2024	NUM
brj-23026	77	26	)	)	PUNCT
brj-23026	77	27	.	.	PUNCT
brj-23026	78	1	“	"	PUNCT
brj-23026	78	2	nir	nir	ADJ
brj-23026	78	3	lignin	lignin	NOUN
brj-23026	78	4	model	model	NOUN
brj-23026	78	5	transfer	transfer	NOUN
brj-23026	78	6	coupling	coupling	NOUN
brj-23026	78	7	,	,	PUNCT
brj-23026	78	8	”	"	PUNCT
brj-23026	78	9	bioresources	bioresource	NOUN
brj-23026	78	10	19(1	19(1	NUM
brj-23026	78	11	)	)	PUNCT
brj-23026	78	12	,	,	PUNCT
brj-23026	78	13	245	245	NUM
brj-23026	78	14	-	-	SYM
brj-23026	78	15	256	256	NUM
brj-23026	78	16	.	.	PUNCT
brj-23026	79	1	249	249	NUM
brj-23026	79	2	rpd<2.5	rpd<2.5	PROPN
brj-23026	79	3	indicates	indicate	VERB
brj-23026	79	4	a	a	DET
brj-23026	79	5	poor	poor	ADJ
brj-23026	79	6	model	model	NOUN
brj-23026	79	7	,	,	PUNCT
brj-23026	79	8	the	the	DET
brj-23026	79	9	value	value	NOUN
brj-23026	79	10	of	of	ADP
brj-23026	79	11	rpd	rpd	PROPN
brj-23026	79	12	is	be	AUX
brj-23026	79	13	from	from	ADP
brj-23026	79	14	2.5	2.5	NUM
brj-23026	79	15	to	to	ADP
brj-23026	79	16	3.0	3.0	NUM
brj-23026	79	17	the	the	DET
brj-23026	79	18	model	model	NOUN
brj-23026	79	19	is	be	AUX
brj-23026	79	20	fair	fair	ADJ
brj-23026	79	21	,	,	PUNCT
brj-23026	79	22	and	and	CCONJ
brj-23026	79	23	rpd>3.0	rpd>3.0	ADV
brj-23026	79	24	the	the	DET
brj-23026	79	25	model	model	NOUN
brj-23026	79	26	is	be	AUX
brj-23026	79	27	good	good	ADJ
brj-23026	79	28	.	.	PUNCT
brj-23026	80	1	since	since	SCONJ
brj-23026	80	2	r2	r2	PROPN
brj-23026	80	3	is	be	AUX
brj-23026	80	4	associated	associate	VERB
brj-23026	80	5	with	with	ADP
brj-23026	80	6	rpd	rpd	PROPN
brj-23026	80	7	,	,	PUNCT
brj-23026	80	8	which	which	PRON
brj-23026	80	9	represents	represent	VERB
brj-23026	80	10	a	a	DET
brj-23026	80	11	more	more	ADV
brj-23026	80	12	straightforward	straightforward	ADJ
brj-23026	80	13	measurement	measurement	NOUN
brj-23026	80	14	of	of	ADP
brj-23026	80	15	the	the	DET
brj-23026	80	16	ability	ability	NOUN
brj-23026	80	17	of	of	ADP
brj-23026	80	18	an	an	DET
brj-23026	80	19	nirs	nirs	NOUN
brj-23026	80	20	model	model	NOUN
brj-23026	80	21	to	to	PART
brj-23026	80	22	predict	predict	VERB
brj-23026	80	23	a	a	DET
brj-23026	80	24	constituent	constituent	NOUN
brj-23026	80	25	,	,	PUNCT
brj-23026	80	26	in	in	ADP
brj-23026	80	27	our	our	PRON
brj-23026	80	28	following	follow	VERB
brj-23026	80	29	study	study	NOUN
brj-23026	80	30	the	the	DET
brj-23026	80	31	rpd	rpd	PROPN
brj-23026	80	32	was	be	AUX
brj-23026	80	33	employed	employ	VERB
brj-23026	80	34	as	as	ADP
brj-23026	80	35	an	an	DET
brj-23026	80	36	evaluation	evaluation	NOUN
brj-23026	80	37	index	index	NOUN
brj-23026	80	38	.	.	PUNCT
brj-23026	81	1	the	the	DET
brj-23026	81	2	aic	aic	PROPN
brj-23026	81	3	is	be	AUX
brj-23026	81	4	defined	define	VERB
brj-23026	81	5	as	as	ADP
brj-23026	81	6	(	(	PUNCT
brj-23026	81	7	rossel	rossel	NOUN
brj-23026	81	8	et	et	PROPN
brj-23026	81	9	al	al	PROPN
brj-23026	81	10	.	.	PROPN
brj-23026	81	11	2010	2010	NUM
brj-23026	81	12	)	)	PUNCT
brj-23026	81	13	,	,	PUNCT
brj-23026	81	14	ln	ln	NOUN
brj-23026	81	15	2ica	2ica	NUM
brj-23026	81	16	n	n	NUM
brj-23026	81	17	rmsep	rmsep	ADV
brj-23026	81	18	p=	p=	NOUN
brj-23026	82	1	+	+	CCONJ
brj-23026	82	2	(	(	PUNCT
brj-23026	82	3	4	4	NUM
brj-23026	82	4	)	)	PUNCT
brj-23026	82	5	where	where	SCONJ
brj-23026	82	6	n	n	PRON
brj-23026	82	7	is	be	AUX
brj-23026	82	8	the	the	DET
brj-23026	82	9	number	number	NOUN
brj-23026	82	10	of	of	ADP
brj-23026	82	11	samples	sample	NOUN
brj-23026	82	12	and	and	CCONJ
brj-23026	82	13	p	p	NOUN
brj-23026	82	14	is	be	AUX
brj-23026	82	15	the	the	DET
brj-23026	82	16	number	number	NOUN
brj-23026	82	17	of	of	ADP
brj-23026	82	18	characteristics	characteristic	NOUN
brj-23026	82	19	of	of	ADP
brj-23026	82	20	the	the	DET
brj-23026	82	21	samples	sample	NOUN
brj-23026	82	22	,	,	PUNCT
brj-23026	82	23	i.e.	i.e.	X
brj-23026	82	24	,	,	PUNCT
brj-23026	82	25	the	the	DET
brj-23026	82	26	number	number	NOUN
brj-23026	82	27	of	of	ADP
brj-23026	82	28	variables	variable	NOUN
brj-23026	82	29	modeled	model	VERB
brj-23026	82	30	.	.	PUNCT
brj-23026	83	1	the	the	DET
brj-23026	83	2	smaller	small	ADJ
brj-23026	83	3	the	the	DET
brj-23026	83	4	aic	aic	PROPN
brj-23026	83	5	value	value	NOUN
brj-23026	83	6	,	,	PUNCT
brj-23026	83	7	the	the	PRON
brj-23026	83	8	more	more	ADV
brj-23026	83	9	streamlined	streamlined	ADJ
brj-23026	83	10	the	the	DET
brj-23026	83	11	model	model	NOUN
brj-23026	83	12	.	.	PUNCT
brj-23026	84	1	software	software	PROPN
brj-23026	84	2	spectral	spectral	PROPN
brj-23026	84	3	data	datum	NOUN
brj-23026	84	4	preprocessing	preprocessing	NOUN
brj-23026	84	5	,	,	PUNCT
brj-23026	84	6	division	division	NOUN
brj-23026	84	7	of	of	ADP
brj-23026	84	8	correction	correction	NOUN
brj-23026	84	9	/	/	SYM
brj-23026	84	10	prediction	prediction	NOUN
brj-23026	84	11	sets	set	NOUN
brj-23026	84	12	,	,	PUNCT
brj-23026	84	13	and	and	CCONJ
brj-23026	84	14	pls	pls	VERB
brj-23026	84	15	modeling	modeling	NOUN
brj-23026	84	16	and	and	CCONJ
brj-23026	84	17	prediction	prediction	NOUN
brj-23026	84	18	were	be	AUX
brj-23026	84	19	performed	perform	VERB
brj-23026	84	20	using	use	VERB
brj-23026	84	21	nirsa	nirsa	ADJ
brj-23026	84	22	software	software	NOUN
brj-23026	84	23	developed	develop	VERB
brj-23026	84	24	in	in	ADP
brj-23026	84	25	-	-	PUNCT
brj-23026	84	26	house	house	NOUN
brj-23026	84	27	by	by	ADP
brj-23026	84	28	the	the	DET
brj-23026	84	29	laboratory	laboratory	NOUN
brj-23026	84	30	.	.	PUNCT
brj-23026	85	1	in	in	ADP
brj-23026	85	2	the	the	DET
brj-23026	85	3	test	test	NOUN
brj-23026	85	4	analysis	analysis	NOUN
brj-23026	85	5	,	,	PUNCT
brj-23026	85	6	nirsa	nirsa	PROPN
brj-23026	85	7	provided	provide	VERB
brj-23026	85	8	similar	similar	ADJ
brj-23026	85	9	results	result	NOUN
brj-23026	85	10	to	to	ADP
brj-23026	85	11	the	the	DET
brj-23026	85	12	unscramblertm	unscramblertm	ADJ
brj-23026	85	13	software	software	NOUN
brj-23026	85	14	(	(	PUNCT
brj-23026	85	15	xiong	xiong	PROPN
brj-23026	85	16	et	et	PROPN
brj-23026	85	17	al	al	PROPN
brj-23026	85	18	.	.	PROPN
brj-23026	85	19	2016	2016	NUM
brj-23026	85	20	)	)	PUNCT
brj-23026	85	21	.	.	PUNCT
brj-23026	86	1	the	the	DET
brj-23026	86	2	swcss	swcss	PROPN
brj-23026	86	3	algorithm	algorithm	PROPN
brj-23026	86	4	as	as	ADV
brj-23026	86	5	well	well	ADV
brj-23026	86	6	as	as	ADP
brj-23026	86	7	wavelength	wavelength	NOUN
brj-23026	86	8	selection	selection	NOUN
brj-23026	86	9	algorithms	algorithm	NOUN
brj-23026	86	10	such	such	ADJ
brj-23026	86	11	as	as	ADP
brj-23026	86	12	cars	car	NOUN
brj-23026	86	13	were	be	AUX
brj-23026	86	14	run	run	VERB
brj-23026	86	15	using	use	VERB
brj-23026	86	16	matlab	matlab	PROPN
brj-23026	86	17	2016	2016	NUM
brj-23026	86	18	software	software	NOUN
brj-23026	86	19	.	.	PUNCT
brj-23026	87	1	results	result	NOUN
brj-23026	87	2	and	and	CCONJ
brj-23026	87	3	discussion	discussion	NOUN
brj-23026	87	4	stable	stable	ADJ
brj-23026	87	5	consistent	consistent	ADJ
brj-23026	87	6	wavelength	wavelength	NOUN
brj-23026	87	7	screening	screening	NOUN
brj-23026	87	8	based	base	VERB
brj-23026	87	9	on	on	ADP
brj-23026	87	10	lignin	lignin	NOUN
brj-23026	87	11	using	use	VERB
brj-23026	87	12	the	the	DET
brj-23026	87	13	standardized	standardized	ADJ
brj-23026	87	14	preprocessing	preprocessing	NOUN
brj-23026	87	15	method	method	NOUN
brj-23026	87	16	combined	combine	VERB
brj-23026	87	17	with	with	ADP
brj-23026	87	18	the	the	DET
brj-23026	87	19	swcss	swcss	PROPN
brj-23026	87	20	method	method	NOUN
brj-23026	87	21	to	to	PART
brj-23026	87	22	screen	screen	VERB
brj-23026	87	23	wavelengths	wavelength	NOUN
brj-23026	87	24	with	with	ADP
brj-23026	87	25	consistent	consistent	ADJ
brj-23026	87	26	and	and	CCONJ
brj-23026	87	27	stable	stable	ADJ
brj-23026	87	28	spectral	spectral	ADJ
brj-23026	87	29	signals	signal	NOUN
brj-23026	87	30	between	between	ADP
brj-23026	87	31	master	master	NOUN
brj-23026	87	32	and	and	CCONJ
brj-23026	87	33	target	target	NOUN
brj-23026	87	34	spectrometers	spectrometer	NOUN
brj-23026	87	35	,	,	PUNCT
brj-23026	87	36	the	the	DET
brj-23026	87	37	number	number	NOUN
brj-23026	87	38	of	of	ADP
brj-23026	87	39	selected	select	VERB
brj-23026	87	40	wavelengths	wavelength	NOUN
brj-23026	87	41	and	and	CCONJ
brj-23026	87	42	wavelength	wavelength	NOUN
brj-23026	87	43	points	point	NOUN
brj-23026	87	44	were	be	AUX
brj-23026	87	45	the	the	DET
brj-23026	87	46	same	same	ADJ
brj-23026	87	47	,	,	PUNCT
brj-23026	87	48	regardless	regardless	ADV
brj-23026	87	49	of	of	ADP
brj-23026	87	50	how	how	SCONJ
brj-23026	87	51	many	many	ADJ
brj-23026	87	52	samples	sample	NOUN
brj-23026	87	53	were	be	AUX
brj-23026	87	54	taken	take	VERB
brj-23026	87	55	to	to	PART
brj-23026	87	56	screen	screen	VERB
brj-23026	87	57	the	the	DET
brj-23026	87	58	wavelengths	wavelength	NOUN
brj-23026	87	59	.	.	PUNCT
brj-23026	88	1	therefore	therefore	ADV
brj-23026	88	2	,	,	PUNCT
brj-23026	88	3	five	five	NUM
brj-23026	88	4	representative	representative	ADJ
brj-23026	88	5	samples	sample	NOUN
brj-23026	88	6	were	be	AUX
brj-23026	88	7	selected	select	VERB
brj-23026	88	8	for	for	ADP
brj-23026	88	9	use	use	NOUN
brj-23026	88	10	in	in	ADP
brj-23026	88	11	the	the	DET
brj-23026	88	12	swcss	swcss	PROPN
brj-23026	88	13	algorithm	algorithm	NOUN
brj-23026	88	14	to	to	PART
brj-23026	88	15	screen	screen	VERB
brj-23026	88	16	for	for	ADP
brj-23026	88	17	consistent	consistent	ADJ
brj-23026	88	18	wavelengths	wavelength	NOUN
brj-23026	88	19	(	(	PUNCT
brj-23026	88	20	denoted	denote	VERB
brj-23026	88	21	as	as	ADP
brj-23026	88	22	u1	u1	NOUN
brj-23026	88	23	and	and	CCONJ
brj-23026	88	24	u2	u2	NOUN
brj-23026	88	25	)	)	PUNCT
brj-23026	88	26	between	between	ADP
brj-23026	88	27	the	the	DET
brj-23026	88	28	master	master	NOUN
brj-23026	88	29	and	and	CCONJ
brj-23026	88	30	target	target	VERB
brj-23026	88	31	1	1	NUM
brj-23026	88	32	and	and	CCONJ
brj-23026	88	33	target	target	VERB
brj-23026	88	34	2	2	NUM
brj-23026	88	35	spectrometers	spectrometer	NOUN
brj-23026	88	36	,	,	PUNCT
brj-23026	88	37	respectively	respectively	ADV
brj-23026	88	38	.	.	PUNCT
brj-23026	89	1	the	the	DET
brj-23026	89	2	wavelength	wavelength	NOUN
brj-23026	89	3	sets	set	VERB
brj-23026	89	4	u1	u1	NOUN
brj-23026	89	5	and	and	CCONJ
brj-23026	89	6	u2	u2	NOUN
brj-23026	89	7	were	be	AUX
brj-23026	89	8	utilized	utilize	VERB
brj-23026	89	9	to	to	PART
brj-23026	89	10	model	model	VERB
brj-23026	89	11	the	the	DET
brj-23026	89	12	host	host	NOUN
brj-23026	89	13	and	and	CCONJ
brj-23026	89	14	predict	predict	VERB
brj-23026	89	15	the	the	DET
brj-23026	89	16	variation	variation	NOUN
brj-23026	89	17	of	of	ADP
brj-23026	89	18	rmsep	rmsep	NOUN
brj-23026	89	19	with	with	ADP
brj-23026	89	20	b	b	NOUN
brj-23026	89	21	-	-	PUNCT
brj-23026	89	22	value	value	NOUN
brj-23026	89	23	for	for	ADP
brj-23026	89	24	the	the	DET
brj-23026	89	25	prediction	prediction	NOUN
brj-23026	89	26	set	set	VERB
brj-23026	89	27	samples	sample	NOUN
brj-23026	89	28	of	of	ADP
brj-23026	89	29	target	target	NOUN
brj-23026	89	30	1	1	NUM
brj-23026	89	31	and	and	CCONJ
brj-23026	89	32	target	target	VERB
brj-23026	89	33	2	2	NUM
brj-23026	89	34	,	,	PUNCT
brj-23026	89	35	respectively	respectively	ADV
brj-23026	89	36	(	(	PUNCT
brj-23026	89	37	fig	fig	NOUN
brj-23026	89	38	.	.	NOUN
brj-23026	90	1	1	1	NUM
brj-23026	90	2	)	)	PUNCT
brj-23026	90	3	.	.	PUNCT
brj-23026	91	1	the	the	DET
brj-23026	91	2	rmsep	rmsep	ADJ
brj-23026	91	3	values	value	NOUN
brj-23026	91	4	of	of	ADP
brj-23026	91	5	the	the	DET
brj-23026	91	6	target	target	NOUN
brj-23026	91	7	samples	sample	NOUN
brj-23026	91	8	analyzed	analyze	VERB
brj-23026	91	9	using	use	VERB
brj-23026	91	10	the	the	DET
brj-23026	91	11	master	master	NOUN
brj-23026	91	12	models	model	NOUN
brj-23026	91	13	built	build	VERB
brj-23026	91	14	by	by	ADP
brj-23026	91	15	u1	u1	NOUN
brj-23026	91	16	and	and	CCONJ
brj-23026	91	17	u2	u2	NOUN
brj-23026	91	18	when	when	SCONJ
brj-23026	91	19	the	the	DET
brj-23026	91	20	b	b	PROPN
brj-23026	91	21	value	value	NOUN
brj-23026	91	22	is	be	AUX
brj-23026	91	23	too	too	ADV
brj-23026	91	24	small	small	ADJ
brj-23026	91	25	were	be	AUX
brj-23026	91	26	8.8832	8.8832	NUM
brj-23026	91	27	and	and	CCONJ
brj-23026	91	28	4.4979	4.4979	NUM
brj-23026	91	29	,	,	PUNCT
brj-23026	91	30	respectively	respectively	ADV
brj-23026	91	31	,	,	PUNCT
brj-23026	91	32	which	which	PRON
brj-23026	91	33	is	be	AUX
brj-23026	91	34	a	a	DET
brj-23026	91	35	poor	poor	ADJ
brj-23026	91	36	prediction	prediction	NOUN
brj-23026	91	37	.	.	PUNCT
brj-23026	92	1	this	this	PRON
brj-23026	92	2	indicates	indicate	VERB
brj-23026	92	3	that	that	SCONJ
brj-23026	92	4	the	the	DET
brj-23026	92	5	number	number	NOUN
brj-23026	92	6	of	of	ADP
brj-23026	92	7	consistent	consistent	ADJ
brj-23026	92	8	wavelengths	wavelength	NOUN
brj-23026	92	9	selected	select	VERB
brj-23026	92	10	for	for	ADP
brj-23026	92	11	the	the	DET
brj-23026	92	12	consistency	consistency	NOUN
brj-23026	92	13	parameter	parameter	NOUN
brj-23026	92	14	b=1	b=1	PROPN
brj-23026	92	15	was	be	AUX
brj-23026	92	16	too	too	ADV
brj-23026	92	17	small	small	ADJ
brj-23026	92	18	,	,	PUNCT
brj-23026	92	19	which	which	PRON
brj-23026	92	20	will	will	AUX
brj-23026	92	21	lose	lose	VERB
brj-23026	92	22	many	many	ADJ
brj-23026	92	23	important	important	ADJ
brj-23026	92	24	information	information	NOUN
brj-23026	92	25	that	that	PRON
brj-23026	92	26	is	be	AUX
brj-23026	92	27	beneficial	beneficial	ADJ
brj-23026	92	28	to	to	ADP
brj-23026	92	29	the	the	DET
brj-23026	92	30	modeling	modeling	NOUN
brj-23026	92	31	,	,	PUNCT
brj-23026	92	32	resulting	result	VERB
brj-23026	92	33	in	in	ADP
brj-23026	92	34	that	that	SCONJ
brj-23026	92	35	the	the	DET
brj-23026	92	36	transfer	transfer	NOUN
brj-23026	92	37	performance	performance	NOUN
brj-23026	92	38	of	of	ADP
brj-23026	92	39	the	the	DET
brj-23026	92	40	calibration	calibration	NOUN
brj-23026	92	41	model	model	NOUN
brj-23026	92	42	built	build	VERB
brj-23026	92	43	by	by	ADP
brj-23026	92	44	the	the	DET
brj-23026	92	45	swcss	swcss	PROPN
brj-23026	92	46	method	method	NOUN
brj-23026	92	47	will	will	AUX
brj-23026	92	48	be	be	AUX
brj-23026	92	49	poor	poor	ADJ
brj-23026	92	50	.	.	PUNCT
brj-23026	93	1	therefore	therefore	ADV
brj-23026	93	2	,	,	PUNCT
brj-23026	93	3	during	during	ADP
brj-23026	93	4	the	the	DET
brj-23026	93	5	experiment	experiment	NOUN
brj-23026	93	6	,	,	PUNCT
brj-23026	93	7	u1	u1	NOUN
brj-23026	93	8	and	and	CCONJ
brj-23026	93	9	u2	u2	NOUN
brj-23026	93	10	were	be	AUX
brj-23026	93	11	screened	screen	VERB
brj-23026	93	12	by	by	ADP
brj-23026	93	13	setting	set	VERB
brj-23026	93	14	b	b	NOUN
brj-23026	93	15	to	to	PART
brj-23026	93	16	take	take	VERB
brj-23026	93	17	1	1	NUM
brj-23026	93	18	to	to	PART
brj-23026	93	19	10	10	NUM
brj-23026	93	20	.	.	PUNCT
brj-23026	94	1	both	both	PRON
brj-23026	94	2	u1	u1	VERB
brj-23026	94	3	and	and	CCONJ
brj-23026	94	4	u2	u2	PROPN
brj-23026	94	5	used	use	VERB
brj-23026	94	6	the	the	DET
brj-23026	94	7	minimum	minimum	ADJ
brj-23026	94	8	value	value	NOUN
brj-23026	94	9	of	of	ADP
brj-23026	94	10	rmsep	rmsep	NOUN
brj-23026	94	11	predicted	predict	VERB
brj-23026	94	12	by	by	ADP
brj-23026	94	13	the	the	DET
brj-23026	94	14	plsr	plsr	PROPN
brj-23026	94	15	model	model	NOUN
brj-23026	94	16	of	of	ADP
brj-23026	94	17	the	the	DET
brj-23026	94	18	host	host	NOUN
brj-23026	94	19	of	of	ADP
brj-23026	94	20	lignin	lignin	NOUN
brj-23026	94	21	indicators	indicator	NOUN
brj-23026	94	22	for	for	ADP
brj-23026	94	23	the	the	DET
brj-23026	94	24	2	2	NUM
brj-23026	94	25	target	target	NOUN
brj-23026	94	26	samples	sample	NOUN
brj-23026	94	27	as	as	ADP
brj-23026	94	28	the	the	DET
brj-23026	94	29	criterion	criterion	NOUN
brj-23026	94	30	for	for	ADP
brj-23026	94	31	selecting	select	VERB
brj-23026	94	32	the	the	DET
brj-23026	94	33	appropriate	appropriate	ADJ
brj-23026	94	34	b	b	NOUN
brj-23026	94	35	value	value	NOUN
brj-23026	94	36	,	,	PUNCT
brj-23026	94	37	and	and	CCONJ
brj-23026	94	38	the	the	DET
brj-23026	94	39	standard	standard	ADJ
brj-23026	94	40	deviation	deviation	NOUN
brj-23026	94	41	was	be	AUX
brj-23026	94	42	calculated	calculate	VERB
brj-23026	94	43	between	between	ADP
brj-23026	94	44	the	the	DET
brj-23026	94	45	master	master	NOUN
brj-23026	94	46	and	and	CCONJ
brj-23026	94	47	the	the	DET
brj-23026	94	48	targets	target	NOUN
brj-23026	94	49	1	1	NUM
brj-23026	94	50	and	and	CCONJ
brj-23026	94	51	2	2	NUM
brj-23026	94	52	,	,	PUNCT
brj-23026	94	53	sddsi1	sddsi1	PROPN
brj-23026	94	54	and	and	CCONJ
brj-23026	94	55	sddsi2	sddsi2	PROPN
brj-23026	94	56	.	.	PUNCT
brj-23026	95	1	taking	take	VERB
brj-23026	95	2	the	the	DET
brj-23026	95	3	intersection	intersection	NOUN
brj-23026	95	4	of	of	ADP
brj-23026	95	5	u1	u1	NOUN
brj-23026	95	6	and	and	CCONJ
brj-23026	95	7	u2	u2	NOUN
brj-23026	95	8	yields	yield	NOUN
brj-23026	95	9	the	the	DET
brj-23026	95	10	intersection	intersection	NOUN
brj-23026	95	11	set	set	NOUN
brj-23026	95	12	of	of	ADP
brj-23026	95	13	uc	uc	PROPN
brj-23026	95	14	,	,	PUNCT
brj-23026	95	15	containing	contain	VERB
brj-23026	95	16	465	465	NUM
brj-23026	95	17	wavelength	wavelength	NOUN
brj-23026	95	18	points	point	NOUN
brj-23026	95	19	.	.	PUNCT
brj-23026	96	1	the	the	DET
brj-23026	96	2	consistent	consistent	ADJ
brj-23026	96	3	wavelength	wavelength	NOUN
brj-23026	96	4	set	set	NOUN
brj-23026	96	5	uc	uc	ADP
brj-23026	96	6	screened	screen	VERB
brj-23026	96	7	by	by	ADP
brj-23026	96	8	the	the	DET
brj-23026	96	9	swcss	swcss	PROPN
brj-23026	96	10	method	method	NOUN
brj-23026	96	11	is	be	AUX
brj-23026	96	12	shown	show	VERB
brj-23026	96	13	in	in	ADP
brj-23026	96	14	fig	fig	NOUN
brj-23026	96	15	.	.	PUNCT
brj-23026	97	1	2	2	NUM
brj-23026	97	2	.	.	X
brj-23026	97	3	peer	peer	NOUN
brj-23026	97	4	-	-	PUNCT
brj-23026	97	5	reviewed	review	VERB
brj-23026	97	6	article	article	NOUN
brj-23026	97	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	97	8	liu	liu	PROPN
brj-23026	97	9	et	et	PROPN
brj-23026	97	10	al	al	PROPN
brj-23026	97	11	.	.	PROPN
brj-23026	98	1	(	(	PUNCT
brj-23026	98	2	2024	2024	NUM
brj-23026	98	3	)	)	PUNCT
brj-23026	98	4	.	.	PUNCT
brj-23026	99	1	“	"	PUNCT
brj-23026	99	2	nir	nir	ADJ
brj-23026	99	3	lignin	lignin	NOUN
brj-23026	99	4	model	model	NOUN
brj-23026	99	5	transfer	transfer	NOUN
brj-23026	99	6	coupling	coupling	NOUN
brj-23026	99	7	,	,	PUNCT
brj-23026	99	8	”	"	PUNCT
brj-23026	99	9	bioresources	bioresource	NOUN
brj-23026	99	10	19(1	19(1	NUM
brj-23026	99	11	)	)	PUNCT
brj-23026	99	12	,	,	PUNCT
brj-23026	99	13	245	245	NUM
brj-23026	99	14	-	-	SYM
brj-23026	99	15	256	256	NUM
brj-23026	99	16	.	.	PUNCT
brj-23026	100	1	250	250	NUM
brj-23026	100	2	fig	fig	NOUN
brj-23026	100	3	.	.	PUNCT
brj-23026	101	1	1	1	X
brj-23026	101	2	.	.	X
brj-23026	102	1	the	the	DET
brj-23026	102	2	change	change	NOUN
brj-23026	102	3	of	of	ADP
brj-23026	102	4	rmsep	rmsep	NOUN
brj-23026	102	5	with	with	ADP
brj-23026	102	6	b	b	NOUN
brj-23026	102	7	value	value	NOUN
brj-23026	102	8	of	of	ADP
brj-23026	102	9	two	two	NUM
brj-23026	102	10	targets	target	NOUN
brj-23026	102	11	samples	sample	NOUN
brj-23026	102	12	analyzed	analyze	VERB
brj-23026	102	13	by	by	ADP
brj-23026	102	14	u1	u1	NOUN
brj-23026	102	15	and	and	CCONJ
brj-23026	102	16	u2	u2	PROPN
brj-23026	102	17	modeling	model	VERB
brj-23026	102	18	the	the	DET
brj-23026	102	19	lignin	lignin	NOUN
brj-23026	102	20	-	-	PUNCT
brj-23026	102	21	based	base	VERB
brj-23026	102	22	consistency	consistency	NOUN
brj-23026	102	23	wavelength	wavelength	NOUN
brj-23026	102	24	points	point	NOUN
brj-23026	102	25	uc	uc	INTJ
brj-23026	102	26	selected	select	VERB
brj-23026	102	27	using	use	VERB
brj-23026	102	28	the	the	DET
brj-23026	102	29	swcss	swcss	PROPN
brj-23026	102	30	method	method	NOUN
brj-23026	102	31	are	be	AUX
brj-23026	102	32	mainly	mainly	ADV
brj-23026	102	33	located	locate	VERB
brj-23026	102	34	in	in	ADP
brj-23026	102	35	the	the	DET
brj-23026	102	36	regions	region	NOUN
brj-23026	102	37	where	where	SCONJ
brj-23026	102	38	the	the	DET
brj-23026	102	39	standard	standard	ADJ
brj-23026	102	40	deviations	deviation	NOUN
brj-23026	102	41	sddsi1	sddsi1	NOUN
brj-23026	102	42	and	and	CCONJ
brj-23026	102	43	sddsi2	sddsi2	NOUN
brj-23026	102	44	between	between	ADP
brj-23026	102	45	the	the	DET
brj-23026	102	46	master	master	NOUN
brj-23026	102	47	and	and	CCONJ
brj-23026	102	48	the	the	DET
brj-23026	102	49	2	2	NUM
brj-23026	102	50	targets	target	NOUN
brj-23026	102	51	are	be	AUX
brj-23026	102	52	small	small	ADJ
brj-23026	102	53	,	,	PUNCT
brj-23026	102	54	and	and	CCONJ
brj-23026	102	55	in	in	ADP
brj-23026	102	56	the	the	DET
brj-23026	102	57	regions	region	NOUN
brj-23026	102	58	where	where	SCONJ
brj-23026	102	59	the	the	DET
brj-23026	102	60	differences	difference	NOUN
brj-23026	102	61	are	be	AUX
brj-23026	102	62	large	large	ADJ
brj-23026	102	63	such	such	ADJ
brj-23026	102	64	as	as	ADP
brj-23026	102	65	900	900	NUM
brj-23026	102	66	,	,	PUNCT
brj-23026	102	67	903	903	NUM
brj-23026	102	68	to	to	ADP
brj-23026	102	69	1412	1412	NUM
brj-23026	102	70	,	,	PUNCT
brj-23026	102	71	1572	1572	NUM
brj-23026	102	72	,	,	PUNCT
brj-23026	102	73	1577	1577	NUM
brj-23026	102	74	to	to	ADP
brj-23026	102	75	1583	1583	NUM
brj-23026	102	76	,	,	PUNCT
brj-23026	102	77	1586	1586	NUM
brj-23026	102	78	to	to	ADP
brj-23026	102	79	1593	1593	NUM
brj-23026	102	80	,	,	PUNCT
brj-23026	102	81	1611	1611	NUM
brj-23026	102	82	,	,	PUNCT
brj-23026	102	83	1614	1614	NUM
brj-23026	102	84	to	to	ADP
brj-23026	102	85	1619	1619	NUM
brj-23026	102	86	,	,	PUNCT
brj-23026	102	87	1627	1627	NUM
brj-23026	102	88	to	to	ADP
brj-23026	102	89	1748	1748	NUM
brj-23026	102	90	,	,	PUNCT
brj-23026	102	91	1750	1750	NUM
brj-23026	102	92	to	to	ADP
brj-23026	102	93	1942	1942	NUM
brj-23026	102	94	,	,	PUNCT
brj-23026	102	95	1974	1974	NUM
brj-23026	102	96	,	,	PUNCT
brj-23026	102	97	1976	1976	NUM
brj-23026	102	98	to	to	ADP
brj-23026	102	99	1977	1977	NUM
brj-23026	102	100	,	,	PUNCT
brj-23026	102	101	1981	1981	NUM
brj-23026	102	102	to	to	ADP
brj-23026	102	103	1984	1984	NUM
brj-23026	102	104	,	,	PUNCT
brj-23026	102	105	2007	2007	NUM
brj-23026	102	106	to	to	ADP
brj-23026	102	107	2008	2008	NUM
brj-23026	102	108	,	,	PUNCT
brj-23026	102	109	2010	2010	NUM
brj-23026	102	110	,	,	PUNCT
brj-23026	102	111	2115	2115	NUM
brj-23026	102	112	to	to	ADP
brj-23026	102	113	2116	2116	NUM
brj-23026	102	114	,	,	PUNCT
brj-23026	102	115	2130	2130	NUM
brj-23026	102	116	to	to	ADP
brj-23026	102	117	2135	2135	NUM
brj-23026	102	118	,	,	PUNCT
brj-23026	102	119	2139	2139	NUM
brj-23026	102	120	,	,	PUNCT
brj-23026	102	121	and	and	CCONJ
brj-23026	102	122	2189	2189	NUM
brj-23026	102	123	to	to	ADP
brj-23026	102	124	2500	2500	NUM
brj-23026	102	125	nm	nm	NOUN
brj-23026	102	126	,	,	PUNCT
brj-23026	102	127	then	then	ADV
brj-23026	102	128	none	none	NOUN
brj-23026	102	129	of	of	ADP
brj-23026	102	130	them	they	PRON
brj-23026	102	131	can	can	AUX
brj-23026	102	132	be	be	AUX
brj-23026	102	133	screened	screen	VERB
brj-23026	102	134	by	by	ADP
brj-23026	102	135	swcss	swcss	PROPN
brj-23026	102	136	algorithm	algorithm	PROPN
brj-23026	102	137	.	.	PUNCT
brj-23026	103	1	fig	fig	NOUN
brj-23026	103	2	.	.	PUNCT
brj-23026	104	1	2	2	X
brj-23026	104	2	.	.	X
brj-23026	104	3	the	the	DET
brj-23026	104	4	position	position	NOUN
brj-23026	104	5	distribution	distribution	NOUN
brj-23026	104	6	of	of	ADP
brj-23026	104	7	the	the	DET
brj-23026	104	8	consistent	consistent	ADJ
brj-23026	104	9	wavelength	wavelength	NOUN
brj-23026	104	10	set	set	NOUN
brj-23026	104	11	uc	uc	PROPN
brj-23026	104	12	selected	select	VERB
brj-23026	104	13	based	base	VERB
brj-23026	104	14	on	on	ADP
brj-23026	104	15	the	the	DET
brj-23026	104	16	swcss	swcss	PROPN
brj-23026	104	17	method	method	PROPN
brj-23026	104	18	stable	stable	ADJ
brj-23026	104	19	consistent	consistent	ADJ
brj-23026	104	20	wavelength	wavelength	NOUN
brj-23026	104	21	screening	screening	NOUN
brj-23026	104	22	based	base	VERB
brj-23026	104	23	on	on	ADP
brj-23026	104	24	lignin	lignin	PROPN
brj-23026	104	25	the	the	DET
brj-23026	104	26	spectral	spectral	ADJ
brj-23026	104	27	wavelength	wavelength	NOUN
brj-23026	104	28	selection	selection	NOUN
brj-23026	104	29	of	of	ADP
brj-23026	104	30	swcss	swcss	PROPN
brj-23026	104	31	is	be	AUX
brj-23026	104	32	only	only	ADV
brj-23026	104	33	for	for	ADP
brj-23026	104	34	wavelength	wavelength	NOUN
brj-23026	104	35	points	point	NOUN
brj-23026	104	36	with	with	ADP
brj-23026	104	37	little	little	ADJ
brj-23026	104	38	inter	inter	ADJ
brj-23026	104	39	-	-	ADJ
brj-23026	104	40	instrumental	instrumental	ADJ
brj-23026	104	41	difference	difference	NOUN
brj-23026	104	42	,	,	PUNCT
brj-23026	104	43	and	and	CCONJ
brj-23026	104	44	its	its	PRON
brj-23026	104	45	screening	screening	NOUN
brj-23026	104	46	results	result	NOUN
brj-23026	104	47	may	may	AUX
brj-23026	104	48	contain	contain	VERB
brj-23026	104	49	wavelengths	wavelength	NOUN
brj-23026	104	50	with	with	ADP
brj-23026	104	51	no	no	DET
brj-23026	104	52	or	or	CCONJ
brj-23026	104	53	little	little	ADJ
brj-23026	104	54	information	information	NOUN
brj-23026	104	55	,	,	PUNCT
brj-23026	104	56	which	which	PRON
brj-23026	104	57	may	may	AUX
brj-23026	104	58	adversely	adversely	ADV
brj-23026	104	59	affect	affect	VERB
brj-23026	104	60	the	the	DET
brj-23026	104	61	calibration	calibration	NOUN
brj-23026	104	62	model	model	NOUN
brj-23026	104	63	,	,	PUNCT
brj-23026	104	64	so	so	SCONJ
brj-23026	104	65	this	this	PRON
brj-23026	104	66	necessitates	necessitate	VERB
brj-23026	104	67	wavelength	wavelength	NOUN
brj-23026	104	68	band	band	NOUN
brj-23026	104	69	optimization	optimization	NOUN
brj-23026	104	70	of	of	ADP
brj-23026	104	71	the	the	DET
brj-23026	104	72	uc	uc	PROPN
brj-23026	104	73	wavelength	wavelength	NOUN
brj-23026	104	74	set	set	VERB
brj-23026	104	75	to	to	PART
brj-23026	104	76	obtain	obtain	VERB
brj-23026	104	77	a	a	DET
brj-23026	104	78	more	more	ADV
brj-23026	104	79	reliable	reliable	ADJ
brj-23026	104	80	calibration	calibration	NOUN
brj-23026	104	81	model	model	NOUN
brj-23026	104	82	.	.	PUNCT
brj-23026	105	1	based	base	VERB
brj-23026	105	2	on	on	ADP
brj-23026	105	3	the	the	DET
brj-23026	105	4	uc	uc	PROPN
brj-23026	105	5	wavelength	wavelength	NOUN
brj-23026	105	6	set	set	NOUN
brj-23026	105	7	,	,	PUNCT
brj-23026	105	8	the	the	DET
brj-23026	105	9	cars	car	NOUN
brj-23026	105	10	algorithm	algorithm	NOUN
brj-23026	105	11	is	be	AUX
brj-23026	105	12	used	use	VERB
brj-23026	105	13	to	to	PART
brj-23026	105	14	optimize	optimize	VERB
brj-23026	105	15	the	the	DET
brj-23026	105	16	uc	uc	PROPN
brj-23026	105	17	wavelength	wavelength	NOUN
brj-23026	105	18	set	set	VERB
brj-23026	105	19	to	to	PART
brj-23026	105	20	obtain	obtain	VERB
brj-23026	105	21	a	a	DET
brj-23026	105	22	new	new	ADJ
brj-23026	105	23	set	set	NOUN
brj-23026	105	24	of	of	ADP
brj-23026	105	25	wavelengths	wavelength	NOUN
brj-23026	105	26	containing	contain	VERB
brj-23026	105	27	24	24	NUM
brj-23026	105	28	0	0	NUM
brj-23026	105	29	2	2	NUM
brj-23026	105	30	4	4	NUM
brj-23026	105	31	6	6	NUM
brj-23026	105	32	8	8	NUM
brj-23026	105	33	10	10	NUM
brj-23026	105	34	0	0	NUM
brj-23026	105	35	2	2	NUM
brj-23026	105	36	4	4	NUM
brj-23026	105	37	6	6	NUM
brj-23026	105	38	8	8	NUM
brj-23026	105	39	10	10	NUM
brj-23026	105	40	r	r	NOUN
brj-23026	105	41	m	m	NOUN
brj-23026	105	42	s	s	NOUN
brj-23026	105	43	e	e	NOUN
brj-23026	105	44	p	p	PROPN
brj-23026	105	45	b	b	PROPN
brj-23026	105	46	value	value	NOUN
brj-23026	105	47	target	target	NOUN
brj-23026	105	48	1	1	NUM
brj-23026	105	49	target	target	NOUN
brj-23026	105	50	2	2	NUM
brj-23026	105	51	900	900	NUM
brj-23026	105	52	1100	1100	NUM
brj-23026	105	53	1300	1300	NUM
brj-23026	105	54	1500	1500	NUM
brj-23026	105	55	1700	1700	NUM
brj-23026	105	56	1900	1900	NUM
brj-23026	105	57	2100	2100	NUM
brj-23026	105	58	2300	2300	NUM
brj-23026	105	59	2500	2500	NUM
brj-23026	105	60	0.1	0.1	NUM
brj-23026	105	61	0.2	0.2	NUM
brj-23026	105	62	0.3	0.3	NUM
brj-23026	105	63	0.4	0.4	NUM
brj-23026	105	64	0.5	0.5	NUM
brj-23026	105	65	0.6	0.6	NUM
brj-23026	105	66	a	a	DET
brj-23026	105	67	b	b	PROPN
brj-23026	105	68	s	s	PART
brj-23026	105	69	o	o	NOUN
brj-23026	105	70	rb	rb	NOUN
brj-23026	105	71	a	a	PRON
brj-23026	105	72	n	n	NOUN
brj-23026	105	73	c	c	NOUN
brj-23026	105	74	e	e	NOUN
brj-23026	105	75	wavelength(nm	wavelength(nm	PROPN
brj-23026	105	76	)	)	PUNCT
brj-23026	105	77	sddsi1	sddsi1	PROPN
brj-23026	105	78	sddsi2	sddsi2	NOUN
brj-23026	105	79	swcss	swcss	PROPN
brj-23026	105	80	peer	peer	NOUN
brj-23026	105	81	-	-	PUNCT
brj-23026	105	82	reviewed	review	VERB
brj-23026	105	83	article	article	NOUN
brj-23026	105	84	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	105	85	liu	liu	PROPN
brj-23026	105	86	et	et	PROPN
brj-23026	105	87	al	al	PROPN
brj-23026	105	88	.	.	PROPN
brj-23026	106	1	(	(	PUNCT
brj-23026	106	2	2024	2024	NUM
brj-23026	106	3	)	)	PUNCT
brj-23026	106	4	.	.	PUNCT
brj-23026	107	1	“	"	PUNCT
brj-23026	107	2	nir	nir	ADJ
brj-23026	107	3	lignin	lignin	NOUN
brj-23026	107	4	model	model	NOUN
brj-23026	107	5	transfer	transfer	NOUN
brj-23026	107	6	coupling	coupling	NOUN
brj-23026	107	7	,	,	PUNCT
brj-23026	107	8	”	"	PUNCT
brj-23026	107	9	bioresources	bioresource	NOUN
brj-23026	107	10	19(1	19(1	NUM
brj-23026	107	11	)	)	PUNCT
brj-23026	107	12	,	,	PUNCT
brj-23026	107	13	245	245	NUM
brj-23026	107	14	-	-	SYM
brj-23026	107	15	256	256	NUM
brj-23026	107	16	.	.	NOUN
brj-23026	107	17	251	251	NUM
brj-23026	107	18	wavelength	wavelength	NOUN
brj-23026	107	19	points	point	NOUN
brj-23026	107	20	,	,	PUNCT
brj-23026	107	21	which	which	PRON
brj-23026	107	22	accounts	account	VERB
brj-23026	107	23	for	for	ADP
brj-23026	107	24	1.50	1.50	NUM
brj-23026	107	25	%	%	NOUN
brj-23026	107	26	of	of	ADP
brj-23026	107	27	the	the	DET
brj-23026	107	28	number	number	NOUN
brj-23026	107	29	of	of	ADP
brj-23026	107	30	full	full	ADJ
brj-23026	107	31	-	-	PUNCT
brj-23026	107	32	spectrum	spectrum	NOUN
brj-23026	107	33	variables	variable	NOUN
brj-23026	107	34	,	,	PUNCT
brj-23026	107	35	as	as	SCONJ
brj-23026	107	36	shown	show	VERB
brj-23026	107	37	in	in	ADP
brj-23026	107	38	fig	fig	NOUN
brj-23026	107	39	.	.	PUNCT
brj-23026	108	1	3	3	X
brj-23026	108	2	.	.	X
brj-23026	108	3	for	for	ADP
brj-23026	108	4	comparison	comparison	NOUN
brj-23026	108	5	,	,	PUNCT
brj-23026	108	6	the	the	DET
brj-23026	108	7	lignin	lignin	NOUN
brj-23026	108	8	-	-	PUNCT
brj-23026	108	9	based	base	VERB
brj-23026	108	10	wavelength	wavelength	NOUN
brj-23026	108	11	sets	set	NOUN
brj-23026	108	12	selected	select	VERB
brj-23026	108	13	by	by	ADP
brj-23026	108	14	the	the	DET
brj-23026	108	15	fullspectrum	fullspectrum	NOUN
brj-23026	108	16	-	-	PUNCT
brj-23026	108	17	based	base	VERB
brj-23026	108	18	spa	spa	NOUN
brj-23026	108	19	,	,	PUNCT
brj-23026	108	20	uve	uve	NOUN
brj-23026	108	21	,	,	PUNCT
brj-23026	108	22	and	and	CCONJ
brj-23026	108	23	cars	car	NOUN
brj-23026	108	24	algorithms	algorithm	NOUN
brj-23026	108	25	(	(	PUNCT
brj-23026	108	26	containing	contain	VERB
brj-23026	108	27	19	19	NUM
brj-23026	108	28	,	,	PUNCT
brj-23026	108	29	1260	1260	NUM
brj-23026	108	30	,	,	PUNCT
brj-23026	108	31	and	and	CCONJ
brj-23026	108	32	34	34	NUM
brj-23026	108	33	wavelength	wavelength	NOUN
brj-23026	108	34	points	point	NOUN
brj-23026	108	35	,	,	PUNCT
brj-23026	108	36	accounting	account	VERB
brj-23026	108	37	for	for	ADP
brj-23026	108	38	1.19	1.19	NUM
brj-23026	108	39	%	%	NOUN
brj-23026	108	40	,	,	PUNCT
brj-23026	108	41	78.70	78.70	NUM
brj-23026	108	42	%	%	NOUN
brj-23026	108	43	,	,	PUNCT
brj-23026	108	44	and	and	CCONJ
brj-23026	108	45	2.12	2.12	NUM
brj-23026	108	46	%	%	NOUN
brj-23026	108	47	of	of	ADP
brj-23026	108	48	the	the	DET
brj-23026	108	49	number	number	NOUN
brj-23026	108	50	of	of	ADP
brj-23026	108	51	fullspectrum	fullspectrum	ADJ
brj-23026	108	52	variables	variable	NOUN
brj-23026	108	53	,	,	PUNCT
brj-23026	108	54	respectively	respectively	ADV
brj-23026	108	55	)	)	PUNCT
brj-23026	108	56	are	be	AUX
brj-23026	108	57	shown	show	VERB
brj-23026	108	58	in	in	ADP
brj-23026	108	59	fig	fig	NOUN
brj-23026	108	60	.	.	PUNCT
brj-23026	109	1	3	3	X
brj-23026	109	2	.	.	X
brj-23026	109	3	fig	fig	NOUN
brj-23026	109	4	.	.	PUNCT
brj-23026	110	1	3	3	X
brj-23026	110	2	.	.	X
brj-23026	110	3	the	the	DET
brj-23026	110	4	position	position	NOUN
brj-23026	110	5	distribution	distribution	NOUN
brj-23026	110	6	of	of	ADP
brj-23026	110	7	the	the	DET
brj-23026	110	8	new	new	ADJ
brj-23026	110	9	wavelength	wavelength	NOUN
brj-23026	110	10	group	group	NOUN
brj-23026	110	11	obtained	obtain	VERB
brj-23026	110	12	by	by	ADP
brj-23026	110	13	wavelength	wavelength	NOUN
brj-23026	110	14	optimization	optimization	NOUN
brj-23026	110	15	of	of	ADP
brj-23026	110	16	uc	uc	PROPN
brj-23026	110	17	and	and	CCONJ
brj-23026	110	18	full	full	ADJ
brj-23026	110	19	spectrum	spectrum	NOUN
brj-23026	110	20	lignin	lignin	NOUN
brj-23026	110	21	model	model	NOUN
brj-23026	110	22	transfer	transfer	NOUN
brj-23026	110	23	results	result	NOUN
brj-23026	110	24	and	and	CCONJ
brj-23026	110	25	analysis	analysis	NOUN
brj-23026	110	26	the	the	DET
brj-23026	110	27	master	master	NOUN
brj-23026	110	28	plsr	plsr	NOUN
brj-23026	110	29	models	model	NOUN
brj-23026	110	30	were	be	AUX
brj-23026	110	31	built	build	VERB
brj-23026	110	32	based	base	VERB
brj-23026	110	33	on	on	ADP
brj-23026	110	34	swcss	swcss	PROPN
brj-23026	110	35	and	and	CCONJ
brj-23026	110	36	uve	uve	PROPN
brj-23026	110	37	,	,	PUNCT
brj-23026	110	38	cars	car	NOUN
brj-23026	110	39	,	,	PUNCT
brj-23026	110	40	and	and	CCONJ
brj-23026	110	41	spa	spa	NOUN
brj-23026	110	42	algorithms	algorithm	NOUN
brj-23026	110	43	alone	alone	ADV
brj-23026	110	44	or	or	CCONJ
brj-23026	110	45	in	in	ADP
brj-23026	110	46	combination	combination	NOUN
brj-23026	110	47	,	,	PUNCT
brj-23026	110	48	and	and	CCONJ
brj-23026	110	49	the	the	DET
brj-23026	110	50	appropriate	appropriate	ADJ
brj-23026	110	51	number	number	NOUN
brj-23026	110	52	of	of	ADP
brj-23026	110	53	latent	latent	NOUN
brj-23026	110	54	variables	variable	NOUN
brj-23026	110	55	(	(	PUNCT
brj-23026	110	56	lv	lv	PROPN
brj-23026	110	57	)	)	PUNCT
brj-23026	110	58	was	be	AUX
brj-23026	110	59	selected	select	VERB
brj-23026	110	60	by	by	ADP
brj-23026	110	61	cross	cross	NOUN
brj-23026	110	62	-	-	NOUN
brj-23026	110	63	validation	validation	ADJ
brj-23026	110	64	using	use	VERB
brj-23026	110	65	the	the	DET
brj-23026	110	66	leave	leave	VERB
brj-23026	110	67	-	-	PUNCT
brj-23026	110	68	one	one	NUM
brj-23026	110	69	-	-	PUNCT
brj-23026	110	70	out	out	ADP
brj-23026	110	71	method	method	NOUN
brj-23026	110	72	.	.	PUNCT
brj-23026	111	1	the	the	DET
brj-23026	111	2	results	result	NOUN
brj-23026	111	3	are	be	AUX
brj-23026	111	4	shown	show	VERB
brj-23026	111	5	in	in	ADP
brj-23026	111	6	table	table	NOUN
brj-23026	111	7	2	2	NUM
brj-23026	111	8	.	.	PUNCT
brj-23026	111	9	table	table	NOUN
brj-23026	111	10	2	2	NUM
brj-23026	111	11	.	.	PUNCT
brj-23026	111	12	master	master	NOUN
brj-23026	111	13	models	model	NOUN
brj-23026	111	14	built	build	VERB
brj-23026	111	15	at	at	ADP
brj-23026	111	16	different	different	ADJ
brj-23026	111	17	wavelength	wavelength	NOUN
brj-23026	111	18	sets	set	NOUN
brj-23026	111	19	and	and	CCONJ
brj-23026	111	20	their	their	PRON
brj-23026	111	21	analytical	analytical	ADJ
brj-23026	111	22	results	result	NOUN
brj-23026	111	23	for	for	ADP
brj-23026	111	24	master	master	NOUN
brj-23026	111	25	samples	sample	NOUN
brj-23026	111	26	method	method	VERB
brj-23026	111	27	lv	lv	PROPN
brj-23026	111	28	correction	correction	NOUN
brj-23026	111	29	set	set	VERB
brj-23026	111	30	prediction	prediction	NOUN
brj-23026	111	31	set	set	NOUN
brj-23026	111	32	r	r	NOUN
brj-23026	111	33	rpd	rpd	NOUN
brj-23026	111	34	rmsecv	rmsecv	NOUN
brj-23026	112	1	r	r	PROPN
brj-23026	112	2	rpd	rpd	PROPN
brj-23026	112	3	rmsep	rmsep	PROPN
brj-23026	112	4	full	full	ADJ
brj-23026	112	5	spectrum	spectrum	NOUN
brj-23026	112	6	9	9	NUM
brj-23026	112	7	0.9858	0.9858	NUM
brj-23026	112	8	5.9468	5.9468	NUM
brj-23026	112	9	0.8050	0.8050	NUM
brj-23026	113	1	0.9843	0.9843	NUM
brj-23026	113	2	5.6438	5.6438	NUM
brj-23026	113	3	0.9426	0.9426	NUM
brj-23026	113	4	swcss	swcs	VERB
brj-23026	113	5	7	7	NUM
brj-23026	113	6	0.9686	0.9686	NUM
brj-23026	113	7	3.8169	3.8169	NUM
brj-23026	113	8	1.2541	1.2541	NUM
brj-23026	113	9	0.9637	0.9637	NUM
brj-23026	113	10	3.4229	3.4229	NUM
brj-23026	113	11	1.3985	1.3985	NUM
brj-23026	113	12	uve	uve	NOUN
brj-23026	113	13	10	10	NUM
brj-23026	113	14	0.9815	0.9815	NUM
brj-23026	113	15	5.0194	5.0194	NUM
brj-23026	113	16	0.9537	0.9537	NUM
brj-23026	113	17	0.9801	0.9801	NUM
brj-23026	113	18	5.0252	5.0252	NUM
brj-23026	113	19	1.0587	1.0587	NUM
brj-23026	113	20	cars	car	NOUN
brj-23026	113	21	6	6	NUM
brj-23026	113	22	0.9866	0.9866	NUM
brj-23026	113	23	6.1199	6.1199	NUM
brj-23026	113	24	0.8689	0.8689	NUM
brj-23026	113	25	0.9738	0.9738	NUM
brj-23026	113	26	4.3058	4.3058	NUM
brj-23026	113	27	1.1118	1.1118	NUM
brj-23026	113	28	spa	spa	NOUN
brj-23026	113	29	9	9	NUM
brj-23026	113	30	0.9896	0.9896	NUM
brj-23026	113	31	6.9338	6.9338	NUM
brj-23026	113	32	0.7671	0.7671	NUM
brj-23026	113	33	0.9882	0.9882	NUM
brj-23026	113	34	6.1605	6.1605	NUM
brj-23026	113	35	0.7770	0.7770	NUM
brj-23026	113	36	swcsscars	swcsscar	VERB
brj-23026	113	37	5	5	NUM
brj-23026	113	38	0.9709	0.9709	NUM
brj-23026	113	39	4.1703	4.1703	NUM
brj-23026	113	40	1.2757	1.2757	NUM
brj-23026	113	41	0.9641	0.9641	NUM
brj-23026	113	42	3.6066	3.6066	NUM
brj-23026	113	43	1.3273	1.3273	NUM
brj-23026	113	44	1000	1000	NUM
brj-23026	113	45	1200	1200	NUM
brj-23026	113	46	1400	1400	NUM
brj-23026	113	47	1600	1600	NUM
brj-23026	113	48	1800	1800	NUM
brj-23026	113	49	2000	2000	NUM
brj-23026	113	50	2200	2200	NUM
brj-23026	113	51	2400	2400	NUM
brj-23026	113	52	0.1	0.1	NUM
brj-23026	113	53	0.2	0.2	NUM
brj-23026	113	54	0.3	0.3	NUM
brj-23026	113	55	0.4	0.4	NUM
brj-23026	113	56	0.5	0.5	NUM
brj-23026	113	57	0.6	0.6	NUM
brj-23026	113	58	0.7	0.7	NUM
brj-23026	113	59	0.8	0.8	NUM
brj-23026	113	60	a	a	DET
brj-23026	113	61	b	b	PROPN
brj-23026	113	62	s	s	PART
brj-23026	113	63	o	o	NOUN
brj-23026	113	64	rb	rb	NOUN
brj-23026	113	65	a	a	DET
brj-23026	113	66	n	n	NOUN
brj-23026	113	67	c	c	NOUN
brj-23026	113	68	e	e	NOUN
brj-23026	113	69	wavelength(nm	wavelength(nm	PROPN
brj-23026	113	70	)	)	PUNCT
brj-23026	113	71	master	master	NOUN
brj-23026	113	72	original	original	ADJ
brj-23026	113	73	spectrum	spectrum	NOUN
brj-23026	113	74	swcss	swcss	PROPN
brj-23026	113	75	(	(	PUNCT
brj-23026	113	76	421	421	NUM
brj-23026	113	77	)	)	PUNCT
brj-23026	113	78	cars	car	NOUN
brj-23026	113	79	(	(	PUNCT
brj-23026	113	80	34	34	NUM
brj-23026	113	81	)	)	PUNCT
brj-23026	113	82	spa(19	spa(19	PROPN
brj-23026	113	83	)	)	PUNCT
brj-23026	113	84	uve(1260	uve(1260	X
brj-23026	113	85	)	)	PUNCT
brj-23026	113	86	swcss	swcss	NOUN
brj-23026	113	87	-	-	PUNCT
brj-23026	113	88	cars	car	NOUN
brj-23026	113	89	(	(	PUNCT
brj-23026	113	90	24	24	NUM
brj-23026	113	91	)	)	PUNCT
brj-23026	113	92	peer	peer	NOUN
brj-23026	113	93	-	-	PUNCT
brj-23026	113	94	reviewed	review	VERB
brj-23026	113	95	article	article	NOUN
brj-23026	113	96	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	113	97	liu	liu	PROPN
brj-23026	113	98	et	et	PROPN
brj-23026	113	99	al	al	PROPN
brj-23026	113	100	.	.	PROPN
brj-23026	114	1	(	(	PUNCT
brj-23026	114	2	2024	2024	NUM
brj-23026	114	3	)	)	PUNCT
brj-23026	114	4	.	.	PUNCT
brj-23026	115	1	“	"	PUNCT
brj-23026	115	2	nir	nir	ADJ
brj-23026	115	3	lignin	lignin	NOUN
brj-23026	115	4	model	model	NOUN
brj-23026	115	5	transfer	transfer	NOUN
brj-23026	115	6	coupling	coupling	NOUN
brj-23026	115	7	,	,	PUNCT
brj-23026	115	8	”	"	PUNCT
brj-23026	115	9	bioresources	bioresource	NOUN
brj-23026	115	10	19(1	19(1	NUM
brj-23026	115	11	)	)	PUNCT
brj-23026	115	12	,	,	PUNCT
brj-23026	115	13	245	245	NUM
brj-23026	115	14	-	-	SYM
brj-23026	115	15	256	256	NUM
brj-23026	115	16	.	.	NUM
brj-23026	115	17	252	252	NUM
brj-23026	115	18	fig	fig	NOUN
brj-23026	115	19	.	.	PUNCT
brj-23026	116	1	4	4	X
brj-23026	116	2	.	.	X
brj-23026	116	3	the	the	DET
brj-23026	116	4	correlation	correlation	NOUN
brj-23026	116	5	between	between	ADP
brj-23026	116	6	the	the	DET
brj-23026	116	7	measured	measured	ADJ
brj-23026	116	8	value	value	NOUN
brj-23026	116	9	and	and	CCONJ
brj-23026	116	10	the	the	DET
brj-23026	116	11	predicted	predict	VERB
brj-23026	116	12	value	value	NOUN
brj-23026	116	13	of	of	ADP
brj-23026	116	14	the	the	DET
brj-23026	116	15	model	model	NOUN
brj-23026	116	16	established	establish	VERB
brj-23026	116	17	by	by	ADP
brj-23026	116	18	different	different	ADJ
brj-23026	116	19	wavelength	wavelength	NOUN
brj-23026	116	20	sets	set	NOUN
brj-23026	116	21	for	for	ADP
brj-23026	116	22	the	the	DET
brj-23026	116	23	analysis	analysis	NOUN
brj-23026	116	24	of	of	ADP
brj-23026	116	25	the	the	DET
brj-23026	116	26	master	master	NOUN
brj-23026	116	27	sample	sample	VERB
brj-23026	116	28	the	the	DET
brj-23026	116	29	distribution	distribution	NOUN
brj-23026	116	30	of	of	ADP
brj-23026	116	31	predicted	predict	VERB
brj-23026	116	32	and	and	CCONJ
brj-23026	116	33	measured	measure	VERB
brj-23026	116	34	lignin	lignin	NOUN
brj-23026	116	35	values	value	NOUN
brj-23026	116	36	for	for	ADP
brj-23026	116	37	the	the	DET
brj-23026	116	38	target	target	NOUN
brj-23026	116	39	samples	sample	NOUN
brj-23026	116	40	analyzed	analyze	VERB
brj-23026	116	41	by	by	ADP
brj-23026	116	42	the	the	DET
brj-23026	116	43	eight	eight	NUM
brj-23026	116	44	host	host	NOUN
brj-23026	116	45	calibration	calibration	NOUN
brj-23026	116	46	models	model	NOUN
brj-23026	116	47	in	in	ADP
brj-23026	116	48	table	table	NOUN
brj-23026	116	49	2	2	NUM
brj-23026	116	50	is	be	AUX
brj-23026	116	51	shown	show	VERB
brj-23026	116	52	in	in	ADP
brj-23026	116	53	fig	fig	NOUN
brj-23026	116	54	.	.	PUNCT
brj-23026	117	1	4	4	X
brj-23026	117	2	.	.	X
brj-23026	117	3	the	the	DET
brj-23026	117	4	prediction	prediction	NOUN
brj-23026	117	5	results	result	NOUN
brj-23026	117	6	of	of	ADP
brj-23026	117	7	these	these	DET
brj-23026	117	8	host	host	NOUN
brj-23026	117	9	correction	correction	NOUN
brj-23026	117	10	models	model	NOUN
brj-23026	117	11	for	for	ADP
brj-23026	117	12	target	target	NOUN
brj-23026	117	13	samples	sample	NOUN
brj-23026	117	14	exceeded	exceed	VERB
brj-23026	117	15	3.0	3.0	NUM
brj-23026	117	16	,	,	PUNCT
brj-23026	117	17	indicating	indicate	VERB
brj-23026	117	18	that	that	SCONJ
brj-23026	117	19	the	the	DET
brj-23026	117	20	host	host	NOUN
brj-23026	117	21	models	model	NOUN
brj-23026	117	22	constructed	construct	VERB
brj-23026	117	23	on	on	ADP
brj-23026	117	24	the	the	DET
brj-23026	117	25	basis	basis	NOUN
brj-23026	117	26	of	of	ADP
brj-23026	117	27	the	the	DET
brj-23026	117	28	above	above	ADJ
brj-23026	117	29	different	different	ADJ
brj-23026	117	30	wavelength	wavelength	NOUN
brj-23026	117	31	selection	selection	NOUN
brj-23026	117	32	methods	method	NOUN
brj-23026	117	33	can	can	AUX
brj-23026	117	34	meet	meet	VERB
brj-23026	117	35	the	the	DET
brj-23026	117	36	needs	need	NOUN
brj-23026	117	37	of	of	ADP
brj-23026	117	38	practical	practical	ADJ
brj-23026	117	39	applications	application	NOUN
brj-23026	117	40	.	.	PUNCT
brj-23026	118	1	the	the	DET
brj-23026	118	2	28	28	NUM
brj-23026	118	3	prediction	prediction	NOUN
brj-23026	118	4	set	set	VERB
brj-23026	118	5	samples	sample	NOUN
brj-23026	118	6	from	from	ADP
brj-23026	118	7	the	the	DET
brj-23026	118	8	2	2	NUM
brj-23026	118	9	targets	target	NOUN
brj-23026	118	10	were	be	AUX
brj-23026	118	11	analyzed	analyze	VERB
brj-23026	118	12	separately	separately	ADV
brj-23026	118	13	using	use	VERB
brj-23026	118	14	the	the	DET
brj-23026	118	15	models	model	NOUN
brj-23026	118	16	in	in	ADP
brj-23026	118	17	table	table	NOUN
brj-23026	118	18	2	2	NUM
brj-23026	118	19	,	,	PUNCT
brj-23026	118	20	and	and	CCONJ
brj-23026	118	21	the	the	DET
brj-23026	118	22	results	result	NOUN
brj-23026	118	23	are	be	AUX
brj-23026	118	24	shown	show	VERB
brj-23026	118	25	in	in	ADP
brj-23026	118	26	table	table	NOUN
brj-23026	118	27	3	3	NUM
brj-23026	118	28	.	.	PUNCT
brj-23026	119	1	when	when	SCONJ
brj-23026	119	2	the	the	DET
brj-23026	119	3	2	2	NUM
brj-23026	119	4	target	target	NOUN
brj-23026	119	5	sample	sample	NOUN
brj-23026	119	6	sets	set	NOUN
brj-23026	119	7	were	be	AUX
brj-23026	119	8	directly	directly	ADV
brj-23026	119	9	substituted	substitute	VERB
brj-23026	119	10	into	into	ADP
brj-23026	119	11	the	the	DET
brj-23026	119	12	host	host	NOUN
brj-23026	119	13	model	model	NOUN
brj-23026	119	14	for	for	ADP
brj-23026	119	15	prediction	prediction	NOUN
brj-23026	119	16	without	without	ADP
brj-23026	119	17	model	model	NOUN
brj-23026	119	18	transfer	transfer	NOUN
brj-23026	119	19	,	,	PUNCT
brj-23026	119	20	the	the	DET
brj-23026	119	21	rmsep	rmsep	NOUN
brj-23026	119	22	of	of	ADP
brj-23026	119	23	the	the	DET
brj-23026	119	24	prediction	prediction	NOUN
brj-23026	119	25	set	set	NOUN
brj-23026	119	26	increased	increase	VERB
brj-23026	119	27	from	from	ADP
brj-23026	119	28	the	the	DET
brj-23026	119	29	original	original	ADJ
brj-23026	119	30	0.9426	0.9426	NUM
brj-23026	119	31	to	to	ADP
brj-23026	119	32	2.4872	2.4872	NUM
brj-23026	119	33	and	and	CCONJ
brj-23026	119	34	2.7488	2.7488	NUM
brj-23026	119	35	,	,	PUNCT
brj-23026	119	36	and	and	CCONJ
brj-23026	119	37	the	the	DET
brj-23026	119	38	prediction	prediction	NOUN
brj-23026	119	39	results	result	NOUN
brj-23026	119	40	exhibited	exhibit	VERB
brj-23026	119	41	a	a	DET
brj-23026	119	42	large	large	ADJ
brj-23026	119	43	deviation	deviation	NOUN
brj-23026	119	44	,	,	PUNCT
brj-23026	119	45	which	which	PRON
brj-23026	119	46	indicates	indicate	VERB
brj-23026	119	47	that	that	SCONJ
brj-23026	119	48	the	the	DET
brj-23026	119	49	target	target	NOUN
brj-23026	119	50	samples	sample	NOUN
brj-23026	119	51	can	can	AUX
brj-23026	119	52	not	not	PART
brj-23026	119	53	be	be	AUX
brj-23026	119	54	directly	directly	ADV
brj-23026	119	55	applied	apply	VERB
brj-23026	119	56	to	to	ADP
brj-23026	119	57	the	the	DET
brj-23026	119	58	host	host	NOUN
brj-23026	119	59	model	model	NOUN
brj-23026	119	60	,	,	PUNCT
brj-23026	119	61	and	and	CCONJ
brj-23026	119	62	it	it	PRON
brj-23026	119	63	is	be	AUX
brj-23026	119	64	necessary	necessary	ADJ
brj-23026	119	65	to	to	PART
brj-23026	119	66	carry	carry	VERB
brj-23026	119	67	out	out	ADP
brj-23026	119	68	model	model	NOUN
brj-23026	119	69	transfer	transfer	NOUN
brj-23026	119	70	for	for	ADP
brj-23026	119	71	the	the	DET
brj-23026	119	72	target	target	NOUN
brj-23026	119	73	samples	sample	NOUN
brj-23026	119	74	.	.	PUNCT
brj-23026	120	1	after	after	ADP
brj-23026	120	2	model	model	NOUN
brj-23026	120	3	transfer	transfer	NOUN
brj-23026	120	4	using	use	VERB
brj-23026	120	5	different	different	ADJ
brj-23026	120	6	wavelength	wavelength	NOUN
brj-23026	120	7	selection	selection	NOUN
brj-23026	120	8	methods	method	NOUN
brj-23026	120	9	,	,	PUNCT
brj-23026	120	10	the	the	DET
brj-23026	120	11	prediction	prediction	NOUN
brj-23026	120	12	accuracies	accuracy	NOUN
brj-23026	120	13	of	of	ADP
brj-23026	120	14	the	the	DET
brj-23026	120	15	established	establish	VERB
brj-23026	120	16	models	model	NOUN
brj-23026	120	17	for	for	ADP
brj-23026	120	18	the	the	DET
brj-23026	120	19	target	target	NOUN
brj-23026	120	20	samples	sample	NOUN
brj-23026	120	21	were	be	AUX
brj-23026	120	22	different	different	ADJ
brj-23026	120	23	,	,	PUNCT
brj-23026	120	24	among	among	ADP
brj-23026	120	25	which	which	PRON
brj-23026	120	26	the	the	DET
brj-23026	120	27	swcss	swcss	PROPN
brj-23026	120	28	-	-	PUNCT
brj-23026	120	29	cars	car	NOUN
brj-23026	120	30	method	method	NOUN
brj-23026	120	31	exhibited	exhibit	VERB
brj-23026	120	32	the	the	DET
brj-23026	120	33	highest	high	ADJ
brj-23026	120	34	accuracy	accuracy	NOUN
brj-23026	120	35	and	and	CCONJ
brj-23026	120	36	stability	stability	NOUN
brj-23026	120	37	,	,	PUNCT
brj-23026	120	38	and	and	CCONJ
brj-23026	120	39	the	the	DET
brj-23026	120	40	transfer	transfer	NOUN
brj-23026	120	41	efficiency	efficiency	NOUN
brj-23026	120	42	was	be	AUX
brj-23026	120	43	significantly	significantly	ADV
brj-23026	120	44	improved	improve	VERB
brj-23026	120	45	.	.	PUNCT
brj-23026	121	1	after	after	ADP
brj-23026	121	2	model	model	NOUN
brj-23026	121	3	transfer	transfer	NOUN
brj-23026	121	4	with	with	ADP
brj-23026	121	5	the	the	DET
brj-23026	121	6	swcsscars	swcsscar	NOUN
brj-23026	121	7	method	method	NOUN
brj-23026	121	8	,	,	PUNCT
brj-23026	121	9	the	the	DET
brj-23026	121	10	rmsep	rmsep	NOUN
brj-23026	121	11	of	of	ADP
brj-23026	121	12	the	the	DET
brj-23026	121	13	two	two	NUM
brj-23026	121	14	targets	target	NOUN
brj-23026	121	15	decreased	decrease	VERB
brj-23026	121	16	to	to	ADP
brj-23026	121	17	1.5016	1.5016	NUM
brj-23026	121	18	and	and	CCONJ
brj-23026	121	19	1.4726	1.4726	NUM
brj-23026	121	20	,	,	PUNCT
brj-23026	121	21	respectively	respectively	ADV
brj-23026	121	22	,	,	PUNCT
brj-23026	121	23	and	and	CCONJ
brj-23026	121	24	the	the	DET
brj-23026	121	25	aic	aic	PROPN
brj-23026	121	26	value	value	NOUN
brj-23026	121	27	decreased	decrease	VERB
brj-23026	121	28	from	from	ADP
brj-23026	121	29	3198.70	3198.70	NUM
brj-23026	121	30	to	to	ADP
brj-23026	121	31	63.86	63.86	NUM
brj-23026	121	32	.	.	PUNCT
brj-23026	122	1	those	those	DET
brj-23026	122	2	values	value	NOUN
brj-23026	122	3	are	be	AUX
brj-23026	122	4	better	well	ADJ
brj-23026	122	5	than	than	ADP
brj-23026	122	6	the	the	DET
brj-23026	122	7	prediction	prediction	NOUN
brj-23026	122	8	effectiveness	effectiveness	NOUN
brj-23026	122	9	and	and	CCONJ
brj-23026	122	10	efficiency	efficiency	NOUN
brj-23026	122	11	of	of	ADP
brj-23026	122	12	the	the	DET
brj-23026	122	13	swcss	swcss	PROPN
brj-23026	122	14	method	method	NOUN
brj-23026	122	15	alone	alone	ADV
brj-23026	122	16	.	.	PUNCT
brj-23026	123	1	this	this	PRON
brj-23026	123	2	is	be	AUX
brj-23026	123	3	due	due	ADJ
brj-23026	123	4	to	to	ADP
brj-23026	123	5	the	the	DET
brj-23026	123	6	fact	fact	NOUN
brj-23026	123	7	that	that	SCONJ
brj-23026	123	8	the	the	DET
brj-23026	123	9	cars	car	NOUN
brj-23026	123	10	method	method	NOUN
brj-23026	123	11	uses	use	VERB
brj-23026	123	12	adaptive	adaptive	ADJ
brj-23026	123	13	reweighted	reweighted	ADJ
brj-23026	123	14	sampling	sample	VERB
brj-23026	123	15	(	(	PUNCT
brj-23026	123	16	ars	ar	NOUN
brj-23026	123	17	)	)	PUNCT
brj-23026	123	18	to	to	PART
brj-23026	123	19	select	select	VERB
brj-23026	123	20	wavelengths	wavelength	NOUN
brj-23026	123	21	,	,	PUNCT
brj-23026	123	22	and	and	CCONJ
brj-23026	123	23	the	the	DET
brj-23026	123	24	wavelength	wavelength	NOUN
brj-23026	123	25	variable	variable	NOUN
brj-23026	123	26	corresponding	correspond	VERB
brj-23026	123	27	to	to	ADP
brj-23026	123	28	the	the	DET
brj-23026	123	29	model	model	NOUN
brj-23026	123	30	with	with	ADP
brj-23026	123	31	the	the	DET
brj-23026	123	32	smallest	small	ADJ
brj-23026	123	33	cross	cross	ADJ
brj-23026	123	34	-	-	ADJ
brj-23026	123	35	validated	validated	ADJ
brj-23026	123	36	root	root	NOUN
brj-23026	123	37	mean	mean	NOUN
brj-23026	123	38	square	square	ADJ
brj-23026	123	39	error	error	NOUN
brj-23026	123	40	(	(	PUNCT
brj-23026	123	41	rmsep	rmsep	PROPN
brj-23026	123	42	)	)	PUNCT
brj-23026	123	43	is	be	AUX
brj-23026	123	44	selected	select	VERB
brj-23026	123	45	as	as	ADP
brj-23026	123	46	the	the	DET
brj-23026	123	47	characteristic	characteristic	ADJ
brj-23026	123	48	wavelength	wavelength	NOUN
brj-23026	123	49	variable	variable	NOUN
brj-23026	123	50	in	in	ADP
brj-23026	123	51	plsr	plsr	PROPN
brj-23026	123	52	modeling	modeling	NOUN
brj-23026	123	53	,	,	PUNCT
brj-23026	123	54	which	which	PRON
brj-23026	123	55	effectively	effectively	ADV
brj-23026	123	56	eliminates	eliminate	VERB
brj-23026	123	57	the	the	DET
brj-23026	123	58	information	information	NOUN
brj-23026	123	59	content	content	NOUN
brj-23026	123	60	of	of	ADP
brj-23026	123	61	the	the	DET
brj-23026	123	62	wavelengths	wavelength	NOUN
brj-23026	123	63	selected	select	VERB
brj-23026	123	64	by	by	ADP
brj-23026	123	65	the	the	DET
brj-23026	123	66	single	single	ADJ
brj-23026	123	67	swcss	swcss	PROPN
brj-23026	123	68	method	method	NOUN
brj-23026	123	69	.	.	PUNCT
brj-23026	124	1	wavelengths	wavelength	NOUN
brj-23026	124	2	with	with	ADP
brj-23026	124	3	little	little	ADJ
brj-23026	124	4	or	or	CCONJ
brj-23026	124	5	no	no	PRON
brj-23026	124	6	information	information	NOUN
brj-23026	124	7	content	content	NOUN
brj-23026	124	8	and	and	CCONJ
brj-23026	124	9	uninformative	uninformative	ADJ
brj-23026	124	10	wavelength	wavelength	NOUN
brj-23026	124	11	variables	variable	NOUN
brj-23026	124	12	present	present	ADJ
brj-23026	124	13	in	in	ADP
brj-23026	124	14	the	the	DET
brj-23026	124	15	wavelengths	wavelength	NOUN
brj-23026	124	16	.	.	PUNCT
brj-23026	125	1	therefore	therefore	ADV
brj-23026	125	2	,	,	PUNCT
brj-23026	125	3	the	the	DET
brj-23026	125	4	wavelengths	wavelength	NOUN
brj-23026	125	5	selected	select	VERB
brj-23026	125	6	by	by	ADP
brj-23026	125	7	the	the	DET
brj-23026	125	8	swcss	swcss	PROPN
brj-23026	125	9	-	-	PUNCT
brj-23026	125	10	cars	car	NOUN
brj-23026	125	11	method	method	NOUN
brj-23026	125	12	have	have	VERB
brj-23026	125	13	better	well	ADJ
brj-23026	125	14	stability	stability	NOUN
brj-23026	125	15	and	and	CCONJ
brj-23026	125	16	transmission	transmission	NOUN
brj-23026	125	17	efficiency	efficiency	NOUN
brj-23026	125	18	than	than	ADP
brj-23026	125	19	the	the	DET
brj-23026	125	20	swcss	swcss	PROPN
brj-23026	125	21	method	method	NOUN
brj-23026	125	22	alone	alone	ADV
brj-23026	125	23	.	.	PUNCT
brj-23026	126	1	tables	table	NOUN
brj-23026	126	2	2	2	NUM
brj-23026	126	3	and	and	CCONJ
brj-23026	126	4	3	3	NUM
brj-23026	126	5	show	show	VERB
brj-23026	126	6	that	that	SCONJ
brj-23026	126	7	the	the	DET
brj-23026	126	8	models	model	NOUN
brj-23026	126	9	built	build	VERB
brj-23026	126	10	based	base	VERB
brj-23026	126	11	on	on	ADP
brj-23026	126	12	the	the	DET
brj-23026	126	13	wavelength	wavelength	NOUN
brj-23026	126	14	sets	set	NOUN
brj-23026	126	15	screened	screen	VERB
brj-23026	126	16	by	by	ADP
brj-23026	126	17	the	the	DET
brj-23026	126	18	full	full	ADJ
brj-23026	126	19	-	-	PUNCT
brj-23026	126	20	spectrum	spectrum	NOUN
brj-23026	126	21	uve	uve	NOUN
brj-23026	126	22	,	,	PUNCT
brj-23026	126	23	cars	car	NOUN
brj-23026	126	24	,	,	PUNCT
brj-23026	126	25	and	and	CCONJ
brj-23026	126	26	spa	spa	NOUN
brj-23026	126	27	algorithms	algorithm	NOUN
brj-23026	126	28	had	have	VERB
brj-23026	126	29	a	a	DET
brj-23026	126	30	somewhat	somewhat	ADV
brj-23026	126	31	improved	improve	VERB
brj-23026	126	32	ability	ability	NOUN
brj-23026	126	33	to	to	PART
brj-23026	126	34	analyse	analyse	VERB
brj-23026	126	35	the	the	DET
brj-23026	126	36	host	host	NOUN
brj-23026	126	37	samples	sample	NOUN
brj-23026	126	38	,	,	PUNCT
brj-23026	126	39	but	but	CCONJ
brj-23026	126	40	a	a	DET
brj-23026	126	41	poorer	poor	ADJ
brj-23026	126	42	ability	ability	NOUN
brj-23026	126	43	to	to	PART
brj-23026	126	44	analyse	analyse	VERB
brj-23026	126	45	the	the	DET
brj-23026	126	46	lignin	lignin	NOUN
brj-23026	126	47	content	content	NOUN
brj-23026	126	48	of	of	ADP
brj-23026	126	49	the	the	DET
brj-23026	126	50	two	two	NUM
brj-23026	126	51	target	target	NOUN
brj-23026	126	52	samples	sample	NOUN
brj-23026	126	53	.	.	PUNCT
brj-23026	127	1	this	this	PRON
brj-23026	127	2	is	be	AUX
brj-23026	127	3	due	due	ADJ
brj-23026	127	4	to	to	ADP
brj-23026	127	5	the	the	DET
brj-23026	127	6	fact	fact	NOUN
brj-23026	127	7	that	that	SCONJ
brj-23026	127	8	none	none	NOUN
brj-23026	127	9	of	of	ADP
brj-23026	127	10	the	the	DET
brj-23026	127	11	three	three	NUM
brj-23026	127	12	methods	method	NOUN
brj-23026	127	13	mentioned	mention	VERB
brj-23026	127	14	above	above	ADV
brj-23026	127	15	is	be	AUX
brj-23026	127	16	17	17	NUM
brj-23026	127	17	19	19	NUM
brj-23026	127	18	21	21	NUM
brj-23026	127	19	23	23	NUM
brj-23026	127	20	25	25	NUM
brj-23026	127	21	27	27	NUM
brj-23026	127	22	29	29	NUM
brj-23026	127	23	31	31	NUM
brj-23026	127	24	33	33	NUM
brj-23026	127	25	35	35	NUM
brj-23026	127	26	18	18	NUM
brj-23026	127	27	20	20	NUM
brj-23026	127	28	22	22	NUM
brj-23026	127	29	24	24	NUM
brj-23026	127	30	26	26	NUM
brj-23026	127	31	28	28	NUM
brj-23026	127	32	30	30	NUM
brj-23026	127	33	32	32	NUM
brj-23026	127	34	34	34	NUM
brj-23026	127	35	36	36	NUM
brj-23026	127	36	full	full	ADJ
brj-23026	127	37	spectrum	spectrum	NOUN
brj-23026	127	38	swcss	swcss	PROPN
brj-23026	127	39	uve	uve	PROPN
brj-23026	127	40	cars	car	NOUN
brj-23026	127	41	spa	spa	VERB
brj-23026	127	42	swcss	swcss	PROPN
brj-23026	127	43	-	-	PUNCT
brj-23026	127	44	cars	car	NOUN
brj-23026	127	45	p	p	NOUN
brj-23026	127	46	re	re	ADP
brj-23026	127	47	d	d	NOUN
brj-23026	127	48	ic	ic	X
brj-23026	127	49	te	te	PROPN
brj-23026	127	50	d	d	PROPN
brj-23026	127	51	v	v	PROPN
brj-23026	127	52	a	a	DET
brj-23026	127	53	lu	lu	NOUN
brj-23026	127	54	e	e	NOUN
brj-23026	127	55	measured	measure	VERB
brj-23026	127	56	value	value	NOUN
brj-23026	127	57	peer	peer	NOUN
brj-23026	127	58	-	-	PUNCT
brj-23026	127	59	reviewed	review	VERB
brj-23026	127	60	article	article	NOUN
brj-23026	127	61	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	127	62	liu	liu	PROPN
brj-23026	127	63	et	et	PROPN
brj-23026	127	64	al	al	PROPN
brj-23026	127	65	.	.	PROPN
brj-23026	128	1	(	(	PUNCT
brj-23026	128	2	2024	2024	NUM
brj-23026	128	3	)	)	PUNCT
brj-23026	128	4	.	.	PUNCT
brj-23026	129	1	“	"	PUNCT
brj-23026	129	2	nir	nir	ADJ
brj-23026	129	3	lignin	lignin	NOUN
brj-23026	129	4	model	model	NOUN
brj-23026	129	5	transfer	transfer	NOUN
brj-23026	129	6	coupling	coupling	NOUN
brj-23026	129	7	,	,	PUNCT
brj-23026	129	8	”	"	PUNCT
brj-23026	129	9	bioresources	bioresource	NOUN
brj-23026	129	10	19(1	19(1	NUM
brj-23026	129	11	)	)	PUNCT
brj-23026	129	12	,	,	PUNCT
brj-23026	129	13	245	245	NUM
brj-23026	129	14	-	-	SYM
brj-23026	129	15	256	256	NUM
brj-23026	129	16	.	.	PUNCT
brj-23026	129	17	253	253	NUM
brj-23026	129	18	based	base	VERB
brj-23026	129	19	on	on	ADP
brj-23026	129	20	stable	stable	ADJ
brj-23026	129	21	wavelength	wavelength	NOUN
brj-23026	129	22	screening	screening	NOUN
brj-23026	129	23	,	,	PUNCT
brj-23026	129	24	and	and	CCONJ
brj-23026	129	25	most	most	ADJ
brj-23026	129	26	of	of	ADP
brj-23026	129	27	the	the	DET
brj-23026	129	28	selected	select	VERB
brj-23026	129	29	wavelengths	wavelength	NOUN
brj-23026	129	30	are	be	AUX
brj-23026	129	31	located	locate	VERB
brj-23026	129	32	in	in	ADP
brj-23026	129	33	the	the	DET
brj-23026	129	34	wavelength	wavelength	NOUN
brj-23026	129	35	region	region	NOUN
brj-23026	129	36	with	with	ADP
brj-23026	129	37	large	large	ADJ
brj-23026	129	38	inter	inter	ADJ
brj-23026	129	39	-	-	ADJ
brj-23026	129	40	instrumental	instrumental	ADJ
brj-23026	129	41	differences	difference	NOUN
brj-23026	129	42	.	.	PUNCT
brj-23026	130	1	this	this	DET
brj-23026	130	2	difference	difference	NOUN
brj-23026	130	3	resulted	result	VERB
brj-23026	130	4	in	in	ADP
brj-23026	130	5	the	the	DET
brj-23026	130	6	poor	poor	ADJ
brj-23026	130	7	analytical	analytical	ADJ
brj-23026	130	8	ability	ability	NOUN
brj-23026	130	9	of	of	ADP
brj-23026	130	10	the	the	DET
brj-23026	130	11	host	host	NOUN
brj-23026	130	12	plsr	plsr	NOUN
brj-23026	130	13	model	model	NOUN
brj-23026	130	14	developed	develop	VERB
brj-23026	130	15	by	by	ADP
brj-23026	130	16	the	the	DET
brj-23026	130	17	method	method	NOUN
brj-23026	130	18	to	to	PART
brj-23026	130	19	analyse	analyse	VERB
brj-23026	130	20	the	the	DET
brj-23026	130	21	spectra	spectra	NOUN
brj-23026	130	22	of	of	ADP
brj-23026	130	23	the	the	DET
brj-23026	130	24	target	target	NOUN
brj-23026	130	25	samples	sample	NOUN
brj-23026	130	26	.	.	PUNCT
brj-23026	131	1	table	table	NOUN
brj-23026	131	2	3	3	NUM
brj-23026	131	3	.	.	PUNCT
brj-23026	131	4	model	model	NOUN
brj-23026	131	5	transfer	transfer	NOUN
brj-23026	131	6	results	result	NOUN
brj-23026	131	7	of	of	ADP
brj-23026	131	8	different	different	ADJ
brj-23026	131	9	wavelength	wavelength	NOUN
brj-23026	131	10	selection	selection	NOUN
brj-23026	131	11	methods	method	NOUN
brj-23026	131	12	method	method	VERB
brj-23026	131	13	wavelength	wavelength	NOUN
brj-23026	131	14	set	set	VERB
brj-23026	131	15	aic	aic	PROPN
brj-23026	131	16	target	target	VERB
brj-23026	131	17	1	1	NUM
brj-23026	131	18	target	target	NOUN
brj-23026	131	19	2	2	NUM
brj-23026	131	20	r	r	NOUN
brj-23026	131	21	rpd	rpd	NOUN
brj-23026	131	22	rmsep	rmsep	PROPN
brj-23026	131	23	r	r	PROPN
brj-23026	131	24	rpd	rpd	PROPN
brj-23026	131	25	rmsep	rmsep	PROPN
brj-23026	131	26	full	full	ADJ
brj-23026	131	27	spectrum	spectrum	NOUN
brj-23026	131	28	1601	1601	NUM
brj-23026	131	29	3198.70	3198.70	NUM
brj-23026	131	30	0.9650	0.9650	NUM
brj-23026	131	31	1.9247	1.9247	NUM
brj-23026	131	32	2.4872	2.4872	NUM
brj-23026	131	33	0.9731	0.9731	NUM
brj-23026	131	34	1.7415	1.7415	NUM
brj-23026	131	35	2.7488	2.7488	NUM
brj-23026	131	36	swcss	swcss	PROPN
brj-23026	131	37	421	421	NUM
brj-23026	131	38	860.78	860.78	NUM
brj-23026	131	39	0.9600	0.9600	NUM
brj-23026	131	40	3.5631	3.5631	NUM
brj-23026	131	41	1.4931	1.4931	NUM
brj-23026	131	42	0.9596	0.9596	NUM
brj-23026	131	43	3.1493	3.1493	NUM
brj-23026	131	44	1.5200	1.5200	NUM
brj-23026	131	45	uve	uve	NOUN
brj-23026	131	46	1260	1260	NUM
brj-23026	131	47	2523.19	2523.19	NUM
brj-23026	131	48	0.9523	0.9523	NUM
brj-23026	131	49	1.4334	1.4334	NUM
brj-23026	131	50	3.3396	3.3396	NUM
brj-23026	131	51	0.9561	0.9561	NUM
brj-23026	131	52	1.5341	1.5341	NUM
brj-23026	131	53	3.1204	3.1204	NUM
brj-23026	131	54	cars	car	NOUN
brj-23026	131	55	34	34	NUM
brj-23026	131	56	73.93	73.93	NUM
brj-23026	131	57	0.9621	0.9621	NUM
brj-23026	131	58	1.9276	1.9276	NUM
brj-23026	131	59	2.4834	2.4834	NUM
brj-23026	131	60	0.9710	0.9710	NUM
brj-23026	131	61	2.5068	2.5068	NUM
brj-23026	131	62	1.9096	1.9096	NUM
brj-23026	131	63	spa	spa	NOUN
brj-23026	131	64	19	19	NUM
brj-23026	131	65	23.87	23.87	NUM
brj-23026	131	66	0.9490	0.9490	NUM
brj-23026	131	67	0.9748	0.9748	NUM
brj-23026	131	68	4.9107	4.9107	NUM
brj-23026	131	69	0.9610	0.9610	NUM
brj-23026	131	70	1.0622	1.0622	NUM
brj-23026	131	71	4.5065	4.5065	NUM
brj-23026	131	72	swcsscars	swcsscar	VERB
brj-23026	131	73	24	24	NUM
brj-23026	131	74	63.86	63.86	NUM
brj-23026	131	75	0.9580	0.9580	NUM
brj-23026	131	76	3.1880	3.1880	NUM
brj-23026	131	77	1.5016	1.5016	NUM
brj-23026	131	78	0.9597	0.9597	NUM
brj-23026	131	79	3.2508	3.2508	NUM
brj-23026	131	80	1.4726	1.4726	NUM
brj-23026	131	81	figure	figure	NOUN
brj-23026	131	82	5	5	NUM
brj-23026	131	83	shows	show	VERB
brj-23026	131	84	the	the	DET
brj-23026	131	85	correlation	correlation	NOUN
brj-23026	131	86	plots	plot	NOUN
brj-23026	131	87	of	of	ADP
brj-23026	131	88	the	the	DET
brj-23026	131	89	measured	measure	VERB
brj-23026	131	90	and	and	CCONJ
brj-23026	131	91	predicted	predict	VERB
brj-23026	131	92	values	value	NOUN
brj-23026	131	93	of	of	ADP
brj-23026	131	94	lignin	lignin	NOUN
brj-23026	131	95	content	content	NOUN
brj-23026	131	96	of	of	ADP
brj-23026	131	97	the	the	DET
brj-23026	131	98	2	2	NUM
brj-23026	131	99	targets	target	NOUN
brj-23026	131	100	before	before	ADV
brj-23026	131	101	and	and	CCONJ
brj-23026	131	102	after	after	ADP
brj-23026	131	103	model	model	NOUN
brj-23026	131	104	transfer	transfer	NOUN
brj-23026	131	105	using	use	VERB
brj-23026	131	106	different	different	ADJ
brj-23026	131	107	methods	method	NOUN
brj-23026	131	108	such	such	ADJ
brj-23026	131	109	as	as	ADP
brj-23026	131	110	swcss	swcss	PROPN
brj-23026	131	111	and	and	CCONJ
brj-23026	131	112	cars	car	NOUN
brj-23026	131	113	algorithms	algorithm	VERB
brj-23026	131	114	independently	independently	ADV
brj-23026	131	115	and	and	CCONJ
brj-23026	131	116	in	in	ADP
brj-23026	131	117	conjunction	conjunction	NOUN
brj-23026	131	118	,	,	PUNCT
brj-23026	131	119	as	as	ADV
brj-23026	131	120	well	well	ADV
brj-23026	131	121	as	as	ADP
brj-23026	131	122	their	their	PRON
brj-23026	131	123	distributions	distribution	NOUN
brj-23026	131	124	.	.	PUNCT
brj-23026	132	1	before	before	ADP
brj-23026	132	2	the	the	DET
brj-23026	132	3	transfer	transfer	NOUN
brj-23026	132	4	,	,	PUNCT
brj-23026	132	5	the	the	DET
brj-23026	132	6	prediction	prediction	NOUN
brj-23026	132	7	error	error	NOUN
brj-23026	132	8	was	be	AUX
brj-23026	132	9	large	large	ADJ
brj-23026	132	10	when	when	SCONJ
brj-23026	132	11	the	the	DET
brj-23026	132	12	target	target	NOUN
brj-23026	132	13	samples	sample	NOUN
brj-23026	132	14	were	be	AUX
brj-23026	132	15	directly	directly	ADV
brj-23026	132	16	substituted	substitute	VERB
brj-23026	132	17	into	into	ADP
brj-23026	132	18	the	the	DET
brj-23026	132	19	master	master	NOUN
brj-23026	132	20	model	model	NOUN
brj-23026	132	21	.	.	PUNCT
brj-23026	133	1	the	the	DET
brj-23026	133	2	separate	separate	ADJ
brj-23026	133	3	uve	uve	PROPN
brj-23026	133	4	,	,	PUNCT
brj-23026	133	5	cars	car	NOUN
brj-23026	133	6	,	,	PUNCT
brj-23026	133	7	and	and	CCONJ
brj-23026	133	8	spa	spa	NOUN
brj-23026	133	9	algorithms	algorithm	NOUN
brj-23026	133	10	were	be	AUX
brj-23026	133	11	directly	directly	ADV
brj-23026	133	12	applied	apply	VERB
brj-23026	133	13	to	to	ADP
brj-23026	133	14	the	the	DET
brj-23026	133	15	two	two	NUM
brj-23026	133	16	target	target	NOUN
brj-23026	133	17	machine	machine	NOUN
brj-23026	133	18	samples	sample	NOUN
brj-23026	133	19	with	with	ADP
brj-23026	133	20	poor	poor	ADJ
brj-23026	133	21	prediction	prediction	NOUN
brj-23026	133	22	results	result	NOUN
brj-23026	133	23	and	and	CCONJ
brj-23026	133	24	large	large	ADJ
brj-23026	133	25	vertical	vertical	ADJ
brj-23026	133	26	deviations	deviation	NOUN
brj-23026	133	27	.	.	PUNCT
brj-23026	134	1	after	after	ADP
brj-23026	134	2	applying	apply	VERB
brj-23026	134	3	the	the	DET
brj-23026	134	4	master	master	NOUN
brj-23026	134	5	lignin	lignin	NOUN
brj-23026	134	6	model	model	NOUN
brj-23026	134	7	constructed	construct	VERB
brj-23026	134	8	by	by	ADP
brj-23026	134	9	swcss	swcss	PROPN
brj-23026	134	10	and	and	CCONJ
brj-23026	134	11	swcss	swcss	PROPN
brj-23026	134	12	-	-	PUNCT
brj-23026	134	13	cars	car	NOUN
brj-23026	134	14	methods	method	NOUN
brj-23026	134	15	to	to	ADP
brj-23026	134	16	the	the	DET
brj-23026	134	17	two	two	NUM
brj-23026	134	18	target	target	NOUN
brj-23026	134	19	samples	sample	NOUN
brj-23026	134	20	,	,	PUNCT
brj-23026	134	21	the	the	DET
brj-23026	134	22	analytical	analytical	ADJ
brj-23026	134	23	errors	error	NOUN
brj-23026	134	24	were	be	AUX
brj-23026	134	25	reduced	reduce	VERB
brj-23026	134	26	.	.	PUNCT
brj-23026	135	1	among	among	ADP
brj-23026	135	2	them	they	PRON
brj-23026	135	3	,	,	PUNCT
brj-23026	135	4	the	the	DET
brj-23026	135	5	swcss	swcss	PROPN
brj-23026	135	6	-	-	PUNCT
brj-23026	135	7	cars	car	NOUN
brj-23026	135	8	method	method	NOUN
brj-23026	135	9	analyzed	analyze	VERB
brj-23026	135	10	the	the	DET
brj-23026	135	11	target	target	NOUN
brj-23026	135	12	2	2	NUM
brj-23026	135	13	samples	sample	NOUN
brj-23026	135	14	with	with	ADP
brj-23026	135	15	the	the	DET
brj-23026	135	16	smallest	small	ADJ
brj-23026	135	17	deviation	deviation	NOUN
brj-23026	135	18	between	between	ADP
brj-23026	135	19	the	the	DET
brj-23026	135	20	predicted	predict	VERB
brj-23026	135	21	values	value	NOUN
brj-23026	135	22	and	and	CCONJ
brj-23026	135	23	the	the	DET
brj-23026	135	24	measured	measured	ADJ
brj-23026	135	25	values	value	NOUN
brj-23026	135	26	,	,	PUNCT
brj-23026	135	27	which	which	PRON
brj-23026	135	28	also	also	ADV
brj-23026	135	29	indicates	indicate	VERB
brj-23026	135	30	that	that	SCONJ
brj-23026	135	31	the	the	DET
brj-23026	135	32	model	model	NOUN
brj-23026	135	33	analyzed	analyze	VERB
brj-23026	135	34	after	after	ADP
brj-23026	135	35	the	the	DET
brj-23026	135	36	delivery	delivery	NOUN
brj-23026	135	37	of	of	ADP
brj-23026	135	38	the	the	DET
brj-23026	135	39	swcss	swcss	PROPN
brj-23026	135	40	-	-	PUNCT
brj-23026	135	41	cars	car	NOUN
brj-23026	135	42	method	method	NOUN
brj-23026	135	43	is	be	AUX
brj-23026	135	44	good	good	ADJ
brj-23026	135	45	.	.	PUNCT
brj-23026	136	1	peer	peer	NOUN
brj-23026	136	2	-	-	PUNCT
brj-23026	136	3	reviewed	review	VERB
brj-23026	136	4	article	article	NOUN
brj-23026	136	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	136	6	liu	liu	PROPN
brj-23026	136	7	et	et	PROPN
brj-23026	136	8	al	al	PROPN
brj-23026	136	9	.	.	PROPN
brj-23026	137	1	(	(	PUNCT
brj-23026	137	2	2024	2024	NUM
brj-23026	137	3	)	)	PUNCT
brj-23026	137	4	.	.	PUNCT
brj-23026	138	1	“	"	PUNCT
brj-23026	138	2	nir	nir	ADJ
brj-23026	138	3	lignin	lignin	NOUN
brj-23026	138	4	model	model	NOUN
brj-23026	138	5	transfer	transfer	NOUN
brj-23026	138	6	coupling	coupling	NOUN
brj-23026	138	7	,	,	PUNCT
brj-23026	138	8	”	"	PUNCT
brj-23026	138	9	bioresources	bioresource	NOUN
brj-23026	138	10	19(1	19(1	NUM
brj-23026	138	11	)	)	PUNCT
brj-23026	138	12	,	,	PUNCT
brj-23026	138	13	245	245	NUM
brj-23026	138	14	-	-	SYM
brj-23026	138	15	256	256	NUM
brj-23026	138	16	.	.	NOUN
brj-23026	138	17	254	254	NUM
brj-23026	138	18	fig	fig	NOUN
brj-23026	138	19	.	.	PUNCT
brj-23026	139	1	5	5	X
brj-23026	139	2	.	.	X
brj-23026	139	3	correlation	correlation	NOUN
brj-23026	139	4	plots	plot	NOUN
brj-23026	139	5	of	of	ADP
brj-23026	139	6	measured	measure	VERB
brj-23026	139	7	and	and	CCONJ
brj-23026	139	8	predicted	predict	VERB
brj-23026	139	9	lignin	lignin	NOUN
brj-23026	139	10	content	content	NOUN
brj-23026	139	11	in	in	ADP
brj-23026	139	12	the	the	DET
brj-23026	139	13	prediction	prediction	NOUN
brj-23026	139	14	set	set	NOUN
brj-23026	139	15	and	and	CCONJ
brj-23026	139	16	their	their	PRON
brj-23026	139	17	distribution	distribution	NOUN
brj-23026	139	18	conclusions	conclusion	NOUN
brj-23026	139	19	1	1	NUM
brj-23026	139	20	.	.	PUNCT
brj-23026	140	1	with	with	ADP
brj-23026	140	2	the	the	DET
brj-23026	140	3	“	"	PUNCT
brj-23026	140	4	screening	screen	VERB
brj-23026	140	5	wavelengths	wavelength	NOUN
brj-23026	140	6	with	with	ADP
brj-23026	140	7	consistent	consistent	ADJ
brj-23026	140	8	stable	stable	ADJ
brj-23026	140	9	signals	signal	NOUN
brj-23026	140	10	–	–	PUNCT
brj-23026	140	11	competitive	competitive	ADJ
brj-23026	140	12	adaptive	adaptive	ADJ
brj-23026	140	13	reweighting	reweighting	NOUN
brj-23026	140	14	sampling	sampling	NOUN
brj-23026	140	15	”	"	PUNCT
brj-23026	140	16	(	(	PUNCT
brj-23026	140	17	swcss	swcss	NOUN
brj-23026	140	18	-	-	PUNCT
brj-23026	140	19	cars	car	NOUN
brj-23026	140	20	)	)	PUNCT
brj-23026	140	21	coupling	coupling	NOUN
brj-23026	140	22	method	method	NOUN
brj-23026	140	23	,	,	PUNCT
brj-23026	140	24	the	the	DET
brj-23026	140	25	wavelength	wavelength	NOUN
brj-23026	140	26	was	be	AUX
brj-23026	140	27	reduced	reduce	VERB
brj-23026	140	28	from	from	ADP
brj-23026	140	29	1601	1601	NUM
brj-23026	140	30	to	to	ADP
brj-23026	140	31	24	24	NUM
brj-23026	140	32	,	,	PUNCT
brj-23026	140	33	which	which	PRON
brj-23026	140	34	means	mean	VERB
brj-23026	140	35	that	that	SCONJ
brj-23026	140	36	the	the	DET
brj-23026	140	37	number	number	NOUN
brj-23026	140	38	of	of	ADP
brj-23026	140	39	variables	variable	NOUN
brj-23026	140	40	used	use	VERB
brj-23026	140	41	in	in	ADP
brj-23026	140	42	the	the	DET
brj-23026	140	43	modeling	modeling	NOUN
brj-23026	140	44	process	process	NOUN
brj-23026	140	45	was	be	AUX
brj-23026	140	46	greatly	greatly	ADV
brj-23026	140	47	reduced	reduce	VERB
brj-23026	140	48	.	.	PUNCT
brj-23026	141	1	for	for	ADP
brj-23026	141	2	target	target	NOUN
brj-23026	141	3	1	1	NUM
brj-23026	141	4	,	,	PUNCT
brj-23026	141	5	the	the	DET
brj-23026	141	6	value	value	NOUN
brj-23026	141	7	of	of	ADP
brj-23026	141	8	ratio	ratio	NOUN
brj-23026	141	9	of	of	ADP
brj-23026	141	10	prediction	prediction	NOUN
brj-23026	141	11	to	to	ADP
brj-23026	141	12	deviation	deviation	NOUN
brj-23026	141	13	(	(	PUNCT
brj-23026	141	14	rpd	rpd	PROPN
brj-23026	141	15	)	)	PUNCT
brj-23026	141	16	was	be	AUX
brj-23026	141	17	increased	increase	VERB
brj-23026	141	18	from	from	ADP
brj-23026	141	19	1.9247	1.9247	NUM
brj-23026	141	20	to	to	ADP
brj-23026	141	21	3.1880	3.1880	NUM
brj-23026	141	22	;	;	PUNCT
brj-23026	141	23	for	for	ADP
brj-23026	141	24	target	target	NOUN
brj-23026	141	25	2	2	NUM
brj-23026	141	26	,	,	PUNCT
brj-23026	141	27	the	the	DET
brj-23026	141	28	value	value	NOUN
brj-23026	141	29	of	of	ADP
brj-23026	141	30	rpd	rpd	PROPN
brj-23026	141	31	was	be	AUX
brj-23026	141	32	improved	improve	VERB
brj-23026	141	33	from	from	ADP
brj-23026	141	34	1.7415	1.7415	NUM
brj-23026	141	35	to	to	ADP
brj-23026	141	36	3.2508	3.2508	NUM
brj-23026	141	37	,	,	PUNCT
brj-23026	141	38	while	while	SCONJ
brj-23026	141	39	aic	aic	PROPN
brj-23026	141	40	decreased	decrease	VERB
brj-23026	141	41	from	from	ADP
brj-23026	141	42	3198.70	3198.70	NUM
brj-23026	141	43	to	to	ADP
brj-23026	141	44	63.86	63.86	NUM
brj-23026	141	45	for	for	ADP
brj-23026	141	46	both	both	PRON
brj-23026	141	47	.	.	PUNCT
brj-23026	142	1	comparative	comparative	ADJ
brj-23026	142	2	experiments	experiment	NOUN
brj-23026	142	3	show	show	VERB
brj-23026	142	4	that	that	SCONJ
brj-23026	142	5	the	the	DET
brj-23026	142	6	wavelengths	wavelength	NOUN
brj-23026	142	7	selected	select	VERB
brj-23026	142	8	using	use	VERB
brj-23026	142	9	the	the	DET
brj-23026	142	10	swcsscars	swcsscar	NOUN
brj-23026	142	11	method	method	NOUN
brj-23026	142	12	can	can	AUX
brj-23026	142	13	provide	provide	VERB
brj-23026	142	14	a	a	DET
brj-23026	142	15	more	more	ADV
brj-23026	142	16	robust	robust	ADJ
brj-23026	142	17	and	and	CCONJ
brj-23026	142	18	simple	simple	ADJ
brj-23026	142	19	correction	correction	NOUN
brj-23026	142	20	model	model	NOUN
brj-23026	142	21	for	for	ADP
brj-23026	142	22	the	the	DET
brj-23026	142	23	prediction	prediction	NOUN
brj-23026	142	24	of	of	ADP
brj-23026	142	25	lignin	lignin	NOUN
brj-23026	142	26	content	content	NOUN
brj-23026	142	27	.	.	PUNCT
brj-23026	143	1	it	it	PRON
brj-23026	143	2	can	can	AUX
brj-23026	143	3	simplify	simplify	VERB
brj-23026	143	4	the	the	DET
brj-23026	143	5	process	process	NOUN
brj-23026	143	6	of	of	ADP
brj-23026	143	7	correction	correction	NOUN
brj-23026	143	8	model	model	NOUN
brj-23026	143	9	transfer	transfer	NOUN
brj-23026	143	10	,	,	PUNCT
brj-23026	143	11	which	which	PRON
brj-23026	143	12	is	be	AUX
brj-23026	143	13	convenient	convenient	ADJ
brj-23026	143	14	for	for	ADP
brj-23026	143	15	application	application	NOUN
brj-23026	143	16	in	in	ADP
brj-23026	143	17	practice	practice	NOUN
brj-23026	143	18	.	.	PUNCT
brj-23026	144	1	2	2	X
brj-23026	144	2	.	.	X
brj-23026	144	3	in	in	ADP
brj-23026	144	4	addition	addition	NOUN
brj-23026	144	5	,	,	PUNCT
brj-23026	144	6	theoretically	theoretically	ADV
brj-23026	144	7	speaking	speak	VERB
brj-23026	144	8	,	,	PUNCT
brj-23026	144	9	besides	besides	SCONJ
brj-23026	144	10	the	the	DET
brj-23026	144	11	cars	car	NOUN
brj-23026	144	12	optimization	optimization	NOUN
brj-23026	144	13	algorithm	algorithm	NOUN
brj-23026	144	14	,	,	PUNCT
brj-23026	144	15	many	many	ADJ
brj-23026	144	16	wavelength	wavelength	NOUN
brj-23026	144	17	selection	selection	NOUN
brj-23026	144	18	methods	method	NOUN
brj-23026	144	19	can	can	AUX
brj-23026	144	20	be	be	AUX
brj-23026	144	21	used	use	VERB
brj-23026	144	22	for	for	ADP
brj-23026	144	23	the	the	DET
brj-23026	144	24	further	further	ADJ
brj-23026	144	25	optimization	optimization	NOUN
brj-23026	144	26	of	of	ADP
brj-23026	144	27	the	the	DET
brj-23026	144	28	consistent	consistent	ADJ
brj-23026	144	29	wavelength	wavelength	NOUN
brj-23026	144	30	set	set	VERB
brj-23026	144	31	uc	uc	PROPN
brj-23026	144	32	,	,	PUNCT
brj-23026	144	33	such	such	ADJ
brj-23026	144	34	as	as	ADP
brj-23026	144	35	the	the	DET
brj-23026	144	36	binary	binary	ADJ
brj-23026	144	37	dragonfly	dragonfly	PROPN
brj-23026	144	38	algorithm	algorithm	NOUN
brj-23026	144	39	(	(	PUNCT
brj-23026	144	40	bda	bda	PROPN
brj-23026	144	41	)	)	PUNCT
brj-23026	144	42	,	,	PUNCT
brj-23026	144	43	genetic	genetic	ADJ
brj-23026	144	44	algorithm	algorithm	NOUN
brj-23026	144	45	(	(	PUNCT
brj-23026	144	46	ga	ga	NOUN
brj-23026	144	47	)	)	PUNCT
brj-23026	144	48	,	,	PUNCT
brj-23026	144	49	and	and	CCONJ
brj-23026	144	50	particle	particle	NOUN
brj-23026	144	51	swarm	swarm	NOUN
brj-23026	144	52	optimization	optimization	NOUN
brj-23026	144	53	(	(	PUNCT
brj-23026	144	54	pso	pso	NOUN
brj-23026	144	55	)	)	PUNCT
brj-23026	144	56	.	.	PUNCT
brj-23026	145	1	for	for	ADP
brj-23026	145	2	the	the	DET
brj-23026	145	3	dataset	dataset	NOUN
brj-23026	145	4	used	use	VERB
brj-23026	145	5	in	in	ADP
brj-23026	145	6	this	this	DET
brj-23026	145	7	study	study	NOUN
brj-23026	145	8	,	,	PUNCT
brj-23026	145	9	not	not	PART
brj-23026	145	10	all	all	DET
brj-23026	145	11	wavelength	wavelength	NOUN
brj-23026	145	12	selection	selection	NOUN
brj-23026	145	13	methods	method	NOUN
brj-23026	145	14	combined	combine	VERB
brj-23026	145	15	with	with	ADP
brj-23026	145	16	the	the	DET
brj-23026	145	17	swcss	swcss	PROPN
brj-23026	145	18	method	method	NOUN
brj-23026	145	19	give	give	VERB
brj-23026	145	20	satisfactory	satisfactory	ADJ
brj-23026	145	21	results	result	NOUN
brj-23026	145	22	for	for	ADP
brj-23026	145	23	the	the	DET
brj-23026	145	24	delivery	delivery	NOUN
brj-23026	145	25	of	of	ADP
brj-23026	145	26	the	the	DET
brj-23026	145	27	calibration	calibration	NOUN
brj-23026	145	28	model	model	NOUN
brj-23026	145	29	.	.	PUNCT
brj-23026	146	1	16	16	NUM
brj-23026	146	2	18	18	NUM
brj-23026	146	3	20	20	NUM
brj-23026	146	4	22	22	NUM
brj-23026	146	5	24	24	NUM
brj-23026	146	6	26	26	NUM
brj-23026	146	7	28	28	NUM
brj-23026	146	8	30	30	NUM
brj-23026	146	9	32	32	NUM
brj-23026	146	10	34	34	NUM
brj-23026	146	11	36	36	NUM
brj-23026	146	12	15	15	NUM
brj-23026	146	13	20	20	NUM
brj-23026	146	14	25	25	NUM
brj-23026	146	15	30	30	NUM
brj-23026	146	16	35	35	NUM
brj-23026	146	17	40	40	NUM
brj-23026	146	18	45	45	NUM
brj-23026	146	19	(	(	PUNCT
brj-23026	146	20	a	a	NOUN
brj-23026	146	21	)	)	PUNCT
brj-23026	146	22	full	full	ADJ
brj-23026	146	23	spectrum	spectrum	NOUN
brj-23026	146	24	swcss	swcss	PROPN
brj-23026	146	25	uve	uve	PROPN
brj-23026	146	26	cars	car	NOUN
brj-23026	146	27	spa	spa	VERB
brj-23026	146	28	swcss	swcss	PROPN
brj-23026	146	29	-	-	PUNCT
brj-23026	146	30	cars	car	NOUN
brj-23026	146	31	p	p	NOUN
brj-23026	146	32	re	re	ADP
brj-23026	146	33	d	d	NOUN
brj-23026	146	34	ic	ic	X
brj-23026	146	35	te	te	PROPN
brj-23026	146	36	d	d	PROPN
brj-23026	146	37	v	v	PROPN
brj-23026	146	38	a	a	DET
brj-23026	146	39	lu	lu	NOUN
brj-23026	146	40	e	e	NOUN
brj-23026	146	41	measured	measure	VERB
brj-23026	146	42	value	value	NOUN
brj-23026	146	43	target	target	NOUN
brj-23026	146	44	1	1	NUM
brj-23026	146	45	0	0	NUM
brj-23026	146	46	5	5	NUM
brj-23026	146	47	10	10	NUM
brj-23026	146	48	15	15	NUM
brj-23026	146	49	20	20	NUM
brj-23026	146	50	25	25	NUM
brj-23026	146	51	30	30	NUM
brj-23026	146	52	15	15	NUM
brj-23026	146	53	20	20	NUM
brj-23026	146	54	25	25	NUM
brj-23026	146	55	30	30	NUM
brj-23026	146	56	35	35	NUM
brj-23026	146	57	40	40	NUM
brj-23026	146	58	45	45	NUM
brj-23026	146	59	(	(	PUNCT
brj-23026	146	60	b	b	NOUN
brj-23026	146	61	)	)	PUNCT
brj-23026	146	62	l	l	NOUN
brj-23026	146	63	ig	ig	PROPN
brj-23026	146	64	n	n	PROPN
brj-23026	146	65	in	in	ADP
brj-23026	146	66	c	c	PROPN
brj-23026	146	67	o	o	NOUN
brj-23026	146	68	n	n	ADP
brj-23026	146	69	te	te	INTJ
brj-23026	146	70	n	n	ADP
brj-23026	146	71	t	t	NOUN
brj-23026	146	72	sample	sample	NOUN
brj-23026	146	73	size	size	NOUN
brj-23026	146	74	measured	measure	VERB
brj-23026	146	75	value	value	NOUN
brj-23026	146	76	full	full	ADJ
brj-23026	146	77	spectrum	spectrum	NOUN
brj-23026	146	78	swcss	swcss	PROPN
brj-23026	146	79	uve	uve	PROPN
brj-23026	146	80	cars	car	NOUN
brj-23026	146	81	spa	spa	VERB
brj-23026	146	82	swcss	swcss	PROPN
brj-23026	146	83	-	-	PUNCT
brj-23026	146	84	cars	car	NOUN
brj-23026	146	85	target	target	VERB
brj-23026	146	86	1	1	NUM
brj-23026	146	87	16	16	NUM
brj-23026	146	88	18	18	NUM
brj-23026	146	89	20	20	NUM
brj-23026	146	90	22	22	NUM
brj-23026	146	91	24	24	NUM
brj-23026	146	92	26	26	NUM
brj-23026	146	93	28	28	NUM
brj-23026	146	94	30	30	NUM
brj-23026	146	95	32	32	NUM
brj-23026	146	96	34	34	NUM
brj-23026	146	97	36	36	NUM
brj-23026	146	98	15	15	NUM
brj-23026	146	99	20	20	NUM
brj-23026	146	100	25	25	NUM
brj-23026	146	101	30	30	NUM
brj-23026	146	102	35	35	NUM
brj-23026	146	103	40	40	NUM
brj-23026	146	104	45	45	NUM
brj-23026	146	105	(	(	PUNCT
brj-23026	146	106	c	c	NOUN
brj-23026	146	107	)	)	PUNCT
brj-23026	146	108	full	full	ADJ
brj-23026	146	109	spectrum	spectrum	NOUN
brj-23026	146	110	swcss	swcss	PROPN
brj-23026	146	111	uve	uve	PROPN
brj-23026	146	112	cars	car	NOUN
brj-23026	146	113	spa	spa	VERB
brj-23026	146	114	swcss	swcss	PROPN
brj-23026	146	115	-	-	PUNCT
brj-23026	146	116	cars	car	NOUN
brj-23026	146	117	p	p	NOUN
brj-23026	146	118	re	re	ADP
brj-23026	146	119	d	d	NOUN
brj-23026	146	120	ic	ic	X
brj-23026	146	121	te	te	PROPN
brj-23026	146	122	d	d	PROPN
brj-23026	146	123	v	v	PROPN
brj-23026	146	124	a	a	DET
brj-23026	146	125	lu	lu	NOUN
brj-23026	146	126	e	e	NOUN
brj-23026	146	127	measured	measure	VERB
brj-23026	146	128	value	value	NOUN
brj-23026	146	129	target	target	NOUN
brj-23026	146	130	2	2	NUM
brj-23026	146	131	0	0	NUM
brj-23026	146	132	5	5	NUM
brj-23026	146	133	10	10	NUM
brj-23026	146	134	15	15	NUM
brj-23026	146	135	20	20	NUM
brj-23026	146	136	25	25	NUM
brj-23026	146	137	30	30	NUM
brj-23026	146	138	15	15	NUM
brj-23026	146	139	20	20	NUM
brj-23026	146	140	25	25	NUM
brj-23026	146	141	30	30	NUM
brj-23026	146	142	35	35	NUM
brj-23026	146	143	40	40	NUM
brj-23026	146	144	45	45	NUM
brj-23026	146	145	target	target	NOUN
brj-23026	146	146	2	2	NUM
brj-23026	146	147	l	l	NOUN
brj-23026	146	148	ig	ig	PROPN
brj-23026	146	149	n	n	PROPN
brj-23026	146	150	in	in	ADP
brj-23026	146	151	c	c	PROPN
brj-23026	146	152	o	o	NOUN
brj-23026	146	153	n	n	ADP
brj-23026	146	154	te	te	INTJ
brj-23026	146	155	n	n	ADP
brj-23026	146	156	t	t	NOUN
brj-23026	146	157	sample	sample	NOUN
brj-23026	146	158	size	size	NOUN
brj-23026	146	159	measured	measure	VERB
brj-23026	146	160	value	value	NOUN
brj-23026	146	161	full	full	ADJ
brj-23026	146	162	spectrum	spectrum	NOUN
brj-23026	146	163	swcss	swcss	PROPN
brj-23026	146	164	uve	uve	PROPN
brj-23026	146	165	cars	car	NOUN
brj-23026	146	166	spa	spa	VERB
brj-23026	146	167	swcss	swcss	PROPN
brj-23026	146	168	-	-	PUNCT
brj-23026	146	169	cars	car	NOUN
brj-23026	146	170	(	(	PUNCT
brj-23026	146	171	d	d	NOUN
brj-23026	146	172	)	)	PUNCT
brj-23026	146	173	(	(	PUNCT
brj-23026	146	174	a	a	X
brj-23026	146	175	)	)	PUNCT
brj-23026	146	176	(	(	PUNCT
brj-23026	146	177	b	b	X
brj-23026	146	178	)	)	PUNCT
brj-23026	146	179	(	(	PUNCT
brj-23026	146	180	c	c	X
brj-23026	146	181	)	)	PUNCT
brj-23026	146	182	(	(	PUNCT
brj-23026	146	183	d	d	X
brj-23026	146	184	)	)	PUNCT
brj-23026	146	185	peer	peer	NOUN
brj-23026	146	186	-	-	PUNCT
brj-23026	146	187	reviewed	review	VERB
brj-23026	146	188	article	article	NOUN
brj-23026	146	189	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	146	190	liu	liu	PROPN
brj-23026	146	191	et	et	PROPN
brj-23026	146	192	al	al	PROPN
brj-23026	146	193	.	.	PROPN
brj-23026	147	1	(	(	PUNCT
brj-23026	147	2	2024	2024	NUM
brj-23026	147	3	)	)	PUNCT
brj-23026	147	4	.	.	PUNCT
brj-23026	148	1	“	"	PUNCT
brj-23026	148	2	nir	nir	ADJ
brj-23026	148	3	lignin	lignin	NOUN
brj-23026	148	4	model	model	NOUN
brj-23026	148	5	transfer	transfer	NOUN
brj-23026	148	6	coupling	coupling	NOUN
brj-23026	148	7	,	,	PUNCT
brj-23026	148	8	”	"	PUNCT
brj-23026	148	9	bioresources	bioresource	NOUN
brj-23026	148	10	19(1	19(1	NUM
brj-23026	148	11	)	)	PUNCT
brj-23026	148	12	,	,	PUNCT
brj-23026	148	13	245	245	NUM
brj-23026	148	14	-	-	SYM
brj-23026	148	15	256	256	NUM
brj-23026	148	16	.	.	PUNCT
brj-23026	148	17	255	255	NUM
brj-23026	148	18	3	3	NUM
brj-23026	148	19	.	.	PUNCT
brj-23026	149	1	at	at	ADP
brj-23026	149	2	present	present	ADJ
brj-23026	149	3	,	,	PUNCT
brj-23026	149	4	the	the	DET
brj-23026	149	5	method	method	NOUN
brj-23026	149	6	only	only	ADV
brj-23026	149	7	realizes	realize	VERB
brj-23026	149	8	model	model	NOUN
brj-23026	149	9	transfer	transfer	NOUN
brj-23026	149	10	between	between	ADP
brj-23026	149	11	the	the	DET
brj-23026	149	12	same	same	ADJ
brj-23026	149	13	type	type	NOUN
brj-23026	149	14	of	of	ADP
brj-23026	149	15	nir	nir	ADJ
brj-23026	149	16	spectrometers	spectrometer	NOUN
brj-23026	149	17	produced	produce	VERB
brj-23026	149	18	by	by	ADP
brj-23026	149	19	the	the	DET
brj-23026	149	20	same	same	ADJ
brj-23026	149	21	manufacturer	manufacturer	NOUN
brj-23026	149	22	,	,	PUNCT
brj-23026	149	23	and	and	CCONJ
brj-23026	149	24	model	model	NOUN
brj-23026	149	25	sharing	sharing	NOUN
brj-23026	149	26	between	between	ADP
brj-23026	149	27	spectroscopic	spectroscopic	ADJ
brj-23026	149	28	instruments	instrument	NOUN
brj-23026	149	29	with	with	ADP
brj-23026	149	30	different	different	ADJ
brj-23026	149	31	wavelength	wavelength	NOUN
brj-23026	149	32	intervals	interval	NOUN
brj-23026	149	33	,	,	PUNCT
brj-23026	149	34	different	different	ADJ
brj-23026	149	35	resolutions	resolution	NOUN
brj-23026	149	36	,	,	PUNCT
brj-23026	149	37	and	and	CCONJ
brj-23026	149	38	large	large	ADJ
brj-23026	149	39	differences	difference	NOUN
brj-23026	149	40	in	in	ADP
brj-23026	149	41	instrument	instrument	NOUN
brj-23026	149	42	structures	structure	NOUN
brj-23026	149	43	requires	require	VERB
brj-23026	149	44	further	further	ADJ
brj-23026	149	45	validation	validation	NOUN
brj-23026	149	46	.	.	PUNCT
brj-23026	150	1	acknowledgments	acknowledgment	NOUN
brj-23026	150	2	this	this	DET
brj-23026	150	3	work	work	NOUN
brj-23026	150	4	was	be	AUX
brj-23026	150	5	funded	fund	VERB
brj-23026	150	6	by	by	ADP
brj-23026	150	7	the	the	DET
brj-23026	150	8	support	support	NOUN
brj-23026	150	9	of	of	ADP
brj-23026	150	10	the	the	DET
brj-23026	150	11	fundamental	fundamental	ADJ
brj-23026	150	12	research	research	NOUN
brj-23026	150	13	funds	fund	NOUN
brj-23026	150	14	of	of	ADP
brj-23026	150	15	research	research	PROPN
brj-23026	150	16	institute	institute	PROPN
brj-23026	150	17	of	of	ADP
brj-23026	150	18	forest	forest	PROPN
brj-23026	150	19	new	new	ADJ
brj-23026	150	20	technology	technology	NOUN
brj-23026	150	21	,	,	PUNCT
brj-23026	150	22	caf(cafybb2019sy039	caf(cafybb2019sy039	PROPN
brj-23026	150	23	)	)	PUNCT
brj-23026	150	24	.	.	PUNCT
brj-23026	151	1	references	reference	NOUN
brj-23026	151	2	cited	cite	VERB
brj-23026	151	3	cai	cai	PROPN
brj-23026	151	4	,	,	PUNCT
brj-23026	151	5	w.	w.	PROPN
brj-23026	151	6	,	,	PUNCT
brj-23026	151	7	li	li	PROPN
brj-23026	151	8	,	,	PUNCT
brj-23026	151	9	y.	y.	PROPN
brj-23026	151	10	,	,	PUNCT
brj-23026	151	11	and	and	CCONJ
brj-23026	151	12	shao	shao	PROPN
brj-23026	151	13	x.	x.	PROPN
brj-23026	151	14	(	(	PUNCT
brj-23026	151	15	2008	2008	NUM
brj-23026	151	16	)	)	PUNCT
brj-23026	151	17	.	.	PUNCT
brj-23026	152	1	“	"	PUNCT
brj-23026	152	2	a	a	DET
brj-23026	152	3	variable	variable	ADJ
brj-23026	152	4	selection	selection	NOUN
brj-23026	152	5	method	method	NOUN
brj-23026	152	6	based	base	VERB
brj-23026	152	7	on	on	ADP
brj-23026	152	8	uninformative	uninformative	ADJ
brj-23026	152	9	variable	variable	ADJ
brj-23026	152	10	elimination	elimination	NOUN
brj-23026	152	11	for	for	ADP
brj-23026	152	12	multivariate	multivariate	NOUN
brj-23026	152	13	calibration	calibration	NOUN
brj-23026	152	14	of	of	ADP
brj-23026	152	15	near	near	ADV
brj-23026	152	16	-	-	PUNCT
brj-23026	152	17	infrared	infrared	ADJ
brj-23026	152	18	spectra	spectra	NOUN
brj-23026	152	19	,	,	PUNCT
brj-23026	152	20	”	"	PUNCT
brj-23026	152	21	chemometrics	chemometric	NOUN
brj-23026	152	22	and	and	CCONJ
brj-23026	152	23	intelligent	intelligent	ADJ
brj-23026	152	24	laboratory	laboratory	NOUN
brj-23026	152	25	systems	system	NOUN
brj-23026	152	26	90(2	90(2	NUM
brj-23026	152	27	)	)	PUNCT
brj-23026	152	28	,	,	PUNCT
brj-23026	152	29	188	188	NUM
brj-23026	152	30	-	-	SYM
brj-23026	152	31	194	194	NUM
brj-23026	152	32	.	.	PUNCT
brj-23026	153	1	doi	doi	NOUN
brj-23026	153	2	:	:	PUNCT
brj-23026	153	3	10.1016	10.1016	NUM
brj-23026	153	4	/	/	SYM
brj-23026	153	5	j.chemolab.2007.10.001	j.chemolab.2007.10.001	PROPN
brj-23026	153	6	castro	castro	PROPN
brj-23026	153	7	,	,	PUNCT
brj-23026	153	8	r.	r.	PROPN
brj-23026	153	9	c.	c.	PROPN
brj-23026	153	10	,	,	PUNCT
brj-23026	153	11	ribeiro	ribeiro	PROPN
brj-23026	153	12	,	,	PUNCT
brj-23026	153	13	d.	d.	PROPN
brj-23026	153	14	s.	s.	PROPN
brj-23026	153	15	m.	m.	PROPN
brj-23026	153	16	,	,	PUNCT
brj-23026	153	17	santos	santos	PROPN
brj-23026	153	18	,	,	PUNCT
brj-23026	153	19	j.	j.	PROPN
brj-23026	153	20	l.m	l.m	PROPN
brj-23026	153	21	.	.	PROPN
brj-23026	153	22	,	,	PUNCT
brj-23026	153	23	and	and	CCONJ
brj-23026	153	24	páscoa	páscoa	PRON
brj-23026	153	25	,	,	PUNCT
brj-23026	153	26	r.	r.	PROPN
brj-23026	153	27	n.	n.	PROPN
brj-23026	153	28	m.	m.	PROPN
brj-23026	153	29	j.	j.	PROPN
brj-23026	153	30	(	(	PUNCT
brj-23026	153	31	2023	2023	NUM
brj-23026	153	32	)	)	PUNCT
brj-23026	153	33	.	.	PUNCT
brj-23026	154	1	“	"	PUNCT
brj-23026	154	2	authentication	authentication	NOUN
brj-23026	154	3	/	/	SYM
brj-23026	154	4	discrimination	discrimination	NOUN
brj-23026	154	5	,	,	PUNCT
brj-23026	154	6	identification	identification	NOUN
brj-23026	154	7	and	and	CCONJ
brj-23026	154	8	quantification	quantification	NOUN
brj-23026	154	9	of	of	ADP
brj-23026	154	10	cinnamon	cinnamon	NOUN
brj-23026	154	11	adulterants	adulterant	NOUN
brj-23026	154	12	using	use	VERB
brj-23026	154	13	nir	nir	ADJ
brj-23026	154	14	spectroscopy	spectroscopy	NOUN
brj-23026	154	15	and	and	CCONJ
brj-23026	154	16	different	different	ADJ
brj-23026	154	17	chemometric	chemometric	ADJ
brj-23026	154	18	tools	tool	NOUN
brj-23026	154	19	:	:	PUNCT
brj-23026	154	20	a	a	DET
brj-23026	154	21	tutorial	tutorial	NOUN
brj-23026	154	22	to	to	PART
brj-23026	154	23	deal	deal	VERB
brj-23026	154	24	with	with	ADP
brj-23026	154	25	counterfeit	counterfeit	ADJ
brj-23026	154	26	samples	sample	NOUN
brj-23026	154	27	,	,	PUNCT
brj-23026	154	28	”	"	PUNCT
brj-23026	154	29	food	food	NOUN
brj-23026	154	30	control	control	NOUN
brj-23026	154	31	109619	109619	NUM
brj-23026	154	32	.	.	PUNCT
brj-23026	155	1	doi	doi	NOUN
brj-23026	155	2	:	:	PUNCT
brj-23026	155	3	10.1016	10.1016	NUM
brj-23026	155	4	/	/	SYM
brj-23026	155	5	j.foodcont.2023.109619	j.foodcont.2023.109619	PROPN
brj-23026	155	6	cortés	cortés	NOUN
brj-23026	155	7	,	,	PUNCT
brj-23026	155	8	v.	v.	PROPN
brj-23026	155	9	,	,	PUNCT
brj-23026	155	10	blasco	blasco	PROPN
brj-23026	155	11	,	,	PUNCT
brj-23026	155	12	j.	j.	PROPN
brj-23026	155	13	,	,	PUNCT
brj-23026	155	14	aleixos	aleixos	PROPN
brj-23026	155	15	,	,	PUNCT
brj-23026	155	16	n.	n.	NOUN
brj-23026	155	17	,	,	PUNCT
brj-23026	155	18	cubero	cubero	NOUN
brj-23026	155	19	,	,	PUNCT
brj-23026	155	20	s.	s.	PROPN
brj-23026	155	21	,	,	PUNCT
brj-23026	155	22	and	and	CCONJ
brj-23026	155	23	talens	talen	NOUN
brj-23026	155	24	,	,	PUNCT
brj-23026	155	25	p.	p.	NOUN
brj-23026	155	26	(	(	PUNCT
brj-23026	155	27	2019	2019	NUM
brj-23026	155	28	)	)	PUNCT
brj-23026	155	29	.	.	PUNCT
brj-23026	156	1	“	"	PUNCT
brj-23026	156	2	monitoring	monitor	VERB
brj-23026	156	3	strategies	strategy	NOUN
brj-23026	156	4	for	for	ADP
brj-23026	156	5	quality	quality	NOUN
brj-23026	156	6	control	control	NOUN
brj-23026	156	7	of	of	ADP
brj-23026	156	8	agricultural	agricultural	ADJ
brj-23026	156	9	products	product	NOUN
brj-23026	156	10	using	use	VERB
brj-23026	156	11	visible	visible	ADJ
brj-23026	156	12	and	and	CCONJ
brj-23026	156	13	near	near	ADV
brj-23026	156	14	-	-	PUNCT
brj-23026	156	15	infrared	infrared	ADJ
brj-23026	156	16	spectroscopy	spectroscopy	NOUN
brj-23026	156	17	:	:	PUNCT
brj-23026	156	18	a	a	DET
brj-23026	156	19	review	review	NOUN
brj-23026	156	20	,	,	PUNCT
brj-23026	156	21	”	"	PUNCT
brj-23026	156	22	trends	trend	NOUN
brj-23026	156	23	in	in	ADP
brj-23026	156	24	food	food	NOUN
brj-23026	156	25	science	science	NOUN
brj-23026	156	26	&	&	CCONJ
brj-23026	156	27	technology	technology	PROPN
brj-23026	156	28	85	85	NUM
brj-23026	156	29	,	,	PUNCT
brj-23026	156	30	138	138	NUM
brj-23026	156	31	-	-	SYM
brj-23026	156	32	148	148	NUM
brj-23026	156	33	.	.	PUNCT
brj-23026	157	1	doi	doi	NOUN
brj-23026	157	2	:	:	PUNCT
brj-23026	157	3	10.1016	10.1016	NUM
brj-23026	157	4	/	/	SYM
brj-23026	157	5	j.tifs.2019.01.015	j.tifs.2019.01.015	PROPN
brj-23026	157	6	dardenne	dardenne	PROPN
brj-23026	157	7	,	,	PUNCT
brj-23026	157	8	p.	p.	NOUN
brj-23026	157	9	(	(	PUNCT
brj-23026	157	10	2002	2002	NUM
brj-23026	157	11	)	)	PUNCT
brj-23026	157	12	.	.	PUNCT
brj-23026	158	1	“	"	PUNCT
brj-23026	158	2	calibration	calibration	NOUN
brj-23026	158	3	transfer	transfer	NOUN
brj-23026	158	4	in	in	ADP
brj-23026	158	5	near	near	ADP
brj-23026	158	6	infrared	infrared	ADJ
brj-23026	158	7	spectroscopy	spectroscopy	NOUN
brj-23026	158	8	,	,	PUNCT
brj-23026	158	9	”	"	PUNCT
brj-23026	158	10	nir	nir	PROPN
brj-23026	158	11	news	news	NOUN
brj-23026	158	12	13.4	13.4	NUM
brj-23026	158	13	,	,	PUNCT
brj-23026	158	14	3	3	NUM
brj-23026	158	15	-	-	SYM
brj-23026	158	16	7	7	NUM
brj-23026	158	17	.	.	PUNCT
brj-23026	158	18	doi	doi	NOUN
brj-23026	158	19	:	:	PUNCT
brj-23026	158	20	10.1255	10.1255	NUM
brj-23026	158	21	/	/	SYM
brj-23026	158	22	nirn.668	nirn.668	PROPN
brj-23026	158	23	jiang	jiang	PROPN
brj-23026	158	24	,	,	PUNCT
brj-23026	158	25	h.	h.	PROPN
brj-23026	158	26	,	,	PUNCT
brj-23026	158	27	zhang	zhang	PROPN
brj-23026	158	28	,	,	PUNCT
brj-23026	158	29	h.	h.	PROPN
brj-23026	158	30	,	,	PUNCT
brj-23026	158	31	chen	chen	PROPN
brj-23026	158	32	,	,	PUNCT
brj-23026	158	33	q.	q.	PROPN
brj-23026	158	34	,	,	PUNCT
brj-23026	158	35	mei	mei	PROPN
brj-23026	158	36	,	,	PUNCT
brj-23026	158	37	c.	c.	PROPN
brj-23026	158	38	,	,	PUNCT
brj-23026	158	39	and	and	CCONJ
brj-23026	158	40	liu	liu	PROPN
brj-23026	158	41	,	,	PUNCT
brj-23026	158	42	g.	g.	PROPN
brj-23026	158	43	(	(	PUNCT
brj-23026	158	44	2015	2015	NUM
brj-23026	158	45	)	)	PUNCT
brj-23026	158	46	.	.	PUNCT
brj-23026	159	1	“	"	PUNCT
brj-23026	159	2	identification	identification	NOUN
brj-23026	159	3	of	of	ADP
brj-23026	159	4	solid	solid	ADJ
brj-23026	159	5	state	state	NOUN
brj-23026	159	6	fermentation	fermentation	NOUN
brj-23026	159	7	degree	degree	NOUN
brj-23026	159	8	with	with	ADP
brj-23026	159	9	ft	ft	NOUN
brj-23026	159	10	-	-	PUNCT
brj-23026	159	11	nir	nir	NOUN
brj-23026	159	12	spectroscopy	spectroscopy	NOUN
brj-23026	159	13	:	:	PUNCT
brj-23026	159	14	comparison	comparison	NOUN
brj-23026	159	15	of	of	ADP
brj-23026	159	16	wavelength	wavelength	NOUN
brj-23026	159	17	variable	variable	ADJ
brj-23026	159	18	selection	selection	NOUN
brj-23026	159	19	methods	method	NOUN
brj-23026	159	20	of	of	ADP
brj-23026	159	21	cars	car	NOUN
brj-23026	159	22	and	and	CCONJ
brj-23026	159	23	scars	scar	NOUN
brj-23026	159	24	,	,	PUNCT
brj-23026	159	25	”	"	PUNCT
brj-23026	159	26	spectrochimica	spectrochimica	NOUN
brj-23026	159	27	acta	acta	PROPN
brj-23026	159	28	part	part	NOUN
brj-23026	159	29	a	a	PRON
brj-23026	159	30	:	:	PUNCT
brj-23026	159	31	molecular	molecular	ADJ
brj-23026	159	32	and	and	CCONJ
brj-23026	159	33	biomolecular	biomolecular	ADJ
brj-23026	159	34	spectroscopy	spectroscopy	VERB
brj-23026	159	35	149	149	NUM
brj-23026	159	36	,	,	PUNCT
brj-23026	159	37	1	1	NUM
brj-23026	159	38	-	-	SYM
brj-23026	159	39	7	7	NUM
brj-23026	159	40	.	.	PUNCT
brj-23026	159	41	doi	doi	NOUN
brj-23026	159	42	:	:	PUNCT
brj-23026	159	43	10.1016	10.1016	NUM
brj-23026	159	44	/	/	SYM
brj-23026	159	45	j.saa.2015.04.024	j.saa.2015.04.024	PROPN
brj-23026	159	46	liang	liang	PROPN
brj-23026	159	47	,	,	PUNCT
brj-23026	159	48	l.	l.	PROPN
brj-23026	159	49	,	,	PUNCT
brj-23026	159	50	wei	wei	PROPN
brj-23026	159	51	,	,	PUNCT
brj-23026	159	52	l.	l.	PROPN
brj-23026	159	53	,	,	PUNCT
brj-23026	159	54	fang	fang	PROPN
brj-23026	159	55	,	,	PUNCT
brj-23026	159	56	g.	g.	PROPN
brj-23026	159	57	,	,	PUNCT
brj-23026	159	58	xu	xu	PROPN
brj-23026	159	59	,	,	PUNCT
brj-23026	159	60	f.	f.	PROPN
brj-23026	159	61	,	,	PUNCT
brj-23026	159	62	deng	deng	PROPN
brj-23026	159	63	,	,	PUNCT
brj-23026	159	64	y.	y.	PROPN
brj-23026	159	65	,	,	PUNCT
brj-23026	159	66	shen	shen	PROPN
brj-23026	159	67	,	,	PUNCT
brj-23026	159	68	k.	k.	PROPN
brj-23026	159	69	,	,	PUNCT
brj-23026	159	70	tian	tian	PROPN
brj-23026	159	71	,	,	PUNCT
brj-23026	159	72	q.	q.	PROPN
brj-23026	159	73	,	,	PUNCT
brj-23026	159	74	wu	wu	PROPN
brj-23026	159	75	,	,	PUNCT
brj-23026	159	76	t.	t.	PROPN
brj-23026	159	77	,	,	PUNCT
brj-23026	159	78	and	and	CCONJ
brj-23026	159	79	zhu	zhu	PROPN
brj-23026	159	80	,	,	PUNCT
brj-23026	159	81	b.	b.	PROPN
brj-23026	159	82	(	(	PUNCT
brj-23026	159	83	2020	2020	NUM
brj-23026	159	84	)	)	PUNCT
brj-23026	159	85	.	.	PUNCT
brj-23026	160	1	“	"	PUNCT
brj-23026	160	2	prediction	prediction	NOUN
brj-23026	160	3	of	of	ADP
brj-23026	160	4	holocellulose	holocellulose	ADJ
brj-23026	160	5	and	and	CCONJ
brj-23026	160	6	lignin	lignin	NOUN
brj-23026	160	7	content	content	NOUN
brj-23026	160	8	of	of	ADP
brj-23026	160	9	pulp	pulp	NOUN
brj-23026	160	10	wood	wood	NOUN
brj-23026	160	11	feedstock	feedstock	NOUN
brj-23026	160	12	using	use	VERB
brj-23026	160	13	near	near	ADP
brj-23026	160	14	infrared	infrared	ADJ
brj-23026	160	15	spectroscopy	spectroscopy	NOUN
brj-23026	160	16	and	and	CCONJ
brj-23026	160	17	variable	variable	ADJ
brj-23026	160	18	selection	selection	NOUN
brj-23026	160	19	,	,	PUNCT
brj-23026	160	20	”	"	PUNCT
brj-23026	160	21	spectrochimica	spectrochimica	NOUN
brj-23026	160	22	acta	acta	PROPN
brj-23026	160	23	part	part	NOUN
brj-23026	160	24	a	a	DET
brj-23026	160	25	:	:	PUNCT
brj-23026	160	26	molecular	molecular	ADJ
brj-23026	160	27	and	and	CCONJ
brj-23026	160	28	biomolecular	biomolecular	ADJ
brj-23026	160	29	spectroscopy	spectroscopy	NOUN
brj-23026	160	30	225	225	NUM
brj-23026	160	31	,	,	PUNCT
brj-23026	160	32	article	article	NOUN
brj-23026	160	33	117515	117515	NUM
brj-23026	160	34	.	.	PUNCT
brj-23026	161	1	doi	doi	NOUN
brj-23026	161	2	:	:	PUNCT
brj-23026	161	3	10.1016	10.1016	NUM
brj-23026	161	4	/	/	SYM
brj-23026	161	5	j.saa.2019.117515	j.saa.2019.117515	PROPN
brj-23026	161	6	morellos	morello	NOUN
brj-23026	161	7	,	,	PUNCT
brj-23026	161	8	a.	a.	NOUN
brj-23026	161	9	,	,	PUNCT
brj-23026	161	10	pantazi	pantazi	ADV
brj-23026	161	11	,	,	PUNCT
brj-23026	161	12	x.	x.	NOUN
brj-23026	161	13	,	,	PUNCT
brj-23026	161	14	moshou	moshou	PROPN
brj-23026	161	15	,	,	PUNCT
brj-23026	161	16	d.	d.	PROPN
brj-23026	161	17	,	,	PUNCT
brj-23026	161	18	alexandridis	alexandridis	PROPN
brj-23026	161	19	,	,	PUNCT
brj-23026	161	20	t.	t.	PROPN
brj-23026	161	21	,	,	PUNCT
brj-23026	161	22	whetton	whetton	PROPN
brj-23026	161	23	,	,	PUNCT
brj-23026	161	24	r.	r.	PROPN
brj-23026	161	25	,	,	PUNCT
brj-23026	161	26	tziotzios	tziotzio	NOUN
brj-23026	161	27	,	,	PUNCT
brj-23026	161	28	g.	g.	PROPN
brj-23026	161	29	,	,	PUNCT
brj-23026	161	30	wiebensohn	wiebensohn	PROPN
brj-23026	161	31	,	,	PUNCT
brj-23026	161	32	j.	j.	PROPN
brj-23026	161	33	,	,	PUNCT
brj-23026	161	34	bill	bill	PROPN
brj-23026	161	35	,	,	PUNCT
brj-23026	161	36	r.	r.	PROPN
brj-23026	161	37	,	,	PUNCT
brj-23026	161	38	and	and	CCONJ
brj-23026	161	39	mouazen	mouazen	PROPN
brj-23026	161	40	,	,	PUNCT
brj-23026	161	41	a.	a.	NOUN
brj-23026	161	42	(	(	PUNCT
brj-23026	161	43	2016	2016	NUM
brj-23026	161	44	)	)	PUNCT
brj-23026	161	45	.	.	PUNCT
brj-23026	162	1	“	"	PUNCT
brj-23026	162	2	machine	machine	NOUN
brj-23026	162	3	learning	learning	NOUN
brj-23026	162	4	based	base	VERB
brj-23026	162	5	prediction	prediction	NOUN
brj-23026	162	6	of	of	ADP
brj-23026	162	7	soil	soil	NOUN
brj-23026	162	8	total	total	NOUN
brj-23026	162	9	nitrogen	nitrogen	NOUN
brj-23026	162	10	,	,	PUNCT
brj-23026	162	11	organic	organic	ADJ
brj-23026	162	12	carbon	carbon	NOUN
brj-23026	162	13	and	and	CCONJ
brj-23026	162	14	moisture	moisture	NOUN
brj-23026	162	15	content	content	NOUN
brj-23026	162	16	by	by	ADP
brj-23026	162	17	using	use	VERB
brj-23026	162	18	visnir	visnir	NOUN
brj-23026	162	19	spectroscopy	spectroscopy	NOUN
brj-23026	162	20	,	,	PUNCT
brj-23026	162	21	”	"	PUNCT
brj-23026	162	22	biosystems	biosystem	NOUN
brj-23026	162	23	engineering	engineer	VERB
brj-23026	162	24	152	152	NUM
brj-23026	162	25	,	,	PUNCT
brj-23026	162	26	104	104	NUM
brj-23026	162	27	-	-	SYM
brj-23026	162	28	116	116	NUM
brj-23026	162	29	.	.	PUNCT
brj-23026	163	1	doi	doi	NOUN
brj-23026	163	2	:	:	PUNCT
brj-23026	163	3	10.1016	10.1016	NUM
brj-23026	163	4	/	/	SYM
brj-23026	163	5	j.biosystemseng.2016.04.018	j.biosystemseng.2016.04.018	PROPN
brj-23026	163	6	ni	ni	PROPN
brj-23026	163	7	,	,	PUNCT
brj-23026	163	8	l.	l.	PROPN
brj-23026	163	9	,	,	PUNCT
brj-23026	163	10	han	han	PROPN
brj-23026	163	11	,	,	PUNCT
brj-23026	163	12	m.	m.	NOUN
brj-23026	163	13	,	,	PUNCT
brj-23026	163	14	luan	luan	PROPN
brj-23026	163	15	,	,	PUNCT
brj-23026	163	16	s.	s.	PROPN
brj-23026	163	17	,	,	PUNCT
brj-23026	163	18	and	and	CCONJ
brj-23026	163	19	zhang	zhang	PROPN
brj-23026	163	20	,	,	PUNCT
brj-23026	163	21	l.	l.	PROPN
brj-23026	163	22	(	(	PUNCT
brj-23026	163	23	2019	2019	NUM
brj-23026	163	24	)	)	PUNCT
brj-23026	163	25	.	.	PUNCT
brj-23026	164	1	“	"	PUNCT
brj-23026	164	2	screening	screen	VERB
brj-23026	164	3	wavelengths	wavelength	NOUN
brj-23026	164	4	with	with	ADP
brj-23026	164	5	consistent	consistent	ADJ
brj-23026	164	6	and	and	CCONJ
brj-23026	164	7	stable	stable	ADJ
brj-23026	164	8	signals	signal	NOUN
brj-23026	164	9	to	to	PART
brj-23026	164	10	realize	realize	VERB
brj-23026	164	11	calibration	calibration	NOUN
brj-23026	164	12	model	model	NOUN
brj-23026	164	13	transfer	transfer	NOUN
brj-23026	164	14	of	of	ADP
brj-23026	164	15	near	near	ADP
brj-23026	164	16	infrared	infrared	PROPN
brj-23026	164	17	spectra	spectra	PROPN
brj-23026	164	18	,	,	PUNCT
brj-23026	164	19	”	"	PUNCT
brj-23026	164	20	spectrochimica	spectrochimica	NOUN
brj-23026	164	21	acta	acta	PROPN
brj-23026	164	22	part	part	NOUN
brj-23026	164	23	a	a	PRON
brj-23026	164	24	:	:	PUNCT
brj-23026	164	25	molecular	molecular	ADJ
brj-23026	164	26	and	and	CCONJ
brj-23026	164	27	biomolecular	biomolecular	ADJ
brj-23026	164	28	spectroscopy	spectroscopy	VERB
brj-23026	164	29	206	206	NUM
brj-23026	164	30	,	,	PUNCT
brj-23026	164	31	350358	350358	NUM
brj-23026	164	32	.	.	PUNCT
brj-23026	165	1	doi	doi	NOUN
brj-23026	165	2	:	:	PUNCT
brj-23026	165	3	10.1016	10.1016	NUM
brj-23026	165	4	/	/	SYM
brj-23026	165	5	j.saa.2018.08.027	j.saa.2018.08.027	PROPN
brj-23026	165	6	peer	peer	NOUN
brj-23026	165	7	-	-	PUNCT
brj-23026	165	8	reviewed	review	VERB
brj-23026	165	9	article	article	NOUN
brj-23026	165	10	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-23026	165	11	liu	liu	PROPN
brj-23026	165	12	et	et	PROPN
brj-23026	165	13	al	al	PROPN
brj-23026	165	14	.	.	PROPN
brj-23026	165	15	(	(	PUNCT
brj-23026	165	16	2024	2024	NUM
brj-23026	165	17	)	)	PUNCT
brj-23026	165	18	.	.	PUNCT
brj-23026	166	1	“	"	PUNCT
brj-23026	166	2	nir	nir	ADJ
brj-23026	166	3	lignin	lignin	NOUN
brj-23026	166	4	model	model	NOUN
brj-23026	166	5	transfer	transfer	NOUN
brj-23026	166	6	coupling	coupling	NOUN
brj-23026	166	7	,	,	PUNCT
brj-23026	166	8	”	"	PUNCT
brj-23026	166	9	bioresources	bioresource	NOUN
brj-23026	166	10	19(1	19(1	NUM
brj-23026	166	11	)	)	PUNCT
brj-23026	166	12	,	,	PUNCT
brj-23026	166	13	245	245	NUM
brj-23026	166	14	-	-	SYM
brj-23026	166	15	256	256	NUM
brj-23026	166	16	.	.	NUM
brj-23026	166	17	256	256	NUM
brj-23026	166	18	ni	ni	PROPN
brj-23026	166	19	,	,	PUNCT
brj-23026	166	20	l.	l.	PROPN
brj-23026	166	21	,	,	PUNCT
brj-23026	166	22	han	han	PROPN
brj-23026	166	23	,	,	PUNCT
brj-23026	166	24	m.	m.	NOUN
brj-23026	166	25	,	,	PUNCT
brj-23026	166	26	zhang	zhang	PROPN
brj-23026	166	27	,	,	PUNCT
brj-23026	166	28	l.	l.	PROPN
brj-23026	166	29	,	,	PUNCT
brj-23026	166	30	n.	n.	PROPN
brj-23026	166	31	m.	m.	PROPN
brj-23026	166	32	y.	y.	PROPN
brj-23026	166	33	,	,	PUNCT
brj-23026	166	34	and	and	CCONJ
brj-23026	166	35	luan	luan	PROPN
brj-23026	166	36	,	,	PUNCT
brj-23026	166	37	s.	s.	PROPN
brj-23026	166	38	(	(	PUNCT
brj-23026	166	39	2018	2018	NUM
brj-23026	166	40	)	)	PUNCT
brj-23026	166	41	.	.	PUNCT
brj-23026	167	1	“	"	PUNCT
brj-23026	167	2	a	a	DET
brj-23026	167	3	novel	novel	ADJ
brj-23026	167	4	calibration	calibration	NOUN
brj-23026	167	5	transfer	transfer	NOUN
brj-23026	167	6	method	method	NOUN
brj-23026	167	7	of	of	ADP
brj-23026	167	8	near	near	ADP
brj-23026	167	9	infrared	infrared	ADJ
brj-23026	167	10	spectral	spectral	ADJ
brj-23026	167	11	model	model	NOUN
brj-23026	167	12	without	without	ADP
brj-23026	167	13	standard	standard	ADJ
brj-23026	167	14	samples	sample	NOUN
brj-23026	167	15	based	base	VERB
brj-23026	167	16	on	on	ADP
brj-23026	167	17	screening	screen	VERB
brj-23026	167	18	stable	stable	ADJ
brj-23026	167	19	and	and	CCONJ
brj-23026	167	20	consistent	consistent	ADJ
brj-23026	167	21	wavelengths	wavelength	NOUN
brj-23026	167	22	,	,	PUNCT
brj-23026	167	23	”	"	PUNCT
brj-23026	167	24	chinese	chinese	ADJ
brj-23026	167	25	journal	journal	NOUN
brj-23026	167	26	of	of	ADP
brj-23026	167	27	analytical	analytical	ADJ
brj-23026	167	28	chemistry	chemistry	NOUN
brj-23026	167	29	46(10	46(10	NUM
brj-23026	167	30	)	)	PUNCT
brj-23026	167	31	,	,	PUNCT
brj-23026	167	32	1660	1660	NUM
brj-23026	167	33	-	-	SYM
brj-23026	167	34	1668	1668	NUM
brj-23026	167	35	.	.	PUNCT
brj-23026	168	1	doi	doi	NOUN
brj-23026	168	2	:	:	PUNCT
brj-23026	168	3	10.11895	10.11895	NUM
brj-23026	168	4	/	/	SYM
brj-23026	168	5	j.issn.0253	j.issn.0253	NOUN
brj-23026	168	6	-	-	PUNCT
brj-23026	168	7	3820.181242	3820.181242	NUM
brj-23026	168	8	rossel	rossel	NOUN
brj-23026	168	9	,	,	PUNCT
brj-23026	168	10	r.	r.	PROPN
brj-23026	168	11	v.	v.	PROPN
brj-23026	168	12	,	,	PUNCT
brj-23026	168	13	and	and	CCONJ
brj-23026	168	14	behrens	behren	NOUN
brj-23026	168	15	,	,	PUNCT
brj-23026	168	16	t.	t.	PROPN
brj-23026	168	17	(	(	PUNCT
brj-23026	168	18	2010	2010	NUM
brj-23026	168	19	)	)	PUNCT
brj-23026	168	20	.	.	PUNCT
brj-23026	169	1	“	"	PUNCT
brj-23026	169	2	using	use	VERB
brj-23026	169	3	data	datum	NOUN
brj-23026	169	4	mining	mining	NOUN
brj-23026	169	5	to	to	ADP
brj-23026	169	6	model	model	NOUN
brj-23026	169	7	and	and	CCONJ
brj-23026	169	8	interpret	interpret	VERB
brj-23026	169	9	soil	soil	NOUN
brj-23026	169	10	diffuse	diffuse	PROPN
brj-23026	169	11	reflectance	reflectance	NOUN
brj-23026	169	12	spectra	spectra	NOUN
brj-23026	169	13	,	,	PUNCT
brj-23026	169	14	”	"	PUNCT
brj-23026	169	15	geoderma	geoderma	X
brj-23026	169	16	158(1	158(1	NUM
brj-23026	169	17	-	-	SYM
brj-23026	169	18	2	2	NUM
brj-23026	169	19	)	)	PUNCT
brj-23026	169	20	,	,	PUNCT
brj-23026	169	21	46	46	NUM
brj-23026	169	22	-	-	SYM
brj-23026	169	23	54	54	NUM
brj-23026	169	24	.	.	PUNCT
brj-23026	170	1	doi	doi	NOUN
brj-23026	170	2	:	:	PUNCT
brj-23026	170	3	10.1016	10.1016	NUM
brj-23026	170	4	/	/	SYM
brj-23026	170	5	j.geoderma.2009.12.025	j.geoderma.2009.12.025	PROPN
brj-23026	170	6	rossel	rossel	NOUN
brj-23026	170	7	,	,	PUNCT
brj-23026	170	8	r.	r.	PROPN
brj-23026	170	9	v.	v.	PROPN
brj-23026	170	10	,	,	PUNCT
brj-23026	170	11	mcglynn	mcglynn	PROPN
brj-23026	170	12	,	,	PUNCT
brj-23026	170	13	r.	r.	PROPN
brj-23026	170	14	n.	n.	PROPN
brj-23026	170	15	,	,	PUNCT
brj-23026	170	16	and	and	CCONJ
brj-23026	170	17	mcbratney	mcbratney	NOUN
brj-23026	170	18	,	,	PUNCT
brj-23026	170	19	a.	a.	PROPN
brj-23026	170	20	b.	b.	PROPN
brj-23026	170	21	(	(	PUNCT
brj-23026	170	22	2006	2006	NUM
brj-23026	170	23	)	)	PUNCT
brj-23026	170	24	.	.	PUNCT
brj-23026	171	1	“	"	PUNCT
brj-23026	171	2	determining	determine	VERB
brj-23026	171	3	the	the	DET
brj-23026	171	4	composition	composition	NOUN
brj-23026	171	5	of	of	ADP
brj-23026	171	6	mineral	mineral	NOUN
brj-23026	171	7	-	-	PUNCT
brj-23026	171	8	organic	organic	ADJ
brj-23026	171	9	mixes	mix	NOUN
brj-23026	171	10	using	use	VERB
brj-23026	171	11	uv	uv	NOUN
brj-23026	171	12	–	–	PUNCT
brj-23026	171	13	vis	vis	X
brj-23026	171	14	–	–	PUNCT
brj-23026	171	15	nir	nir	ADJ
brj-23026	171	16	diffuse	diffuse	NOUN
brj-23026	171	17	reflectance	reflectance	NOUN
brj-23026	171	18	spectroscopy	spectroscopy	NOUN
brj-23026	171	19	,	,	PUNCT
brj-23026	171	20	”	"	PUNCT
brj-23026	171	21	geoderma	geoderma	NOUN
brj-23026	171	22	137(1	137(1	NUM
brj-23026	171	23	-	-	SYM
brj-23026	171	24	2	2	NUM
brj-23026	171	25	)	)	PUNCT
brj-23026	171	26	,	,	PUNCT
brj-23026	171	27	70	70	NUM
brj-23026	171	28	-	-	SYM
brj-23026	171	29	82	82	NUM
brj-23026	171	30	.	.	PUNCT
brj-23026	172	1	doi	doi	NOUN
brj-23026	172	2	:	:	PUNCT
brj-23026	172	3	10.1016	10.1016	NUM
brj-23026	172	4	/	/	SYM
brj-23026	172	5	j.geoderma.2006.07.004	j.geoderma.2006.07.004	PROPN
brj-23026	172	6	soares	soares	PROPN
brj-23026	172	7	,	,	PUNCT
brj-23026	172	8	s.	s.	PROPN
brj-23026	172	9	f.	f.	PROPN
brj-23026	172	10	c.	c.	PROPN
brj-23026	172	11	s.	s.	PROPN
brj-23026	172	12	,	,	PUNCT
brj-23026	172	13	gomes	gomes	PROPN
brj-23026	172	14	a.	a.	NOUN
brj-23026	172	15	a.	a.	PROPN
brj-23026	172	16	,	,	PUNCT
brj-23026	172	17	araujo	araujo	NOUN
brj-23026	172	18	,	,	PUNCT
brj-23026	172	19	m.	m.	NOUN
brj-23026	172	20	c.	c.	PROPN
brj-23026	172	21	u.	u.	PROPN
brj-23026	172	22	,	,	PUNCT
brj-23026	172	23	filho	filho	PROPN
brj-23026	172	24	,	,	PUNCT
brj-23026	172	25	a.	a.	PROPN
brj-23026	172	26	r.	r.	PROPN
brj-23026	172	27	g.	g.	PROPN
brj-23026	172	28	,	,	PUNCT
brj-23026	172	29	and	and	CCONJ
brj-23026	172	30	galvão	galvão	NOUN
brj-23026	172	31	,	,	PUNCT
brj-23026	172	32	r.	r.	PROPN
brj-23026	172	33	k.	k.	PROPN
brj-23026	172	34	h.	h.	PROPN
brj-23026	173	1	(	(	PUNCT
brj-23026	173	2	2013	2013	NUM
brj-23026	173	3	)	)	PUNCT
brj-23026	173	4	.	.	PUNCT
brj-23026	174	1	“	"	PUNCT
brj-23026	174	2	the	the	DET
brj-23026	174	3	successive	successive	ADJ
brj-23026	174	4	projections	projection	NOUN
brj-23026	174	5	algorithm	algorithm	NOUN
brj-23026	174	6	,	,	PUNCT
brj-23026	174	7	”	"	PUNCT
brj-23026	174	8	trac	trac	NOUN
brj-23026	174	9	trends	trend	NOUN
brj-23026	174	10	in	in	ADP
brj-23026	174	11	analytical	analytical	ADJ
brj-23026	174	12	chemistry	chemistry	NOUN
brj-23026	174	13	42	42	NUM
brj-23026	174	14	,	,	PUNCT
brj-23026	174	15	84	84	NUM
brj-23026	174	16	-	-	SYM
brj-23026	174	17	98	98	NUM
brj-23026	174	18	.	.	PUNCT
brj-23026	175	1	doi	doi	NOUN
brj-23026	175	2	:	:	PUNCT
brj-23026	175	3	10.1016	10.1016	NUM
brj-23026	175	4	/	/	SYM
brj-23026	175	5	j.trac.2012.09.006	j.trac.2012.09.006	PROPN
brj-23026	175	6	son	son	NOUN
brj-23026	175	7	,	,	PUNCT
brj-23026	175	8	d.	d.	PROPN
brj-23026	175	9	,	,	PUNCT
brj-23026	175	10	kwon	kwon	PROPN
brj-23026	175	11	,	,	PUNCT
brj-23026	175	12	h.	h.	PROPN
brj-23026	175	13	,	,	PUNCT
brj-23026	175	14	and	and	CCONJ
brj-23026	175	15	lee	lee	PROPN
brj-23026	175	16	,	,	PUNCT
brj-23026	175	17	s.	s.	PROPN
brj-23026	175	18	(	(	PUNCT
brj-23026	175	19	2020	2020	NUM
brj-23026	175	20	)	)	PUNCT
brj-23026	175	21	.	.	PUNCT
brj-23026	176	1	“	"	PUNCT
brj-23026	176	2	visible	visible	ADJ
brj-23026	176	3	and	and	CCONJ
brj-23026	176	4	near	near	ADV
brj-23026	176	5	-	-	PUNCT
brj-23026	176	6	infrared	infrared	ADJ
brj-23026	176	7	image	image	NOUN
brj-23026	176	8	synthesis	synthesis	NOUN
brj-23026	176	9	using	use	VERB
brj-23026	176	10	pca	pca	NOUN
brj-23026	176	11	fusion	fusion	NOUN
brj-23026	176	12	of	of	ADP
brj-23026	176	13	multiscale	multiscale	ADJ
brj-23026	176	14	layers	layer	NOUN
brj-23026	176	15	,	,	PUNCT
brj-23026	176	16	”	"	PUNCT
brj-23026	176	17	applied	apply	VERB
brj-23026	176	18	sciences	science	NOUN
brj-23026	176	19	10(23	10(23	NOUN
brj-23026	176	20	)	)	PUNCT
brj-23026	176	21	,	,	PUNCT
brj-23026	176	22	article	article	NOUN
brj-23026	176	23	8702	8702	NUM
brj-23026	176	24	.	.	PUNCT
brj-23026	177	1	doi	doi	NOUN
brj-23026	177	2	:	:	PUNCT
brj-23026	177	3	10.3390	10.3390	NUM
brj-23026	177	4	/	/	SYM
brj-23026	177	5	app10238702	app10238702	PROPN
brj-23026	177	6	wang	wang	PROPN
brj-23026	177	7	,	,	PUNCT
brj-23026	177	8	a.	a.	PROPN
brj-23026	177	9	,	,	PUNCT
brj-23026	177	10	yang	yang	PROPN
brj-23026	177	11	,	,	PUNCT
brj-23026	177	12	p.	p.	PROPN
brj-23026	177	13	,	,	PUNCT
brj-23026	177	14	chen	chen	PROPN
brj-23026	177	15	,	,	PUNCT
brj-23026	177	16	j.	j.	PROPN
brj-23026	177	17	,	,	PUNCT
brj-23026	177	18	wu	wu	PROPN
brj-23026	177	19	,	,	PUNCT
brj-23026	177	20	z.	z.	PROPN
brj-23026	177	21	,	,	PUNCT
brj-23026	177	22	jia	jia	PROPN
brj-23026	177	23	,	,	PUNCT
brj-23026	177	24	y.	y.	PROPN
brj-23026	177	25	,	,	PUNCT
brj-23026	177	26	ma	ma	PROPN
brj-23026	177	27	,	,	PUNCT
brj-23026	177	28	c.	c.	PROPN
brj-23026	177	29	,	,	PUNCT
brj-23026	177	30	and	and	CCONJ
brj-23026	177	31	zhan	zhan	NUM
brj-23026	177	32	,	,	PUNCT
brj-23026	177	33	x.	x.	NOUN
brj-23026	177	34	(	(	PUNCT
brj-23026	177	35	2019	2019	NUM
brj-23026	177	36	)	)	PUNCT
brj-23026	177	37	.	.	PUNCT
brj-23026	178	1	“	"	PUNCT
brj-23026	178	2	a	a	DET
brj-23026	178	3	new	new	ADJ
brj-23026	178	4	calibration	calibration	NOUN
brj-23026	178	5	model	model	NOUN
brj-23026	178	6	transferring	transfer	VERB
brj-23026	178	7	strategy	strategy	NOUN
brj-23026	178	8	maintaining	maintain	VERB
brj-23026	178	9	the	the	DET
brj-23026	178	10	predictive	predictive	ADJ
brj-23026	178	11	abilities	ability	NOUN
brj-23026	178	12	of	of	ADP
brj-23026	178	13	nir	nir	ADJ
brj-23026	178	14	multivariate	multivariate	NOUN
brj-23026	178	15	calibration	calibration	NOUN
brj-23026	178	16	model	model	NOUN
brj-23026	178	17	applied	apply	VERB
brj-23026	178	18	in	in	ADP
brj-23026	178	19	different	different	ADJ
brj-23026	178	20	batches	batch	NOUN
brj-23026	178	21	process	process	NOUN
brj-23026	178	22	of	of	ADP
brj-23026	178	23	extraction	extraction	NOUN
brj-23026	178	24	,	,	PUNCT
brj-23026	178	25	”	"	PUNCT
brj-23026	178	26	infrared	infrared	PROPN
brj-23026	178	27	physics	physics	PROPN
brj-23026	178	28	&	&	CCONJ
brj-23026	178	29	technology	technology	PROPN
brj-23026	178	30	103	103	NUM
brj-23026	178	31	,	,	PUNCT
brj-23026	178	32	article	article	NOUN
brj-23026	178	33	103046	103046	NUM
brj-23026	178	34	.	.	PUNCT
brj-23026	179	1	doi	doi	NOUN
brj-23026	179	2	:	:	PUNCT
brj-23026	179	3	10.1016	10.1016	NUM
brj-23026	179	4	/	/	SYM
brj-23026	179	5	j.infrared.2019.103046	j.infrared.2019.103046	PROPN
brj-23026	179	6	wang	wang	PROPN
brj-23026	179	7	,	,	PUNCT
brj-23026	179	8	h.	h.	PROPN
brj-23026	179	9	,	,	PUNCT
brj-23026	179	10	xiong	xiong	PROPN
brj-23026	179	11	,	,	PUNCT
brj-23026	179	12	z.	z.	PROPN
brj-23026	179	13	,	,	PUNCT
brj-23026	179	14	hu	hu	PROPN
brj-23026	179	15	,	,	PUNCT
brj-23026	179	16	y.	y.	PROPN
brj-23026	179	17	,	,	PUNCT
brj-23026	179	18	liu	liu	PROPN
brj-23026	179	19	,	,	PUNCT
brj-23026	179	20	z.	z.	PROPN
brj-23026	179	21	,	,	PUNCT
brj-23026	179	22	and	and	CCONJ
brj-23026	179	23	liang	liang	PROPN
brj-23026	179	24	,	,	PUNCT
brj-23026	179	25	l.	l.	PROPN
brj-23026	179	26	,	,	PUNCT
brj-23026	179	27	(	(	PUNCT
brj-23026	179	28	2022	2022	NUM
brj-23026	179	29	)	)	PUNCT
brj-23026	179	30	.	.	PUNCT
brj-23026	180	1	“	"	PUNCT
brj-23026	180	2	transfer	transfer	VERB
brj-23026	180	3	strategy	strategy	NOUN
brj-23026	180	4	for	for	ADP
brj-23026	180	5	near	near	ADV
brj-23026	180	6	infrared	infrared	ADJ
brj-23026	180	7	analysis	analysis	NOUN
brj-23026	180	8	model	model	NOUN
brj-23026	180	9	of	of	ADP
brj-23026	180	10	holocellulose	holocellulose	NOUN
brj-23026	180	11	and	and	CCONJ
brj-23026	180	12	lignin	lignin	NOUN
brj-23026	180	13	based	base	VERB
brj-23026	180	14	on	on	ADP
brj-23026	180	15	improved	improved	ADJ
brj-23026	180	16	slope	slope	NOUN
brj-23026	180	17	/	/	SYM
brj-23026	180	18	bias	bias	NOUN
brj-23026	180	19	algorithm	algorithm	NOUN
brj-23026	180	20	,	,	PUNCT
brj-23026	180	21	”	"	PUNCT
brj-23026	180	22	bioresources	bioresource	NOUN
brj-23026	180	23	17.4	17.4	NUM
brj-23026	180	24	,	,	PUNCT
brj-23026	180	25	6476	6476	NUM
brj-23026	180	26	.	.	PUNCT
brj-23026	181	1	doi	doi	NOUN
brj-23026	181	2	:	:	PUNCT
brj-23026	181	3	10.15376	10.15376	NUM
brj-23026	181	4	/	/	SYM
brj-23026	181	5	biores.17.4.6476	biores.17.4.6476	PROPN
brj-23026	181	6	-	-	PUNCT
brj-23026	181	7	6489	6489	NUM
brj-23026	181	8	xiong	xiong	PROPN
brj-23026	181	9	,	,	PUNCT
brj-23026	181	10	z.	z.	PROPN
brj-23026	181	11	,	,	PUNCT
brj-23026	181	12	pfeifer	pfeifer	PROPN
brj-23026	181	13	,	,	PUNCT
brj-23026	181	14	f.	f.	PROPN
brj-23026	181	15	,	,	PUNCT
brj-23026	181	16	and	and	CCONJ
brj-23026	181	17	siesler	siesler	NOUN
brj-23026	181	18	,	,	PUNCT
brj-23026	181	19	h.	h.	PROPN
brj-23026	181	20	(	(	PUNCT
brj-23026	181	21	2016	2016	NUM
brj-23026	181	22	)	)	PUNCT
brj-23026	181	23	.	.	PUNCT
brj-23026	182	1	“	"	PUNCT
brj-23026	182	2	evaluating	evaluate	VERB
brj-23026	182	3	the	the	DET
brj-23026	182	4	molecular	molecular	ADJ
brj-23026	182	5	interaction	interaction	NOUN
brj-23026	182	6	of	of	ADP
brj-23026	182	7	organic	organic	ADJ
brj-23026	182	8	liquid	liquid	ADJ
brj-23026	182	9	mixtures	mixture	NOUN
brj-23026	182	10	using	use	VERB
brj-23026	182	11	near	near	ADV
brj-23026	182	12	-	-	PUNCT
brj-23026	182	13	infrared	infrared	ADJ
brj-23026	182	14	spectroscopy	spectroscopy	NOUN
brj-23026	182	15	,	,	PUNCT
brj-23026	182	16	”	"	PUNCT
brj-23026	182	17	applied	apply	VERB
brj-23026	182	18	spectroscopy	spectroscopy	NOUN
brj-23026	182	19	70.4	70.4	NUM
brj-23026	182	20	,	,	PUNCT
brj-23026	182	21	635	635	NUM
brj-23026	182	22	-	-	SYM
brj-23026	182	23	644	644	NUM
brj-23026	182	24	.	.	PUNCT
brj-23026	183	1	doi	doi	NOUN
brj-23026	183	2	:	:	PUNCT
brj-23026	183	3	10.1177/0003702816631301	10.1177/0003702816631301	NUM
brj-23026	183	4	yin	yin	PROPN
brj-23026	183	5	,	,	PUNCT
brj-23026	183	6	l.	l.	PROPN
brj-23026	183	7	,	,	PUNCT
brj-23026	183	8	zhou	zhou	PROPN
brj-23026	183	9	,	,	PUNCT
brj-23026	183	10	j.	j.	PROPN
brj-23026	183	11	,	,	PUNCT
brj-23026	183	12	chen	chen	PROPN
brj-23026	183	13	,	,	PUNCT
brj-23026	183	14	d.	d.	PROPN
brj-23026	183	15	,	,	PUNCT
brj-23026	183	16	han	han	PROPN
brj-23026	183	17	,	,	PUNCT
brj-23026	183	18	t.	t.	PROPN
brj-23026	183	19	,	,	PUNCT
brj-23026	183	20	zheng	zheng	PROPN
brj-23026	183	21	,	,	PUNCT
brj-23026	183	22	b.	b.	PROPN
brj-23026	183	23	,	,	PUNCT
brj-23026	183	24	younis	younis	PROPN
brj-23026	183	25	a.	a.	NOUN
brj-23026	183	26	,	,	PUNCT
brj-23026	183	27	and	and	CCONJ
brj-23026	183	28	shao	shao	PROPN
brj-23026	183	29	,	,	PUNCT
brj-23026	183	30	q.	q.	PROPN
brj-23026	183	31	(	(	PUNCT
brj-23026	183	32	2019	2019	NUM
brj-23026	183	33	)	)	PUNCT
brj-23026	183	34	.	.	PUNCT
brj-23026	184	1	“	"	PUNCT
brj-23026	184	2	a	a	DET
brj-23026	184	3	review	review	NOUN
brj-23026	184	4	of	of	ADP
brj-23026	184	5	the	the	DET
brj-23026	184	6	application	application	NOUN
brj-23026	184	7	of	of	ADP
brj-23026	184	8	near	near	ADV
brj-23026	184	9	-	-	PUNCT
brj-23026	184	10	infrared	infrared	ADJ
brj-23026	184	11	spectroscopy	spectroscopy	NOUN
brj-23026	184	12	to	to	ADP
brj-23026	184	13	rare	rare	ADJ
brj-23026	184	14	traditional	traditional	ADJ
brj-23026	184	15	chinese	chinese	ADJ
brj-23026	184	16	medicine	medicine	NOUN
brj-23026	184	17	,	,	PUNCT
brj-23026	184	18	”	"	PUNCT
brj-23026	184	19	spectrochimica	spectrochimica	NOUN
brj-23026	184	20	acta	acta	PROPN
brj-23026	184	21	part	part	NOUN
brj-23026	184	22	a	a	PRON
brj-23026	184	23	:	:	PUNCT
brj-23026	184	24	molecular	molecular	ADJ
brj-23026	184	25	and	and	CCONJ
brj-23026	184	26	biomolecular	biomolecular	ADJ
brj-23026	184	27	spectroscopy	spectroscopy	VERB
brj-23026	184	28	221	221	NUM
brj-23026	184	29	,	,	PUNCT
brj-23026	184	30	article	article	NOUN
brj-23026	184	31	117208	117208	NUM
brj-23026	184	32	.	.	PUNCT
brj-23026	185	1	doi	doi	NOUN
brj-23026	185	2	:	:	PUNCT
brj-23026	185	3	10.1016	10.1016	NUM
brj-23026	185	4	/	/	SYM
brj-23026	185	5	j.saa.2019.117208	j.saa.2019.117208	PROPN
brj-23026	185	6	zhang	zhang	PROPN
brj-23026	185	7	,	,	PUNCT
brj-23026	185	8	l.	l.	PROPN
brj-23026	185	9	,	,	PUNCT
brj-23026	185	10	li	li	PROPN
brj-23026	185	11	,	,	PUNCT
brj-23026	185	12	y.	y.	PROPN
brj-23026	185	13	,	,	PUNCT
brj-23026	185	14	huang	huang	PROPN
brj-23026	185	15	,	,	PUNCT
brj-23026	185	16	w.	w.	PROPN
brj-23026	185	17	,	,	PUNCT
brj-23026	185	18	ni	ni	PROPN
brj-23026	185	19	,	,	PUNCT
brj-23026	185	20	l.	l.	PROPN
brj-23026	185	21	,	,	PUNCT
brj-23026	185	22	and	and	CCONJ
brj-23026	185	23	ge	ge	PROPN
brj-23026	185	24	,	,	PUNCT
brj-23026	185	25	j.	j.	PROPN
brj-23026	185	26	(	(	PUNCT
brj-23026	185	27	2020	2020	NUM
brj-23026	185	28	)	)	PUNCT
brj-23026	185	29	.	.	PUNCT
brj-23026	186	1	“	"	PUNCT
brj-23026	186	2	the	the	DET
brj-23026	186	3	method	method	NOUN
brj-23026	186	4	of	of	ADP
brj-23026	186	5	calibration	calibration	NOUN
brj-23026	186	6	model	model	NOUN
brj-23026	186	7	transfer	transfer	NOUN
brj-23026	186	8	by	by	ADP
brj-23026	186	9	optimizing	optimize	VERB
brj-23026	186	10	wavelength	wavelength	NOUN
brj-23026	186	11	combinations	combination	NOUN
brj-23026	186	12	based	base	VERB
brj-23026	186	13	on	on	ADP
brj-23026	186	14	consistent	consistent	ADJ
brj-23026	186	15	and	and	CCONJ
brj-23026	186	16	stable	stable	ADJ
brj-23026	186	17	spectral	spectral	ADJ
brj-23026	186	18	signals	signal	NOUN
brj-23026	186	19	,	,	PUNCT
brj-23026	186	20	”	"	PUNCT
brj-23026	186	21	spectrochimica	spectrochimica	NOUN
brj-23026	186	22	acta	acta	PROPN
brj-23026	186	23	part	part	NOUN
brj-23026	186	24	a	a	PRON
brj-23026	186	25	:	:	PUNCT
brj-23026	186	26	molecular	molecular	ADJ
brj-23026	186	27	and	and	CCONJ
brj-23026	186	28	biomolecular	biomolecular	ADJ
brj-23026	186	29	spectroscopy	spectroscopy	VERB
brj-23026	186	30	227	227	NUM
brj-23026	186	31	,	,	PUNCT
brj-23026	186	32	article	article	NOUN
brj-23026	186	33	117647	117647	NUM
brj-23026	186	34	.	.	PUNCT
brj-23026	187	1	doi	doi	NOUN
brj-23026	187	2	:	:	PUNCT
brj-23026	187	3	10.1016	10.1016	NUM
brj-23026	187	4	/	/	SYM
brj-23026	187	5	j.saa.2019.117647	j.saa.2019.117647	NOUN
brj-23026	187	6	article	article	NOUN
brj-23026	187	7	submitted	submit	VERB
brj-23026	187	8	:	:	PUNCT
brj-23026	187	9	october	october	PROPN
brj-23026	187	10	9	9	NUM
brj-23026	187	11	,	,	PUNCT
brj-23026	187	12	2023	2023	NUM
brj-23026	187	13	;	;	PUNCT
brj-23026	187	14	peer	peer	NOUN
brj-23026	187	15	review	review	NOUN
brj-23026	187	16	completed	complete	VERB
brj-23026	187	17	:	:	PUNCT
brj-23026	187	18	october	october	PROPN
brj-23026	187	19	28	28	NUM
brj-23026	187	20	,	,	PUNCT
brj-23026	187	21	2023	2023	NUM
brj-23026	187	22	;	;	PUNCT
brj-23026	187	23	revised	revise	VERB
brj-23026	187	24	version	version	NOUN
brj-23026	187	25	received	receive	VERB
brj-23026	187	26	and	and	CCONJ
brj-23026	187	27	accepted	accept	VERB
brj-23026	187	28	:	:	PUNCT
brj-23026	187	29	november	november	PROPN
brj-23026	187	30	5	5	NUM
brj-23026	187	31	,	,	PUNCT
brj-23026	187	32	2023	2023	NUM
brj-23026	187	33	;	;	PUNCT
brj-23026	187	34	published	publish	VERB
brj-23026	187	35	:	:	PUNCT
brj-23026	187	36	november	november	PROPN
brj-23026	187	37	14	14	NUM
brj-23026	187	38	,	,	PUNCT
brj-23026	187	39	2023	2023	NUM
brj-23026	187	40	.	.	PUNCT
brj-23026	188	1	doi	doi	NOUN
brj-23026	188	2	:	:	PUNCT
brj-23026	188	3	10.15376	10.15376	NUM
brj-23026	188	4	/	/	SYM
brj-23026	188	5	biores.19.1.245	biores.19.1.245	NOUN
brj-23026	188	6	-	-	SYM
brj-23026	188	7	256	256	NUM