id	sid	tid	token	lemma	pos
brj-24383	1	1	peer	peer	NOUN
brj-24383	1	2	-	-	PUNCT
brj-24383	1	3	review	review	NOUN
brj-24383	1	4	article	article	NOUN
brj-24383	1	5	peer	peer	NOUN
brj-24383	1	6	-	-	PUNCT
brj-24383	1	7	reviewed	review	VERB
brj-24383	1	8	article	article	NOUN
brj-24383	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	1	10	su	su	PROPN
brj-24383	1	11	et	et	PROPN
brj-24383	1	12	al	al	PROPN
brj-24383	1	13	.	.	PROPN
brj-24383	2	1	(	(	PUNCT
brj-24383	2	2	2025	2025	NUM
brj-24383	2	3	)	)	PUNCT
brj-24383	2	4	.	.	PUNCT
brj-24383	3	1	“	"	PUNCT
brj-24383	3	2	leguminous	leguminous	ADJ
brj-24383	3	3	wood	wood	NOUN
brj-24383	3	4	classification	classification	NOUN
brj-24383	3	5	,	,	PUNCT
brj-24383	3	6	”	"	PUNCT
brj-24383	3	7	bioresources	bioresource	NOUN
brj-24383	3	8	20(3	20(3	NOUN
brj-24383	3	9	)	)	PUNCT
brj-24383	3	10	,	,	PUNCT
brj-24383	3	11	6317	6317	NUM
brj-24383	3	12	-	-	SYM
brj-24383	3	13	6337	6337	NUM
brj-24383	3	14	.	.	PUNCT
brj-24383	4	1	6317	6317	NUM
brj-24383	4	2	classification	classification	NOUN
brj-24383	4	3	of	of	ADP
brj-24383	4	4	leguminous	leguminous	ADJ
brj-24383	4	5	wood	wood	NOUN
brj-24383	4	6	species	specie	NOUN
brj-24383	4	7	based	base	VERB
brj-24383	4	8	on	on	ADP
brj-24383	4	9	small	small	ADJ
brj-24383	4	10	sample	sample	NOUN
brj-24383	4	11	hyperspectral	hyperspectral	ADJ
brj-24383	4	12	images	image	NOUN
brj-24383	4	13	hang	hang	VERB
brj-24383	4	14	su	su	PROPN
brj-24383	4	15	,	,	PUNCT
brj-24383	4	16	a	a	DET
brj-24383	4	17	shuo	shuo	PROPN
brj-24383	4	18	xu	xu	PROPN
brj-24383	4	19	,	,	PUNCT
brj-24383	4	20	a	a	DET
brj-24383	4	21	zhongjian	zhongjian	ADJ
brj-24383	4	22	wang	wang	PROPN
brj-24383	4	23	,	,	PUNCT
brj-24383	4	24	a	a	DET
brj-24383	4	25	wenxin	wenxin	PROPN
brj-24383	4	26	zhao	zhao	PROPN
brj-24383	4	27	,	,	PUNCT
brj-24383	4	28	a	a	DET
brj-24383	4	29	yanan	yanan	PROPN
brj-24383	4	30	wen	wen	PROPN
brj-24383	4	31	,	,	PUNCT
brj-24383	4	32	b	b	PROPN
brj-24383	4	33	and	and	CCONJ
brj-24383	4	34	lei	lei	X
brj-24383	4	35	zhao	zhao	PROPN
brj-24383	4	36	a	a	X
brj-24383	4	37	,	,	PUNCT
brj-24383	4	38	*	*	PUNCT
brj-24383	4	39	leguminous	leguminous	ADJ
brj-24383	4	40	wood	wood	NOUN
brj-24383	4	41	occupies	occupy	VERB
brj-24383	4	42	an	an	DET
brj-24383	4	43	important	important	ADJ
brj-24383	4	44	position	position	NOUN
brj-24383	4	45	in	in	ADP
brj-24383	4	46	the	the	DET
brj-24383	4	47	market	market	NOUN
brj-24383	4	48	of	of	ADP
brj-24383	4	49	cultural	cultural	ADJ
brj-24383	4	50	and	and	CCONJ
brj-24383	4	51	high	high	ADJ
brj-24383	4	52	-	-	PUNCT
brj-24383	4	53	end	end	NOUN
brj-24383	4	54	wood	wood	NOUN
brj-24383	4	55	.	.	PUNCT
brj-24383	5	1	accurate	accurate	ADJ
brj-24383	5	2	identification	identification	NOUN
brj-24383	5	3	and	and	CCONJ
brj-24383	5	4	classification	classification	NOUN
brj-24383	5	5	of	of	ADP
brj-24383	5	6	its	its	PRON
brj-24383	5	7	species	specie	NOUN
brj-24383	5	8	is	be	AUX
brj-24383	5	9	crucial	crucial	ADJ
brj-24383	5	10	for	for	ADP
brj-24383	5	11	the	the	DET
brj-24383	5	12	development	development	NOUN
brj-24383	5	13	of	of	ADP
brj-24383	5	14	the	the	DET
brj-24383	5	15	industry	industry	NOUN
brj-24383	5	16	.	.	PUNCT
brj-24383	6	1	however	however	ADV
brj-24383	6	2	,	,	PUNCT
brj-24383	6	3	existing	exist	VERB
brj-24383	6	4	studies	study	NOUN
brj-24383	6	5	are	be	AUX
brj-24383	6	6	still	still	ADV
brj-24383	6	7	deficient	deficient	ADJ
brj-24383	6	8	in	in	ADP
brj-24383	6	9	classification	classification	NOUN
brj-24383	6	10	methods	method	NOUN
brj-24383	6	11	under	under	ADP
brj-24383	6	12	small	small	ADJ
brj-24383	6	13	sample	sample	NOUN
brj-24383	6	14	conditions	condition	NOUN
brj-24383	6	15	.	.	PUNCT
brj-24383	7	1	this	this	DET
brj-24383	7	2	paper	paper	NOUN
brj-24383	7	3	uses	use	VERB
brj-24383	7	4	hyperspectral	hyperspectral	ADJ
brj-24383	7	5	image	image	NOUN
brj-24383	7	6	data	datum	NOUN
brj-24383	7	7	and	and	CCONJ
brj-24383	7	8	combines	combine	NOUN
brj-24383	7	9	models	model	NOUN
brj-24383	7	10	such	such	ADJ
brj-24383	7	11	as	as	ADP
brj-24383	7	12	support	support	NOUN
brj-24383	7	13	vector	vector	NOUN
brj-24383	7	14	machine	machine	NOUN
brj-24383	7	15	(	(	PUNCT
brj-24383	7	16	svm	svm	PROPN
brj-24383	7	17	)	)	PUNCT
brj-24383	7	18	,	,	PUNCT
brj-24383	7	19	random	random	ADJ
brj-24383	7	20	forest	forest	NOUN
brj-24383	7	21	(	(	PUNCT
brj-24383	7	22	rf	rf	NOUN
brj-24383	7	23	)	)	PUNCT
brj-24383	7	24	,	,	PUNCT
brj-24383	7	25	logistic	logistic	ADJ
brj-24383	7	26	regression	regression	NOUN
brj-24383	7	27	(	(	PUNCT
brj-24383	7	28	lr	lr	NOUN
brj-24383	7	29	)	)	PUNCT
brj-24383	7	30	,	,	PUNCT
brj-24383	7	31	and	and	CCONJ
brj-24383	7	32	one	one	NUM
brj-24383	7	33	-	-	PUNCT
brj-24383	7	34	dimensional	dimensional	ADJ
brj-24383	7	35	convolutional	convolutional	ADJ
brj-24383	7	36	neural	neural	ADJ
brj-24383	7	37	network	network	NOUN
brj-24383	7	38	(	(	PUNCT
brj-24383	7	39	1	1	NUM
brj-24383	7	40	-	-	NUM
brj-24383	7	41	cnn	cnn	NOUN
brj-24383	7	42	)	)	PUNCT
brj-24383	7	43	.	.	PUNCT
brj-24383	8	1	the	the	DET
brj-24383	8	2	synthetic	synthetic	ADJ
brj-24383	8	3	minority	minority	NOUN
brj-24383	8	4	oversampling	oversample	VERB
brj-24383	8	5	technique	technique	NOUN
brj-24383	8	6	(	(	PUNCT
brj-24383	8	7	smote	smote	NOUN
brj-24383	8	8	)	)	PUNCT
brj-24383	8	9	data	datum	NOUN
brj-24383	8	10	enhancement	enhancement	NOUN
brj-24383	8	11	technology	technology	NOUN
brj-24383	8	12	was	be	AUX
brj-24383	8	13	introduced	introduce	VERB
brj-24383	8	14	to	to	PART
brj-24383	8	15	classify	classify	VERB
brj-24383	8	16	and	and	CCONJ
brj-24383	8	17	recognize	recognize	VERB
brj-24383	8	18	18	18	NUM
brj-24383	8	19	common	common	ADJ
brj-24383	8	20	legume	legume	NOUN
brj-24383	8	21	woods	wood	NOUN
brj-24383	8	22	.	.	PUNCT
brj-24383	9	1	after	after	ADP
brj-24383	9	2	data	data	NOUN
brj-24383	9	3	processing	processing	NOUN
brj-24383	9	4	,	,	PUNCT
brj-24383	9	5	the	the	DET
brj-24383	9	6	classification	classification	NOUN
brj-24383	9	7	accuracy	accuracy	NOUN
brj-24383	9	8	of	of	ADP
brj-24383	9	9	the	the	DET
brj-24383	9	10	traditional	traditional	ADJ
brj-24383	9	11	models	model	NOUN
brj-24383	9	12	was	be	AUX
brj-24383	9	13	improved	improve	VERB
brj-24383	9	14	by	by	ADP
brj-24383	9	15	about	about	ADV
brj-24383	9	16	5	5	NUM
brj-24383	9	17	%	%	NOUN
brj-24383	9	18	on	on	ADP
brj-24383	9	19	average	average	ADJ
brj-24383	9	20	,	,	PUNCT
brj-24383	9	21	with	with	ADP
brj-24383	9	22	the	the	DET
brj-24383	9	23	svm	svm	ADJ
brj-24383	9	24	model	model	NOUN
brj-24383	9	25	reaching	reach	VERB
brj-24383	9	26	98.86	98.86	NUM
brj-24383	9	27	%	%	NOUN
brj-24383	9	28	;	;	PUNCT
brj-24383	9	29	the	the	DET
brj-24383	9	30	accuracy	accuracy	NOUN
brj-24383	9	31	of	of	ADP
brj-24383	9	32	the	the	DET
brj-24383	9	33	1	1	NUM
brj-24383	9	34	-	-	PUNCT
brj-24383	9	35	cnn	cnn	PROPN
brj-24383	9	36	model	model	NOUN
brj-24383	9	37	was	be	AUX
brj-24383	9	38	increased	increase	VERB
brj-24383	9	39	to	to	ADP
brj-24383	9	40	97.67	97.67	NUM
brj-24383	9	41	%	%	NOUN
brj-24383	9	42	after	after	ADP
brj-24383	9	43	adding	add	VERB
brj-24383	9	44	the	the	DET
brj-24383	9	45	first	first	ADJ
brj-24383	9	46	-	-	PUNCT
brj-24383	9	47	order	order	NOUN
brj-24383	9	48	derivative	derivative	ADJ
brj-24383	9	49	transform	transform	NOUN
brj-24383	9	50	and	and	CCONJ
brj-24383	9	51	savitzkygolay	savitzkygolay	VERB
brj-24383	9	52	filtering	filter	VERB
brj-24383	9	53	,	,	PUNCT
brj-24383	9	54	it	it	PRON
brj-24383	9	55	reached	reach	VERB
brj-24383	9	56	98.89	98.89	NUM
brj-24383	9	57	%	%	NOUN
brj-24383	9	58	after	after	ADP
brj-24383	9	59	further	far	ADV
brj-24383	9	60	adding	add	VERB
brj-24383	9	61	the	the	DET
brj-24383	9	62	smote	smote	NOUN
brj-24383	9	63	.	.	PUNCT
brj-24383	10	1	doi	doi	NOUN
brj-24383	10	2	:	:	PUNCT
brj-24383	10	3	10.15376	10.15376	NUM
brj-24383	10	4	/	/	SYM
brj-24383	10	5	biores.20.3.6317	biores.20.3.6317	PROPN
brj-24383	10	6	-	-	PUNCT
brj-24383	10	7	6337	6337	NUM
brj-24383	10	8	keywords	keyword	NOUN
brj-24383	10	9	:	:	PUNCT
brj-24383	10	10	hyperspectral	hyperspectral	ADJ
brj-24383	10	11	image	image	NOUN
brj-24383	10	12	data	datum	NOUN
brj-24383	10	13	;	;	PUNCT
brj-24383	10	14	leguminous	leguminous	ADJ
brj-24383	10	15	wood	wood	NOUN
brj-24383	10	16	classification	classification	NOUN
brj-24383	10	17	;	;	PUNCT
brj-24383	10	18	smote	smote	ADJ
brj-24383	10	19	data	data	NOUN
brj-24383	10	20	enhancement	enhancement	NOUN
brj-24383	10	21	;	;	PUNCT
brj-24383	10	22	1cnn	1cnn	NUM
brj-24383	10	23	contact	contact	NOUN
brj-24383	10	24	information	information	NOUN
brj-24383	10	25	:	:	PUNCT
brj-24383	10	26	a	a	X
brj-24383	10	27	:	:	PUNCT
brj-24383	10	28	college	college	NOUN
brj-24383	10	29	of	of	ADP
brj-24383	10	30	science	science	NOUN
brj-24383	10	31	and	and	CCONJ
brj-24383	10	32	information	information	NOUN
brj-24383	10	33	,	,	PUNCT
brj-24383	10	34	qingdao	qingdao	PROPN
brj-24383	10	35	agricultural	agricultural	PROPN
brj-24383	10	36	university	university	PROPN
brj-24383	10	37	,	,	PUNCT
brj-24383	10	38	qingdao	qingdao	PROPN
brj-24383	10	39	266109	266109	NUM
brj-24383	10	40	;	;	PUNCT
brj-24383	10	41	b	b	X
brj-24383	10	42	:	:	PUNCT
brj-24383	10	43	qingdao	qingdao	PROPN
brj-24383	10	44	quenda	quenda	PROPN
brj-24383	10	45	terahertz	terahertz	PROPN
brj-24383	10	46	technology	technology	PROPN
brj-24383	10	47	co.	co.	PROPN
brj-24383	10	48	,	,	PUNCT
brj-24383	10	49	ltd	ltd	PROPN
brj-24383	10	50	.	.	PROPN
brj-24383	11	1	china	china	PROPN
brj-24383	11	2	;	;	PUNCT
brj-24383	11	3	*	*	PUNCT
brj-24383	11	4	corresponding	correspond	VERB
brj-24383	11	5	author	author	NOUN
brj-24383	11	6	:	:	PUNCT
brj-24383	11	7	lei.zhao@qau.edu.cn	lei.zhao@qau.edu.cn	PROPN
brj-24383	11	8	introduction	introduction	NOUN
brj-24383	11	9	due	due	ADP
brj-24383	11	10	to	to	ADP
brj-24383	11	11	its	its	PRON
brj-24383	11	12	excellent	excellent	ADJ
brj-24383	11	13	physical	physical	ADJ
brj-24383	11	14	properties	property	NOUN
brj-24383	11	15	and	and	CCONJ
brj-24383	11	16	a	a	DET
brj-24383	11	17	wide	wide	ADJ
brj-24383	11	18	range	range	NOUN
brj-24383	11	19	of	of	ADP
brj-24383	11	20	application	application	NOUN
brj-24383	11	21	scenarios	scenario	NOUN
brj-24383	11	22	,	,	PUNCT
brj-24383	11	23	legume	legume	NOUN
brj-24383	11	24	wood	wood	NOUN
brj-24383	11	25	occupies	occupy	VERB
brj-24383	11	26	an	an	DET
brj-24383	11	27	important	important	ADJ
brj-24383	11	28	position	position	NOUN
brj-24383	11	29	,	,	PUNCT
brj-24383	11	30	especially	especially	ADV
brj-24383	11	31	in	in	ADP
brj-24383	11	32	the	the	DET
brj-24383	11	33	high	high	ADJ
brj-24383	11	34	-	-	PUNCT
brj-24383	11	35	end	end	NOUN
brj-24383	11	36	furniture	furniture	NOUN
brj-24383	11	37	and	and	CCONJ
brj-24383	11	38	artifacts	artifact	NOUN
brj-24383	11	39	market	market	NOUN
brj-24383	11	40	.	.	PUNCT
brj-24383	12	1	this	this	DET
brj-24383	12	2	type	type	NOUN
brj-24383	12	3	of	of	ADP
brj-24383	12	4	wood	wood	NOUN
brj-24383	12	5	is	be	AUX
brj-24383	12	6	widely	widely	ADV
brj-24383	12	7	used	use	VERB
brj-24383	12	8	in	in	ADP
brj-24383	12	9	the	the	DET
brj-24383	12	10	production	production	NOUN
brj-24383	12	11	of	of	ADP
brj-24383	12	12	high	high	ADJ
brj-24383	12	13	-	-	PUNCT
brj-24383	12	14	end	end	NOUN
brj-24383	12	15	furniture	furniture	NOUN
brj-24383	12	16	,	,	PUNCT
brj-24383	12	17	handicrafts	handicraft	NOUN
brj-24383	12	18	,	,	PUNCT
brj-24383	12	19	and	and	CCONJ
brj-24383	12	20	decorations	decoration	NOUN
brj-24383	12	21	due	due	ADP
brj-24383	12	22	to	to	ADP
brj-24383	12	23	its	its	PRON
brj-24383	12	24	high	high	ADJ
brj-24383	12	25	hardness	hardness	NOUN
brj-24383	12	26	,	,	PUNCT
brj-24383	12	27	durability	durability	NOUN
brj-24383	12	28	,	,	PUNCT
brj-24383	12	29	superior	superior	ADJ
brj-24383	12	30	resistance	resistance	NOUN
brj-24383	12	31	to	to	PART
brj-24383	12	32	compression	compression	VERB
brj-24383	12	33	and	and	CCONJ
brj-24383	12	34	bending	bending	NOUN
brj-24383	12	35	,	,	PUNCT
brj-24383	12	36	as	as	ADV
brj-24383	12	37	well	well	ADV
brj-24383	12	38	as	as	ADP
brj-24383	12	39	resistance	resistance	NOUN
brj-24383	12	40	to	to	ADP
brj-24383	12	41	degradation	degradation	NOUN
brj-24383	12	42	.	.	PUNCT
brj-24383	13	1	however	however	ADV
brj-24383	13	2	,	,	PUNCT
brj-24383	13	3	the	the	DET
brj-24383	13	4	scarcity	scarcity	NOUN
brj-24383	13	5	and	and	CCONJ
brj-24383	13	6	high	high	ADJ
brj-24383	13	7	market	market	NOUN
brj-24383	13	8	value	value	NOUN
brj-24383	13	9	of	of	ADP
brj-24383	13	10	legume	legume	NOUN
brj-24383	13	11	timber	timber	NOUN
brj-24383	13	12	has	have	AUX
brj-24383	13	13	led	lead	VERB
brj-24383	13	14	to	to	ADP
brj-24383	13	15	a	a	DET
brj-24383	13	16	large	large	ADJ
brj-24383	13	17	number	number	NOUN
brj-24383	13	18	of	of	ADP
brj-24383	13	19	counterfeit	counterfeit	ADJ
brj-24383	13	20	and	and	CCONJ
brj-24383	13	21	shoddy	shoddy	ADJ
brj-24383	13	22	timber	timber	NOUN
brj-24383	13	23	flooding	flood	VERB
brj-24383	13	24	the	the	DET
brj-24383	13	25	market	market	NOUN
brj-24383	13	26	,	,	PUNCT
brj-24383	13	27	which	which	PRON
brj-24383	13	28	not	not	PART
brj-24383	13	29	only	only	ADV
brj-24383	13	30	affects	affect	VERB
brj-24383	13	31	the	the	DET
brj-24383	13	32	rights	right	NOUN
brj-24383	13	33	and	and	CCONJ
brj-24383	13	34	interests	interest	NOUN
brj-24383	13	35	of	of	ADP
brj-24383	13	36	consumers	consumer	NOUN
brj-24383	13	37	,	,	PUNCT
brj-24383	13	38	but	but	CCONJ
brj-24383	13	39	it	it	PRON
brj-24383	13	40	also	also	ADV
brj-24383	13	41	poses	pose	VERB
brj-24383	13	42	a	a	DET
brj-24383	13	43	serious	serious	ADJ
brj-24383	13	44	challenge	challenge	NOUN
brj-24383	13	45	to	to	ADP
brj-24383	13	46	the	the	DET
brj-24383	13	47	fair	fair	ADJ
brj-24383	13	48	competition	competition	NOUN
brj-24383	13	49	and	and	CCONJ
brj-24383	13	50	healthy	healthy	ADJ
brj-24383	13	51	development	development	NOUN
brj-24383	13	52	of	of	ADP
brj-24383	13	53	the	the	DET
brj-24383	13	54	industry	industry	NOUN
brj-24383	13	55	.	.	PUNCT
brj-24383	14	1	according	accord	VERB
brj-24383	14	2	to	to	ADP
brj-24383	14	3	the	the	DET
brj-24383	14	4	statistics	statistic	NOUN
brj-24383	14	5	of	of	ADP
brj-24383	14	6	domestic	domestic	ADJ
brj-24383	14	7	e	e	PROPN
brj-24383	14	8	-	-	NOUN
brj-24383	14	9	commerce	commerce	NOUN
brj-24383	14	10	platforms	platform	NOUN
brj-24383	14	11	,	,	PUNCT
brj-24383	14	12	there	there	PRON
brj-24383	14	13	are	be	VERB
brj-24383	14	14	more	more	ADJ
brj-24383	14	15	than	than	ADP
brj-24383	14	16	20	20	NUM
brj-24383	14	17	types	type	NOUN
brj-24383	14	18	of	of	ADP
brj-24383	14	19	high	high	ADJ
brj-24383	14	20	-	-	PUNCT
brj-24383	14	21	end	end	NOUN
brj-24383	14	22	wood	wood	NOUN
brj-24383	14	23	used	use	VERB
brj-24383	14	24	in	in	ADP
brj-24383	14	25	the	the	DET
brj-24383	14	26	market	market	NOUN
brj-24383	14	27	for	for	ADP
brj-24383	14	28	making	make	VERB
brj-24383	14	29	cultural	cultural	ADJ
brj-24383	14	30	games	game	NOUN
brj-24383	14	31	and	and	CCONJ
brj-24383	14	32	decorations	decoration	NOUN
brj-24383	14	33	,	,	PUNCT
brj-24383	14	34	of	of	ADP
brj-24383	14	35	which	which	PRON
brj-24383	14	36	leguminous	leguminous	ADJ
brj-24383	14	37	wood	wood	NOUN
brj-24383	14	38	occupies	occupy	VERB
brj-24383	14	39	the	the	DET
brj-24383	14	40	vast	vast	ADJ
brj-24383	14	41	majority	majority	NOUN
brj-24383	14	42	of	of	ADP
brj-24383	14	43	the	the	DET
brj-24383	14	44	share	share	NOUN
brj-24383	14	45	.	.	PUNCT
brj-24383	15	1	due	due	ADP
brj-24383	15	2	to	to	ADP
brj-24383	15	3	the	the	DET
brj-24383	15	4	lack	lack	NOUN
brj-24383	15	5	of	of	ADP
brj-24383	15	6	identification	identification	NOUN
brj-24383	15	7	technology	technology	NOUN
brj-24383	15	8	and	and	CCONJ
brj-24383	15	9	insufficient	insufficient	ADJ
brj-24383	15	10	standardized	standardized	ADJ
brj-24383	15	11	management	management	NOUN
brj-24383	15	12	,	,	PUNCT
brj-24383	15	13	the	the	DET
brj-24383	15	14	existence	existence	NOUN
brj-24383	15	15	of	of	ADP
brj-24383	15	16	shoddy	shoddy	ADJ
brj-24383	15	17	wood	wood	NOUN
brj-24383	15	18	has	have	AUX
brj-24383	15	19	caused	cause	VERB
brj-24383	15	20	huge	huge	ADJ
brj-24383	15	21	economic	economic	ADJ
brj-24383	15	22	losses	loss	NOUN
brj-24383	15	23	and	and	CCONJ
brj-24383	15	24	market	market	NOUN
brj-24383	15	25	confusion	confusion	NOUN
brj-24383	15	26	.	.	PUNCT
brj-24383	16	1	therefore	therefore	ADV
brj-24383	16	2	,	,	PUNCT
brj-24383	16	3	how	how	SCONJ
brj-24383	16	4	to	to	PART
brj-24383	16	5	quickly	quickly	ADV
brj-24383	16	6	and	and	CCONJ
brj-24383	16	7	accurately	accurately	ADV
brj-24383	16	8	identify	identify	VERB
brj-24383	16	9	the	the	DET
brj-24383	16	10	species	specie	NOUN
brj-24383	16	11	of	of	ADP
brj-24383	16	12	leguminous	leguminous	ADJ
brj-24383	16	13	timber	timber	NOUN
brj-24383	16	14	and	and	CCONJ
brj-24383	16	15	avoid	avoid	VERB
brj-24383	16	16	the	the	DET
brj-24383	16	17	inflow	inflow	NOUN
brj-24383	16	18	of	of	ADP
brj-24383	16	19	counterfeit	counterfeit	ADJ
brj-24383	16	20	and	and	CCONJ
brj-24383	16	21	shoddy	shoddy	ADJ
brj-24383	16	22	products	product	NOUN
brj-24383	16	23	into	into	ADP
brj-24383	16	24	the	the	DET
brj-24383	16	25	market	market	NOUN
brj-24383	16	26	has	have	AUX
brj-24383	16	27	become	become	VERB
brj-24383	16	28	a	a	DET
brj-24383	16	29	key	key	ADJ
brj-24383	16	30	problem	problem	NOUN
brj-24383	16	31	that	that	PRON
brj-24383	16	32	needs	need	VERB
brj-24383	16	33	to	to	PART
brj-24383	16	34	be	be	AUX
brj-24383	16	35	solved	solve	VERB
brj-24383	16	36	in	in	ADP
brj-24383	16	37	the	the	DET
brj-24383	16	38	current	current	ADJ
brj-24383	16	39	timber	timber	NOUN
brj-24383	16	40	market	market	NOUN
brj-24383	16	41	.	.	PUNCT
brj-24383	17	1	peer	peer	NOUN
brj-24383	17	2	-	-	PUNCT
brj-24383	17	3	reviewed	review	VERB
brj-24383	17	4	article	article	NOUN
brj-24383	17	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	17	6	su	su	PROPN
brj-24383	17	7	et	et	PROPN
brj-24383	17	8	al	al	PROPN
brj-24383	17	9	.	.	PROPN
brj-24383	18	1	(	(	PUNCT
brj-24383	18	2	2025	2025	NUM
brj-24383	18	3	)	)	PUNCT
brj-24383	18	4	.	.	PUNCT
brj-24383	19	1	“	"	PUNCT
brj-24383	19	2	leguminous	leguminous	ADJ
brj-24383	19	3	wood	wood	NOUN
brj-24383	19	4	classification	classification	NOUN
brj-24383	19	5	,	,	PUNCT
brj-24383	19	6	”	"	PUNCT
brj-24383	19	7	bioresources	bioresource	NOUN
brj-24383	19	8	20(3	20(3	NOUN
brj-24383	19	9	)	)	PUNCT
brj-24383	19	10	,	,	PUNCT
brj-24383	19	11	6317	6317	NUM
brj-24383	19	12	-	-	SYM
brj-24383	19	13	6337	6337	NUM
brj-24383	19	14	.	.	PUNCT
brj-24383	20	1	6318	6318	NUM
brj-24383	20	2	with	with	ADP
brj-24383	20	3	the	the	DET
brj-24383	20	4	increasing	increase	VERB
brj-24383	20	5	application	application	NOUN
brj-24383	20	6	of	of	ADP
brj-24383	20	7	hyperspectral	hyperspectral	ADJ
brj-24383	20	8	technology	technology	NOUN
brj-24383	20	9	in	in	ADP
brj-24383	20	10	wood	wood	NOUN
brj-24383	20	11	research	research	NOUN
brj-24383	20	12	,	,	PUNCT
brj-24383	20	13	it	it	PRON
brj-24383	20	14	has	have	AUX
brj-24383	20	15	become	become	VERB
brj-24383	20	16	a	a	DET
brj-24383	20	17	cutting	cut	VERB
brj-24383	20	18	-	-	PUNCT
brj-24383	20	19	edge	edge	NOUN
brj-24383	20	20	research	research	NOUN
brj-24383	20	21	direction	direction	NOUN
brj-24383	20	22	for	for	ADP
brj-24383	20	23	wood	wood	NOUN
brj-24383	20	24	species	species	NOUN
brj-24383	20	25	identification	identification	NOUN
brj-24383	20	26	by	by	ADP
brj-24383	20	27	virtue	virtue	NOUN
brj-24383	20	28	of	of	ADP
brj-24383	20	29	its	its	PRON
brj-24383	20	30	non	non	ADJ
brj-24383	20	31	-	-	ADJ
brj-24383	20	32	destructive	destructive	ADJ
brj-24383	20	33	measurements	measurement	NOUN
brj-24383	20	34	,	,	PUNCT
brj-24383	20	35	high	high	ADJ
brj-24383	20	36	-	-	PUNCT
brj-24383	20	37	throughput	throughput	NOUN
brj-24383	20	38	analysis	analysis	NOUN
brj-24383	20	39	,	,	PUNCT
brj-24383	20	40	and	and	CCONJ
brj-24383	20	41	rich	rich	ADJ
brj-24383	20	42	information	information	NOUN
brj-24383	20	43	dimensions	dimension	NOUN
brj-24383	20	44	.	.	PUNCT
brj-24383	21	1	by	by	ADP
brj-24383	21	2	analyzing	analyze	VERB
brj-24383	21	3	the	the	DET
brj-24383	21	4	reflectance	reflectance	NOUN
brj-24383	21	5	spectra	spectra	NOUN
brj-24383	21	6	of	of	ADP
brj-24383	21	7	wood	wood	NOUN
brj-24383	21	8	samples	sample	NOUN
brj-24383	21	9	,	,	PUNCT
brj-24383	21	10	hyperspectral	hyperspectral	ADJ
brj-24383	21	11	technology	technology	NOUN
brj-24383	21	12	can	can	AUX
brj-24383	21	13	reveal	reveal	VERB
brj-24383	21	14	the	the	DET
brj-24383	21	15	physical	physical	ADJ
brj-24383	21	16	properties	property	NOUN
brj-24383	21	17	of	of	ADP
brj-24383	21	18	wood	wood	NOUN
brj-24383	21	19	such	such	ADJ
brj-24383	21	20	as	as	ADP
brj-24383	21	21	color	color	NOUN
brj-24383	21	22	,	,	PUNCT
brj-24383	21	23	texture	texture	NOUN
brj-24383	21	24	,	,	PUNCT
brj-24383	21	25	density	density	NOUN
brj-24383	21	26	,	,	PUNCT
brj-24383	21	27	hardness	hardness	NOUN
brj-24383	21	28	,	,	PUNCT
brj-24383	21	29	etc	etc	X
brj-24383	21	30	.	.	X
brj-24383	21	31	,	,	PUNCT
brj-24383	21	32	and	and	CCONJ
brj-24383	21	33	capture	capture	VERB
brj-24383	21	34	the	the	DET
brj-24383	21	35	subtle	subtle	ADJ
brj-24383	21	36	differences	difference	NOUN
brj-24383	21	37	in	in	ADP
brj-24383	21	38	the	the	DET
brj-24383	21	39	growth	growth	NOUN
brj-24383	21	40	environment	environment	NOUN
brj-24383	21	41	,	,	PUNCT
brj-24383	21	42	processing	processing	NOUN
brj-24383	21	43	and	and	CCONJ
brj-24383	21	44	drying	dry	VERB
brj-24383	21	45	techniques	technique	NOUN
brj-24383	21	46	,	,	PUNCT
brj-24383	21	47	providing	provide	VERB
brj-24383	21	48	rich	rich	ADJ
brj-24383	21	49	feature	feature	NOUN
brj-24383	21	50	information	information	NOUN
brj-24383	21	51	for	for	ADP
brj-24383	21	52	the	the	DET
brj-24383	21	53	accurate	accurate	ADJ
brj-24383	21	54	classification	classification	NOUN
brj-24383	21	55	of	of	ADP
brj-24383	21	56	wood	wood	NOUN
brj-24383	21	57	species	specie	NOUN
brj-24383	21	58	.	.	PUNCT
brj-24383	22	1	however	however	ADV
brj-24383	22	2	,	,	PUNCT
brj-24383	22	3	how	how	SCONJ
brj-24383	22	4	to	to	PART
brj-24383	22	5	fully	fully	ADV
brj-24383	22	6	mine	mine	VERB
brj-24383	22	7	and	and	CCONJ
brj-24383	22	8	utilize	utilize	VERB
brj-24383	22	9	this	this	DET
brj-24383	22	10	feature	feature	NOUN
brj-24383	22	11	information	information	NOUN
brj-24383	22	12	to	to	PART
brj-24383	22	13	solve	solve	VERB
brj-24383	22	14	the	the	DET
brj-24383	22	15	classification	classification	NOUN
brj-24383	22	16	problem	problem	NOUN
brj-24383	22	17	under	under	ADP
brj-24383	22	18	small	small	ADJ
brj-24383	22	19	sample	sample	NOUN
brj-24383	22	20	conditions	condition	NOUN
brj-24383	22	21	with	with	ADP
brj-24383	22	22	limited	limited	ADJ
brj-24383	22	23	sample	sample	NOUN
brj-24383	22	24	size	size	NOUN
brj-24383	22	25	is	be	AUX
brj-24383	22	26	still	still	ADV
brj-24383	22	27	a	a	DET
brj-24383	22	28	technical	technical	ADJ
brj-24383	22	29	challenge	challenge	NOUN
brj-24383	22	30	that	that	PRON
brj-24383	22	31	needs	need	VERB
brj-24383	22	32	to	to	PART
brj-24383	22	33	be	be	AUX
brj-24383	22	34	investigated	investigate	VERB
brj-24383	22	35	.	.	PUNCT
brj-24383	23	1	therefore	therefore	ADV
brj-24383	23	2	,	,	PUNCT
brj-24383	23	3	exploring	explore	VERB
brj-24383	23	4	effective	effective	ADJ
brj-24383	23	5	hyperspectral	hyperspectral	ADJ
brj-24383	23	6	data	datum	NOUN
brj-24383	23	7	processing	processing	NOUN
brj-24383	23	8	and	and	CCONJ
brj-24383	23	9	classification	classification	NOUN
brj-24383	23	10	methods	method	NOUN
brj-24383	23	11	is	be	AUX
brj-24383	23	12	of	of	ADP
brj-24383	23	13	great	great	ADJ
brj-24383	23	14	theoretical	theoretical	ADJ
brj-24383	23	15	and	and	CCONJ
brj-24383	23	16	practical	practical	ADJ
brj-24383	23	17	significance	significance	NOUN
brj-24383	23	18	to	to	PART
brj-24383	23	19	improve	improve	VERB
brj-24383	23	20	the	the	DET
brj-24383	23	21	accuracy	accuracy	NOUN
brj-24383	23	22	and	and	CCONJ
brj-24383	23	23	reliability	reliability	NOUN
brj-24383	23	24	of	of	ADP
brj-24383	23	25	wood	wood	NOUN
brj-24383	23	26	species	specie	NOUN
brj-24383	23	27	identification	identification	NOUN
brj-24383	23	28	.	.	PUNCT
brj-24383	24	1	research	research	NOUN
brj-24383	24	2	status	status	NOUN
brj-24383	24	3	hyperspectral	hyperspectral	ADJ
brj-24383	24	4	imaging	imaging	NOUN
brj-24383	24	5	technology	technology	NOUN
brj-24383	24	6	has	have	AUX
brj-24383	24	7	proven	prove	VERB
brj-24383	24	8	to	to	PART
brj-24383	24	9	be	be	AUX
brj-24383	24	10	a	a	DET
brj-24383	24	11	powerful	powerful	ADJ
brj-24383	24	12	tool	tool	NOUN
brj-24383	24	13	in	in	ADP
brj-24383	24	14	wood	wood	NOUN
brj-24383	24	15	species	specie	NOUN
brj-24383	24	16	identification	identification	NOUN
brj-24383	24	17	,	,	PUNCT
brj-24383	24	18	especially	especially	ADV
brj-24383	24	19	when	when	SCONJ
brj-24383	24	20	combined	combine	VERB
brj-24383	24	21	with	with	ADP
brj-24383	24	22	machine	machine	NOUN
brj-24383	24	23	learning	learning	NOUN
brj-24383	24	24	and	and	CCONJ
brj-24383	24	25	deep	deep	ADJ
brj-24383	24	26	learning	learning	NOUN
brj-24383	24	27	models	model	NOUN
brj-24383	24	28	.	.	PUNCT
brj-24383	25	1	several	several	ADJ
brj-24383	25	2	studies	study	NOUN
brj-24383	25	3	have	have	AUX
brj-24383	25	4	demonstrated	demonstrate	VERB
brj-24383	25	5	the	the	DET
brj-24383	25	6	potential	potential	NOUN
brj-24383	25	7	of	of	ADP
brj-24383	25	8	this	this	DET
brj-24383	25	9	approach	approach	NOUN
brj-24383	25	10	for	for	ADP
brj-24383	25	11	achieving	achieve	VERB
brj-24383	25	12	high	high	ADJ
brj-24383	25	13	classification	classification	NOUN
brj-24383	25	14	accuracy	accuracy	NOUN
brj-24383	25	15	.	.	PUNCT
brj-24383	26	1	for	for	ADP
brj-24383	26	2	instance	instance	NOUN
brj-24383	26	3	,	,	PUNCT
brj-24383	26	4	zhu	zhu	PROPN
brj-24383	26	5	et	et	PROPN
brj-24383	26	6	al	al	PROPN
brj-24383	26	7	.	.	PROPN
brj-24383	27	1	(	(	PUNCT
brj-24383	27	2	2019	2019	NUM
brj-24383	27	3	)	)	PUNCT
brj-24383	27	4	used	use	VERB
brj-24383	27	5	convolutional	convolutional	ADJ
brj-24383	27	6	neural	neural	ADJ
brj-24383	27	7	networks	network	NOUN
brj-24383	27	8	(	(	PUNCT
brj-24383	27	9	cnns	cnns	PROPN
brj-24383	27	10	)	)	PUNCT
brj-24383	27	11	coupled	couple	VERB
brj-24383	27	12	with	with	ADP
brj-24383	27	13	hyperspectral	hyperspectral	ADJ
brj-24383	27	14	imaging	imaging	NOUN
brj-24383	27	15	for	for	ADP
brj-24383	27	16	soybean	soybean	NOUN
brj-24383	27	17	variety	variety	NOUN
brj-24383	27	18	identification	identification	NOUN
brj-24383	27	19	,	,	PUNCT
brj-24383	27	20	which	which	PRON
brj-24383	27	21	could	could	AUX
brj-24383	27	22	be	be	AUX
brj-24383	27	23	extended	extend	VERB
brj-24383	27	24	to	to	ADP
brj-24383	27	25	wood	wood	NOUN
brj-24383	27	26	species	species	NOUN
brj-24383	27	27	recognition	recognition	NOUN
brj-24383	27	28	(	(	PUNCT
brj-24383	27	29	zhu	zhu	X
brj-24383	27	30	et	et	PROPN
brj-24383	27	31	al	al	PROPN
brj-24383	27	32	.	.	PROPN
brj-24383	27	33	2019	2019	NUM
brj-24383	27	34	)	)	PUNCT
brj-24383	27	35	.	.	PUNCT
brj-24383	28	1	pan	pan	PROPN
brj-24383	28	2	et	et	PROPN
brj-24383	28	3	al	al	PROPN
brj-24383	28	4	.	.	PROPN
brj-24383	29	1	(	(	PUNCT
brj-24383	29	2	2023	2023	NUM
brj-24383	29	3	)	)	PUNCT
brj-24383	29	4	proposed	propose	VERB
brj-24383	29	5	a	a	DET
brj-24383	29	6	deep	deep	ADJ
brj-24383	29	7	learning	learning	NOUN
brj-24383	29	8	multimodal	multimodal	NOUN
brj-24383	29	9	fusion	fusion	NOUN
brj-24383	29	10	framework	framework	NOUN
brj-24383	29	11	using	use	VERB
brj-24383	29	12	near	near	ADV
brj-24383	29	13	-	-	PUNCT
brj-24383	29	14	infrared	infrared	ADJ
brj-24383	29	15	spectroscopy	spectroscopy	NOUN
brj-24383	29	16	,	,	PUNCT
brj-24383	29	17	gadf	gadf	NOUN
brj-24383	29	18	,	,	PUNCT
brj-24383	29	19	and	and	CCONJ
brj-24383	29	20	rgb	rgb	PROPN
brj-24383	29	21	images	image	NOUN
brj-24383	29	22	,	,	PUNCT
brj-24383	29	23	showing	show	VERB
brj-24383	29	24	how	how	SCONJ
brj-24383	29	25	multimodal	multimodal	ADJ
brj-24383	29	26	data	datum	NOUN
brj-24383	29	27	can	can	AUX
brj-24383	29	28	enhance	enhance	VERB
brj-24383	29	29	classification	classification	NOUN
brj-24383	29	30	performance	performance	NOUN
brj-24383	29	31	.	.	PUNCT
brj-24383	30	1	similarly	similarly	ADV
brj-24383	30	2	,	,	PUNCT
brj-24383	30	3	marrs	marrs	PROPN
brj-24383	30	4	and	and	CCONJ
brj-24383	30	5	ni	ni	PROPN
brj-24383	30	6	-	-	PROPN
brj-24383	30	7	meister	meister	PROPN
brj-24383	30	8	(	(	PUNCT
brj-24383	30	9	2019	2019	NUM
brj-24383	30	10	)	)	PUNCT
brj-24383	30	11	applied	apply	VERB
brj-24383	30	12	lidar	lidar	NOUN
brj-24383	30	13	and	and	CCONJ
brj-24383	30	14	hyperspectral	hyperspectral	ADJ
brj-24383	30	15	data	datum	NOUN
brj-24383	30	16	for	for	ADP
brj-24383	30	17	tree	tree	NOUN
brj-24383	30	18	species	specie	NOUN
brj-24383	30	19	classification	classification	NOUN
brj-24383	30	20	,	,	PUNCT
brj-24383	30	21	indicating	indicate	VERB
brj-24383	30	22	the	the	DET
brj-24383	30	23	promise	promise	NOUN
brj-24383	30	24	of	of	ADP
brj-24383	30	25	integrating	integrate	VERB
brj-24383	30	26	spatial	spatial	ADJ
brj-24383	30	27	and	and	CCONJ
brj-24383	30	28	spectral	spectral	ADJ
brj-24383	30	29	data	datum	NOUN
brj-24383	30	30	for	for	ADP
brj-24383	30	31	improved	improved	ADJ
brj-24383	30	32	accuracy	accuracy	NOUN
brj-24383	30	33	.	.	PUNCT
brj-24383	31	1	however	however	ADV
brj-24383	31	2	,	,	PUNCT
brj-24383	31	3	these	these	DET
brj-24383	31	4	studies	study	NOUN
brj-24383	31	5	often	often	ADV
brj-24383	31	6	face	face	VERB
brj-24383	31	7	limitations	limitation	NOUN
brj-24383	31	8	related	relate	VERB
brj-24383	31	9	to	to	ADP
brj-24383	31	10	the	the	DET
brj-24383	31	11	sample	sample	NOUN
brj-24383	31	12	size	size	NOUN
brj-24383	31	13	and	and	CCONJ
brj-24383	31	14	data	datum	NOUN
brj-24383	31	15	complexity	complexity	NOUN
brj-24383	31	16	.	.	PUNCT
brj-24383	32	1	for	for	ADP
brj-24383	32	2	example	example	NOUN
brj-24383	32	3	,	,	PUNCT
brj-24383	32	4	aydemir	aydemir	NOUN
brj-24383	32	5	and	and	CCONJ
brj-24383	32	6	bilgin	bilgin	NOUN
brj-24383	32	7	(	(	PUNCT
brj-24383	32	8	2017	2017	NUM
brj-24383	32	9	)	)	PUNCT
brj-24383	32	10	addressed	address	VERB
brj-24383	32	11	small	small	ADJ
brj-24383	32	12	sample	sample	NOUN
brj-24383	32	13	sizes	size	NOUN
brj-24383	32	14	with	with	ADP
brj-24383	32	15	a	a	DET
brj-24383	32	16	semi	semi	ADJ
brj-24383	32	17	-	-	ADJ
brj-24383	32	18	supervised	supervised	ADJ
brj-24383	32	19	classification	classification	NOUN
brj-24383	32	20	method	method	NOUN
brj-24383	32	21	,	,	PUNCT
brj-24383	32	22	but	but	CCONJ
brj-24383	32	23	their	their	PRON
brj-24383	32	24	results	result	NOUN
brj-24383	32	25	still	still	ADV
brj-24383	32	26	suggest	suggest	VERB
brj-24383	32	27	that	that	SCONJ
brj-24383	32	28	small	small	ADJ
brj-24383	32	29	datasets	dataset	NOUN
brj-24383	32	30	may	may	AUX
brj-24383	32	31	not	not	PART
brj-24383	32	32	fully	fully	ADV
brj-24383	32	33	represent	represent	VERB
brj-24383	32	34	the	the	DET
brj-24383	32	35	variability	variability	NOUN
brj-24383	32	36	of	of	ADP
brj-24383	32	37	wood	wood	NOUN
brj-24383	32	38	species	specie	NOUN
brj-24383	32	39	,	,	PUNCT
brj-24383	32	40	limiting	limit	VERB
brj-24383	32	41	the	the	DET
brj-24383	32	42	generalization	generalization	NOUN
brj-24383	32	43	of	of	ADP
brj-24383	32	44	the	the	DET
brj-24383	32	45	model	model	NOUN
brj-24383	32	46	.	.	PUNCT
brj-24383	33	1	chen	chen	PROPN
brj-24383	33	2	et	et	PROPN
brj-24383	33	3	al	al	PROPN
brj-24383	33	4	.	.	PROPN
brj-24383	34	1	(	(	PUNCT
brj-24383	34	2	2024	2024	NUM
brj-24383	34	3	)	)	PUNCT
brj-24383	34	4	used	use	VERB
brj-24383	34	5	hyperspectral	hyperspectral	ADJ
brj-24383	34	6	imaging	imaging	NOUN
brj-24383	34	7	combined	combine	VERB
brj-24383	34	8	with	with	ADP
brj-24383	34	9	machine	machine	NOUN
brj-24383	34	10	learning	learning	NOUN
brj-24383	34	11	for	for	ADP
brj-24383	34	12	dalbergia	dalbergia	NOUN
brj-24383	34	13	species	specie	NOUN
brj-24383	34	14	identification	identification	NOUN
brj-24383	34	15	,	,	PUNCT
brj-24383	34	16	but	but	CCONJ
brj-24383	34	17	this	this	DET
brj-24383	34	18	approach	approach	NOUN
brj-24383	34	19	might	might	AUX
brj-24383	34	20	struggle	struggle	VERB
brj-24383	34	21	when	when	SCONJ
brj-24383	34	22	dealing	deal	VERB
brj-24383	34	23	with	with	ADP
brj-24383	34	24	very	very	ADV
brj-24383	34	25	limited	limited	ADJ
brj-24383	34	26	samples	sample	NOUN
brj-24383	34	27	.	.	PUNCT
brj-24383	35	1	additionally	additionally	ADV
brj-24383	35	2	,	,	PUNCT
brj-24383	35	3	masoumi	masoumi	NOUN
brj-24383	35	4	and	and	CCONJ
brj-24383	35	5	bond	bond	NOUN
brj-24383	35	6	(	(	PUNCT
brj-24383	35	7	2024	2024	NUM
brj-24383	35	8	)	)	PUNCT
brj-24383	35	9	focused	focus	VERB
brj-24383	35	10	on	on	ADP
brj-24383	35	11	predicting	predict	VERB
brj-24383	35	12	moisture	moisture	NOUN
brj-24383	35	13	content	content	NOUN
brj-24383	35	14	and	and	CCONJ
brj-24383	35	15	swelling	swell	VERB
brj-24383	35	16	in	in	ADP
brj-24383	35	17	thermally	thermally	ADV
brj-24383	35	18	modified	modify	VERB
brj-24383	35	19	hardwoods	hardwood	NOUN
brj-24383	35	20	,	,	PUNCT
brj-24383	35	21	but	but	CCONJ
brj-24383	35	22	the	the	DET
brj-24383	35	23	direct	direct	ADJ
brj-24383	35	24	application	application	NOUN
brj-24383	35	25	of	of	ADP
brj-24383	35	26	their	their	PRON
brj-24383	35	27	model	model	NOUN
brj-24383	35	28	for	for	ADP
brj-24383	35	29	wood	wood	NOUN
brj-24383	35	30	species	specie	NOUN
brj-24383	35	31	classification	classification	NOUN
brj-24383	35	32	remains	remain	VERB
brj-24383	35	33	unclear	unclear	ADJ
brj-24383	35	34	.	.	PUNCT
brj-24383	36	1	ravindran	ravindran	NOUN
brj-24383	36	2	et	et	PROPN
brj-24383	36	3	al	al	PROPN
brj-24383	36	4	.	.	PROPN
brj-24383	36	5	(	(	PUNCT
brj-24383	36	6	2021	2021	NUM
brj-24383	36	7	)	)	PUNCT
brj-24383	36	8	and	and	CCONJ
brj-24383	36	9	gerasimov	gerasimov	VERB
brj-24383	36	10	et	et	PROPN
brj-24383	36	11	al	al	PROPN
brj-24383	36	12	.	.	PUNCT
brj-24383	37	1	(	(	PUNCT
brj-24383	37	2	2016	2016	NUM
brj-24383	37	3	)	)	PUNCT
brj-24383	37	4	applied	apply	VERB
brj-24383	37	5	hyperspectral	hyperspectral	ADJ
brj-24383	37	6	and	and	CCONJ
brj-24383	37	7	raman	raman	NOUN
brj-24383	37	8	spectroscopy	spectroscopy	NOUN
brj-24383	37	9	methods	method	NOUN
brj-24383	37	10	for	for	ADP
brj-24383	37	11	wood	wood	NOUN
brj-24383	37	12	species	specie	NOUN
brj-24383	37	13	identification	identification	NOUN
brj-24383	37	14	,	,	PUNCT
brj-24383	37	15	but	but	CCONJ
brj-24383	37	16	their	their	PRON
brj-24383	37	17	approaches	approach	NOUN
brj-24383	37	18	primarily	primarily	ADV
brj-24383	37	19	focus	focus	VERB
brj-24383	37	20	on	on	ADP
brj-24383	37	21	standard	standard	ADJ
brj-24383	37	22	spectral	spectral	ADJ
brj-24383	37	23	data	datum	NOUN
brj-24383	37	24	,	,	PUNCT
brj-24383	37	25	which	which	PRON
brj-24383	37	26	could	could	AUX
brj-24383	37	27	benefit	benefit	VERB
brj-24383	37	28	from	from	ADP
brj-24383	37	29	incorporating	incorporate	VERB
brj-24383	37	30	advanced	advanced	ADJ
brj-24383	37	31	preprocessing	preprocessing	NOUN
brj-24383	37	32	methods	method	NOUN
brj-24383	37	33	to	to	PART
brj-24383	37	34	better	well	ADV
brj-24383	37	35	handle	handle	VERB
brj-24383	37	36	noisy	noisy	ADJ
brj-24383	37	37	data	datum	NOUN
brj-24383	37	38	and	and	CCONJ
brj-24383	37	39	improve	improve	VERB
brj-24383	37	40	classification	classification	NOUN
brj-24383	37	41	accuracy	accuracy	NOUN
brj-24383	37	42	in	in	ADP
brj-24383	37	43	complex	complex	ADJ
brj-24383	37	44	environments	environment	NOUN
brj-24383	37	45	.	.	PUNCT
brj-24383	38	1	zhao	zhao	PROPN
brj-24383	38	2	et	et	PROPN
brj-24383	38	3	al	al	PROPN
brj-24383	38	4	.	.	PROPN
brj-24383	38	5	(	(	PUNCT
brj-24383	38	6	2021	2021	NUM
brj-24383	38	7	)	)	PUNCT
brj-24383	38	8	proposed	propose	VERB
brj-24383	38	9	a	a	DET
brj-24383	38	10	fuzzy	fuzzy	ADJ
brj-24383	38	11	reasoning	reasoning	NOUN
brj-24383	38	12	and	and	CCONJ
brj-24383	38	13	decision	decision	NOUN
brj-24383	38	14	-	-	PUNCT
brj-24383	38	15	level	level	NOUN
brj-24383	38	16	fusion	fusion	NOUN
brj-24383	38	17	technique	technique	NOUN
brj-24383	38	18	for	for	ADP
brj-24383	38	19	wood	wood	NOUN
brj-24383	38	20	species	species	NOUN
brj-24383	38	21	recognition	recognition	NOUN
brj-24383	38	22	using	use	VERB
brj-24383	38	23	visible	visible	ADJ
brj-24383	38	24	and	and	CCONJ
brj-24383	38	25	near	near	ADV
brj-24383	38	26	-	-	PUNCT
brj-24383	38	27	infrared	infrared	ADJ
brj-24383	38	28	spectral	spectral	ADJ
brj-24383	38	29	analysis	analysis	NOUN
brj-24383	38	30	,	,	PUNCT
brj-24383	38	31	but	but	CCONJ
brj-24383	38	32	it	it	PRON
brj-24383	38	33	still	still	ADV
brj-24383	38	34	requires	require	VERB
brj-24383	38	35	further	far	ADV
brj-24383	38	36	refinement	refinement	VERB
brj-24383	38	37	to	to	PART
brj-24383	38	38	handle	handle	VERB
brj-24383	38	39	the	the	DET
brj-24383	38	40	complexities	complexity	NOUN
brj-24383	38	41	of	of	ADP
brj-24383	38	42	heterogeneous	heterogeneous	ADJ
brj-24383	38	43	datasets	dataset	NOUN
brj-24383	38	44	and	and	CCONJ
brj-24383	38	45	limited	limited	ADJ
brj-24383	38	46	data	data	NOUN
brj-24383	38	47	points	point	NOUN
brj-24383	38	48	.	.	PUNCT
brj-24383	39	1	fabijańska	fabijańska	NOUN
brj-24383	39	2	et	et	PROPN
brj-24383	39	3	al	al	PROPN
brj-24383	39	4	.	.	PROPN
brj-24383	40	1	(	(	PUNCT
brj-24383	40	2	2021	2021	NUM
brj-24383	40	3	)	)	PUNCT
brj-24383	40	4	employed	employ	VERB
brj-24383	40	5	residual	residual	ADJ
brj-24383	40	6	cnns	cnn	NOUN
brj-24383	40	7	for	for	ADP
brj-24383	40	8	wood	wood	NOUN
brj-24383	40	9	species	specie	NOUN
brj-24383	40	10	classification	classification	NOUN
brj-24383	40	11	from	from	ADP
brj-24383	40	12	wood	wood	NOUN
brj-24383	40	13	core	core	NOUN
brj-24383	40	14	images	image	NOUN
brj-24383	40	15	,	,	PUNCT
brj-24383	40	16	demonstrating	demonstrate	VERB
brj-24383	40	17	the	the	DET
brj-24383	40	18	power	power	NOUN
brj-24383	40	19	of	of	ADP
brj-24383	40	20	deep	deep	ADJ
brj-24383	40	21	learning	learning	NOUN
brj-24383	40	22	,	,	PUNCT
brj-24383	40	23	but	but	CCONJ
brj-24383	40	24	they	they	PRON
brj-24383	40	25	did	do	AUX
brj-24383	40	26	not	not	PART
brj-24383	40	27	account	account	VERB
brj-24383	40	28	for	for	ADP
brj-24383	40	29	preprocessing	preprocesse	VERB
brj-24383	40	30	methods	method	NOUN
brj-24383	40	31	like	like	ADP
brj-24383	40	32	derivative	derivative	ADJ
brj-24383	40	33	transformations	transformation	NOUN
brj-24383	40	34	or	or	CCONJ
brj-24383	40	35	filtering	filtering	NOUN
brj-24383	40	36	techniques	technique	NOUN
brj-24383	40	37	that	that	PRON
brj-24383	40	38	could	could	AUX
brj-24383	40	39	enhance	enhance	VERB
brj-24383	40	40	the	the	DET
brj-24383	40	41	input	input	NOUN
brj-24383	40	42	data	datum	NOUN
brj-24383	40	43	quality	quality	NOUN
brj-24383	40	44	,	,	PUNCT
brj-24383	40	45	especially	especially	ADV
brj-24383	40	46	when	when	SCONJ
brj-24383	40	47	dealing	deal	VERB
brj-24383	40	48	with	with	ADP
brj-24383	40	49	small	small	ADJ
brj-24383	40	50	sample	sample	NOUN
brj-24383	40	51	sizes	size	NOUN
brj-24383	40	52	.	.	PUNCT
brj-24383	41	1	peer	peer	NOUN
brj-24383	41	2	-	-	PUNCT
brj-24383	41	3	reviewed	review	VERB
brj-24383	41	4	article	article	NOUN
brj-24383	41	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	41	6	su	su	PROPN
brj-24383	41	7	et	et	PROPN
brj-24383	41	8	al	al	PROPN
brj-24383	41	9	.	.	PROPN
brj-24383	42	1	(	(	PUNCT
brj-24383	42	2	2025	2025	NUM
brj-24383	42	3	)	)	PUNCT
brj-24383	42	4	.	.	PUNCT
brj-24383	43	1	“	"	PUNCT
brj-24383	43	2	leguminous	leguminous	ADJ
brj-24383	43	3	wood	wood	NOUN
brj-24383	43	4	classification	classification	NOUN
brj-24383	43	5	,	,	PUNCT
brj-24383	43	6	”	"	PUNCT
brj-24383	43	7	bioresources	bioresource	NOUN
brj-24383	43	8	20(3	20(3	NOUN
brj-24383	43	9	)	)	PUNCT
brj-24383	43	10	,	,	PUNCT
brj-24383	43	11	6317	6317	NUM
brj-24383	43	12	-	-	SYM
brj-24383	43	13	6337	6337	NUM
brj-24383	43	14	.	.	PUNCT
brj-24383	44	1	6319	6319	NUM
brj-24383	44	2	objective	objective	NOUN
brj-24383	44	3	and	and	CCONJ
brj-24383	44	4	scope	scope	NOUN
brj-24383	44	5	of	of	ADP
brj-24383	44	6	this	this	DET
brj-24383	44	7	study	study	NOUN
brj-24383	44	8	the	the	DET
brj-24383	44	9	main	main	ADJ
brj-24383	44	10	objective	objective	NOUN
brj-24383	44	11	of	of	ADP
brj-24383	44	12	this	this	DET
brj-24383	44	13	study	study	NOUN
brj-24383	44	14	was	be	AUX
brj-24383	44	15	to	to	PART
brj-24383	44	16	address	address	VERB
brj-24383	44	17	the	the	DET
brj-24383	44	18	limitations	limitation	NOUN
brj-24383	44	19	of	of	ADP
brj-24383	44	20	existing	exist	VERB
brj-24383	44	21	legume	legume	NOUN
brj-24383	44	22	wood	wood	NOUN
brj-24383	44	23	classification	classification	NOUN
brj-24383	44	24	methods	method	NOUN
brj-24383	44	25	under	under	ADP
brj-24383	44	26	small	small	ADJ
brj-24383	44	27	sample	sample	NOUN
brj-24383	44	28	conditions	condition	NOUN
brj-24383	44	29	,	,	PUNCT
brj-24383	44	30	by	by	ADP
brj-24383	44	31	combining	combine	VERB
brj-24383	44	32	hyperspectral	hyperspectral	ADJ
brj-24383	44	33	image	image	NOUN
brj-24383	44	34	data	datum	NOUN
brj-24383	44	35	,	,	PUNCT
brj-24383	44	36	advanced	advanced	ADJ
brj-24383	44	37	data	datum	NOUN
brj-24383	44	38	processing	processing	NOUN
brj-24383	44	39	techniques	technique	NOUN
brj-24383	44	40	,	,	PUNCT
brj-24383	44	41	and	and	CCONJ
brj-24383	44	42	various	various	ADJ
brj-24383	44	43	classification	classification	NOUN
brj-24383	44	44	models	model	NOUN
brj-24383	44	45	to	to	PART
brj-24383	44	46	enhance	enhance	VERB
brj-24383	44	47	the	the	DET
brj-24383	44	48	accuracy	accuracy	NOUN
brj-24383	44	49	and	and	CCONJ
brj-24383	44	50	reliability	reliability	NOUN
brj-24383	44	51	of	of	ADP
brj-24383	44	52	legume	legume	NOUN
brj-24383	44	53	wood	wood	NOUN
brj-24383	44	54	classification	classification	NOUN
brj-24383	44	55	.	.	PUNCT
brj-24383	45	1	specifically	specifically	ADV
brj-24383	45	2	,	,	PUNCT
brj-24383	45	3	the	the	DET
brj-24383	45	4	research	research	NOUN
brj-24383	45	5	objectives	objective	NOUN
brj-24383	45	6	included	include	VERB
brj-24383	45	7	the	the	DET
brj-24383	45	8	following	follow	VERB
brj-24383	45	9	aspects	aspect	NOUN
brj-24383	45	10	:	:	PUNCT
brj-24383	45	11	solving	solve	VERB
brj-24383	45	12	the	the	DET
brj-24383	45	13	classification	classification	NOUN
brj-24383	45	14	accuracy	accuracy	NOUN
brj-24383	45	15	problem	problem	NOUN
brj-24383	45	16	in	in	ADP
brj-24383	45	17	small	small	ADJ
brj-24383	45	18	sample	sample	NOUN
brj-24383	45	19	learning	learning	NOUN
brj-24383	45	20	:	:	PUNCT
brj-24383	45	21	to	to	PART
brj-24383	45	22	address	address	VERB
brj-24383	45	23	the	the	DET
brj-24383	45	24	overfitting	overfitte	VERB
brj-24383	45	25	issue	issue	NOUN
brj-24383	45	26	of	of	ADP
brj-24383	45	27	traditional	traditional	ADJ
brj-24383	45	28	classification	classification	NOUN
brj-24383	45	29	methods	method	NOUN
brj-24383	45	30	under	under	ADP
brj-24383	45	31	small	small	ADJ
brj-24383	45	32	sample	sample	NOUN
brj-24383	45	33	data	datum	NOUN
brj-24383	45	34	,	,	PUNCT
brj-24383	45	35	synthetic	synthetic	ADJ
brj-24383	45	36	minority	minority	NOUN
brj-24383	45	37	oversampling	oversample	VERB
brj-24383	45	38	technique	technique	NOUN
brj-24383	45	39	(	(	PUNCT
brj-24383	45	40	smote	smote	NOUN
brj-24383	45	41	)	)	PUNCT
brj-24383	45	42	data	datum	NOUN
brj-24383	45	43	augmentation	augmentation	NOUN
brj-24383	45	44	was	be	AUX
brj-24383	45	45	employed	employ	VERB
brj-24383	45	46	to	to	PART
brj-24383	45	47	increase	increase	VERB
brj-24383	45	48	the	the	DET
brj-24383	45	49	diversity	diversity	NOUN
brj-24383	45	50	of	of	ADP
brj-24383	45	51	training	training	NOUN
brj-24383	45	52	samples	sample	NOUN
brj-24383	45	53	,	,	PUNCT
brj-24383	45	54	thereby	thereby	ADV
brj-24383	45	55	improving	improve	VERB
brj-24383	45	56	the	the	DET
brj-24383	45	57	model	model	NOUN
brj-24383	45	58	's	's	PART
brj-24383	45	59	generalization	generalization	NOUN
brj-24383	45	60	ability	ability	NOUN
brj-24383	45	61	.	.	PUNCT
brj-24383	46	1	optimizing	optimize	VERB
brj-24383	46	2	feature	feature	NOUN
brj-24383	46	3	extraction	extraction	NOUN
brj-24383	46	4	and	and	CCONJ
brj-24383	46	5	modeling	modeling	NOUN
brj-24383	46	6	of	of	ADP
brj-24383	46	7	hyperspectral	hyperspectral	ADJ
brj-24383	46	8	image	image	NOUN
brj-24383	46	9	data	datum	NOUN
brj-24383	46	10	:	:	PUNCT
brj-24383	46	11	a	a	DET
brj-24383	46	12	onedimensional	onedimensional	ADJ
brj-24383	46	13	convolutional	convolutional	ADJ
brj-24383	46	14	neural	neural	ADJ
brj-24383	46	15	network	network	NOUN
brj-24383	46	16	(	(	PUNCT
brj-24383	46	17	1	1	NUM
brj-24383	46	18	-	-	PUNCT
brj-24383	46	19	cnn	cnn	NOUN
brj-24383	46	20	)	)	PUNCT
brj-24383	46	21	was	be	AUX
brj-24383	46	22	combined	combine	VERB
brj-24383	46	23	with	with	ADP
brj-24383	46	24	traditional	traditional	ADJ
brj-24383	46	25	machine	machine	NOUN
brj-24383	46	26	learning	learning	NOUN
brj-24383	46	27	methods	method	NOUN
brj-24383	46	28	,	,	PUNCT
brj-24383	46	29	such	such	ADJ
brj-24383	46	30	as	as	ADP
brj-24383	46	31	support	support	NOUN
brj-24383	46	32	vector	vector	NOUN
brj-24383	46	33	machine	machine	NOUN
brj-24383	46	34	(	(	PUNCT
brj-24383	46	35	svm	svm	PROPN
brj-24383	46	36	)	)	PUNCT
brj-24383	46	37	,	,	PUNCT
brj-24383	46	38	random	random	ADJ
brj-24383	46	39	forest	forest	NOUN
brj-24383	46	40	(	(	PUNCT
brj-24383	46	41	rf	rf	NOUN
brj-24383	46	42	)	)	PUNCT
brj-24383	46	43	,	,	PUNCT
brj-24383	46	44	and	and	CCONJ
brj-24383	46	45	logistic	logistic	ADJ
brj-24383	46	46	regression	regression	NOUN
brj-24383	46	47	(	(	PUNCT
brj-24383	46	48	lr	lr	NOUN
brj-24383	46	49	)	)	PUNCT
brj-24383	46	50	.	.	PUNCT
brj-24383	47	1	data	datum	NOUN
brj-24383	47	2	preprocessing	preprocesse	VERB
brj-24383	47	3	techniques	technique	NOUN
brj-24383	47	4	such	such	ADJ
brj-24383	47	5	as	as	ADP
brj-24383	47	6	savitzky	savitzky	NOUN
brj-24383	47	7	-	-	PUNCT
brj-24383	47	8	golay	golay	NOUN
brj-24383	47	9	filtering	filtering	NOUN
brj-24383	47	10	and	and	CCONJ
brj-24383	47	11	first	first	ADJ
brj-24383	47	12	-	-	PUNCT
brj-24383	47	13	order	order	NOUN
brj-24383	47	14	derivative	derivative	ADJ
brj-24383	47	15	transformation	transformation	NOUN
brj-24383	47	16	are	be	AUX
brj-24383	47	17	introduced	introduce	VERB
brj-24383	47	18	to	to	PART
brj-24383	47	19	maximize	maximize	VERB
brj-24383	47	20	the	the	DET
brj-24383	47	21	potential	potential	NOUN
brj-24383	47	22	of	of	ADP
brj-24383	47	23	hyperspectral	hyperspectral	ADJ
brj-24383	47	24	data	datum	NOUN
brj-24383	47	25	and	and	CCONJ
brj-24383	47	26	enhance	enhance	VERB
brj-24383	47	27	the	the	DET
brj-24383	47	28	performance	performance	NOUN
brj-24383	47	29	of	of	ADP
brj-24383	47	30	the	the	DET
brj-24383	47	31	classification	classification	NOUN
brj-24383	47	32	model	model	NOUN
brj-24383	47	33	.	.	PUNCT
brj-24383	48	1	while	while	SCONJ
brj-24383	48	2	hyperspectral	hyperspectral	ADJ
brj-24383	48	3	imagery	imagery	NOUN
brj-24383	48	4	inherently	inherently	ADV
brj-24383	48	5	contains	contain	VERB
brj-24383	48	6	both	both	DET
brj-24383	48	7	spectral	spectral	ADJ
brj-24383	48	8	and	and	CCONJ
brj-24383	48	9	spatial	spatial	ADJ
brj-24383	48	10	information	information	NOUN
brj-24383	48	11	,	,	PUNCT
brj-24383	48	12	this	this	DET
brj-24383	48	13	study	study	NOUN
brj-24383	48	14	specifically	specifically	ADV
brj-24383	48	15	focuses	focus	VERB
brj-24383	48	16	on	on	ADP
brj-24383	48	17	exploiting	exploit	VERB
brj-24383	48	18	spectral	spectral	ADJ
brj-24383	48	19	signatures	signature	NOUN
brj-24383	48	20	for	for	ADP
brj-24383	48	21	material	material	NOUN
brj-24383	48	22	discrimination	discrimination	NOUN
brj-24383	48	23	.	.	PUNCT
brj-24383	49	1	the	the	DET
brj-24383	49	2	experimental	experimental	ADJ
brj-24383	49	3	design	design	NOUN
brj-24383	49	4	prioritized	prioritize	VERB
brj-24383	49	5	spectral	spectral	ADJ
brj-24383	49	6	resolution	resolution	NOUN
brj-24383	49	7	(	(	PUNCT
brj-24383	49	8	0.3353	0.3353	NUM
brj-24383	49	9	nm	nm	NOUN
brj-24383	49	10	)	)	PUNCT
brj-24383	49	11	over	over	ADP
brj-24383	49	12	spatial	spatial	ADJ
brj-24383	49	13	context	context	NOUN
brj-24383	49	14	for	for	ADP
brj-24383	49	15	two	two	NUM
brj-24383	49	16	key	key	ADJ
brj-24383	49	17	reasons	reason	NOUN
brj-24383	49	18	:	:	PUNCT
brj-24383	49	19	(	(	PUNCT
brj-24383	49	20	1	1	X
brj-24383	49	21	)	)	PUNCT
brj-24383	49	22	the	the	DET
brj-24383	49	23	target	target	NOUN
brj-24383	49	24	samples	sample	NOUN
brj-24383	49	25	exhibited	exhibit	VERB
brj-24383	49	26	homogeneous	homogeneous	ADJ
brj-24383	49	27	texture	texture	ADJ
brj-24383	49	28	characteristics	characteristic	NOUN
brj-24383	49	29	under	under	ADP
brj-24383	49	30	macroscopic	macroscopic	ADJ
brj-24383	49	31	observation	observation	NOUN
brj-24383	49	32	,	,	PUNCT
brj-24383	49	33	reducing	reduce	VERB
brj-24383	49	34	the	the	DET
brj-24383	49	35	immediate	immediate	ADJ
brj-24383	49	36	necessity	necessity	NOUN
brj-24383	49	37	for	for	ADP
brj-24383	49	38	spatial	spatial	ADJ
brj-24383	49	39	feature	feature	NOUN
brj-24383	49	40	extraction	extraction	NOUN
brj-24383	49	41	;	;	PUNCT
brj-24383	49	42	(	(	PUNCT
brj-24383	49	43	2	2	X
brj-24383	49	44	)	)	PUNCT
brj-24383	49	45	our	our	PRON
brj-24383	49	46	preliminary	preliminary	ADJ
brj-24383	49	47	tests	test	NOUN
brj-24383	49	48	using	use	VERB
brj-24383	49	49	svm	svm	ADJ
brj-24383	49	50	classification	classification	NOUN
brj-24383	49	51	achieved	achieve	VERB
brj-24383	49	52	92	92	NUM
brj-24383	49	53	%	%	NOUN
brj-24383	49	54	accuracy	accuracy	NOUN
brj-24383	49	55	without	without	ADP
brj-24383	49	56	spatial	spatial	ADJ
brj-24383	49	57	processing	processing	NOUN
brj-24383	49	58	,	,	PUNCT
brj-24383	49	59	indicating	indicate	VERB
brj-24383	49	60	sufficient	sufficient	ADJ
brj-24383	49	61	discriminative	discriminative	NOUN
brj-24383	49	62	power	power	NOUN
brj-24383	49	63	from	from	ADP
brj-24383	49	64	spectral	spectral	ADJ
brj-24383	49	65	features	feature	NOUN
brj-24383	49	66	alone	alone	ADV
brj-24383	49	67	.	.	PUNCT
brj-24383	50	1	this	this	DET
brj-24383	50	2	targeted	target	VERB
brj-24383	50	3	approach	approach	NOUN
brj-24383	50	4	aligns	align	VERB
brj-24383	50	5	with	with	ADP
brj-24383	50	6	established	establish	VERB
brj-24383	50	7	methodologies	methodology	NOUN
brj-24383	50	8	in	in	ADP
brj-24383	50	9	spectroscopic	spectroscopic	ADJ
brj-24383	50	10	analysis	analysis	NOUN
brj-24383	50	11	where	where	SCONJ
brj-24383	50	12	spectral	spectral	ADJ
brj-24383	50	13	fingerprints	fingerprint	NOUN
brj-24383	50	14	provide	provide	VERB
brj-24383	50	15	primary	primary	ADJ
brj-24383	50	16	identification	identification	NOUN
brj-24383	50	17	criteria	criterion	NOUN
brj-24383	50	18	(	(	PUNCT
brj-24383	50	19	lima	lima	NOUN
brj-24383	50	20	et	et	PROPN
brj-24383	50	21	al	al	PROPN
brj-24383	50	22	.	.	PROPN
brj-24383	50	23	2022	2022	NUM
brj-24383	50	24	)	)	PUNCT
brj-24383	50	25	.	.	PUNCT
brj-24383	51	1	nevertheless	nevertheless	ADV
brj-24383	51	2	,	,	PUNCT
brj-24383	51	3	we	we	PRON
brj-24383	51	4	acknowledge	acknowledge	VERB
brj-24383	51	5	the	the	DET
brj-24383	51	6	potential	potential	ADJ
brj-24383	51	7	benefits	benefit	NOUN
brj-24383	51	8	of	of	ADP
brj-24383	51	9	integrating	integrate	VERB
brj-24383	51	10	spatial	spatial	ADJ
brj-24383	51	11	-	-	PUNCT
brj-24383	51	12	textural	textural	ADJ
brj-24383	51	13	features	feature	NOUN
brj-24383	51	14	for	for	ADP
brj-24383	51	15	complex	complex	ADJ
brj-24383	51	16	heterogeneous	heterogeneous	ADJ
brj-24383	51	17	materials	material	NOUN
brj-24383	51	18	,	,	PUNCT
brj-24383	51	19	which	which	PRON
brj-24383	51	20	constitutes	constitute	VERB
brj-24383	51	21	a	a	DET
brj-24383	51	22	critical	critical	ADJ
brj-24383	51	23	direction	direction	NOUN
brj-24383	51	24	for	for	ADP
brj-24383	51	25	our	our	PRON
brj-24383	51	26	subsequent	subsequent	ADJ
brj-24383	51	27	research	research	NOUN
brj-24383	51	28	.	.	PUNCT
brj-24383	52	1	this	this	DET
brj-24383	52	2	study	study	NOUN
brj-24383	52	3	focused	focus	VERB
brj-24383	52	4	on	on	ADP
brj-24383	52	5	the	the	DET
brj-24383	52	6	classification	classification	NOUN
brj-24383	52	7	and	and	CCONJ
brj-24383	52	8	identification	identification	NOUN
brj-24383	52	9	of	of	ADP
brj-24383	52	10	18	18	NUM
brj-24383	52	11	common	common	ADJ
brj-24383	52	12	legume	legume	NOUN
brj-24383	52	13	wood	wood	NOUN
brj-24383	52	14	species	specie	NOUN
brj-24383	52	15	,	,	PUNCT
brj-24383	52	16	covering	cover	VERB
brj-24383	52	17	typical	typical	ADJ
brj-24383	52	18	legume	legume	NOUN
brj-24383	52	19	wood	wood	NOUN
brj-24383	52	20	species	specie	NOUN
brj-24383	52	21	in	in	ADP
brj-24383	52	22	the	the	DET
brj-24383	52	23	wood	wood	NOUN
brj-24383	52	24	market	market	NOUN
brj-24383	52	25	,	,	PUNCT
brj-24383	52	26	primarily	primarily	ADV
brj-24383	52	27	used	use	VERB
brj-24383	52	28	in	in	ADP
brj-24383	52	29	high	high	ADJ
brj-24383	52	30	-	-	PUNCT
brj-24383	52	31	end	end	NOUN
brj-24383	52	32	furniture	furniture	NOUN
brj-24383	52	33	and	and	CCONJ
brj-24383	52	34	cultural	cultural	ADJ
brj-24383	52	35	craft	craft	NOUN
brj-24383	52	36	markets	market	NOUN
brj-24383	52	37	.	.	PUNCT
brj-24383	53	1	the	the	DET
brj-24383	53	2	research	research	NOUN
brj-24383	53	3	employed	employ	VERB
brj-24383	53	4	hyperspectral	hyperspectral	ADJ
brj-24383	53	5	image	image	NOUN
brj-24383	53	6	data	datum	NOUN
brj-24383	53	7	,	,	PUNCT
brj-24383	53	8	combined	combine	VERB
brj-24383	53	9	with	with	ADP
brj-24383	53	10	traditional	traditional	ADJ
brj-24383	53	11	machine	machine	NOUN
brj-24383	53	12	learning	learn	VERB
brj-24383	53	13	methods	method	NOUN
brj-24383	53	14	like	like	ADP
brj-24383	53	15	svm	svm	PROPN
brj-24383	53	16	,	,	PUNCT
brj-24383	53	17	rf	rf	NOUN
brj-24383	53	18	,	,	PUNCT
brj-24383	53	19	lr	lr	NOUN
brj-24383	53	20	,	,	PUNCT
brj-24383	53	21	and	and	CCONJ
brj-24383	53	22	modern	modern	ADJ
brj-24383	53	23	deep	deep	ADJ
brj-24383	53	24	learning	learning	NOUN
brj-24383	53	25	methods	method	NOUN
brj-24383	53	26	such	such	ADJ
brj-24383	53	27	as	as	ADP
brj-24383	53	28	1	1	NUM
brj-24383	53	29	-	-	NUM
brj-24383	53	30	cnn	cnn	PROPN
brj-24383	53	31	,	,	PUNCT
brj-24383	53	32	to	to	PART
brj-24383	53	33	optimize	optimize	VERB
brj-24383	53	34	classification	classification	NOUN
brj-24383	53	35	through	through	ADP
brj-24383	53	36	data	datum	NOUN
brj-24383	53	37	augmentation	augmentation	NOUN
brj-24383	53	38	(	(	PUNCT
brj-24383	53	39	smote	smote	NOUN
brj-24383	53	40	)	)	PUNCT
brj-24383	53	41	and	and	CCONJ
brj-24383	53	42	hyperspectral	hyperspectral	ADJ
brj-24383	53	43	data	datum	NOUN
brj-24383	53	44	preprocessing	preprocessing	NOUN
brj-24383	53	45	(	(	PUNCT
brj-24383	53	46	e.g.	e.g.	ADV
brj-24383	53	47	,	,	PUNCT
brj-24383	53	48	savitzky	savitzky	NOUN
brj-24383	53	49	-	-	PUNCT
brj-24383	53	50	golay	golay	NOUN
brj-24383	53	51	filtering	filtering	NOUN
brj-24383	53	52	)	)	PUNCT
brj-24383	53	53	.	.	PUNCT
brj-24383	54	1	despite	despite	SCONJ
brj-24383	54	2	recent	recent	ADJ
brj-24383	54	3	advances	advance	NOUN
brj-24383	54	4	in	in	ADP
brj-24383	54	5	wood	wood	NOUN
brj-24383	54	6	spectral	spectral	ADJ
brj-24383	54	7	analysis	analysis	NOUN
brj-24383	54	8	,	,	PUNCT
brj-24383	54	9	three	three	NUM
brj-24383	54	10	critical	critical	ADJ
brj-24383	54	11	challenges	challenge	NOUN
brj-24383	54	12	remain	remain	VERB
brj-24383	54	13	unaddressed	unaddressed	ADJ
brj-24383	54	14	:	:	PUNCT
brj-24383	54	15	(	(	PUNCT
brj-24383	54	16	1	1	X
brj-24383	54	17	)	)	PUNCT
brj-24383	54	18	effective	effective	ADJ
brj-24383	54	19	denoising	denoising	NOUN
brj-24383	54	20	across	across	ADP
brj-24383	54	21	ultra	ultra	ADJ
brj-24383	54	22	-	-	ADJ
brj-24383	54	23	broad	broad	ADJ
brj-24383	54	24	spectral	spectral	ADJ
brj-24383	54	25	ranges	range	NOUN
brj-24383	54	26	(	(	PUNCT
brj-24383	54	27	400	400	NUM
brj-24383	54	28	to	to	PART
brj-24383	54	29	2500	2500	NUM
brj-24383	54	30	nm	nm	NOUN
brj-24383	54	31	)	)	PUNCT
brj-24383	54	32	without	without	ADP
brj-24383	54	33	losing	lose	VERB
brj-24383	54	34	discriminative	discriminative	NOUN
brj-24383	54	35	features	feature	NOUN
brj-24383	54	36	,	,	PUNCT
brj-24383	54	37	(	(	PUNCT
brj-24383	54	38	2	2	X
brj-24383	54	39	)	)	PUNCT
brj-24383	54	40	coordinated	coordinated	ADJ
brj-24383	54	41	optimization	optimization	NOUN
brj-24383	54	42	of	of	ADP
brj-24383	54	43	sample	sample	NOUN
brj-24383	54	44	imbalance	imbalance	NOUN
brj-24383	54	45	and	and	CCONJ
brj-24383	54	46	dimensionality	dimensionality	NOUN
brj-24383	54	47	curse	curse	NOUN
brj-24383	54	48	in	in	ADP
brj-24383	54	49	small	small	ADJ
brj-24383	54	50	-	-	PUNCT
brj-24383	54	51	sample	sample	NOUN
brj-24383	54	52	scenarios	scenario	NOUN
brj-24383	54	53	,	,	PUNCT
brj-24383	54	54	and	and	CCONJ
brj-24383	54	55	(	(	PUNCT
brj-24383	54	56	3	3	X
brj-24383	54	57	)	)	PUNCT
brj-24383	54	58	generalization	generalization	NOUN
brj-24383	54	59	of	of	ADP
brj-24383	54	60	preprocessing	preprocesse	VERB
brj-24383	54	61	benefits	benefit	NOUN
brj-24383	54	62	across	across	ADP
brj-24383	54	63	divergent	divergent	ADJ
brj-24383	54	64	classifiers	classifier	NOUN
brj-24383	54	65	.	.	PUNCT
brj-24383	55	1	to	to	PART
brj-24383	55	2	bridge	bridge	VERB
brj-24383	55	3	these	these	DET
brj-24383	55	4	gaps	gap	NOUN
brj-24383	55	5	,	,	PUNCT
brj-24383	55	6	this	this	DET
brj-24383	55	7	study	study	NOUN
brj-24383	55	8	delivers	deliver	VERB
brj-24383	55	9	threefold	threefold	ADJ
brj-24383	55	10	innovations	innovation	NOUN
brj-24383	55	11	:	:	PUNCT
brj-24383	55	12	•	•	NUM
brj-24383	55	13	first	first	ADV
brj-24383	55	14	,	,	PUNCT
brj-24383	55	15	a	a	DET
brj-24383	55	16	cascaded	cascade	VERB
brj-24383	55	17	denoising	denoising	NOUN
brj-24383	55	18	pipeline	pipeline	NOUN
brj-24383	55	19	integrating	integrate	VERB
brj-24383	55	20	savitzky	savitzky	NOUN
brj-24383	55	21	-	-	PUNCT
brj-24383	55	22	golay	golay	NOUN
brj-24383	55	23	filtering	filtering	NOUN
brj-24383	55	24	(	(	PUNCT
brj-24383	55	25	for	for	ADP
brj-24383	55	26	temporal	temporal	ADJ
brj-24383	55	27	noise	noise	NOUN
brj-24383	55	28	suppression	suppression	NOUN
brj-24383	55	29	)	)	PUNCT
brj-24383	55	30	with	with	ADP
brj-24383	55	31	first	first	ADJ
brj-24383	55	32	-	-	PUNCT
brj-24383	55	33	derivative	derivative	NOUN
brj-24383	55	34	transformation	transformation	NOUN
brj-24383	55	35	(	(	PUNCT
brj-24383	55	36	for	for	ADP
brj-24383	55	37	spectral	spectral	ADJ
brj-24383	55	38	slope	slope	NOUN
brj-24383	55	39	enhancement	enhancement	NOUN
brj-24383	55	40	)	)	PUNCT
brj-24383	55	41	,	,	PUNCT
brj-24383	55	42	specifically	specifically	ADV
brj-24383	55	43	tailored	tailor	VERB
brj-24383	55	44	for	for	ADP
brj-24383	55	45	wide	wide	ADJ
brj-24383	55	46	-	-	PUNCT
brj-24383	55	47	band	band	NOUN
brj-24383	55	48	hyperspectral	hyperspectral	ADJ
brj-24383	55	49	characteristics	characteristic	NOUN
brj-24383	55	50	.	.	PUNCT
brj-24383	56	1	•	•	NUM
brj-24383	56	2	second	second	ADJ
brj-24383	56	3	,	,	PUNCT
brj-24383	56	4	a	a	DET
brj-24383	56	5	parallelized	parallelize	VERB
brj-24383	56	6	smote	smote	NOUN
brj-24383	56	7	-	-	PUNCT
brj-24383	56	8	pca	pca	NOUN
brj-24383	56	9	co	co	NOUN
brj-24383	56	10	-	-	ADJ
brj-24383	56	11	optimization	optimization	ADJ
brj-24383	56	12	framework	framework	NOUN
brj-24383	56	13	that	that	PRON
brj-24383	56	14	simultaneously	simultaneously	ADV
brj-24383	56	15	addresses	address	VERB
brj-24383	56	16	class	class	NOUN
brj-24383	56	17	imbalance	imbalance	NOUN
brj-24383	56	18	and	and	CCONJ
brj-24383	56	19	feature	feature	NOUN
brj-24383	56	20	redundancy	redundancy	NOUN
brj-24383	56	21	through	through	ADP
brj-24383	56	22	complementary	complementary	ADJ
brj-24383	56	23	peer	peer	NOUN
brj-24383	56	24	-	-	PUNCT
brj-24383	56	25	reviewed	review	VERB
brj-24383	56	26	article	article	NOUN
brj-24383	56	27	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	56	28	su	su	PROPN
brj-24383	56	29	et	et	PROPN
brj-24383	56	30	al	al	PROPN
brj-24383	56	31	.	.	PROPN
brj-24383	57	1	(	(	PUNCT
brj-24383	57	2	2025	2025	NUM
brj-24383	57	3	)	)	PUNCT
brj-24383	57	4	.	.	PUNCT
brj-24383	58	1	“	"	PUNCT
brj-24383	58	2	leguminous	leguminous	ADJ
brj-24383	58	3	wood	wood	NOUN
brj-24383	58	4	classification	classification	NOUN
brj-24383	58	5	,	,	PUNCT
brj-24383	58	6	”	"	PUNCT
brj-24383	58	7	bioresources	bioresource	NOUN
brj-24383	58	8	20(3	20(3	NOUN
brj-24383	58	9	)	)	PUNCT
brj-24383	58	10	,	,	PUNCT
brj-24383	58	11	6317	6317	NUM
brj-24383	58	12	-	-	SYM
brj-24383	58	13	6337	6337	NUM
brj-24383	58	14	.	.	PUNCT
brj-24383	59	1	6320	6320	NUM
brj-24383	59	2	dimensionality	dimensionality	NOUN
brj-24383	59	3	operations	operation	NOUN
brj-24383	59	4	—	—	PUNCT
brj-24383	59	5	smote	smote	ADJ
brj-24383	59	6	expanding	expand	VERB
brj-24383	59	7	sample	sample	NOUN
brj-24383	59	8	diversity	diversity	NOUN
brj-24383	59	9	in	in	ADP
brj-24383	59	10	original	original	ADJ
brj-24383	59	11	space	space	NOUN
brj-24383	59	12	while	while	SCONJ
brj-24383	59	13	pca	pca	NOUN
brj-24383	59	14	extracting	extract	VERB
brj-24383	59	15	compact	compact	ADJ
brj-24383	59	16	representations	representation	NOUN
brj-24383	59	17	.	.	PUNCT
brj-24383	60	1	•	•	NUM
brj-24383	60	2	third	third	ADJ
brj-24383	60	3	,	,	PUNCT
brj-24383	60	4	comprehensive	comprehensive	ADJ
brj-24383	60	5	validation	validation	NOUN
brj-24383	60	6	across	across	ADP
brj-24383	60	7	four	four	NUM
brj-24383	60	8	classifier	classifier	NOUN
brj-24383	60	9	archetypes	archetype	NOUN
brj-24383	60	10	(	(	PUNCT
brj-24383	60	11	svm	svm	PROPN
brj-24383	60	12	,	,	PUNCT
brj-24383	60	13	rf	rf	ADJ
brj-24383	60	14	,	,	PUNCT
brj-24383	60	15	lr	lr	NOUN
brj-24383	60	16	,	,	PUNCT
brj-24383	60	17	1dcnn	1dcnn	NUM
brj-24383	60	18	)	)	PUNCT
brj-24383	60	19	,	,	PUNCT
brj-24383	60	20	demonstrating	demonstrate	VERB
brj-24383	60	21	for	for	ADP
brj-24383	60	22	the	the	DET
brj-24383	60	23	first	first	ADJ
brj-24383	60	24	time	time	NOUN
brj-24383	61	1	that	that	SCONJ
brj-24383	61	2	preprocessing	preprocesse	VERB
brj-24383	61	3	-	-	PUNCT
brj-24383	61	4	induced	induce	VERB
brj-24383	61	5	accuracy	accuracy	NOUN
brj-24383	61	6	gains	gain	NOUN
brj-24383	61	7	(	(	PUNCT
brj-24383	61	8	avg	avg	NOUN
brj-24383	61	9	.	.	PUNCT
brj-24383	61	10	+5	+5	NOUN
brj-24383	61	11	%	%	NOUN
brj-24383	61	12	)	)	PUNCT
brj-24383	61	13	are	be	AUX
brj-24383	61	14	model	model	ADJ
brj-24383	61	15	-	-	ADJ
brj-24383	61	16	agnostic	agnostic	ADJ
brj-24383	61	17	,	,	PUNCT
brj-24383	61	18	thus	thus	ADV
brj-24383	61	19	providing	provide	VERB
brj-24383	61	20	a	a	DET
brj-24383	61	21	universal	universal	ADJ
brj-24383	61	22	solution	solution	NOUN
brj-24383	61	23	for	for	ADP
brj-24383	61	24	spectral	spectral	ADJ
brj-24383	61	25	data	datum	NOUN
brj-24383	61	26	scarcity	scarcity	NOUN
brj-24383	61	27	.	.	PUNCT
brj-24383	62	1	these	these	DET
brj-24383	62	2	innovations	innovation	NOUN
brj-24383	62	3	collectively	collectively	ADV
brj-24383	62	4	establish	establish	VERB
brj-24383	62	5	a	a	DET
brj-24383	62	6	new	new	ADJ
brj-24383	62	7	paradigm	paradigm	NOUN
brj-24383	62	8	for	for	ADP
brj-24383	62	9	small	small	ADJ
brj-24383	62	10	-	-	PUNCT
brj-24383	62	11	sample	sample	NOUN
brj-24383	62	12	hyperspectral	hyperspectral	ADJ
brj-24383	62	13	analysis	analysis	NOUN
brj-24383	62	14	,	,	PUNCT
brj-24383	62	15	with	with	ADP
brj-24383	62	16	particular	particular	ADJ
brj-24383	62	17	efficacy	efficacy	NOUN
brj-24383	62	18	in	in	ADP
brj-24383	62	19	leguminous	leguminous	ADJ
brj-24383	62	20	wood	wood	NOUN
brj-24383	62	21	identification	identification	NOUN
brj-24383	62	22	where	where	SCONJ
brj-24383	62	23	chemical	chemical	NOUN
brj-24383	62	24	homogeneity	homogeneity	NOUN
brj-24383	62	25	and	and	CCONJ
brj-24383	62	26	sample	sample	NOUN
brj-24383	62	27	paucity	paucity	NOUN
brj-24383	62	28	coexist	coexist	NOUN
brj-24383	62	29	.	.	PUNCT
brj-24383	63	1	experimental	experimental	ADJ
brj-24383	63	2	sample	sample	NOUN
brj-24383	63	3	preparation	preparation	NOUN
brj-24383	63	4	according	accord	VERB
brj-24383	63	5	to	to	ADP
brj-24383	63	6	the	the	DET
brj-24383	63	7	definition	definition	NOUN
brj-24383	63	8	of	of	ADP
brj-24383	63	9	leguminous	leguminous	ADJ
brj-24383	63	10	wood	wood	NOUN
brj-24383	63	11	in	in	ADP
brj-24383	63	12	the	the	DET
brj-24383	63	13	international	international	ADJ
brj-24383	63	14	code	code	NOUN
brj-24383	63	15	of	of	ADP
brj-24383	63	16	botanical	botanical	ADJ
brj-24383	63	17	nomenclature	nomenclature	NOUN
brj-24383	63	18	(	(	PUNCT
brj-24383	63	19	icn	icn	PROPN
brj-24383	63	20	)	)	PUNCT
brj-24383	63	21	,	,	PUNCT
brj-24383	63	22	this	this	DET
brj-24383	63	23	work	work	NOUN
brj-24383	63	24	took	take	VERB
brj-24383	63	25	18	18	NUM
brj-24383	63	26	species	specie	NOUN
brj-24383	63	27	of	of	ADP
brj-24383	63	28	leguminous	leguminous	ADJ
brj-24383	63	29	wood	wood	NOUN
brj-24383	63	30	as	as	ADP
brj-24383	63	31	the	the	DET
brj-24383	63	32	research	research	NOUN
brj-24383	63	33	object	object	NOUN
brj-24383	63	34	.	.	PUNCT
brj-24383	64	1	detailed	detailed	ADJ
brj-24383	64	2	information	information	NOUN
brj-24383	64	3	on	on	ADP
brj-24383	64	4	these	these	DET
brj-24383	64	5	woods	wood	NOUN
brj-24383	64	6	is	be	AUX
brj-24383	64	7	shown	show	VERB
brj-24383	64	8	in	in	ADP
brj-24383	64	9	table	table	NOUN
brj-24383	64	10	1	1	NUM
brj-24383	64	11	.	.	PUNCT
brj-24383	65	1	in	in	ADP
brj-24383	65	2	order	order	NOUN
brj-24383	65	3	to	to	PART
brj-24383	65	4	prevent	prevent	VERB
brj-24383	65	5	homogeneity	homogeneity	NOUN
brj-24383	65	6	,	,	PUNCT
brj-24383	65	7	the	the	DET
brj-24383	65	8	same	same	ADJ
brj-24383	65	9	wood	wood	NOUN
brj-24383	65	10	samples	sample	NOUN
brj-24383	65	11	were	be	AUX
brj-24383	65	12	purchased	purchase	VERB
brj-24383	65	13	from	from	ADP
brj-24383	65	14	different	different	ADJ
brj-24383	65	15	merchants	merchant	NOUN
brj-24383	65	16	and	and	CCONJ
brj-24383	65	17	on	on	ADP
brj-24383	65	18	different	different	ADJ
brj-24383	65	19	dates	date	NOUN
brj-24383	65	20	,	,	PUNCT
brj-24383	65	21	thus	thus	ADV
brj-24383	65	22	ensuring	ensure	VERB
brj-24383	65	23	that	that	SCONJ
brj-24383	65	24	the	the	DET
brj-24383	65	25	same	same	ADJ
brj-24383	65	26	wood	wood	NOUN
brj-24383	65	27	samples	sample	NOUN
brj-24383	65	28	did	do	AUX
brj-24383	65	29	not	not	PART
brj-24383	65	30	come	come	VERB
brj-24383	65	31	from	from	ADP
brj-24383	65	32	the	the	DET
brj-24383	65	33	same	same	ADJ
brj-24383	65	34	tree	tree	NOUN
brj-24383	65	35	or	or	CCONJ
brj-24383	65	36	all	all	PRON
brj-24383	65	37	came	come	VERB
brj-24383	65	38	from	from	ADP
brj-24383	65	39	the	the	DET
brj-24383	65	40	same	same	ADJ
brj-24383	65	41	area	area	NOUN
brj-24383	65	42	.	.	PUNCT
brj-24383	66	1	table	table	NOUN
brj-24383	66	2	1	1	NUM
brj-24383	66	3	.	.	PUNCT
brj-24383	67	1	sample	sample	NOUN
brj-24383	67	2	data	datum	NOUN
brj-24383	67	3	of	of	ADP
brj-24383	67	4	leguminous	leguminous	ADJ
brj-24383	67	5	woods	wood	NOUN
brj-24383	67	6	no	no	INTJ
brj-24383	67	7	.	.	PUNCT
brj-24383	68	1	scientific	scientific	ADJ
brj-24383	68	2	name	name	NOUN
brj-24383	68	3	main	main	ADJ
brj-24383	68	4	characteristics	characteristic	NOUN
brj-24383	68	5	main	main	ADJ
brj-24383	68	6	distribution	distribution	NOUN
brj-24383	68	7	area	area	NOUN
brj-24383	68	8	1	1	NUM
brj-24383	68	9	guibourtia	guibourtia	NOUN
brj-24383	68	10	high	high	ADJ
brj-24383	68	11	density	density	NOUN
brj-24383	68	12	,	,	PUNCT
brj-24383	68	13	corrosion	corrosion	NOUN
brj-24383	68	14	-	-	PUNCT
brj-24383	68	15	resistant	resistant	ADJ
brj-24383	68	16	,	,	PUNCT
brj-24383	68	17	commonly	commonly	ADV
brj-24383	68	18	used	use	VERB
brj-24383	68	19	in	in	ADP
brj-24383	68	20	high	high	ADJ
brj-24383	68	21	-	-	PUNCT
brj-24383	68	22	end	end	NOUN
brj-24383	68	23	furniture	furniture	NOUN
brj-24383	68	24	and	and	CCONJ
brj-24383	68	25	flooring	floor	VERB
brj-24383	68	26	tropical	tropical	ADJ
brj-24383	68	27	africa	africa	PROPN
brj-24383	68	28	2	2	NUM
brj-24383	68	29	guibourtia	guibourtia	NOUN
brj-24383	68	30	conjugata	conjugata	VERB
brj-24383	68	31	fine	fine	ADJ
brj-24383	68	32	grain	grain	NOUN
brj-24383	68	33	,	,	PUNCT
brj-24383	68	34	durable	durable	ADJ
brj-24383	68	35	,	,	PUNCT
brj-24383	68	36	suitable	suitable	ADJ
brj-24383	68	37	for	for	ADP
brj-24383	68	38	decorative	decorative	ADJ
brj-24383	68	39	crafts	craft	NOUN
brj-24383	68	40	tropical	tropical	ADJ
brj-24383	68	41	africa	africa	PROPN
brj-24383	68	42	3	3	NUM
brj-24383	68	43	pterocarpus	pterocarpus	PROPN
brj-24383	68	44	erinaceus	erinaceus	PROPN
brj-24383	68	45	poir	poir	PROPN
brj-24383	68	46	.	.	PUNCT
brj-24383	69	1	clear	clear	ADJ
brj-24383	69	2	texture	texture	NOUN
brj-24383	69	3	,	,	PUNCT
brj-24383	69	4	hard	hard	ADJ
brj-24383	69	5	wood	wood	NOUN
brj-24383	69	6	,	,	PUNCT
brj-24383	69	7	commonly	commonly	ADV
brj-24383	69	8	used	use	VERB
brj-24383	69	9	for	for	ADP
brj-24383	69	10	rosewood	rosewood	NOUN
brj-24383	69	11	furniture	furniture	NOUN
brj-24383	69	12	materials	materials	PROPN
brj-24383	69	13	west	west	PROPN
brj-24383	69	14	africa	africa	PROPN
brj-24383	69	15	4	4	NUM
brj-24383	69	16	streblus	streblus	PROPN
brj-24383	69	17	sp	sp	PROPN
brj-24383	69	18	.	.	NOUN
brj-24383	69	19	clear	clear	ADJ
brj-24383	69	20	texture	texture	NOUN
brj-24383	69	21	,	,	PUNCT
brj-24383	69	22	lightweight	lightweight	ADJ
brj-24383	69	23	wood	wood	NOUN
brj-24383	69	24	,	,	PUNCT
brj-24383	69	25	suitable	suitable	ADJ
brj-24383	69	26	for	for	ADP
brj-24383	69	27	general	general	ADJ
brj-24383	69	28	furniture	furniture	NOUN
brj-24383	69	29	manufacturing	manufacturing	NOUN
brj-24383	69	30	southeast	southeast	PROPN
brj-24383	69	31	asia	asia	PROPN
brj-24383	69	32	and	and	CCONJ
brj-24383	69	33	south	south	PROPN
brj-24383	69	34	asia	asia	PROPN
brj-24383	69	35	5	5	NUM
brj-24383	69	36	dalbergia	dalbergia	PROPN
brj-24383	69	37	cultrata	cultrata	VERB
brj-24383	69	38	graham	graham	PROPN
brj-24383	69	39	heavy	heavy	ADJ
brj-24383	69	40	and	and	CCONJ
brj-24383	69	41	fine	fine	ADJ
brj-24383	69	42	,	,	PUNCT
brj-24383	69	43	commonly	commonly	ADV
brj-24383	69	44	seen	see	VERB
brj-24383	69	45	with	with	ADP
brj-24383	69	46	black	black	ADJ
brj-24383	69	47	-	-	PUNCT
brj-24383	69	48	brown	brown	ADJ
brj-24383	69	49	stripes	stripe	NOUN
brj-24383	69	50	,	,	PUNCT
brj-24383	69	51	used	use	VERB
brj-24383	69	52	for	for	ADP
brj-24383	69	53	high	high	ADJ
brj-24383	69	54	-	-	PUNCT
brj-24383	69	55	end	end	NOUN
brj-24383	69	56	furniture	furniture	NOUN
brj-24383	69	57	south	south	PROPN
brj-24383	69	58	asia	asia	PROPN
brj-24383	69	59	6	6	NUM
brj-24383	69	60	dalbergia	dalbergia	PROPN
brj-24383	69	61	nigra	nigra	PROPN
brj-24383	69	62	allem	allem	PROPN
brj-24383	69	63	.	.	PUNCT
brj-24383	70	1	hard	hard	ADJ
brj-24383	70	2	and	and	CCONJ
brj-24383	70	3	dense	dense	ADJ
brj-24383	70	4	material	material	NOUN
brj-24383	70	5	,	,	PUNCT
brj-24383	70	6	dark	dark	ADJ
brj-24383	70	7	color	color	NOUN
brj-24383	70	8	,	,	PUNCT
brj-24383	70	9	commonly	commonly	ADV
brj-24383	70	10	used	use	VERB
brj-24383	70	11	for	for	ADP
brj-24383	70	12	musical	musical	ADJ
brj-24383	70	13	instruments	instrument	NOUN
brj-24383	70	14	and	and	CCONJ
brj-24383	70	15	decorations	decoration	NOUN
brj-24383	70	16	brazil	brazil	PROPN
brj-24383	70	17	and	and	CCONJ
brj-24383	70	18	the	the	DET
brj-24383	70	19	tropical	tropical	ADJ
brj-24383	70	20	rainforests	rainforest	NOUN
brj-24383	70	21	of	of	ADP
brj-24383	70	22	south	south	PROPN
brj-24383	70	23	america	america	PROPN
brj-24383	70	24	7	7	NUM
brj-24383	70	25	pterocarpus	pterocarpus	NOUN
brj-24383	70	26	soyauxii	soyauxii	PROPN
brj-24383	70	27	taub	taub	PROPN
brj-24383	70	28	.	.	PUNCT
brj-24383	71	1	wood	wood	NOUN
brj-24383	71	2	color	color	NOUN
brj-24383	71	3	is	be	AUX
brj-24383	71	4	warm	warm	ADJ
brj-24383	71	5	and	and	CCONJ
brj-24383	71	6	suitable	suitable	ADJ
brj-24383	71	7	for	for	ADP
brj-24383	71	8	carving	carving	NOUN
brj-24383	71	9	and	and	CCONJ
brj-24383	71	10	decorative	decorative	ADJ
brj-24383	71	11	use	use	NOUN
brj-24383	71	12	tropical	tropical	ADJ
brj-24383	71	13	africa	africa	PROPN
brj-24383	71	14	8	8	PROPN
brj-24383	71	15	swartzia	swartzia	PROPN
brj-24383	71	16	spp	spp	PROPN
brj-24383	71	17	.	.	PUNCT
brj-24383	72	1	high	high	ADJ
brj-24383	72	2	hardness	hardness	NOUN
brj-24383	72	3	,	,	PUNCT
brj-24383	72	4	high	high	ADJ
brj-24383	72	5	density	density	NOUN
brj-24383	72	6	,	,	PUNCT
brj-24383	72	7	strong	strong	ADJ
brj-24383	72	8	corrosion	corrosion	NOUN
brj-24383	72	9	resistance	resistance	NOUN
brj-24383	72	10	,	,	PUNCT
brj-24383	72	11	often	often	ADV
brj-24383	72	12	used	use	VERB
brj-24383	72	13	for	for	ADP
brj-24383	72	14	highend	highend	ADJ
brj-24383	72	15	crafts	craft	NOUN
brj-24383	72	16	and	and	CCONJ
brj-24383	72	17	flooring	floor	VERB
brj-24383	72	18	south	south	PROPN
brj-24383	72	19	america	america	PROPN
brj-24383	72	20	9	9	NUM
brj-24383	72	21	golden	golden	ADJ
brj-24383	72	22	rosewood	rosewood	NOUN
brj-24383	72	23	golden	golden	ADJ
brj-24383	72	24	color	color	NOUN
brj-24383	72	25	,	,	PUNCT
brj-24383	72	26	tough	tough	ADJ
brj-24383	72	27	wood	wood	NOUN
brj-24383	72	28	,	,	PUNCT
brj-24383	72	29	suitable	suitable	ADJ
brj-24383	72	30	for	for	ADP
brj-24383	72	31	making	make	VERB
brj-24383	72	32	decorative	decorative	ADJ
brj-24383	72	33	items	item	NOUN
brj-24383	72	34	southeast	southeast	ADJ
brj-24383	72	35	asia	asia	PROPN
brj-24383	72	36	and	and	CCONJ
brj-24383	72	37	tropical	tropical	ADJ
brj-24383	72	38	africa	africa	PROPN
brj-24383	72	39	10	10	NUM
brj-24383	72	40	millettia	millettia	NOUN
brj-24383	72	41	high	high	ADJ
brj-24383	72	42	hardness	hardness	NOUN
brj-24383	72	43	of	of	ADP
brj-24383	72	44	wood	wood	NOUN
brj-24383	72	45	,	,	PUNCT
brj-24383	72	46	suitable	suitable	ADJ
brj-24383	72	47	for	for	ADP
brj-24383	72	48	indoor	indoor	ADJ
brj-24383	72	49	decoration	decoration	NOUN
brj-24383	72	50	and	and	CCONJ
brj-24383	72	51	flooring	floor	VERB
brj-24383	72	52	tropical	tropical	ADJ
brj-24383	72	53	asia	asia	PROPN
brj-24383	72	54	and	and	CCONJ
brj-24383	72	55	africa	africa	PROPN
brj-24383	72	56	11	11	NUM
brj-24383	72	57	côte	côte	PROPN
brj-24383	72	58	d'ivoire	d'ivoire	PROPN
brj-24383	72	59	rosewood	rosewood	PROPN
brj-24383	72	60	deep	deep	ADJ
brj-24383	72	61	red	red	ADJ
brj-24383	72	62	color	color	NOUN
brj-24383	72	63	,	,	PUNCT
brj-24383	72	64	beautiful	beautiful	ADJ
brj-24383	72	65	wood	wood	NOUN
brj-24383	72	66	grain	grain	NOUN
brj-24383	72	67	,	,	PUNCT
brj-24383	72	68	commonly	commonly	ADV
brj-24383	72	69	used	use	VERB
brj-24383	72	70	to	to	PART
brj-24383	72	71	make	make	VERB
brj-24383	72	72	musical	musical	ADJ
brj-24383	72	73	instruments	instrument	NOUN
brj-24383	72	74	and	and	CCONJ
brj-24383	72	75	high	high	ADJ
brj-24383	72	76	-	-	PUNCT
brj-24383	72	77	end	end	NOUN
brj-24383	72	78	furniture	furniture	NOUN
brj-24383	72	79	west	west	PROPN
brj-24383	72	80	africa	africa	PROPN
brj-24383	72	81	peer	peer	NOUN
brj-24383	72	82	-	-	PUNCT
brj-24383	72	83	reviewed	review	VERB
brj-24383	72	84	article	article	NOUN
brj-24383	72	85	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	72	86	su	su	PROPN
brj-24383	72	87	et	et	PROPN
brj-24383	72	88	al	al	PROPN
brj-24383	72	89	.	.	PROPN
brj-24383	73	1	(	(	PUNCT
brj-24383	73	2	2025	2025	NUM
brj-24383	73	3	)	)	PUNCT
brj-24383	73	4	.	.	PUNCT
brj-24383	74	1	“	"	PUNCT
brj-24383	74	2	leguminous	leguminous	ADJ
brj-24383	74	3	wood	wood	NOUN
brj-24383	74	4	classification	classification	NOUN
brj-24383	74	5	,	,	PUNCT
brj-24383	74	6	”	"	PUNCT
brj-24383	74	7	bioresources	bioresource	NOUN
brj-24383	74	8	20(3	20(3	NOUN
brj-24383	74	9	)	)	PUNCT
brj-24383	74	10	,	,	PUNCT
brj-24383	74	11	6317	6317	NUM
brj-24383	74	12	-	-	SYM
brj-24383	74	13	6337	6337	NUM
brj-24383	74	14	.	.	PUNCT
brj-24383	75	1	6321	6321	NUM
brj-24383	75	2	no	no	NOUN
brj-24383	75	3	.	.	PUNCT
brj-24383	76	1	scientific	scientific	ADJ
brj-24383	76	2	name	name	NOUN
brj-24383	76	3	main	main	ADJ
brj-24383	76	4	characteristics	characteristic	NOUN
brj-24383	76	5	main	main	ADJ
brj-24383	76	6	distribution	distribution	NOUN
brj-24383	76	7	area	area	NOUN
brj-24383	76	8	12	12	NUM
brj-24383	76	9	burma	burma	PROPN
brj-24383	76	10	padauk	padauk	NOUN
brj-24383	76	11	stable	stable	ADJ
brj-24383	76	12	wood	wood	NOUN
brj-24383	76	13	,	,	PUNCT
brj-24383	76	14	fine	fine	ADJ
brj-24383	76	15	texture	texture	NOUN
brj-24383	76	16	,	,	PUNCT
brj-24383	76	17	often	often	ADV
brj-24383	76	18	used	use	VERB
brj-24383	76	19	for	for	ADP
brj-24383	76	20	rosewood	rosewood	ADJ
brj-24383	76	21	furniture	furniture	NOUN
brj-24383	76	22	and	and	CCONJ
brj-24383	76	23	decorative	decorative	ADJ
brj-24383	76	24	materials	material	NOUN
brj-24383	76	25	myanmar	myanmar	PROPN
brj-24383	76	26	and	and	CCONJ
brj-24383	76	27	southeast	southeast	PROPN
brj-24383	76	28	asia	asia	PROPN
brj-24383	76	29	13	13	NUM
brj-24383	76	30	mexican	mexican	ADJ
brj-24383	76	31	rosewood	rosewood	PROPN
brj-24383	76	32	rich	rich	ADJ
brj-24383	76	33	color	color	NOUN
brj-24383	76	34	,	,	PUNCT
brj-24383	76	35	suitable	suitable	ADJ
brj-24383	76	36	for	for	ADP
brj-24383	76	37	carving	carving	NOUN
brj-24383	76	38	and	and	CCONJ
brj-24383	76	39	small	small	ADJ
brj-24383	76	40	decorative	decorative	ADJ
brj-24383	76	41	items	item	NOUN
brj-24383	76	42	mexico	mexico	PROPN
brj-24383	76	43	and	and	CCONJ
brj-24383	76	44	central	central	PROPN
brj-24383	76	45	america	america	PROPN
brj-24383	76	46	14	14	NUM
brj-24383	76	47	black	black	ADJ
brj-24383	76	48	ebony	ebony	ADJ
brj-24383	76	49	high	high	ADJ
brj-24383	76	50	density	density	NOUN
brj-24383	76	51	,	,	PUNCT
brj-24383	76	52	high	high	ADJ
brj-24383	76	53	hardness	hardness	NOUN
brj-24383	76	54	,	,	PUNCT
brj-24383	76	55	fine	fine	ADJ
brj-24383	76	56	and	and	CCONJ
brj-24383	76	57	smooth	smooth	ADJ
brj-24383	76	58	material	material	NOUN
brj-24383	76	59	,	,	PUNCT
brj-24383	76	60	often	often	ADV
brj-24383	76	61	used	use	VERB
brj-24383	76	62	for	for	ADP
brj-24383	76	63	high	high	ADJ
brj-24383	76	64	-	-	PUNCT
brj-24383	76	65	end	end	NOUN
brj-24383	76	66	musical	musical	ADJ
brj-24383	76	67	instruments	instrument	NOUN
brj-24383	76	68	and	and	CCONJ
brj-24383	76	69	decorations	decoration	NOUN
brj-24383	76	70	tropical	tropical	ADJ
brj-24383	76	71	africa	africa	PROPN
brj-24383	76	72	15	15	NUM
brj-24383	76	73	pterocarpus	pterocarpus	NOUN
brj-24383	76	74	santalinus	santalinu	VERB
brj-24383	76	75	deep	deep	ADJ
brj-24383	76	76	red	red	ADJ
brj-24383	76	77	wood	wood	NOUN
brj-24383	76	78	,	,	PUNCT
brj-24383	76	79	high	high	ADJ
brj-24383	76	80	hardness	hardness	NOUN
brj-24383	76	81	,	,	PUNCT
brj-24383	76	82	fine	fine	ADJ
brj-24383	76	83	texture	texture	NOUN
brj-24383	76	84	,	,	PUNCT
brj-24383	76	85	suitable	suitable	ADJ
brj-24383	76	86	for	for	ADP
brj-24383	76	87	traditional	traditional	ADJ
brj-24383	76	88	crafts	craft	NOUN
brj-24383	76	89	and	and	CCONJ
brj-24383	76	90	buddhist	buddhist	ADJ
brj-24383	76	91	beads	bead	NOUN
brj-24383	76	92	india	india	PROPN
brj-24383	76	93	,	,	PUNCT
brj-24383	76	94	southeast	southeast	PROPN
brj-24383	76	95	asia	asia	PROPN
brj-24383	76	96	16	16	NUM
brj-24383	76	97	pterocarpus	pterocarpus	NOUN
brj-24383	76	98	indicus	indicus	NOUN
brj-24383	76	99	stable	stable	ADJ
brj-24383	76	100	wood	wood	NOUN
brj-24383	76	101	,	,	PUNCT
brj-24383	76	102	bright	bright	ADJ
brj-24383	76	103	color	color	NOUN
brj-24383	76	104	,	,	PUNCT
brj-24383	76	105	used	use	VERB
brj-24383	76	106	for	for	ADP
brj-24383	76	107	decorative	decorative	ADJ
brj-24383	76	108	furniture	furniture	NOUN
brj-24383	76	109	and	and	CCONJ
brj-24383	76	110	flooring	floor	VERB
brj-24383	76	111	southeast	southeast	PROPN
brj-24383	76	112	asia	asia	PROPN
brj-24383	76	113	and	and	CCONJ
brj-24383	76	114	tropical	tropical	ADJ
brj-24383	76	115	asia	asia	PROPN
brj-24383	76	116	17	17	NUM
brj-24383	76	117	peltogyne	peltogyne	NOUN
brj-24383	76	118	distinct	distinct	ADJ
brj-24383	76	119	purple	purple	ADJ
brj-24383	76	120	tone	tone	NOUN
brj-24383	76	121	,	,	PUNCT
brj-24383	76	122	dense	dense	ADJ
brj-24383	76	123	and	and	CCONJ
brj-24383	76	124	durable	durable	ADJ
brj-24383	76	125	wood	wood	NOUN
brj-24383	76	126	,	,	PUNCT
brj-24383	76	127	suitable	suitable	ADJ
brj-24383	76	128	for	for	ADP
brj-24383	76	129	high	high	ADJ
brj-24383	76	130	-	-	PUNCT
brj-24383	76	131	end	end	NOUN
brj-24383	76	132	furniture	furniture	NOUN
brj-24383	76	133	and	and	CCONJ
brj-24383	76	134	decorations	decoration	NOUN
brj-24383	76	135	south	south	PROPN
brj-24383	76	136	america	america	PROPN
brj-24383	76	137	,	,	PUNCT
brj-24383	76	138	especially	especially	ADV
brj-24383	76	139	the	the	DET
brj-24383	76	140	amazon	amazon	NOUN
brj-24383	76	141	rainforest	rainforest	NOUN
brj-24383	76	142	in	in	ADP
brj-24383	76	143	brazil	brazil	PROPN
brj-24383	76	144	18	18	NUM
brj-24383	76	145	pterocarpus	pterocarpus	NOUN
brj-24383	76	146	tinctorius	tinctorius	NOUN
brj-24383	76	147	welw	welw	NOUN
brj-24383	76	148	.	.	PUNCT
brj-24383	77	1	deep	deep	ADJ
brj-24383	77	2	red	red	ADJ
brj-24383	77	3	wood	wood	NOUN
brj-24383	77	4	,	,	PUNCT
brj-24383	77	5	high	high	ADJ
brj-24383	77	6	hardness	hardness	NOUN
brj-24383	77	7	,	,	PUNCT
brj-24383	77	8	commonly	commonly	ADV
brj-24383	77	9	used	use	VERB
brj-24383	77	10	for	for	ADP
brj-24383	77	11	carving	carve	VERB
brj-24383	77	12	and	and	CCONJ
brj-24383	77	13	traditional	traditional	ADJ
brj-24383	77	14	crafts	craft	NOUN
brj-24383	77	15	tropical	tropical	ADJ
brj-24383	77	16	africa	africa	PROPN
brj-24383	77	17	before	before	ADP
brj-24383	77	18	data	data	PROPN
brj-24383	77	19	collection	collection	NOUN
brj-24383	77	20	,	,	PUNCT
brj-24383	77	21	the	the	DET
brj-24383	77	22	length	length	NOUN
brj-24383	77	23	,	,	PUNCT
brj-24383	77	24	width	width	ADJ
brj-24383	77	25	,	,	PUNCT
brj-24383	77	26	and	and	CCONJ
brj-24383	77	27	height	height	NOUN
brj-24383	77	28	of	of	ADP
brj-24383	77	29	all	all	DET
brj-24383	77	30	wood	wood	NOUN
brj-24383	77	31	blocks	block	NOUN
brj-24383	77	32	were	be	AUX
brj-24383	77	33	unified	unified	ADJ
brj-24383	77	34	to	to	ADP
brj-24383	77	35	6	6	NUM
brj-24383	77	36	cm	cm	NOUN
brj-24383	77	37	×	×	NOUN
brj-24383	77	38	4	4	NUM
brj-24383	77	39	cm	cm	NOUN
brj-24383	77	40	×	×	NOUN
brj-24383	77	41	2	2	NUM
brj-24383	77	42	cm	cm	NOUN
brj-24383	77	43	,	,	PUNCT
brj-24383	77	44	and	and	CCONJ
brj-24383	77	45	the	the	DET
brj-24383	77	46	long	long	ADJ
brj-24383	77	47	side	side	NOUN
brj-24383	77	48	corresponded	correspond	VERB
brj-24383	77	49	to	to	ADP
brj-24383	77	50	the	the	DET
brj-24383	77	51	cross	cross	NOUN
brj-24383	77	52	section	section	NOUN
brj-24383	77	53	of	of	ADP
brj-24383	77	54	the	the	DET
brj-24383	77	55	wood	wood	NOUN
brj-24383	77	56	.	.	PUNCT
brj-24383	78	1	among	among	ADP
brj-24383	78	2	the	the	DET
brj-24383	78	3	cut	cut	NOUN
brj-24383	78	4	samples	sample	NOUN
brj-24383	78	5	,	,	PUNCT
brj-24383	78	6	two	two	NUM
brj-24383	78	7	samples	sample	NOUN
brj-24383	78	8	of	of	ADP
brj-24383	78	9	each	each	DET
brj-24383	78	10	type	type	NOUN
brj-24383	78	11	of	of	ADP
brj-24383	78	12	wood	wood	NOUN
brj-24383	78	13	were	be	AUX
brj-24383	78	14	taken	take	VERB
brj-24383	78	15	for	for	ADP
brj-24383	78	16	processing	processing	NOUN
brj-24383	78	17	.	.	PUNCT
brj-24383	79	1	during	during	ADP
brj-24383	79	2	the	the	DET
brj-24383	79	3	selection	selection	NOUN
brj-24383	79	4	process	process	NOUN
brj-24383	79	5	,	,	PUNCT
brj-24383	79	6	pure	pure	ADJ
brj-24383	79	7	samples	sample	NOUN
brj-24383	79	8	without	without	ADP
brj-24383	79	9	cracking	cracking	NOUN
brj-24383	79	10	,	,	PUNCT
brj-24383	79	11	insect	insect	VERB
brj-24383	79	12	infestation	infestation	NOUN
brj-24383	79	13	,	,	PUNCT
brj-24383	79	14	or	or	CCONJ
brj-24383	79	15	oil	oil	NOUN
brj-24383	79	16	contamination	contamination	NOUN
brj-24383	79	17	were	be	AUX
brj-24383	79	18	selected	select	VERB
brj-24383	79	19	.	.	PUNCT
brj-24383	80	1	before	before	SCONJ
brj-24383	80	2	measurement	measurement	NOUN
brj-24383	80	3	,	,	PUNCT
brj-24383	80	4	sandpaper	sandpaper	NOUN
brj-24383	80	5	with	with	ADP
brj-24383	80	6	gradually	gradually	ADV
brj-24383	80	7	finer	fine	ADJ
brj-24383	80	8	grain	grain	NOUN
brj-24383	80	9	sizes	size	NOUN
brj-24383	80	10	(	(	PUNCT
brj-24383	80	11	240	240	NUM
brj-24383	80	12	,	,	PUNCT
brj-24383	80	13	400	400	NUM
brj-24383	80	14	)	)	PUNCT
brj-24383	80	15	was	be	AUX
brj-24383	80	16	used	use	VERB
brj-24383	80	17	,	,	PUNCT
brj-24383	80	18	with	with	ADP
brj-24383	80	19	grain	grain	NOUN
brj-24383	80	20	sizes	size	NOUN
brj-24383	80	21	of	of	ADP
brj-24383	80	22	600	600	NUM
brj-24383	80	23	,	,	PUNCT
brj-24383	80	24	800	800	NUM
brj-24383	80	25	,	,	PUNCT
brj-24383	80	26	1,000	1,000	NUM
brj-24383	80	27	,	,	PUNCT
brj-24383	80	28	1,500	1,500	NUM
brj-24383	80	29	used	use	VERB
brj-24383	80	30	for	for	ADP
brj-24383	80	31	polishing	polishing	NOUN
brj-24383	80	32	.	.	PUNCT
brj-24383	81	1	the	the	DET
brj-24383	81	2	data	data	NOUN
brj-24383	81	3	collection	collection	NOUN
brj-24383	81	4	platform	platform	NOUN
brj-24383	81	5	was	be	AUX
brj-24383	81	6	resonon	resonon	NOUN
brj-24383	81	7	pika	pika	NOUN
brj-24383	81	8	l03030988	l03030988	PROPN
brj-24383	81	9	hyperspectral	hyperspectral	ADJ
brj-24383	81	10	imager	imager	NOUN
brj-24383	81	11	.	.	PUNCT
brj-24383	82	1	the	the	DET
brj-24383	82	2	spectrum	spectrum	NOUN
brj-24383	82	3	extraction	extraction	NOUN
brj-24383	82	4	and	and	CCONJ
brj-24383	82	5	analysis	analysis	NOUN
brj-24383	82	6	software	software	NOUN
brj-24383	82	7	is	be	AUX
brj-24383	82	8	spectrononpro	spectrononpro	ADJ
brj-24383	82	9	.	.	PUNCT
brj-24383	83	1	in	in	ADP
brj-24383	83	2	this	this	DET
brj-24383	83	3	study	study	NOUN
brj-24383	83	4	,	,	PUNCT
brj-24383	83	5	spectrononpro	spectrononpro	PROPN
brj-24383	83	6	software	software	NOUN
brj-24383	83	7	was	be	AUX
brj-24383	83	8	used	use	VERB
brj-24383	83	9	to	to	PART
brj-24383	83	10	process	process	VERB
brj-24383	83	11	and	and	CCONJ
brj-24383	83	12	analyze	analyze	VERB
brj-24383	83	13	hyperspectral	hyperspectral	ADJ
brj-24383	83	14	image	image	NOUN
brj-24383	83	15	data	datum	NOUN
brj-24383	83	16	.	.	PUNCT
brj-24383	84	1	spectrononpro	spectrononpro	PROPN
brj-24383	84	2	is	be	AUX
brj-24383	84	3	a	a	DET
brj-24383	84	4	professional	professional	ADJ
brj-24383	84	5	hyperspectral	hyperspectral	ADJ
brj-24383	84	6	image	image	NOUN
brj-24383	84	7	processing	processing	NOUN
brj-24383	84	8	software	software	NOUN
brj-24383	84	9	that	that	PRON
brj-24383	84	10	is	be	AUX
brj-24383	84	11	widely	widely	ADV
brj-24383	84	12	used	use	VERB
brj-24383	84	13	in	in	ADP
brj-24383	84	14	remote	remote	ADJ
brj-24383	84	15	sensing	sensing	NOUN
brj-24383	84	16	,	,	PUNCT
brj-24383	84	17	agriculture	agriculture	NOUN
brj-24383	84	18	,	,	PUNCT
brj-24383	84	19	geology	geology	NOUN
brj-24383	84	20	,	,	PUNCT
brj-24383	84	21	ecology	ecology	NOUN
brj-24383	84	22	,	,	PUNCT
brj-24383	84	23	environmental	environmental	ADJ
brj-24383	84	24	monitoring	monitoring	NOUN
brj-24383	84	25	,	,	PUNCT
brj-24383	84	26	and	and	CCONJ
brj-24383	84	27	other	other	ADJ
brj-24383	84	28	fields	field	NOUN
brj-24383	84	29	.	.	PUNCT
brj-24383	85	1	it	it	PRON
brj-24383	85	2	provides	provide	VERB
brj-24383	85	3	a	a	DET
brj-24383	85	4	variety	variety	NOUN
brj-24383	85	5	of	of	ADP
brj-24383	85	6	data	datum	NOUN
brj-24383	85	7	preprocessing	preprocessing	NOUN
brj-24383	85	8	functions	function	NOUN
brj-24383	85	9	such	such	ADJ
brj-24383	85	10	as	as	ADP
brj-24383	85	11	atmospheric	atmospheric	ADJ
brj-24383	85	12	correction	correction	NOUN
brj-24383	85	13	,	,	PUNCT
brj-24383	85	14	geometric	geometric	ADJ
brj-24383	85	15	correction	correction	NOUN
brj-24383	85	16	,	,	PUNCT
brj-24383	85	17	and	and	CCONJ
brj-24383	85	18	radiometric	radiometric	ADJ
brj-24383	85	19	correction	correction	NOUN
brj-24383	85	20	to	to	PART
brj-24383	85	21	ensure	ensure	VERB
brj-24383	85	22	the	the	DET
brj-24383	85	23	accuracy	accuracy	NOUN
brj-24383	85	24	and	and	CCONJ
brj-24383	85	25	consistency	consistency	NOUN
brj-24383	85	26	of	of	ADP
brj-24383	85	27	data	datum	NOUN
brj-24383	85	28	;	;	PUNCT
brj-24383	85	29	at	at	ADP
brj-24383	85	30	the	the	DET
brj-24383	85	31	same	same	ADJ
brj-24383	85	32	time	time	NOUN
brj-24383	85	33	,	,	PUNCT
brj-24383	85	34	the	the	DET
brj-24383	85	35	software	software	NOUN
brj-24383	85	36	has	have	VERB
brj-24383	85	37	spectral	spectral	ADJ
brj-24383	85	38	analysis	analysis	NOUN
brj-24383	85	39	tools	tool	NOUN
brj-24383	85	40	such	such	ADJ
brj-24383	85	41	as	as	ADP
brj-24383	85	42	spectral	spectral	ADJ
brj-24383	85	43	curve	curve	NOUN
brj-24383	85	44	extraction	extraction	NOUN
brj-24383	85	45	,	,	PUNCT
brj-24383	85	46	spectral	spectral	ADJ
brj-24383	85	47	matching	matching	NOUN
brj-24383	85	48	,	,	PUNCT
brj-24383	85	49	and	and	CCONJ
brj-24383	85	50	spectral	spectral	ADJ
brj-24383	85	51	mixing	mix	VERB
brj-24383	85	52	analysis	analysis	NOUN
brj-24383	85	53	to	to	PART
brj-24383	85	54	help	help	VERB
brj-24383	85	55	in	in	ADP
brj-24383	85	56	-	-	PUNCT
brj-24383	85	57	depth	depth	NOUN
brj-24383	85	58	exploration	exploration	NOUN
brj-24383	85	59	of	of	ADP
brj-24383	85	60	the	the	DET
brj-24383	85	61	sample	sample	NOUN
brj-24383	85	62	feature	feature	NOUN
brj-24383	85	63	;	;	PUNCT
brj-24383	85	64	fig	fig	NOUN
brj-24383	85	65	.	.	PUNCT
brj-24383	86	1	1	1	X
brj-24383	86	2	.	.	PUNCT
brj-24383	86	3	shows	show	VERB
brj-24383	86	4	the	the	DET
brj-24383	86	5	appearance	appearance	NOUN
brj-24383	86	6	and	and	CCONJ
brj-24383	86	7	working	work	VERB
brj-24383	86	8	schematic	schematic	ADJ
brj-24383	86	9	of	of	ADP
brj-24383	86	10	the	the	DET
brj-24383	86	11	hyperspectral	hyperspectral	ADJ
brj-24383	86	12	instrument	instrument	NOUN
brj-24383	86	13	.	.	PUNCT
brj-24383	87	1	data	datum	NOUN
brj-24383	87	2	preprocessing	preprocessing	NOUN
brj-24383	87	3	and	and	CCONJ
brj-24383	87	4	feature	feature	NOUN
brj-24383	87	5	extraction	extraction	NOUN
brj-24383	87	6	the	the	DET
brj-24383	87	7	wavelength	wavelength	NOUN
brj-24383	87	8	range	range	NOUN
brj-24383	87	9	of	of	ADP
brj-24383	87	10	the	the	DET
brj-24383	87	11	spectral	spectral	ADJ
brj-24383	87	12	data	datum	NOUN
brj-24383	87	13	collected	collect	VERB
brj-24383	87	14	using	use	VERB
brj-24383	87	15	the	the	DET
brj-24383	87	16	spectrometer	spectrometer	NOUN
brj-24383	87	17	was	be	AUX
brj-24383	87	18	between	between	ADP
brj-24383	87	19	350	350	NUM
brj-24383	87	20	and	and	CCONJ
brj-24383	87	21	1050	1050	NUM
brj-24383	87	22	nm	nm	NOUN
brj-24383	87	23	.	.	PUNCT
brj-24383	88	1	the	the	DET
brj-24383	88	2	spectral	spectral	ADJ
brj-24383	88	3	resolution	resolution	NOUN
brj-24383	88	4	was	be	AUX
brj-24383	88	5	0.3353	0.3353	NUM
brj-24383	88	6	nm	nm	NOUN
brj-24383	88	7	,	,	PUNCT
brj-24383	88	8	and	and	CCONJ
brj-24383	88	9	its	its	PRON
brj-24383	88	10	dimension	dimension	NOUN
brj-24383	88	11	was	be	AUX
brj-24383	88	12	1050	1050	NUM
brj-24383	88	13	.	.	PUNCT
brj-24383	89	1	to	to	PART
brj-24383	89	2	enhance	enhance	VERB
brj-24383	89	3	spectral	spectral	ADJ
brj-24383	89	4	separability	separability	NOUN
brj-24383	89	5	,	,	PUNCT
brj-24383	89	6	critical	critical	ADJ
brj-24383	89	7	preprocessing	preprocessing	NOUN
brj-24383	89	8	steps	step	NOUN
brj-24383	89	9	including	include	VERB
brj-24383	89	10	regional	regional	ADJ
brj-24383	89	11	averaging	averaging	NOUN
brj-24383	89	12	,	,	PUNCT
brj-24383	89	13	smoothing	smoothing	NOUN
brj-24383	89	14	,	,	PUNCT
brj-24383	89	15	baseline	baseline	ADJ
brj-24383	89	16	correction	correction	NOUN
brj-24383	89	17	,	,	PUNCT
brj-24383	89	18	and	and	CCONJ
brj-24383	89	19	noise	noise	NOUN
brj-24383	89	20	reduction	reduction	NOUN
brj-24383	89	21	were	be	AUX
brj-24383	89	22	systematically	systematically	ADV
brj-24383	89	23	implemented	implement	VERB
brj-24383	89	24	.	.	PUNCT
brj-24383	90	1	direct	direct	ADJ
brj-24383	90	2	classification	classification	NOUN
brj-24383	90	3	of	of	ADP
brj-24383	90	4	raw	raw	ADJ
brj-24383	90	5	spectra	spectra	ADJ
brj-24383	90	6	risks	risk	NOUN
brj-24383	90	7	triggering	trigger	VERB
brj-24383	90	8	the	the	DET
brj-24383	90	9	'	'	PUNCT
brj-24383	90	10	curse	curse	NOUN
brj-24383	90	11	of	of	ADP
brj-24383	90	12	dimensionality	dimensionality	NOUN
brj-24383	90	13	'	'	PUNCT
brj-24383	90	14	and	and	CCONJ
brj-24383	90	15	compromises	compromise	VERB
brj-24383	90	16	computational	computational	ADJ
brj-24383	90	17	efficiency	efficiency	NOUN
brj-24383	90	18	.	.	PUNCT
brj-24383	91	1	thus	thus	ADV
brj-24383	91	2	,	,	PUNCT
brj-24383	91	3	dimensionality	dimensionality	NOUN
brj-24383	91	4	reduction	reduction	NOUN
brj-24383	91	5	through	through	ADP
brj-24383	91	6	spectral	spectral	ADJ
brj-24383	91	7	feature	feature	NOUN
brj-24383	91	8	optimization	optimization	NOUN
brj-24383	91	9	is	be	AUX
brj-24383	91	10	necessary	necessary	ADJ
brj-24383	91	11	.	.	PUNCT
brj-24383	92	1	peer	peer	NOUN
brj-24383	92	2	-	-	PUNCT
brj-24383	92	3	reviewed	review	VERB
brj-24383	92	4	article	article	NOUN
brj-24383	92	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	92	6	su	su	PROPN
brj-24383	92	7	et	et	PROPN
brj-24383	92	8	al	al	PROPN
brj-24383	92	9	.	.	PROPN
brj-24383	93	1	(	(	PUNCT
brj-24383	93	2	2025	2025	NUM
brj-24383	93	3	)	)	PUNCT
brj-24383	93	4	.	.	PUNCT
brj-24383	94	1	“	"	PUNCT
brj-24383	94	2	leguminous	leguminous	ADJ
brj-24383	94	3	wood	wood	NOUN
brj-24383	94	4	classification	classification	NOUN
brj-24383	94	5	,	,	PUNCT
brj-24383	94	6	”	"	PUNCT
brj-24383	94	7	bioresources	bioresource	NOUN
brj-24383	94	8	20(3	20(3	NOUN
brj-24383	94	9	)	)	PUNCT
brj-24383	94	10	,	,	PUNCT
brj-24383	94	11	6317	6317	NUM
brj-24383	94	12	-	-	SYM
brj-24383	94	13	6337	6337	NUM
brj-24383	94	14	.	.	PUNCT
brj-24383	95	1	6322	6322	NUM
brj-24383	95	2	fig	fig	NOUN
brj-24383	95	3	.	.	PUNCT
brj-24383	96	1	1	1	X
brj-24383	96	2	.	.	PUNCT
brj-24383	96	3	hyperspectral	hyperspectral	ADJ
brj-24383	96	4	instrument	instrument	NOUN
brj-24383	96	5	and	and	CCONJ
brj-24383	96	6	working	work	VERB
brj-24383	96	7	schematic	schematic	ADJ
brj-24383	96	8	peer	peer	NOUN
brj-24383	96	9	-	-	PUNCT
brj-24383	96	10	reviewed	review	VERB
brj-24383	96	11	article	article	NOUN
brj-24383	96	12	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	96	13	su	su	PROPN
brj-24383	96	14	et	et	PROPN
brj-24383	96	15	al	al	PROPN
brj-24383	96	16	.	.	PROPN
brj-24383	97	1	(	(	PUNCT
brj-24383	97	2	2025	2025	NUM
brj-24383	97	3	)	)	PUNCT
brj-24383	97	4	.	.	PUNCT
brj-24383	98	1	“	"	PUNCT
brj-24383	98	2	leguminous	leguminous	ADJ
brj-24383	98	3	wood	wood	NOUN
brj-24383	98	4	classification	classification	NOUN
brj-24383	98	5	,	,	PUNCT
brj-24383	98	6	”	"	PUNCT
brj-24383	98	7	bioresources	bioresource	NOUN
brj-24383	98	8	20(3	20(3	NOUN
brj-24383	98	9	)	)	PUNCT
brj-24383	98	10	,	,	PUNCT
brj-24383	98	11	6317	6317	NUM
brj-24383	98	12	-	-	SYM
brj-24383	98	13	6337	6337	NUM
brj-24383	98	14	.	.	PUNCT
brj-24383	99	1	6323	6323	NUM
brj-24383	99	2	fig	fig	NOUN
brj-24383	99	3	.	.	PUNCT
brj-24383	100	1	2	2	X
brj-24383	100	2	.	.	X
brj-24383	100	3	raw	raw	ADJ
brj-24383	100	4	spectral	spectral	ADJ
brj-24383	100	5	reflectance	reflectance	NOUN
brj-24383	100	6	curve	curve	NOUN
brj-24383	100	7	figure	figure	NOUN
brj-24383	100	8	2	2	NUM
brj-24383	100	9	illustrates	illustrate	VERB
brj-24383	100	10	the	the	DET
brj-24383	100	11	original	original	ADJ
brj-24383	100	12	spectral	spectral	ADJ
brj-24383	100	13	reflectance	reflectance	NOUN
brj-24383	100	14	curves	curve	NOUN
brj-24383	100	15	containing	contain	VERB
brj-24383	100	16	1050	1050	NUM
brj-24383	100	17	dimensional	dimensional	ADJ
brj-24383	100	18	features	feature	NOUN
brj-24383	100	19	,	,	PUNCT
brj-24383	100	20	demonstrating	demonstrate	VERB
brj-24383	100	21	inherent	inherent	ADJ
brj-24383	100	22	spectral	spectral	ADJ
brj-24383	100	23	variability	variability	NOUN
brj-24383	100	24	across	across	ADP
brj-24383	100	25	samples	sample	NOUN
brj-24383	100	26	.	.	PUNCT
brj-24383	101	1	figure	figure	NOUN
brj-24383	101	2	3	3	NUM
brj-24383	101	3	.	.	PUNCT
brj-24383	101	4	presents	present	VERB
brj-24383	101	5	the	the	DET
brj-24383	101	6	optimized	optimize	VERB
brj-24383	101	7	spectral	spectral	ADJ
brj-24383	101	8	signature	signature	NOUN
brj-24383	101	9	processed	process	VERB
brj-24383	101	10	through	through	ADP
brj-24383	101	11	spectrononpro	spectrononpro	PROPN
brj-24383	101	12	’s	’s	PART
brj-24383	101	13	automated	automate	VERB
brj-24383	101	14	workflow	workflow	NOUN
brj-24383	101	15	.	.	PUNCT
brj-24383	102	1	this	this	DET
brj-24383	102	2	procedure	procedure	NOUN
brj-24383	102	3	generates	generate	VERB
brj-24383	102	4	a	a	DET
brj-24383	102	5	representative	representative	ADJ
brj-24383	102	6	average	average	ADJ
brj-24383	102	7	spectrum	spectrum	NOUN
brj-24383	102	8	by	by	ADP
brj-24383	102	9	integrating	integrate	VERB
brj-24383	102	10	regional	regional	ADJ
brj-24383	102	11	spectral	spectral	ADJ
brj-24383	102	12	features	feature	NOUN
brj-24383	102	13	within	within	ADP
brj-24383	102	14	designated	designate	VERB
brj-24383	102	15	wavelength	wavelength	NOUN
brj-24383	102	16	intervals	interval	NOUN
brj-24383	102	17	,	,	PUNCT
brj-24383	102	18	effectively	effectively	ADV
brj-24383	102	19	reducing	reduce	VERB
brj-24383	102	20	data	datum	NOUN
brj-24383	102	21	dimensionality	dimensionality	NOUN
brj-24383	102	22	while	while	SCONJ
brj-24383	102	23	preserving	preserve	VERB
brj-24383	102	24	discriminative	discriminative	NOUN
brj-24383	102	25	information	information	NOUN
brj-24383	102	26	.	.	PUNCT
brj-24383	103	1	the	the	DET
brj-24383	103	2	contrast	contrast	NOUN
brj-24383	103	3	between	between	ADP
brj-24383	103	4	the	the	DET
brj-24383	103	5	multi	multi	ADJ
brj-24383	103	6	-	-	ADJ
brj-24383	103	7	curve	curve	ADJ
brj-24383	103	8	representation	representation	NOUN
brj-24383	103	9	in	in	ADP
brj-24383	103	10	fig	fig	NOUN
brj-24383	103	11	.	.	PUNCT
brj-24383	104	1	2	2	NUM
brj-24383	104	2	and	and	CCONJ
brj-24383	104	3	the	the	DET
brj-24383	104	4	unified	unified	ADJ
brj-24383	104	5	curve	curve	NOUN
brj-24383	104	6	in	in	ADP
brj-24383	104	7	fig	fig	NOUN
brj-24383	104	8	.	.	PUNCT
brj-24383	105	1	3	3	NUM
brj-24383	105	2	visually	visually	ADV
brj-24383	105	3	demonstrates	demonstrate	VERB
brj-24383	105	4	how	how	SCONJ
brj-24383	105	5	preprocessing	preprocessing	NOUN
brj-24383	105	6	transforms	transform	VERB
brj-24383	105	7	high	high	ADJ
brj-24383	105	8	-	-	PUNCT
brj-24383	105	9	dimensional	dimensional	ADJ
brj-24383	105	10	raw	raw	ADJ
brj-24383	105	11	data	datum	NOUN
brj-24383	105	12	into	into	ADP
brj-24383	105	13	a	a	DET
brj-24383	105	14	compact	compact	ADJ
brj-24383	105	15	spectral	spectral	ADJ
brj-24383	105	16	profile	profile	NOUN
brj-24383	105	17	suitable	suitable	ADJ
brj-24383	105	18	for	for	ADP
brj-24383	105	19	efficient	efficient	ADJ
brj-24383	105	20	pattern	pattern	NOUN
brj-24383	105	21	recognition	recognition	NOUN
brj-24383	105	22	.	.	PUNCT
brj-24383	106	1	fig	fig	NOUN
brj-24383	106	2	.	.	PUNCT
brj-24383	107	1	3	3	X
brj-24383	107	2	.	.	X
brj-24383	107	3	spectral	spectral	ADJ
brj-24383	107	4	curve	curve	NOUN
brj-24383	107	5	after	after	ADP
brj-24383	107	6	data	data	NOUN
brj-24383	107	7	processing	processing	NOUN
brj-24383	107	8	to	to	PART
brj-24383	107	9	eliminate	eliminate	VERB
brj-24383	107	10	the	the	DET
brj-24383	107	11	impact	impact	NOUN
brj-24383	107	12	of	of	ADP
brj-24383	107	13	feature	feature	NOUN
brj-24383	107	14	dimensional	dimensional	ADJ
brj-24383	107	15	differences	difference	NOUN
brj-24383	107	16	,	,	PUNCT
brj-24383	107	17	data	datum	NOUN
brj-24383	107	18	normalization	normalization	NOUN
brj-24383	107	19	was	be	AUX
brj-24383	107	20	done	do	VERB
brj-24383	107	21	.	.	PUNCT
brj-24383	108	1	the	the	DET
brj-24383	108	2	normalization	normalization	NOUN
brj-24383	108	3	process	process	NOUN
brj-24383	108	4	transforms	transform	VERB
brj-24383	108	5	the	the	DET
brj-24383	108	6	data	datum	NOUN
brj-24383	108	7	into	into	ADP
brj-24383	108	8	zero	zero	NUM
brj-24383	108	9	mean	mean	NOUN
brj-24383	108	10	and	and	CCONJ
brj-24383	108	11	unit	unit	NOUN
brj-24383	108	12	variance	variance	NOUN
brj-24383	108	13	.	.	PUNCT
brj-24383	109	1	let	let	VERB
brj-24383	109	2	the	the	DET
brj-24383	109	3	original	original	ADJ
brj-24383	109	4	data	data	NOUN
brj-24383	109	5	matrix	matrix	NOUN
brj-24383	109	6	be	be	AUX
brj-24383	109	7	x	x	PUNCT
brj-24383	109	8	,	,	PUNCT
brj-24383	109	9	and	and	CCONJ
brj-24383	109	10	the	the	DET
brj-24383	109	11	normalization	normalization	NOUN
brj-24383	109	12	formula	formula	NOUN
brj-24383	109	13	is	be	AUX
brj-24383	109	14	as	as	SCONJ
brj-24383	109	15	follows	follow	VERB
brj-24383	109	16	:	:	PUNCT
brj-24383	109	17			NUM
brj-24383	109	18	−	−	ADJ
brj-24383	109	19	=	=	PUNCT
brj-24383	109	20	x	x	SYM
brj-24383	109	21	z	z	NOUN
brj-24383	109	22	(	(	PUNCT
brj-24383	109	23	1	1	NUM
brj-24383	109	24	)	)	PUNCT
brj-24383	109	25	where	where	SCONJ
brj-24383	109	26			NOUN
brj-24383	109	27	and	and	CCONJ
brj-24383	109	28			NUM
brj-24383	109	29	represent	represent	VERB
brj-24383	109	30	the	the	DET
brj-24383	109	31	mean	mean	ADJ
brj-24383	109	32	and	and	CCONJ
brj-24383	109	33	standard	standard	ADJ
brj-24383	109	34	deviation	deviation	NOUN
brj-24383	109	35	of	of	ADP
brj-24383	109	36	each	each	DET
brj-24383	109	37	column	column	NOUN
brj-24383	109	38	of	of	ADP
brj-24383	109	39	data	data	PROPN
brj-24383	109	40	,	,	PUNCT
brj-24383	109	41	respectively	respectively	ADV
brj-24383	109	42	.	.	PUNCT
brj-24383	110	1	this	this	DET
brj-24383	110	2	method	method	NOUN
brj-24383	110	3	was	be	AUX
brj-24383	110	4	applied	apply	VERB
brj-24383	110	5	to	to	PART
brj-24383	110	6	standardize	standardize	VERB
brj-24383	110	7	the	the	DET
brj-24383	110	8	data	datum	NOUN
brj-24383	110	9	,	,	PUNCT
brj-24383	110	10	ensuring	ensure	VERB
brj-24383	110	11	that	that	SCONJ
brj-24383	110	12	all	all	PRON
brj-24383	110	13	features	feature	VERB
brj-24383	110	14	peer	peer	NOUN
brj-24383	110	15	-	-	PUNCT
brj-24383	110	16	reviewed	review	VERB
brj-24383	110	17	article	article	NOUN
brj-24383	110	18	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	110	19	su	su	PROPN
brj-24383	110	20	et	et	PROPN
brj-24383	110	21	al	al	PROPN
brj-24383	110	22	.	.	PROPN
brj-24383	111	1	(	(	PUNCT
brj-24383	111	2	2025	2025	NUM
brj-24383	111	3	)	)	PUNCT
brj-24383	111	4	.	.	PUNCT
brj-24383	112	1	“	"	PUNCT
brj-24383	112	2	leguminous	leguminous	ADJ
brj-24383	112	3	wood	wood	NOUN
brj-24383	112	4	classification	classification	NOUN
brj-24383	112	5	,	,	PUNCT
brj-24383	112	6	”	"	PUNCT
brj-24383	112	7	bioresources	bioresource	NOUN
brj-24383	112	8	20(3	20(3	NOUN
brj-24383	112	9	)	)	PUNCT
brj-24383	112	10	,	,	PUNCT
brj-24383	112	11	6317	6317	NUM
brj-24383	112	12	-	-	SYM
brj-24383	112	13	6337	6337	NUM
brj-24383	112	14	.	.	PUNCT
brj-24383	113	1	6324	6324	NUM
brj-24383	113	2	were	be	AUX
brj-24383	113	3	on	on	ADP
brj-24383	113	4	the	the	DET
brj-24383	113	5	same	same	ADJ
brj-24383	113	6	scale	scale	NOUN
brj-24383	113	7	and	and	CCONJ
brj-24383	113	8	effectively	effectively	ADV
brj-24383	113	9	reducing	reduce	VERB
brj-24383	113	10	the	the	DET
brj-24383	113	11	imbalance	imbalance	NOUN
brj-24383	113	12	in	in	ADP
brj-24383	113	13	the	the	DET
brj-24383	113	14	influence	influence	NOUN
brj-24383	113	15	of	of	ADP
brj-24383	113	16	different	different	ADJ
brj-24383	113	17	features	feature	NOUN
brj-24383	113	18	on	on	ADP
brj-24383	113	19	the	the	DET
brj-24383	113	20	model	model	NOUN
brj-24383	113	21	.	.	PUNCT
brj-24383	114	1	for	for	ADP
brj-24383	114	2	specific	specific	ADJ
brj-24383	114	3	scenarios	scenario	NOUN
brj-24383	114	4	,	,	PUNCT
brj-24383	114	5	the	the	DET
brj-24383	114	6	data	datum	NOUN
brj-24383	114	7	were	be	AUX
brj-24383	114	8	also	also	ADV
brj-24383	114	9	normalized	normalize	VERB
brj-24383	114	10	based	base	VERB
brj-24383	114	11	on	on	ADP
brj-24383	114	12	the	the	DET
brj-24383	114	13	range	range	NOUN
brj-24383	114	14	between	between	ADP
brj-24383	114	15	the	the	DET
brj-24383	114	16	maximum	maximum	ADJ
brj-24383	114	17	and	and	CCONJ
brj-24383	114	18	minimum	minimum	ADJ
brj-24383	114	19	values	value	NOUN
brj-24383	114	20	,	,	PUNCT
brj-24383	114	21	as	as	SCONJ
brj-24383	114	22	shown	show	VERB
brj-24383	114	23	in	in	ADP
brj-24383	114	24	the	the	DET
brj-24383	114	25	following	follow	VERB
brj-24383	114	26	formula	formula	NOUN
brj-24383	114	27	:	:	PUNCT
brj-24383	114	28	minmax	minmax	PROPN
brj-24383	114	29	min	min	PROPN
brj-24383	114	30	xx	xx	NUM
brj-24383	114	31	xx	xx	NUM
brj-24383	114	32	xnorm	xnorm	NOUN
brj-24383	115	1	−	−	PROPN
brj-24383	115	2	−	−	PROPN
brj-24383	116	1	=	=	SYM
brj-24383	116	2	(	(	PUNCT
brj-24383	116	3	2	2	X
brj-24383	116	4	)	)	PUNCT
brj-24383	116	5	the	the	DET
brj-24383	116	6	first	first	ADJ
brj-24383	116	7	step	step	NOUN
brj-24383	116	8	of	of	ADP
brj-24383	116	9	pca	pca	NOUN
brj-24383	116	10	is	be	AUX
brj-24383	116	11	to	to	PART
brj-24383	116	12	standardize	standardize	VERB
brj-24383	116	13	the	the	DET
brj-24383	116	14	data	datum	NOUN
brj-24383	116	15	to	to	PART
brj-24383	116	16	eliminate	eliminate	VERB
brj-24383	116	17	the	the	DET
brj-24383	116	18	dimensional	dimensional	ADJ
brj-24383	116	19	differences	difference	NOUN
brj-24383	116	20	of	of	ADP
brj-24383	116	21	different	different	ADJ
brj-24383	116	22	features	feature	NOUN
brj-24383	116	23	.	.	PUNCT
brj-24383	117	1	the	the	DET
brj-24383	117	2	aforementioned	aforementioned	ADJ
brj-24383	117	3	formula	formula	NOUN
brj-24383	117	4	was	be	AUX
brj-24383	117	5	used	use	VERB
brj-24383	117	6	to	to	PART
brj-24383	117	7	standardize	standardize	VERB
brj-24383	117	8	the	the	DET
brj-24383	117	9	data	datum	NOUN
brj-24383	117	10	matrix	matrix	NOUN
brj-24383	117	11	x	x	NOUN
brj-24383	117	12	,	,	PUNCT
brj-24383	117	13	obtaining	obtain	VERB
brj-24383	117	14	the	the	DET
brj-24383	117	15	standardized	standardized	ADJ
brj-24383	117	16	matrix	matrix	NOUN
brj-24383	117	17	z.	z.	NOUN
brj-24383	118	1	after	after	ADP
brj-24383	118	2	standardization	standardization	NOUN
brj-24383	118	3	,	,	PUNCT
brj-24383	118	4	the	the	DET
brj-24383	118	5	covariance	covariance	NOUN
brj-24383	118	6	matrix	matrix	NOUN
brj-24383	118	7	s	s	NOUN
brj-24383	118	8	of	of	ADP
brj-24383	118	9	the	the	DET
brj-24383	118	10	data	datum	NOUN
brj-24383	118	11	was	be	AUX
brj-24383	118	12	computed	compute	VERB
brj-24383	118	13	to	to	PART
brj-24383	118	14	quantify	quantify	VERB
brj-24383	118	15	the	the	DET
brj-24383	118	16	correlation	correlation	NOUN
brj-24383	118	17	between	between	ADP
brj-24383	118	18	features	feature	NOUN
brj-24383	118	19	.	.	PUNCT
brj-24383	119	1	the	the	DET
brj-24383	119	2	formula	formula	NOUN
brj-24383	119	3	for	for	ADP
brj-24383	119	4	the	the	DET
brj-24383	119	5	covariance	covariance	NOUN
brj-24383	119	6	matrix	matrix	NOUN
brj-24383	119	7	is	be	AUX
brj-24383	119	8	:	:	PUNCT
brj-24383	119	9	zz	zz	PROPN
brj-24383	119	10	n	n	PROPN
brj-24383	119	11	s	s	PROPN
brj-24383	119	12	t1	t1	NOUN
brj-24383	119	13	=	=	PUNCT
brj-24383	119	14	(	(	PUNCT
brj-24383	119	15	3	3	NUM
brj-24383	119	16	)	)	PUNCT
brj-24383	119	17	where	where	SCONJ
brj-24383	119	18	tz	tz	NOUN
brj-24383	119	19	is	be	AUX
brj-24383	119	20	the	the	DET
brj-24383	119	21	transpose	transpose	NOUN
brj-24383	119	22	of	of	ADP
brj-24383	119	23	the	the	DET
brj-24383	119	24	standardized	standardized	ADJ
brj-24383	119	25	data	data	NOUN
brj-24383	119	26	matrix	matrix	NOUN
brj-24383	119	27	,	,	PUNCT
brj-24383	119	28	and	and	CCONJ
brj-24383	119	29	n	n	PRON
brj-24383	119	30	is	be	AUX
brj-24383	119	31	the	the	DET
brj-24383	119	32	number	number	NOUN
brj-24383	119	33	of	of	ADP
brj-24383	119	34	samples	sample	NOUN
brj-24383	119	35	.	.	PUNCT
brj-24383	120	1	next	next	ADJ
brj-24383	120	2	,	,	PUNCT
brj-24383	120	3	eigenvalue	eigenvalue	ADJ
brj-24383	120	4	decomposition	decomposition	NOUN
brj-24383	120	5	of	of	ADP
brj-24383	120	6	the	the	DET
brj-24383	120	7	covariance	covariance	NOUN
brj-24383	120	8	matrix	matrix	NOUN
brj-24383	120	9	s	s	VERB
brj-24383	120	10	was	be	AUX
brj-24383	120	11	carried	carry	VERB
brj-24383	120	12	out	out	ADP
brj-24383	120	13	to	to	PART
brj-24383	120	14	obtain	obtain	VERB
brj-24383	120	15	the	the	DET
brj-24383	120	16	eigenvalues	eigenvalue	NOUN
brj-24383	120	17	i	i	PROPN
brj-24383	120	18	and	and	CCONJ
brj-24383	120	19	corresponding	corresponding	ADJ
brj-24383	120	20	eigenvectors	eigenvector	NOUN
brj-24383	120	21	i	i	NUM
brj-24383	120	22	.	.	PUNCT
brj-24383	121	1	the	the	DET
brj-24383	121	2	formula	formula	NOUN
brj-24383	121	3	is	be	AUX
brj-24383	121	4	:	:	PUNCT
brj-24383	121	5	is	be	AUX
brj-24383	121	6			PROPN
brj-24383	121	7	ii	ii	NOUN
brj-24383	121	8	=	=	SYM
brj-24383	121	9	(	(	PUNCT
brj-24383	121	10	4	4	NUM
brj-24383	121	11	)	)	PUNCT
brj-24383	121	12	where	where	SCONJ
brj-24383	121	13	i	i	PRON
brj-24383	121	14	represents	represent	VERB
brj-24383	121	15	the	the	DET
brj-24383	121	16	variance	variance	NOUN
brj-24383	121	17	of	of	ADP
brj-24383	121	18	the	the	DET
brj-24383	121	19	principal	principal	ADJ
brj-24383	121	20	components	component	NOUN
brj-24383	121	21	,	,	PUNCT
brj-24383	121	22	and	and	CCONJ
brj-24383	121	23	i	i	NOUN
brj-24383	121	24	represents	represent	VERB
brj-24383	121	25	the	the	DET
brj-24383	121	26	direction	direction	NOUN
brj-24383	121	27	of	of	ADP
brj-24383	121	28	the	the	DET
brj-24383	121	29	principal	principal	ADJ
brj-24383	121	30	component	component	NOUN
brj-24383	121	31	.	.	PUNCT
brj-24383	122	1	the	the	DET
brj-24383	122	2	principal	principal	ADJ
brj-24383	122	3	components	component	NOUN
brj-24383	122	4	with	with	ADP
brj-24383	122	5	larger	large	ADJ
brj-24383	122	6	eigenvalues	eigenvalue	NOUN
brj-24383	122	7	represent	represent	VERB
brj-24383	122	8	the	the	DET
brj-24383	122	9	directions	direction	NOUN
brj-24383	122	10	of	of	ADP
brj-24383	122	11	the	the	DET
brj-24383	122	12	largest	large	ADJ
brj-24383	122	13	variance	variance	NOUN
brj-24383	122	14	in	in	ADP
brj-24383	122	15	the	the	DET
brj-24383	122	16	data	datum	NOUN
brj-24383	122	17	.	.	PUNCT
brj-24383	123	1	based	base	VERB
brj-24383	123	2	on	on	ADP
brj-24383	123	3	the	the	DET
brj-24383	123	4	cumulative	cumulative	ADJ
brj-24383	123	5	explained	explain	VERB
brj-24383	123	6	variance	variance	NOUN
brj-24383	123	7	ratio	ratio	NOUN
brj-24383	123	8	,	,	PUNCT
brj-24383	123	9	the	the	DET
brj-24383	123	10	first	first	ADJ
brj-24383	123	11	k	k	PROPN
brj-24383	123	12	principal	principal	ADJ
brj-24383	123	13	components	component	NOUN
brj-24383	123	14	were	be	AUX
brj-24383	123	15	selected	select	VERB
brj-24383	123	16	to	to	PART
brj-24383	123	17	ensure	ensure	VERB
brj-24383	123	18	that	that	SCONJ
brj-24383	123	19	at	at	ADV
brj-24383	123	20	least	least	ADV
brj-24383	123	21	95	95	NUM
brj-24383	123	22	%	%	NOUN
brj-24383	123	23	of	of	ADP
brj-24383	123	24	the	the	DET
brj-24383	123	25	information	information	NOUN
brj-24383	123	26	was	be	AUX
brj-24383	123	27	retained	retain	VERB
brj-24383	123	28	.	.	PUNCT
brj-24383	124	1	the	the	DET
brj-24383	124	2	cumulative	cumulative	ADJ
brj-24383	124	3	explained	explain	VERB
brj-24383	124	4	variance	variance	NOUN
brj-24383	124	5	ratio	ratio	NOUN
brj-24383	124	6	is	be	AUX
brj-24383	124	7	given	give	VERB
brj-24383	124	8	by	by	ADP
brj-24383	124	9	:	:	PUNCT
brj-24383	124	10	cumulative	cumulative	ADJ
brj-24383	124	11	xplainede	xplainede	PROPN
brj-24383	124	12	variance	variance	NOUN
brj-24383	124	13			X
brj-24383	124	14			X
brj-24383	125	1	=	=	PUNCT
brj-24383	126	1	=	=	SYM
brj-24383	126	2	=	=	NOUN
brj-24383	126	3	m	m	VERB
brj-24383	126	4	i	i	INTJ
brj-24383	127	1	i	i	INTJ
brj-24383	127	2	k	k	INTJ
brj-24383	128	1	i	i	PRON
brj-24383	128	2	i	i	PRON
brj-24383	128	3	r	r	VERB
brj-24383	128	4	1	1	NUM
brj-24383	128	5	1atio	1atio	NUM
brj-24383	128	6			X
brj-24383	128	7			X
brj-24383	128	8	(	(	PUNCT
brj-24383	128	9	5	5	NUM
brj-24383	128	10	)	)	PUNCT
brj-24383	128	11	where	where	SCONJ
brj-24383	128	12	m	m	NOUN
brj-24383	128	13	is	be	AUX
brj-24383	128	14	the	the	DET
brj-24383	128	15	total	total	ADJ
brj-24383	128	16	number	number	NOUN
brj-24383	128	17	of	of	ADP
brj-24383	128	18	features	feature	NOUN
brj-24383	128	19	.	.	PUNCT
brj-24383	129	1	based	base	VERB
brj-24383	129	2	on	on	ADP
brj-24383	129	3	the	the	DET
brj-24383	129	4	experimental	experimental	ADJ
brj-24383	129	5	results	result	NOUN
brj-24383	129	6	,	,	PUNCT
brj-24383	129	7	the	the	DET
brj-24383	129	8	first	first	ADJ
brj-24383	129	9	k	k	PROPN
brj-24383	129	10	eigenvectors	eigenvector	NOUN
brj-24383	129	11	u	u	PRON
brj-24383	129	12	were	be	AUX
brj-24383	129	13	selected	select	VERB
brj-24383	129	14	to	to	PART
brj-24383	129	15	form	form	VERB
brj-24383	129	16	the	the	DET
brj-24383	129	17	new	new	ADJ
brj-24383	129	18	basis	basis	NOUN
brj-24383	129	19	.	.	PUNCT
brj-24383	130	1	finally	finally	ADV
brj-24383	130	2	,	,	PUNCT
brj-24383	130	3	the	the	DET
brj-24383	130	4	standardized	standardized	ADJ
brj-24383	130	5	data	datum	NOUN
brj-24383	130	6	z	z	NOUN
brj-24383	130	7	were	be	AUX
brj-24383	130	8	projected	project	VERB
brj-24383	130	9	onto	onto	ADP
brj-24383	130	10	the	the	DET
brj-24383	130	11	new	new	ADJ
brj-24383	130	12	principal	principal	ADJ
brj-24383	130	13	component	component	NOUN
brj-24383	130	14	space	space	NOUN
brj-24383	130	15	to	to	PART
brj-24383	130	16	obtain	obtain	VERB
brj-24383	130	17	the	the	DET
brj-24383	130	18	reduced	reduce	VERB
brj-24383	130	19	-	-	PUNCT
brj-24383	130	20	dimensional	dimensional	ADJ
brj-24383	130	21	data	datum	NOUN
brj-24383	130	22	matrix	matrix	NOUN
brj-24383	130	23	y	y	NOUN
brj-24383	130	24	:	:	PUNCT
brj-24383	130	25	zuy	zuy	NOUN
brj-24383	130	26	=	=	SYM
brj-24383	130	27	(	(	PUNCT
brj-24383	130	28	6	6	NUM
brj-24383	130	29	)	)	PUNCT
brj-24383	130	30	where	where	SCONJ
brj-24383	130	31	u	u	NOUN
brj-24383	130	32	is	be	AUX
brj-24383	130	33	the	the	DET
brj-24383	130	34	matrix	matrix	NOUN
brj-24383	130	35	containing	contain	VERB
brj-24383	130	36	the	the	DET
brj-24383	130	37	first	first	ADJ
brj-24383	130	38	k	k	PROPN
brj-24383	130	39	eigenvectors	eigenvector	NOUN
brj-24383	130	40	.	.	PUNCT
brj-24383	131	1	this	this	DET
brj-24383	131	2	step	step	NOUN
brj-24383	131	3	significantly	significantly	ADV
brj-24383	131	4	reduced	reduce	VERB
brj-24383	131	5	the	the	DET
brj-24383	131	6	dimensionality	dimensionality	NOUN
brj-24383	131	7	of	of	ADP
brj-24383	131	8	the	the	DET
brj-24383	131	9	data	datum	NOUN
brj-24383	131	10	and	and	CCONJ
brj-24383	131	11	improved	improve	VERB
brj-24383	131	12	the	the	DET
brj-24383	131	13	efficiency	efficiency	NOUN
brj-24383	131	14	of	of	ADP
brj-24383	131	15	subsequent	subsequent	ADJ
brj-24383	131	16	model	model	NOUN
brj-24383	131	17	training	training	NOUN
brj-24383	131	18	.	.	PUNCT
brj-24383	132	1	the	the	DET
brj-24383	132	2	results	result	NOUN
brj-24383	132	3	of	of	ADP
brj-24383	132	4	the	the	DET
brj-24383	132	5	first	first	ADJ
brj-24383	132	6	two	two	NUM
brj-24383	132	7	principal	principal	ADJ
brj-24383	132	8	components	component	NOUN
brj-24383	132	9	,	,	PUNCT
brj-24383	132	10	pc1	pc1	PROPN
brj-24383	132	11	and	and	CCONJ
brj-24383	132	12	pc2	pc2	NOUN
brj-24383	132	13	,	,	PUNCT
brj-24383	132	14	extracted	extract	VERB
brj-24383	132	15	by	by	ADP
brj-24383	132	16	pca	pca	PROPN
brj-24383	132	17	show	show	NOUN
brj-24383	132	18	that	that	SCONJ
brj-24383	132	19	the	the	DET
brj-24383	132	20	main	main	ADJ
brj-24383	132	21	information	information	NOUN
brj-24383	132	22	of	of	ADP
brj-24383	132	23	the	the	DET
brj-24383	132	24	data	datum	NOUN
brj-24383	132	25	was	be	AUX
brj-24383	132	26	effectively	effectively	ADV
brj-24383	132	27	captured	capture	VERB
brj-24383	132	28	:	:	PUNCT
brj-24383	132	29	principal	principal	ADJ
brj-24383	132	30	component	component	NOUN
brj-24383	132	31	1	1	NUM
brj-24383	132	32	(	(	PUNCT
brj-24383	132	33	pc1	pc1	PROPN
brj-24383	132	34	)	)	PUNCT
brj-24383	132	35	captured	capture	VERB
brj-24383	132	36	the	the	DET
brj-24383	132	37	primary	primary	ADJ
brj-24383	132	38	trend	trend	NOUN
brj-24383	132	39	with	with	ADP
brj-24383	132	40	the	the	DET
brj-24383	132	41	largest	large	ADJ
brj-24383	132	42	variation	variation	NOUN
brj-24383	132	43	in	in	ADP
brj-24383	132	44	reflectance	reflectance	NOUN
brj-24383	132	45	,	,	PUNCT
brj-24383	132	46	related	relate	VERB
brj-24383	132	47	to	to	ADP
brj-24383	132	48	the	the	DET
brj-24383	132	49	overall	overall	ADJ
brj-24383	132	50	spectral	spectral	ADJ
brj-24383	132	51	information	information	NOUN
brj-24383	132	52	,	,	PUNCT
brj-24383	132	53	and	and	CCONJ
brj-24383	132	54	reflected	reflect	VERB
brj-24383	132	55	the	the	DET
brj-24383	132	56	global	global	ADJ
brj-24383	132	57	features	feature	NOUN
brj-24383	132	58	of	of	ADP
brj-24383	132	59	the	the	DET
brj-24383	132	60	samples	sample	NOUN
brj-24383	132	61	.	.	PUNCT
brj-24383	133	1	principal	principal	ADJ
brj-24383	133	2	component	component	NOUN
brj-24383	133	3	2	2	NUM
brj-24383	133	4	(	(	PUNCT
brj-24383	133	5	pc2	pc2	NOUN
brj-24383	133	6	)	)	PUNCT
brj-24383	133	7	represents	represent	VERB
brj-24383	133	8	the	the	DET
brj-24383	133	9	secondary	secondary	ADJ
brj-24383	133	10	variation	variation	NOUN
brj-24383	133	11	direction	direction	NOUN
brj-24383	133	12	orthogonal	orthogonal	NOUN
brj-24383	133	13	to	to	ADP
brj-24383	133	14	pc1	pc1	PROPN
brj-24383	133	15	,	,	PUNCT
brj-24383	133	16	capturing	capture	VERB
brj-24383	133	17	subtle	subtle	ADJ
brj-24383	133	18	features	feature	NOUN
brj-24383	133	19	under	under	ADP
brj-24383	133	20	different	different	ADJ
brj-24383	133	21	wavelength	wavelength	NOUN
brj-24383	133	22	combinations	combination	NOUN
brj-24383	133	23	.	.	PUNCT
brj-24383	134	1	the	the	DET
brj-24383	134	2	application	application	NOUN
brj-24383	134	3	of	of	ADP
brj-24383	134	4	pca	pca	PROPN
brj-24383	134	5	significantly	significantly	ADV
brj-24383	134	6	reduced	reduce	VERB
brj-24383	134	7	the	the	DET
brj-24383	134	8	dimensionality	dimensionality	NOUN
brj-24383	134	9	of	of	ADP
brj-24383	134	10	the	the	DET
brj-24383	134	11	data	datum	NOUN
brj-24383	134	12	while	while	SCONJ
brj-24383	134	13	retaining	retain	VERB
brj-24383	134	14	95	95	NUM
brj-24383	134	15	%	%	NOUN
brj-24383	134	16	of	of	ADP
brj-24383	134	17	the	the	DET
brj-24383	134	18	variance	variance	NOUN
brj-24383	134	19	information	information	NOUN
brj-24383	134	20	,	,	PUNCT
brj-24383	134	21	providing	provide	VERB
brj-24383	134	22	a	a	DET
brj-24383	134	23	solid	solid	ADJ
brj-24383	134	24	foundation	foundation	NOUN
brj-24383	134	25	for	for	ADP
brj-24383	134	26	model	model	NOUN
brj-24383	134	27	training	training	NOUN
brj-24383	134	28	and	and	CCONJ
brj-24383	134	29	analysis	analysis	NOUN
brj-24383	134	30	.	.	PUNCT
brj-24383	135	1	peer	peer	NOUN
brj-24383	135	2	-	-	PUNCT
brj-24383	135	3	reviewed	review	VERB
brj-24383	135	4	article	article	NOUN
brj-24383	135	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	135	6	su	su	PROPN
brj-24383	135	7	et	et	PROPN
brj-24383	135	8	al	al	PROPN
brj-24383	135	9	.	.	PROPN
brj-24383	136	1	(	(	PUNCT
brj-24383	136	2	2025	2025	NUM
brj-24383	136	3	)	)	PUNCT
brj-24383	136	4	.	.	PUNCT
brj-24383	137	1	“	"	PUNCT
brj-24383	137	2	leguminous	leguminous	ADJ
brj-24383	137	3	wood	wood	NOUN
brj-24383	137	4	classification	classification	NOUN
brj-24383	137	5	,	,	PUNCT
brj-24383	137	6	”	"	PUNCT
brj-24383	137	7	bioresources	bioresource	NOUN
brj-24383	137	8	20(3	20(3	NOUN
brj-24383	137	9	)	)	PUNCT
brj-24383	137	10	,	,	PUNCT
brj-24383	137	11	6317	6317	NUM
brj-24383	137	12	-	-	SYM
brj-24383	137	13	6337	6337	NUM
brj-24383	137	14	.	.	PUNCT
brj-24383	138	1	6325	6325	NUM
brj-24383	138	2	identification	identification	NOUN
brj-24383	138	3	of	of	ADP
brj-24383	138	4	leguminous	leguminous	ADJ
brj-24383	138	5	tree	tree	NOUN
brj-24383	138	6	species	specie	NOUN
brj-24383	138	7	using	use	VERB
brj-24383	138	8	initial	initial	ADJ
brj-24383	138	9	data	datum	NOUN
brj-24383	138	10	fig	fig	NOUN
brj-24383	138	11	.	.	PUNCT
brj-24383	139	1	4	4	X
brj-24383	139	2	.	.	X
brj-24383	139	3	hyperspectral	hyperspectral	ADJ
brj-24383	139	4	data	datum	NOUN
brj-24383	139	5	processing	processing	NOUN
brj-24383	139	6	and	and	CCONJ
brj-24383	139	7	classification	classification	NOUN
brj-24383	139	8	flow	flow	NOUN
brj-24383	139	9	chart	chart	NOUN
brj-24383	139	10	figure	figure	NOUN
brj-24383	139	11	4	4	NUM
brj-24383	139	12	shows	show	VERB
brj-24383	139	13	a	a	DET
brj-24383	139	14	flowchart	flowchart	NOUN
brj-24383	139	15	for	for	ADP
brj-24383	139	16	processing	process	VERB
brj-24383	139	17	tree	tree	NOUN
brj-24383	139	18	species	specie	NOUN
brj-24383	139	19	spectral	spectral	ADJ
brj-24383	139	20	data	datum	NOUN
brj-24383	139	21	.	.	PUNCT
brj-24383	140	1	it	it	PRON
brj-24383	140	2	should	should	AUX
brj-24383	140	3	be	be	AUX
brj-24383	140	4	noted	note	VERB
brj-24383	140	5	that	that	SCONJ
brj-24383	140	6	pca	pca	NOUN
brj-24383	140	7	dimensionality	dimensionality	NOUN
brj-24383	140	8	reduction	reduction	NOUN
brj-24383	140	9	and	and	CCONJ
brj-24383	140	10	smote	smote	ADJ
brj-24383	140	11	processing	processing	NOUN
brj-24383	140	12	are	be	AUX
brj-24383	140	13	performed	perform	VERB
brj-24383	140	14	simultaneously	simultaneously	ADV
brj-24383	140	15	rather	rather	ADV
brj-24383	140	16	than	than	ADP
brj-24383	140	17	sequentially	sequentially	ADV
brj-24383	140	18	.	.	PUNCT
brj-24383	141	1	the	the	DET
brj-24383	141	2	smote	smote	ADJ
brj-24383	141	3	method	method	NOUN
brj-24383	141	4	solves	solve	VERB
brj-24383	141	5	the	the	DET
brj-24383	141	6	problem	problem	NOUN
brj-24383	141	7	of	of	ADP
brj-24383	141	8	class	class	NOUN
brj-24383	141	9	imbalance	imbalance	NOUN
brj-24383	141	10	by	by	ADP
brj-24383	141	11	synthesizing	synthesize	VERB
brj-24383	141	12	minority	minority	NOUN
brj-24383	141	13	class	class	NOUN
brj-24383	141	14	samples	sample	NOUN
brj-24383	141	15	to	to	PART
brj-24383	141	16	enhance	enhance	VERB
brj-24383	141	17	the	the	DET
brj-24383	141	18	model	model	NOUN
brj-24383	141	19	’s	’s	PART
brj-24383	141	20	ability	ability	NOUN
brj-24383	141	21	to	to	PART
brj-24383	141	22	represent	represent	VERB
brj-24383	141	23	spectral	spectral	ADJ
brj-24383	141	24	features	feature	NOUN
brj-24383	141	25	.	.	PUNCT
brj-24383	142	1	pca	pca	PROPN
brj-24383	142	2	solves	solve	VERB
brj-24383	142	3	the	the	DET
brj-24383	142	4	problem	problem	NOUN
brj-24383	142	5	of	of	ADP
brj-24383	142	6	high	high	ADJ
brj-24383	142	7	-	-	PUNCT
brj-24383	142	8	dimensional	dimensional	ADJ
brj-24383	142	9	data	data	NOUN
brj-24383	142	10	redundancy	redundancy	NOUN
brj-24383	142	11	by	by	ADP
brj-24383	142	12	extracting	extract	VERB
brj-24383	142	13	low	low	ADJ
brj-24383	142	14	dimensional	dimensional	ADJ
brj-24383	142	15	principal	principal	ADJ
brj-24383	142	16	components	component	NOUN
brj-24383	142	17	of	of	ADP
brj-24383	142	18	spectral	spectral	ADJ
brj-24383	142	19	data	datum	NOUN
brj-24383	142	20	through	through	ADP
brj-24383	142	21	orthogonal	orthogonal	ADJ
brj-24383	142	22	transformation	transformation	NOUN
brj-24383	142	23	,	,	PUNCT
brj-24383	142	24	alleviating	alleviate	VERB
brj-24383	142	25	the	the	DET
brj-24383	142	26	curse	curse	NOUN
brj-24383	142	27	of	of	ADP
brj-24383	142	28	dimensionality	dimensionality	NOUN
brj-24383	142	29	.	.	PUNCT
brj-24383	143	1	the	the	DET
brj-24383	143	2	two	two	NUM
brj-24383	143	3	are	be	AUX
brj-24383	143	4	not	not	PART
brj-24383	143	5	simply	simply	ADV
brj-24383	143	6	upstream	upstream	ADJ
brj-24383	143	7	and	and	CCONJ
brj-24383	143	8	downstream	downstream	ADJ
brj-24383	143	9	relationships	relationship	NOUN
brj-24383	143	10	,	,	PUNCT
brj-24383	143	11	but	but	CCONJ
brj-24383	143	12	independent	independent	ADJ
brj-24383	143	13	optimizations	optimization	NOUN
brj-24383	143	14	for	for	ADP
brj-24383	143	15	data	data	NOUN
brj-24383	143	16	distribution	distribution	NOUN
brj-24383	143	17	(	(	PUNCT
brj-24383	143	18	category	category	NOUN
brj-24383	143	19	balance	balance	NOUN
brj-24383	143	20	)	)	PUNCT
brj-24383	143	21	and	and	CCONJ
brj-24383	143	22	feature	feature	NOUN
brj-24383	143	23	space	space	NOUN
brj-24383	143	24	(	(	PUNCT
brj-24383	143	25	dimensional	dimensional	ADJ
brj-24383	143	26	redundancy	redundancy	NOUN
brj-24383	143	27	)	)	PUNCT
brj-24383	143	28	.	.	PUNCT
brj-24383	144	1	parallel	parallel	ADJ
brj-24383	144	2	processing	processing	NOUN
brj-24383	144	3	can	can	AUX
brj-24383	144	4	avoid	avoid	VERB
brj-24383	144	5	coupling	couple	VERB
brj-24383	144	6	interference	interference	NOUN
brj-24383	144	7	in	in	ADP
brj-24383	144	8	sequential	sequential	ADJ
brj-24383	144	9	operations	operation	NOUN
brj-24383	144	10	.	.	PUNCT
brj-24383	145	1	if	if	SCONJ
brj-24383	145	2	smote	smote	ADJ
brj-24383	145	3	is	be	AUX
brj-24383	145	4	first	first	ADV
brj-24383	145	5	followed	follow	VERB
brj-24383	145	6	by	by	ADP
brj-24383	145	7	pca	pca	PROPN
brj-24383	145	8	,	,	PUNCT
brj-24383	145	9	then	then	ADV
brj-24383	145	10	the	the	DET
brj-24383	145	11	synthesized	synthesize	VERB
brj-24383	145	12	high	high	ADJ
brj-24383	145	13	-	-	PUNCT
brj-24383	145	14	dimensional	dimensional	ADJ
brj-24383	145	15	samples	sample	NOUN
brj-24383	145	16	may	may	AUX
brj-24383	145	17	lose	lose	VERB
brj-24383	145	18	key	key	ADJ
brj-24383	145	19	discriminative	discriminative	NOUN
brj-24383	145	20	features	feature	NOUN
brj-24383	145	21	during	during	ADP
brj-24383	145	22	the	the	DET
brj-24383	145	23	dimensionality	dimensionality	NOUN
brj-24383	145	24	reduction	reduction	NOUN
brj-24383	145	25	process	process	NOUN
brj-24383	145	26	,	,	PUNCT
brj-24383	145	27	weakening	weaken	VERB
brj-24383	145	28	the	the	DET
brj-24383	145	29	data	data	NOUN
brj-24383	145	30	augmentation	augmentation	NOUN
brj-24383	145	31	effect	effect	NOUN
brj-24383	145	32	.	.	PUNCT
brj-24383	146	1	if	if	SCONJ
brj-24383	146	2	pca	pca	PROPN
brj-24383	146	3	is	be	AUX
brj-24383	146	4	first	first	ADV
brj-24383	146	5	followed	follow	VERB
brj-24383	146	6	by	by	ADP
brj-24383	146	7	smote	smote	NOUN
brj-24383	146	8	,	,	PUNCT
brj-24383	146	9	the	the	DET
brj-24383	146	10	low	low	ADJ
brj-24383	146	11	dimensional	dimensional	ADJ
brj-24383	146	12	space	space	NOUN
brj-24383	146	13	after	after	SCONJ
brj-24383	146	14	dimensionality	dimensionality	NOUN
brj-24383	146	15	reduction	reduction	NOUN
brj-24383	146	16	is	be	AUX
brj-24383	146	17	difficult	difficult	ADJ
brj-24383	146	18	to	to	PART
brj-24383	146	19	accurately	accurately	ADV
brj-24383	146	20	depict	depict	VERB
brj-24383	146	21	the	the	DET
brj-24383	146	22	original	original	ADJ
brj-24383	146	23	spectral	spectral	ADJ
brj-24383	146	24	distribution	distribution	NOUN
brj-24383	146	25	,	,	PUNCT
brj-24383	146	26	resulting	result	VERB
brj-24383	146	27	in	in	ADP
brj-24383	146	28	synthesized	synthesize	VERB
brj-24383	146	29	samples	sample	NOUN
brj-24383	146	30	deviating	deviate	VERB
brj-24383	146	31	from	from	ADP
brj-24383	146	32	the	the	DET
brj-24383	146	33	true	true	ADJ
brj-24383	146	34	feature	feature	NOUN
brj-24383	146	35	space	space	NOUN
brj-24383	146	36	.	.	PUNCT
brj-24383	147	1	the	the	DET
brj-24383	147	2	parallel	parallel	ADJ
brj-24383	147	3	branch	branch	NOUN
brj-24383	147	4	design	design	NOUN
brj-24383	147	5	allows	allow	VERB
brj-24383	147	6	the	the	DET
brj-24383	147	7	original	original	ADJ
brj-24383	147	8	standardized	standardized	ADJ
brj-24383	147	9	data	datum	NOUN
brj-24383	147	10	to	to	PART
brj-24383	147	11	enter	enter	VERB
brj-24383	147	12	two	two	NUM
brj-24383	147	13	independent	independent	ADJ
brj-24383	147	14	processing	processing	NOUN
brj-24383	147	15	channels	channel	NOUN
brj-24383	147	16	simultaneously	simultaneously	ADV
brj-24383	147	17	.	.	PUNCT
brj-24383	148	1	the	the	DET
brj-24383	148	2	smote	smote	ADJ
brj-24383	148	3	branch	branch	NOUN
brj-24383	148	4	generates	generate	VERB
brj-24383	148	5	synthetic	synthetic	ADJ
brj-24383	148	6	samples	sample	NOUN
brj-24383	148	7	that	that	PRON
brj-24383	148	8	conform	conform	VERB
brj-24383	148	9	to	to	ADP
brj-24383	148	10	the	the	DET
brj-24383	148	11	spectral	spectral	ADJ
brj-24383	148	12	data	datum	NOUN
brj-24383	148	13	distribution	distribution	NOUN
brj-24383	148	14	in	in	ADP
brj-24383	148	15	the	the	DET
brj-24383	148	16	original	original	ADJ
brj-24383	148	17	high	high	ADJ
brj-24383	148	18	-	-	PUNCT
brj-24383	148	19	dimensional	dimensional	ADJ
brj-24383	148	20	space	space	NOUN
brj-24383	148	21	,	,	PUNCT
brj-24383	148	22	ensuring	ensure	VERB
brj-24383	148	23	class	class	NOUN
brj-24383	148	24	balance	balance	NOUN
brj-24383	148	25	.	.	PUNCT
brj-24383	149	1	pca	pca	NOUN
brj-24383	149	2	branch	branch	NOUN
brj-24383	149	3	extracts	extract	VERB
brj-24383	149	4	low	low	ADJ
brj-24383	149	5	dimensional	dimensional	ADJ
brj-24383	149	6	discriminative	discriminative	NOUN
brj-24383	149	7	principal	principal	ADJ
brj-24383	149	8	components	component	NOUN
brj-24383	149	9	,	,	PUNCT
brj-24383	149	10	eliminates	eliminate	VERB
brj-24383	149	11	noise	noise	NOUN
brj-24383	149	12	and	and	CCONJ
brj-24383	149	13	redundancy	redundancy	NOUN
brj-24383	149	14	,	,	PUNCT
brj-24383	149	15	and	and	CCONJ
brj-24383	149	16	improves	improve	VERB
brj-24383	149	17	computational	computational	ADJ
brj-24383	149	18	efficiency	efficiency	NOUN
brj-24383	149	19	.	.	PUNCT
brj-24383	150	1	in	in	ADP
brj-24383	150	2	this	this	DET
brj-24383	150	3	study	study	NOUN
brj-24383	150	4	,	,	PUNCT
brj-24383	150	5	four	four	NUM
brj-24383	150	6	different	different	ADJ
brj-24383	150	7	models	model	NOUN
brj-24383	150	8	were	be	AUX
brj-24383	150	9	used	use	VERB
brj-24383	150	10	to	to	PART
brj-24383	150	11	classify	classify	VERB
brj-24383	150	12	the	the	DET
brj-24383	150	13	hyperspectral	hyperspectral	ADJ
brj-24383	150	14	images	image	NOUN
brj-24383	150	15	of	of	ADP
brj-24383	150	16	18	18	NUM
brj-24383	150	17	species	specie	NOUN
brj-24383	150	18	of	of	ADP
brj-24383	150	19	leguminous	leguminous	ADJ
brj-24383	150	20	trees	tree	NOUN
brj-24383	150	21	.	.	PUNCT
brj-24383	151	1	to	to	PART
brj-24383	151	2	ensure	ensure	VERB
brj-24383	151	3	the	the	DET
brj-24383	151	4	reliability	reliability	NOUN
brj-24383	151	5	of	of	ADP
brj-24383	151	6	the	the	DET
brj-24383	151	7	results	result	NOUN
brj-24383	151	8	,	,	PUNCT
brj-24383	151	9	we	we	PRON
brj-24383	151	10	first	first	ADV
brj-24383	151	11	performed	perform	VERB
brj-24383	151	12	a	a	DET
brj-24383	151	13	preliminary	preliminary	ADJ
brj-24383	151	14	evaluation	evaluation	NOUN
brj-24383	151	15	of	of	ADP
brj-24383	151	16	the	the	DET
brj-24383	151	17	accuracy	accuracy	NOUN
brj-24383	151	18	for	for	ADP
brj-24383	151	19	each	each	DET
brj-24383	151	20	model	model	NOUN
brj-24383	151	21	.	.	PUNCT
brj-24383	152	1	the	the	DET
brj-24383	152	2	classification	classification	NOUN
brj-24383	152	3	accuracy	accuracy	NOUN
brj-24383	152	4	of	of	ADP
brj-24383	152	5	each	each	DET
brj-24383	152	6	model	model	NOUN
brj-24383	152	7	was	be	AUX
brj-24383	152	8	recorded	record	VERB
brj-24383	152	9	without	without	ADP
brj-24383	152	10	using	use	VERB
brj-24383	152	11	the	the	DET
brj-24383	152	12	smote	smote	ADJ
brj-24383	152	13	method	method	NOUN
brj-24383	152	14	.	.	PUNCT
brj-24383	153	1	random	random	ADJ
brj-24383	153	2	forest	forest	NOUN
brj-24383	153	3	model	model	NOUN
brj-24383	153	4	:	:	PUNCT
brj-24383	153	5	the	the	DET
brj-24383	153	6	accuracy	accuracy	NOUN
brj-24383	153	7	of	of	ADP
brj-24383	153	8	the	the	DET
brj-24383	153	9	random	random	ADJ
brj-24383	153	10	forest	forest	NOUN
brj-24383	153	11	model	model	NOUN
brj-24383	153	12	reached	reach	VERB
brj-24383	153	13	88	88	NUM
brj-24383	153	14	%	%	NOUN
brj-24383	153	15	.	.	PUNCT
brj-24383	154	1	this	this	DET
brj-24383	154	2	model	model	NOUN
brj-24383	154	3	performed	perform	VERB
brj-24383	154	4	well	well	ADV
brj-24383	154	5	in	in	ADP
brj-24383	154	6	classifying	classify	VERB
brj-24383	154	7	most	most	ADJ
brj-24383	154	8	of	of	ADP
brj-24383	154	9	the	the	DET
brj-24383	154	10	tree	tree	NOUN
brj-24383	154	11	species	specie	NOUN
brj-24383	154	12	,	,	PUNCT
brj-24383	154	13	but	but	CCONJ
brj-24383	154	14	there	there	PRON
brj-24383	154	15	were	be	VERB
brj-24383	154	16	still	still	ADV
brj-24383	154	17	some	some	DET
brj-24383	154	18	errors	error	NOUN
brj-24383	154	19	when	when	SCONJ
brj-24383	154	20	handling	handle	VERB
brj-24383	154	21	certain	certain	ADJ
brj-24383	154	22	categories	category	NOUN
brj-24383	154	23	.	.	PUNCT
brj-24383	155	1	support	support	NOUN
brj-24383	155	2	vector	vector	NOUN
brj-24383	155	3	machine	machine	NOUN
brj-24383	155	4	model	model	NOUN
brj-24383	155	5	(	(	PUNCT
brj-24383	155	6	svm	svm	PROPN
brj-24383	155	7	):	):	PUNCT
brj-24383	155	8	the	the	DET
brj-24383	155	9	accuracy	accuracy	NOUN
brj-24383	155	10	of	of	ADP
brj-24383	155	11	the	the	DET
brj-24383	155	12	svm	svm	ADJ
brj-24383	155	13	model	model	NOUN
brj-24383	155	14	was	be	AUX
brj-24383	155	15	92	92	NUM
brj-24383	155	16	%	%	NOUN
brj-24383	155	17	.	.	PUNCT
brj-24383	156	1	the	the	DET
brj-24383	156	2	svm	svm	PROPN
brj-24383	156	3	performed	perform	VERB
brj-24383	156	4	better	well	ADV
brj-24383	156	5	than	than	ADP
brj-24383	156	6	the	the	DET
brj-24383	156	7	random	random	ADJ
brj-24383	156	8	forest	forest	NOUN
brj-24383	156	9	model	model	NOUN
brj-24383	156	10	and	and	CCONJ
brj-24383	156	11	was	be	AUX
brj-24383	156	12	able	able	ADJ
brj-24383	156	13	to	to	PART
brj-24383	156	14	handle	handle	VERB
brj-24383	156	15	the	the	DET
brj-24383	156	16	complex	complex	ADJ
brj-24383	156	17	patterns	pattern	NOUN
brj-24383	156	18	in	in	ADP
brj-24383	156	19	the	the	DET
brj-24383	156	20	hyperspectral	hyperspectral	ADJ
brj-24383	156	21	data	datum	NOUN
brj-24383	156	22	more	more	ADV
brj-24383	156	23	effectively	effectively	ADV
brj-24383	156	24	.	.	PUNCT
brj-24383	157	1	peer	peer	NOUN
brj-24383	157	2	-	-	PUNCT
brj-24383	157	3	reviewed	review	VERB
brj-24383	157	4	article	article	NOUN
brj-24383	157	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	157	6	su	su	PROPN
brj-24383	157	7	et	et	PROPN
brj-24383	157	8	al	al	PROPN
brj-24383	157	9	.	.	PROPN
brj-24383	158	1	(	(	PUNCT
brj-24383	158	2	2025	2025	NUM
brj-24383	158	3	)	)	PUNCT
brj-24383	158	4	.	.	PUNCT
brj-24383	159	1	“	"	PUNCT
brj-24383	159	2	leguminous	leguminous	ADJ
brj-24383	159	3	wood	wood	NOUN
brj-24383	159	4	classification	classification	NOUN
brj-24383	159	5	,	,	PUNCT
brj-24383	159	6	”	"	PUNCT
brj-24383	159	7	bioresources	bioresource	NOUN
brj-24383	159	8	20(3	20(3	NOUN
brj-24383	159	9	)	)	PUNCT
brj-24383	159	10	,	,	PUNCT
brj-24383	159	11	6317	6317	NUM
brj-24383	159	12	-	-	SYM
brj-24383	159	13	6337	6337	NUM
brj-24383	159	14	.	.	PUNCT
brj-24383	160	1	6326	6326	NUM
brj-24383	160	2	logistic	logistic	ADJ
brj-24383	160	3	regression	regression	NOUN
brj-24383	160	4	model	model	NOUN
brj-24383	160	5	:	:	PUNCT
brj-24383	160	6	the	the	DET
brj-24383	160	7	classification	classification	NOUN
brj-24383	160	8	accuracy	accuracy	NOUN
brj-24383	160	9	of	of	ADP
brj-24383	160	10	the	the	DET
brj-24383	160	11	logistic	logistic	ADJ
brj-24383	160	12	regression	regression	NOUN
brj-24383	160	13	model	model	NOUN
brj-24383	160	14	was	be	AUX
brj-24383	160	15	93	93	NUM
brj-24383	160	16	%	%	NOUN
brj-24383	160	17	.	.	PUNCT
brj-24383	161	1	this	this	DET
brj-24383	161	2	model	model	NOUN
brj-24383	161	3	performed	perform	VERB
brj-24383	161	4	excellently	excellently	ADV
brj-24383	161	5	,	,	PUNCT
brj-24383	161	6	accurately	accurately	ADV
brj-24383	161	7	classifying	classify	VERB
brj-24383	161	8	most	most	ADJ
brj-24383	161	9	of	of	ADP
brj-24383	161	10	the	the	DET
brj-24383	161	11	samples	sample	NOUN
brj-24383	161	12	,	,	PUNCT
brj-24383	161	13	but	but	CCONJ
brj-24383	161	14	its	its	PRON
brj-24383	161	15	sensitivity	sensitivity	NOUN
brj-24383	161	16	to	to	ADP
brj-24383	161	17	imbalanced	imbalanced	ADJ
brj-24383	161	18	data	datum	NOUN
brj-24383	161	19	might	might	AUX
brj-24383	161	20	have	have	AUX
brj-24383	161	21	affected	affect	VERB
brj-24383	161	22	the	the	DET
brj-24383	161	23	overall	overall	ADJ
brj-24383	161	24	performance	performance	NOUN
brj-24383	161	25	.	.	PUNCT
brj-24383	162	1	the	the	DET
brj-24383	162	2	class	class	NOUN
brj-24383	162	3	imbalance	imbalance	NOUN
brj-24383	162	4	in	in	ADP
brj-24383	162	5	our	our	PRON
brj-24383	162	6	dataset	dataset	NOUN
brj-24383	162	7	inherently	inherently	ADV
brj-24383	162	8	reflects	reflect	VERB
brj-24383	162	9	the	the	DET
brj-24383	162	10	objective	objective	ADJ
brj-24383	162	11	imprint	imprint	NOUN
brj-24383	162	12	of	of	ADP
brj-24383	162	13	economic	economic	ADJ
brj-24383	162	14	principles	principle	NOUN
brj-24383	162	15	governing	govern	VERB
brj-24383	162	16	the	the	DET
brj-24383	162	17	collectible	collectible	ADJ
brj-24383	162	18	wood	wood	NOUN
brj-24383	162	19	market	market	NOUN
brj-24383	162	20	within	within	ADP
brj-24383	162	21	research	research	NOUN
brj-24383	162	22	samples	sample	NOUN
brj-24383	162	23	.	.	PUNCT
brj-24383	163	1	as	as	ADP
brj-24383	163	2	core	core	ADJ
brj-24383	163	3	commodities	commodity	NOUN
brj-24383	163	4	in	in	ADP
brj-24383	163	5	china	china	PROPN
brj-24383	163	6	’s	’s	PART
brj-24383	163	7	premium	premium	NOUN
brj-24383	163	8	timber	timber	NOUN
brj-24383	163	9	sector	sector	NOUN
brj-24383	163	10	,	,	PUNCT
brj-24383	163	11	leguminous	leguminous	ADJ
brj-24383	163	12	rosewoods	rosewood	NOUN
brj-24383	163	13	(	(	PUNCT
brj-24383	163	14	dalbergia	dalbergia	NOUN
brj-24383	163	15	/	/	SYM
brj-24383	163	16	pterocarpus	pterocarpus	NOUN
brj-24383	163	17	spp	spp	NOUN
brj-24383	163	18	.	.	PUNCT
brj-24383	163	19	)	)	PUNCT
brj-24383	163	20	face	face	VERB
brj-24383	163	21	triple	triple	ADJ
brj-24383	163	22	supply	supply	NOUN
brj-24383	163	23	-	-	PUNCT
brj-24383	163	24	chain	chain	NOUN
brj-24383	163	25	constraints	constraint	NOUN
brj-24383	163	26	:	:	PUNCT
brj-24383	163	27	stringent	stringent	ADJ
brj-24383	163	28	international	international	ADJ
brj-24383	163	29	trade	trade	NOUN
brj-24383	163	30	quotas	quota	NOUN
brj-24383	163	31	under	under	ADP
brj-24383	163	32	cites	cite	NOUN
brj-24383	163	33	for	for	ADP
brj-24383	163	34	endangered	endanger	VERB
brj-24383	163	35	species	specie	NOUN
brj-24383	163	36	such	such	ADJ
brj-24383	163	37	as	as	ADP
brj-24383	163	38	dalbergia	dalbergia	NOUN
brj-24383	163	39	odorifera	odorifera	NOUN
brj-24383	163	40	severely	severely	ADV
brj-24383	163	41	restrict	restrict	VERB
brj-24383	163	42	global	global	ADJ
brj-24383	163	43	circulation	circulation	NOUN
brj-24383	163	44	volumes	volume	NOUN
brj-24383	163	45	.	.	PUNCT
brj-24383	164	1	custom	custom	NOUN
brj-24383	164	2	records	record	NOUN
brj-24383	164	3	indicate	indicate	VERB
brj-24383	164	4	that	that	SCONJ
brj-24383	164	5	annual	annual	ADJ
brj-24383	164	6	imports	import	NOUN
brj-24383	164	7	of	of	ADP
brj-24383	164	8	regulated	regulated	ADJ
brj-24383	164	9	species	specie	NOUN
brj-24383	164	10	account	account	VERB
brj-24383	164	11	for	for	ADP
brj-24383	164	12	less	less	ADJ
brj-24383	164	13	than	than	ADP
brj-24383	164	14	12	12	NUM
brj-24383	164	15	%	%	NOUN
brj-24383	164	16	of	of	ADP
brj-24383	164	17	non	non	ADJ
brj-24383	164	18	-	-	ADJ
brj-24383	164	19	restricted	restricted	ADJ
brj-24383	164	20	varieties	variety	NOUN
brj-24383	164	21	,	,	PUNCT
brj-24383	164	22	legally	legally	ADV
brj-24383	164	23	constraining	constrain	VERB
brj-24383	164	24	the	the	DET
brj-24383	164	25	naturally	naturally	ADV
brj-24383	164	26	diminished	diminish	VERB
brj-24383	164	27	sample	sample	NOUN
brj-24383	164	28	pool	pool	NOUN
brj-24383	164	29	accessible	accessible	ADJ
brj-24383	164	30	to	to	ADP
brj-24383	164	31	researchers	researcher	NOUN
brj-24383	164	32	.	.	PUNCT
brj-24383	165	1	market	market	NOUN
brj-24383	165	2	pricing	pricing	NOUN
brj-24383	165	3	mechanisms	mechanism	NOUN
brj-24383	165	4	exacerbate	exacerbate	VERB
brj-24383	165	5	sample	sample	NOUN
brj-24383	165	6	disparity	disparity	NOUN
brj-24383	165	7	.	.	PUNCT
brj-24383	166	1	mass	mass	ADJ
brj-24383	166	2	-	-	PUNCT
brj-24383	166	3	producible	producible	ADJ
brj-24383	166	4	species	specie	NOUN
brj-24383	166	5	such	such	ADJ
brj-24383	166	6	as	as	ADP
brj-24383	166	7	dalbergia	dalbergia	NOUN
brj-24383	166	8	cochinchinensis	cochinchinensis	NOUN
brj-24383	166	9	(	(	PUNCT
brj-24383	166	10	priced	price	VERB
brj-24383	166	11	below	below	ADP
brj-24383	166	12	600,000	600,000	NUM
brj-24383	166	13	cny	cny	NOUN
brj-24383	166	14	/	/	SYM
brj-24383	166	15	ton	ton	NOUN
brj-24383	166	16	)	)	PUNCT
brj-24383	166	17	dominate	dominate	VERB
brj-24383	166	18	76	76	NUM
brj-24383	166	19	%	%	NOUN
brj-24383	166	20	of	of	ADP
brj-24383	166	21	manufacturers	manufacturer	NOUN
brj-24383	166	22	’	’	PART
brj-24383	166	23	procurement	procurement	NOUN
brj-24383	166	24	,	,	PUNCT
brj-24383	166	25	while	while	SCONJ
brj-24383	166	26	premium	premium	ADJ
brj-24383	166	27	-	-	PUNCT
brj-24383	166	28	grade	grade	NOUN
brj-24383	166	29	d.	d.	NOUN
brj-24383	166	30	odorifera	odorifera	NOUN
brj-24383	166	31	(	(	PUNCT
brj-24383	166	32	exceeding	exceed	VERB
brj-24383	166	33	1.2	1.2	NUM
brj-24383	166	34	million	million	NUM
brj-24383	166	35	cny	cny	PROPN
brj-24383	166	36	/	/	SYM
brj-24383	166	37	ton	ton	NOUN
brj-24383	166	38	)	)	PUNCT
brj-24383	166	39	becomes	becomes	AUX
brj-24383	166	40	hoarded	hoard	VERB
brj-24383	166	41	by	by	ADP
brj-24383	166	42	investment	investment	NOUN
brj-24383	166	43	entities	entity	NOUN
brj-24383	166	44	,	,	PUNCT
brj-24383	166	45	creating	create	VERB
brj-24383	166	46	a	a	DET
brj-24383	166	47	paradoxical	paradoxical	ADJ
brj-24383	166	48	“	"	PUNCT
brj-24383	166	49	circulation	circulation	NOUN
brj-24383	166	50	without	without	ADP
brj-24383	166	51	accessibility	accessibility	NOUN
brj-24383	166	52	”	"	PUNCT
brj-24383	166	53	scenario	scenario	NOUN
brj-24383	166	54	for	for	ADP
brj-24383	166	55	scientific	scientific	ADJ
brj-24383	166	56	inquiry	inquiry	NOUN
brj-24383	166	57	.	.	PUNCT
brj-24383	167	1	this	this	DET
brj-24383	167	2	price	price	NOUN
brj-24383	167	3	hierarchy	hierarchy	NOUN
brj-24383	167	4	functionally	functionally	ADV
brj-24383	167	5	filters	filter	VERB
brj-24383	167	6	samples	sample	NOUN
brj-24383	167	7	,	,	PUNCT
brj-24383	167	8	compelling	compelling	ADJ
brj-24383	167	9	research	research	NOUN
brj-24383	167	10	reliance	reliance	NOUN
brj-24383	167	11	on	on	ADP
brj-24383	167	12	readily	readily	ADV
brj-24383	167	13	available	available	ADJ
brj-24383	167	14	mid	mid	ADJ
brj-24383	167	15	-	-	ADJ
brj-24383	167	16	tier	tier	ADJ
brj-24383	167	17	species	specie	NOUN
brj-24383	167	18	.	.	PUNCT
brj-24383	168	1	high	high	ADJ
brj-24383	168	2	-	-	PUNCT
brj-24383	168	3	value	value	NOUN
brj-24383	168	4	wood	wood	NOUN
brj-24383	168	5	sampling	sampling	NOUN
brj-24383	168	6	incurs	incur	VERB
brj-24383	168	7	disproportionate	disproportionate	ADJ
brj-24383	168	8	expenses	expense	NOUN
brj-24383	168	9	:	:	PUNCT
brj-24383	168	10	destructive	destructive	ADJ
brj-24383	168	11	testing	testing	NOUN
brj-24383	168	12	of	of	ADP
brj-24383	168	13	auction	auction	NOUN
brj-24383	168	14	-	-	PUNCT
brj-24383	168	15	grade	grade	NOUN
brj-24383	168	16	materials	material	NOUN
brj-24383	168	17	requires	require	VERB
brj-24383	168	18	substantial	substantial	ADJ
brj-24383	168	19	security	security	NOUN
brj-24383	168	20	deposits	deposit	NOUN
brj-24383	168	21	,	,	PUNCT
brj-24383	168	22	while	while	SCONJ
brj-24383	168	23	non	non	ADJ
brj-24383	168	24	-	-	ADJ
brj-24383	168	25	destructive	destructive	ADJ
brj-24383	168	26	microsampling	microsample	VERB
brj-24383	168	27	techniques	technique	NOUN
brj-24383	168	28	(	(	PUNCT
brj-24383	168	29	to	to	PART
brj-24383	168	30	preserve	preserve	VERB
brj-24383	168	31	material	material	NOUN
brj-24383	168	32	integrity	integrity	NOUN
brj-24383	168	33	)	)	PUNCT
brj-24383	168	34	prolong	prolong	VERB
brj-24383	168	35	processing	processing	NOUN
brj-24383	168	36	time	time	NOUN
brj-24383	168	37	by	by	ADP
brj-24383	168	38	2.8×	2.8×	NUM
brj-24383	168	39	per	per	ADP
brj-24383	168	40	specimen	speciman	NOUN
brj-24383	168	41	.	.	PUNCT
brj-24383	169	1	these	these	DET
brj-24383	169	2	combined	combine	VERB
brj-24383	169	3	economic	economic	ADJ
brj-24383	169	4	and	and	CCONJ
brj-24383	169	5	technical	technical	ADJ
brj-24383	169	6	barriers	barrier	NOUN
brj-24383	169	7	objectively	objectively	ADV
brj-24383	169	8	compress	compress	VERB
brj-24383	169	9	sampling	sample	VERB
brj-24383	169	10	scales	scale	NOUN
brj-24383	169	11	for	for	ADP
brj-24383	169	12	rare	rare	ADJ
brj-24383	169	13	species	specie	NOUN
brj-24383	169	14	.	.	PUNCT
brj-24383	170	1	as	as	ADP
brj-24383	170	2	per	per	ADP
brj-24383	170	3	2023	2023	NUM
brj-24383	170	4	china	china	PROPN
brj-24383	170	5	collectibles	collectible	NOUN
brj-24383	170	6	market	market	NOUN
brj-24383	170	7	report	report	NOUN
brj-24383	170	8	,	,	PUNCT
brj-24383	170	9	the	the	DET
brj-24383	170	10	circulation	circulation	NOUN
brj-24383	170	11	of	of	ADP
brj-24383	170	12	sandalwood	sandalwood	NOUN
brj-24383	170	13	exhibits	exhibit	VERB
brj-24383	170	14	the	the	DET
brj-24383	170	15	matthew	matthew	PROPN
brj-24383	170	16	effect	effect	NOUN
brj-24383	170	17	:	:	PUNCT
brj-24383	170	18	dalbergia	dalbergia	PROPN
brj-24383	170	19	nigra	nigra	NOUN
brj-24383	170	20	accounts	account	VERB
brj-24383	170	21	for	for	ADP
brj-24383	170	22	38.7	38.7	NUM
brj-24383	170	23	%	%	NOUN
brj-24383	170	24	of	of	ADP
brj-24383	170	25	the	the	DET
brj-24383	170	26	total	total	NOUN
brj-24383	170	27	.	.	PUNCT
brj-24383	171	1	guibourtia	guibourtia	PROPN
brj-24383	171	2	accounts	account	VERB
brj-24383	171	3	for	for	ADP
brj-24383	171	4	29.1	29.1	NUM
brj-24383	171	5	%	%	NOUN
brj-24383	171	6	of	of	ADP
brj-24383	171	7	the	the	DET
brj-24383	171	8	total	total	NOUN
brj-24383	171	9	.	.	PUNCT
brj-24383	172	1	pterocarpus	pterocarpus	PROPN
brj-24383	172	2	soyauxii	soyauxii	NOUN
brj-24383	172	3	accounts	account	VERB
brj-24383	172	4	for	for	ADP
brj-24383	172	5	only	only	ADV
brj-24383	172	6	5.3	5.3	NUM
brj-24383	172	7	%	%	NOUN
brj-24383	172	8	.	.	PUNCT
brj-24383	173	1	1d	1d	NUM
brj-24383	173	2	convolutional	convolutional	ADJ
brj-24383	173	3	neural	neural	ADJ
brj-24383	173	4	network	network	NOUN
brj-24383	173	5	model	model	NOUN
brj-24383	173	6	(	(	PUNCT
brj-24383	173	7	1	1	NUM
brj-24383	173	8	-	-	PUNCT
brj-24383	173	9	cnn	cnn	NOUN
brj-24383	173	10	):	):	PUNCT
brj-24383	173	11	the	the	DET
brj-24383	173	12	initial	initial	ADJ
brj-24383	173	13	classification	classification	NOUN
brj-24383	173	14	accuracy	accuracy	NOUN
brj-24383	173	15	of	of	ADP
brj-24383	173	16	the	the	DET
brj-24383	173	17	1	1	NUM
brj-24383	173	18	-	-	PUNCT
brj-24383	173	19	cnn	cnn	PROPN
brj-24383	173	20	model	model	NOUN
brj-24383	173	21	was	be	AUX
brj-24383	173	22	92.25	92.25	NUM
brj-24383	173	23	%	%	NOUN
brj-24383	173	24	.	.	PUNCT
brj-24383	174	1	in	in	ADP
brj-24383	174	2	hyperspectral	hyperspectral	ADJ
brj-24383	174	3	image	image	NOUN
brj-24383	174	4	classification	classification	NOUN
brj-24383	174	5	,	,	PUNCT
brj-24383	174	6	due	due	ADP
brj-24383	174	7	to	to	ADP
brj-24383	174	8	the	the	DET
brj-24383	174	9	class	class	NOUN
brj-24383	174	10	imbalance	imbalance	NOUN
brj-24383	174	11	in	in	ADP
brj-24383	174	12	the	the	DET
brj-24383	174	13	dataset	dataset	NOUN
brj-24383	174	14	,	,	PUNCT
brj-24383	174	15	minority	minority	NOUN
brj-24383	174	16	class	class	NOUN
brj-24383	174	17	samples	sample	NOUN
brj-24383	174	18	often	often	ADV
brj-24383	174	19	lead	lead	VERB
brj-24383	174	20	to	to	ADP
brj-24383	174	21	the	the	DET
brj-24383	174	22	model	model	NOUN
brj-24383	174	23	being	be	AUX
brj-24383	174	24	biased	bias	VERB
brj-24383	174	25	towards	towards	ADP
brj-24383	174	26	the	the	DET
brj-24383	174	27	majority	majority	NOUN
brj-24383	174	28	class	class	NOUN
brj-24383	174	29	,	,	PUNCT
brj-24383	174	30	thereby	thereby	ADV
brj-24383	174	31	affecting	affect	VERB
brj-24383	174	32	classification	classification	NOUN
brj-24383	174	33	accuracy	accuracy	NOUN
brj-24383	174	34	.	.	PUNCT
brj-24383	175	1	to	to	PART
brj-24383	175	2	address	address	VERB
brj-24383	175	3	this	this	DET
brj-24383	175	4	issue	issue	NOUN
brj-24383	175	5	,	,	PUNCT
brj-24383	175	6	this	this	DET
brj-24383	175	7	study	study	NOUN
brj-24383	175	8	adopted	adopt	VERB
brj-24383	175	9	three	three	NUM
brj-24383	175	10	methods	method	NOUN
brj-24383	175	11	:	:	PUNCT
brj-24383	175	12	first	first	ADJ
brj-24383	175	13	-	-	PUNCT
brj-24383	175	14	order	order	NOUN
brj-24383	175	15	derivative	derivative	ADJ
brj-24383	175	16	transformation	transformation	NOUN
brj-24383	175	17	,	,	PUNCT
brj-24383	175	18	savitzky	savitzky	NOUN
brj-24383	175	19	-	-	PUNCT
brj-24383	175	20	golay	golay	NOUN
brj-24383	175	21	filtering	filtering	NOUN
brj-24383	175	22	,	,	PUNCT
brj-24383	175	23	and	and	CCONJ
brj-24383	175	24	smote	smote	ADJ
brj-24383	175	25	(	(	PUNCT
brj-24383	175	26	synthetic	synthetic	ADJ
brj-24383	175	27	minority	minority	NOUN
brj-24383	175	28	over	over	ADP
brj-24383	175	29	-	-	PUNCT
brj-24383	175	30	sampling	sample	VERB
brj-24383	175	31	technique	technique	NOUN
brj-24383	175	32	)	)	PUNCT
brj-24383	175	33	to	to	PART
brj-24383	175	34	process	process	VERB
brj-24383	175	35	the	the	DET
brj-24383	175	36	data	datum	NOUN
brj-24383	175	37	,	,	PUNCT
brj-24383	175	38	aiming	aim	VERB
brj-24383	175	39	to	to	PART
brj-24383	175	40	improve	improve	VERB
brj-24383	175	41	classification	classification	NOUN
brj-24383	175	42	accuracy	accuracy	NOUN
brj-24383	175	43	.	.	PUNCT
brj-24383	176	1	the	the	DET
brj-24383	176	2	first	first	ADJ
brj-24383	176	3	-	-	PUNCT
brj-24383	176	4	order	order	NOUN
brj-24383	176	5	derivative	derivative	ADJ
brj-24383	176	6	transformation	transformation	NOUN
brj-24383	176	7	is	be	AUX
brj-24383	176	8	a	a	DET
brj-24383	176	9	differential	differential	ADJ
brj-24383	176	10	processing	processing	NOUN
brj-24383	176	11	technique	technique	NOUN
brj-24383	176	12	applied	apply	VERB
brj-24383	176	13	to	to	ADP
brj-24383	176	14	spectral	spectral	ADJ
brj-24383	176	15	data	datum	NOUN
brj-24383	176	16	to	to	PART
brj-24383	176	17	emphasize	emphasize	VERB
brj-24383	176	18	the	the	DET
brj-24383	176	19	trend	trend	NOUN
brj-24383	176	20	of	of	ADP
brj-24383	176	21	changes	change	NOUN
brj-24383	176	22	and	and	CCONJ
brj-24383	176	23	characteristic	characteristic	ADJ
brj-24383	176	24	points	point	NOUN
brj-24383	176	25	on	on	ADP
brj-24383	176	26	the	the	DET
brj-24383	176	27	spectral	spectral	ADJ
brj-24383	176	28	curve	curve	NOUN
brj-24383	176	29	.	.	PUNCT
brj-24383	177	1	in	in	ADP
brj-24383	177	2	hyperspectral	hyperspectral	ADJ
brj-24383	177	3	data	datum	NOUN
brj-24383	177	4	,	,	PUNCT
brj-24383	177	5	many	many	ADJ
brj-24383	177	6	important	important	ADJ
brj-24383	177	7	spectral	spectral	ADJ
brj-24383	177	8	features	feature	NOUN
brj-24383	177	9	(	(	PUNCT
brj-24383	177	10	such	such	ADJ
brj-24383	177	11	as	as	ADP
brj-24383	177	12	absorption	absorption	NOUN
brj-24383	177	13	peaks	peak	NOUN
brj-24383	177	14	and	and	CCONJ
brj-24383	177	15	reflection	reflection	NOUN
brj-24383	177	16	peaks	peak	NOUN
brj-24383	177	17	)	)	PUNCT
brj-24383	177	18	manifest	manifest	VERB
brj-24383	177	19	themselves	themselves	PRON
brj-24383	177	20	as	as	ADP
brj-24383	177	21	changes	change	NOUN
brj-24383	177	22	in	in	ADP
brj-24383	177	23	the	the	DET
brj-24383	177	24	spectral	spectral	ADJ
brj-24383	177	25	curve	curve	NOUN
brj-24383	177	26	.	.	PUNCT
brj-24383	178	1	by	by	ADP
brj-24383	178	2	calculating	calculate	VERB
brj-24383	178	3	the	the	DET
brj-24383	178	4	rate	rate	NOUN
brj-24383	178	5	of	of	ADP
brj-24383	178	6	change	change	NOUN
brj-24383	178	7	for	for	ADP
brj-24383	178	8	each	each	DET
brj-24383	178	9	spectral	spectral	ADJ
brj-24383	178	10	band	band	NOUN
brj-24383	178	11	,	,	PUNCT
brj-24383	178	12	the	the	DET
brj-24383	178	13	response	response	NOUN
brj-24383	178	14	in	in	ADP
brj-24383	178	15	these	these	DET
brj-24383	178	16	significantly	significantly	ADV
brj-24383	178	17	changing	change	VERB
brj-24383	178	18	parts	part	NOUN
brj-24383	178	19	can	can	AUX
brj-24383	178	20	be	be	AUX
brj-24383	178	21	enhanced	enhance	VERB
brj-24383	178	22	,	,	PUNCT
brj-24383	178	23	helping	help	VERB
brj-24383	178	24	the	the	DET
brj-24383	178	25	model	model	NOUN
brj-24383	178	26	better	well	ADV
brj-24383	178	27	identify	identify	VERB
brj-24383	178	28	subtle	subtle	ADJ
brj-24383	178	29	differences	difference	NOUN
brj-24383	178	30	between	between	ADP
brj-24383	178	31	tree	tree	NOUN
brj-24383	178	32	species	specie	NOUN
brj-24383	178	33	.	.	PUNCT
brj-24383	179	1	let	let	VERB
brj-24383	179	2	)	)	PUNCT
brj-24383	179	3	(	(	PUNCT
brj-24383	179	4	s	s	ADV
brj-24383	179	5	represent	represent	VERB
brj-24383	179	6	the	the	DET
brj-24383	179	7	value	value	NOUN
brj-24383	179	8	of	of	ADP
brj-24383	179	9	the	the	DET
brj-24383	179	10	spectral	spectral	ADJ
brj-24383	179	11	curve	curve	NOUN
brj-24383	179	12	at	at	ADP
brj-24383	179	13	wavelength	wavelength	NOUN
brj-24383	179	14			ADJ
brj-24383	179	15	,	,	PUNCT
brj-24383	179	16	then	then	ADV
brj-24383	179	17	the	the	DET
brj-24383	179	18	formula	formula	NOUN
brj-24383	179	19	for	for	ADP
brj-24383	179	20	the	the	DET
brj-24383	179	21	first	first	ADJ
brj-24383	179	22	-	-	PUNCT
brj-24383	179	23	order	order	NOUN
brj-24383	179	24	derivative	derivative	NOUN
brj-24383	179	25	is	be	AUX
brj-24383	179	26	:	:	PUNCT
brj-24383	179	27			ADJ
brj-24383	179	28			PROPN
brj-24383	179	29			ADJ
brj-24383	179	30			ADJ
brj-24383	179	31			DET
brj-24383	179	32	−+	−+	X
brj-24383	179	33	=	=	PUNCT
brj-24383	179	34	)	)	PUNCT
brj-24383	179	35	(	(	PUNCT
brj-24383	179	36	)	)	PUNCT
brj-24383	179	37	(	(	PUNCT
brj-24383	179	38	)	)	PUNCT
brj-24383	179	39	(	(	PUNCT
brj-24383	179	40	ss	ss	NOUN
brj-24383	179	41	d	d	X
brj-24383	179	42	ds	ds	X
brj-24383	179	43	(	(	PUNCT
brj-24383	179	44	7	7	NUM
brj-24383	179	45	)	)	PUNCT
brj-24383	179	46	peer	peer	NOUN
brj-24383	179	47	-	-	PUNCT
brj-24383	179	48	reviewed	review	VERB
brj-24383	179	49	article	article	NOUN
brj-24383	179	50	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	179	51	su	su	PROPN
brj-24383	179	52	et	et	PROPN
brj-24383	179	53	al	al	PROPN
brj-24383	179	54	.	.	PROPN
brj-24383	180	1	(	(	PUNCT
brj-24383	180	2	2025	2025	NUM
brj-24383	180	3	)	)	PUNCT
brj-24383	180	4	.	.	PUNCT
brj-24383	181	1	“	"	PUNCT
brj-24383	181	2	leguminous	leguminous	ADJ
brj-24383	181	3	wood	wood	NOUN
brj-24383	181	4	classification	classification	NOUN
brj-24383	181	5	,	,	PUNCT
brj-24383	181	6	”	"	PUNCT
brj-24383	181	7	bioresources	bioresource	NOUN
brj-24383	181	8	20(3	20(3	NOUN
brj-24383	181	9	)	)	PUNCT
brj-24383	181	10	,	,	PUNCT
brj-24383	181	11	6317	6317	NUM
brj-24383	181	12	-	-	SYM
brj-24383	181	13	6337	6337	NUM
brj-24383	181	14	.	.	PUNCT
brj-24383	182	1	6327	6327	NUM
brj-24383	182	2	where	where	SCONJ
brj-24383	182	3			PROPN
brj-24383	182	4	is	be	AUX
brj-24383	182	5	the	the	DET
brj-24383	182	6	wavelength	wavelength	NOUN
brj-24383	182	7	step	step	NOUN
brj-24383	182	8	size	size	NOUN
brj-24383	182	9	.	.	PUNCT
brj-24383	183	1	this	this	DET
brj-24383	183	2	method	method	NOUN
brj-24383	183	3	highlights	highlight	VERB
brj-24383	183	4	abrupt	abrupt	ADJ
brj-24383	183	5	changes	change	NOUN
brj-24383	183	6	or	or	CCONJ
brj-24383	183	7	variation	variation	NOUN
brj-24383	183	8	regions	region	NOUN
brj-24383	183	9	in	in	ADP
brj-24383	183	10	the	the	DET
brj-24383	183	11	spectral	spectral	ADJ
brj-24383	183	12	data	datum	NOUN
brj-24383	183	13	,	,	PUNCT
brj-24383	183	14	helping	help	VERB
brj-24383	183	15	the	the	DET
brj-24383	183	16	classification	classification	NOUN
brj-24383	183	17	model	model	NOUN
brj-24383	183	18	focus	focus	NOUN
brj-24383	183	19	on	on	ADP
brj-24383	183	20	key	key	ADJ
brj-24383	183	21	features	feature	NOUN
brj-24383	183	22	.	.	PUNCT
brj-24383	184	1	the	the	DET
brj-24383	184	2	savitzky	savitzky	PROPN
brj-24383	184	3	-	-	PUNCT
brj-24383	184	4	golay	golay	NOUN
brj-24383	184	5	filtering	filtering	NOUN
brj-24383	184	6	is	be	AUX
brj-24383	184	7	a	a	DET
brj-24383	184	8	commonly	commonly	ADV
brj-24383	184	9	used	use	VERB
brj-24383	184	10	smoothing	smoothing	NOUN
brj-24383	184	11	technique	technique	NOUN
brj-24383	184	12	,	,	PUNCT
brj-24383	184	13	especially	especially	ADV
brj-24383	184	14	suitable	suitable	ADJ
brj-24383	184	15	for	for	ADP
brj-24383	184	16	noise	noise	NOUN
brj-24383	184	17	removal	removal	NOUN
brj-24383	184	18	while	while	SCONJ
brj-24383	184	19	retaining	retain	VERB
brj-24383	184	20	the	the	DET
brj-24383	184	21	main	main	ADJ
brj-24383	184	22	features	feature	NOUN
brj-24383	184	23	of	of	ADP
brj-24383	184	24	the	the	DET
brj-24383	184	25	signal	signal	NOUN
brj-24383	184	26	.	.	PUNCT
brj-24383	185	1	in	in	ADP
brj-24383	185	2	hyperspectral	hyperspectral	ADJ
brj-24383	185	3	data	datum	NOUN
brj-24383	185	4	,	,	PUNCT
brj-24383	185	5	noise	noise	NOUN
brj-24383	185	6	can	can	AUX
brj-24383	185	7	interfere	interfere	VERB
brj-24383	185	8	with	with	ADP
brj-24383	185	9	the	the	DET
brj-24383	185	10	model	model	NOUN
brj-24383	185	11	training	training	NOUN
brj-24383	185	12	process	process	NOUN
brj-24383	185	13	,	,	PUNCT
brj-24383	185	14	leading	lead	VERB
brj-24383	185	15	to	to	ADP
brj-24383	185	16	incorrect	incorrect	ADJ
brj-24383	185	17	classification	classification	NOUN
brj-24383	185	18	results	result	NOUN
brj-24383	185	19	.	.	PUNCT
brj-24383	186	1	the	the	DET
brj-24383	186	2	savitzky	savitzky	NOUN
brj-24383	186	3	-	-	PUNCT
brj-24383	186	4	golay	golay	NOUN
brj-24383	186	5	filter	filter	NOUN
brj-24383	186	6	smooths	smooth	VERB
brj-24383	186	7	the	the	DET
brj-24383	186	8	data	datum	NOUN
brj-24383	186	9	by	by	ADP
brj-24383	186	10	performing	perform	VERB
brj-24383	186	11	local	local	ADJ
brj-24383	186	12	polynomial	polynomial	NOUN
brj-24383	186	13	fitting	fit	VERB
brj-24383	186	14	on	on	ADP
brj-24383	186	15	the	the	DET
brj-24383	186	16	spectral	spectral	ADJ
brj-24383	186	17	data	datum	NOUN
brj-24383	186	18	,	,	PUNCT
brj-24383	186	19	thus	thus	ADV
brj-24383	186	20	reducing	reduce	VERB
brj-24383	186	21	the	the	DET
brj-24383	186	22	noise	noise	NOUN
brj-24383	186	23	’s	’s	PART
brj-24383	186	24	influence	influence	NOUN
brj-24383	186	25	.	.	PUNCT
brj-24383	187	1	this	this	DET
brj-24383	187	2	filtering	filtering	NOUN
brj-24383	187	3	method	method	NOUN
brj-24383	187	4	is	be	AUX
brj-24383	187	5	a	a	DET
brj-24383	187	6	filtering	filter	VERB
brj-24383	187	7	method	method	NOUN
brj-24383	187	8	based	base	VERB
brj-24383	187	9	on	on	ADP
brj-24383	187	10	local	local	ADJ
brj-24383	187	11	polynomial	polynomial	ADJ
brj-24383	187	12	least	least	ADJ
brj-24383	187	13	squares	square	NOUN
brj-24383	187	14	fitting	fit	VERB
brj-24383	187	15	in	in	ADP
brj-24383	187	16	the	the	DET
brj-24383	187	17	time	time	NOUN
brj-24383	187	18	domain	domain	NOUN
brj-24383	187	19	.	.	PUNCT
brj-24383	188	1	its	its	PRON
brj-24383	188	2	biggest	big	ADJ
brj-24383	188	3	feature	feature	NOUN
brj-24383	188	4	is	be	AUX
brj-24383	188	5	that	that	SCONJ
brj-24383	188	6	it	it	PRON
brj-24383	188	7	can	can	AUX
brj-24383	188	8	keep	keep	VERB
brj-24383	188	9	the	the	DET
brj-24383	188	10	shape	shape	NOUN
brj-24383	188	11	and	and	CCONJ
brj-24383	188	12	width	width	NOUN
brj-24383	188	13	of	of	ADP
brj-24383	188	14	the	the	DET
brj-24383	188	15	signal	signal	NOUN
brj-24383	188	16	unchanged	unchanged	ADJ
brj-24383	188	17	while	while	SCONJ
brj-24383	188	18	filtering	filter	VERB
brj-24383	188	19	out	out	ADP
brj-24383	188	20	noise	noise	NOUN
brj-24383	188	21	.	.	PUNCT
brj-24383	189	1	the	the	DET
brj-24383	189	2	basic	basic	ADJ
brj-24383	189	3	principle	principle	NOUN
brj-24383	189	4	is	be	AUX
brj-24383	189	5	to	to	PART
brj-24383	189	6	fit	fit	VERB
brj-24383	189	7	the	the	DET
brj-24383	189	8	data	datum	NOUN
brj-24383	189	9	with	with	ADP
brj-24383	189	10	a	a	DET
brj-24383	189	11	sliding	slide	VERB
brj-24383	189	12	window	window	NOUN
brj-24383	189	13	,	,	PUNCT
brj-24383	189	14	using	use	VERB
brj-24383	189	15	local	local	ADJ
brj-24383	189	16	polynomial	polynomial	ADJ
brj-24383	189	17	approximations	approximation	NOUN
brj-24383	189	18	to	to	PART
brj-24383	189	19	smooth	smooth	VERB
brj-24383	189	20	the	the	DET
brj-24383	189	21	data	datum	NOUN
brj-24383	189	22	.	.	PUNCT
brj-24383	190	1	the	the	DET
brj-24383	190	2	algorithm	algorithm	NOUN
brj-24383	190	3	idea	idea	NOUN
brj-24383	190	4	is	be	AUX
brj-24383	190	5	to	to	PART
brj-24383	190	6	suppress	suppress	VERB
brj-24383	190	7	noise	noise	NOUN
brj-24383	190	8	through	through	ADP
brj-24383	190	9	motion	motion	NOUN
brj-24383	190	10	smoothing	smooth	VERB
brj-24383	190	11	.	.	PUNCT
brj-24383	191	1	figure	figure	NOUN
brj-24383	191	2	5	5	NUM
brj-24383	191	3	explains	explain	VERB
brj-24383	191	4	the	the	DET
brj-24383	191	5	principle	principle	NOUN
brj-24383	191	6	of	of	ADP
brj-24383	191	7	how	how	SCONJ
brj-24383	191	8	the	the	DET
brj-24383	191	9	sliding	slide	VERB
brj-24383	191	10	window	window	NOUN
brj-24383	191	11	smoothes	smooth	VERB
brj-24383	191	12	the	the	DET
brj-24383	191	13	function	function	NOUN
brj-24383	191	14	graph	graph	NOUN
brj-24383	192	1	.	.	PUNCT
brj-24383	192	2	fig	fig	NOUN
brj-24383	192	3	.	.	PUNCT
brj-24383	193	1	5	5	NUM
brj-24383	193	2	.	.	X
brj-24383	193	3	schematic	schematic	ADJ
brj-24383	193	4	diagram	diagram	NOUN
brj-24383	193	5	of	of	ADP
brj-24383	193	6	sliding	slide	VERB
brj-24383	193	7	window	window	NOUN
brj-24383	193	8	noise	noise	NOUN
brj-24383	193	9	suppression	suppression	NOUN
brj-24383	193	10	the	the	DET
brj-24383	193	11	key	key	ADJ
brj-24383	193	12	parameters	parameter	NOUN
brj-24383	193	13	of	of	ADP
brj-24383	193	14	sg	sg	PROPN
brj-24383	193	15	filtering	filtering	NOUN
brj-24383	193	16	are	be	AUX
brj-24383	193	17	the	the	DET
brj-24383	193	18	number	number	NOUN
brj-24383	193	19	of	of	ADP
brj-24383	193	20	window	window	NOUN
brj-24383	193	21	points	point	NOUN
brj-24383	193	22	(	(	PUNCT
brj-24383	193	23	non	non	X
brj-24383	193	24	physical	physical	ADJ
brj-24383	193	25	width	width	NOUN
brj-24383	193	26	)	)	PUNCT
brj-24383	193	27	,	,	PUNCT
brj-24383	193	28	and	and	CCONJ
brj-24383	193	29	the	the	DET
brj-24383	193	30	window	window	NOUN
brj-24383	193	31	slides	slide	VERB
brj-24383	193	32	along	along	ADP
brj-24383	193	33	the	the	DET
brj-24383	193	34	standardized	standardized	ADJ
brj-24383	193	35	data	data	NOUN
brj-24383	193	36	point	point	NOUN
brj-24383	193	37	number	number	NOUN
brj-24383	193	38	(	(	PUNCT
brj-24383	193	39	step	step	NOUN
brj-24383	193	40	size=1	size=1	PROPN
brj-24383	193	41	point	point	NOUN
brj-24383	193	42	)	)	PUNCT
brj-24383	193	43	.	.	PUNCT
brj-24383	194	1	during	during	ADP
brj-24383	194	2	processing	processing	NOUN
brj-24383	194	3	,	,	PUNCT
brj-24383	194	4	it	it	PRON
brj-24383	194	5	does	do	AUX
brj-24383	194	6	not	not	PART
brj-24383	194	7	rely	rely	VERB
brj-24383	194	8	on	on	ADP
brj-24383	194	9	wavelength	wavelength	NOUN
brj-24383	194	10	calibration	calibration	NOUN
brj-24383	194	11	but	but	CCONJ
brj-24383	194	12	only	only	ADV
brj-24383	194	13	focuses	focus	VERB
brj-24383	194	14	on	on	ADP
brj-24383	194	15	the	the	DET
brj-24383	194	16	numerical	numerical	ADJ
brj-24383	194	17	relationship	relationship	NOUN
brj-24383	194	18	between	between	ADP
brj-24383	194	19	adjacent	adjacent	ADJ
brj-24383	194	20	data	datum	NOUN
brj-24383	194	21	points	point	NOUN
brj-24383	194	22	.	.	PUNCT
brj-24383	195	1	the	the	DET
brj-24383	195	2	5	5	NUM
brj-24383	195	3	-	-	PUNCT
brj-24383	195	4	point	point	NOUN
brj-24383	195	5	window	window	NOUN
brj-24383	195	6	(	(	PUNCT
brj-24383	195	7	the	the	DET
brj-24383	195	8	optimal	optimal	ADJ
brj-24383	195	9	value	value	NOUN
brj-24383	195	10	in	in	ADP
brj-24383	195	11	this	this	DET
brj-24383	195	12	experiment	experiment	NOUN
brj-24383	195	13	)	)	PUNCT
brj-24383	195	14	balances	balance	VERB
brj-24383	195	15	the	the	DET
brj-24383	195	16	noise	noise	NOUN
brj-24383	195	17	suppression	suppression	NOUN
brj-24383	195	18	requirements	requirement	NOUN
brj-24383	195	19	with	with	ADP
brj-24383	195	20	the	the	DET
brj-24383	195	21	ability	ability	NOUN
brj-24383	195	22	to	to	PART
brj-24383	195	23	preserve	preserve	VERB
brj-24383	195	24	spectral	spectral	ADJ
brj-24383	195	25	peak	peak	PROPN
brj-24383	195	26	valley	valley	NOUN
brj-24383	195	27	features	feature	NOUN
brj-24383	195	28	.	.	PUNCT
brj-24383	196	1	the	the	DET
brj-24383	196	2	formula	formula	NOUN
brj-24383	196	3	is	be	AUX
brj-24383	196	4	as	as	SCONJ
brj-24383	196	5	follows	follow	VERB
brj-24383	196	6	:	:	PUNCT
brj-24383	196	7			X
brj-24383	196	8	−=	−=	VERB
brj-24383	196	9	+	+	SYM
brj-24383	196	10	=	=	NOUN
brj-24383	196	11	m	m	VERB
brj-24383	196	12	mk	mk	NOUN
brj-24383	196	13	k	k	PROPN
brj-24383	196	14	ktxbty	ktxbty	PROPN
brj-24383	196	15	)	)	PUNCT
brj-24383	196	16	(	(	PUNCT
brj-24383	196	17	)	)	PUNCT
brj-24383	196	18	(	(	PUNCT
brj-24383	196	19	(	(	PUNCT
brj-24383	196	20	8)	8)	NUM
brj-24383	196	21	where	where	SCONJ
brj-24383	196	22	y(t	y(t	NOUN
brj-24383	196	23	)	)	PUNCT
brj-24383	196	24	is	be	AUX
brj-24383	196	25	the	the	DET
brj-24383	196	26	smoothed	smooth	VERB
brj-24383	196	27	data	datum	NOUN
brj-24383	196	28	,	,	PUNCT
brj-24383	196	29	x(t+k	x(t+k	NUM
brj-24383	196	30	)	)	PUNCT
brj-24383	196	31	is	be	AUX
brj-24383	196	32	the	the	DET
brj-24383	196	33	original	original	ADJ
brj-24383	196	34	data	datum	NOUN
brj-24383	196	35	point	point	NOUN
brj-24383	196	36	,	,	PUNCT
brj-24383	196	37	kb	kb	PROPN
brj-24383	196	38	is	be	AUX
brj-24383	196	39	the	the	DET
brj-24383	196	40	filter	filter	NOUN
brj-24383	196	41	coefficient	coefficient	NOUN
brj-24383	196	42	,	,	PUNCT
brj-24383	196	43	and	and	CCONJ
brj-24383	196	44	m	m	PROPN
brj-24383	196	45	is	be	AUX
brj-24383	196	46	the	the	DET
brj-24383	196	47	window	window	NOUN
brj-24383	196	48	size	size	NOUN
brj-24383	196	49	.	.	PUNCT
brj-24383	197	1	this	this	DET
brj-24383	197	2	method	method	NOUN
brj-24383	197	3	allows	allow	VERB
brj-24383	197	4	the	the	DET
brj-24383	197	5	savitzky	savitzky	NOUN
brj-24383	197	6	-	-	PUNCT
brj-24383	197	7	golay	golay	NOUN
brj-24383	197	8	filter	filter	NOUN
brj-24383	197	9	to	to	PART
brj-24383	197	10	smooth	smooth	VERB
brj-24383	197	11	spectral	spectral	ADJ
brj-24383	197	12	data	datum	NOUN
brj-24383	197	13	while	while	SCONJ
brj-24383	197	14	effectively	effectively	ADV
brj-24383	197	15	preserving	preserve	VERB
brj-24383	197	16	spectral	spectral	ADJ
brj-24383	197	17	feature	feature	NOUN
brj-24383	197	18	information	information	NOUN
brj-24383	197	19	,	,	PUNCT
brj-24383	197	20	improving	improve	VERB
brj-24383	197	21	the	the	DET
brj-24383	197	22	quality	quality	NOUN
brj-24383	197	23	of	of	ADP
brj-24383	197	24	the	the	DET
brj-24383	197	25	data	datum	NOUN
brj-24383	197	26	.	.	PUNCT
brj-24383	198	1	figure	figure	NOUN
brj-24383	198	2	6	6	NUM
brj-24383	198	3	shows	show	VERB
brj-24383	198	4	the	the	DET
brj-24383	198	5	process	process	NOUN
brj-24383	198	6	of	of	ADP
brj-24383	198	7	window	window	NOUN
brj-24383	198	8	sliding	slide	VERB
brj-24383	198	9	and	and	CCONJ
brj-24383	198	10	local	local	ADJ
brj-24383	198	11	fitting	fit	VERB
brj-24383	198	12	during	during	ADP
brj-24383	198	13	the	the	DET
brj-24383	198	14	filtering	filtering	NOUN
brj-24383	198	15	process	process	NOUN
brj-24383	198	16	.	.	PUNCT
brj-24383	199	1	figure	figure	VERB
brj-24383	199	2	7	7	NUM
brj-24383	199	3	presents	present	VERB
brj-24383	199	4	a	a	DET
brj-24383	199	5	comparative	comparative	ADJ
brj-24383	199	6	analysis	analysis	NOUN
brj-24383	199	7	of	of	ADP
brj-24383	199	8	the	the	DET
brj-24383	199	9	spectral	spectral	ADJ
brj-24383	199	10	reflectance	reflectance	NOUN
brj-24383	199	11	curves	curve	NOUN
brj-24383	199	12	for	for	ADP
brj-24383	199	13	selected	select	VERB
brj-24383	199	14	wood	wood	NOUN
brj-24383	199	15	species	species	NOUN
brj-24383	199	16	samples	sample	NOUN
brj-24383	199	17	before	before	ADP
brj-24383	199	18	and	and	CCONJ
brj-24383	199	19	after	after	ADP
brj-24383	199	20	the	the	DET
brj-24383	199	21	application	application	NOUN
brj-24383	199	22	of	of	ADP
brj-24383	199	23	the	the	DET
brj-24383	199	24	savitzky	savitzky	NOUN
brj-24383	199	25	-	-	PUNCT
brj-24383	199	26	golay	golay	PROPN
brj-24383	199	27	(	(	PUNCT
brj-24383	199	28	sg	sg	NOUN
brj-24383	199	29	)	)	PUNCT
brj-24383	199	30	filtering	filter	VERB
brj-24383	199	31	technique	technique	NOUN
brj-24383	199	32	.	.	PUNCT
brj-24383	200	1	the	the	DET
brj-24383	200	2	left	left	ADJ
brj-24383	200	3	subplot	subplot	NOUN
brj-24383	200	4	displays	display	VERB
brj-24383	200	5	the	the	DET
brj-24383	200	6	original	original	ADJ
brj-24383	200	7	spectral	spectral	ADJ
brj-24383	200	8	curves	curve	NOUN
brj-24383	200	9	,	,	PUNCT
brj-24383	200	10	illustrating	illustrate	VERB
brj-24383	200	11	the	the	DET
brj-24383	200	12	inherent	inherent	ADJ
brj-24383	200	13	variability	variability	NOUN
brj-24383	200	14	and	and	CCONJ
brj-24383	200	15	noise	noise	NOUN
brj-24383	200	16	present	present	ADJ
brj-24383	200	17	in	in	ADP
brj-24383	200	18	the	the	DET
brj-24383	200	19	raw	raw	ADJ
brj-24383	200	20	hyperspectral	hyperspectral	ADJ
brj-24383	200	21	data	datum	NOUN
brj-24383	200	22	.	.	PUNCT
brj-24383	201	1	each	each	DET
brj-24383	201	2	blue	blue	ADJ
brj-24383	201	3	line	line	NOUN
brj-24383	201	4	represents	represent	VERB
brj-24383	201	5	the	the	DET
brj-24383	201	6	reflectance	reflectance	NOUN
brj-24383	201	7	spectrum	spectrum	NOUN
brj-24383	201	8	of	of	ADP
brj-24383	201	9	an	an	DET
brj-24383	201	10	individual	individual	ADJ
brj-24383	201	11	sample	sample	NOUN
brj-24383	201	12	,	,	PUNCT
brj-24383	201	13	capturing	capture	VERB
brj-24383	201	14	the	the	DET
brj-24383	201	15	detailed	detailed	ADJ
brj-24383	201	16	fluctuations	fluctuation	NOUN
brj-24383	201	17	across	across	ADP
brj-24383	201	18	the	the	DET
brj-24383	201	19	wavelength	wavelength	NOUN
brj-24383	201	20	range	range	NOUN
brj-24383	201	21	from	from	ADP
brj-24383	201	22	350	350	NUM
brj-24383	201	23	to	to	PART
brj-24383	201	24	1050	1050	NUM
brj-24383	201	25	nm	nm	NOUN
brj-24383	201	26	.	.	PUNCT
brj-24383	202	1	in	in	ADP
brj-24383	202	2	contrast	contrast	NOUN
brj-24383	202	3	,	,	PUNCT
brj-24383	202	4	the	the	DET
brj-24383	202	5	right	right	ADJ
brj-24383	202	6	subplot	subplot	NOUN
brj-24383	202	7	showcases	showcase	VERB
brj-24383	202	8	the	the	DET
brj-24383	202	9	filtered	filter	VERB
brj-24383	202	10	spectral	spectral	ADJ
brj-24383	202	11	curves	curve	NOUN
brj-24383	202	12	,	,	PUNCT
brj-24383	202	13	depicted	depict	VERB
brj-24383	202	14	as	as	ADP
brj-24383	202	15	red	red	ADJ
brj-24383	202	16	dashed	dash	VERB
brj-24383	202	17	lines	line	NOUN
brj-24383	202	18	.	.	PUNCT
brj-24383	203	1	peer	peer	NOUN
brj-24383	203	2	-	-	PUNCT
brj-24383	203	3	reviewed	review	VERB
brj-24383	203	4	article	article	NOUN
brj-24383	203	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	203	6	su	su	PROPN
brj-24383	203	7	et	et	PROPN
brj-24383	203	8	al	al	PROPN
brj-24383	203	9	.	.	PROPN
brj-24383	204	1	(	(	PUNCT
brj-24383	204	2	2025	2025	NUM
brj-24383	204	3	)	)	PUNCT
brj-24383	204	4	.	.	PUNCT
brj-24383	205	1	“	"	PUNCT
brj-24383	205	2	leguminous	leguminous	ADJ
brj-24383	205	3	wood	wood	NOUN
brj-24383	205	4	classification	classification	NOUN
brj-24383	205	5	,	,	PUNCT
brj-24383	205	6	”	"	PUNCT
brj-24383	205	7	bioresources	bioresource	NOUN
brj-24383	205	8	20(3	20(3	NOUN
brj-24383	205	9	)	)	PUNCT
brj-24383	205	10	,	,	PUNCT
brj-24383	205	11	6317	6317	NUM
brj-24383	205	12	-	-	SYM
brj-24383	205	13	6337	6337	NUM
brj-24383	205	14	.	.	PUNCT
brj-24383	206	1	6328	6328	NUM
brj-24383	206	2	fig	fig	NOUN
brj-24383	206	3	.	.	PUNCT
brj-24383	207	1	6	6	NUM
brj-24383	207	2	.	.	NOUN
brj-24383	207	3	process	process	NOUN
brj-24383	207	4	diagram	diagram	NOUN
brj-24383	207	5	of	of	ADP
brj-24383	207	6	window	window	NOUN
brj-24383	207	7	sliding	slide	VERB
brj-24383	207	8	and	and	CCONJ
brj-24383	207	9	local	local	ADJ
brj-24383	207	10	fitting	fitting	ADJ
brj-24383	207	11	(	(	PUNCT
brj-24383	207	12	the	the	DET
brj-24383	207	13	horizontal	horizontal	ADJ
brj-24383	207	14	axis	axis	NOUN
brj-24383	207	15	represents	represent	VERB
brj-24383	207	16	the	the	DET
brj-24383	207	17	standardized	standardized	ADJ
brj-24383	207	18	time	time	NOUN
brj-24383	207	19	point	point	NOUN
brj-24383	207	20	)	)	PUNCT
brj-24383	207	21	fig	fig	NOUN
brj-24383	207	22	.	.	PUNCT
brj-24383	208	1	7	7	X
brj-24383	208	2	.	.	X
brj-24383	208	3	spectral	spectral	ADJ
brj-24383	208	4	curves	curve	NOUN
brj-24383	208	5	comparison	comparison	NOUN
brj-24383	208	6	before	before	ADV
brj-24383	208	7	and	and	CCONJ
brj-24383	208	8	after	after	ADP
brj-24383	208	9	savitzky	savitzky	NOUN
brj-24383	208	10	-	-	PUNCT
brj-24383	208	11	golay	golay	NOUN
brj-24383	208	12	filtering	filter	VERB
brj-24383	208	13	peer	peer	NOUN
brj-24383	208	14	-	-	PUNCT
brj-24383	208	15	reviewed	review	VERB
brj-24383	208	16	article	article	NOUN
brj-24383	208	17	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	208	18	su	su	PROPN
brj-24383	208	19	et	et	PROPN
brj-24383	208	20	al	al	PROPN
brj-24383	208	21	.	.	PROPN
brj-24383	209	1	(	(	PUNCT
brj-24383	209	2	2025	2025	NUM
brj-24383	209	3	)	)	PUNCT
brj-24383	209	4	.	.	PUNCT
brj-24383	210	1	“	"	PUNCT
brj-24383	210	2	leguminous	leguminous	ADJ
brj-24383	210	3	wood	wood	NOUN
brj-24383	210	4	classification	classification	NOUN
brj-24383	210	5	,	,	PUNCT
brj-24383	210	6	”	"	PUNCT
brj-24383	210	7	bioresources	bioresource	NOUN
brj-24383	210	8	20(3	20(3	NOUN
brj-24383	210	9	)	)	PUNCT
brj-24383	210	10	,	,	PUNCT
brj-24383	210	11	6317	6317	NUM
brj-24383	210	12	-	-	SYM
brj-24383	210	13	6337	6337	NUM
brj-24383	210	14	.	.	PUNCT
brj-24383	211	1	6329	6329	NUM
brj-24383	211	2	the	the	DET
brj-24383	211	3	sg	sg	PROPN
brj-24383	211	4	filter	filter	NOUN
brj-24383	211	5	effectively	effectively	ADV
brj-24383	211	6	smooths	smooth	VERB
brj-24383	211	7	out	out	ADP
brj-24383	211	8	high	high	ADJ
brj-24383	211	9	-	-	PUNCT
brj-24383	211	10	frequency	frequency	NOUN
brj-24383	211	11	noise	noise	NOUN
brj-24383	211	12	while	while	SCONJ
brj-24383	211	13	preserving	preserve	VERB
brj-24383	211	14	essential	essential	ADJ
brj-24383	211	15	spectral	spectral	ADJ
brj-24383	211	16	features	feature	NOUN
brj-24383	211	17	,	,	PUNCT
brj-24383	211	18	resulting	result	VERB
brj-24383	211	19	in	in	ADP
brj-24383	211	20	more	more	ADV
brj-24383	211	21	continuous	continuous	ADJ
brj-24383	211	22	and	and	CCONJ
brj-24383	211	23	less	less	ADV
brj-24383	211	24	erratic	erratic	ADJ
brj-24383	211	25	reflectance	reflectance	NOUN
brj-24383	211	26	profiles	profile	NOUN
brj-24383	211	27	.	.	PUNCT
brj-24383	212	1	this	this	DET
brj-24383	212	2	enhancement	enhancement	NOUN
brj-24383	212	3	facilitates	facilitate	VERB
brj-24383	212	4	better	well	ADJ
brj-24383	212	5	visualization	visualization	NOUN
brj-24383	212	6	and	and	CCONJ
brj-24383	212	7	subsequent	subsequent	ADJ
brj-24383	212	8	analysis	analysis	NOUN
brj-24383	212	9	by	by	ADP
brj-24383	212	10	mitigating	mitigate	VERB
brj-24383	212	11	the	the	DET
brj-24383	212	12	impact	impact	NOUN
brj-24383	212	13	of	of	ADP
brj-24383	212	14	random	random	ADJ
brj-24383	212	15	noise	noise	NOUN
brj-24383	212	16	without	without	ADP
brj-24383	212	17	distorting	distort	VERB
brj-24383	212	18	significant	significant	ADJ
brj-24383	212	19	spectral	spectral	ADJ
brj-24383	212	20	characteristics	characteristic	NOUN
brj-24383	212	21	.	.	PUNCT
brj-24383	213	1	the	the	DET
brj-24383	213	2	side	side	NOUN
brj-24383	213	3	-	-	PUNCT
brj-24383	213	4	byside	byside	ADJ
brj-24383	213	5	comparison	comparison	NOUN
brj-24383	213	6	underscores	underscore	VERB
brj-24383	213	7	the	the	DET
brj-24383	213	8	efficacy	efficacy	NOUN
brj-24383	213	9	of	of	ADP
brj-24383	213	10	the	the	DET
brj-24383	213	11	savitzky	savitzky	NOUN
brj-24383	213	12	-	-	PUNCT
brj-24383	213	13	golay	golay	NOUN
brj-24383	213	14	filter	filter	NOUN
brj-24383	213	15	in	in	ADP
brj-24383	213	16	improving	improve	VERB
brj-24383	213	17	data	datum	NOUN
brj-24383	213	18	quality	quality	NOUN
brj-24383	213	19	,	,	PUNCT
brj-24383	213	20	thereby	thereby	ADV
brj-24383	213	21	enabling	enable	VERB
brj-24383	213	22	more	more	ADV
brj-24383	213	23	accurate	accurate	ADJ
brj-24383	213	24	and	and	CCONJ
brj-24383	213	25	reliable	reliable	ADJ
brj-24383	213	26	interpretations	interpretation	NOUN
brj-24383	213	27	of	of	ADP
brj-24383	213	28	the	the	DET
brj-24383	213	29	hyperspectral	hyperspectral	ADJ
brj-24383	213	30	information	information	NOUN
brj-24383	213	31	.	.	PUNCT
brj-24383	214	1	figure	figure	NOUN
brj-24383	214	2	8	8	NUM
brj-24383	214	3	illustrates	illustrate	VERB
brj-24383	214	4	the	the	DET
brj-24383	214	5	quantitative	quantitative	ADJ
brj-24383	214	6	impact	impact	NOUN
brj-24383	214	7	of	of	ADP
brj-24383	214	8	savitzky	savitzky	NOUN
brj-24383	214	9	-	-	PUNCT
brj-24383	214	10	golay	golay	NOUN
brj-24383	214	11	filtering	filtering	NOUN
brj-24383	214	12	on	on	ADP
brj-24383	214	13	the	the	DET
brj-24383	214	14	spectral	spectral	ADJ
brj-24383	214	15	reflectance	reflectance	NOUN
brj-24383	214	16	data	datum	NOUN
brj-24383	214	17	by	by	ADP
brj-24383	214	18	plotting	plot	VERB
brj-24383	214	19	the	the	DET
brj-24383	214	20	difference	difference	NOUN
brj-24383	214	21	between	between	ADP
brj-24383	214	22	the	the	DET
brj-24383	214	23	filtered	filter	VERB
brj-24383	214	24	and	and	CCONJ
brj-24383	214	25	original	original	ADJ
brj-24383	214	26	reflectance	reflectance	NOUN
brj-24383	214	27	values	value	NOUN
brj-24383	214	28	for	for	ADP
brj-24383	214	29	each	each	DET
brj-24383	214	30	selected	select	VERB
brj-24383	214	31	sample	sample	NOUN
brj-24383	214	32	.	.	PUNCT
brj-24383	215	1	each	each	DET
brj-24383	215	2	plot	plot	NOUN
brj-24383	215	3	represents	represent	VERB
brj-24383	215	4	the	the	DET
brj-24383	215	5	reflectance	reflectance	NOUN
brj-24383	215	6	difference	difference	NOUN
brj-24383	215	7	(	(	PUNCT
brj-24383	215	8	filtered	filter	VERB
brj-24383	215	9	original	original	ADJ
brj-24383	215	10	)	)	PUNCT
brj-24383	215	11	across	across	ADP
brj-24383	215	12	the	the	DET
brj-24383	215	13	wavelength	wavelength	NOUN
brj-24383	215	14	range	range	NOUN
brj-24383	215	15	of	of	ADP
brj-24383	215	16	350	350	NUM
brj-24383	215	17	to	to	PART
brj-24383	215	18	1050	1050	NUM
brj-24383	215	19	nm	nm	NOUN
brj-24383	215	20	for	for	ADP
brj-24383	215	21	an	an	DET
brj-24383	215	22	individual	individual	ADJ
brj-24383	215	23	wood	wood	NOUN
brj-24383	215	24	species	specie	NOUN
brj-24383	215	25	sample	sample	NOUN
brj-24383	215	26	.	.	PUNCT
brj-24383	216	1	the	the	DET
brj-24383	216	2	purple	purple	ADJ
brj-24383	216	3	line	line	NOUN
brj-24383	216	4	indicates	indicate	VERB
brj-24383	216	5	the	the	DET
brj-24383	216	6	magnitude	magnitude	NOUN
brj-24383	216	7	and	and	CCONJ
brj-24383	216	8	direction	direction	NOUN
brj-24383	216	9	of	of	ADP
brj-24383	216	10	changes	change	NOUN
brj-24383	216	11	introduced	introduce	VERB
brj-24383	216	12	by	by	ADP
brj-24383	216	13	the	the	DET
brj-24383	216	14	filtering	filtering	NOUN
brj-24383	216	15	process	process	NOUN
brj-24383	216	16	.	.	PUNCT
brj-24383	217	1	positive	positive	ADJ
brj-24383	217	2	values	value	NOUN
brj-24383	217	3	signify	signify	VERB
brj-24383	217	4	an	an	DET
brj-24383	217	5	increase	increase	NOUN
brj-24383	217	6	in	in	ADP
brj-24383	217	7	reflectance	reflectance	NOUN
brj-24383	217	8	postfiltering	postfiltere	VERB
brj-24383	217	9	,	,	PUNCT
brj-24383	217	10	while	while	SCONJ
brj-24383	217	11	negative	negative	ADJ
brj-24383	217	12	values	value	NOUN
brj-24383	217	13	denote	denote	VERB
brj-24383	217	14	a	a	DET
brj-24383	217	15	decrease	decrease	NOUN
brj-24383	217	16	.	.	PUNCT
brj-24383	218	1	this	this	DET
brj-24383	218	2	visualization	visualization	NOUN
brj-24383	218	3	highlights	highlight	VERB
brj-24383	218	4	the	the	DET
brj-24383	218	5	areas	area	NOUN
brj-24383	218	6	where	where	SCONJ
brj-24383	218	7	the	the	DET
brj-24383	218	8	sg	sg	PROPN
brj-24383	218	9	filter	filter	NOUN
brj-24383	218	10	has	have	AUX
brj-24383	218	11	smoothed	smooth	VERB
brj-24383	218	12	out	out	ADP
brj-24383	218	13	noise	noise	NOUN
brj-24383	218	14	and	and	CCONJ
brj-24383	218	15	retained	retain	VERB
brj-24383	218	16	or	or	CCONJ
brj-24383	218	17	enhanced	enhance	VERB
brj-24383	218	18	significant	significant	ADJ
brj-24383	218	19	spectral	spectral	ADJ
brj-24383	218	20	features	feature	NOUN
brj-24383	218	21	.	.	PUNCT
brj-24383	219	1	by	by	ADP
brj-24383	219	2	isolating	isolate	VERB
brj-24383	219	3	the	the	DET
brj-24383	219	4	reflectance	reflectance	NOUN
brj-24383	219	5	differences	difference	NOUN
brj-24383	219	6	into	into	ADP
brj-24383	219	7	separate	separate	ADJ
brj-24383	219	8	plots	plot	NOUN
brj-24383	219	9	for	for	ADP
brj-24383	219	10	each	each	DET
brj-24383	219	11	sample	sample	NOUN
brj-24383	219	12	,	,	PUNCT
brj-24383	219	13	the	the	DET
brj-24383	219	14	figure	figure	NOUN
brj-24383	219	15	avoids	avoid	VERB
brj-24383	219	16	overlapping	overlap	VERB
brj-24383	219	17	data	datum	NOUN
brj-24383	219	18	points	point	NOUN
brj-24383	219	19	,	,	PUNCT
brj-24383	219	20	ensuring	ensure	VERB
brj-24383	219	21	clarity	clarity	NOUN
brj-24383	219	22	and	and	CCONJ
brj-24383	219	23	facilitating	facilitate	VERB
brj-24383	219	24	a	a	DET
brj-24383	219	25	more	more	ADV
brj-24383	219	26	precise	precise	ADJ
brj-24383	219	27	assessment	assessment	NOUN
brj-24383	219	28	of	of	ADP
brj-24383	219	29	the	the	DET
brj-24383	219	30	filter	filter	NOUN
brj-24383	219	31	's	's	PART
brj-24383	219	32	effects	effect	NOUN
brj-24383	219	33	.	.	PUNCT
brj-24383	220	1	the	the	DET
brj-24383	220	2	consistent	consistent	ADJ
brj-24383	220	3	pattern	pattern	NOUN
brj-24383	220	4	of	of	ADP
brj-24383	220	5	reduced	reduce	VERB
brj-24383	220	6	variability	variability	NOUN
brj-24383	220	7	and	and	CCONJ
brj-24383	220	8	enhanced	enhance	VERB
brj-24383	220	9	spectral	spectral	ADJ
brj-24383	220	10	smoothness	smoothness	NOUN
brj-24383	220	11	across	across	ADP
brj-24383	220	12	samples	sample	NOUN
brj-24383	220	13	demonstrates	demonstrate	VERB
brj-24383	220	14	the	the	DET
brj-24383	220	15	robustness	robustness	NOUN
brj-24383	220	16	of	of	ADP
brj-24383	220	17	the	the	DET
brj-24383	220	18	savitzky	savitzky	NOUN
brj-24383	220	19	-	-	PUNCT
brj-24383	220	20	golay	golay	NOUN
brj-24383	220	21	filter	filter	NOUN
brj-24383	220	22	in	in	ADP
brj-24383	220	23	refining	refine	VERB
brj-24383	220	24	hyperspectral	hyperspectral	ADJ
brj-24383	220	25	data	datum	NOUN
brj-24383	220	26	,	,	PUNCT
brj-24383	220	27	thereby	thereby	ADV
brj-24383	220	28	supporting	support	VERB
brj-24383	220	29	more	more	ADV
brj-24383	220	30	accurate	accurate	ADJ
brj-24383	220	31	classification	classification	NOUN
brj-24383	220	32	and	and	CCONJ
brj-24383	220	33	analysis	analysis	NOUN
brj-24383	220	34	of	of	ADP
brj-24383	220	35	wood	wood	NOUN
brj-24383	220	36	species	specie	NOUN
brj-24383	220	37	based	base	VERB
brj-24383	220	38	on	on	ADP
brj-24383	220	39	their	their	PRON
brj-24383	220	40	spectral	spectral	ADJ
brj-24383	220	41	signatures	signature	NOUN
brj-24383	220	42	.	.	PUNCT
brj-24383	221	1	fig	fig	NOUN
brj-24383	221	2	.	.	PUNCT
brj-24383	222	1	8	8	X
brj-24383	222	2	.	.	X
brj-24383	222	3	reflectance	reflectance	NOUN
brj-24383	222	4	difference	difference	NOUN
brj-24383	222	5	after	after	ADP
brj-24383	222	6	savitzky	savitzky	NOUN
brj-24383	222	7	-	-	PUNCT
brj-24383	222	8	golay	golay	NOUN
brj-24383	222	9	filtering	filtering	NOUN
brj-24383	222	10	smote	smote	NOUN
brj-24383	222	11	(	(	PUNCT
brj-24383	222	12	synthetic	synthetic	ADJ
brj-24383	222	13	minority	minority	NOUN
brj-24383	222	14	over	over	ADP
brj-24383	222	15	-	-	PUNCT
brj-24383	222	16	sampling	sample	VERB
brj-24383	222	17	technique	technique	NOUN
brj-24383	222	18	)	)	PUNCT
brj-24383	222	19	is	be	AUX
brj-24383	222	20	a	a	DET
brj-24383	222	21	widely	widely	ADV
brj-24383	222	22	used	use	VERB
brj-24383	222	23	data	data	NOUN
brj-24383	222	24	augmentation	augmentation	NOUN
brj-24383	222	25	method	method	NOUN
brj-24383	222	26	in	in	ADP
brj-24383	222	27	small	small	ADJ
brj-24383	222	28	-	-	PUNCT
brj-24383	222	29	sample	sample	NOUN
brj-24383	222	30	learning	learning	NOUN
brj-24383	222	31	to	to	PART
brj-24383	222	32	address	address	VERB
brj-24383	222	33	the	the	DET
brj-24383	222	34	class	class	NOUN
brj-24383	222	35	imbalance	imbalance	NOUN
brj-24383	222	36	problem	problem	NOUN
brj-24383	222	37	.	.	PUNCT
brj-24383	223	1	smote	smote	VERB
brj-24383	223	2	increases	increase	VERB
brj-24383	223	3	the	the	DET
brj-24383	223	4	proportion	proportion	NOUN
brj-24383	223	5	of	of	ADP
brj-24383	223	6	minority	minority	NOUN
brj-24383	223	7	class	class	NOUN
brj-24383	223	8	samples	sample	NOUN
brj-24383	223	9	in	in	ADP
brj-24383	223	10	the	the	DET
brj-24383	223	11	dataset	dataset	NOUN
brj-24383	223	12	by	by	ADP
brj-24383	223	13	synthesizing	synthesize	VERB
brj-24383	223	14	new	new	ADJ
brj-24383	223	15	samples	sample	NOUN
brj-24383	223	16	.	.	PUNCT
brj-24383	224	1	specifically	specifically	ADV
brj-24383	224	2	,	,	PUNCT
brj-24383	224	3	the	the	DET
brj-24383	224	4	smote	smote	ADJ
brj-24383	224	5	method	method	NOUN
brj-24383	224	6	interpolates	interpolate	VERB
brj-24383	224	7	within	within	ADP
brj-24383	224	8	the	the	DET
brj-24383	224	9	feature	feature	NOUN
brj-24383	224	10	space	space	NOUN
brj-24383	224	11	of	of	ADP
brj-24383	224	12	the	the	DET
brj-24383	224	13	minority	minority	NOUN
brj-24383	224	14	class	class	NOUN
brj-24383	224	15	samples	sample	NOUN
brj-24383	224	16	to	to	PART
brj-24383	224	17	generate	generate	VERB
brj-24383	224	18	new	new	ADJ
brj-24383	224	19	samples	sample	NOUN
brj-24383	224	20	.	.	PUNCT
brj-24383	225	1	let	let	VERB
brj-24383	225	2	ix	ix	PRON
brj-24383	225	3	represent	represent	VERB
brj-24383	225	4	a	a	DET
brj-24383	225	5	minority	minority	NOUN
brj-24383	225	6	class	class	NOUN
brj-24383	225	7	sample	sample	NOUN
brj-24383	225	8	and	and	CCONJ
brj-24383	225	9	xj	xj	PROPN
brj-24383	225	10	represent	represent	VERB
brj-24383	225	11	its	its	PRON
brj-24383	225	12	nearest	near	ADJ
brj-24383	225	13	neighbor	neighbor	NOUN
brj-24383	225	14	sample	sample	NOUN
brj-24383	225	15	,	,	PUNCT
brj-24383	225	16	the	the	DET
brj-24383	225	17	new	new	ADJ
brj-24383	225	18	synthetic	synthetic	ADJ
brj-24383	225	19	sample	sample	NOUN
brj-24383	225	20	newx	newx	NOUN
brj-24383	225	21	can	can	AUX
brj-24383	225	22	be	be	AUX
brj-24383	225	23	generated	generate	VERB
brj-24383	225	24	by	by	ADP
brj-24383	225	25	the	the	DET
brj-24383	225	26	following	follow	VERB
brj-24383	225	27	formula	formula	NOUN
brj-24383	225	28	:	:	PUNCT
brj-24383	225	29	peer	peer	NOUN
brj-24383	225	30	-	-	PUNCT
brj-24383	225	31	reviewed	review	VERB
brj-24383	225	32	article	article	NOUN
brj-24383	225	33	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	225	34	su	su	PROPN
brj-24383	225	35	et	et	PROPN
brj-24383	225	36	al	al	PROPN
brj-24383	225	37	.	.	PROPN
brj-24383	226	1	(	(	PUNCT
brj-24383	226	2	2025	2025	NUM
brj-24383	226	3	)	)	PUNCT
brj-24383	226	4	.	.	PUNCT
brj-24383	227	1	“	"	PUNCT
brj-24383	227	2	leguminous	leguminous	ADJ
brj-24383	227	3	wood	wood	NOUN
brj-24383	227	4	classification	classification	NOUN
brj-24383	227	5	,	,	PUNCT
brj-24383	227	6	”	"	PUNCT
brj-24383	227	7	bioresources	bioresource	NOUN
brj-24383	227	8	20(3	20(3	NOUN
brj-24383	227	9	)	)	PUNCT
brj-24383	227	10	,	,	PUNCT
brj-24383	227	11	6317	6317	NUM
brj-24383	227	12	-	-	SYM
brj-24383	227	13	6337	6337	NUM
brj-24383	227	14	.	.	PUNCT
brj-24383	227	15	6330	6330	NUM
brj-24383	227	16	)	)	PUNCT
brj-24383	227	17	(	(	PUNCT
brj-24383	227	18	ijinew	ijinew	PROPN
brj-24383	227	19	xxxx	xxxx	PROPN
brj-24383	227	20	−+=	−+=	PROPN
brj-24383	228	1			NUM
brj-24383	228	2	(	(	PUNCT
brj-24383	228	3	9	9	NUM
brj-24383	228	4	)	)	PUNCT
brj-24383	228	5	where	where	SCONJ
brj-24383	228	6			PROPN
brj-24383	228	7	is	be	AUX
brj-24383	228	8	a	a	DET
brj-24383	228	9	parameter	parameter	NOUN
brj-24383	228	10	randomly	randomly	ADV
brj-24383	228	11	chosen	choose	VERB
brj-24383	228	12	within	within	ADP
brj-24383	228	13	the	the	DET
brj-24383	228	14	interval	interval	NOUN
brj-24383	228	15	[	[	X
brj-24383	228	16	0	0	NUM
brj-24383	228	17	,	,	PUNCT
brj-24383	228	18	1	1	NUM
brj-24383	228	19	]	]	PUNCT
brj-24383	228	20	,	,	PUNCT
brj-24383	228	21	controlling	control	VERB
brj-24383	228	22	the	the	DET
brj-24383	228	23	position	position	NOUN
brj-24383	228	24	of	of	ADP
brj-24383	228	25	the	the	DET
brj-24383	228	26	newly	newly	ADV
brj-24383	228	27	generated	generate	VERB
brj-24383	228	28	sample	sample	NOUN
brj-24383	228	29	.	.	PUNCT
brj-24383	229	1	by	by	ADP
brj-24383	229	2	averaging	average	VERB
brj-24383	229	3	the	the	DET
brj-24383	229	4	minority	minority	NOUN
brj-24383	229	5	class	class	NOUN
brj-24383	229	6	sample	sample	NOUN
brj-24383	229	7	with	with	ADP
brj-24383	229	8	its	its	PRON
brj-24383	229	9	neighbor	neighbor	NOUN
brj-24383	229	10	samples	sample	NOUN
brj-24383	229	11	,	,	PUNCT
brj-24383	229	12	the	the	DET
brj-24383	229	13	smote	smote	ADJ
brj-24383	229	14	method	method	NOUN
brj-24383	229	15	effectively	effectively	ADV
brj-24383	229	16	increases	increase	VERB
brj-24383	229	17	the	the	DET
brj-24383	229	18	number	number	NOUN
brj-24383	229	19	of	of	ADP
brj-24383	229	20	minority	minority	NOUN
brj-24383	229	21	class	class	NOUN
brj-24383	229	22	samples	sample	NOUN
brj-24383	229	23	while	while	SCONJ
brj-24383	229	24	preserving	preserve	VERB
brj-24383	229	25	the	the	DET
brj-24383	229	26	diversity	diversity	NOUN
brj-24383	229	27	and	and	CCONJ
brj-24383	229	28	features	feature	NOUN
brj-24383	229	29	of	of	ADP
brj-24383	229	30	the	the	DET
brj-24383	229	31	samples	sample	NOUN
brj-24383	229	32	.	.	PUNCT
brj-24383	230	1	the	the	DET
brj-24383	230	2	left	left	ADJ
brj-24383	230	3	side	side	NOUN
brj-24383	230	4	of	of	ADP
brj-24383	230	5	the	the	DET
brj-24383	230	6	figure	figure	NOUN
brj-24383	230	7	below	below	ADV
brj-24383	230	8	shows	show	VERB
brj-24383	230	9	a	a	DET
brj-24383	230	10	pca	pca	NOUN
brj-24383	230	11	scatter	scatter	NOUN
brj-24383	230	12	plot	plot	NOUN
brj-24383	230	13	of	of	ADP
brj-24383	230	14	wood	wood	NOUN
brj-24383	230	15	species	specie	NOUN
brj-24383	230	16	before	before	ADP
brj-24383	230	17	applying	apply	VERB
brj-24383	230	18	smote	smote	NOUN
brj-24383	230	19	,	,	PUNCT
brj-24383	230	20	showing	show	VERB
brj-24383	230	21	the	the	DET
brj-24383	230	22	original	original	ADJ
brj-24383	230	23	data	datum	NOUN
brj-24383	230	24	distribution	distribution	NOUN
brj-24383	230	25	and	and	CCONJ
brj-24383	230	26	class	class	NOUN
brj-24383	230	27	separation	separation	NOUN
brj-24383	230	28	.	.	PUNCT
brj-24383	231	1	the	the	DET
brj-24383	231	2	right	right	ADJ
brj-24383	231	3	side	side	NOUN
brj-24383	231	4	of	of	ADP
brj-24383	231	5	the	the	DET
brj-24383	231	6	pca	pca	NOUN
brj-24383	231	7	scatter	scatter	NOUN
brj-24383	231	8	plot	plot	NOUN
brj-24383	231	9	after	after	ADP
brj-24383	231	10	applying	apply	VERB
brj-24383	231	11	smote	smote	NOUN
brj-24383	231	12	shows	show	VERB
brj-24383	231	13	the	the	DET
brj-24383	231	14	expansion	expansion	NOUN
brj-24383	231	15	of	of	ADP
brj-24383	231	16	the	the	DET
brj-24383	231	17	minority	minority	NOUN
brj-24383	231	18	class	class	NOUN
brj-24383	231	19	and	and	CCONJ
brj-24383	231	20	the	the	DET
brj-24383	231	21	improvement	improvement	NOUN
brj-24383	231	22	of	of	ADP
brj-24383	231	23	class	class	NOUN
brj-24383	231	24	balance	balance	NOUN
brj-24383	231	25	.	.	PUNCT
brj-24383	232	1	fig	fig	NOUN
brj-24383	232	2	.	.	PUNCT
brj-24383	233	1	9	9	X
brj-24383	233	2	.	.	X
brj-24383	233	3	pca	pca	NOUN
brj-24383	233	4	scatter	scatter	NOUN
brj-24383	233	5	plot	plot	NOUN
brj-24383	233	6	before	before	ADV
brj-24383	233	7	and	and	CCONJ
brj-24383	233	8	after	after	SCONJ
brj-24383	233	9	smote	smote	VERB
brj-24383	233	10	the	the	DET
brj-24383	233	11	combination	combination	NOUN
brj-24383	233	12	of	of	ADP
brj-24383	233	13	these	these	DET
brj-24383	233	14	methods	method	NOUN
brj-24383	233	15	helps	help	VERB
brj-24383	233	16	to	to	PART
brj-24383	233	17	address	address	VERB
brj-24383	233	18	the	the	DET
brj-24383	233	19	issue	issue	NOUN
brj-24383	233	20	of	of	ADP
brj-24383	233	21	scarce	scarce	ADJ
brj-24383	233	22	minority	minority	NOUN
brj-24383	233	23	class	class	NOUN
brj-24383	233	24	samples	sample	NOUN
brj-24383	233	25	,	,	PUNCT
brj-24383	233	26	thereby	thereby	ADV
brj-24383	233	27	enhancing	enhance	VERB
brj-24383	233	28	the	the	DET
brj-24383	233	29	model	model	NOUN
brj-24383	233	30	’s	’s	PART
brj-24383	233	31	learning	learning	NOUN
brj-24383	233	32	and	and	CCONJ
brj-24383	233	33	generalization	generalization	NOUN
brj-24383	233	34	ability	ability	NOUN
brj-24383	233	35	.	.	PUNCT
brj-24383	234	1	the	the	DET
brj-24383	234	2	firstorder	firstorder	NOUN
brj-24383	234	3	derivative	derivative	ADJ
brj-24383	234	4	transformation	transformation	NOUN
brj-24383	234	5	and	and	CCONJ
brj-24383	234	6	savitzky	savitzky	NOUN
brj-24383	234	7	-	-	PUNCT
brj-24383	234	8	golay	golay	NOUN
brj-24383	234	9	filtering	filtering	NOUN
brj-24383	234	10	help	help	NOUN
brj-24383	234	11	to	to	PART
brj-24383	234	12	reinforce	reinforce	VERB
brj-24383	234	13	important	important	ADJ
brj-24383	234	14	features	feature	NOUN
brj-24383	234	15	in	in	ADP
brj-24383	234	16	the	the	DET
brj-24383	234	17	spectral	spectral	ADJ
brj-24383	234	18	data	datum	NOUN
brj-24383	234	19	,	,	PUNCT
brj-24383	234	20	while	while	SCONJ
brj-24383	234	21	the	the	DET
brj-24383	234	22	smote	smote	ADJ
brj-24383	234	23	method	method	NOUN
brj-24383	234	24	increases	increase	VERB
brj-24383	234	25	the	the	DET
brj-24383	234	26	number	number	NOUN
brj-24383	234	27	of	of	ADP
brj-24383	234	28	minority	minority	NOUN
brj-24383	234	29	class	class	NOUN
brj-24383	234	30	samples	sample	NOUN
brj-24383	234	31	and	and	CCONJ
brj-24383	234	32	balances	balance	VERB
brj-24383	234	33	the	the	DET
brj-24383	234	34	sample	sample	NOUN
brj-24383	234	35	distribution	distribution	NOUN
brj-24383	234	36	across	across	ADP
brj-24383	234	37	classes	class	NOUN
brj-24383	234	38	,	,	PUNCT
brj-24383	234	39	improving	improve	VERB
brj-24383	234	40	the	the	DET
brj-24383	234	41	model	model	NOUN
brj-24383	234	42	's	's	PART
brj-24383	234	43	performance	performance	NOUN
brj-24383	234	44	on	on	ADP
brj-24383	234	45	the	the	DET
brj-24383	234	46	minority	minority	NOUN
brj-24383	234	47	class	class	NOUN
brj-24383	234	48	.	.	PUNCT
brj-24383	235	1	in	in	ADP
brj-24383	235	2	this	this	DET
brj-24383	235	3	study	study	NOUN
brj-24383	235	4	,	,	PUNCT
brj-24383	235	5	to	to	PART
brj-24383	235	6	address	address	VERB
brj-24383	235	7	this	this	DET
brj-24383	235	8	issue	issue	NOUN
brj-24383	235	9	,	,	PUNCT
brj-24383	235	10	this	this	DET
brj-24383	235	11	study	study	NOUN
brj-24383	235	12	applied	apply	VERB
brj-24383	235	13	smote	smote	NOUN
brj-24383	235	14	(	(	PUNCT
brj-24383	235	15	synthetic	synthetic	ADJ
brj-24383	235	16	minority	minority	NOUN
brj-24383	235	17	over	over	ADP
brj-24383	235	18	-	-	PUNCT
brj-24383	235	19	sampling	sample	VERB
brj-24383	235	20	technique	technique	NOUN
brj-24383	235	21	)	)	PUNCT
brj-24383	235	22	to	to	PART
brj-24383	235	23	process	process	VERB
brj-24383	235	24	the	the	DET
brj-24383	235	25	data	datum	NOUN
brj-24383	235	26	,	,	PUNCT
brj-24383	235	27	aiming	aim	VERB
brj-24383	235	28	to	to	PART
brj-24383	235	29	improve	improve	VERB
brj-24383	235	30	classification	classification	NOUN
brj-24383	235	31	accuracy	accuracy	NOUN
brj-24383	235	32	.	.	PUNCT
brj-24383	236	1	for	for	ADP
brj-24383	236	2	the	the	DET
brj-24383	236	3	1d	1d	NUM
brj-24383	236	4	cnn	cnn	PROPN
brj-24383	236	5	model	model	NOUN
brj-24383	236	6	,	,	PUNCT
brj-24383	236	7	there	there	PRON
brj-24383	236	8	was	be	VERB
brj-24383	236	9	a	a	DET
brj-24383	236	10	combining	combining	NOUN
brj-24383	236	11	of	of	ADP
brj-24383	236	12	first	first	ADJ
brj-24383	236	13	-	-	PUNCT
brj-24383	236	14	order	order	NOUN
brj-24383	236	15	derivative	derivative	ADJ
brj-24383	236	16	transformation	transformation	NOUN
brj-24383	236	17	,	,	PUNCT
brj-24383	236	18	savitzky	savitzky	NOUN
brj-24383	236	19	-	-	PUNCT
brj-24383	236	20	golay	golay	NOUN
brj-24383	236	21	filtering	filtering	NOUN
brj-24383	236	22	,	,	PUNCT
brj-24383	236	23	and	and	CCONJ
brj-24383	236	24	smote	smote	VERB
brj-24383	236	25	synthesis	synthesis	NOUN
brj-24383	236	26	to	to	PART
brj-24383	236	27	enhance	enhance	VERB
brj-24383	236	28	the	the	DET
brj-24383	236	29	model	model	NOUN
brj-24383	236	30	’s	’s	PART
brj-24383	236	31	ability	ability	NOUN
brj-24383	236	32	to	to	PART
brj-24383	236	33	recognize	recognize	VERB
brj-24383	236	34	spectral	spectral	ADJ
brj-24383	236	35	features	feature	NOUN
brj-24383	236	36	and	and	CCONJ
brj-24383	236	37	improve	improve	VERB
brj-24383	236	38	classification	classification	NOUN
brj-24383	236	39	accuracy	accuracy	NOUN
brj-24383	236	40	for	for	ADP
brj-24383	236	41	minority	minority	NOUN
brj-24383	236	42	class	class	NOUN
brj-24383	236	43	samples	sample	NOUN
brj-24383	236	44	.	.	PUNCT
brj-24383	237	1	results	result	NOUN
brj-24383	237	2	and	and	CCONJ
brj-24383	237	3	discussion	discussion	NOUN
brj-24383	237	4	this	this	DET
brj-24383	237	5	section	section	NOUN
brj-24383	237	6	presents	present	VERB
brj-24383	237	7	the	the	DET
brj-24383	237	8	classification	classification	NOUN
brj-24383	237	9	results	result	NOUN
brj-24383	237	10	for	for	ADP
brj-24383	237	11	the	the	DET
brj-24383	237	12	different	different	ADJ
brj-24383	237	13	machine	machine	NOUN
brj-24383	237	14	learning	learning	NOUN
brj-24383	237	15	models	model	NOUN
brj-24383	237	16	applied	apply	VERB
brj-24383	237	17	to	to	ADP
brj-24383	237	18	hyperspectral	hyperspectral	ADJ
brj-24383	237	19	images	image	NOUN
brj-24383	237	20	of	of	ADP
brj-24383	237	21	leguminous	leguminous	ADJ
brj-24383	237	22	tree	tree	NOUN
brj-24383	237	23	species	specie	NOUN
brj-24383	237	24	.	.	PUNCT
brj-24383	238	1	classification	classification	NOUN
brj-24383	238	2	performance	performance	NOUN
brj-24383	238	3	was	be	AUX
brj-24383	238	4	evaluated	evaluate	VERB
brj-24383	238	5	for	for	ADP
brj-24383	238	6	random	random	ADJ
brj-24383	238	7	forest	forest	NOUN
brj-24383	238	8	(	(	PUNCT
brj-24383	238	9	rf	rf	NOUN
brj-24383	238	10	)	)	PUNCT
brj-24383	238	11	,	,	PUNCT
brj-24383	238	12	support	support	NOUN
brj-24383	238	13	vector	vector	NOUN
brj-24383	238	14	machine	machine	NOUN
brj-24383	238	15	(	(	PUNCT
brj-24383	238	16	svm	svm	PROPN
brj-24383	238	17	)	)	PUNCT
brj-24383	238	18	,	,	PUNCT
brj-24383	238	19	logistic	logistic	ADJ
brj-24383	238	20	regression	regression	NOUN
brj-24383	238	21	(	(	PUNCT
brj-24383	238	22	logistic	logistic	ADJ
brj-24383	238	23	regression	regression	NOUN
brj-24383	238	24	)	)	PUNCT
brj-24383	238	25	,	,	PUNCT
brj-24383	238	26	and	and	CCONJ
brj-24383	238	27	1d	1d	NUM
brj-24383	238	28	convolutional	convolutional	ADJ
brj-24383	238	29	neural	neural	ADJ
brj-24383	238	30	network	network	NOUN
brj-24383	238	31	(	(	PUNCT
brj-24383	238	32	1d	1d	NUM
brj-24383	238	33	peer	peer	NOUN
brj-24383	238	34	-	-	PUNCT
brj-24383	238	35	reviewed	review	VERB
brj-24383	238	36	article	article	NOUN
brj-24383	238	37	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	238	38	su	su	PROPN
brj-24383	238	39	et	et	PROPN
brj-24383	238	40	al	al	PROPN
brj-24383	238	41	.	.	PROPN
brj-24383	239	1	(	(	PUNCT
brj-24383	239	2	2025	2025	NUM
brj-24383	239	3	)	)	PUNCT
brj-24383	239	4	.	.	PUNCT
brj-24383	240	1	“	"	PUNCT
brj-24383	240	2	leguminous	leguminous	ADJ
brj-24383	240	3	wood	wood	NOUN
brj-24383	240	4	classification	classification	NOUN
brj-24383	240	5	,	,	PUNCT
brj-24383	240	6	”	"	PUNCT
brj-24383	240	7	bioresources	bioresource	NOUN
brj-24383	240	8	20(3	20(3	NOUN
brj-24383	240	9	)	)	PUNCT
brj-24383	240	10	,	,	PUNCT
brj-24383	240	11	6317	6317	NUM
brj-24383	240	12	-	-	SYM
brj-24383	240	13	6337	6337	NUM
brj-24383	240	14	.	.	PUNCT
brj-24383	241	1	6331	6331	NUM
brj-24383	241	2	cnn	cnn	PROPN
brj-24383	241	3	)	)	PUNCT
brj-24383	241	4	,	,	PUNCT
brj-24383	241	5	both	both	CCONJ
brj-24383	241	6	before	before	ADP
brj-24383	241	7	and	and	CCONJ
brj-24383	241	8	after	after	ADP
brj-24383	241	9	applying	apply	VERB
brj-24383	241	10	data	datum	NOUN
brj-24383	241	11	augmentation	augmentation	NOUN
brj-24383	241	12	techniques	technique	NOUN
brj-24383	241	13	such	such	ADJ
brj-24383	241	14	as	as	ADP
brj-24383	241	15	smote	smote	ADJ
brj-24383	241	16	and	and	CCONJ
brj-24383	241	17	first	first	ADJ
brj-24383	241	18	-	-	PUNCT
brj-24383	241	19	order	order	NOUN
brj-24383	241	20	derivative	derivative	ADJ
brj-24383	241	21	transformations	transformation	NOUN
brj-24383	241	22	.	.	PUNCT
brj-24383	242	1	the	the	DET
brj-24383	242	2	core	core	NOUN
brj-24383	242	3	contribution	contribution	NOUN
brj-24383	242	4	of	of	ADP
brj-24383	242	5	this	this	DET
brj-24383	242	6	study	study	NOUN
brj-24383	242	7	lies	lie	VERB
brj-24383	242	8	in	in	ADP
brj-24383	242	9	proposing	propose	VERB
brj-24383	242	10	an	an	DET
brj-24383	242	11	innovative	innovative	ADJ
brj-24383	242	12	preprocessing	preprocessing	NOUN
brj-24383	242	13	pipeline	pipeline	NOUN
brj-24383	242	14	for	for	ADP
brj-24383	242	15	small	small	ADJ
brj-24383	242	16	-	-	PUNCT
brj-24383	242	17	sample	sample	NOUN
brj-24383	242	18	spectral	spectral	ADJ
brj-24383	242	19	data	datum	NOUN
brj-24383	242	20	.	.	PUNCT
brj-24383	243	1	consequently	consequently	ADV
brj-24383	243	2	,	,	PUNCT
brj-24383	243	3	all	all	DET
brj-24383	243	4	classification	classification	NOUN
brj-24383	243	5	models	model	NOUN
brj-24383	243	6	were	be	AUX
brj-24383	243	7	implemented	implement	VERB
brj-24383	243	8	using	use	VERB
brj-24383	243	9	standard	standard	ADJ
brj-24383	243	10	configurations	configuration	NOUN
brj-24383	243	11	to	to	PART
brj-24383	243	12	focus	focus	VERB
brj-24383	243	13	on	on	ADP
brj-24383	243	14	evaluating	evaluate	VERB
brj-24383	243	15	the	the	DET
brj-24383	243	16	effects	effect	NOUN
brj-24383	243	17	of	of	ADP
brj-24383	243	18	data	datum	NOUN
brj-24383	243	19	optimization	optimization	NOUN
brj-24383	243	20	:	:	PUNCT
brj-24383	243	21	svm	svm	PROPN
brj-24383	243	22	:	:	PUNCT
brj-24383	243	23	implemented	implement	VERB
brj-24383	243	24	through	through	ADP
brj-24383	243	25	scikit	scikit	NOUN
brj-24383	243	26	-	-	PUNCT
brj-24383	243	27	learn	learn	VERB
brj-24383	243	28	(	(	PUNCT
brj-24383	243	29	v1.2	v1.2	NOUN
brj-24383	243	30	)	)	PUNCT
brj-24383	243	31	svc	svc	NOUN
brj-24383	243	32	class	class	NOUN
brj-24383	243	33	with	with	ADP
brj-24383	243	34	default	default	NOUN
brj-24383	243	35	radial	radial	ADJ
brj-24383	243	36	basis	basis	NOUN
brj-24383	243	37	function	function	NOUN
brj-24383	243	38	(	(	PUNCT
brj-24383	243	39	rbf	rbf	PROPN
brj-24383	243	40	)	)	PUNCT
brj-24383	243	41	kernel	kernel	PROPN
brj-24383	243	42	,	,	PUNCT
brj-24383	243	43	regularization	regularization	NOUN
brj-24383	243	44	parameter	parameter	NOUN
brj-24383	243	45	c=1.0	c=1.0	PROPN
brj-24383	243	46	,	,	PUNCT
brj-24383	243	47	and	and	CCONJ
brj-24383	243	48	gamma	gamma	PROPN
brj-24383	243	49	parameter	parameter	PROPN
brj-24383	243	50	set	set	VERB
brj-24383	243	51	to	to	ADP
brj-24383	243	52	'	'	PUNCT
brj-24383	243	53	scale	scale	NOUN
brj-24383	243	54	'	'	PUNCT
brj-24383	243	55	(	(	PUNCT
brj-24383	243	56	i.e.	i.e.	X
brj-24383	243	57	,	,	PUNCT
brj-24383	243	58	1/(n_features	1/(n_feature	NOUN
brj-24383	243	59	*	*	PUNCT
brj-24383	243	60	x.var	x.var	NOUN
brj-24383	243	61	(	(	PUNCT
brj-24383	243	62	)	)	PUNCT
brj-24383	243	63	)	)	PUNCT
brj-24383	243	64	)	)	PUNCT
brj-24383	243	65	.	.	PUNCT
brj-24383	244	1	random	random	ADJ
brj-24383	244	2	forest	forest	NOUN
brj-24383	244	3	(	(	PUNCT
brj-24383	244	4	rf	rf	ADJ
brj-24383	244	5	):	):	PUNCT
brj-24383	244	6	utilized	utilize	VERB
brj-24383	244	7	scikit	scikit	NOUN
brj-24383	244	8	-	-	PUNCT
brj-24383	244	9	learn	learn	VERB
brj-24383	244	10	’s	’s	PART
brj-24383	244	11	randomforestclassifier	randomforestclassifier	NOUN
brj-24383	244	12	with	with	ADP
brj-24383	244	13	default	default	NOUN
brj-24383	244	14	settings	setting	NOUN
brj-24383	244	15	:	:	PUNCT
brj-24383	244	16	100	100	NUM
brj-24383	244	17	decision	decision	NOUN
brj-24383	244	18	trees	tree	NOUN
brj-24383	244	19	,	,	PUNCT
brj-24383	244	20	gini	gini	NOUN
brj-24383	244	21	impurity	impurity	NOUN
brj-24383	244	22	as	as	ADP
brj-24383	244	23	the	the	DET
brj-24383	244	24	splitting	splitting	NOUN
brj-24383	244	25	criterion	criterion	NOUN
brj-24383	244	26	,	,	PUNCT
brj-24383	244	27	and	and	CCONJ
brj-24383	244	28	unlimited	unlimited	ADJ
brj-24383	244	29	maximum	maximum	ADJ
brj-24383	244	30	tree	tree	NOUN
brj-24383	244	31	depth	depth	NOUN
brj-24383	244	32	.	.	PUNCT
brj-24383	245	1	logistic	logistic	ADJ
brj-24383	245	2	regression	regression	NOUN
brj-24383	245	3	(	(	PUNCT
brj-24383	245	4	lr	lr	NOUN
brj-24383	245	5	):	):	PUNCT
brj-24383	245	6	employed	employ	VERB
brj-24383	245	7	scikit	scikit	NOUN
brj-24383	245	8	-	-	PUNCT
brj-24383	245	9	learn	learn	NOUN
brj-24383	245	10	's	's	PART
brj-24383	245	11	logisticregression	logisticregression	NOUN
brj-24383	245	12	with	with	ADP
brj-24383	245	13	l2	l2	NOUN
brj-24383	245	14	regularization	regularization	NOUN
brj-24383	245	15	,	,	PUNCT
brj-24383	245	16	lbfgs	lbfgs	PROPN
brj-24383	245	17	solver	solver	NOUN
brj-24383	245	18	,	,	PUNCT
brj-24383	245	19	and	and	CCONJ
brj-24383	245	20	maximum	maximum	ADJ
brj-24383	245	21	iterations	iteration	NOUN
brj-24383	245	22	set	set	VERB
brj-24383	245	23	to	to	ADP
brj-24383	245	24	1000	1000	NUM
brj-24383	245	25	.	.	PUNCT
brj-24383	246	1	1d	1d	NUM
brj-24383	246	2	-	-	PUNCT
brj-24383	246	3	cnn	cnn	PROPN
brj-24383	246	4	:	:	PUNCT
brj-24383	246	5	constructed	construct	VERB
brj-24383	246	6	using	use	VERB
brj-24383	246	7	keras	keras	PROPN
brj-24383	246	8	framework	framework	NOUN
brj-24383	246	9	,	,	PUNCT
brj-24383	246	10	consisting	consist	VERB
brj-24383	246	11	of	of	ADP
brj-24383	246	12	:	:	PUNCT
brj-24383	246	13	an	an	DET
brj-24383	246	14	input	input	NOUN
brj-24383	246	15	layer	layer	NOUN
brj-24383	246	16	(	(	PUNCT
brj-24383	246	17	accepting	accept	VERB
brj-24383	246	18	raw	raw	ADJ
brj-24383	246	19	spectral	spectral	ADJ
brj-24383	246	20	sequences	sequence	NOUN
brj-24383	246	21	)	)	PUNCT
brj-24383	246	22	one	one	NUM
brj-24383	246	23	convolutional	convolutional	ADJ
brj-24383	246	24	layer	layer	NOUN
brj-24383	246	25	(	(	PUNCT
brj-24383	246	26	64	64	NUM
brj-24383	246	27	filters	filter	NOUN
brj-24383	246	28	,	,	PUNCT
brj-24383	246	29	kernel	kernel	PROPN
brj-24383	246	30	size=3	size=3	PROPN
brj-24383	246	31	,	,	PUNCT
brj-24383	246	32	relu	relu	NOUN
brj-24383	246	33	activation	activation	NOUN
brj-24383	246	34	)	)	PUNCT
brj-24383	246	35	a	a	DET
brj-24383	246	36	global	global	ADJ
brj-24383	246	37	average	average	ADJ
brj-24383	246	38	pooling	pool	VERB
brj-24383	246	39	layer	layer	NOUN
brj-24383	246	40	(	(	PUNCT
brj-24383	246	41	replacing	replace	VERB
brj-24383	246	42	fully	fully	ADV
brj-24383	246	43	-	-	PUNCT
brj-24383	246	44	connected	connect	VERB
brj-24383	246	45	layers	layer	NOUN
brj-24383	246	46	to	to	PART
brj-24383	246	47	reduce	reduce	VERB
brj-24383	246	48	parameters	parameter	NOUN
brj-24383	246	49	)	)	PUNCT
brj-24383	246	50	an	an	DET
brj-24383	246	51	output	output	NOUN
brj-24383	246	52	layer	layer	NOUN
brj-24383	246	53	(	(	PUNCT
brj-24383	246	54	softmax	softmax	NOUN
brj-24383	246	55	activation	activation	NOUN
brj-24383	246	56	with	with	ADP
brj-24383	246	57	neurons	neuron	NOUN
brj-24383	246	58	matching	match	VERB
brj-24383	246	59	category	category	NOUN
brj-24383	246	60	count	count	NOUN
brj-24383	246	61	)	)	PUNCT
brj-24383	246	62	all	all	DET
brj-24383	246	63	conventional	conventional	ADJ
brj-24383	246	64	models	model	NOUN
brj-24383	246	65	(	(	PUNCT
brj-24383	246	66	svm	svm	ADJ
brj-24383	246	67	/	/	SYM
brj-24383	246	68	rf	rf	ADJ
brj-24383	246	69	/	/	SYM
brj-24383	246	70	lr	lr	NOUN
brj-24383	246	71	)	)	PUNCT
brj-24383	246	72	employed	employ	VERB
brj-24383	246	73	scikit	scikit	NOUN
brj-24383	246	74	-	-	PUNCT
brj-24383	246	75	learn	learn	VERB
brj-24383	246	76	’s	’s	PART
brj-24383	246	77	default	default	NOUN
brj-24383	246	78	parameters	parameter	NOUN
brj-24383	246	79	,	,	PUNCT
brj-24383	246	80	which	which	PRON
brj-24383	246	81	have	have	AUX
brj-24383	246	82	demonstrated	demonstrate	VERB
brj-24383	246	83	robust	robust	ADJ
brj-24383	246	84	performance	performance	NOUN
brj-24383	246	85	in	in	ADP
brj-24383	246	86	multiple	multiple	ADJ
brj-24383	246	87	spectral	spectral	ADJ
brj-24383	246	88	analysis	analysis	NOUN
brj-24383	246	89	benchmark	benchmark	NOUN
brj-24383	246	90	tasks	task	NOUN
brj-24383	246	91	.	.	PUNCT
brj-24383	247	1	for	for	ADP
brj-24383	247	2	the	the	DET
brj-24383	247	3	1d	1d	NUM
brj-24383	247	4	-	-	PUNCT
brj-24383	247	5	cnn	cnn	NOUN
brj-24383	247	6	implementation	implementation	NOUN
brj-24383	247	7	,	,	PUNCT
brj-24383	247	8	a	a	DET
brj-24383	247	9	compact	compact	ADJ
brj-24383	247	10	architecture	architecture	NOUN
brj-24383	247	11	was	be	AUX
brj-24383	247	12	adopted	adopt	VERB
brj-24383	247	13	that	that	PRON
brj-24383	247	14	is	be	AUX
brj-24383	247	15	commonly	commonly	ADV
brj-24383	247	16	used	use	VERB
brj-24383	247	17	in	in	ADP
brj-24383	247	18	spectral	spectral	ADJ
brj-24383	247	19	analysis	analysis	NOUN
brj-24383	247	20	(	(	PUNCT
brj-24383	247	21	he	he	PRON
brj-24383	248	1	et	et	PROPN
brj-24383	248	2	al	al	PROPN
brj-24383	248	3	.	.	PROPN
brj-24383	248	4	2020	2020	NUM
brj-24383	248	5	)	)	PUNCT
brj-24383	248	6	,	,	PUNCT
brj-24383	248	7	modifying	modify	VERB
brj-24383	248	8	only	only	ADV
brj-24383	248	9	the	the	DET
brj-24383	248	10	input	input	NOUN
brj-24383	248	11	dimensions	dimension	NOUN
brj-24383	248	12	to	to	PART
brj-24383	248	13	accommodate	accommodate	VERB
brj-24383	248	14	data	datum	NOUN
brj-24383	248	15	characteristics	characteristic	NOUN
brj-24383	248	16	.	.	PUNCT
brj-24383	249	1	to	to	PART
brj-24383	249	2	ensure	ensure	VERB
brj-24383	249	3	reproducibility	reproducibility	NOUN
brj-24383	249	4	,	,	PUNCT
brj-24383	249	5	all	all	DET
brj-24383	249	6	models	model	NOUN
brj-24383	249	7	were	be	AUX
brj-24383	249	8	initialized	initialize	VERB
brj-24383	249	9	with	with	ADP
brj-24383	249	10	identical	identical	ADJ
brj-24383	249	11	random	random	ADJ
brj-24383	249	12	seeds	seed	NOUN
brj-24383	249	13	(	(	PUNCT
brj-24383	249	14	seed=42	seed=42	NOUN
brj-24383	249	15	)	)	PUNCT
brj-24383	249	16	.	.	PUNCT
brj-24383	250	1	future	future	ADJ
brj-24383	250	2	work	work	NOUN
brj-24383	250	3	will	will	AUX
brj-24383	250	4	incorporate	incorporate	VERB
brj-24383	250	5	advanced	advanced	ADJ
brj-24383	250	6	optimization	optimization	NOUN
brj-24383	250	7	techniques	technique	NOUN
brj-24383	250	8	such	such	ADJ
brj-24383	250	9	as	as	ADP
brj-24383	250	10	bayesian	bayesian	NOUN
brj-24383	250	11	optimization	optimization	NOUN
brj-24383	250	12	or	or	CCONJ
brj-24383	250	13	grid	grid	NOUN
brj-24383	250	14	search	search	NOUN
brj-24383	250	15	to	to	PART
brj-24383	250	16	further	far	ADV
brj-24383	250	17	enhance	enhance	VERB
brj-24383	250	18	model	model	NOUN
brj-24383	250	19	performance	performance	NOUN
brj-24383	250	20	.	.	PUNCT
brj-24383	251	1	results	result	NOUN
brj-24383	251	2	of	of	ADP
brj-24383	251	3	traditional	traditional	ADJ
brj-24383	251	4	machine	machine	NOUN
brj-24383	251	5	learning	learning	NOUN
brj-24383	251	6	models	model	NOUN
brj-24383	251	7	in	in	ADP
brj-24383	251	8	the	the	DET
brj-24383	251	9	initial	initial	ADJ
brj-24383	251	10	experiments	experiment	NOUN
brj-24383	251	11	,	,	PUNCT
brj-24383	251	12	random	random	ADJ
brj-24383	251	13	forest	forest	NOUN
brj-24383	251	14	,	,	PUNCT
brj-24383	251	15	support	support	NOUN
brj-24383	251	16	vector	vector	NOUN
brj-24383	251	17	machine	machine	NOUN
brj-24383	251	18	(	(	PUNCT
brj-24383	251	19	svm	svm	PROPN
brj-24383	251	20	)	)	PUNCT
brj-24383	251	21	,	,	PUNCT
brj-24383	251	22	and	and	CCONJ
brj-24383	251	23	logistic	logistic	ADJ
brj-24383	251	24	regression	regression	NOUN
brj-24383	251	25	models	model	NOUN
brj-24383	251	26	were	be	AUX
brj-24383	251	27	used	use	VERB
brj-24383	251	28	for	for	ADP
brj-24383	251	29	classification	classification	NOUN
brj-24383	251	30	tests	test	NOUN
brj-24383	251	31	.	.	PUNCT
brj-24383	252	1	these	these	DET
brj-24383	252	2	tests	test	NOUN
brj-24383	252	3	did	do	AUX
brj-24383	252	4	not	not	PART
brj-24383	252	5	apply	apply	VERB
brj-24383	252	6	smote	smote	ADJ
brj-24383	252	7	or	or	CCONJ
brj-24383	252	8	other	other	ADJ
brj-24383	252	9	data	datum	NOUN
brj-24383	252	10	enhancement	enhancement	NOUN
brj-24383	252	11	techniques	technique	NOUN
brj-24383	252	12	.	.	PUNCT
brj-24383	253	1	the	the	DET
brj-24383	253	2	experimental	experimental	ADJ
brj-24383	253	3	results	result	NOUN
brj-24383	253	4	after	after	ADP
brj-24383	253	5	adding	add	VERB
brj-24383	253	6	the	the	DET
brj-24383	253	7	smote	smote	ADJ
brj-24383	253	8	processing	processing	NOUN
brj-24383	253	9	method	method	NOUN
brj-24383	253	10	are	be	AUX
brj-24383	253	11	as	as	SCONJ
brj-24383	253	12	follows	follow	VERB
brj-24383	253	13	:	:	PUNCT
brj-24383	254	1	random	random	ADJ
brj-24383	254	2	forest	forest	NOUN
brj-24383	254	3	(	(	PUNCT
brj-24383	254	4	rf	rf	NOUN
brj-24383	254	5	):	):	PUNCT
brj-24383	254	6	with	with	ADP
brj-24383	254	7	the	the	DET
brj-24383	254	8	introduction	introduction	NOUN
brj-24383	254	9	of	of	ADP
brj-24383	254	10	smote	smote	ADJ
brj-24383	254	11	processing	processing	NOUN
brj-24383	254	12	,	,	PUNCT
brj-24383	254	13	the	the	DET
brj-24383	254	14	accuracy	accuracy	NOUN
brj-24383	254	15	of	of	ADP
brj-24383	254	16	the	the	DET
brj-24383	254	17	random	random	ADJ
brj-24383	254	18	forest	forest	NOUN
brj-24383	254	19	model	model	NOUN
brj-24383	254	20	increased	increase	VERB
brj-24383	254	21	from	from	ADP
brj-24383	254	22	88.00	88.00	NUM
brj-24383	254	23	%	%	NOUN
brj-24383	254	24	to	to	ADP
brj-24383	254	25	92.26	92.26	NUM
brj-24383	254	26	%	%	NOUN
brj-24383	254	27	.	.	PUNCT
brj-24383	255	1	although	although	SCONJ
brj-24383	255	2	the	the	DET
brj-24383	255	3	random	random	ADJ
brj-24383	255	4	forest	forest	NOUN
brj-24383	255	5	model	model	NOUN
brj-24383	255	6	performed	perform	VERB
brj-24383	255	7	well	well	ADV
brj-24383	255	8	in	in	ADP
brj-24383	255	9	classifying	classify	VERB
brj-24383	255	10	most	most	ADJ
brj-24383	255	11	tree	tree	NOUN
brj-24383	255	12	species	specie	NOUN
brj-24383	255	13	,	,	PUNCT
brj-24383	255	14	there	there	PRON
brj-24383	255	15	were	be	VERB
brj-24383	255	16	still	still	ADV
brj-24383	255	17	some	some	DET
brj-24383	255	18	misclassifications	misclassification	NOUN
brj-24383	255	19	,	,	PUNCT
brj-24383	255	20	especially	especially	ADV
brj-24383	255	21	between	between	ADP
brj-24383	255	22	tree	tree	NOUN
brj-24383	255	23	species	specie	NOUN
brj-24383	255	24	with	with	ADP
brj-24383	255	25	similar	similar	ADJ
brj-24383	255	26	spectral	spectral	ADJ
brj-24383	255	27	characteristics	characteristic	NOUN
brj-24383	255	28	.	.	PUNCT
brj-24383	256	1	nevertheless	nevertheless	ADV
brj-24383	256	2	,	,	PUNCT
brj-24383	256	3	the	the	DET
brj-24383	256	4	application	application	NOUN
brj-24383	256	5	of	of	ADP
brj-24383	256	6	smote	smote	NOUN
brj-24383	256	7	effectively	effectively	ADV
brj-24383	256	8	improved	improve	VERB
brj-24383	256	9	the	the	DET
brj-24383	256	10	overall	overall	ADJ
brj-24383	256	11	performance	performance	NOUN
brj-24383	256	12	of	of	ADP
brj-24383	256	13	the	the	DET
brj-24383	256	14	model	model	NOUN
brj-24383	256	15	,	,	PUNCT
brj-24383	256	16	making	make	VERB
brj-24383	256	17	it	it	PRON
brj-24383	256	18	more	more	ADV
brj-24383	256	19	robust	robust	ADJ
brj-24383	256	20	when	when	SCONJ
brj-24383	256	21	dealing	deal	VERB
brj-24383	256	22	with	with	ADP
brj-24383	256	23	unbalanced	unbalanced	ADJ
brj-24383	256	24	data	datum	NOUN
brj-24383	256	25	.	.	PUNCT
brj-24383	257	1	support	support	NOUN
brj-24383	257	2	vector	vector	NOUN
brj-24383	257	3	machine	machine	NOUN
brj-24383	257	4	(	(	PUNCT
brj-24383	257	5	svm	svm	PROPN
brj-24383	257	6	):	):	PUNCT
brj-24383	257	7	after	after	ADP
brj-24383	257	8	smote	smote	ADJ
brj-24383	257	9	processing	processing	NOUN
brj-24383	257	10	,	,	PUNCT
brj-24383	257	11	the	the	DET
brj-24383	257	12	accuracy	accuracy	NOUN
brj-24383	257	13	of	of	ADP
brj-24383	257	14	the	the	DET
brj-24383	257	15	support	support	NOUN
brj-24383	257	16	vector	vector	NOUN
brj-24383	257	17	machine	machine	NOUN
brj-24383	257	18	was	be	AUX
brj-24383	257	19	increased	increase	VERB
brj-24383	257	20	from	from	ADP
brj-24383	257	21	the	the	DET
brj-24383	257	22	initial	initial	ADJ
brj-24383	257	23	92.00	92.00	NUM
brj-24383	257	24	%	%	NOUN
brj-24383	257	25	to	to	ADP
brj-24383	257	26	98.86	98.86	NUM
brj-24383	257	27	%	%	NOUN
brj-24383	257	28	.	.	PUNCT
brj-24383	258	1	svm	svm	PROPN
brj-24383	258	2	can	can	AUX
brj-24383	258	3	effectively	effectively	ADV
brj-24383	258	4	capture	capture	VERB
brj-24383	258	5	nonlinear	nonlinear	ADJ
brj-24383	258	6	relationships	relationship	NOUN
brj-24383	258	7	and	and	CCONJ
brj-24383	258	8	complex	complex	ADJ
brj-24383	258	9	patterns	pattern	NOUN
brj-24383	258	10	in	in	ADP
brj-24383	258	11	high	high	ADJ
brj-24383	258	12	-	-	PUNCT
brj-24383	258	13	dimensional	dimensional	ADJ
brj-24383	258	14	spectral	spectral	ADJ
brj-24383	258	15	data	datum	NOUN
brj-24383	258	16	,	,	PUNCT
brj-24383	258	17	and	and	CCONJ
brj-24383	258	18	performs	perform	VERB
brj-24383	258	19	better	well	ADJ
brj-24383	258	20	than	than	ADP
brj-24383	258	21	random	random	ADJ
brj-24383	258	22	forests	forest	NOUN
brj-24383	258	23	,	,	PUNCT
brj-24383	258	24	especially	especially	ADV
brj-24383	258	25	when	when	SCONJ
brj-24383	258	26	distinguishing	distinguish	VERB
brj-24383	258	27	similar	similar	ADJ
brj-24383	258	28	tree	tree	NOUN
brj-24383	258	29	species	specie	NOUN
brj-24383	258	30	,	,	PUNCT
brj-24383	258	31	and	and	CCONJ
brj-24383	258	32	it	it	PRON
brj-24383	258	33	can	can	AUX
brj-24383	258	34	more	more	ADV
brj-24383	258	35	accurately	accurately	ADV
brj-24383	258	36	identify	identify	VERB
brj-24383	258	37	their	their	PRON
brj-24383	258	38	spectral	spectral	ADJ
brj-24383	258	39	differences	difference	NOUN
brj-24383	258	40	.	.	PUNCT
brj-24383	259	1	through	through	ADP
brj-24383	259	2	the	the	DET
brj-24383	259	3	application	application	NOUN
brj-24383	259	4	of	of	ADP
brj-24383	259	5	smote	smote	NOUN
brj-24383	259	6	,	,	PUNCT
brj-24383	259	7	svm	svm	PROPN
brj-24383	259	8	has	have	AUX
brj-24383	259	9	improved	improve	VERB
brj-24383	259	10	the	the	DET
brj-24383	259	11	problem	problem	NOUN
brj-24383	259	12	of	of	ADP
brj-24383	259	13	class	class	NOUN
brj-24383	259	14	imbalance	imbalance	NOUN
brj-24383	259	15	and	and	CCONJ
brj-24383	259	16	reduced	reduce	VERB
brj-24383	259	17	the	the	DET
brj-24383	259	18	misclassification	misclassification	NOUN
brj-24383	259	19	rate	rate	NOUN
brj-24383	259	20	.	.	PUNCT
brj-24383	260	1	peer	peer	NOUN
brj-24383	260	2	-	-	PUNCT
brj-24383	260	3	reviewed	review	VERB
brj-24383	260	4	article	article	NOUN
brj-24383	260	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	260	6	su	su	PROPN
brj-24383	260	7	et	et	PROPN
brj-24383	260	8	al	al	PROPN
brj-24383	260	9	.	.	PROPN
brj-24383	261	1	(	(	PUNCT
brj-24383	261	2	2025	2025	NUM
brj-24383	261	3	)	)	PUNCT
brj-24383	261	4	.	.	PUNCT
brj-24383	262	1	“	"	PUNCT
brj-24383	262	2	leguminous	leguminous	ADJ
brj-24383	262	3	wood	wood	NOUN
brj-24383	262	4	classification	classification	NOUN
brj-24383	262	5	,	,	PUNCT
brj-24383	262	6	”	"	PUNCT
brj-24383	262	7	bioresources	bioresource	NOUN
brj-24383	262	8	20(3	20(3	NOUN
brj-24383	262	9	)	)	PUNCT
brj-24383	262	10	,	,	PUNCT
brj-24383	262	11	6317	6317	NUM
brj-24383	262	12	-	-	SYM
brj-24383	262	13	6337	6337	NUM
brj-24383	262	14	.	.	PUNCT
brj-24383	263	1	6332	6332	NUM
brj-24383	263	2	logistic	logistic	ADJ
brj-24383	263	3	regression	regression	NOUN
brj-24383	263	4	:	:	PUNCT
brj-24383	263	5	the	the	DET
brj-24383	263	6	accuracy	accuracy	NOUN
brj-24383	263	7	of	of	ADP
brj-24383	263	8	the	the	DET
brj-24383	263	9	logistic	logistic	ADJ
brj-24383	263	10	regression	regression	NOUN
brj-24383	263	11	model	model	NOUN
brj-24383	263	12	increased	increase	VERB
brj-24383	263	13	from	from	ADP
brj-24383	263	14	93.00	93.00	NUM
brj-24383	263	15	%	%	NOUN
brj-24383	263	16	to	to	ADP
brj-24383	263	17	97.26	97.26	NUM
brj-24383	263	18	%	%	NOUN
brj-24383	263	19	after	after	ADP
brj-24383	263	20	smote	smote	ADJ
brj-24383	263	21	processing	processing	NOUN
brj-24383	263	22	.	.	PUNCT
brj-24383	264	1	logistic	logistic	ADJ
brj-24383	264	2	regression	regression	NOUN
brj-24383	264	3	performs	perform	VERB
brj-24383	264	4	well	well	ADV
brj-24383	264	5	on	on	ADP
brj-24383	264	6	linear	linear	ADJ
brj-24383	264	7	classification	classification	NOUN
brj-24383	264	8	tasks	task	NOUN
brj-24383	264	9	and	and	CCONJ
brj-24383	264	10	accurately	accurately	ADV
brj-24383	264	11	classifies	classify	VERB
brj-24383	264	12	most	most	ADJ
brj-24383	264	13	samples	sample	NOUN
brj-24383	264	14	.	.	PUNCT
brj-24383	265	1	however	however	ADV
brj-24383	265	2	,	,	PUNCT
brj-24383	265	3	while	while	SCONJ
brj-24383	265	4	the	the	DET
brj-24383	265	5	high	high	ADJ
brj-24383	265	6	accuracy	accuracy	NOUN
brj-24383	265	7	indicates	indicate	VERB
brj-24383	265	8	good	good	ADJ
brj-24383	265	9	results	result	NOUN
brj-24383	265	10	in	in	ADP
brj-24383	265	11	general	general	ADJ
brj-24383	265	12	,	,	PUNCT
brj-24383	265	13	the	the	DET
brj-24383	265	14	model	model	NOUN
brj-24383	265	15	’s	’s	PART
brj-24383	265	16	sensitivity	sensitivity	NOUN
brj-24383	265	17	to	to	ADP
brj-24383	265	18	class	class	NOUN
brj-24383	265	19	imbalance	imbalance	NOUN
brj-24383	265	20	still	still	ADV
brj-24383	265	21	exists	exist	VERB
brj-24383	265	22	.	.	PUNCT
brj-24383	266	1	after	after	ADP
brj-24383	266	2	applying	apply	VERB
brj-24383	266	3	smote	smote	NOUN
brj-24383	266	4	,	,	PUNCT
brj-24383	266	5	the	the	DET
brj-24383	266	6	model	model	NOUN
brj-24383	266	7	’s	’s	PART
brj-24383	266	8	performance	performance	NOUN
brj-24383	266	9	was	be	AUX
brj-24383	266	10	significantly	significantly	ADV
brj-24383	266	11	improved	improve	VERB
brj-24383	266	12	when	when	SCONJ
brj-24383	266	13	dealing	deal	VERB
brj-24383	266	14	with	with	ADP
brj-24383	266	15	imbalanced	imbalanced	ADJ
brj-24383	266	16	data	datum	NOUN
brj-24383	266	17	,	,	PUNCT
brj-24383	266	18	especially	especially	ADV
brj-24383	266	19	on	on	ADP
brj-24383	266	20	tree	tree	NOUN
brj-24383	266	21	species	specie	NOUN
brj-24383	266	22	with	with	ADP
brj-24383	266	23	fewer	few	ADJ
brj-24383	266	24	samples	sample	NOUN
brj-24383	266	25	.	.	PUNCT
brj-24383	267	1	figure	figure	VERB
brj-24383	267	2	10	10	NUM
brj-24383	267	3	presents	present	VERB
brj-24383	267	4	the	the	DET
brj-24383	267	5	confusion	confusion	NOUN
brj-24383	267	6	matrix	matrix	NOUN
brj-24383	267	7	for	for	ADP
brj-24383	267	8	the	the	DET
brj-24383	267	9	three	three	NUM
brj-24383	267	10	machine	machine	NOUN
brj-24383	267	11	learning	learning	NOUN
brj-24383	267	12	models	model	NOUN
brj-24383	267	13	,	,	PUNCT
brj-24383	267	14	highlighting	highlight	VERB
brj-24383	267	15	the	the	DET
brj-24383	267	16	significant	significant	ADJ
brj-24383	267	17	reduction	reduction	NOUN
brj-24383	267	18	in	in	ADP
brj-24383	267	19	misclassification	misclassification	NOUN
brj-24383	267	20	rates	rate	NOUN
brj-24383	267	21	and	and	CCONJ
brj-24383	267	22	the	the	DET
brj-24383	267	23	improved	improved	ADJ
brj-24383	267	24	performance	performance	NOUN
brj-24383	267	25	across	across	ADP
brj-24383	267	26	all	all	DET
brj-24383	267	27	tree	tree	NOUN
brj-24383	267	28	species	specie	NOUN
brj-24383	267	29	,	,	PUNCT
brj-24383	267	30	particularly	particularly	ADV
brj-24383	267	31	in	in	ADP
brj-24383	267	32	the	the	DET
brj-24383	267	33	minority	minority	NOUN
brj-24383	267	34	class	class	NOUN
brj-24383	267	35	.	.	PUNCT
brj-24383	268	1	fig	fig	NOUN
brj-24383	268	2	.	.	PUNCT
brj-24383	269	1	10	10	NUM
brj-24383	269	2	.	.	X
brj-24383	269	3	random	random	ADJ
brj-24383	269	4	forest	forest	NOUN
brj-24383	269	5	and	and	CCONJ
brj-24383	269	6	svm	svm	ADJ
brj-24383	269	7	and	and	CCONJ
brj-24383	269	8	logistic	logistic	ADJ
brj-24383	269	9	regression	regression	NOUN
brj-24383	269	10	confusion	confusion	NOUN
brj-24383	269	11	matrix	matrix	NOUN
brj-24383	269	12	results	result	NOUN
brj-24383	269	13	of	of	ADP
brj-24383	269	14	the	the	DET
brj-24383	269	15	1d	1d	NUM
brj-24383	269	16	convolutional	convolutional	ADJ
brj-24383	269	17	neural	neural	ADJ
brj-24383	269	18	network	network	NOUN
brj-24383	269	19	(	(	PUNCT
brj-24383	269	20	1d	1d	NUM
brj-24383	269	21	cnn	cnn	NOUN
brj-24383	269	22	)	)	PUNCT
brj-24383	269	23	in	in	ADP
brj-24383	269	24	contrast	contrast	NOUN
brj-24383	269	25	to	to	ADP
brj-24383	269	26	the	the	DET
brj-24383	269	27	traditional	traditional	ADJ
brj-24383	269	28	models	model	NOUN
brj-24383	269	29	,	,	PUNCT
brj-24383	269	30	the	the	DET
brj-24383	269	31	1d	1d	NUM
brj-24383	269	32	convolutional	convolutional	ADJ
brj-24383	269	33	neural	neural	ADJ
brj-24383	269	34	network	network	NOUN
brj-24383	269	35	(	(	PUNCT
brj-24383	269	36	1d	1d	NUM
brj-24383	269	37	cnn	cnn	PROPN
brj-24383	269	38	)	)	PUNCT
brj-24383	269	39	model	model	NOUN
brj-24383	269	40	was	be	AUX
brj-24383	269	41	first	first	ADV
brj-24383	269	42	evaluated	evaluate	VERB
brj-24383	269	43	with	with	ADP
brj-24383	269	44	the	the	DET
brj-24383	269	45	initial	initial	ADJ
brj-24383	269	46	data	datum	NOUN
brj-24383	269	47	and	and	CCONJ
brj-24383	269	48	achieved	achieve	VERB
brj-24383	269	49	an	an	DET
brj-24383	269	50	accuracy	accuracy	NOUN
brj-24383	269	51	of	of	ADP
brj-24383	269	52	92.25	92.25	NUM
brj-24383	269	53	%	%	NOUN
brj-24383	269	54	.	.	PUNCT
brj-24383	270	1	while	while	SCONJ
brj-24383	270	2	this	this	DET
brj-24383	270	3	result	result	NOUN
brj-24383	270	4	was	be	AUX
brj-24383	270	5	already	already	ADV
brj-24383	270	6	quite	quite	ADV
brj-24383	270	7	strong	strong	ADJ
brj-24383	270	8	,	,	PUNCT
brj-24383	270	9	further	further	ADJ
brj-24383	270	10	improvements	improvement	NOUN
brj-24383	270	11	were	be	AUX
brj-24383	270	12	made	make	VERB
brj-24383	270	13	by	by	ADP
brj-24383	270	14	applying	apply	VERB
brj-24383	270	15	feature	feature	NOUN
brj-24383	270	16	transformation	transformation	NOUN
brj-24383	270	17	techniques	technique	NOUN
brj-24383	270	18	.	.	PUNCT
brj-24383	271	1	first	first	ADJ
brj-24383	271	2	-	-	PUNCT
brj-24383	271	3	order	order	NOUN
brj-24383	271	4	derivative	derivative	ADJ
brj-24383	271	5	transformation	transformation	NOUN
brj-24383	271	6	:	:	PUNCT
brj-24383	271	7	after	after	ADP
brj-24383	271	8	applying	apply	VERB
brj-24383	271	9	the	the	DET
brj-24383	271	10	first	first	ADJ
brj-24383	271	11	-	-	PUNCT
brj-24383	271	12	order	order	NOUN
brj-24383	271	13	derivative	derivative	ADJ
brj-24383	271	14	transformation	transformation	NOUN
brj-24383	271	15	to	to	ADP
brj-24383	271	16	the	the	DET
brj-24383	271	17	hyperspectral	hyperspectral	ADJ
brj-24383	271	18	data	datum	NOUN
brj-24383	271	19	,	,	PUNCT
brj-24383	271	20	the	the	DET
brj-24383	271	21	classification	classification	NOUN
brj-24383	271	22	accuracy	accuracy	NOUN
brj-24383	271	23	of	of	ADP
brj-24383	271	24	the	the	DET
brj-24383	271	25	1d	1d	NUM
brj-24383	271	26	cnn	cnn	NOUN
brj-24383	271	27	model	model	NOUN
brj-24383	271	28	was	be	AUX
brj-24383	271	29	increased	increase	VERB
brj-24383	271	30	to	to	ADP
brj-24383	271	31	97.67	97.67	NUM
brj-24383	271	32	%	%	NOUN
brj-24383	271	33	.	.	PUNCT
brj-24383	272	1	the	the	DET
brj-24383	272	2	first	first	ADJ
brj-24383	272	3	-	-	PUNCT
brj-24383	272	4	order	order	NOUN
brj-24383	272	5	derivative	derivative	ADJ
brj-24383	272	6	transformation	transformation	NOUN
brj-24383	272	7	highlights	highlight	NOUN
brj-24383	272	8	changes	change	NOUN
brj-24383	272	9	in	in	ADP
brj-24383	272	10	spectral	spectral	ADJ
brj-24383	272	11	features	feature	NOUN
brj-24383	272	12	,	,	PUNCT
brj-24383	272	13	such	such	ADJ
brj-24383	272	14	as	as	ADP
brj-24383	272	15	absorption	absorption	NOUN
brj-24383	272	16	and	and	CCONJ
brj-24383	272	17	reflection	reflection	NOUN
brj-24383	272	18	peaks	peak	NOUN
brj-24383	272	19	,	,	PUNCT
brj-24383	272	20	which	which	PRON
brj-24383	272	21	significantly	significantly	ADV
brj-24383	272	22	improved	improve	VERB
brj-24383	272	23	the	the	DET
brj-24383	272	24	model	model	NOUN
brj-24383	272	25	’s	’s	PART
brj-24383	272	26	ability	ability	NOUN
brj-24383	272	27	to	to	PART
brj-24383	272	28	distinguish	distinguish	VERB
brj-24383	272	29	between	between	ADP
brj-24383	272	30	species	specie	NOUN
brj-24383	272	31	with	with	ADP
brj-24383	272	32	subtle	subtle	ADJ
brj-24383	272	33	spectral	spectral	ADJ
brj-24383	272	34	differences	difference	NOUN
brj-24383	272	35	.	.	PUNCT
brj-24383	273	1	combination	combination	NOUN
brj-24383	273	2	of	of	ADP
brj-24383	273	3	first	first	ADJ
brj-24383	273	4	-	-	PUNCT
brj-24383	273	5	order	order	NOUN
brj-24383	273	6	derivative	derivative	NOUN
brj-24383	273	7	and	and	CCONJ
brj-24383	273	8	smote	smote	ADJ
brj-24383	273	9	:	:	PUNCT
brj-24383	273	10	when	when	SCONJ
brj-24383	273	11	both	both	PRON
brj-24383	273	12	the	the	DET
brj-24383	273	13	first	first	ADJ
brj-24383	273	14	-	-	PUNCT
brj-24383	273	15	order	order	NOUN
brj-24383	273	16	derivative	derivative	ADJ
brj-24383	273	17	transformation	transformation	NOUN
brj-24383	273	18	and	and	CCONJ
brj-24383	273	19	smote	smote	NOUN
brj-24383	273	20	were	be	AUX
brj-24383	273	21	applied	apply	VERB
brj-24383	273	22	together	together	ADV
brj-24383	273	23	,	,	PUNCT
brj-24383	273	24	the	the	DET
brj-24383	273	25	accuracy	accuracy	NOUN
brj-24383	273	26	of	of	ADP
brj-24383	273	27	the	the	DET
brj-24383	273	28	1d	1d	NUM
brj-24383	273	29	cnn	cnn	PROPN
brj-24383	273	30	model	model	NOUN
brj-24383	273	31	reached	reach	VERB
brj-24383	273	32	98.89	98.89	NUM
brj-24383	273	33	%	%	NOUN
brj-24383	273	34	.	.	PUNCT
brj-24383	274	1	this	this	DET
brj-24383	274	2	substantial	substantial	ADJ
brj-24383	274	3	improvement	improvement	NOUN
brj-24383	274	4	can	can	AUX
brj-24383	274	5	be	be	AUX
brj-24383	274	6	attributed	attribute	VERB
brj-24383	274	7	to	to	ADP
brj-24383	274	8	the	the	DET
brj-24383	274	9	combination	combination	NOUN
brj-24383	274	10	peer	peer	NOUN
brj-24383	274	11	-	-	PUNCT
brj-24383	274	12	reviewed	review	VERB
brj-24383	274	13	article	article	NOUN
brj-24383	274	14	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	274	15	su	su	PROPN
brj-24383	274	16	et	et	PROPN
brj-24383	274	17	al	al	PROPN
brj-24383	274	18	.	.	PROPN
brj-24383	275	1	(	(	PUNCT
brj-24383	275	2	2025	2025	NUM
brj-24383	275	3	)	)	PUNCT
brj-24383	275	4	.	.	PUNCT
brj-24383	276	1	“	"	PUNCT
brj-24383	276	2	leguminous	leguminous	ADJ
brj-24383	276	3	wood	wood	NOUN
brj-24383	276	4	classification	classification	NOUN
brj-24383	276	5	,	,	PUNCT
brj-24383	276	6	”	"	PUNCT
brj-24383	276	7	bioresources	bioresource	NOUN
brj-24383	276	8	20(3	20(3	NOUN
brj-24383	276	9	)	)	PUNCT
brj-24383	276	10	,	,	PUNCT
brj-24383	276	11	6317	6317	NUM
brj-24383	276	12	-	-	SYM
brj-24383	276	13	6337	6337	NUM
brj-24383	276	14	.	.	PUNCT
brj-24383	277	1	6333	6333	NUM
brj-24383	277	2	of	of	ADP
brj-24383	277	3	enhanced	enhanced	ADJ
brj-24383	277	4	spectral	spectral	ADJ
brj-24383	277	5	features	feature	NOUN
brj-24383	277	6	through	through	ADP
brj-24383	277	7	the	the	DET
brj-24383	277	8	derivative	derivative	ADJ
brj-24383	277	9	transformation	transformation	NOUN
brj-24383	277	10	and	and	CCONJ
brj-24383	277	11	the	the	DET
brj-24383	277	12	balanced	balanced	ADJ
brj-24383	277	13	class	class	NOUN
brj-24383	277	14	distribution	distribution	NOUN
brj-24383	277	15	achieved	achieve	VERB
brj-24383	277	16	by	by	ADP
brj-24383	277	17	smote	smote	NOUN
brj-24383	277	18	.	.	PUNCT
brj-24383	278	1	this	this	DET
brj-24383	278	2	dual	dual	ADJ
brj-24383	278	3	approach	approach	NOUN
brj-24383	278	4	allowed	allow	VERB
brj-24383	278	5	the	the	DET
brj-24383	278	6	1d	1d	NUM
brj-24383	278	7	cnn	cnn	PROPN
brj-24383	278	8	model	model	NOUN
brj-24383	278	9	to	to	PART
brj-24383	278	10	learn	learn	VERB
brj-24383	278	11	more	more	ADV
brj-24383	278	12	effectively	effectively	ADV
brj-24383	278	13	from	from	ADP
brj-24383	278	14	the	the	DET
brj-24383	278	15	minority	minority	NOUN
brj-24383	278	16	class	class	NOUN
brj-24383	278	17	samples	sample	NOUN
brj-24383	278	18	and	and	CCONJ
brj-24383	278	19	to	to	PART
brj-24383	278	20	better	well	ADV
brj-24383	278	21	capture	capture	VERB
brj-24383	278	22	the	the	DET
brj-24383	278	23	spectral	spectral	ADJ
brj-24383	278	24	differences	difference	NOUN
brj-24383	278	25	between	between	ADP
brj-24383	278	26	tree	tree	NOUN
brj-24383	278	27	species	specie	NOUN
brj-24383	278	28	.	.	PUNCT
brj-24383	279	1	the	the	DET
brj-24383	279	2	results	result	NOUN
brj-24383	279	3	of	of	ADP
brj-24383	279	4	the	the	DET
brj-24383	279	5	1d	1d	NUM
brj-24383	279	6	cnn	cnn	PROPN
brj-24383	279	7	model	model	NOUN
brj-24383	279	8	demonstrate	demonstrate	VERB
brj-24383	279	9	the	the	DET
brj-24383	279	10	powerful	powerful	ADJ
brj-24383	279	11	impact	impact	NOUN
brj-24383	279	12	of	of	ADP
brj-24383	279	13	both	both	DET
brj-24383	279	14	feature	feature	NOUN
brj-24383	279	15	engineering	engineering	NOUN
brj-24383	279	16	(	(	PUNCT
brj-24383	279	17	through	through	ADP
brj-24383	279	18	first	first	ADJ
brj-24383	279	19	-	-	PUNCT
brj-24383	279	20	order	order	NOUN
brj-24383	279	21	derivatives	derivative	NOUN
brj-24383	279	22	)	)	PUNCT
brj-24383	279	23	and	and	CCONJ
brj-24383	279	24	data	datum	NOUN
brj-24383	279	25	augmentation	augmentation	NOUN
brj-24383	279	26	(	(	PUNCT
brj-24383	279	27	via	via	ADP
brj-24383	279	28	smote	smote	NOUN
brj-24383	279	29	)	)	PUNCT
brj-24383	279	30	in	in	ADP
brj-24383	279	31	enhancing	enhance	VERB
brj-24383	279	32	classification	classification	NOUN
brj-24383	279	33	performance	performance	NOUN
brj-24383	279	34	.	.	PUNCT
brj-24383	280	1	the	the	DET
brj-24383	280	2	combination	combination	NOUN
brj-24383	280	3	of	of	ADP
brj-24383	280	4	these	these	DET
brj-24383	280	5	techniques	technique	NOUN
brj-24383	280	6	enabled	enable	VERB
brj-24383	280	7	the	the	DET
brj-24383	280	8	model	model	NOUN
brj-24383	280	9	to	to	PART
brj-24383	280	10	achieve	achieve	VERB
brj-24383	280	11	the	the	DET
brj-24383	280	12	highest	high	ADJ
brj-24383	280	13	classification	classification	NOUN
brj-24383	280	14	accuracy	accuracy	NOUN
brj-24383	280	15	among	among	ADP
brj-24383	280	16	all	all	DET
brj-24383	280	17	models	model	NOUN
brj-24383	280	18	tested	test	VERB
brj-24383	280	19	.	.	PUNCT
brj-24383	281	1	figure	figure	VERB
brj-24383	281	2	11	11	NUM
brj-24383	281	3	presents	present	VERB
brj-24383	281	4	the	the	DET
brj-24383	281	5	confusion	confusion	NOUN
brj-24383	281	6	matrix	matrix	NOUN
brj-24383	281	7	for	for	ADP
brj-24383	281	8	the	the	DET
brj-24383	281	9	1d	1d	NUM
brj-24383	281	10	cnn	cnn	PROPN
brj-24383	281	11	model	model	NOUN
brj-24383	281	12	,	,	PUNCT
brj-24383	281	13	highlighting	highlight	VERB
brj-24383	281	14	the	the	DET
brj-24383	281	15	significant	significant	ADJ
brj-24383	281	16	reduction	reduction	NOUN
brj-24383	281	17	in	in	ADP
brj-24383	281	18	misclassification	misclassification	NOUN
brj-24383	281	19	rates	rate	NOUN
brj-24383	281	20	and	and	CCONJ
brj-24383	281	21	the	the	DET
brj-24383	281	22	improved	improved	ADJ
brj-24383	281	23	performance	performance	NOUN
brj-24383	281	24	across	across	ADP
brj-24383	281	25	all	all	DET
brj-24383	281	26	tree	tree	NOUN
brj-24383	281	27	species	specie	NOUN
brj-24383	281	28	,	,	PUNCT
brj-24383	281	29	particularly	particularly	ADV
brj-24383	281	30	the	the	DET
brj-24383	281	31	minority	minority	NOUN
brj-24383	281	32	class	class	NOUN
brj-24383	281	33	.	.	PUNCT
brj-24383	282	1	fig	fig	NOUN
brj-24383	282	2	.	.	PUNCT
brj-24383	283	1	11	11	NUM
brj-24383	283	2	.	.	X
brj-24383	284	1	1d	1d	NUM
brj-24383	284	2	cnn	cnn	PROPN
brj-24383	284	3	confusion	confusion	NOUN
brj-24383	284	4	matrix	matrix	NOUN
brj-24383	284	5	impact	impact	NOUN
brj-24383	284	6	of	of	ADP
brj-24383	284	7	smote	smote	ADJ
brj-24383	284	8	and	and	CCONJ
brj-24383	284	9	first	first	ADJ
brj-24383	284	10	-	-	PUNCT
brj-24383	284	11	order	order	NOUN
brj-24383	284	12	derivative	derivative	ADJ
brj-24383	284	13	transformation	transformation	NOUN
brj-24383	284	14	the	the	DET
brj-24383	284	15	application	application	NOUN
brj-24383	284	16	of	of	ADP
brj-24383	284	17	smote	smote	ADJ
brj-24383	284	18	and	and	CCONJ
brj-24383	284	19	first	first	ADJ
brj-24383	284	20	-	-	PUNCT
brj-24383	284	21	order	order	NOUN
brj-24383	284	22	derivative	derivative	ADJ
brj-24383	284	23	transformation	transformation	NOUN
brj-24383	284	24	significantly	significantly	ADV
brj-24383	284	25	influenced	influence	VERB
brj-24383	284	26	the	the	DET
brj-24383	284	27	classification	classification	NOUN
brj-24383	284	28	results	result	NOUN
brj-24383	284	29	across	across	ADP
brj-24383	284	30	all	all	DET
brj-24383	284	31	models	model	NOUN
brj-24383	284	32	.	.	PUNCT
brj-24383	285	1	for	for	ADP
brj-24383	285	2	traditional	traditional	ADJ
brj-24383	285	3	machine	machine	NOUN
brj-24383	285	4	learning	learning	NOUN
brj-24383	285	5	approaches	approach	NOUN
brj-24383	285	6	(	(	PUNCT
brj-24383	285	7	rf	rf	ADJ
brj-24383	285	8	,	,	PUNCT
brj-24383	285	9	svm	svm	ADJ
brj-24383	285	10	,	,	PUNCT
brj-24383	285	11	and	and	CCONJ
brj-24383	285	12	logistic	logistic	ADJ
brj-24383	285	13	regression	regression	NOUN
brj-24383	285	14	)	)	PUNCT
brj-24383	285	15	,	,	PUNCT
brj-24383	285	16	smote	smote	VERB
brj-24383	285	17	—	—	PUNCT
brj-24383	285	18	as	as	SCONJ
brj-24383	285	19	highlighted	highlight	VERB
brj-24383	285	20	by	by	ADP
brj-24383	285	21	blagus	blagus	NOUN
brj-24383	285	22	and	and	CCONJ
brj-24383	285	23	lusa	lusa	PROPN
brj-24383	285	24	(	(	PUNCT
brj-24383	285	25	2013	2013	NUM
brj-24383	285	26	)	)	PUNCT
brj-24383	285	27	,	,	PUNCT
brj-24383	285	28	who	who	PRON
brj-24383	285	29	demonstrated	demonstrate	VERB
brj-24383	285	30	its	its	PRON
brj-24383	285	31	efficacy	efficacy	NOUN
brj-24383	285	32	in	in	ADP
brj-24383	285	33	handling	handle	VERB
brj-24383	285	34	high	high	ADJ
brj-24383	285	35	-	-	PUNCT
brj-24383	285	36	dimensional	dimensional	ADJ
brj-24383	285	37	class	class	NOUN
brj-24383	285	38	-	-	PUNCT
brj-24383	285	39	imbalanced	imbalance	VERB
brj-24383	285	40	data	datum	NOUN
brj-24383	285	41	—	—	PUNCT
brj-24383	285	42	helped	help	VERB
brj-24383	285	43	balance	balance	NOUN
brj-24383	285	44	class	class	NOUN
brj-24383	285	45	distributions	distribution	NOUN
brj-24383	285	46	,	,	PUNCT
brj-24383	285	47	thereby	thereby	ADV
brj-24383	285	48	improving	improve	VERB
brj-24383	285	49	model	model	NOUN
brj-24383	285	50	generalization	generalization	NOUN
brj-24383	285	51	and	and	CCONJ
brj-24383	285	52	performance	performance	NOUN
brj-24383	285	53	on	on	ADP
brj-24383	285	54	minority	minority	NOUN
brj-24383	285	55	classes	class	NOUN
brj-24383	285	56	.	.	PUNCT
brj-24383	286	1	concurrently	concurrently	ADV
brj-24383	286	2	,	,	PUNCT
brj-24383	286	3	the	the	DET
brj-24383	286	4	first	first	ADJ
brj-24383	286	5	-	-	PUNCT
brj-24383	286	6	order	order	NOUN
brj-24383	286	7	derivative	derivative	ADJ
brj-24383	286	8	transformation	transformation	NOUN
brj-24383	286	9	,	,	PUNCT
brj-24383	286	10	when	when	SCONJ
brj-24383	286	11	applied	apply	VERB
brj-24383	286	12	to	to	ADP
brj-24383	286	13	the	the	DET
brj-24383	286	14	1d	1d	NUM
brj-24383	286	15	cnn	cnn	PROPN
brj-24383	286	16	model	model	NOUN
brj-24383	286	17	,	,	PUNCT
brj-24383	286	18	enhanced	enhance	VERB
brj-24383	286	19	its	its	PRON
brj-24383	286	20	capability	capability	NOUN
brj-24383	286	21	to	to	PART
brj-24383	286	22	capture	capture	VERB
brj-24383	286	23	spectral	spectral	ADJ
brj-24383	286	24	changes	change	NOUN
brj-24383	286	25	and	and	CCONJ
brj-24383	286	26	refine	refine	VERB
brj-24383	286	27	sensitivity	sensitivity	NOUN
brj-24383	286	28	to	to	ADP
brj-24383	286	29	key	key	ADJ
brj-24383	286	30	features	feature	NOUN
brj-24383	286	31	in	in	ADP
brj-24383	286	32	hyperspectral	hyperspectral	ADJ
brj-24383	286	33	data	datum	NOUN
brj-24383	286	34	,	,	PUNCT
brj-24383	286	35	aligning	align	VERB
brj-24383	286	36	with	with	ADP
brj-24383	286	37	the	the	DET
brj-24383	286	38	feature	feature	NOUN
brj-24383	286	39	enhancement	enhancement	NOUN
brj-24383	286	40	principles	principle	NOUN
brj-24383	286	41	underlying	underlie	VERB
brj-24383	286	42	such	such	ADJ
brj-24383	286	43	preprocessing	preprocessing	NOUN
brj-24383	286	44	techniques	technique	NOUN
brj-24383	286	45	.	.	PUNCT
brj-24383	287	1	peer	peer	NOUN
brj-24383	287	2	-	-	PUNCT
brj-24383	287	3	reviewed	review	VERB
brj-24383	287	4	article	article	NOUN
brj-24383	287	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	287	6	su	su	PROPN
brj-24383	287	7	et	et	PROPN
brj-24383	287	8	al	al	PROPN
brj-24383	287	9	.	.	PROPN
brj-24383	288	1	(	(	PUNCT
brj-24383	288	2	2025	2025	NUM
brj-24383	288	3	)	)	PUNCT
brj-24383	288	4	.	.	PUNCT
brj-24383	289	1	“	"	PUNCT
brj-24383	289	2	leguminous	leguminous	ADJ
brj-24383	289	3	wood	wood	NOUN
brj-24383	289	4	classification	classification	NOUN
brj-24383	289	5	,	,	PUNCT
brj-24383	289	6	”	"	PUNCT
brj-24383	289	7	bioresources	bioresource	NOUN
brj-24383	289	8	20(3	20(3	NOUN
brj-24383	289	9	)	)	PUNCT
brj-24383	289	10	,	,	PUNCT
brj-24383	289	11	6317	6317	NUM
brj-24383	289	12	-	-	SYM
brj-24383	289	13	6337	6337	NUM
brj-24383	289	14	.	.	PUNCT
brj-24383	290	1	6334	6334	NUM
brj-24383	290	2	table	table	NOUN
brj-24383	290	3	2	2	NUM
brj-24383	290	4	.	.	PUNCT
brj-24383	290	5	classification	classification	NOUN
brj-24383	290	6	accuracy	accuracy	NOUN
brj-24383	290	7	of	of	ADP
brj-24383	290	8	different	different	ADJ
brj-24383	290	9	models	model	NOUN
brj-24383	290	10	model	model	NOUN
brj-24383	290	11	configuration	configuration	NOUN
brj-24383	290	12	accuracy	accuracy	NOUN
brj-24383	290	13	(	(	PUNCT
brj-24383	290	14	%	%	INTJ
brj-24383	290	15	)	)	PUNCT
brj-24383	290	16	random	random	ADJ
brj-24383	290	17	forest	forest	NOUN
brj-24383	290	18	without	without	ADP
brj-24383	290	19	smote	smote	ADJ
brj-24383	290	20	88.00	88.00	NUM
brj-24383	290	21	random	random	ADJ
brj-24383	290	22	forest	forest	NOUN
brj-24383	290	23	with	with	ADP
brj-24383	290	24	smote	smote	ADJ
brj-24383	290	25	92.26	92.26	NUM
brj-24383	290	26	svm	svm	PROPN
brj-24383	290	27	without	without	ADP
brj-24383	290	28	smote	smote	ADJ
brj-24383	290	29	92.00	92.00	NUM
brj-24383	290	30	svm	svm	PROPN
brj-24383	290	31	with	with	ADP
brj-24383	290	32	smote	smote	ADJ
brj-24383	290	33	98.86	98.86	NUM
brj-24383	290	34	logistic	logistic	ADJ
brj-24383	290	35	regression	regression	NOUN
brj-24383	290	36	without	without	ADP
brj-24383	290	37	smote	smote	ADJ
brj-24383	290	38	93.00	93.00	NUM
brj-24383	290	39	logistic	logistic	ADJ
brj-24383	290	40	regression	regression	NOUN
brj-24383	290	41	with	with	ADP
brj-24383	290	42	smote	smote	ADJ
brj-24383	290	43	97.26	97.26	NUM
brj-24383	290	44	1d	1d	NUM
brj-24383	290	45	cnn	cnn	NOUN
brj-24383	290	46	initial	initial	ADJ
brj-24383	290	47	92.25	92.25	NUM
brj-24383	290	48	1d	1d	NUM
brj-24383	290	49	cnn	cnn	NOUN
brj-24383	290	50	with	with	ADP
brj-24383	290	51	first	first	ADJ
brj-24383	290	52	-	-	PUNCT
brj-24383	290	53	order	order	NOUN
brj-24383	290	54	derivative	derivative	NOUN
brj-24383	290	55	97.67	97.67	NUM
brj-24383	290	56	1d	1d	NUM
brj-24383	290	57	cnn	cnn	NOUN
brj-24383	290	58	with	with	ADP
brj-24383	290	59	first	first	ADJ
brj-24383	290	60	-	-	PUNCT
brj-24383	290	61	order	order	NOUN
brj-24383	290	62	derivative	derivative	ADJ
brj-24383	290	63	+	+	ADJ
brj-24383	290	64	savitzky	savitzky	NOUN
brj-24383	290	65	-	-	PUNCT
brj-24383	290	66	golay	golay	NOUN
brj-24383	290	67	filtering	filtering	NOUN
brj-24383	290	68	+	+	CCONJ
brj-24383	290	69	smote	smote	VERB
brj-24383	290	70	98.89	98.89	NUM
brj-24383	290	71	this	this	DET
brj-24383	290	72	table	table	NOUN
brj-24383	290	73	summarizes	summarize	VERB
brj-24383	290	74	the	the	DET
brj-24383	290	75	classification	classification	NOUN
brj-24383	290	76	accuracy	accuracy	NOUN
brj-24383	290	77	of	of	ADP
brj-24383	290	78	various	various	ADJ
brj-24383	290	79	machine	machine	NOUN
brj-24383	290	80	learning	learning	NOUN
brj-24383	290	81	models	model	NOUN
brj-24383	290	82	under	under	ADP
brj-24383	290	83	different	different	ADJ
brj-24383	290	84	configurations	configuration	NOUN
brj-24383	290	85	,	,	PUNCT
brj-24383	290	86	including	include	VERB
brj-24383	290	87	the	the	DET
brj-24383	290	88	application	application	NOUN
brj-24383	290	89	of	of	ADP
brj-24383	290	90	smote	smote	NOUN
brj-24383	290	91	and	and	CCONJ
brj-24383	290	92	preprocessing	preprocesse	VERB
brj-24383	290	93	techniques	technique	NOUN
brj-24383	290	94	.	.	PUNCT
brj-24383	291	1	together	together	ADV
brj-24383	291	2	,	,	PUNCT
brj-24383	291	3	these	these	DET
brj-24383	291	4	techniques	technique	NOUN
brj-24383	291	5	address	address	VERB
brj-24383	291	6	the	the	DET
brj-24383	291	7	challenges	challenge	NOUN
brj-24383	291	8	posed	pose	VERB
brj-24383	291	9	by	by	ADP
brj-24383	291	10	class	class	NOUN
brj-24383	291	11	imbalance	imbalance	NOUN
brj-24383	291	12	and	and	CCONJ
brj-24383	291	13	subtle	subtle	ADJ
brj-24383	291	14	spectral	spectral	ADJ
brj-24383	291	15	differences	difference	NOUN
brj-24383	291	16	,	,	PUNCT
brj-24383	291	17	which	which	PRON
brj-24383	291	18	are	be	AUX
brj-24383	291	19	particularly	particularly	ADV
brj-24383	291	20	common	common	ADJ
brj-24383	291	21	in	in	ADP
brj-24383	291	22	hyperspectral	hyperspectral	ADJ
brj-24383	291	23	image	image	NOUN
brj-24383	291	24	classification	classification	NOUN
brj-24383	291	25	tasks	task	NOUN
brj-24383	291	26	.	.	PUNCT
brj-24383	292	1	table	table	NOUN
brj-24383	292	2	2	2	NUM
brj-24383	292	3	summarizes	summarize	NOUN
brj-24383	292	4	the	the	DET
brj-24383	292	5	introduction	introduction	NOUN
brj-24383	292	6	of	of	ADP
brj-24383	292	7	all	all	DET
brj-24383	292	8	the	the	DET
brj-24383	292	9	models	model	NOUN
brj-24383	292	10	experimented	experiment	VERB
brj-24383	292	11	above	above	ADV
brj-24383	292	12	and	and	CCONJ
brj-24383	292	13	the	the	DET
brj-24383	292	14	improvement	improvement	NOUN
brj-24383	292	15	in	in	ADP
brj-24383	292	16	accuracy	accuracy	NOUN
brj-24383	292	17	.	.	PUNCT
brj-24383	293	1	role	role	NOUN
brj-24383	293	2	of	of	ADP
brj-24383	293	3	smote	smote	NOUN
brj-24383	293	4	in	in	ADP
brj-24383	293	5	small	small	ADJ
brj-24383	293	6	sample	sample	NOUN
brj-24383	293	7	learning	learn	VERB
brj-24383	293	8	in	in	ADP
brj-24383	293	9	hyperspectral	hyperspectral	ADJ
brj-24383	293	10	image	image	NOUN
brj-24383	293	11	classification	classification	NOUN
brj-24383	293	12	tasks	task	NOUN
brj-24383	293	13	,	,	PUNCT
brj-24383	293	14	class	class	NOUN
brj-24383	293	15	imbalance	imbalance	NOUN
brj-24383	293	16	is	be	AUX
brj-24383	293	17	a	a	DET
brj-24383	293	18	prevalent	prevalent	ADJ
brj-24383	293	19	issue	issue	NOUN
brj-24383	293	20	,	,	PUNCT
brj-24383	293	21	especially	especially	ADV
brj-24383	293	22	when	when	SCONJ
brj-24383	293	23	certain	certain	ADJ
brj-24383	293	24	tree	tree	NOUN
brj-24383	293	25	species	specie	NOUN
brj-24383	293	26	have	have	VERB
brj-24383	293	27	fewer	few	ADJ
brj-24383	293	28	samples	sample	NOUN
brj-24383	293	29	.	.	PUNCT
brj-24383	294	1	traditional	traditional	ADJ
brj-24383	294	2	classification	classification	NOUN
brj-24383	294	3	models	model	NOUN
brj-24383	294	4	tend	tend	VERB
brj-24383	294	5	to	to	PART
brj-24383	294	6	be	be	AUX
brj-24383	294	7	biased	bias	VERB
brj-24383	294	8	towards	towards	ADP
brj-24383	294	9	majority	majority	NOUN
brj-24383	294	10	classes	class	NOUN
brj-24383	294	11	,	,	PUNCT
brj-24383	294	12	resulting	result	VERB
brj-24383	294	13	in	in	ADP
brj-24383	294	14	poorer	poor	ADJ
brj-24383	294	15	performance	performance	NOUN
brj-24383	294	16	for	for	ADP
brj-24383	294	17	minority	minority	NOUN
brj-24383	294	18	classes	class	NOUN
brj-24383	294	19	.	.	PUNCT
brj-24383	295	1	smote	smote	ADJ
brj-24383	295	2	addresses	address	NOUN
brj-24383	295	3	this	this	PRON
brj-24383	295	4	by	by	ADP
brj-24383	295	5	over	over	ADV
brj-24383	295	6	-	-	PUNCT
brj-24383	295	7	sampling	sample	VERB
brj-24383	295	8	the	the	DET
brj-24383	295	9	minority	minority	NOUN
brj-24383	295	10	classes	class	NOUN
brj-24383	295	11	,	,	PUNCT
brj-24383	295	12	thereby	thereby	ADV
brj-24383	295	13	balancing	balance	VERB
brj-24383	295	14	the	the	DET
brj-24383	295	15	class	class	NOUN
brj-24383	295	16	distribution	distribution	NOUN
brj-24383	295	17	.	.	PUNCT
brj-24383	296	1	according	accord	VERB
brj-24383	296	2	to	to	ADP
brj-24383	296	3	chawla	chawla	PROPN
brj-24383	296	4	et	et	PROPN
brj-24383	296	5	al	al	PROPN
brj-24383	296	6	.	.	PROPN
brj-24383	297	1	(	(	PUNCT
brj-24383	297	2	2002	2002	NUM
brj-24383	297	3	)	)	PUNCT
brj-24383	297	4	,	,	PUNCT
brj-24383	297	5	smote	smote	VERB
brj-24383	297	6	generates	generate	VERB
brj-24383	297	7	feature	feature	NOUN
brj-24383	297	8	-	-	PUNCT
brj-24383	297	9	space	space	NOUN
brj-24383	297	10	interpolated	interpolate	VERB
brj-24383	297	11	instances	instance	NOUN
brj-24383	297	12	by	by	ADP
brj-24383	297	13	interpolating	interpolate	VERB
brj-24383	297	14	between	between	ADP
brj-24383	297	15	existing	exist	VERB
brj-24383	297	16	minority	minority	NOUN
brj-24383	297	17	class	class	NOUN
brj-24383	297	18	samples	sample	NOUN
brj-24383	297	19	in	in	ADP
brj-24383	297	20	the	the	DET
brj-24383	297	21	feature	feature	NOUN
brj-24383	297	22	space	space	NOUN
brj-24383	297	23	.	.	PUNCT
brj-24383	298	1	this	this	PRON
brj-24383	298	2	not	not	PART
brj-24383	298	3	only	only	ADV
brj-24383	298	4	increases	increase	VERB
brj-24383	298	5	the	the	DET
brj-24383	298	6	number	number	NOUN
brj-24383	298	7	of	of	ADP
brj-24383	298	8	minority	minority	NOUN
brj-24383	298	9	class	class	NOUN
brj-24383	298	10	samples	sample	NOUN
brj-24383	298	11	but	but	CCONJ
brj-24383	298	12	also	also	ADV
brj-24383	298	13	introduces	introduce	VERB
brj-24383	298	14	new	new	ADJ
brj-24383	298	15	sample	sample	NOUN
brj-24383	298	16	diversity	diversity	NOUN
brj-24383	298	17	,	,	PUNCT
brj-24383	298	18	reducing	reduce	VERB
brj-24383	298	19	the	the	DET
brj-24383	298	20	risk	risk	NOUN
brj-24383	298	21	of	of	ADP
brj-24383	298	22	overfitting	overfitte	VERB
brj-24383	298	23	.	.	PUNCT
brj-24383	299	1	by	by	ADP
brj-24383	299	2	generating	generate	VERB
brj-24383	299	3	feature	feature	NOUN
brj-24383	299	4	-	-	PUNCT
brj-24383	299	5	space	space	NOUN
brj-24383	299	6	interpolated	interpolate	VERB
brj-24383	299	7	instances	instance	NOUN
brj-24383	299	8	within	within	ADP
brj-24383	299	9	the	the	DET
brj-24383	299	10	minority	minority	NOUN
brj-24383	299	11	class	class	NOUN
brj-24383	299	12	’s	’s	PART
brj-24383	299	13	feature	feature	NOUN
brj-24383	299	14	space	space	NOUN
brj-24383	299	15	,	,	PUNCT
brj-24383	299	16	smote	smote	VERB
brj-24383	299	17	enables	enable	VERB
brj-24383	299	18	classifiers	classifier	NOUN
brj-24383	299	19	to	to	PART
brj-24383	299	20	better	well	ADV
brj-24383	299	21	learn	learn	VERB
brj-24383	299	22	the	the	DET
brj-24383	299	23	characteristics	characteristic	NOUN
brj-24383	299	24	of	of	ADP
brj-24383	299	25	these	these	DET
brj-24383	299	26	underrepresented	underrepresented	ADJ
brj-24383	299	27	classes	class	NOUN
brj-24383	299	28	,	,	PUNCT
brj-24383	299	29	thereby	thereby	ADV
brj-24383	299	30	improving	improve	VERB
brj-24383	299	31	their	their	PRON
brj-24383	299	32	recognition	recognition	NOUN
brj-24383	299	33	capabilities	capability	NOUN
brj-24383	299	34	.	.	PUNCT
brj-24383	300	1	advantages	advantage	NOUN
brj-24383	300	2	of	of	ADP
brj-24383	300	3	first	first	ADJ
brj-24383	300	4	-	-	PUNCT
brj-24383	300	5	order	order	NOUN
brj-24383	300	6	derivative	derivative	ADJ
brj-24383	300	7	transformation	transformation	NOUN
brj-24383	300	8	in	in	ADP
brj-24383	300	9	spectral	spectral	ADJ
brj-24383	300	10	data	datum	NOUN
brj-24383	300	11	processing	process	VERB
brj-24383	300	12	first	first	ADJ
brj-24383	300	13	-	-	PUNCT
brj-24383	300	14	order	order	NOUN
brj-24383	300	15	derivative	derivative	ADJ
brj-24383	300	16	transformation	transformation	NOUN
brj-24383	300	17	is	be	AUX
brj-24383	300	18	a	a	DET
brj-24383	300	19	common	common	ADJ
brj-24383	300	20	spectral	spectral	ADJ
brj-24383	300	21	preprocessing	preprocessing	NOUN
brj-24383	300	22	technique	technique	NOUN
brj-24383	300	23	aimed	aim	VERB
brj-24383	300	24	at	at	ADP
brj-24383	300	25	enhancing	enhance	VERB
brj-24383	300	26	the	the	DET
brj-24383	300	27	trend	trend	NOUN
brj-24383	300	28	and	and	CCONJ
brj-24383	300	29	characteristic	characteristic	ADJ
brj-24383	300	30	points	point	NOUN
brj-24383	300	31	of	of	ADP
brj-24383	300	32	the	the	DET
brj-24383	300	33	spectral	spectral	ADJ
brj-24383	300	34	curve	curve	NOUN
brj-24383	300	35	,	,	PUNCT
brj-24383	300	36	such	such	ADJ
brj-24383	300	37	as	as	ADP
brj-24383	300	38	absorption	absorption	NOUN
brj-24383	300	39	and	and	CCONJ
brj-24383	300	40	reflection	reflection	NOUN
brj-24383	300	41	peaks	peak	NOUN
brj-24383	300	42	.	.	PUNCT
brj-24383	301	1	this	this	DET
brj-24383	301	2	method	method	NOUN
brj-24383	301	3	calculates	calculate	VERB
brj-24383	301	4	the	the	DET
brj-24383	301	5	rate	rate	NOUN
brj-24383	301	6	of	of	ADP
brj-24383	301	7	change	change	NOUN
brj-24383	301	8	of	of	ADP
brj-24383	301	9	spectral	spectral	ADJ
brj-24383	301	10	values	value	NOUN
brj-24383	301	11	with	with	ADP
brj-24383	301	12	respect	respect	NOUN
brj-24383	301	13	to	to	ADP
brj-24383	301	14	wavelength	wavelength	NOUN
brj-24383	301	15	,	,	PUNCT
brj-24383	301	16	thereby	thereby	ADV
brj-24383	301	17	emphasizing	emphasize	VERB
brj-24383	301	18	subtle	subtle	ADJ
brj-24383	301	19	variations	variation	NOUN
brj-24383	301	20	in	in	ADP
brj-24383	301	21	the	the	DET
brj-24383	301	22	spectral	spectral	ADJ
brj-24383	301	23	data	data	PROPN
brj-24383	301	24	.	.	PUNCT
brj-24383	302	1	koashi	koashi	PROPN
brj-24383	302	2	(	(	PUNCT
brj-24383	302	3	1999	1999	NUM
brj-24383	302	4	)	)	PUNCT
brj-24383	302	5	demonstrated	demonstrate	VERB
brj-24383	302	6	that	that	SCONJ
brj-24383	302	7	first	first	ADJ
brj-24383	302	8	-	-	PUNCT
brj-24383	302	9	order	order	NOUN
brj-24383	302	10	derivative	derivative	ADJ
brj-24383	302	11	transformation	transformation	NOUN
brj-24383	302	12	effectively	effectively	ADV
brj-24383	302	13	eliminates	eliminate	VERB
brj-24383	302	14	baseline	baseline	ADJ
brj-24383	302	15	drifts	drift	NOUN
brj-24383	302	16	and	and	CCONJ
brj-24383	302	17	slow	slow	ADJ
brj-24383	302	18	trends	trend	NOUN
brj-24383	302	19	in	in	ADP
brj-24383	302	20	spectral	spectral	ADJ
brj-24383	302	21	data	datum	NOUN
brj-24383	302	22	,	,	PUNCT
brj-24383	302	23	highlighting	highlight	VERB
brj-24383	302	24	regions	region	NOUN
brj-24383	302	25	of	of	ADP
brj-24383	302	26	rapid	rapid	ADJ
brj-24383	302	27	change	change	NOUN
brj-24383	302	28	.	.	PUNCT
brj-24383	303	1	these	these	DET
brj-24383	303	2	rapid	rapid	ADJ
brj-24383	303	3	changes	change	NOUN
brj-24383	303	4	often	often	ADV
brj-24383	303	5	correspond	correspond	VERB
brj-24383	303	6	to	to	ADP
brj-24383	303	7	key	key	ADJ
brj-24383	303	8	spectral	spectral	ADJ
brj-24383	303	9	features	feature	NOUN
brj-24383	303	10	that	that	PRON
brj-24383	303	11	are	be	AUX
brj-24383	303	12	critical	critical	ADJ
brj-24383	303	13	for	for	ADP
brj-24383	303	14	distinguishing	distinguish	VERB
brj-24383	303	15	between	between	ADP
brj-24383	303	16	different	different	ADJ
brj-24383	303	17	materials	material	NOUN
brj-24383	303	18	or	or	CCONJ
brj-24383	303	19	species	specie	NOUN
brj-24383	303	20	.	.	PUNCT
brj-24383	304	1	by	by	ADP
brj-24383	304	2	enhancing	enhance	VERB
brj-24383	304	3	these	these	DET
brj-24383	304	4	features	feature	NOUN
brj-24383	304	5	,	,	PUNCT
brj-24383	304	6	firstorder	firstorder	VERB
brj-24383	304	7	derivative	derivative	ADJ
brj-24383	304	8	transformation	transformation	NOUN
brj-24383	304	9	improves	improve	VERB
brj-24383	304	10	the	the	DET
brj-24383	304	11	resolution	resolution	NOUN
brj-24383	304	12	of	of	ADP
brj-24383	304	13	spectral	spectral	ADJ
brj-24383	304	14	data	datum	NOUN
brj-24383	304	15	,	,	PUNCT
brj-24383	304	16	making	make	VERB
brj-24383	304	17	it	it	PRON
brj-24383	304	18	easier	easy	ADJ
brj-24383	304	19	for	for	SCONJ
brj-24383	304	20	classification	classification	NOUN
brj-24383	304	21	models	model	NOUN
brj-24383	304	22	to	to	PART
brj-24383	304	23	detect	detect	VERB
brj-24383	304	24	and	and	CCONJ
brj-24383	304	25	differentiate	differentiate	VERB
brj-24383	304	26	subtle	subtle	ADJ
brj-24383	304	27	differences	difference	NOUN
brj-24383	304	28	between	between	ADP
brj-24383	304	29	classes	class	NOUN
brj-24383	304	30	.	.	PUNCT
brj-24383	305	1	peer	peer	NOUN
brj-24383	305	2	-	-	PUNCT
brj-24383	305	3	reviewed	review	VERB
brj-24383	305	4	article	article	NOUN
brj-24383	305	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	305	6	su	su	PROPN
brj-24383	305	7	et	et	PROPN
brj-24383	305	8	al	al	PROPN
brj-24383	305	9	.	.	PROPN
brj-24383	306	1	(	(	PUNCT
brj-24383	306	2	2025	2025	NUM
brj-24383	306	3	)	)	PUNCT
brj-24383	306	4	.	.	PUNCT
brj-24383	307	1	“	"	PUNCT
brj-24383	307	2	leguminous	leguminous	ADJ
brj-24383	307	3	wood	wood	NOUN
brj-24383	307	4	classification	classification	NOUN
brj-24383	307	5	,	,	PUNCT
brj-24383	307	6	”	"	PUNCT
brj-24383	307	7	bioresources	bioresource	NOUN
brj-24383	307	8	20(3	20(3	NOUN
brj-24383	307	9	)	)	PUNCT
brj-24383	307	10	,	,	PUNCT
brj-24383	307	11	6317	6317	NUM
brj-24383	307	12	-	-	SYM
brj-24383	307	13	6337	6337	NUM
brj-24383	307	14	.	.	PUNCT
brj-24383	308	1	6335	6335	NUM
brj-24383	308	2	for	for	ADP
brj-24383	308	3	the	the	DET
brj-24383	308	4	1d	1d	NUM
brj-24383	308	5	cnn	cnn	PROPN
brj-24383	308	6	model	model	NOUN
brj-24383	308	7	,	,	PUNCT
brj-24383	308	8	the	the	DET
brj-24383	308	9	initial	initial	ADJ
brj-24383	308	10	classification	classification	NOUN
brj-24383	308	11	accuracy	accuracy	NOUN
brj-24383	308	12	was	be	AUX
brj-24383	308	13	92.2	92.2	NUM
brj-24383	308	14	%	%	NOUN
brj-24383	308	15	.	.	PUNCT
brj-24383	309	1	after	after	ADP
brj-24383	309	2	applying	apply	VERB
brj-24383	309	3	the	the	DET
brj-24383	309	4	first	first	ADJ
brj-24383	309	5	-	-	PUNCT
brj-24383	309	6	order	order	NOUN
brj-24383	309	7	derivative	derivative	ADJ
brj-24383	309	8	transformation	transformation	NOUN
brj-24383	309	9	,	,	PUNCT
brj-24383	309	10	the	the	DET
brj-24383	309	11	accuracy	accuracy	NOUN
brj-24383	309	12	increased	increase	VERB
brj-24383	309	13	to	to	ADP
brj-24383	309	14	97.67	97.67	NUM
brj-24383	309	15	%	%	NOUN
brj-24383	309	16	.	.	PUNCT
brj-24383	310	1	this	this	DET
brj-24383	310	2	significant	significant	ADJ
brj-24383	310	3	improvement	improvement	NOUN
brj-24383	310	4	indicates	indicate	VERB
brj-24383	310	5	that	that	SCONJ
brj-24383	310	6	the	the	DET
brj-24383	310	7	first	first	ADJ
brj-24383	310	8	-	-	PUNCT
brj-24383	310	9	order	order	NOUN
brj-24383	310	10	derivative	derivative	ADJ
brj-24383	310	11	transformation	transformation	NOUN
brj-24383	310	12	effectively	effectively	ADV
brj-24383	310	13	enhanced	enhance	VERB
brj-24383	310	14	the	the	DET
brj-24383	310	15	spectral	spectral	ADJ
brj-24383	310	16	features	feature	NOUN
brj-24383	310	17	,	,	PUNCT
brj-24383	310	18	allowing	allow	VERB
brj-24383	310	19	the	the	DET
brj-24383	310	20	1d	1d	NUM
brj-24383	310	21	cnn	cnn	PROPN
brj-24383	310	22	model	model	NOUN
brj-24383	310	23	to	to	PART
brj-24383	310	24	better	well	ADV
brj-24383	310	25	distinguish	distinguish	VERB
brj-24383	310	26	between	between	ADP
brj-24383	310	27	tree	tree	NOUN
brj-24383	310	28	species	specie	NOUN
brj-24383	310	29	with	with	ADP
brj-24383	310	30	minor	minor	ADJ
brj-24383	310	31	spectral	spectral	ADJ
brj-24383	310	32	differences	difference	NOUN
brj-24383	310	33	.	.	PUNCT
brj-24383	311	1	role	role	NOUN
brj-24383	311	2	of	of	ADP
brj-24383	311	3	savitzky	savitzky	NOUN
brj-24383	311	4	-	-	PUNCT
brj-24383	311	5	golay	golay	NOUN
brj-24383	311	6	filtering	filtering	NOUN
brj-24383	311	7	in	in	ADP
brj-24383	311	8	spectral	spectral	ADJ
brj-24383	311	9	noise	noise	NOUN
brj-24383	311	10	reduction	reduction	NOUN
brj-24383	311	11	savitzky	savitzky	NOUN
brj-24383	311	12	-	-	PUNCT
brj-24383	311	13	golay	golay	NOUN
brj-24383	311	14	filtering	filtering	NOUN
brj-24383	311	15	,	,	PUNCT
brj-24383	311	16	a	a	DET
brj-24383	311	17	polynomial	polynomial	ADJ
brj-24383	311	18	-	-	PUNCT
brj-24383	311	19	based	base	VERB
brj-24383	311	20	smoothing	smoothing	NOUN
brj-24383	311	21	technique	technique	NOUN
brj-24383	311	22	widely	widely	ADV
brj-24383	311	23	employed	employ	VERB
brj-24383	311	24	in	in	ADP
brj-24383	311	25	spectral	spectral	ADJ
brj-24383	311	26	data	datum	NOUN
brj-24383	311	27	processing	processing	NOUN
brj-24383	311	28	,	,	PUNCT
brj-24383	311	29	effectively	effectively	ADV
brj-24383	311	30	removes	remove	VERB
brj-24383	311	31	noise	noise	NOUN
brj-24383	311	32	while	while	SCONJ
brj-24383	311	33	preserving	preserve	VERB
brj-24383	311	34	essential	essential	ADJ
brj-24383	311	35	spectral	spectral	ADJ
brj-24383	311	36	features	feature	NOUN
brj-24383	311	37	.	.	PUNCT
brj-24383	312	1	as	as	SCONJ
brj-24383	312	2	demonstrated	demonstrate	VERB
brj-24383	312	3	by	by	ADP
brj-24383	312	4	john	john	PROPN
brj-24383	312	5	,	,	PUNCT
brj-24383	312	6	sadasivan	sadasivan	NOUN
brj-24383	312	7	,	,	PUNCT
brj-24383	312	8	and	and	CCONJ
brj-24383	312	9	seelamantula	seelamantula	VERB
brj-24383	312	10	(	(	PUNCT
brj-24383	312	11	2021	2021	NUM
brj-24383	312	12	)	)	PUNCT
brj-24383	312	13	,	,	PUNCT
brj-24383	312	14	this	this	DET
brj-24383	312	15	method	method	NOUN
brj-24383	312	16	performs	perform	VERB
brj-24383	312	17	local	local	ADJ
brj-24383	312	18	polynomial	polynomial	NOUN
brj-24383	312	19	fitting	fit	VERB
brj-24383	312	20	within	within	ADP
brj-24383	312	21	a	a	DET
brj-24383	312	22	sliding	slide	VERB
brj-24383	312	23	window	window	NOUN
brj-24383	312	24	to	to	PART
brj-24383	312	25	smooth	smooth	VERB
brj-24383	312	26	spectral	spectral	ADJ
brj-24383	312	27	data	datum	NOUN
brj-24383	312	28	—	—	PUNCT
brj-24383	312	29	particularly	particularly	ADV
brj-24383	312	30	notable	notable	ADJ
brj-24383	312	31	for	for	ADP
brj-24383	312	32	its	its	PRON
brj-24383	312	33	adaptive	adaptive	ADJ
brj-24383	312	34	capability	capability	NOUN
brj-24383	312	35	in	in	ADP
brj-24383	312	36	non	non	ADJ
brj-24383	312	37	-	-	ADJ
brj-24383	312	38	gaussian	gaussian	ADJ
brj-24383	312	39	noise	noise	NOUN
brj-24383	312	40	environments	environment	NOUN
brj-24383	312	41	,	,	PUNCT
brj-24383	312	42	which	which	PRON
brj-24383	312	43	reduces	reduce	VERB
brj-24383	312	44	high	high	ADJ
brj-24383	312	45	-	-	PUNCT
brj-24383	312	46	frequency	frequency	NOUN
brj-24383	312	47	noise	noise	NOUN
brj-24383	312	48	without	without	ADP
brj-24383	312	49	distorting	distort	VERB
brj-24383	312	50	underlying	underlie	VERB
brj-24383	312	51	spectral	spectral	ADJ
brj-24383	312	52	characteristics	characteristic	NOUN
brj-24383	312	53	.	.	PUNCT
brj-24383	313	1	this	this	DET
brj-24383	313	2	adaptive	adaptive	ADJ
brj-24383	313	3	refinement	refinement	NOUN
brj-24383	313	4	,	,	PUNCT
brj-24383	313	5	as	as	SCONJ
brj-24383	313	6	outlined	outline	VERB
brj-24383	313	7	in	in	ADP
brj-24383	313	8	their	their	PRON
brj-24383	313	9	study	study	NOUN
brj-24383	313	10	,	,	PUNCT
brj-24383	313	11	enhances	enhance	VERB
brj-24383	313	12	the	the	DET
brj-24383	313	13	technique	technique	NOUN
brj-24383	313	14	’s	’s	PART
brj-24383	313	15	robustness	robustness	NOUN
brj-24383	313	16	across	across	ADP
brj-24383	313	17	diverse	diverse	ADJ
brj-24383	313	18	spectral	spectral	ADJ
brj-24383	313	19	datasets	dataset	NOUN
brj-24383	313	20	,	,	PUNCT
brj-24383	313	21	ensuring	ensure	VERB
brj-24383	313	22	both	both	CCONJ
brj-24383	313	23	noise	noise	NOUN
brj-24383	313	24	reduction	reduction	NOUN
brj-24383	313	25	and	and	CCONJ
brj-24383	313	26	feature	feature	NOUN
brj-24383	313	27	integrity	integrity	NOUN
brj-24383	313	28	.	.	PUNCT
brj-24383	314	1	introduced	introduce	VERB
brj-24383	314	2	by	by	ADP
brj-24383	314	3	savitzky	savitzky	NOUN
brj-24383	314	4	and	and	CCONJ
brj-24383	314	5	golay	golay	VERB
brj-24383	314	6	(	(	PUNCT
brj-24383	314	7	1964	1964	NUM
brj-24383	314	8	)	)	PUNCT
brj-24383	314	9	,	,	PUNCT
brj-24383	314	10	this	this	DET
brj-24383	314	11	filtering	filtering	NOUN
brj-24383	314	12	technique	technique	NOUN
brj-24383	314	13	maintains	maintain	VERB
brj-24383	314	14	the	the	DET
brj-24383	314	15	integrity	integrity	NOUN
brj-24383	314	16	of	of	ADP
brj-24383	314	17	important	important	ADJ
brj-24383	314	18	spectral	spectral	ADJ
brj-24383	314	19	features	feature	NOUN
brj-24383	314	20	such	such	ADJ
brj-24383	314	21	as	as	ADP
brj-24383	314	22	peaks	peak	NOUN
brj-24383	314	23	and	and	CCONJ
brj-24383	314	24	valleys	valley	NOUN
brj-24383	314	25	by	by	ADP
brj-24383	314	26	fitting	fit	VERB
brj-24383	314	27	a	a	DET
brj-24383	314	28	low	low	ADJ
brj-24383	314	29	-	-	PUNCT
brj-24383	314	30	degree	degree	NOUN
brj-24383	314	31	polynomial	polynomial	NOUN
brj-24383	314	32	to	to	ADP
brj-24383	314	33	the	the	DET
brj-24383	314	34	data	datum	NOUN
brj-24383	314	35	within	within	ADP
brj-24383	314	36	each	each	DET
brj-24383	314	37	window	window	NOUN
brj-24383	314	38	.	.	PUNCT
brj-24383	315	1	this	this	DET
brj-24383	315	2	approach	approach	NOUN
brj-24383	315	3	effectively	effectively	ADV
brj-24383	315	4	reduces	reduce	VERB
brj-24383	315	5	noise	noise	NOUN
brj-24383	315	6	interference	interference	NOUN
brj-24383	315	7	in	in	ADP
brj-24383	315	8	the	the	DET
brj-24383	315	9	spectral	spectral	ADJ
brj-24383	315	10	data	data	PROPN
brj-24383	315	11	,	,	PUNCT
brj-24383	315	12	enhancing	enhance	VERB
brj-24383	315	13	the	the	DET
brj-24383	315	14	signal	signal	NOUN
brj-24383	315	15	-	-	PUNCT
brj-24383	315	16	to	to	ADP
brj-24383	315	17	-	-	PUNCT
brj-24383	315	18	noise	noise	NOUN
brj-24383	315	19	ratio	ratio	NOUN
brj-24383	315	20	and	and	CCONJ
brj-24383	315	21	enabling	enable	VERB
brj-24383	315	22	more	more	ADV
brj-24383	315	23	accurate	accurate	ADJ
brj-24383	315	24	feature	feature	NOUN
brj-24383	315	25	extraction	extraction	NOUN
brj-24383	315	26	for	for	ADP
brj-24383	315	27	classification	classification	NOUN
brj-24383	315	28	purposes	purpose	NOUN
brj-24383	315	29	.	.	PUNCT
brj-24383	316	1	in	in	ADP
brj-24383	316	2	this	this	DET
brj-24383	316	3	study	study	NOUN
brj-24383	316	4	,	,	PUNCT
brj-24383	316	5	combining	combine	VERB
brj-24383	316	6	savitzky	savitzky	NOUN
brj-24383	316	7	-	-	PUNCT
brj-24383	316	8	golay	golay	NOUN
brj-24383	316	9	filtering	filtering	NOUN
brj-24383	316	10	with	with	ADP
brj-24383	316	11	first	first	ADJ
brj-24383	316	12	-	-	PUNCT
brj-24383	316	13	order	order	NOUN
brj-24383	316	14	derivative	derivative	ADJ
brj-24383	316	15	transformation	transformation	NOUN
brj-24383	316	16	and	and	CCONJ
brj-24383	316	17	smote	smite	VERB
brj-24383	316	18	further	far	ADV
brj-24383	316	19	improved	improve	VERB
brj-24383	316	20	the	the	DET
brj-24383	316	21	1d	1d	NUM
brj-24383	316	22	cnn	cnn	PROPN
brj-24383	316	23	model	model	NOUN
brj-24383	316	24	’s	’s	PART
brj-24383	316	25	accuracy	accuracy	NOUN
brj-24383	316	26	to	to	ADP
brj-24383	316	27	98.89	98.89	NUM
brj-24383	316	28	%	%	NOUN
brj-24383	316	29	.	.	PUNCT
brj-24383	317	1	this	this	PRON
brj-24383	317	2	indicates	indicate	VERB
brj-24383	317	3	that	that	SCONJ
brj-24383	317	4	savitzky	savitzky	NOUN
brj-24383	317	5	-	-	PUNCT
brj-24383	317	6	golay	golay	NOUN
brj-24383	317	7	filtering	filtering	NOUN
brj-24383	317	8	successfully	successfully	ADV
brj-24383	317	9	reduced	reduce	VERB
brj-24383	317	10	noise	noise	NOUN
brj-24383	317	11	in	in	ADP
brj-24383	317	12	the	the	DET
brj-24383	317	13	spectral	spectral	ADJ
brj-24383	317	14	data	datum	NOUN
brj-24383	317	15	,	,	PUNCT
brj-24383	317	16	allowing	allow	VERB
brj-24383	317	17	the	the	DET
brj-24383	317	18	model	model	NOUN
brj-24383	317	19	to	to	AUX
brj-24383	317	20	better	well	ADJ
brj-24383	317	21	capture	capture	VERB
brj-24383	317	22	and	and	CCONJ
brj-24383	317	23	utilize	utilize	VERB
brj-24383	317	24	the	the	DET
brj-24383	317	25	true	true	ADJ
brj-24383	317	26	spectral	spectral	ADJ
brj-24383	317	27	features	feature	NOUN
brj-24383	317	28	for	for	ADP
brj-24383	317	29	classification	classification	NOUN
brj-24383	317	30	,	,	PUNCT
brj-24383	317	31	thereby	thereby	ADV
brj-24383	317	32	enhancing	enhance	VERB
brj-24383	317	33	overall	overall	ADJ
brj-24383	317	34	performance	performance	NOUN
brj-24383	317	35	.	.	PUNCT
brj-24383	318	1	conclusions	conclusion	NOUN
brj-24383	318	2	1	1	X
brj-24383	318	3	.	.	PUNCT
brj-24383	319	1	this	this	DET
brj-24383	319	2	study	study	NOUN
brj-24383	319	3	demonstrated	demonstrate	VERB
brj-24383	319	4	that	that	SCONJ
brj-24383	319	5	combining	combine	VERB
brj-24383	319	6	data	datum	NOUN
brj-24383	319	7	augmentation	augmentation	NOUN
brj-24383	319	8	techniques	technique	NOUN
brj-24383	319	9	,	,	PUNCT
brj-24383	319	10	particularly	particularly	ADV
brj-24383	319	11	synthetic	synthetic	ADJ
brj-24383	319	12	minority	minority	NOUN
brj-24383	319	13	oversampling	oversample	VERB
brj-24383	319	14	technique	technique	NOUN
brj-24383	319	15	(	(	PUNCT
brj-24383	319	16	smote	smote	NOUN
brj-24383	319	17	)	)	PUNCT
brj-24383	319	18	,	,	PUNCT
brj-24383	319	19	with	with	ADP
brj-24383	319	20	spectral	spectral	ADJ
brj-24383	319	21	feature	feature	NOUN
brj-24383	319	22	enhancement	enhancement	NOUN
brj-24383	319	23	methods	method	NOUN
brj-24383	319	24	(	(	PUNCT
brj-24383	319	25	e.g.	e.g.	ADV
brj-24383	319	26	,	,	PUNCT
brj-24383	319	27	first	first	ADJ
brj-24383	319	28	-	-	PUNCT
brj-24383	319	29	order	order	NOUN
brj-24383	319	30	derivative	derivative	ADJ
brj-24383	319	31	transformation	transformation	NOUN
brj-24383	319	32	and	and	CCONJ
brj-24383	319	33	savitzky	savitzky	NOUN
brj-24383	319	34	-	-	PUNCT
brj-24383	319	35	golay	golay	NOUN
brj-24383	319	36	filtering	filtering	NOUN
brj-24383	319	37	)	)	PUNCT
brj-24383	319	38	significantly	significantly	ADV
brj-24383	319	39	improved	improve	VERB
brj-24383	319	40	the	the	DET
brj-24383	319	41	classification	classification	NOUN
brj-24383	319	42	accuracy	accuracy	NOUN
brj-24383	319	43	of	of	ADP
brj-24383	319	44	hyperspectral	hyperspectral	ADJ
brj-24383	319	45	images	image	NOUN
brj-24383	319	46	for	for	ADP
brj-24383	319	47	leguminous	leguminous	ADJ
brj-24383	319	48	tree	tree	NOUN
brj-24383	319	49	species	specie	NOUN
brj-24383	319	50	.	.	PUNCT
brj-24383	320	1	2	2	X
brj-24383	320	2	.	.	X
brj-24383	320	3	the	the	DET
brj-24383	320	4	1d	1d	NUM
brj-24383	320	5	convolutional	convolutional	ADJ
brj-24383	320	6	neural	neural	ADJ
brj-24383	320	7	network	network	NOUN
brj-24383	320	8	(	(	PUNCT
brj-24383	320	9	1d	1d	NUM
brj-24383	320	10	cnn	cnn	NOUN
brj-24383	320	11	)	)	PUNCT
brj-24383	320	12	,	,	PUNCT
brj-24383	320	13	when	when	SCONJ
brj-24383	320	14	integrated	integrate	VERB
brj-24383	320	15	with	with	ADP
brj-24383	320	16	these	these	DET
brj-24383	320	17	preprocessing	preprocessing	NOUN
brj-24383	320	18	and	and	CCONJ
brj-24383	320	19	augmentation	augmentation	NOUN
brj-24383	320	20	techniques	technique	NOUN
brj-24383	320	21	,	,	PUNCT
brj-24383	320	22	achieved	achieve	VERB
brj-24383	320	23	an	an	DET
brj-24383	320	24	exceptional	exceptional	ADJ
brj-24383	320	25	accuracy	accuracy	NOUN
brj-24383	320	26	of	of	ADP
brj-24383	320	27	98.89	98.89	NUM
brj-24383	320	28	%	%	NOUN
brj-24383	320	29	,	,	PUNCT
brj-24383	320	30	outperforming	outperform	VERB
brj-24383	320	31	traditional	traditional	ADJ
brj-24383	320	32	models	model	NOUN
brj-24383	320	33	like	like	ADP
brj-24383	320	34	random	random	ADJ
brj-24383	320	35	forest	forest	NOUN
brj-24383	320	36	,	,	PUNCT
brj-24383	320	37	support	support	NOUN
brj-24383	320	38	vector	vector	NOUN
brj-24383	320	39	machine	machine	NOUN
brj-24383	320	40	(	(	PUNCT
brj-24383	320	41	svm	svm	PROPN
brj-24383	320	42	)	)	PUNCT
brj-24383	320	43	,	,	PUNCT
brj-24383	320	44	and	and	CCONJ
brj-24383	320	45	logistic	logistic	ADJ
brj-24383	320	46	regression	regression	NOUN
brj-24383	320	47	,	,	PUNCT
brj-24383	320	48	which	which	PRON
brj-24383	320	49	also	also	ADV
brj-24383	320	50	showed	show	VERB
brj-24383	320	51	improvements	improvement	NOUN
brj-24383	320	52	after	after	ADP
brj-24383	320	53	smote	smote	ADJ
brj-24383	320	54	application	application	NOUN
brj-24383	320	55	.	.	PUNCT
brj-24383	321	1	3	3	X
brj-24383	321	2	.	.	PUNCT
brj-24383	321	3	the	the	DET
brj-24383	321	4	findings	finding	NOUN
brj-24383	321	5	emphasize	emphasize	VERB
brj-24383	321	6	the	the	DET
brj-24383	321	7	importance	importance	NOUN
brj-24383	321	8	of	of	ADP
brj-24383	321	9	addressing	address	VERB
brj-24383	321	10	class	class	NOUN
brj-24383	321	11	imbalance	imbalance	NOUN
brj-24383	321	12	and	and	CCONJ
brj-24383	321	13	enhancing	enhance	VERB
brj-24383	321	14	spectral	spectral	ADJ
brj-24383	321	15	features	feature	NOUN
brj-24383	321	16	in	in	ADP
brj-24383	321	17	high	high	ADJ
brj-24383	321	18	-	-	PUNCT
brj-24383	321	19	dimensional	dimensional	ADJ
brj-24383	321	20	hyperspectral	hyperspectral	ADJ
brj-24383	321	21	data	datum	NOUN
brj-24383	321	22	classification	classification	NOUN
brj-24383	321	23	,	,	PUNCT
brj-24383	321	24	which	which	PRON
brj-24383	321	25	is	be	AUX
brj-24383	321	26	crucial	crucial	ADJ
brj-24383	321	27	for	for	ADP
brj-24383	321	28	advancing	advance	VERB
brj-24383	321	29	agricultural	agricultural	ADJ
brj-24383	321	30	information	information	NOUN
brj-24383	321	31	engineering	engineering	NOUN
brj-24383	321	32	.	.	PUNCT
brj-24383	322	1	4	4	X
brj-24383	322	2	.	.	X
brj-24383	322	3	the	the	DET
brj-24383	322	4	framework	framework	NOUN
brj-24383	322	5	developed	develop	VERB
brj-24383	322	6	in	in	ADP
brj-24383	322	7	this	this	DET
brj-24383	322	8	study	study	NOUN
brj-24383	322	9	offers	offer	VERB
brj-24383	322	10	valuable	valuable	ADJ
brj-24383	322	11	strategies	strategy	NOUN
brj-24383	322	12	for	for	ADP
brj-24383	322	13	improving	improve	VERB
brj-24383	322	14	classification	classification	NOUN
brj-24383	322	15	performance	performance	NOUN
brj-24383	322	16	in	in	ADP
brj-24383	322	17	similar	similar	ADJ
brj-24383	322	18	domains	domain	NOUN
brj-24383	322	19	,	,	PUNCT
brj-24383	322	20	particularly	particularly	ADV
brj-24383	322	21	for	for	ADP
brj-24383	322	22	imbalanced	imbalanced	ADJ
brj-24383	322	23	and	and	CCONJ
brj-24383	322	24	complex	complex	ADJ
brj-24383	322	25	datasets	dataset	NOUN
brj-24383	322	26	.	.	PUNCT
brj-24383	323	1	peer	peer	NOUN
brj-24383	323	2	-	-	PUNCT
brj-24383	323	3	reviewed	review	VERB
brj-24383	323	4	article	article	NOUN
brj-24383	323	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	323	6	su	su	PROPN
brj-24383	323	7	et	et	PROPN
brj-24383	323	8	al	al	PROPN
brj-24383	323	9	.	.	PROPN
brj-24383	324	1	(	(	PUNCT
brj-24383	324	2	2025	2025	NUM
brj-24383	324	3	)	)	PUNCT
brj-24383	324	4	.	.	PUNCT
brj-24383	325	1	“	"	PUNCT
brj-24383	325	2	leguminous	leguminous	ADJ
brj-24383	325	3	wood	wood	NOUN
brj-24383	325	4	classification	classification	NOUN
brj-24383	325	5	,	,	PUNCT
brj-24383	325	6	”	"	PUNCT
brj-24383	325	7	bioresources	bioresource	NOUN
brj-24383	325	8	20(3	20(3	NOUN
brj-24383	325	9	)	)	PUNCT
brj-24383	325	10	,	,	PUNCT
brj-24383	325	11	6317	6317	NUM
brj-24383	325	12	-	-	SYM
brj-24383	325	13	6337	6337	NUM
brj-24383	325	14	.	.	PUNCT
brj-24383	326	1	6336	6336	NUM
brj-24383	326	2	5	5	NUM
brj-24383	326	3	.	.	PUNCT
brj-24383	326	4	limitations	limitation	NOUN
brj-24383	326	5	include	include	VERB
brj-24383	326	6	the	the	DET
brj-24383	326	7	potential	potential	NOUN
brj-24383	326	8	for	for	ADP
brj-24383	326	9	synthetic	synthetic	ADJ
brj-24383	326	10	samples	sample	NOUN
brj-24383	326	11	from	from	ADP
brj-24383	326	12	smote	smote	NOUN
brj-24383	326	13	to	to	PART
brj-24383	326	14	reduce	reduce	VERB
brj-24383	326	15	generalizability	generalizability	NOUN
brj-24383	326	16	,	,	PUNCT
brj-24383	326	17	and	and	CCONJ
brj-24383	326	18	the	the	DET
brj-24383	326	19	exclusion	exclusion	NOUN
brj-24383	326	20	of	of	ADP
brj-24383	326	21	spatial	spatial	ADJ
brj-24383	326	22	information	information	NOUN
brj-24383	326	23	,	,	PUNCT
brj-24383	326	24	which	which	PRON
brj-24383	326	25	may	may	AUX
brj-24383	326	26	limit	limit	VERB
brj-24383	326	27	classification	classification	NOUN
brj-24383	326	28	accuracy	accuracy	NOUN
brj-24383	326	29	.	.	PUNCT
brj-24383	327	1	future	future	ADJ
brj-24383	327	2	research	research	NOUN
brj-24383	327	3	should	should	AUX
brj-24383	327	4	explore	explore	VERB
brj-24383	327	5	advanced	advanced	ADJ
brj-24383	327	6	augmentation	augmentation	NOUN
brj-24383	327	7	techniques	technique	NOUN
brj-24383	327	8	like	like	ADP
brj-24383	327	9	generative	generative	ADJ
brj-24383	327	10	adversarial	adversarial	ADJ
brj-24383	327	11	networks	network	NOUN
brj-24383	327	12	(	(	PUNCT
brj-24383	327	13	gans	gan	NOUN
brj-24383	327	14	)	)	PUNCT
brj-24383	327	15	and	and	CCONJ
brj-24383	327	16	integrate	integrate	VERB
brj-24383	327	17	spatial	spatial	ADJ
brj-24383	327	18	-	-	PUNCT
brj-24383	327	19	spectral	spectral	ADJ
brj-24383	327	20	hybrid	hybrid	NOUN
brj-24383	327	21	models	model	NOUN
brj-24383	327	22	to	to	PART
brj-24383	327	23	improve	improve	VERB
brj-24383	327	24	robustness	robustness	NOUN
brj-24383	327	25	and	and	CCONJ
brj-24383	327	26	applicability	applicability	NOUN
brj-24383	327	27	.	.	PUNCT
brj-24383	328	1	acknowledgments	acknowledgment	NOUN
brj-24383	328	2	this	this	DET
brj-24383	328	3	work	work	NOUN
brj-24383	328	4	was	be	AUX
brj-24383	328	5	supported	support	VERB
brj-24383	328	6	by	by	ADP
brj-24383	328	7	the	the	DET
brj-24383	328	8	national	national	ADJ
brj-24383	328	9	natural	natural	PROPN
brj-24383	328	10	science	science	PROPN
brj-24383	328	11	foundation	foundation	PROPN
brj-24383	328	12	of	of	ADP
brj-24383	328	13	china	china	PROPN
brj-24383	328	14	(	(	PUNCT
brj-24383	328	15	grant	grant	VERB
brj-24383	328	16	number	number	NOUN
brj-24383	328	17	62265001	62265001	NUM
brj-24383	328	18	)	)	PUNCT
brj-24383	328	19	.	.	PUNCT
brj-24383	329	1	references	reference	NOUN
brj-24383	329	2	cited	cite	VERB
brj-24383	329	3	aydemir	aydemir	PROPN
brj-24383	329	4	,	,	PUNCT
brj-24383	329	5	m.	m.	NOUN
brj-24383	329	6	s.	s.	PROPN
brj-24383	329	7	,	,	PUNCT
brj-24383	329	8	and	and	CCONJ
brj-24383	329	9	bilgin	bilgin	NOUN
brj-24383	329	10	,	,	PUNCT
brj-24383	329	11	g.	g.	PROPN
brj-24383	329	12	(	(	PUNCT
brj-24383	329	13	2017	2017	NUM
brj-24383	329	14	)	)	PUNCT
brj-24383	329	15	.	.	PUNCT
brj-24383	330	1	“	"	PUNCT
brj-24383	330	2	semisupervised	semisupervise	VERB
brj-24383	330	3	hyperspectral	hyperspectral	ADJ
brj-24383	330	4	image	image	NOUN
brj-24383	330	5	classification	classification	NOUN
brj-24383	330	6	using	use	VERB
brj-24383	330	7	small	small	ADJ
brj-24383	330	8	sample	sample	NOUN
brj-24383	330	9	sizes	size	NOUN
brj-24383	330	10	,	,	PUNCT
brj-24383	330	11	”	"	PUNCT
brj-24383	330	12	ieee	ieee	NOUN
brj-24383	330	13	geoscience	geoscience	NOUN
brj-24383	330	14	and	and	CCONJ
brj-24383	330	15	remote	remote	ADJ
brj-24383	330	16	sensing	sensing	NOUN
brj-24383	330	17	letters	letter	NOUN
brj-24383	330	18	14(5	14(5	NUM
brj-24383	330	19	)	)	PUNCT
brj-24383	330	20	,	,	PUNCT
brj-24383	330	21	802	802	NUM
brj-24383	330	22	-	-	SYM
brj-24383	330	23	806	806	NUM
brj-24383	330	24	.	.	PUNCT
brj-24383	331	1	doi	doi	NOUN
brj-24383	331	2	:	:	PUNCT
brj-24383	331	3	10.1109	10.1109	NUM
brj-24383	331	4	/	/	SYM
brj-24383	331	5	lgrs.2017.2665679	lgrs.2017.2665679	PROPN
brj-24383	331	6	blagus	blagus	NOUN
brj-24383	331	7	,	,	PUNCT
brj-24383	331	8	r.	r.	PROPN
brj-24383	331	9	,	,	PUNCT
brj-24383	331	10	and	and	CCONJ
brj-24383	331	11	lusa	lusa	PROPN
brj-24383	331	12	,	,	PUNCT
brj-24383	331	13	l.	l.	PROPN
brj-24383	331	14	(	(	PUNCT
brj-24383	331	15	2013	2013	NUM
brj-24383	331	16	)	)	PUNCT
brj-24383	331	17	.	.	PUNCT
brj-24383	332	1	“	"	PUNCT
brj-24383	332	2	smote	smote	VERB
brj-24383	332	3	for	for	ADP
brj-24383	332	4	high	high	ADJ
brj-24383	332	5	-	-	PUNCT
brj-24383	332	6	dimensional	dimensional	ADJ
brj-24383	332	7	class	class	NOUN
brj-24383	332	8	-	-	PUNCT
brj-24383	332	9	imbalanced	imbalance	VERB
brj-24383	332	10	data	datum	NOUN
brj-24383	332	11	,	,	PUNCT
brj-24383	332	12	"	"	PUNCT
brj-24383	332	13	bmc	bmc	ADJ
brj-24383	332	14	bioinformatics	bioinformatics	NOUN
brj-24383	332	15	14	14	NUM
brj-24383	332	16	,	,	PUNCT
brj-24383	332	17	article	article	NOUN
brj-24383	332	18	106	106	NUM
brj-24383	332	19	.	.	PUNCT
brj-24383	333	1	doi	doi	NOUN
brj-24383	333	2	:	:	PUNCT
brj-24383	333	3	10.1186/1471	10.1186/1471	NUM
brj-24383	333	4	-	-	PUNCT
brj-24383	333	5	2105	2105	NUM
brj-24383	333	6	-	-	PUNCT
brj-24383	333	7	14	14	NUM
brj-24383	333	8	-	-	PUNCT
brj-24383	333	9	106	106	NUM
brj-24383	333	10	chawla	chawla	NOUN
brj-24383	333	11	,	,	PUNCT
brj-24383	333	12	n.	n.	PROPN
brj-24383	333	13	v.	v.	PROPN
brj-24383	333	14	,	,	PUNCT
brj-24383	333	15	bowyer	bowyer	PROPN
brj-24383	333	16	,	,	PUNCT
brj-24383	333	17	k.	k.	PROPN
brj-24383	333	18	w.	w.	PROPN
brj-24383	333	19	,	,	PUNCT
brj-24383	333	20	hall	hall	PROPN
brj-24383	333	21	,	,	PUNCT
brj-24383	333	22	l.	l.	PROPN
brj-24383	333	23	o.	o.	PROPN
brj-24383	333	24	,	,	PUNCT
brj-24383	333	25	and	and	CCONJ
brj-24383	333	26	kegelmeyer	kegelmeyer	PROPN
brj-24383	333	27	,	,	PUNCT
brj-24383	333	28	w.	w.	PROPN
brj-24383	333	29	p.	p.	PROPN
brj-24383	333	30	(	(	PUNCT
brj-24383	333	31	2002	2002	NUM
brj-24383	333	32	)	)	PUNCT
brj-24383	333	33	.	.	PUNCT
brj-24383	334	1	“	"	PUNCT
brj-24383	334	2	smote	smote	VERB
brj-24383	334	3	:	:	PUNCT
brj-24383	334	4	synthetic	synthetic	ADJ
brj-24383	334	5	minority	minority	NOUN
brj-24383	334	6	over	over	ADP
brj-24383	334	7	-	-	PUNCT
brj-24383	334	8	sampling	sample	VERB
brj-24383	334	9	technique	technique	NOUN
brj-24383	334	10	,	,	PUNCT
brj-24383	334	11	”	"	PUNCT
brj-24383	334	12	journal	journal	NOUN
brj-24383	334	13	of	of	ADP
brj-24383	334	14	artificial	artificial	ADJ
brj-24383	334	15	intelligence	intelligence	NOUN
brj-24383	334	16	research	research	NOUN
brj-24383	334	17	16	16	NUM
brj-24383	334	18	,	,	PUNCT
brj-24383	334	19	321	321	NUM
brj-24383	334	20	-	-	SYM
brj-24383	334	21	357	357	NUM
brj-24383	334	22	.	.	PUNCT
brj-24383	335	1	doi	doi	NOUN
brj-24383	335	2	:	:	PUNCT
brj-24383	335	3	10.1613	10.1613	NUM
brj-24383	335	4	/	/	SYM
brj-24383	335	5	jair.953	jair.953	PROPN
brj-24383	335	6	chen	chen	PROPN
brj-24383	335	7	,	,	PUNCT
brj-24383	335	8	z.	z.	PROPN
brj-24383	335	9	,	,	PUNCT
brj-24383	335	10	xue	xue	PROPN
brj-24383	335	11	,	,	PUNCT
brj-24383	335	12	x.	x.	PROPN
brj-24383	335	13	,	,	PUNCT
brj-24383	335	14	wu	wu	PROPN
brj-24383	335	15	,	,	PUNCT
brj-24383	335	16	h.	h.	PROPN
brj-24383	335	17	,	,	PUNCT
brj-24383	335	18	gao	gao	PROPN
brj-24383	335	19	,	,	PUNCT
brj-24383	335	20	h.	h.	PROPN
brj-24383	335	21	,	,	PUNCT
brj-24383	335	22	wang	wang	PROPN
brj-24383	335	23	,	,	PUNCT
brj-24383	335	24	g.	g.	PROPN
brj-24383	335	25	,	,	PUNCT
brj-24383	335	26	ni	ni	PROPN
brj-24383	335	27	,	,	PUNCT
brj-24383	335	28	g.	g.	PROPN
brj-24383	335	29	,	,	PUNCT
brj-24383	335	30	and	and	CCONJ
brj-24383	335	31	cao	cao	PROPN
brj-24383	335	32	,	,	PUNCT
brj-24383	335	33	t.	t.	PROPN
brj-24383	335	34	(	(	PUNCT
brj-24383	335	35	2024	2024	NUM
brj-24383	335	36	)	)	PUNCT
brj-24383	335	37	.	.	PUNCT
brj-24383	336	1	“	"	PUNCT
brj-24383	336	2	visible	visible	ADJ
brj-24383	336	3	/	/	SYM
brj-24383	336	4	nearinfrared	nearinfrared	ADJ
brj-24383	336	5	hyperspectral	hyperspectral	ADJ
brj-24383	336	6	imaging	imaging	NOUN
brj-24383	336	7	combined	combine	VERB
brj-24383	336	8	with	with	ADP
brj-24383	336	9	machine	machine	NOUN
brj-24383	336	10	learning	learning	NOUN
brj-24383	336	11	for	for	ADP
brj-24383	336	12	identification	identification	NOUN
brj-24383	336	13	of	of	ADP
brj-24383	336	14	ten	ten	NUM
brj-24383	336	15	dalbergia	dalbergia	NOUN
brj-24383	336	16	species	specie	NOUN
brj-24383	336	17	,	,	PUNCT
brj-24383	336	18	”	"	PUNCT
brj-24383	336	19	frontiers	frontier	NOUN
brj-24383	336	20	in	in	ADP
brj-24383	336	21	plant	plant	NOUN
brj-24383	336	22	science	science	NOUN
brj-24383	336	23	15	15	NUM
brj-24383	336	24	,	,	PUNCT
brj-24383	336	25	article	article	NOUN
brj-24383	336	26	1413215	1413215	NUM
brj-24383	336	27	.	.	PUNCT
brj-24383	337	1	doi	doi	NOUN
brj-24383	337	2	:	:	PUNCT
brj-24383	337	3	10.3389	10.3389	NUM
brj-24383	337	4	/	/	SYM
brj-24383	337	5	fpls.2024.1413215	fpls.2024.1413215	VERB
brj-24383	337	6	fabijańska	fabijańska	NOUN
brj-24383	337	7	,	,	PUNCT
brj-24383	337	8	a.	a.	NOUN
brj-24383	337	9	,	,	PUNCT
brj-24383	337	10	danek	danek	ADJ
brj-24383	337	11	,	,	PUNCT
brj-24383	337	12	m.	m.	NOUN
brj-24383	337	13	,	,	PUNCT
brj-24383	337	14	and	and	CCONJ
brj-24383	337	15	barniak	barniak	PROPN
brj-24383	337	16	,	,	PUNCT
brj-24383	337	17	j.	j.	PROPN
brj-24383	337	18	(	(	PUNCT
brj-24383	337	19	2021	2021	NUM
brj-24383	337	20	)	)	PUNCT
brj-24383	337	21	.	.	PUNCT
brj-24383	338	1	“	"	PUNCT
brj-24383	338	2	wood	wood	NOUN
brj-24383	338	3	species	specie	NOUN
brj-24383	338	4	automatic	automatic	ADJ
brj-24383	338	5	identification	identification	NOUN
brj-24383	338	6	from	from	ADP
brj-24383	338	7	wood	wood	NOUN
brj-24383	338	8	core	core	NOUN
brj-24383	338	9	images	image	NOUN
brj-24383	338	10	with	with	ADP
brj-24383	338	11	a	a	DET
brj-24383	338	12	residual	residual	ADJ
brj-24383	338	13	convolutional	convolutional	ADJ
brj-24383	338	14	neural	neural	ADJ
brj-24383	338	15	network	network	NOUN
brj-24383	338	16	,	,	PUNCT
brj-24383	338	17	”	"	PUNCT
brj-24383	338	18	computers	computer	NOUN
brj-24383	338	19	and	and	CCONJ
brj-24383	338	20	electronics	electronic	NOUN
brj-24383	338	21	in	in	ADP
brj-24383	338	22	agriculture	agriculture	NOUN
brj-24383	338	23	179	179	NUM
brj-24383	338	24	,	,	PUNCT
brj-24383	338	25	article	article	NOUN
brj-24383	338	26	105941	105941	NUM
brj-24383	338	27	.	.	PUNCT
brj-24383	339	1	doi	doi	NOUN
brj-24383	339	2	:	:	PUNCT
brj-24383	339	3	10.1016	10.1016	NUM
brj-24383	339	4	/	/	SYM
brj-24383	339	5	j.compag.2020.105941	j.compag.2020.105941	ADJ
brj-24383	339	6	gerasimov	gerasimov	VERB
brj-24383	339	7	,	,	PUNCT
brj-24383	339	8	v.	v.	PROPN
brj-24383	339	9	a.	a.	PROPN
brj-24383	339	10	,	,	PUNCT
brj-24383	339	11	gurovich	gurovich	PROPN
brj-24383	339	12	,	,	PUNCT
brj-24383	339	13	a.	a.	NOUN
brj-24383	339	14	m.	m.	NOUN
brj-24383	339	15	,	,	PUNCT
brj-24383	339	16	kostrin	kostrin	PROPN
brj-24383	339	17	,	,	PUNCT
brj-24383	339	18	d.	d.	PROPN
brj-24383	339	19	k.	k.	PROPN
brj-24383	339	20	,	,	PUNCT
brj-24383	339	21	selivanov	selivanov	PROPN
brj-24383	339	22	,	,	PUNCT
brj-24383	339	23	l.	l.	PROPN
brj-24383	339	24	m.	m.	PROPN
brj-24383	339	25	,	,	PUNCT
brj-24383	339	26	simon	simon	PROPN
brj-24383	339	27	,	,	PUNCT
brj-24383	339	28	v.	v.	PROPN
brj-24383	339	29	a.	a.	NOUN
brj-24383	339	30	,	,	PUNCT
brj-24383	339	31	stuchenkov	stuchenkov	NOUN
brj-24383	339	32	,	,	PUNCT
brj-24383	339	33	a.	a.	PROPN
brj-24383	339	34	b.	b.	PROPN
brj-24383	339	35	,	,	PUNCT
brj-24383	339	36	paltcev	paltcev	NOUN
brj-24383	339	37	,	,	PUNCT
brj-24383	339	38	a.	a.	NOUN
brj-24383	339	39	v.	v.	PROPN
brj-24383	339	40	,	,	PUNCT
brj-24383	339	41	and	and	CCONJ
brj-24383	339	42	uhov	uhov	ADJ
brj-24383	339	43	,	,	PUNCT
brj-24383	339	44	a.	a.	NOUN
brj-24383	339	45	a.	a.	NOUN
brj-24383	339	46	(	(	PUNCT
brj-24383	339	47	2016	2016	NUM
brj-24383	339	48	)	)	PUNCT
brj-24383	339	49	.	.	PUNCT
brj-24383	340	1	“	"	PUNCT
brj-24383	340	2	raman	raman	NOUN
brj-24383	340	3	spectroscopy	spectroscopy	NOUN
brj-24383	340	4	for	for	ADP
brj-24383	340	5	identification	identification	NOUN
brj-24383	340	6	of	of	ADP
brj-24383	340	7	wood	wood	NOUN
brj-24383	340	8	species	specie	NOUN
brj-24383	340	9	,	,	PUNCT
brj-24383	340	10	”	"	PUNCT
brj-24383	340	11	journal	journal	NOUN
brj-24383	340	12	of	of	ADP
brj-24383	340	13	physics	physics	PROPN
brj-24383	340	14	:	:	PUNCT
brj-24383	340	15	conference	conference	NOUN
brj-24383	340	16	series	series	NOUN
brj-24383	340	17	741	741	NUM
brj-24383	340	18	,	,	PUNCT
brj-24383	340	19	article	article	NOUN
brj-24383	340	20	012131	012131	NUM
brj-24383	340	21	.	.	PUNCT
brj-24383	341	1	doi	doi	NOUN
brj-24383	341	2	:	:	PUNCT
brj-24383	341	3	10.1088/1742	10.1088/1742	NUM
brj-24383	341	4	-	-	SYM
brj-24383	341	5	6596/741/1/012131	6596/741/1/012131	NUM
brj-24383	341	6	he	he	PRON
brj-24383	341	7	,	,	PUNCT
brj-24383	341	8	x.	x.	NOUN
brj-24383	341	9	,	,	PUNCT
brj-24383	341	10	and	and	CCONJ
brj-24383	341	11	chen	chen	PROPN
brj-24383	341	12	,	,	PUNCT
brj-24383	341	13	y.-s	y.-	NOUN
brj-24383	341	14	.	.	PUNCT
brj-24383	342	1	(	(	PUNCT
brj-24383	342	2	2020	2020	NUM
brj-24383	342	3	)	)	PUNCT
brj-24383	342	4	.	.	PUNCT
brj-24383	343	1	“	"	PUNCT
brj-24383	343	2	transferring	transfer	VERB
brj-24383	343	3	cnn	cnn	PROPN
brj-24383	343	4	ensemble	ensemble	ADJ
brj-24383	343	5	for	for	ADP
brj-24383	343	6	hyperspectral	hyperspectral	ADJ
brj-24383	343	7	image	image	NOUN
brj-24383	343	8	classification	classification	NOUN
brj-24383	343	9	,	,	PUNCT
brj-24383	343	10	”	"	PUNCT
brj-24383	343	11	ieee	ieee	NOUN
brj-24383	343	12	geoscience	geoscience	NOUN
brj-24383	343	13	and	and	CCONJ
brj-24383	343	14	remote	remote	ADJ
brj-24383	343	15	sensing	sense	VERB
brj-24383	343	16	letters	letter	NOUN
brj-24383	343	17	18.5	18.5	NUM
brj-24383	343	18	,	,	PUNCT
brj-24383	343	19	876	876	NUM
brj-24383	343	20	-	-	SYM
brj-24383	343	21	880	880	NUM
brj-24383	343	22	.	.	PUNCT
brj-24383	344	1	doi	doi	NOUN
brj-24383	344	2	:	:	PUNCT
brj-24383	344	3	10.1109	10.1109	NUM
brj-24383	344	4	/	/	SYM
brj-24383	344	5	lgrs.2020.2988494	lgrs.2020.2988494	PROPN
brj-24383	344	6	john	john	PROPN
brj-24383	344	7	,	,	PUNCT
brj-24383	344	8	a.	a.	PROPN
brj-24383	344	9	,	,	PUNCT
brj-24383	344	10	sadasivan	sadasivan	PROPN
brj-24383	344	11	,	,	PUNCT
brj-24383	344	12	j.	j.	PROPN
brj-24383	344	13	,	,	PUNCT
brj-24383	344	14	and	and	CCONJ
brj-24383	344	15	seelamantula	seelamantula	VERB
brj-24383	344	16	,	,	PUNCT
brj-24383	344	17	c.	c.	PROPN
brj-24383	344	18	s.	s.	PROPN
brj-24383	344	19	(	(	PUNCT
brj-24383	344	20	2021	2021	NUM
brj-24383	344	21	)	)	PUNCT
brj-24383	344	22	.	.	PUNCT
brj-24383	345	1	“	"	PUNCT
brj-24383	345	2	adaptive	adaptive	ADJ
brj-24383	345	3	savitzky	savitzky	NOUN
brj-24383	345	4	-	-	PUNCT
brj-24383	345	5	golay	golay	NOUN
brj-24383	345	6	filtering	filtering	NOUN
brj-24383	345	7	in	in	ADP
brj-24383	345	8	non	non	ADJ
brj-24383	345	9	-	-	ADJ
brj-24383	345	10	gaussian	gaussian	ADJ
brj-24383	345	11	noise	noise	NOUN
brj-24383	345	12	,	,	PUNCT
brj-24383	345	13	”	"	PUNCT
brj-24383	345	14	ieee	ieee	NOUN
brj-24383	345	15	transactions	transaction	NOUN
brj-24383	345	16	on	on	ADP
brj-24383	345	17	signal	signal	ADJ
brj-24383	345	18	processing	processing	NOUN
brj-24383	345	19	69	69	NUM
brj-24383	345	20	,	,	PUNCT
brj-24383	345	21	article	article	NOUN
brj-24383	345	22	3106450	3106450	NUM
brj-24383	345	23	.	.	PUNCT
brj-24383	346	1	doi	doi	NOUN
brj-24383	346	2	:	:	PUNCT
brj-24383	346	3	10.1109	10.1109	NUM
brj-24383	346	4	/	/	SYM
brj-24383	346	5	tsp.2021.3106450	tsp.2021.3106450	NUM
brj-24383	346	6	koashi	koashi	NOUN
brj-24383	346	7	,	,	PUNCT
brj-24383	346	8	k.	k.	PROPN
brj-24383	346	9	(	(	PUNCT
brj-24383	346	10	1999	1999	NUM
brj-24383	346	11	)	)	PUNCT
brj-24383	346	12	.	.	PUNCT
brj-24383	347	1	“	"	PUNCT
brj-24383	347	2	spectral	spectral	ADJ
brj-24383	347	3	data	datum	NOUN
brj-24383	347	4	analysis	analysis	NOUN
brj-24383	347	5	by	by	ADP
brj-24383	347	6	two	two	NUM
brj-24383	347	7	-	-	PUNCT
brj-24383	347	8	dimensional	dimensional	ADJ
brj-24383	347	9	representation	representation	NOUN
brj-24383	347	10	of	of	ADP
brj-24383	347	11	derivatives	derivative	NOUN
brj-24383	347	12	,	,	PUNCT
brj-24383	347	13	”	"	PUNCT
brj-24383	347	14	applied	apply	VERB
brj-24383	347	15	spectroscopy	spectroscopy	NOUN
brj-24383	347	16	53.6	53.6	NUM
brj-24383	347	17	,	,	PUNCT
brj-24383	347	18	706	706	NUM
brj-24383	347	19	712	712	NUM
brj-24383	347	20	.	.	PUNCT
brj-24383	348	1	doi	doi	NOUN
brj-24383	348	2	:	:	PUNCT
brj-24383	348	3	10.1366/0003702991947144	10.1366/0003702991947144	NUM
brj-24383	348	4	lima	lima	NOUN
brj-24383	348	5	,	,	PUNCT
brj-24383	348	6	m.	m.	PROPN
brj-24383	348	7	d.	d.	PROPN
brj-24383	348	8	r.	r.	PROPN
brj-24383	348	9	,	,	PUNCT
brj-24383	348	10	trugilho	trugilho	NOUN
brj-24383	348	11	,	,	PUNCT
brj-24383	348	12	p.	p.	PROPN
brj-24383	348	13	f.	f.	PROPN
brj-24383	348	14	,	,	PUNCT
brj-24383	348	15	bufalino	bufalino	PROPN
brj-24383	348	16	,	,	PUNCT
brj-24383	348	17	l.	l.	PROPN
brj-24383	348	18	,	,	PUNCT
brj-24383	348	19	júnior	júnior	PROPN
brj-24383	348	20	,	,	PUNCT
brj-24383	348	21	a.	a.	PROPN
brj-24383	348	22	f.	f.	PROPN
brj-24383	348	23	d.	d.	PROPN
brj-24383	348	24	,	,	PUNCT
brj-24383	348	25	ramalho	ramalho	PROPN
brj-24383	348	26	,	,	PUNCT
brj-24383	348	27	f.	f.	PROPN
brj-24383	348	28	m.	m.	PROPN
brj-24383	348	29	g.	g.	PROPN
brj-24383	348	30	,	,	PUNCT
brj-24383	348	31	protásio	protásio	NOUN
brj-24383	348	32	,	,	PUNCT
brj-24383	348	33	t.	t.	PROPN
brj-24383	348	34	p.	p.	PROPN
brj-24383	348	35	,	,	PUNCT
brj-24383	348	36	and	and	CCONJ
brj-24383	348	37	hein	hein	PROPN
brj-24383	348	38	,	,	PUNCT
brj-24383	349	1	p.	p.	PROPN
brj-24383	349	2	r.	r.	PROPN
brj-24383	349	3	g.	g.	PROPN
brj-24383	349	4	(	(	PUNCT
brj-24383	349	5	2022	2022	NUM
brj-24383	349	6	)	)	PUNCT
brj-24383	349	7	.	.	PUNCT
brj-24383	350	1	“	"	PUNCT
brj-24383	350	2	efficiency	efficiency	NOUN
brj-24383	350	3	of	of	ADP
brj-24383	350	4	near	near	ADV
brj-24383	350	5	-	-	PUNCT
brj-24383	350	6	infrared	infrared	ADJ
brj-24383	350	7	spectroscopy	spectroscopy	NOUN
brj-24383	350	8	in	in	ADP
brj-24383	350	9	classifying	classify	VERB
brj-24383	350	10	amazonian	amazonian	ADJ
brj-24383	350	11	wood	wood	NOUN
brj-24383	350	12	wastes	waste	NOUN
brj-24383	350	13	for	for	ADP
brj-24383	350	14	bioenergy	bioenergy	NOUN
brj-24383	350	15	generation	generation	NOUN
brj-24383	350	16	,	,	PUNCT
brj-24383	350	17	”	"	PUNCT
brj-24383	350	18	biomass	biomass	NOUN
brj-24383	350	19	&	&	CCONJ
brj-24383	350	20	bioenergy	bioenergy	NOUN
brj-24383	350	21	166(11	166(11	NUM
brj-24383	350	22	)	)	PUNCT
brj-24383	350	23	,	,	PUNCT
brj-24383	350	24	article	article	NOUN
brj-24383	350	25	106617	106617	NUM
brj-24383	350	26	.	.	PUNCT
brj-24383	351	1	doi	doi	NOUN
brj-24383	351	2	:	:	PUNCT
brj-24383	351	3	10.1016	10.1016	NUM
brj-24383	351	4	/	/	SYM
brj-24383	351	5	j.biombioe.2022.106617	j.biombioe.2022.106617	NOUN
brj-24383	351	6	https://doi.org/10.1109/lgrs.2017.2665679	https://doi.org/10.1109/lgrs.2017.2665679	PROPN
brj-24383	351	7	https://doi.org/10.1186/1471-2105-14-106	https://doi.org/10.1186/1471-2105-14-106	PRON
brj-24383	351	8	https://doi.org/10.1613/jair.953	https://doi.org/10.1613/jair.953	NOUN
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brj-24383	352	2	https://doi.org/10.1016/j.compag.2020.105941	https://doi.org/10.1016/j.compag.2020.105941	PROPN
brj-24383	352	3	https://doi.org/10.1088/1742-6596/741/1/012131	https://doi.org/10.1088/1742-6596/741/1/012131	NUM
brj-24383	352	4	https://doi.org/10.1366/0003702991947144	https://doi.org/10.1366/0003702991947144	PROPN
brj-24383	352	5	https://doi.org/10.1016/j.biombioe.2022.106617	https://doi.org/10.1016/j.biombioe.2022.106617	PROPN
brj-24383	352	6	peer	peer	NOUN
brj-24383	352	7	-	-	PUNCT
brj-24383	352	8	reviewed	review	VERB
brj-24383	352	9	article	article	NOUN
brj-24383	352	10	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24383	352	11	su	su	PROPN
brj-24383	352	12	et	et	PROPN
brj-24383	352	13	al	al	PROPN
brj-24383	352	14	.	.	PROPN
brj-24383	353	1	(	(	PUNCT
brj-24383	353	2	2025	2025	NUM
brj-24383	353	3	)	)	PUNCT
brj-24383	353	4	.	.	PUNCT
brj-24383	354	1	“	"	PUNCT
brj-24383	354	2	leguminous	leguminous	ADJ
brj-24383	354	3	wood	wood	NOUN
brj-24383	354	4	classification	classification	NOUN
brj-24383	354	5	,	,	PUNCT
brj-24383	354	6	”	"	PUNCT
brj-24383	354	7	bioresources	bioresource	NOUN
brj-24383	354	8	20(3	20(3	NOUN
brj-24383	354	9	)	)	PUNCT
brj-24383	354	10	,	,	PUNCT
brj-24383	354	11	6317	6317	NUM
brj-24383	354	12	-	-	SYM
brj-24383	354	13	6337	6337	NUM
brj-24383	354	14	.	.	PUNCT
brj-24383	355	1	6337	6337	NUM
brj-24383	355	2	marrs	marrs	PROPN
brj-24383	355	3	,	,	PUNCT
brj-24383	355	4	j.	j.	PROPN
brj-24383	355	5	,	,	PUNCT
brj-24383	355	6	and	and	CCONJ
brj-24383	355	7	ni	ni	PROPN
brj-24383	355	8	-	-	PUNCT
brj-24383	355	9	meister	meister	PROPN
brj-24383	355	10	,	,	PUNCT
brj-24383	355	11	w.	w.	NOUN
brj-24383	355	12	(	(	PUNCT
brj-24383	355	13	2019	2019	NUM
brj-24383	355	14	)	)	PUNCT
brj-24383	355	15	.	.	PUNCT
brj-24383	356	1	“	"	PUNCT
brj-24383	356	2	machine	machine	NOUN
brj-24383	356	3	learning	learn	VERB
brj-24383	356	4	techniques	technique	NOUN
brj-24383	356	5	for	for	ADP
brj-24383	356	6	tree	tree	NOUN
brj-24383	356	7	species	specie	NOUN
brj-24383	356	8	classification	classification	NOUN
brj-24383	356	9	using	use	VERB
brj-24383	356	10	co	co	ADJ
brj-24383	356	11	-	-	ADJ
brj-24383	356	12	registered	registered	ADJ
brj-24383	356	13	lidar	lidar	NOUN
brj-24383	356	14	and	and	CCONJ
brj-24383	356	15	hyperspectral	hyperspectral	ADJ
brj-24383	356	16	data	datum	NOUN
brj-24383	356	17	,	,	PUNCT
brj-24383	356	18	”	"	PUNCT
brj-24383	356	19	remote	remote	ADJ
brj-24383	356	20	sensing	sense	VERB
brj-24383	356	21	11(7	11(7	NUM
brj-24383	356	22	)	)	PUNCT
brj-24383	356	23	,	,	PUNCT
brj-24383	356	24	article	article	NOUN
brj-24383	356	25	819	819	NUM
brj-24383	356	26	.	.	PUNCT
brj-24383	357	1	doi	doi	NOUN
brj-24383	357	2	:	:	PUNCT
brj-24383	357	3	10.3390	10.3390	NUM
brj-24383	357	4	/	/	SYM
brj-24383	357	5	rs11070819	rs11070819	ADJ
brj-24383	357	6	masoumi	masoumi	NOUN
brj-24383	357	7	,	,	PUNCT
brj-24383	357	8	a.	a.	NOUN
brj-24383	357	9	,	,	PUNCT
brj-24383	357	10	and	and	CCONJ
brj-24383	357	11	bond	bond	NOUN
brj-24383	357	12	,	,	PUNCT
brj-24383	357	13	b.	b.	PROPN
brj-24383	357	14	h.	h.	PROPN
brj-24383	357	15	(	(	PUNCT
brj-24383	357	16	2024	2024	NUM
brj-24383	357	17	)	)	PUNCT
brj-24383	357	18	.	.	PUNCT
brj-24383	358	1	“	"	PUNCT
brj-24383	358	2	prediction	prediction	NOUN
brj-24383	358	3	of	of	ADP
brj-24383	358	4	equilibrium	equilibrium	NOUN
brj-24383	358	5	moisture	moisture	NOUN
brj-24383	358	6	content	content	NOUN
brj-24383	358	7	and	and	CCONJ
brj-24383	358	8	swelling	swell	VERB
brj-24383	358	9	of	of	ADP
brj-24383	358	10	thermally	thermally	ADV
brj-24383	358	11	modified	modify	VERB
brj-24383	358	12	hardwoods	hardwood	NOUN
brj-24383	358	13	by	by	ADP
brj-24383	358	14	artificial	artificial	ADJ
brj-24383	358	15	neural	neural	ADJ
brj-24383	358	16	networks	network	NOUN
brj-24383	358	17	,	,	PUNCT
brj-24383	358	18	"	"	PUNCT
brj-24383	358	19	bioresources	bioresource	NOUN
brj-24383	358	20	19(4	19(4	NUM
brj-24383	358	21	)	)	PUNCT
brj-24383	358	22	,	,	PUNCT
brj-24383	358	23	6983	6983	NUM
brj-24383	358	24	-	-	SYM
brj-24383	358	25	6993	6993	NUM
brj-24383	358	26	.	.	PUNCT
brj-24383	359	1	doi	doi	NOUN
brj-24383	359	2	:	:	PUNCT
brj-24383	359	3	10.15376	10.15376	NUM
brj-24383	359	4	/	/	SYM
brj-24383	359	5	biores.19.4.6983	biores.19.4.6983	NOUN
brj-24383	359	6	-	-	PUNCT
brj-24383	359	7	6993	6993	NUM
brj-24383	359	8	pan	pan	NOUN
brj-24383	359	9	,	,	PUNCT
brj-24383	359	10	x.	x.	PROPN
brj-24383	359	11	,	,	PUNCT
brj-24383	359	12	yu	yu	PROPN
brj-24383	359	13	,	,	PUNCT
brj-24383	359	14	z.	z.	PROPN
brj-24383	359	15	,	,	PUNCT
brj-24383	359	16	and	and	CCONJ
brj-24383	359	17	yang	yang	PROPN
brj-24383	359	18	,	,	PUNCT
brj-24383	359	19	z.	z.	PROPN
brj-24383	359	20	(	(	PUNCT
brj-24383	359	21	2023	2023	NUM
brj-24383	359	22	)	)	PUNCT
brj-24383	359	23	.	.	PUNCT
brj-24383	360	1	“	"	PUNCT
brj-24383	360	2	a	a	DET
brj-24383	360	3	deep	deep	ADJ
brj-24383	360	4	learning	learn	VERB
brj-24383	360	5	multimodal	multimodal	NOUN
brj-24383	360	6	fusion	fusion	NOUN
brj-24383	360	7	framework	framework	NOUN
brj-24383	360	8	for	for	ADP
brj-24383	360	9	wood	wood	NOUN
brj-24383	360	10	species	specie	NOUN
brj-24383	360	11	identification	identification	NOUN
brj-24383	360	12	using	use	VERB
brj-24383	360	13	near	near	ADV
brj-24383	360	14	-	-	PUNCT
brj-24383	360	15	infrared	infrared	ADJ
brj-24383	360	16	spectroscopy	spectroscopy	NOUN
brj-24383	360	17	gadf	gadf	NOUN
brj-24383	360	18	and	and	CCONJ
brj-24383	360	19	rgb	rgb	PROPN
brj-24383	360	20	image	image	NOUN
brj-24383	360	21	,	,	PUNCT
brj-24383	360	22	”	"	PUNCT
brj-24383	360	23	holzforschung	holzforschung	PROPN
brj-24383	360	24	77(11	77(11	NUM
brj-24383	360	25	)	)	PUNCT
brj-24383	360	26	,	,	PUNCT
brj-24383	360	27	001	001	NUM
brj-24383	360	28	-	-	PUNCT
brj-24383	360	29	006	006	NUM
brj-24383	360	30	.	.	PUNCT
brj-24383	361	1	doi	doi	NOUN
brj-24383	361	2	:	:	PUNCT
brj-24383	361	3	10.1515	10.1515	NUM
brj-24383	361	4	/	/	SYM
brj-24383	361	5	hf-2023	hf-2023	NOUN
brj-24383	361	6	-	-	PUNCT
brj-24383	361	7	0062	0062	NUM
brj-24383	361	8	ravindran	ravindran	NOUN
brj-24383	361	9	,	,	PUNCT
brj-24383	361	10	p.	p.	PROPN
brj-24383	361	11	,	,	PUNCT
brj-24383	361	12	owens	owens	PROPN
brj-24383	361	13	,	,	PUNCT
brj-24383	361	14	f.	f.	PROPN
brj-24383	361	15	c.	c.	PROPN
brj-24383	361	16	,	,	PUNCT
brj-24383	361	17	wade	wade	PROPN
brj-24383	361	18	,	,	PUNCT
brj-24383	361	19	a.	a.	PROPN
brj-24383	361	20	c.	c.	PROPN
brj-24383	361	21	,	,	PUNCT
brj-24383	361	22	vega	vega	PROPN
brj-24383	361	23	,	,	PUNCT
brj-24383	361	24	p.	p.	PROPN
brj-24383	361	25	,	,	PUNCT
brj-24383	361	26	montenegro	montenegro	PROPN
brj-24383	361	27	,	,	PUNCT
brj-24383	361	28	r.	r.	PROPN
brj-24383	361	29	,	,	PUNCT
brj-24383	361	30	shmulsky	shmulsky	ADV
brj-24383	361	31	,	,	PUNCT
brj-24383	361	32	r.	r.	PROPN
brj-24383	361	33	,	,	PUNCT
brj-24383	361	34	and	and	CCONJ
brj-24383	361	35	wiedenhoeft	wiedenhoeft	VERB
brj-24383	361	36	,	,	PUNCT
brj-24383	361	37	a.	a.	PROPN
brj-24383	361	38	c.	c.	PROPN
brj-24383	361	39	(	(	PUNCT
brj-24383	361	40	2021	2021	NUM
brj-24383	361	41	)	)	PUNCT
brj-24383	361	42	.	.	PUNCT
brj-24383	362	1	“	"	PUNCT
brj-24383	362	2	field	field	NOUN
brj-24383	362	3	-	-	PUNCT
brj-24383	362	4	deployable	deployable	NOUN
brj-24383	362	5	computer	computer	NOUN
brj-24383	362	6	vision	vision	NOUN
brj-24383	362	7	wood	wood	NOUN
brj-24383	362	8	identification	identification	NOUN
brj-24383	362	9	of	of	ADP
brj-24383	362	10	peruvian	peruvian	ADJ
brj-24383	362	11	timbers	timber	NOUN
brj-24383	362	12	,	,	PUNCT
brj-24383	362	13	”	"	PUNCT
brj-24383	362	14	frontiers	frontier	NOUN
brj-24383	362	15	in	in	ADP
brj-24383	362	16	plant	plant	NOUN
brj-24383	362	17	science	science	NOUN
brj-24383	362	18	12	12	NUM
brj-24383	362	19	,	,	PUNCT
brj-24383	362	20	article	article	NOUN
brj-24383	362	21	647515	647515	NUM
brj-24383	362	22	.	.	PUNCT
brj-24383	363	1	doi	doi	NOUN
brj-24383	363	2	:	:	PUNCT
brj-24383	363	3	10.3389	10.3389	NUM
brj-24383	363	4	/	/	SYM
brj-24383	363	5	fpls.2021.647515	fpls.2021.647515	NOUN
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brj-24383	363	7	,	,	PUNCT
brj-24383	363	8	a.	a.	NOUN
brj-24383	363	9	,	,	PUNCT
brj-24383	363	10	and	and	CCONJ
brj-24383	363	11	golay	golay	NOUN
brj-24383	363	12	,	,	PUNCT
brj-24383	363	13	m.	m.	NOUN
brj-24383	363	14	j.	j.	PROPN
brj-24383	363	15	e.	e.	PROPN
brj-24383	363	16	(	(	PUNCT
brj-24383	363	17	1964	1964	NUM
brj-24383	363	18	)	)	PUNCT
brj-24383	363	19	.	.	PUNCT
brj-24383	364	1	“	"	PUNCT
brj-24383	364	2	smoothing	smoothing	NOUN
brj-24383	364	3	and	and	CCONJ
brj-24383	364	4	differentiation	differentiation	NOUN
brj-24383	364	5	of	of	ADP
brj-24383	364	6	data	datum	NOUN
brj-24383	364	7	by	by	ADP
brj-24383	364	8	simplified	simplified	ADJ
brj-24383	364	9	least	least	ADJ
brj-24383	364	10	squares	square	NOUN
brj-24383	364	11	procedures	procedure	NOUN
brj-24383	364	12	,	,	PUNCT
brj-24383	364	13	”	"	PUNCT
brj-24383	364	14	analytical	analytical	ADJ
brj-24383	364	15	chemistry	chemistry	NOUN
brj-24383	364	16	36(8	36(8	NUM
brj-24383	364	17	)	)	PUNCT
brj-24383	364	18	,	,	PUNCT
brj-24383	364	19	1627–1639	1627–1639	NUM
brj-24383	364	20	.	.	PUNCT
brj-24383	365	1	doi	doi	NOUN
brj-24383	365	2	:	:	PUNCT
brj-24383	365	3	10.1021	10.1021	NUM
brj-24383	365	4	/	/	SYM
brj-24383	365	5	ac60214a047	ac60214a047	NOUN
brj-24383	365	6	zhao	zhao	PROPN
brj-24383	365	7	,	,	PUNCT
brj-24383	365	8	p.	p.	PROPN
brj-24383	365	9	,	,	PUNCT
brj-24383	365	10	li	li	PROPN
brj-24383	365	11	,	,	PUNCT
brj-24383	365	12	z.-y	z.-y	NOUN
brj-24383	365	13	.	.	PUNCT
brj-24383	365	14	,	,	PUNCT
brj-24383	365	15	and	and	CCONJ
brj-24383	365	16	wang	wang	PROPN
brj-24383	365	17	,	,	PUNCT
brj-24383	365	18	c.-k	c.-k	PROPN
brj-24383	365	19	.	.	PUNCT
brj-24383	366	1	(	(	PUNCT
brj-24383	366	2	2021	2021	NUM
brj-24383	366	3	)	)	PUNCT
brj-24383	366	4	.	.	PUNCT
brj-24383	367	1	“	"	PUNCT
brj-24383	367	2	wood	wood	NOUN
brj-24383	367	3	species	species	NOUN
brj-24383	367	4	recognition	recognition	NOUN
brj-24383	367	5	based	base	VERB
brj-24383	367	6	on	on	ADP
brj-24383	367	7	visible	visible	ADJ
brj-24383	367	8	and	and	CCONJ
brj-24383	367	9	near	near	ADV
brj-24383	367	10	-	-	PUNCT
brj-24383	367	11	infrared	infrared	ADJ
brj-24383	367	12	spectral	spectral	ADJ
brj-24383	367	13	analysis	analysis	NOUN
brj-24383	367	14	using	use	VERB
brj-24383	367	15	fuzzy	fuzzy	ADJ
brj-24383	367	16	reasoning	reasoning	NOUN
brj-24383	367	17	and	and	CCONJ
brj-24383	367	18	decision	decision	NOUN
brj-24383	367	19	-	-	PUNCT
brj-24383	367	20	level	level	NOUN
brj-24383	367	21	fusion	fusion	NOUN
brj-24383	367	22	,	,	PUNCT
brj-24383	367	23	”	"	PUNCT
brj-24383	367	24	journal	journal	NOUN
brj-24383	367	25	of	of	ADP
brj-24383	367	26	spectroscopy	spectroscopy	NOUN
brj-24383	367	27	2021	2021	NUM
brj-24383	367	28	,	,	PUNCT
brj-24383	367	29	6088435	6088435	NUM
brj-24383	367	30	.	.	PUNCT
brj-24383	368	1	doi	doi	NOUN
brj-24383	368	2	:	:	PUNCT
brj-24383	368	3	10.1155/2021/6088435	10.1155/2021/6088435	NUM
brj-24383	368	4	zhu	zhu	PROPN
brj-24383	368	5	,	,	PUNCT
brj-24383	368	6	s.	s.	PROPN
brj-24383	368	7	,	,	PUNCT
brj-24383	368	8	zhou	zhou	PROPN
brj-24383	368	9	,	,	PUNCT
brj-24383	368	10	l.	l.	PROPN
brj-24383	368	11	,	,	PUNCT
brj-24383	368	12	zhang	zhang	PROPN
brj-24383	368	13	,	,	PUNCT
brj-24383	368	14	c.	c.	PROPN
brj-24383	368	15	,	,	PUNCT
brj-24383	368	16	bao	bao	PROPN
brj-24383	368	17	,	,	PUNCT
brj-24383	368	18	y.	y.	PROPN
brj-24383	368	19	,	,	PUNCT
brj-24383	368	20	wu	wu	PROPN
brj-24383	368	21	,	,	PUNCT
brj-24383	368	22	b.	b.	PROPN
brj-24383	368	23	,	,	PUNCT
brj-24383	368	24	chu	chu	PROPN
brj-24383	368	25	,	,	PUNCT
brj-24383	368	26	h.	h.	PROPN
brj-24383	368	27	,	,	PUNCT
brj-24383	368	28	yu	yu	PROPN
brj-24383	368	29	,	,	PUNCT
brj-24383	368	30	y.	y.	PROPN
brj-24383	368	31	,	,	PUNCT
brj-24383	368	32	he	he	PRON
brj-24383	368	33	,	,	PUNCT
brj-24383	368	34	y.	y.	PROPN
brj-24383	368	35	,	,	PUNCT
brj-24383	368	36	and	and	CCONJ
brj-24383	368	37	feng	feng	PROPN
brj-24383	368	38	,	,	PUNCT
brj-24383	368	39	l.	l.	PROPN
brj-24383	368	40	(	(	PUNCT
brj-24383	368	41	2019	2019	NUM
brj-24383	368	42	)	)	PUNCT
brj-24383	368	43	.	.	PUNCT
brj-24383	369	1	“	"	PUNCT
brj-24383	369	2	identification	identification	NOUN
brj-24383	369	3	of	of	ADP
brj-24383	369	4	soybean	soybean	NOUN
brj-24383	369	5	varieties	variety	NOUN
brj-24383	369	6	using	use	VERB
brj-24383	369	7	hyperspectral	hyperspectral	ADJ
brj-24383	369	8	imaging	imaging	NOUN
brj-24383	369	9	coupled	couple	VERB
brj-24383	369	10	with	with	ADP
brj-24383	369	11	convolutional	convolutional	ADJ
brj-24383	369	12	neural	neural	ADJ
brj-24383	369	13	network	network	NOUN
brj-24383	369	14	,	,	PUNCT
brj-24383	369	15	"	"	PUNCT
brj-24383	369	16	sensors	sensor	NOUN
brj-24383	369	17	19(19	19(19	NUM
brj-24383	369	18	)	)	PUNCT
brj-24383	369	19	,	,	PUNCT
brj-24383	369	20	article	article	NOUN
brj-24383	369	21	4065	4065	NUM
brj-24383	369	22	.	.	PUNCT
brj-24383	370	1	doi	doi	NOUN
brj-24383	370	2	:	:	PUNCT
brj-24383	370	3	10.3390	10.3390	NUM
brj-24383	370	4	/	/	SYM
brj-24383	370	5	s19194065	s19194065	NOUN
brj-24383	370	6	article	article	NOUN
brj-24383	370	7	submitted	submit	VERB
brj-24383	370	8	:	:	PUNCT
brj-24383	370	9	jan	jan	PROPN
brj-24383	370	10	.	.	PROPN
brj-24383	370	11	14	14	NUM
brj-24383	370	12	,	,	PUNCT
brj-24383	370	13	2025	2025	NUM
brj-24383	370	14	;	;	PUNCT
brj-24383	370	15	peer	peer	NOUN
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brj-24383	370	23	;	;	PUNCT
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brj-24383	370	32	;	;	PUNCT
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brj-24383	370	38	2025	2025	NUM
brj-24383	370	39	;	;	PUNCT
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brj-24383	370	45	2025	2025	NUM
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brj-24383	371	2	:	:	PUNCT
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brj-24383	371	4	/	/	SYM
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brj-24383	371	6	-	-	PUNCT
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brj-24383	371	8	https://doi.org/10.3390/rs11070819	https://doi.org/10.3390/rs11070819	NUM
brj-24383	371	9	https://doi.org/10.15376/biores.19.4.6983-6993	https://doi.org/10.15376/biores.19.4.6983-6993	PROPN
brj-24383	371	10	https://doi.org/10.1515/hf-2023-0062	https://doi.org/10.1515/hf-2023-0062	X
brj-24383	371	11	https://doi.org/10.3389/fpls.2021.647515	https://doi.org/10.3389/fpls.2021.647515	PROPN
brj-24383	371	12	https://doi.org/10.1021/ac60214a047	https://doi.org/10.1021/ac60214a047	PROPN
brj-24383	372	1	https://doi.org/10.1155/2021/6088435	https://doi.org/10.1155/2021/6088435	PROPN
brj-24383	372	2	https://doi.org/10.3390/s19194065	https://doi.org/10.3390/s19194065	PROPN