Table 3. Performance evaluation of PLS models to predict wood chemical composition.

Species	Component	PP	LV	${\mathbf{R}}_{\mathbf{c}}^2$	RMSEC	${\mathbf{R}}_{\mathit{p}}^2$	RMSEP	RPDa	RPDb
Capirona	Cellulose	2ndDer	4	85.1	1.57	74.0	2.06	1.96	1.96
	Hemicellulose	1stDer	5	77.3	0.52	61.1	0.68	1.60	1.60
	Holocellulose	2ndDer	4	83.0	1.62	72.1	2.07	1.89	1.90
	Lignin	2ndDer	4	90.2	0.31	75.4	0.48	2.02	2.06
Bolaina	Cellulose	MM-N	4	97.3	1.83	97.3	1.82	6.14	6.14
	Hemicellulose	1stDer	5	85.1	1.98	81.3	2.12	2.31	2.32
	Holocellulose	V-N	6	89.3	3.09	88.8	3.16	2.99	2.99
	Lignin	1stDer	6	82.6	0.72	77.7	0.81	2.12	2.12

PP: Pre-processing methods: (1^st Der: First derivative, 2^nd Der: Second derivative. MM-N: Min—Max normalization and V-N: Vector normalization).

LV: Latent variables according to Gowen et al. [21].

^a RPD according to Santos et al. [25].

^b RPD according to Li et al. [26].