Table 1.

Calibration, cross-validation and prediction results of the PPC, TVB-N values and sensory score of rainbow-trout samples by hyperspectral imaging system.

Model	n	LVs	Calibration		Cross-validation		Prediction			RDP
			R2C(adj)	RSMEC	R2CV(adj)	RSMECV	R2P(adj)	RSMEP	Bias
PPC (Log 10 CFU/g)
PLSR	9	6	0.923	0.504	0.901	0.548	0.899	0.579	0.077	3.17
MLR	9	—	0.923	0.522	0.908	0.551	0.918	0.533	0.106	3.43
LS-SVM	9	—	0.920	0.515	0.904	0.505	0.917	0.517	0.103	3.55
BP-ANN	9	—	0.922	0.508	909	0.550	0.921	0.504	0.128	3.64
TVB-N (mg N/100 g)
PLSR	9	5	0.889	3.084	0.874	3.304	0.857	3.585	0.257	2.645
MLR	9	—	0.892	3.164	0.870	3.355	0.855	3.593	0.174	2.640
LS-SVM	9	—	0.889	3.101	0.872	3.327	0.862	3.542	−0.208	2.678
BP-ANN	9	—	0.881	3.307	0.862	3.526	0.853	3.643	0.073	2.603
Sensory score (6–30)
PLSR	9	4	0.927	1.572	0.919	1.604	0.902	1.987	−1.009	3.024
MLR	9	—	0.930	1.544	0.918	1.609	0.909	1.991	−0.983	3.018
LS-SVM	9	—	0.928	1.523	0.921	1.599	0.912	1.802	−0.996	3.335
BP-ANN	9	—	0.920	1.597	0.913	1.664	0.910	1.848	−1.001	3.251

LV: latent variable; R²_C(adj): adjusted determination coefficient of calibration; R²_{CV (adj)}: adjusted determination coefficient of cross-validation; R²_P(adj): adjusted determination coefficient of prediction; RMSEC: root-mean-square errors estimated by calibration; RMSECV: root-mean-square errors estimated by cross-validation; RMSEP: root-mean-square errors estimated by prediction; MLR: Multi-linear regression; PLSR: partial least squares regression; LS-SVM: least squares support vector machine BP-ANN: back-propagation artificial neural network.