Table 2.

Results of classification models using full spectra and effective wavelengths.

Classifier	Full Spectra (%)			Effective Wavelengths (%)
	Calibration	Validation	Prediction	Calibration	Validation	Prediction
CNN-SoftMax a	91.191	89.065	88.838	87.629	84.071	82.860
CNN-LR	94.060	88.611	87.752	90.070	83.731	83.276
CNN-PLS-DA	91.112	88.082	86.644	87.088	82.709	82.027
CNN-SVM	93.695	89.255	88.006	89.970	84.487	84.260
ResNet-SoftMax	95.381	85.698	86.039	92.273	79.985	79.228
ResNet-LR	99.585	84.335	82.324	98.238	76.040	75.952
ResNet-PLS-DA	95.130	85.585	85.358	91.707	78.509	77.677
ResNet-SVM	96.325	85.963	85.887	94.098	79.153	79.115
LR	84.156	82.406	83.012	62.736	62.429	65.305
PLS-DA	81.764	79.947	80.401	78.870	77.261	77.147
SVM	93.557	89.217	88.422	89.441	84.147	84.033

^a. CNN-SoftMax means using SoftMax function as classifier for the CNN model.