## 3 Results and Discussion
### Classification Models
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Spectral Range a**} & \multicolumn{8}{c}{**Calibration Set**} & \multicolumn{8}{c}{**Prediction Set**} \\ \cline{2-13}  & \multicolumn{1}{c}{**1 c**} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**5**} & \multirow{2}{*}{**6**} & \multicolumn{2}{c}{**Acc. b**} & \multirow{2}{*}{**1**} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**5**} & \multirow{2}{*}{**6**} & \multirow{2}{*}{**Acc. (\%)**} \\  & & & & & & & & & & & & & & & & **(\%)** \\ \hline \multirow{8}{*}{1} & 1 & 764 & 29 & 1 & 0 & 3 & 9 & 94.79 & 236 & 27 & 0 & 1 & 0 & 4 & 88.06 \\  & 2 & 19 & 590 & 0 & 5 & 2 & 4 & 95.16 & 4 & 157 & 0 & 0 & 0 & 0 & 97.52 \\  & 3 & 0 & 3 & 619 & 5 & 6 & 1 & 97.63 & 1 & 1 & 206 & 2 & 1 & 0 & 97.63 \\  & 4 & 3 & 4 & 1 & 669 & 6 & 12 & 96.26 & 1 & 0 & 0 & 223 & 1 & 6 & 96.54 \\  & 5 & 6 & 3 & 1 & 17 & 770 & 81 & 87.70 & 6 & 1 & 0 & 8 & 239 & 39 & 81.57 \\  & 6 & 12 & 12 & 0 & 2 & 86 & 945 & 89.40 & 4 & 0 & 0 & 0 & 122 & 226 & 64.20 \\  & total & & & & & & & 92.90 & & & & & & & & & 84.89 \\ \hline \multirow{8}{*}{2} & 1 & 858 & 12 & 0 & 0 & 0 & 0 & 98.62 & 267 & 23 & 0 & 0 & 0 & 0 & 0 & 92.07 \\  & 2 & 112 & 550 & 1 & 2 & 0 & 0 & 0 & 82.70 & 30 & 188 & 2 & 1 & 0 & 0 & 85.07 \\ \cline{1-1}  & 3 & 0 & 2 & 576 & 0 & 10 & 1 & 97.79 & 0 & 1 & 183 & 5 & 7 & 0 & 98.47 \\ \cline{1-1}  & 4 & 1 & 1 & 8 & 752 & 6 & 10 & 96.65 & 0 & 0 & 1 & 256 & 2 & 0 & 98.84 \\ \cline{1-1}  & 5 & 0 & 0 & 21 & 27 & 619 & 110 & 79.67 & 0 & 1 & 2 & 8 & 206 & 42 & 79.54 \\ \cline{1-1}  & 6 & 0 & 0 & 0 & 3 & 10 & 1104 & 98.84 & 0 & 0 & 0 & 0 & 3 & 369 & 99.19 \\ \cline{1-1}  & total & & & & & & & 92.97 & & & & & & & 91.98 \\ \hline \hline \end{tabular}  
* the spectral ranges used for analysis, spectral range 1: 502–947 nm; spectral range 2: 975–1646 nm. b: Acc. represents accuracy. c: 1, 2, 3, 4, 5 and corresponds to Bashan Village, TongWu Village, Meijiawu Village, Tiedian Village, Xinxin Village, and Huijaling Village.  
\end{table} 
Table 3: Confusion matrix of partial least squares discriminant analysis (PLS-DA) models using full spectra for spectral range 1 and spectral range 2.  
samples from different geographical origins, the classification accuracy of each geographical origin was over 90% in the calibration set, with worse classification results in the prediction set (all over 80%). For the SVM model using full spectra of spectral range 2, the optimal model parameters (C, g) were (256, 25.8576). As shown in Table 2, good classification results were obtained, with a classification accuracy of the calibration set and the prediction set of 94.31% and 87.66%, respectively. For the samples from different geographical origins, the classification accuracy of each geographical origin was over 89% in the calibration set, with worse classification results in the prediction set, especially for samples from Bashan Village (less than 80%). As shown in Table 3, close results were also obtained by PLS-DA models using full spectra of spectral range 1 and spectral range 2, and variations were also found on the results of samples from different geographical origins. For the PLS-DA model using full spectra of spectral range 1, classification accuracy of the samples from most geographical origins was over 80% in the calibration and prediction sets, and classification accuracy of the prediction set of Huijaling Village was the lowest (only 64.20%). For the PLS-DA model using full spectra of spectral range 2, classification accuracy of the samples from Xinxin Village was lower than 80% in the calibration and prediction sets, and classification accuracy of the samples from the remaining five geographical was all over 80%. Overall, the PLS-DA model using spectral range 2 showed better results than the PLS-DA model using spectral range 1. In different classification models and different spectral ranges, the classification accuracy of the samples from different geographical origins varied.