## 3 Results and Discussion
### Partial Least Square Regression (PLSR)  
The multilinear regression model in this study was constructed using 288 spectra ranging from 900 to 1800 nm for both seed and powder samples. The regression results of the PLSR model for each pre-processing method developed using all samples comprising all varieties and harvesting time are presented in Table 3. The obtained model of seed samples showed an acceptable performance indicated by a high correlation coefficient (\(R^{2}\)), more than 0.85 and 0.75 for the calibration and validation dataset, respectively. The model performances of the powder sample were better than those of seed samples, denoted by higher \(R^{2}\), which were more than 0.9 for both the calibration and validation dataset for all pre-processed and raw data, except for Savitzky-Golay's (SG) first derivative on the validation dataset. Anthocyanins in rice are not distributed uniformly throughout the seed, which affects sample homogeneity. In addition, the standard error of the models developed based on the seed sample was slightly higher than powder, indicating more scattered data. Furthermore, the model performance developed using a mixed sample of 2019 and 2020 samples was similar to the model constructed based on the separated harvesting time. This phenomenon indicated the consistency of the model and proved the robustness of the PLSR method to predict anthocyanins in rice using NIR-HSI data. Overall, the SNV pre-processing method exhibited the highest performance (\(R^{2}\)) of 0.93 in the calibration dataset and 0.90 on the validation dataset for predicting rice anthocyanins based on seed spectra of mixed samples. This performance is comparable to the previous study conducted by [PERSON] et al. (2020) that predicted anthocyanins of dry goji berries and provided \(R^{2}\) of 0.91-0.95 [30]. The NIR-HSI technique coupled with LS-SVM also was efficiently employed with \(R^{2}\)\({}_{p}\) 0.96 and RMSE 0.146 mg/g to quantify total anthocyanins in mulberry fruit (_Morus_ sp.) [39]. Another study conducted by [PERSON] et al. (2015), which also proves the feasibility of NIR-HSI to measure anthocyanins in wine grapes, obtained the highest \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Pre-Processing**} & \multicolumn{6}{c}{**Seed Sample**} & \multicolumn{6}{c}{**Powder Sample**} \\ \cline{3-10}  & & \(\mathbf{Rc^{2}}\) & \(\mathbf{SEC}\) & \(\mathbf{Rv^{2}}\) & \(\mathbf{SEP}\) & \(\mathbf{Re^{2}}\) & \(\mathbf{SEC}\) & \(\mathbf{Rv^{2}}\) & \(\mathbf{SEP}\) \\ \hline \multirow{8}{*}{2019} & Raw & 0.85 & 0.12 & 0.81 & 0.13 & 0.95 & 0.07 & 0.93 & 0.09 \\  & Mean Normalization & 0.88 & 0.11 & 0.77 & 0.14 & 0.95 & 0.07 & 0.92 & 0.09 \\  & MSC & 0.92 & 0.09 & 0.80 & 0.13 & 0.96 & 0.06 & 0.92 & 0.10 \\  & SNV & 0.87 & 0.12 & 0.85 & 0.11 & 0.95 & 0.07 & 0.92 & 0.09 \\  & SG first derivative & 0.94 & 0.07 & 0.79 & 0.13 & 0.95 & 0.06 & 0.88 & 0.11 \\  & Raw & 0.95 & 0.13 & 0.88 & 0.21 & 0.95 & 0.13 & 0.93 & 0.16 \\  & Mean Normalization & 0.93 & 0.15 & 0.89 & 0.19 & 0.96 & 0.12 & 0.93 & 0.16 \\
2020 & MSC & 0.95 & 0.13 & 0.92 & 0.17 & 0.96 & 0.12 & 0.93 & 0.17 \\  & SNV & 0.95 & 0.12 & 0.92 & 0.17 & 0.96 & 0.12 & 0.95 & 0.14 \\  & SG first derivative & 0.91 & 0.17 & 0.80 & 0.26 & 0.95 & 0.13 & 0.81 & 0.28 \\  & Raw & 0.87 & 0.19 & 0.79 & 0.24 & 0.94 & 0.13 & 0.91 & 0.17 \\  & Mean Normalization & 0.91 & 0.15 & 0.79 & 0.23 & 0.94 & 0.13 & 0.90 & 0.17 \\  & MSC & 0.89 & 0.17 & 0.85 & 0.20 & 0.94 & 0.13 & 0.92 & 0.16 \\  & SNV & 0.93 & 0.14 & 0.90 & 0.16 & 0.94 & 0.13 & 0.92 & 0.15 \\  & SG first derivative & 0.85 & 0.20 & 0.75 & 0.26 & 0.93 & 0.14 & 0.84 & 0.22 \\ \hline \hline \end{tabular} MSC: Multiplication scatter correction; SNV: Standard normal variate; SG: Savitzky–Golay; \(\mathbf{SEC}\): Standard error of calibration; \(\mathbf{SEP}\): Standard error of prediction in validation.  
\end{table}