## 4 Discussion  
Sample size and representativeness have great influence on the model effect. The NIR prediction model for sucrose content in peanut kernels built by [PERSON] et al. (2015), using 72 peanut resources with edible oil, showed that the coefficient of determination of the optimal model was only 0.822, while [PERSON] et al. (2016) expanded the number of modeling samples to 119 and the coefficient of determination of the optimal peanut sucrose prediction model increased to 0.898. [PERSON] et al. (2017) used 40 samples to build the near-infrared model for buckwheat grain protein and total flavonoids, but the prediction value of the built model for protein and flavonoids was not ideal, which might be related to the small sample size. [PERSON] et al. (2018) used 217 buckwheat samples to build a prediction model for grain protein and found that the coefficient of determination of the prediction model was 0.9481 and the root mean square of the cross-validation was 0.68. A total of 230 samples were used in this work, and the coefficient of determination of the established models was mostly above 0.90. The next experiment is planned to expand the number of samples and try to include representative samples to further improve the representativeness of the samples. It was found that the multivariate calibration method has a great influence on the modeling effect. Due to the different substances, the best multivariate calibration methods are also different. [PERSON] et al. (2019) used the Mahalanobis distance discrimination method, the C support vector machine, and the V support vector machine, to build models for discriminating starch types, and it was found that the support vector machine classification model was effective. [PERSON] et al. (2019) used partial least squares (PLS), artificial neural network (ANN), and support vector machine (SVM) to predict amino acids in soybean seeds except for cystine and tryptophan. [PERSON] et al. (2019) developed the continuous projection algorithm (SPA), genetic algorithm (GA) and simulated cooling algorithm (SA) using near infrared spectroscopy combined with partial least square number with collaborative interval to build the free amino acid model of matcha with excellent predictive power. [PERSON] et al. (2019) used the method of UV-visible near-infrared spectroscopy in combination with multivariate analysis to analyze the classification and identification of buckwheat and oats, and also obtained good results. [PERSON] et al. performed Savitzky-Golay filtering (SG) and standard normal variable transformation (SNV) to preprocess the collected original \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Preprocessing** & **Principal** & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{3}{c}{**Validation Set**} \\ \cline{3-8}
**Methods** & **Component** & **R\({}^{2}\)** & **SEC** & **RSD\%** & **R\({}^{2}\)** & **SEC** & **RSD\%** \\ \hline No-preprocessing & 18 & 0.9076 & 0.15 & 5.54 & 0.9042 & 0.15 & 5.44 \\ Centralization & 17 & 0.9009 & 0.15 & 5.74 & 0.8861 & 0.16 & 5.94 \\ Range normalization & 19 & 0.9248 & 0.13 & 5.00 & 0.9060 & 0.14 & 5.39 \\ Vector correction & 17 & 0.9140 & 0.14 & 5.34 & 0.9059 & 0.14 & 5.40 \\ Scatter correction & 18 & 0.9157 & 0.14 & 5.29 & 0.8946 & 0.15 & 5.71 \\ First derivative (11) & 11 & 0.9016 & 0.15 & 5.72 & 0.9012 & 0.15 & 5.53 \\ First derivative (13) & 11 & 0.8991 & 0.15 & 5.79 & 0.8808 & 0.16 & 6.07 \\ First derivative (15) & 14 & 0.9199 & 0.14 & 5.16 & 0.9074 & 0.14 & 5.35 \\ First derivative (17) & 15 & 0.9182 & 0.14 & 5.21 & 0.9071 & 0.14 & 5.36 \\ Second derivative (11) & 16 & 0.8855 & 0.16 & 6.17 & 0.8806 & 0.16 & 6.08 \\ Second derivative (13) & 18 & 0.9135 & 0.14 & 5.36 & 0.9082 & 0.14 & 5.33 \\ Second derivative (15) & 17 & 0.8964 & 0.16 & 5.87 & 0.8955 & 0.15 & 5.69 \\ Second derivative (17) & 19 & 0.9204 & 0.14 & 5.14 & 0.9138 & 0.14 & 5.16 \\ Average & 16 & 0.9090 & 0.15 & 5.49 & 0.8993 & 0.15 & 5.57 \\ \hline \hline \end{tabular}
\end{table}