## 3 Results
### Establishment of the Model  
#### 3.3.1 Influence of Spectral Region on the Model  
Following previous studies, eight spectral ranges such as 4000-5000 cm\({}^{-1}\), 4000-6000 cm\({}^{-1}\), 4000-7000 cm\({}^{-1}\), 4000-8000 cm\({}^{-1}\), 4000-9000 cm\({}^{-1}\), 4000-10,000 cm\({}^{-1}\), 5000-9000 cm\({}^{-1}\) and 5000-8000 cm\({}^{-1}\) were selected to construct the NIR determination model of leucine. The total number of modeling samples was 226. In each modeling, 181 samples were randomly selected as the calibration set and the other 45 samples were the validation set.  
In the construction of the determination model for leucine, the modeling effect of all spectral regions was good, and the determination coefficient between the calibration set and the validation set was above 0.91. Among these, the best modeling effect was obtained when the spectrum of 4000-9000 cm\({}^{-1}\) was selected, and the coefficient of determination of both the calibration set and the validation set was more than 0.93. The average coefficients of determination of the calibration set and the validation set of the eight spectral regions were 0.9313 and 0.9225, respectively, indicating that the model was relatively stable (Table 2).  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Spectral** & **Principal** & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{4}{c}{**Validation Set**} \\ \cline{3-8}
**Regions/cm\({}^{-1}\)** & **Component** & **R\({}^{2}\)** & **SEC** & **RSD/\%** & **R\({}^{2}\)** & **SEC** & **RSD/\%** \\ \hline
4000–5000 & 12 & 0.9211 & 0.62 & 4.74 & 0.9130 & 0.44 & 3.41 \\
4000–6000 & 13 & 0.9298 & 0.58 & 4.47 & 0.9129 & 0.44 & 3.42 \\
4000–7000 & 15 & 0.9440 & 0.52 & 3.99 & 0.9190 & 0.42 & 3.29 \\
4000–8000 & 16 & 0.9423 & 0.53 & 4.05 & 0.9282 & 0.40 & 3.10 \\
4000–9000 & 13 & 0.9320 & 0.58 & 4.40 & 0.9370 & 0.37 & 2.91 \\
4000–10,000 & 13 & 0.9318 & 0.58 & 4.41 & 0.9371 & 0.37 & 2.90 \\
5000–9000 & 11 & 0.9210 & 0.62 & 4.74 & 0.9205 & 0.42 & 3.26 \\
5000–8000 & 14 & 0.9285 & 0.59 & 4.51 & 0.9124 & 0.44 & 3.43 \\ Average & 13 & 0.9313 & 0.58 & 4.41 & 0.9225 & 0.41 & 3.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influence of spectral range on NIR prediction results (Leu).  
Figure 1: Golden Tartary buckwheat leaves’ original near-infrared spectrum. Sample 1 and sample 2 represents: two varieties of Golden Tartary buckwheat with different leucine and tyrosine contents in leaves.  
In building the tyrosine determination model, eight spectral regions were selected, as shown in Table 3, covering the range 4000-10,000 cm\({}^{-1}\). During modeling, it was found that the model created when the spectrum of 4000-10,000 cm\({}^{-1}\) was selected was better. The coefficients of determination of the calibration set and the validation set were 0.9076 and 0.9042, respectively, and the mean relative error was less than 10%. The disadvantage was that the number of principal components was high. The spectral range with a low coefficient of determination was 4000-7000 cm\({}^{-1}\), but the coefficient of determination here was also above 0.85. The average coefficient of determination of the calibration set and the validation set for the eight spectral ranges were 0.8874 and 0.8761, respectively, which achieved the goal of fast detection (Table 3).