## 3 Results
### Establishment of the Model
#### 3.3.2 Influence of Modeling Samples on the Model  
To further investigate the feasibility and stability of near-infrared spectroscopy combined with the partial least squares method for detecting leucine and tyrosine content in Tartary buckwheat leaves, the optimal spectral ranges of the two amino acids were selected for modeling, and different proportions of calibration and validation test samples were randomly selected for model construction and verification.  
The results showed that the coefficient of determination of the model was above 0.92 for different samples. The average determination coefficient of leucine in the calibration and validation set was 0.9510 and 0.9392, respectively, when different samples were used for modeling. This shows that the model for determination of leucine content in Tartary buckwheat leaves constructed using NIR spectral information in the spectral range of 4000-9000 cm\({}^{-1}\) was stable and reliable (Table 4).  
It was found that when modeling different samples, the average determination coefficient of tyrosine calibration set was 0.8959, and that of validation set was 0.8781, which indicated that the determination model of buckwheat leaf tyrosine content established by using near infrared spectral information in the spectral range of 4000-10,000 cm  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Calibration Set:** & **Principal** & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{3}{c}{**Validation Set**} \\ \cline{3-8}
**Validation Set** & **Component** & **R\({}^{2}\)** & **SEC** & **RSD/\%** & **R\({}^{2}\)** & **SEC** & **RSD/\%** \\ \hline
1:1 & 16 & 0.9429 & 0.50 & 3.82 & 0.9239 & 0.57 & 4.41 \\
2:1 & 20 & 0.9628 & 0.37 & 2.87 & 0.9655 & 0.43 & 3.32 \\
3:1 & 18 & 0.9474 & 0.49 & 3.80 & 0.9435 & 0.44 & 3.37 \\
4:1 & 16 & 0.9423 & 0.53 & 4.05 & 0.9282 & 0.40 & 3.10 \\
5:1 & 20 & 0.9612 & 0.41 & 3.16 & 0.9284 & 0.55 & 4.22 \\
6:1 & 21 & 0.9493 & 0.47 & 3.58 & 0.9456 & 0.49 & 3.81 \\ Average & 19 & 0.9510 & 0.46 & 3.55 & 0.9392 & 0.48 & 3.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: NIR prediction results of the model for different samples (Leu).  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Spectral** & \multicolumn{2}{c}{**Principal**} & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{3}{c}{**Validation Set**} \\ \cline{3-8}
**Regions/cm\({}^{-1}\)** & **Component** & **R\({}^{2}\)** & **SEC** & **RSD/\%** & **R\({}^{2}\)** & **SEC** & **RSD/\%** \\ \hline
4000–5000 & 17 & 0.8730 & 0.18 & 6.32 & 0.8690 & 0.17 & 6.59 \\
4000–6000 & 16 & 0.8730 & 0.17 & 6.49 & 0.8617 & 0.17 & 6.54 \\
4000–7000 & 15 & 0.8589 & 0.18 & 6.82 & 0.8687 & 0.17 & 6.37 \\
4000–8000 & 17 & 0.8749 & 0.17 & 6.45 & 0.8727 & 0.17 & 6.28 \\
4000–9000 & 18 & 0.8936 & 0.16 & 5.95 & 0.8774 & 0.16 & 6.16 \\
4000–10,000 & 18 & 0.9076 & 0.15 & 5.54 & 0.9042 & 0.15 & 5.44 \\
5000–9000 & 16 & 0.8887 & 0.16 & 6.08 & 0.8623 & 0.17 & 6.53 \\
5000–8000 & 19 & 0.9298 & 0.13 & 4.83 & 0.8929 & 0.15 & 5.75 \\ Average & 17 & 0.8874 & 0.16 & 6.06 & 0.8761 & 0.16 & 6.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence of spectral range on NIR prediction results (Tyr).  
was stable and reliable. When the ratio of calibration set to validation set was 4:1, the constructed model has a better effect (Table 5).