## 3 Results
### Establishment of the Model
#### 3.3.3 Optimization of the Model  
To further optimize the model, different mathematical methods were used to preprocess the spectrum in this experiment. The results show that after preprocessing the spectrum, the average determination coefficient of leucine in the calibration set and validation set was 0.9574 and 0.9507, respectively. The results also proved that the model for determining the leucine content of Tartary buckwheat leaves was stable and reliable based on the spectral information in the near infrared in the spectral range of 4000-9000 cm\({}^{-1}\). The preprocessing of spectral modeling by centralization, range normalization, vector correction, scattering correction, and first derivative can not only improve the coefficient of determination of the model, but also significantly reduce the number of principal components. For example, the number of principal components in the calibration set and validation set decreased from 21 to 13 when the spectra were preprocessed with the first derivative (11 smoothing points), and the coefficient of determination was also significantly higher than the non-spectral treatment (Table 6). It was observed that the tyrosine model created by preprocessing spectra with various methods did not have a significant change in the coefficient of determination between the calibration set and the validation set, but after preprocessing spectra with the first derivative, centralization, vector correction, and other methods, the principal component of the model was significantly decreased. For example, after preprocessing the spectrum with the first derivative (smoothing the number of points 11), the number of principal components of the model decreased from 18 to 11, compared to the model without preprocessing the spectrum, \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Preprocessing Methods**} & \multirow{2}{*}{**Principal Component**} & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Validation Set**} \\ \cline{3-8}  & & **R\({}^{2}\)\%** & **SEC** & **RSD\%** & **R\({}^{2}\)\%** & **SEC** & **RSD\%** \\ \hline No-preprocessing & 21 & 0.9493 & 0.47 & 3.58 & 0.9456 & 0.49 & 3.81 \\ Centralization & 19 & 0.9587 & 0.42 & 3.23 & 0.9517 & 0.46 & 3.59 \\ Range normalization & 19 & 0.9560 & 0.44 & 3.34 & 0.9427 & 0.50 & 3.91 \\ Vector correction & 17 & 0.9523 & 0.45 & 3.47 & 0.9522 & 0.46 & 3.57 \\ Scatter correction & 19 & 0.9621 & 0.40 & 3.10 & 0.9567 & 0.43 & 3.40 \\ First derivative (11) & 13 & 0.9529 & 0.45 & 3.45 & 0.9502 & 0.47 & 3.65 \\ First derivative (13) & 13 & 0.9527 & 0.45 & 3.46 & 0.9245 & 0.57 & 4.49 \\ First derivative (15) & 14 & 0.9501 & 0.46 & 3.55 & 0.9475 & 0.48 & 3.74 \\ First derivative (17) & 15 & 0.9519 & 0.46 & 3.48 & 0.9472 & 0.48 & 3.76 \\ Second derivative (11) & 20 & 0.9672 & 0.38 & 2.88 & 0.9537 & 0.45 & 3.52 \\ Second derivative (13) & 20 & 0.9639 & 0.39 & 3.02 & 0.9631 & 0.40 & 3.14 \\ Second derivative (15) & 20 & 0.9618 & 0.41 & 3.11 & 0.9613 & 0.41 & 3.22 \\ Second derivative (17) & 21 & 0.9670 & 0.38 & 2.89 & 0.9631 & 0.40 & 3.14 \\ Average & 18 & 0.9574 & 0.43 & 3.27 & 0.9507 & 0.46 & 3.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The influence of different pretreatment and smoothing methods on NIRS prediction results (Leu).