## 5 Conclusions  
The obtained results clearly demonstrate the effectiveness of using VIS-NIR spectroscopy in conjunction with wavelength selection methods and linear discriminant analysis to distinguish between the four types of organic leafy vegetables and their non-organic counterparts. Utilizing either the leaves or stems of these leafy vegetables can achieve excellent results, providing a comprehensive approach for authentication. The principal research conclusions are as follows:  
1. The primary accomplishment lies in the identification of key spectral bands for the classification of organic leafy vegetables. We analyzed the distribution of wavelengths selected by a genetic algorithm, combined with the distribution of the ten most important wavelengths, as well as the number of the selected wavelengths distributed in a certain location. Spectral classification bands for the leaves and stems were defined in the ranges of 550-910 nm and 1380-1500 nm and 750-900 nm and 1700-1820 nm, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spectra** & **Method** & **Sensitivity** & **Specificity** & **Accuracy** & **F1** \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{3}{*}{**Leaf**} & **PCA-LDA** & \multirow{3}{*}{81.4} & \multirow{3}{*}{77.1} & \multirow{3}{*}{79.2} & \multirow{3}{*}{79.8} \\  & with 10 principal variables & & & & \\  & **PCA-SVM** & & & & \\  & with 10 principal variables & & & & \\  & **PLS-DA** & & & & \\  & with 10 principal variables & & & & \\  & **GA-LDA** & & & & \\  & with selected bands & & & & \\ \hline \multirow{3}{*}{**Stem**} & **PCA-LDA** & \multirow{3}{*}{85.3} & \multirow{3}{*}{86.9} & \multirow{3}{*}{86.1} & \multirow{3}{*}{85.8} \\  & with 10 principal variables & & & & \\  & with 10 principal variables & & & & \\  & **PLS-DA** & & & & \\  & with 10 principal variables & & & & \\  & with 10 principal variables & & & & \\  & **GA-LDA** & & & & \\  & with first 10 important wavelengths & & & & \\  & **GA-LDA** & & & & \\  & with selected bands & & & & \\ \hline \hline \end{tabular} Abbreviations: PCA, principal component analysis; LDA, linear discriminant analysis, SVM, support vector machine; PLS-DA, partial least squares–discriminant analysis; GA, genetic algorithm. For the GA-LDA method, the important value of the GA-selected wavelengths was evaluated with SS (stability selection).  
\end{table} 
Table 7: Classification results of the testing set of the leaf and stem spectra by using different methods. respectively. Utilizing these selected bands for classification, we achieved an accuracy of 98.3% for both leaf and stem spectral classifications. This analysis also revealed that specific wavelengths, such as those around 700 nm, 820 nm, and 1400 nm, significantly impact leaf spectral classification, while wavelengths near 800 nm, 1780 nm, and 2400 nm play a substantial role in stem spectral classification. The identification of key spectral bands is of utmost significance as it allows for the effective identification of organic leafy vegetables instead of using the full spectral bands, thereby reducing the costs associated with visible and near-infrared spectrometers. 2. Our approach not only achieved high classification accuracy but also proved to be as efficient as the methods utilizing the entire visible and near-infrared spectrum, such as principal component analysis-linear discriminant analysis, principal component analysis-support vector machine, and partial least squares-discriminant analysis. Furthermore, it provides interpretability by revealing the wavelengths significantly influencing vegetable spectral classification. 3. Additionally, we found that using spectroscopic pre-processing methods, such as the Savitzky-Golay method, enhances the accuracy of the linear discriminant analysis model for classification.