## 4 Discussion  
In this endeavor, we enhance the discernment of organic from non-organic leafy vegetables by judiciously selecting wavelengths within the visible and near-infrared spectrum. This judicious wavelength selection augments the classification model's accuracy, reduces computational intricacies, and imbues the resultant classifications with heightened interpretability to a considerable degree. As delineated in Table 7, it becomes evident that the performance of the GA-LDA approach surpasses that of other methods reliant upon the entire VIS-NIR spectrum when it comes to classifying the leaf and stem spectra of these leafy vegetables. For the GA-LDA approach, when employing the first 10 important wavelengths evaluated by SS, it achieves classification accuracies of 92.1% and 91.1% for the leaf and stem spectra of the vegetables, respectively; when utilizing the selected spectral bands for classification (550-910 nm and 1380-1500 nm for the leaf spectra, 750-900 nm and 1700-1820 nm for the stem spectra), the accuracies for the leaf and stem spectra of the vegetables notably rise to 98.3% each. There are few studies on the identification of organic leafy vegetables. NIR combined with LDA was used to classify organic and traditional cultivation as well as hydroponic lettuce, which obtained classification accuracies of 77.3% and 68.2% when using a successive projections algorithm (SPA)-LDA and stepwise formulation (SW)-LDA methods, respectively [12]. [PERSON] et al. used ambient mass spectrometry and inductively coupled plasma mass spectrometry technology to distinguish between organic and non-organic leeks, achieving an accuracy ranging from 92.5% to 98.1% [11]. [PERSON] et al. used a combination of multiple elements and machine learning methods to distinguish between organic and non-organic lettuce, achieving a classification accuracy of 92% [15]. Compared to the aforementioned studies, this research has also achieved excellent classification results. When using selected spectral bands for classification, it can achieve an accuracy of 98%. Furthermore, this study encompasses four different categories of leafy vegetables, not just one. Analyzing the wavelength distribution selected by the GA in Figure 6, it becomes evident that the classification outcomes in the leaf spectra primarily relied upon wavelengths proximate to 700 nm, 820 nm, and 1400 nm due to their abundance. Furthermore, the 10 most influential wavelengths assessed with SS were predominantly situated around 700 nm, 820 nm, 1400 nm, 2000 nm, and 2500 nm. The classification results in the stem spectra were predominantly affected by wavelengths in the vicinity of 800 nm, 1780 nm, and 2400 nm. By combining these findings with Figure 4, we can infer that the authentication of the four organic leafy vegetables, based on their leaves, may be significantly influenced by factors such as chlorophyll a, b (around 700 nm), water molecules, ROH, ArOH, and substances containing CONH\({}_{2}\), CONHR, CH\({}_{3}\), CH\({}_{2}\), or CH groups (around 1400 nm). However, to the best of our knowledge, the selected wavelengths in the stem spectra do not provide conclusive insights into the substances influencing the classification results for the stems of the four leafy vegetables. Notably, the wavelength near 800 nm exhibits significance not only in the identification of the leaves but also in the stems of these organic leafy vegetables. Leafy vegetables contain varying levels of different phytochemicals and compounds, such as glucosinolates, vitamin C, polyphenols, and carotenoids [1; 2]. The content of these substances affects the VIS-NIR spectral reflectance of the leafy vegetables. Additionally, the physical properties of the vegetables, such as texture, color, and size, also influence their spectral reflectance. All of these factors pose challenges for the spectral classification of organic and non-organic leafy vegetables.