## 3 Results
### Selected Wavelengths and Classification Results
#### 3.3.2 Classification Results  
In order to assess the reliability of the SS, RF, and ANOVA methodologies, wavelengths were ranked in descending order of importance. The foremost 10 pivotal wavelengths and the trailing 10 of significance were meticulously selected as inputs for the LDA classifier, respectively. Subsequently, a comparative analysis of their classification performance was conducted.  
Tables 4 and 5, respectively, present the classification outcomes of the testing set of the leaf spectra and stem spectra. The training set was composed of 70% of the spectra, while the remaining 30% constituted the test set. To ensure robustness, the classification process was repeated 20 times, with the dataset shuffled prior to each iteration. The reported results represent the average performance across these 20 iterations.  
The effectiveness of the SS, RF, and ANOVA methods in wavelength evaluation was evident. It was observed that utilizing the first 10 important wavelengths for classification yielded superior outcomes compared to employing the last 10 important wavelengths. Nevertheless, this does not imply that the latter wavelengths were devoid of utility; rather, they carried a relatively lower degree of discerning information for classification purposes. Furthermore, the classification results were subject to the interplay of various factors, including the number of wavelengths and their specific combinations. Notably, alternative combinations of wavelengths, encompassing both the initial and final important wavelengths, and an ample inclusion of the latter important wavelengths in the classification process had the potential to yield improved results.  
The SS method emerged as the more reliable approach for evaluating the importance of wavelengths. Firstly, when utilizing the first 10 important wavelengths identified through SS, the classification accuracy and f1 score exceeded 90%, surpassing the performance achieved using RF and ANOVA. Secondly, a notable discrepancy in classification accuracy and f1 score was observed between the first 10 important wavelengths and the last 10 important wavelengths in SS, amounting to a 7% difference. Consequently, GA combined with SS emerged as a trustworthy and optimal method for wavelength selection. By leveraging this method, the classification process was optimized through the selection of the most pertinent and crucial variables, leading to a significant reduction in the number of variables and a subsequent decrease in computational complexity. Moreover, it allowed for the flexibility of choosing an appropriate number of variables based on specific requirements and practical considerations.  
No discernible differentiation was observed between the utilization of vegetable leaves and stems for the purpose of identifying the four organic leafy vegetables. Both approaches proved effective in distinguishing the four types of organic vegetables from their non-organic counterparts. Remarkably, employing a mere 10 wavelengths achieved a classification accuracy of 92.1% in the leaf spectra and 91.1% in the stem spectra. Furthermore, it is worth noting that the classification accuracy can possibly be further enhanced by increasing the number of important wavelengths.