## 3 Results and Discussion
### Clilling Injury Detection Based on Spectral Features  
The performance of LDA classifier using different numbers of spectral features that were selected by random frog was investigated. Figure 6 displays two-class classification results for chilled and normal green jujubes based on spectral analysis. For different spectral resolutions, the classification performance at low scanning speed (8 mm/s) was better than that of a higher speed (20 mm/s) in general, since low speed leads to more scan lines. The detection accuracy of LDA model was sensitive to the number of selected features, and an increasing tendency of classification accuracy  
Figure 5: The first five selected spectral images of representative samples selected by Random frog at different scanning speeds and spectral resolutions. was observed with the increasing number of features. The results demonstrated the importance of feature number for jujube chilling detection. The optimal feature number for hyperspectral images with spectral resolution of 1.25 nm was 7 and 6 at the 8 mm/s and 20 mm/s, respectively, with the best discriminating accuracies of 95.0% and 93.3%, respectively (Figure 6a). When considering the two-class classification performance using 5.03 nm, the LDA classifier provided the best classification accuracy of 99.2% and 98.3% at 8 mm/s and 20 mm/s, respectively, with smallest feature number of 5 (839 nm, 875 nm, 870 nm, 865 nm, and 736 nm for 8 mm/s; 844 nm, 880 nm, 870 nm, 849 nm, and 865 nm for 20 mm/s) (Figure 6b). For two-class classification using data with spectral resolution of 2.51 nm, it reached the highest discriminating accuracies of 99.2% and 98.3% for 8 mm/s and 20 mm/s, with optimal feature number of 7 and 8, respectively (Figure S2a). Compared with 5.03 nm, it obtained the same classification results with two more selected features at 20 mm/s (6 for 5.03 nm). However, the classification performance for data with 10.08 nm spectral resolution was the worst (Figure S2b). Additionally, the classification performance based on 2.51 nm and 5.03 nm spectral resolution was relatively better than that of 1.25 nm and 10.08 nm, which might be because the selected wavelengths over 800 nm might provide more useful information about chilling injury (Table 1). Additionally, the evaluation of different levels of chilling damage could contribute to the improvement in quality control in jujube production. In this study, three-class chilling damage identification was also carried out based on the selected spectral features. The three-class classification results based on different number of selected features at different scanning speeds and spectral resolutions are presented in Figure 7. In general, the overall accuracies for the three-class chilling damage classification were relatively poorer than those of the two-class classification, as shown in Figure 6, which was due to misclassification between slightly chilling and severely chilling samples. Similar to two-class chilling detection, the LDA model achieved a better performance to discriminate normal, slightly, and severely samples based on data with 5.03 nm spectral resolution with the overall classification accuracies of 95.0% and 93.3% for 8 mm/s and 20 mm/s, respectively, using the optimum feature subsets of 6 (839 nm, 875 nm, 870 nm, 865 nm, 736 nm and 844 nm for 8 mm/s; 844 nm, 880 nm, 870 nm, 849 nm, 865 nm, and 736 nm for 20 mm/s) (Figure 7b). The three-class classification using data with 2.51 nm spectral resolution reached the highest discriminating accuracies of 97.5% and 93.3% for 8 mm/s and 20 mm/s with optimal feature number of 7 and 6, respectively (Figure S3a), which were comparable to the those of 5.03 nm. However, the performance for three-class classification based on 10.08 nm was not as good as the other combinations (Figure S3b). Figure 6: Two-class performance comparison based on spectral features at different scanning speeds and spectral resolutions of 1.25 nm (**a**) and 5.03 nm (**b**), respectively.