## 3 Results  
Based on the previously described criteria for occlusal state validation with PLM, the investigated teeth were categorized as'stained' or as'stained lesion'. Table 1 shows the data distribution after histological assignment of each sample's occlusal state. Of the analyzed 65 teeth, 12 were categorized as stained and 53 as stained lesions, resulting in an imbalanced data set. The spectral characteristics of the stained and stained lesion groups are contrasted in the mean reflectance spectrum (Figure 3). The difference spectrum (_difference_ = _reflectance_\({}_{stained}\) - _reflectance_\({}_{stained}\)_) illustrates more clearly that the mean reflectance of stained enamel is higher than that of stained lesions in the wavelength range 525-710 nm. In contrast, stained lesions showed a higher mean reflectance in the wavelength range 710-1000 nm. This relationship is also visible in the raw reflectance images, where stains appear transparent at near-infrared wavelengths (Figure 3, dotted white lines), but stained lesions show an increased reflectance compared to the surrounding sound enamel (Figure 3, white arrows). Figure 3: Mean reflectance spectra of stains and stained lesions, respectively, and their difference spectrum (dotted blue line, _difference_ = _reflectance_\({}_{stained}\) - _reflectance_\({}_{stained}\)_), together with sample reflectance images. The mean reflectance of stained lesions exceeds that of stained enamel for wavelengths above 710 nm, whereas it is lower or indifferent for wavelengths below 710 nm. The wavelength region 505–525 nm was eliminated due to high noise. The reflectance images show how stains visible at a wavelength of 600 nm are rendered transparent at a wavelength of 900 nm, and underlying demineralization (white arrows) becomes visible. a.u. : arbitrary units. Classification algorithms were able to distinguish well between the spectra of stained enamel and stained lesions. A kNN classification algorithm considering \(k=1\) neighbor, utilizing Euclidian distance metric and equal distance weights (\"fine kNN\") achieved the highest performance (\(MCC\) = 0.75, sensitivity of 0.95, specificity of 0.80) when trained on the filtered dataset. Algorithms performed slightly better on when trained on filtered data than when trained on raw, unfiltered reflectance data. The confusion matrices of the highest-performing algorithm trained on the filtered and on the unfiltered dataset, respectively, of 65 teeth are shown in Table 2.  
The classification model generated by the fine kNN algorithm was used to classify the fissural systems of five additional teeth, which were not included in the training and evaluation of the algorithm (Table 3). The projection maps show that the classified enamel state is in good agreement with the PLM images in the histologically validated area: Stained samples show a nearly homogeneous distribution of the predicted enamel state \"stained\". However, some clearly circumscribed regions away from the histologically validated area are classified as \"stained lesion\". Stained lesion samples exhibit a more heterogeneous distribution of enamel health states: A large proportion of fissural areas are classified as \"stained lesion\"; however, there are numerous \"stained\" regions of varying sizes dispersed in between.