## 2 Materials and Methods
Oclusal surfaces were located 350 mm below the lens and aligned parallel to the tabletop. Two external 40 W halogen bulbs with an aluminum-coated reflector (OSRAM Halopar GU10, OSRAM GmbH, Munich, Germany) illuminated the samples in the analyzed wavelength range. In order to ensure a reproducible and stable spectrum output of the illumination unit, a warm-up time of 60 s prior to measurements was applied and the recommended voltage (230 V) was supplied by a stabilized power supply. The camera calibration and data normalization procedure was described in more detail previously [28]. Briefly, the reflected intensity of the tooth is normalized to a previously measured dark image with closed shutter as well as to a white image with a reflectance standard (\(>\)98% reflectance, Optopolymer(r), Optopolymer, Munich, Germany) in order to correct sensor noise and illumination inhomogeneities. The resulting normalized reflectance image at each wavelength was represented as a grayscale image according to its reflectance intensity (range: 0-1; dark: low reflectance intensity, bright: high reflectance intensity). Polarized light microscopy:PLM of histological cross-sections was used to determine the reference occlusal health state. Cross-sections of approx. 80 \(\upmu\)m thickness were created at the clinically most questionable region of the central fissure of each tooth using [PERSON]'s thin-section technique [29]. The sections were analyzed using a Leica DMRB polarization microscope (Leica Microsystems GmbH, Wetzlar, Germany) with crossed polarizer and analyzer and an additional full-wave retardation plate (red I plate) prior to the analyzer. Digital photographs were taken of all sections with a full-frame digital camera (SONY Alpha 7, Sony Europe B.V., Surrey, UK) attached to the microscope and in combination with a 2.5\(\times\) magnification objective. No further image processing was performed. Two dentists experienced in tooth histology and not involved in the teeth's visual examination consensually determined each tooth's occlusal health state as intact but with stained enamel (hereafter'stained') or stained and demineralized enamel (hereafter'stained lesion'). Fissural areas that showed natural positive birefringence exhibiting a distinctive change of color (yellow--violet) were associated with stained enamel. Stained lesions were identified by increased absorption, i.e., reduced transparency, and specific polarization properties such as negative birefringence or depolarization with a characteristic brown-dark, often droplet-shaped involvement of the surrounding enamel. _Classification and statistical analysis:_ Using custom MATLAB(r) (MathWorks, Natick, MA, USA) code, a graphical user interface (GUI) for reading, visualizing and selecting hyperspectral data was developed. Within this GUI, ten data points in close proximity to the histologically validated region were selected manually for each tooth (Figure 2a). The corresponding reflectance spectra were saved for further processing and associated with the histological occlusal state by binarization (0 = stained, 1 = stained lesion). The number of teeth and spectra within each occlusal health state group is shown in Table 1. In order to remove potential noise from the spectra, an additional second dataset was generated by applying a Savitzky-Golay filter (order = 2, frame length = 7) to the selected raw reflectance spectra (Figure 2b). The Savitzky-Golay filter is well established in (hyper)spectral data analysis because it enables an increase in data precision without distorting the underlying signal [30]. Intensities at the wavelengths 505-525 nm were eliminated due to the high noise level of the camera sensor in this region. The filtered, cropped spectra were used to train 24 classification algorithms of artificial intelligence. These include algorithms of the subgroups support vector machines (SVM), nearest-neighbor (kNN) and decision trees. An SVM tries to find the best plane (line) that divides data points into their respective classes.