## 3 Results and Discussion  
In Figure 3a, we show the averaged spectra associated with CS and EC. The shaded areas represent the related standard deviation. The corresponding peak assignments are reported in Table 1. The general behavior of the averaged spectra highlight a decrease in the Raman signal of CS with respect to EC. In particular, we qualitatively observed the largest differences between CS and EC in the Raman intensities in the following spectral bands: The most intense peak (\(\sim\)1003 cm\({}^{-1}\)) is attributed to phenylalanine (Phe), recognized as a strongly Raman-active molecule [33]. Phe is a precursor of several amino acids, such as tyrosine, represented by the peaks at 815, 1172, and 1207 cm\({}^{-1}\). Tyrosine has previously been reported to be an amino acid regulating the production and activity of osteoclasts [34]. The presence of Phe is also shown by the peak at 1609 cm\({}^{-1}\). We attributed the two narrow peaks at \(\sim\)729 cm\({}^{-1}\) to carbonates that, alongside bioapatites (604, 849, 1035, 1057, and 1098 cm\({}^{-1}\)), represent one of the most abundant inorganic components of bone tissue. Other relevant spectral components can be found in the peaks at 830 and 1453 cm\({}^{-1}\), assigned to proline (collagen) and CH\({}_{2}\) wagging, respectively. Figure 3: (**a**) Averaged Raman spectra of CS and EC; (**b**) averaged spectra of G1, G2, and G3. The shaded areas represent the standard deviation. We did not normalize the Raman signal, in order to exploit the information about the number of photon counts for the following data analysis. signals of G1, G2, and G3, observed in Figure 3b and, on the other hand, the small standard deviation of the CS Raman signal in comparison to the averaged intensity (see Figure 3a). In view of the employment of the collected Raman spectra to classify the analyzed tissues, we emphasize that the apparent macroscopic difference in the signal intensity of CS and EC observed in Figure 3a is not necessarily sufficient to perform an accurate classification based solely on a visual interpretation of the Raman spectra. We stress that the shaded areas associated with the mean spectra represent the standard deviation. For this reason, CS and EC could have a superposition region in the intensity domain, due to Raman spectra corresponding to the tails of the related intensity distributions, a possible source of misclassification problems. In this case, the employment of ML algorithms allowed us to rely on additional information, provided by the spectral components, in a systematic and efficient fashion. In Table 2, we summarize the performances of the ML and DL models for the examined classification problems. In particular, we average the accuracy \(A\), the sensitivity \(S\), and the specificity \(SP\) over the five folds and over the values of the label. The most evident result highlighted is the substantial difference between the performances of the linear SVM with respect to the other non-linear models, for all the classification problems considered. In turn, RFC and MLPC led to similar classification performances, as shown in Table 2, with only slight improvements provided by MLPC. This feature reveals how non-linearities in the definition of the models result in enhanced classification performances. A possible reason for this behavior can be presumably attributed to the strong non-linear nature of RFC and MLPC. In other words, while SVM tries to distinguish the classes of interest by separating the points corresponding to the Raman spectra through a hyperplane, RFC and MLPC carry out this procedure with a non-linear surface. The reason for the small difference between the performances of RFC and MLPC is probably attributable to the different techniques employed to introduce non-linearities, leading to non-linear surfaces of different shapes. Despite non-linearities potentially leading to enhanced classification performances, they could be a source of overfitting.