## 1 Introduction
In addition, the presence of a large amount of information characterizing a single Raman spectrum dramatically increases the difficulties in the visual interpretation of Raman spectra. This last issue assumes particular significance in the study of biological tissues, often characterized by complex chemical composition. This class of samples is characterized by frequent cases of subtle molecular differences between tissues corresponding to different diseases affecting the same type of organ, often associated with partially overlapped Raman bands. In this sense, the employment of ML algorithms to interpret Raman-derived experimental data could represent a powerful solution to retrieve the information of interest in a time compatible with the final use requirements [11; 12; 13]. In particular, several works [14; 15; 16; 17; 18; 19; 20; 21] have shown how RS in combination with ML allows for the detection of malignant tissues on timescales of minutes or even seconds, potentially helping in the intraoperative estimation of tumor margins. Analogously, recently RS has been applied to chondrogenic tumor classification with excellent classification performances, by employing either classical ML or algorithms, belonging to the sub-field DL [6; 22; 23]. Such investigations demonstrated the effectiveness of such algorithms in systematically interpreting Raman-derived experimental data for the diagnosis of bone tumors. This finding represents an advantage over the analysis of stained tissue sections, requiring expert pathologists to express an ultimate diagnosis. In addition, the high level of detail of such an approach could potentially detect sub-types or substages of a disease, intermediate to the conventionally accepted ones; e.g., the three different grades of CS. This outcome could represent a breakthrough toward an accurate oncological diagnosis, and therefore the planning of personalized therapies. In this paper, we improved the ability of ML and DL models to perform an accurate diagnosis of CS and EC from Raman-based data collected from ex vivo tissues. For this investigation we adopted a set of Raman spectra collected in the framework of a previous study [6]. Our analysis was focused either on the basic problem of distinguishing CS from EC (or on assigning a correct grade to the CS cases), or on the crucial question to identify the most relevant Raman bands for any malignant degree. On the first question, the results demonstrated the effectiveness of ML and DL models in identifying the aforementioned CS tumor, with classification accuracy reaching 99.7%. But much more relevant, the method for the identification of the Raman Band Identification (RBI), i.e., the Raman bands evidencing the degree of malignancy of tissues under investigation, basically inspired by permutation feature importance [24], allowed us to identify Raman spectral components more relevant to the classification process. Such results represent the most original contribution of our paper. This is because the analysis of RBI potentially represents an important tool in the attempt to restrict the spectral range, to reduce the acquisition time and the computational cost of the classification phase.