## 4 Conclusions  
The information obtained in this study provides promising perspectives for the effective use of FTIR in the classification of EVOO against other olive oil categories. The characteristic spectral bands of triacylglycerlecrols related to the carbonyl groups present in triacylglycerlecrols (C=O) located near 1744 cm\({}^{-1}\) (specific features: 1739, 1748, and 1751 cm\({}^{-1}\)), the fingerprinting area 1250-1000 cm\({}^{-1}\) (specific features: 1088, 1094, 1116, 1123, 1124, 1158, 1162, 1236, 1240, and 1247 cm\({}^{-1}\)), which correspond to CH bending, and 1680 cm\({}^{-1}\), which is associated with unsaturated aldehydes, were observed to constitute the main basis of the discrimination of EVOO from the \"other\" class. The reference method employed in the classification of the olive oil samples was based on the sensory analysis of well-established and regulated taste panels. RF analysis was employed as a supervised machine learning approach to correlate spectral data and olive oil sample sensory classes (EVOO vs. other olive oil samples). The combination of RF feature selection with S-G smoothing provides an effective strategy for reducing the dimensionality of spectral data while maintaining high classification accuracy. Careful tuning of the S-G parameters and the feature importance threshold was essential to optimize the model performance, demonstrating the value of well-designed pre-processing and feature selection steps in spectroscopic analysis for food authentication. The analysis indicated that the choice of the feature importance threshold significantly affected the number of features selected and the subsequent classification accuracy. Specifically, the 0.001 threshold generally selected fewer features, simplifying the model while maintaining or even improving classification performance compared to the 0.0001 threshold. The highest accuracy (0.91) was achieved using various combinationsof smoothing parameters, suggesting that careful selection of pre-processing parameters is crucial for the optimal classification of olive oil categories. Furthermore, the feature selection ability of the RF model reduced the number of spectral features from 3164 to 352, which facilitated faster model training while improving interpretability by focusing on the most critical spectral regions for quality classification. Overall, the use of RF in conjunction with S-G smoothing provided an effective methodology for differentiating EVOO from other olive oil categories. The ability of the model to achieve high classification accuracy demonstrates the robustness of FTIR spectral data combined with advanced machine learning techniques or the authentication of olive oil quality. Due to the limited number of olive oil samples that a sensory panel can access in a day, this method provides new perspectives in the high-throughput characterization of the EVOO olive oil against other olive oil categories. Combined with the lower cost and more rapid analysis time afforded by FTIR, it presents a promising option for industrial olive oil classification, increasing the credibility of the olive oil sector in the provision of high-quality products to consumers. To inspire further studies in the field, it is suggested that future research should focus on collecting a larger and more diverse dataset, including olive oil samples from different regions, cultivars, and production methods. This would enhance the generalizability of the model and allow for the development of more robust classification systems. Furthermore, while RF has proven effective in this study, exploring other machine learning techniques, such as deep learning (e.g., convolutional neural networks) or ensemble methods combining multiple algorithms, could further improve the classification accuracy and feature extraction. Conceptualization, E.Z.P. ; methodology, E.Z.P. ; software, P.A. and E.Z.P. ; validation, P.A. and E.Z.P. ; formal analysis, P.A. ; C.G., and E.Z.P. ; investigation, S.S. and C.G. ; resources, C.G. ; and M.K.