## 4 Conclusions  
The obtained results point out that the FTIR spectra measured on cell samples are able to discriminate healthy colon cells from cancerous ones. Although the spectra are very similar, the analysis of the intensity of the absorption peaks highlights small differences mainly in the lipid content, which is greater in normal cells, and in the protein content, which is higher in cancerous cells. However, the intensity of specific absorption peaks is not a reliable parameter for spectral classification with high accuracy. Therefore, we combined the measured FTIR spectra of healthy FHC cells and cancerous CaCo-2 cells with several machine learning algorithms in order to estimate the prediction capability of such models and possibly identify which of them is able to provide the best results regarding spectra classification, so that they can be proposed and translated in the clinical diagnostic field. The performance evaluation of the investigated algorithms was carried out in two successive steps. First, the whole FTIR spectra dataset was divided into a calibration set, including 70% of the spectra for the two cell types, and a test set, including the remaining 30% of the spectra. The first set had the role of allowing for a comparison between the various models, particularly regarding the classification accuracy. The second set served to confirm this accuracy for the models that offered the best performances during the first step. The experimental results indicate that the classification accuracy was >87% for all of the investigated models in both LWR and HWR. In particular, the NN method was revealed to be the most effective, with an accuracy of 98.7%, a sensitivity of 100%, and a specificity of 97.1% in both spectral ranges. The SVM algorithm, which classified spectra with 100% accuracy, was not considered as a very reliable model for our data due to the high classification sensitivity of spectra whose classes were randomized. A significant result obtained from our experiments is that the classification performance was similar in the two spectral ranges. This is particularly important for the use of the FTIR technique in the diagnostic field, as the glass-based supports commonly used in medical practice are opaque to IR radiation in the LWR. Hence, FTIR reflection measurements are not possible in any range with biological samples on glass slides, whereas FTIR transmission measurements are possible only in the HWR. Nonetheless, the measurements carried out only in the latter range were sufficient for a correct classification of biological samples. However, our investigation had some critical issues which should be overcome before considering the possibility of transferring the FTIR measurements and machine learning analysis from the research field to diagnostic practice. First, this study was based on cultured cell lines rather than cells from patients. Thus, this work can be considered as a proof of feasibility of the proposed diagnostic analysis, and further experiments should be performed involving cytological samples from hospital patients. Second, our method should be tested on samples characterized by pathologies other than colon cancer and/or characterized by different degrees of a certain pathology. Lastly, the investigation should include a classification of tissue and liquid biopsies in order to allow for a clear evaluation of how the method can be adopted in the clinical setting. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app131810325/s1](https://www.mdpi.com/article/10.3390/app131810325/s1)]([https://www.mdpi.com/article/10.3390/app131810325/s1](https://www.mdpi.com/article/10.3390/app131810325/s1)), Table S1: Performance parameters of algorithms for test set spectra; Figure S1: ROC curves and AUC parameters of algorithms for test set spectra. Conceptualization, G.P., C.G. and V.C. ; methodology, G.P. and M.L. ; software, C.G. ; validation, G.P. and C.G. ; formal analysis, G.P. ; investigation, G.P. and M.L. ; data curation, G.P. and C.G.