## 3 Results and Discussion
### Discrimination of Colon Tissue Samples Based on Raman Data  
To properly diagnose colorectal cancer, it is necessary to distinguish between samples originating from normal and abnormal colon tissue, as well as between benign and malignant colorectal polyps. This goal can be achieved by comparing individual characteristic Raman bands of biochemical markers of carcinogenesis or through the discriminatory analysis of Raman spectra using multivariate statistical methods. Previous works have used various approaches to separate and classify normal and pathological tissue and biofluid samples based on Raman spectra, such as HCA [6; 43], PCA [40], partial least squares discriminant analysis (PLS-DA) [51; 52], SVM and other machine learning methods [43], PCA in combination with linear discriminant analysis (LDA), SVM, SIMCA [53] and other discriminating parameters like Mahalanobis distance or spectral residuals [45; 54; 55; 56; 57], and convolutional neural networks (CNN) [58; 59; 60; 61]. Of particular interest for early cancer diagnosis is the immediate analysis of intracellular Raman spectra using a biomolecular component analysis (BCA) algorithm [62]. In the current work, we used multivariate statistical methods, namely, HCA, PCA, SIMCA, and SVM, to discriminate and classify the individual smoother and baseline corrected Raman spectra of normal colon tissue, adenomatous polyp, and adenocarcinoma samples recorded using handheld, modular, and laboratory Raman instruments at the 25-30 independent sites. Figure 6 demonstrates the component score plots PC1 versus PC2 (left panels) and dendrograms of similarity (right panels) obtained via HCA using [PERSON]'s clustering method and squared Euclidean distances. For all Raman devices, the combination of principal components PC1 and PC2 was found to demonstrate significant differences between the three samples. The best discrimination was observed for spectra measured with Ahura, where well-separated heap clusters of samples were formed. The normal colon tissue sample showed positive PC1 scores, while the corresponding scores of the two abnormal tissue samples were mostly negative. PC2 scores, in turn, separated adenomatous polyp and adenocarcinoma samples into positive and negative regions, respectively. In the case of i-Raman Plus spectra, PC1 partially separated the normal colon tissue sample, which showed negative values for this component, from the abnormal samples, which had mostly positive scores. In turn, PC2 separated the adenocarcinoma sample with negative score values from the remaining two samples. For DXR SmartRaman spectra, PC1 scores of adenocarcinoma fell below \(-40\) and were, thus, separated from the other two samples having higher scores. PC2 separated normal colon tissue and adenomatous polyp, showing negative and mostly positive scores, respectively. Dendrograms of similarity obtained for the Raman spectra of all three devices showed a good separation of the clusters corresponding to normal colon tissue, adenomatous polyp, and adenocarcinoma. The best discrimination was in the case of Ahura, where all clusters were separated at a distance near 4.5; the corresponding distances were \(\sim\)6 for i-Raman Plus and \(\sim\)8.5 for DXR SmartRaman. The Raman spectra measured with both portable instruments showed similar clustering. Firstly, the cluster of normal colon tissue was separated from the abnormal colon tissues. Then, the spectra of adenomatous polyp and adenocarcinoma were split into their clusters. Conversely, for DXR SmartRaman, firstly, the adenocarcinoma was separated from the other two samples, and a discrimination of normal colon tissue and adenomatous polyp occurred at a relatively high distance. We used PCA-based SIMCA supervised classification to demonstrate Raman spectroscopy's potential in screening colon tissue samples. Among the multivariate data analysis methods, SIMCA can provide a good classification of study groups and high within-class predictability even with small-size datasets [63; 64; 65].