## 3 Results and Discussion
### Discrimination of Colon Tissue Samples Based on Raman Data
The SIMCA model was implemented using Raman data in the working spectral region (600-1800 cm\({}^{-1}\)) for all three devices based on the three independent PCA models with a significance level of 5%. Three-dimensional Cooman's plots from SIMCA (Figure 7a,c,e) were obtained to visualize the results of the classification analysis. These plots display residual distances for independent measurement points in each sample representing individual classes. As can be seen from the graphs, a clear differentiation of the groups, which referred to the normal colon tissue, adenomatous polyp, and adenocarcinoma, was found for all Raman instruments, and these groups were well classified. For the Ahura FirstDefender and DXR SmartRaman devices (Figure 7a,e), the compact group of normal colon tissue was significantly distant from the more scattered groups of adenomatous polyps and adenocarcinoma, and the latter two groups were closer to each other. However, there was no misclassification of pathological groups. In contrast, in the case of i-Raman Plus (Figure 7c), all three groups were more scattered and equidistant, but even in this case, there was no overlapping of the groups. Figure 6: Discrimination of the samples of normal colon tissue (blue squares), adenomatous polyp (red circles), and adenocarcinoma (green triangles) based on the PCA and HCA of individual Raman spectra recorded using handheld (**a,b**), modular (**c,d**), and laboratory (**e,f**) Raman instruments at the 25–30 independent sites of samples: (left panels) component score plots of PC1 versus PC2; (right panels) dendrograms of similarity obtained via HCA using [PERSON]’s clustering method and squared Euclidean distances. SVM is an effective tool for classifying spectral data due to the following advantages [54,66]: (a) it produces a robust classifier that can work with small or limited-size datasets; (b) it uses a structural risk minimization (SRM) approach, which reduces the risk of overfitting the data; (c) it gives reproducible solutions when using the same classifier parameters; (d) it can draw class boundaries with complex conditions, replacing kernel functions. Three Raman datasets obtained from the laboratory, modular, and portable devices, which include 25 spectra for each group, were used as training datasets to perform  
Figure 7: Classification of the samples of normal colon tissue (blue squares), adenomatous polyp (red circles), and adenocarcinoma (green triangles) based on the analyses of individual Raman data recorded with handheld (**a,b**), modular (**c,d**), and laboratory (**e,f**) Raman instruments at the 25–30 independent sites of samples: (left panels) 3D Cooman’s plots for the SIMCA classification; (right panels) scattergram based on SVM classification. SVM classification training to fully distinguish the three types of samples, i.e., normal colon tissue, adenomatous polyp, and adenocarcinoma. The scattergrams, characterizing the three Raman instruments, are shown in Figure 7b,d,f. All the obtained SVM classifications allowed us to distinguish between the three mentioned types of colon tissue.