## 4 Discussion
During PCA analysis, the loadings of parameters describing particle size on the first factor were lower compared to the loadings of the same parameters on the first component before rotation. Now, it was easier to determine what Factor 1 actually represented and what it did not. The factor showed positive loadings of 0.575 (EI), 0.579 (AR), and 0.533 (RD). Based on the extracted parameters, it can be seen that the first factor defined particle form. The second factor isolated all size parameters. Positive loadings included Feret's diameter (0.397) and particle length (0.372), while negative loadings included area (\(-\)0.429), perimeter (\(-\)0.362), width (\(-\)0.4), and equivalent circle diameter (\(-\)0.431). Negative loadings indicated what the factor did not represent. Due to the way rotation was performed, the factor was named based on the highest loading regardless of the sign. The highest factor loading actually indicated the variables that had the strongest correlation with that factor. The third factor isolated parameters defining particle morphology, specifically those on which F1 did not show loadings. Positive loadings were observed on the rectangularity parameter (0.407), while parameters CVX (\(-\)0.460), Circ (\(-\)0.426), SLD (\(-\)0.520), and CP (\(-\)0.393) exhibited negative loadings. Based on the obtained results, each factor could be identified, so Factor 1 represents form, Factor 2 size, and Factor 3 particle roughness. The results obtained using the clustering method enabled the extraction of parameters defining the sample itself. Considering the PCA results, correlation analysis, and clustering analysis together, it can be concluded that the equivalent circle diameter emerged as the parameter that best described the particle size in this sample. The correlation analysis showed a strong association with other size parameters. PCA analysis exhibited the highest loading on this parameter, as well as in clustering, where DCE showed significant correlation with other variables, thus being singled out as representative. This result is consistent with the relationships observed in the correlation matrix and also supports the results of the PCA. The results also allowed the reduction of eight shape parameters to the three defining form, morphological, and textural roughness. EI, RD, and AR were singled out into a separate group, where AR exhibited slightly higher value on the first factor in PCA analysis (Table 5) and was thus chosen as the form representative parameter. For morphological roughness, the solidity parameter stood out independently in the clustering analysis and also showed the highest loading in the third factor of PCA analysis. Convexity isolated textural roughness with the highest loading in the third factor from this group of data. Solidity and convexity were also singled out in correlation analysis as independent parameters, with weaker correlation with other parameters. To reduce the number of variables, considering the results of multivariate statistical analysis on the sample generated by the dental restoration grinding process, one size parameter and three shape parameters that sufficiently defined particle geometry in the sample can be singled out. Area and equivalent circle diameter emerged with almost equal loadings and high correlation with other size parameters. Area showed slightly higher value in PCA analysis, allowing it to be singled out as representative. Regarding particle shape descriptors, a correlation between solidity and circularity could already be observed in the correlation analysis. Circularity showed significantly higher loading in the F3 principal component analysis, while, in the clustering analysis, the loadings and associations were at the same level. Based on the results, circularity emerged as a representative of particle morphological roughness.