For principal component (PC) analyses, the results showed that varying the processing parameters (mass tolerance and intensity threshold) affected the maximum (suitable) number of computed PCs, optimized using seven-fold cross-validation, to explain the variation in data X: changing from a five-PCs to a six-PCs models (Table 2). Notably, only the “R1” significant components, i.e., those producing an increase in Q2, were retained. Although, visually, the sample clustering in the PCA scores space (constructed from the first two PCs) show no significant difference across the four datasets (Figure 1A,B and Figure S1A,B), the model quality was clearly affected. This can be assessed by inspecting the PCA parameters and diagnostic tools, which are computed and displayed graphically or numerically. In computing a PC model, strong and moderate outliers (observations that are extreme or do not fit the model) are often formed. Strong outliers have a high leverage on the model, shifting it significantly and reducing the predictability, whereas the moderate outliers correspond to the temporary perturbations (in the process/study), indicating a shift in the process/study behavior .