The results indicate that the calculated models have much higher R2 and Q2 values (Figure 2B and Table 2) and, thus, the computed true OPLS-DA models are statistically far better than the 50 permutated models for each dataset. Assessing the total variation in X-space (predictive and orthogonal) explained by the models, the results show that the R2X values were different: a change in mass tolerance and intensity threshold affect the amount of variation explained by the computed models (Table 2). For variable selection, the OPLS-DA loading S-plots were evaluated (Figure 2C). This loading plot has an S-shape provided the data are centered/Pareto-scaled, and aids in identifying variables which differ between groups (discriminating variables), i.e., variables situated at the upper right or lower left sections in the S-plot. The p1-axis describes the influence of each X-variable on the group separation (modeled covariation), and the p(corr)1-axis represents the reliability of each X-variable for accomplishing the group separation (modeled correlation). Variables that combine high model influence (high covariation/magnitude) with high reliability (i.e., smaller risk for spurious correlation) are statistically relevant as possible discriminating variables : |p| ≥ 0.05 and |p(corr)| ≥ 0.5 in this study.