Furthermore, to avoid variable selection bias , the significance of the variables from the loading S-plot was assessed using, firstly, the variable importance in projection (VIP) plot (SIMCA 14 software). The latter summarizes the importance of the variables both to explain X and to correlate to Y. The higher the VIP value (exceeding 1.0) the more significant is the variable in the complex analysis in comparing the difference between two or more groups . Each selected variable from the S-plot (with high model influence and reliability, and VIP score >> 1.0) was further evaluated using a dot plot (Figure 2D). The latter is similar to a histogram and kernel density estimation (but algorithmically different), computing each observation as a unit: the observations are sorted into “bins” representing variable sub-ranges . A very strong discriminating variable has no overlap between groups (Figure 2D). The mathematical description of the mentioned algorithms and methods (e.g., VIP, dot plot, etc.) is beyond the scope of this paper; the reader is, thus, referred to the cited literature.