The MarkerLynxTM-generated data matrices were exported into SIMCA software, version 14 (Umetrics, Umea, Sweden) for statistical analyses. An unsupervised method, principal component analysis (PCA), and a supervised modeling, orthogonal projection to latent structures-discriminant analysis (OPLS-DA), were employed. The data pre-treatment methods used included scaling and transformation. These two types of data pre-treatment were explored as described in Section 2.2. The scaling methods looked at were center (Ctr), autoscaling, (also known as unit variance, UV) and Pareto, and the transformation methods used were logarithmic and power transformation. The formulae (or mathematical description of these methods) can be found in the cited literature  and in the SIMCA version 13 manual (User’s Guide to SIMCA 13, 2012). In this study, the logarithmic transformation was 10Log (C1 × X + C2) where C1 = 1 and C2 = 0; and the power transformation was (C1 × X + C2) C3 where C1 = 1, C2 = 0, and C3 = 2. As described in the results, the computed models were validated.