The created matrices (Table 1) were then imported into SIMCA (Soft Independent Modeling of Class Analogy) version 14 software (Umetrics, Umea, Sweden) for statistical modeling: principal component analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) (generally used approaches in metabolomics data analysis for data overview/descriptive exploration, and explicative/predictive analysis, respectively). Total variation in metabolomic data is multifactorial, comprising of the sum of biological variation (induced and non-induced) and technical variation . Therefore such data, with its inherent properties and structures (including large number of variables, nonlinearity, heteroscedasticity, missing values), imperatively requires special attention during statistical handling to avoid a risk of model overfitting , manipulation, and confusion of statistical findings and distortion of the results, which can lead to incorrect data interpretation and false discovery .