Before performing PCA and OPLS-DA, the data was mean-centered (to put all variables on equal footing) and Pareto-scaled (to adjust for measurement errors as to have homoscedasticity in the data). There are different methods for dealing with missing values, and each method/approach impacts on downstream statistical analyses . In the present study, the SIMCA software uses an adjusted nonlinear iterative partial least squares (NIPALS) algorithm (with a correction factor of 3.0)  in handling the missing values. The threshold of missing values is, by default, 50%, and in the four matrices (from the same raw data, Table 1) no observations or variables had missing values exceeding the permitted tolerance. Thus, to assess the effect of processing parameters (mass tolerance and intensity threshold, Table 1) on the statistical output, the quality and characteristics of computed PCA and OPLS-DA models were comparatively evaluated. The statistically-extracted discriminating variables from the four scenarios (Table 1) were also compared.