We used PLS-DA, SVM, Decision Trees and Naïve Bayes implemented in MPP software package (version 2.2, Agilent Technologies, Santa Clara, USA) for all multivariate and machine learning analysis in this study. Partial least squares is a regression method using the information contained in X data matrix (predictor variables) to predict the behavior of Y data matrix (response variables). PLS method models both X and Y variables simultaneously to find the latent variables in X that will predict the latent variables in Y [44] . The application of PLS as a classification method is indicated as PLS-DA [45, 46] . SVM separates two classes by generating the hyperplane (in a highdimensional feature space) which maximizes the distance from the hyperplane to the closest training examples [47] . In Decision Trees a sample gets classified by following the appropriate path down the decision tree. The Naive Bayesian model is built based on the probability distribution function of the training data along each feature. Since Decision Trees and Naïve Bayes directly handle multi-class problems, we have used the default parameters for these techniques. The SVMs are trained using sequential minimal optimization with a linear kernel. Validation of the obtained predictive models was performed using a standard K-fold cross-validation procedure: observations in input data were randomly divided into three equal parts, two parts were used for model training, and the remaining samples were classified using the constructed model. The whole process was repeated for 10 times.