2.4.2 Model Development for Partial Least Squares Discriminant Analysis
The UnscramblerX software, similar to the PCA model development, was used to develop the PLS-DA models. Three PLS-DA models were developed to discriminate between the three microorganisms. These models were designed for binary classification with two classes: samples containing specific microorganisms (y = 1) or those without microorganisms (y = -1). The models were developed using 7 latent variables, cross-validated results with 20 segments of random samples, and the scores and loadings were calculated using the NIPALS algorithm. The threshold for distinguishing between the two classes was zero (0). Several figures of merit were used to evaluate the PLS-DA model performance: the percent of correctly classified samples (%T), percent of incorrectly classified samples (%F), sensitivity (SEN), specificity (ΞΎ), and Matthews correlation coefficient (MCC). A value of negative one (-1) for MCC indicates the perfect classification of the opposite variable, a value of one (1) is the correct classification of the class, and zero (0) indicates random classification.