Discrimination and classification of FTIR spectra of red, blue and green spray paints using a multivariate statistical approach

Following that, classification and prediction of future samples were evaluated by means of supervised techniques of classification such as linear/quadratic discriminant analysis (LDA/QDA), support vector machines (SVM), soft independent modeling of classes analogies (SIMCA) and partial least squares discriminant analysis (PLS-DA). SIMCA was the preferred method, as it provided the smallest false negative rates together with a correct classification rate of about 95%. From an investigative point-of-view the presence of false positives was considered acceptable, as it is preferable to have a longer list of possible sources but have confidence that the true source belongs to it.