One-Class Classification Models for the Authentication of Analgesic Tablet Reference Medicine Using Differential Scanning Calorimetry and Visible-Near Infrared Spectroscopy

Abstract

The aim of this study was to develop a fast, simple and accurate analytical method for the classification of reference tablet analgesic drugs containing dipyrone, orphenadrine and caffeine using differential scanning calorimetry (DSC) and visible-near infrared spectroscopy (VNIRS) associated with one-class chemometric classification algorithm. The training set is based on reference medicine with 15 samples as target class. Three different brands with 10 samples each and five reference medicine samples, obtaining 35 samples, were used as the test set. Chemometric models based on principal component analysis (PCA) and data-driven soft independent modelling of class analogy (DD-SIMCA) were used to obtain the results. Two DD-SIMCA models obtained 100% sensitivity, specificity, and accuracy using DSC and VNIRS, both with a significance level of 0.01. This method using one-class classification as a chemometric tool proved to be a good alternative for quality control of pharmaceutical samples.