Drug identification by electroanalysis with multiple classification approaches

Abstract

Selective approaches in analytical chemistry are essential in drug analysis. Spectroscopic methods are often performed to identify drugs, and electroanalysis is mostly used in quantitative assays. However, chemometrics are tools that can be applied to analytical methods in order to enhance selectivity. Thus, the aim of this work was to propose a novel drug identification approach with electroanalysis with several classification algorithms and to compare those results with Raman spectroscopy. The commercial tablets and analytical standards of diclofenac, amoxicillin, hydrochlorothiazide, bromazepam, piroxicam, sulfadiazine, albendazole, cyclobenzaprine and ibuprofen were studied. The classification approaches of linear discrimination analysis (LDA), partial least squares discriminant analysis (PLSDA), support vector machine and k-nearest neighbours (KNN) were employed. Differential pulse voltammetry and Raman spectroscopy were used. The models AS, CT and AS-CT were proposed by the division of the dataset in three parts, being only the analytical standards (AS), only the commercial tablets (CT) and both analytical standards and commercial tablets (AS-CT). For voltammetry and Raman LDA and PLSDA yielded the best accuracy in model AS. For the commercial tablets (model CT), voltammetry showed better performance than Raman. In model AS-CT the KNN classifications displayed better accuracy for both voltammetry and Raman. Voltammetry showed a great performance in the identification of analytical standards and commercial tablet drugs, higher versatility and easier employment due to the nonrequirement of variable selection in comparison to Raman and showed a better performance than Raman for the identification of commercial tablets.