Classification of Selected Anti-malarial Herbal Drugs Using Laser-induced Autofluorescence (Liaf) Combined With Multivariate Data Analysis

Abstract

ABSTRACT Laser-induced autofluorescence (LIAF) was used to characterize and classify some selected commercial anti-malarial herbal drugs (AMHDs) to avoid sample destruction. Results from deconvoluted peaks of the LIAF spectra of AMHDs showed secondary metabolites belonging to derivatives of alkaloids and classes of phenolic compounds (tannins, lignin, flavonoid, and coumarins) present in all the AMHDs samples. Analyses with unsupervised methods (Principal component analysis (PCA), K-means clustering and Hierarchical Clustering Analysis (HCA)) classified the AMHDs that were similar in physicochemical properties. Based on two PCs, supervised pattern recognition methods such as Supervised Vector Machine (SVM), K-Nearest Neighbor (KNN), Quadratic Discriminant Analysis (QDA), and Linear Discriminant Analysis (LDA) models generated were used to identify and classify unknown AMHDs with 100.00, 100.00, 99.52 and 99.04 % accuracy respectively. LIAF technique in combination with multivariate analysis may offer non-destructive characterization and classification of AMHDs.