Discrimination and classification techniques applied on Mallotus and Phyllanthus high performance liquid chromatography fingerprints - Viaene, J.; Goodarzi, M.; Dejaegher, B.; Tistaert, C.; Hoang Le Tuan, A.; Nguyen Hoai, N.; Chau Van, M.; Quetin-Leclercq, J.; Vander Heyden, Y. (Analytica chimica acta, 2015)
•HPLC fingerprints from six species (two genera) of herbal samples were measured.•Models were built to distinguish the samples according to genus and to species.•LDA, QDA, CART and some SIMCA approaches best discriminate the two genera (100% specificity and sensitivity).•LDA, QDA and SIMCA best discriminate the six species (100% specificity and sensitivity).•SIMCA classification is satisfactory (no type I, but some type II errors observed). Mallotus and Phyllanthus genera, both containing several species commonly used as traditional medicines around the world, are the subjects of this discrimination and classification study. The objective of this study was to compare different discrimination and classification techniques to distinguish the two genera (Mallotus and Phyllanthus) on the one hand, and the six species (Mallotus apelta, Mallotus paniculatus, Phyllanthus emblica, Phyllanthus reticulatus, Phyllanthus urinaria L. and Phyllanthus amarus), on the other. Fingerprints of 36 samples from the 6 species were developed using reversed-phase high-performance liquid chromatography with ultraviolet detection (RP-HPLC-UV). After fingerprint data pretreatment, first an exploratory data analysis was performed using Principal Component Analysis (PCA), revealing two outlying samples, which were excluded from the calibration set used to develop the discrimination and classification models. Models were built by means of Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Classification and Regression Trees (CART) and Soft Independent Modeling of Class Analogy (SIMCA). Application of the models on the total data set (outliers included) confirmed a possible labeling issue for the outliers. LDA, QDA and CART, independently of the pretreatment, or SIMCA after “normalization and column centering (N_CC)” or after “Standard Normal Variate transformation and column centering (SNV_CC)” were found best to discriminate the two genera, while LDA after column centering (CC), N_CC or SNV_CC; QDA after SNV_CC; and SIMCA after N_CC or after SNV_CC best distinguished between the 6 species. As classification technique, SIMCA after N_CC or after SNV_CC results in the best overall sensitivity and specificity.