Quality control of herbal medicines by using spectroscopic techniques and multivariate statistical analysis.

Herbal medicines play an important role in modern human life and have significant effects on treating diseases; however, the quality and safety of these herbal products has now become a serious issue due to increasing pollution in air, water, soil, etc. The present study proposes Fourier transform infrared spectroscopy (FTIR) along with the statistical method principal component analysis (PCA) to identify and discriminate herbal medicines for quality control. Herbal plants have been characterized using FTIR spectroscopy. Characteristic peaks (strong and weak) have been marked for each herbal sample in the fingerprint region (400-2000 cm(-1)). The ratio of the areas of any two marked characteristic peaks was found to be nearly consistent for the same plant from different regions, and thus the present idea suggests an additional discrimination method for herbal medicines. PCA clusters herbal medicines into different groups, clearly showing that this method can adequately discriminate different herbal medicines using FTIR data. Toxic metal contents (Cd, Pb, Cr, and As) have been determined and the results compared with the higher permissible daily intake limit of heavy metals proposed by the World Health Organization (WHO).