Abstract
Herbal medicines are popular natural medicines that have been used for decades. The use of alternative medicines continues to expand rapidly across the world. The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications, as their therapeutic potential varies between different geographic origins, plant species, and varieties. Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach. Their quality should be considered based on a complete metabolic profile, as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds. A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization. In this study, a rapid and effective quality assessment system for geographical traceability, species, and variety-specific authenticity of the widely used natural medicines turmeric, Ocimum, and Withania somnifera was investigated using Fourier transform near-infrared (FT-NIR) spectroscopy-based metabolic fingerprinting. Four different geographical origins of turmeric, five different Ocimum species, and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches. Extremely good discrimination (R2 > 0.98, Q2 > 0.97, and accuracy = 1.0) with sensitivity and specificity of 100% was achieved using this metabolic fingerprinting strategy. Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.