Integrating a greener RP-HPLC method and chemical pattern recognition for the identification of Acorus calamus L. and its common adulterants in Indian markets.

This work deals with the development of a greener RP-HPLC method and chemical pattern recognition for the identification of Acorus calamus L. collected from different natural sources and samples traded as 'Vacha' in Indian herbal drug markets. The simultaneous quantification of α- and β-asarone was performed using 0.10% orthophosphoric acid (A) and acetonitrile (B) as solvents in an isocratic manner (35 : 65 v/v) at 0.70 mL min-1. The developed method was found to be greener using various metrics such as AES, GAPI, NEMI, AGREE, and AGREEprep than existing methods. The LC chromatogram obtained in this study showed 11 common peaks, with β-asarone (peak 10) and α-asarone (peak 11) as major markers, which were identified at an Rt of 10.69 ± 0.08 and 10 ± 0.06 minutes, respectively, while their content varied from 0.02 ± 0.001 to 0.71 ± 0.07 and 2.76 ± 0.03 to 39.26 ± 0.14 μg mg-1, respectively. Chemical pattern recognition was applied for quality evaluation of Acorus samples, and hierarchical cluster analysis bifurcated 17 samples into two categories. Principal component analysis integrated with orthogonal partial least squares discriminant analysis showed that the first two principal components efficiently reflect the similarity and differences among populations. Further, the components P7, P10 (β-asarone) and P11 (α-asarone) will serve as 'landmark components' for quality evaluation of natural and marketed samples. Results show that traded Vacha samples share chemical similarity with authentic Acorus, while common adulterants (Costus speciosus, Typha angustifolia, and Alpinia galanga) have entirely different profiles. The integration of HPLC fingerprint and chemical pattern recognition will provide a scientific rationale for the identification of the authentic raw drug and quality control of its adulterants/substitutes.