Resolving identification issues of Saraca asoca from its adulterant and commercial samples using phytochemical markers

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Abstract
Pharmacognosy Magazine,2017,13,50s,s266-s272.
Published:July 2017
Type:Original Article
Authors:
Author(s) affiliations:

Satisha Hegde1, Harsha Vasudev Hegde2, Sunil Satyappa Jalalpure3, Malleswara Rao Peram4, Sandeep Ramachandra Pai5, Subarna Roy2
1Regional Medical Research Centre, Indian Council of Medical Research; KLE Academy of Higher Education and Research (KLE University), Belagavi, Karnataka, India
2Regional Medical Research Centre, Indian Council of Medical Research, Belagavi, Karnataka, India
3Dr. Prabhakar Kore Basic Science Research Centre, KLE University; Department of Pharmacognosy, KLE University's College of Pharmacy, Belagavi, Karnataka, India
4Dr. Prabhakar Kore Basic Science Research Centre, KLE University, Belagavi, Karnataka, India
5Regional Medical Research Centre, Indian Council of Medical Research; Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, India

Abstract:

Saraca asoca (Roxb.) De Wilde (Ashoka) is a highly valued endangered medicinal tree species from Western Ghats of India. Besides treating cardiac and circulatory problems, S. asoca provides immense relief in gynecological disorders. Higher price and demand, in contrast to the smaller population size of the plant, have motivated adulteration with other plants such as Polyalthia longifolia (Sonnerat) Thwaites. The fundamental concerns in quality control of S. asoca arise due to its part of medicinal value (Bark) and the chemical composition. Phytochemical fingerprinting with proper selection of analytical markers is a promising method in addressing quality control issues. In the present study, high-performance liquid chromatography of phenolic compounds (gallic acid, catechin, and epicatechin) coupled to multivariate analysis was used. Five samples each of S. asocaP. longifolia from two localities alongside five commercial market samples showed evidence of adulteration. Subsequently, multivariate hierarchical cluster analysis and principal component analysis was established to discriminate the adulterants of S. asoca. The proposed method ascertains identification of S. asoca from its putative adulterant P. longifolia and commercial market samples. The data generated may also serve as baseline data to form a quality standard for pharmacopoeias

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