Quantification of adulteration in traded ayurvedic raw drugs employing machine learning approaches with DNA barcode database
Authors
Suma Dev
Forest Genetics and Biotechnology Division, Kerala Forest Research Institute, Peechi, Thrissur, 680653 Kerala India
Remya Unnikrishnan
Forest Genetics and Biotechnology Division, Kerala Forest Research Institute, Peechi, Thrissur, 680653 Kerala India
R Jayaraj
Forest Ecology and Biodiversity Conservation Division, Kerala Forest Research Institute, Peechi, Thrissur, 680653 Kerala India
P Sujanapal
Sustainable Forest Management Division, Kerala Forest Research Institute, Peechi, Thrissur, 680653 Kerala India
V Anitha
Forestry and Human Dimensions Division, Kerala Forest Research Institute, Peechi, Thrissur, 680653 Kerala India
Keywords:
Machine Learning Algorithm, Artificial Intelligence, DNA barcoding, Ayurvedic raw drug
Abstract
Adulteration of expensive raw drugs with inferior taxa has become a routine practice, conceding the quality and safety of derived herbal products. In this regard, the study addresses the development of an integrated approach encompassing DNA barcode and HPTLC fingerprinting to authenticate chiefly traded ayurvedic raw drugs in south India [viz. Saraca asoca (Roxb.) Willd., Terminalia arjuna (Roxb. ex DC.) Wight and Arn., Sida alnifolia L. and Desmodium gangeticum (L.) DC.] from its adulterants. Consortium of Barcode of Life (CBOL) recommended DNA barcode gene regions viz. nuclear ribosomal—Internal Transcribed Spacer (nrDNA-ITS), maturase K (matK), ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) and psbA-trnH spacer regions along with HPTLC profiling were experimented and a reference database was created. Further, an integrated analytical approach employing genetic distance-based Maximum Likelihood phylogenetic tree and Artificial Intelligence (AI)based Machine Learning Algorithms (MLA)-Waikato Environment for Knowledge Analysis (WEKA) and Barcoding with Logic (BLOG) were employed to prove efficacy of DNA barcode tool. Even though, among the four barcodes, psbA-trnH (S. alnifolia and its adulterants, T. arjuna and its adulterants) or ITS region (S. asoca and its adulterants, D. gangeticum and its adulterants) showed highest inter specific divergences in the selected Biological Reference Materials (BRMs), rbcL or matK barcode regions alone were successful for authentication of traded samples. The automated species identification techniques, WEKA and BLOG, experimented for the first time in India for raw drug validation, could achieve rapid and precise identification. A national certification agency for raw drug authentication employing an integrated approach involving a DNA barcoding tool along with standard organoleptic and analytical methods can strengthen and ensure safety and quality of herbal medicines in India.
Supplementary Information
The online version contains supplementary material available at 10.1007/s13205-021-03001-5.
Keywords: Machine Learning Algorithm, Artificial Intelligence, DNA barcoding, Ayurvedic raw drug
Author Biography
Remya Unnikrishnan, Forest Genetics and Biotechnology Division, Kerala Forest Research Institute, Peechi, Thrissur, 680653 Kerala India
Cochin University of Science and Technology, Kochi, Kerala India
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