Abstract
Extreme difficulties in species identification of illegally sourced wood with conventional tools have accelerated illicit logging activities, leading to the destruction of natural resources in India. In this regard, the study primarily focused on developing a DNA barcode database for 41 commercial timber tree species which are highly vulnerable to adulteration in south India. The developed DNA barcode database was validated using an integrated approach involving wood anatomical features of traded wood samples collected from south India. Traded wood samples were primarily identified using wood anatomical features using IAWA list of microscopic features for hardwood identification. Consortium of Barcode of Life (CBOL) recommended barcode gene regions (rbcL, matK & psbA-trnH) were employed for developing DNA barcode database. Secondly, we employed artificial intelligence (AI) analytical platform, Waikato Environment for Knowledge Analysis (WEKA) for analyzing DNA barcode sequence database which could append precision, speed, and accuracy for the entire identification process. Among the four classification algorithms implemented in the machine learning algorithm (WEKA), best performance was shown by SMO, which could clearly allocate individual samples to their respective sequence database of biological reference materials (BRM) with 100 % accuracy, indicating its efficiency in authenticating the traded timber species. Major advantage of AI is the ability to analyze huge data sets with more precision and also provides a large platform for rapid authentication of species, which subsequently reduces human labor and time.
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Data availability
The barcode sequence data generated through the study has been deposited in GenBank (https://ncbi.nlm.nih.gov) and gene accession numbers are provided as supplementary Table S4.
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This study was supported by Kerala Forest Development Fund (KFDF), Government of Kerala, India (Sanction No. A 10-22698/2016 dated 13.10.2016).
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SAD and VBS contributed towards conceptualization, methodology, designing experiments, data interpretation, writing, editing and finalization of manuscript as well as fund acquisition. RU, KS, PSP and AA conducted wet lab experiments, data analysis and manuscript writing, EVA developed wood anatomical keys and edited the manuscript, SV facilitated the conduct of the study and involved in manuscript preparation.
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Dev, S.A., Unnikrishnan, R., Prathibha, P.S. et al. Artificial intelligence in timber forensics employing DNA barcode database. 3 Biotech 13, 183 (2023). https://doi.org/10.1007/s13205-023-03604-0
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DOI: https://doi.org/10.1007/s13205-023-03604-0