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BackgroundA variety of plants in Acanthaceae have long been used in traditional Thai ailment and commercialised with significant economic value. Nowadays medicinal plants are sold in processed forms and thus morphological authentication is almost impossible. Full identification requires comparison of the specimen with some authoritative sources, such as a full and accurate description and verification of the species deposited in herbarium. Intake of wrong herbals can cause adverse effects. Identification of both raw materials and end products is therefore needed.MethodsHere, the potential of a DNA-based identification method, called Bar-HRM (DNA barcoding coupled with High Resolution Melting analysis), in raw material species identification is investigated. DNA barcode sequences from five regions (matK, rbcL, trnH-psbA spacer region, trnL and ITS2) of Acanthaceae species were retrieved for in silico analysis. Then the specific primer pairs were used in HRM assay to generate unique melting profiles for each plants species.ResultsThe method allows identification of samples lacking necessary morphological parts. In silico analyses of all five selected regions suggested that ITS2 is the most suitable marker for Bar-HRM in this study. The HRM analysis on dried samples of 16 Acanthaceae medicinal species was then performed using primer pair derived from ITS2 region. 100% discrimination of the tested samples at both genus and species level was observed. However, two samples documented as Clinacanthus nutans and Clinacanthus siamensis were recognised as the same species from the HRM analysis. Further investigation reveals that C. siamensis is now accepted as C. nutans.ConclusionsThe results here proved that Bar-HRM is a promising technique in species identification of the studied medicinal plants in Acanthaceae. In addition, molecular biological data is currently used in plant taxonomy and increasingly popular in recent years. Here, DNA barcode sequence data should be incorporated with morphological characters in the species identification.