Species identification using high resolution melting (HRM) analysis with random forest classification

Sorelle Bowman*, Dennis McNevin, Samantha J. Venables, Paul Roffey, Alice Richardson, Michelle E. Gahan

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)


    Species identification is an important facet of forensic investigation. In this study, human and non-human species (cow, chicken, pig, sheep, cat, dog, rabbit, fox, kangaroo and wombat) were assayed on the ViiA 7 Real-Time PCR System (Thermo Fisher Scientific) to rapidly screen for their species of origin using the high resolution melt (HRM) analysis targeting the 16S rRNA gene. Classification of HRM difference profiles using the onboard ViiA 7 software resulted in a classification accuracy of <20%. Derivative profiles (temperature versus negative first derivative of fluorescence, –dF/dT) were classified using random forest algorithms supplemented by bagging and boosting, with either a randomly partitioned test set or a variety of folds of cross-classification, in addition to a range of trees and variables. Random forest classification with bagging conditions (constructed over 500 trees) was found to considerably outperform the ViiA 7 software for species differentiation with 100% classification accuracy for biological material from humans, domestic pets (cat and dog) and consumable meats (chicken and sheep) with an average classification accuracy of 70% across all species.

    Original languageEnglish
    Pages (from-to)57-72
    Number of pages16
    JournalAustralian Journal of Forensic Sciences
    Issue number1
    Publication statusPublished - 2 Jan 2019


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