Machine learning for LC-MS medicinal plants identification

D. V. Nazarenko, P. V. Kharyuk, I. V. Oseledets, I. A. Rodin, O. A. Shpigun

    Research output: Contribution to journalArticlepeer-review

    16 Citations (Scopus)


    Herbal medicines are vigorously marketed, but poorly regulated. Analysis methodology for this field is still forming. One particular analytical task is confirmation of plant species identity for medicinal plants used as ingredients. In this work, machine learning approach has been implemented for LC-MS plant species identification. Samples for 36 plant species have been analyzed. Peak data (m/z, abundance) from respective samples have been used for development of classification algorithms. Namely, logistic regression (LR), support vector machine (SVM) and random forest (RF) techniques were used. For most of used machine learning algorithms, classification accuracy of 95% higher were obtained on cross-validation dataset. Now, massive training datasets are needed for full-scale application of this approach.

    Original languageEnglish
    Pages (from-to)174-180
    Number of pages7
    JournalChemometrics and Intelligent Laboratory Systems
    Publication statusPublished - 15 Aug 2016


    • Liquid chromatography-mass spectrometry
    • Machine learning
    • Multiclass classification
    • Plant species identification


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