Machine learning for LC-MS medicinal plants identification

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

    Результат исследований: Вклад в журналСтатьярецензирование

    16 Цитирования (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.

    Язык оригиналаАнглийский
    Страницы (с-по)174-180
    Число страниц7
    ЖурналChemometrics and Intelligent Laboratory Systems
    СостояниеОпубликовано - 15 авг. 2016


    Подробные сведения о темах исследования «Machine learning for LC-MS medicinal plants identification». Вместе они формируют уникальный семантический отпечаток (fingerprint).