A new PARAFAC-based algorithm for HPLC-MS data treatment: herbal extracts identification.

Abstract

Role of highly informative high-performance liquid chromatography mass spectrometry (HPLC-MS) methods in quality control is increasing. Complex herbal products and formulations can simultaneously contain extracts from different plants. Therefore, due to the leads to lack of commercial standards it is important to develop novel approaches for comprehensive treatment of big datasets. The aim of this study is to create a straightforward and information-saving algorithm for the identification of plants extracts in commercial products. In total, 34 samples, including Glycyrrhiza glabra and Panax ginseng dried roots; and Abrus precatorius dried leaves, their double and triple mixtures and flavoured oolong tea samples were analysed by HPLC-MS and combined in a three-dimensional dataset (retention time-mass-to-charge ratio (m/z)-samples). This dataset was subjected to smoothing and denoising techniques and further decomposed using parallel factor analysis (PARAFAC). Samples were divided into eight clusters; loading matrices were interpreted and the presence of the most characteristic triterpene glycoside groups was demonstrated and supported by the characteristic chromatogram approach. The occurrence of Abrus precatorius and G. glabra additives in flavoured tea was confirmed. Developed HPLC-MS-PARAFAC method is potentially reliable and an efficient tool for handling untreated experimental data and its future development may lead to more comprehensive evaluation of chemical composition and quality control of food additives and other complex mixtures.