Combining stable isotope, multielement and untargeted metabolomics with chemometrics to discriminate the geographical origins of ginger (Zingiber officinale Roscoe)

An efficient method for rapid screening of triterpenoid saponins in three Glycyrrhiza species using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry combined with mass defect filtering

Triterpenoid saponins, a major bioactive component of liquorice, possess high hydrophilicity and often co-occur with other impurities of similar polarity. Additionally, subtle structural differences of some triterpenoid saponins bring challenges to comprehensive characterisation. In this study, triterpenoid saponins of three Glycyrrhiza species were systematically analysed using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry (RRLC-Q-TOF-MS) coupled with mass defect filtering (MDF). Firstly, comprehensive date acquisition was achieved using RRLC-Q-TOF-MS. Secondly, a polygonal MDF method was established by summarizing known and speculated substituents and modifications based on the core structure to rapidly screen potential triterpenoid saponins. Thirdly, based on the fragmentation patterns of reference compounds, an identification strategy for characterisation of triterpenoid saponins was proposed. The strategy divided triterpenoid saponins into three distinct classes. By this strategy, 98 triterpenoid saponins including 10 potential new ones were tentatively characterised. Finally, triterpenoid saponins of three Glycyrrhiza species were further analysed using principle component analysis (PCA) and orthogonality partial least squares discriminant analysis (OPLS-DA). Among these, 18 compounds with variable importance in projections (VIP) > 1.0 and P values < 0.05 were selected to distinguish three Glycyrrhiza species. Overall, our study provided a reference for quality control and rational use of the three species.

Development and Application of a Selective Analytical Method for Indole Metabolites in Urine: Dietary Exposure Biomarkers for Broccoli Consumption

The identification of dietary exposure biomarkers is crucial for advancing our understanding of the health benefits of specific foods. Broccoli, a vegetable with well-known anticancer properties, contains active ingredients, such as isothiocyanates with indole side chains. Hence, indole metabolites related to broccoli consumption have the potential to serve as biomarkers of dietary exposure. In this work, we developed a new analytical method for indole metabolites in urine using a poly(deep eutectic solvents)-molecularly imprinted polymer/vinyl-functionalized graphene oxide (PDESs-MIP/VGO) in miniaturized centrifugal pipet-tip solid-phase extraction (CPT-SPE) coupled with liquid chromatography. This method integrates the strengths of PDESs-MIP/VGO, including rich adsorption interactions, high adsorption capacity, and excellent selectivity, with the simplicity and cost-effectiveness of CPT-SPE. The proposed method demonstrated low limits of quantification (1.2-2.5 ng mL-1), high accuracy (91.7-104.8%), and good precision (relative standard deviation ≤4.4%). By applying this method to analyze indole metabolites in urine, our results suggested that indole-3-carbinol and indole-3-acetonitrile have the potential to emerge as reliable dietary exposure biomarkers for broccoli intake. Furthermore, highly selective analytical methods based on molecular imprinting technology are advantageous for precise screening and analysis of dietary exposure biomarkers associated with food consumption.

Geographical verification of Pleuropterus multiflorus thunb. by functional compounds, stable isotope ratios, and multielement combined with machine learning methods

The dry roots of Pleuropterus multiflorus Thunb. (PM) have been traditionally utilized as functional foods and medicines China and various Asian countries. They are extensively cultivated in multiple provinces in China, with variations in prices and qualities. This study aims to investigate the regional characteristics of PM by 4 stable isotope ratios, 40 multielement and 16 functional compounds contents, using a total of 357 samples from 8 different geographical origins. Machine learning methods were developed to authenticating the geographical origins of PM, yielding the accuracy range from 94.44 % to 100 % in the test set. Notably, the protected designation of origin, Deqing PM, exhibited a high accuracy of 100 % in most models, A total of 30 significant prediction variables, encompassing 16 functional compounds, δ2H, 12 rare earth elements, and Cu, were identified. Additionally, the study identified altitude, high temperature, and dry or moisture index as the primary influenced environmental factors.