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Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

IntroductionGout is a common type of inflammatory arthritis. Vitamin C is a potent antioxidant that neutralizes reactive oxygen species. However, the association between dietary vitamin C levels and gout remains unclear. This study evaluated the relationship between dietary vitamin C intake and gout.MethodsCross-sectional data from individuals aged > 20 years who participated in the National Health and Nutrition Examination Survey between 2013 and 2018 were collected. Details on gout, dietary vitamin C intake, and several other essential variables were recorded.ResultsThere were 12589 participants, 5% (652/12589) of whom experienced gout. Compared with individuals with lower vitamin C consumption in the Q1 group (≤19.9 mg/day), the adjusted odds ratio(OR)values for dietary vitamin C intake and gout in the Q2 group (19.9–49.7 mg/day), Q3 group (49.7–110.375 mg/day), and Q4 group (≥110.375 mg/day) were 0.87 (95% confidence interval (CI): 0.69–1.1, P = 0.237), 0.81 (95% CI: 0.64–1.02, P = 0.076), and 0.77 (95% CI: 0.6–0.99, P= 0.042), respectively. Accordingly, the association between dietary vitamin C intake and gout exhibited an L-shaped curve (nonlinear, P = 0.245) in a restricted cubic spline. Subgroup analysis revealed significant interactions between vitamin C levels and gout according to sex (P < 0.05). When we used data on dietary vitamin C from the second survey, we observed a similar inverse association between vitamin C intake and gout. The vitamin C was also negatively associated with hyperuricemia (OR, 0.94; 95% CI, 0.9–0.98, P=0.005). Compared with Q1, the adjusted OR values for dietary vitamin C and hyperuricemia in Q2, Q3, and Q4 were 0.77 (95% CI: 0.69–0.86, P = 0.65), 0.81 (95% CI: 0.72–0.91, P = 0.014), and 0.72 (95% CI: 0.64–0.81, P < 0.001), respectively. No association was observed between vitamin C supplementation and gout.ConclusionThe population-based data indicate that dietary vitamin C intake is inversely associated with gout. These findings support the potential role of vitamin C in preventing gout.

The raw and steamed products of ginseng exhibit different efficacy, and elucidation of the underlying chemical transformation is significant for their rational application and quality control. This work was designed to holistically depict the chemical variation of Panax ginseng (PG), P. quinquefolius (PQ), and P. notoginseng (PN) induced by the steaming, and to unveil the steaming-associated markers diagnostic for differentiating between the raw and processed products, following a three-step strategy: 1) systematic ginsenosides characterization by hybrid scan (namely HDDIDDA) available on the Vion™ ion mobility-quadrupole time-of-flight mass spectrometer coupled with reversed-phase ultra-high performance liquid chromatography; 2) holistic depiction of the chemical variation and discovery of potential markers by the pattern recognition untargeted metabolomics analysis; and 3) construction of steaming-induced transformation network combined with desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Consequently, 542 ginsenosides were characterized from the raw and processed PG/PQ/PN. Steaming at 1–10 h could cause significant chemical variation, and separately 26, 28, and 18 potential markers were found for the steaming of PG, PQ, and PN. Steaming-induced transformation network mainly involved hydration of the malonyl group, hydrolysis of the glycosyl moiety, and dehydration at C-20. DESI-MSI further revealed spatial distribution of marker saponins in the cork layer, phloem, and xylem, and primarily confirmed the hydrolysis reactions occurring to ginseng steaming. Conclusively, the established strategy is practical to unveil the holistic ginsenosides variation of ginseng induced by the processing, which is useful in the quality control of both the herbal medicines and foods.

Abstract Background Panax japonicus var. major (PJM) belongs to the well-known ginseng species used in west China for hundreds of years, which has the effects of lung tonifying and yin nourishing, and exerts the analgesic, antitussive, and hemostatic activities. Compared with the other Panax species, the chemical composition and the spatial tissue distribution of the bioactive ginsenosides in PJM have seldom been investigated. Methods Ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS) and desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) were integrated for the systematic characterization and spatial tissue distribution studies of ginsenosides in the rhizome of PJM. Considering the great difficulty in exposing the minor saponins, apart from the conventional Auto MS/MS (M1), two different precursor ions list-including data-dependent acquisition (PIL-DDA) approaches, involving the direct input of an in-house library containing 579 known ginsenosides (M2) and the inclusion of the target precursors screened from the MS1 data by mass defect filtering (M3), were developed. The in situ spatial distribution of various ginsenosides in PJM was profiled based on DESI-MSI with a mass range of m/z 100–1500 in the negative ion mode, with the imaging data processed by the High Definition Imaging (HDI) software. Results Under the optimized condition, 272 ginsenosides were identified or tentatively characterized, and 138 thereof were possibly not ever reported from the Panax genus. They were composed by 75 oleanolic acid type, 22 protopanaxadiol type, 52 protopanaxatriol type, 16 octillol type, 19 malonylated, 35 C-17 side-chain varied, and 53 others. In addition, the DESI-MSI experiment unveiled the differentiated distribution of saponins, but the main location in the cork layer and phloem of the rhizome. The abundance of the oleanolic acid ginsenosides was high in the rhizome slice of PJM, which was consistent with the results obtained by UHPLC/QTOF-MS. Conclusion Comprehensive characterization of the ginsenosides in the rhizome of PJM was achieved, with a large amount of unknown structures unveiled primarily. We, for the first time, reported the spatial tissue distribution of different subtypes of ginsenosides in the rhizome slice of PJM. These results can benefit the quality control and further development of PJM and the other ginseng species.

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.