Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning

Differentiation of Citri Reticulatae Pericarpium varieties via HPLC fingerprinting of polysaccharides combined with machine learning

To accurately and reliably distinguish different varieties of Citri Reticulatae Pericarpium (CRP), we propose a novel classification strategy combining polysaccharide fingerprinting and machine learning (ML). First, extraction conditions are optimized using the one-variable-at-a-time method and response surface methodology, and the extraction yield of total polysaccharides reaches 25.15%, with different varieties exhibiting different anti-oxidant abilities. Next, the hydrolysis conditions are optimized for constructing a polysaccharide HPLC fingerprinting, followed by the identification 10 common peaks, including D-Man, L-Rha and D-GalA. Thereafter, among nine supervised ML models, five models with high accuracy (> 0.911) and precision (> 0.926) are selected. Finally, upon combining ML for the classification of CRPs, D-Man, D-Gal, D-Xyl and L-Ara are screened as Q-markers with accuracy, and precision more than 0.944. In summary, we demonstrate the reliability of combining polysaccharide fingerprinting and ML for classifying varieties of CRPs, providing a novel quality evaluation method for the distinguishing natural herbal medicines. CHEMICAL COMPOUNDS STUDIED IN THIS ARTICLE: D-Glucose (PubChem CID: 5793); D-Mannose (PubChem CID: 18950); D-Galactose (PubChem CID: 6036); D-Galacturonic acid (PubChem CID: 439215); D-Xylose (PubChem CID: 135191); L-Rhamnose (PubChem CID: 25310); L-Arabinose (PubChem CID: 439195); Sulphuric acid (PubChem CID: 1118).

Unraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/GC-MS-based metabolomics and machine learning

Introduction

Colored onions are favored by consumers due to their distinctive aroma, rich phytochemical content, and diverse biological activities. However, comprehensive analyses of their phytochemical profiles and volatile metabolites remain limited.

Methods

In this study, total phenols, flavonoids, anthocyanins, carotenoids, and antioxidant activities of three colored onion bulbs were evaluated. Volatile metabolites were identified using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS). Multivariate statistical analyses, feature selection techniques (SelectKBest, LASSO), and machine learning models were applied to further analyze and classify the metabolite profiles.

Results

Significant differences in phytochemical composition and antioxidant activities were observed among the three onion types. A total of 243 volatile metabolites were detected, with sulfur compounds accounting for 51-64%, followed by organic acids and their derivatives (4-19%). Multivariate analysis revealed distinct volatile profiles, and 19 key metabolites were identified as biomarkers. Additionally, 33 and 38 feature metabolites were selected by SelectKBest and LASSO, respectively. The 38 features selected by LASSO enabled clear differentiation of onion types via PCA, UMAP, and k-means clustering. Among the four machine learning models tested, the random forest model achieved the highest classification accuracy (1.00). SHAP analysis further confirmed 20 metabolites as potential key markers.

Conclusion

The findings suggest that the combination of HS-SPME/GC-MS and machine learning, particularly the random forest algorithm, is a powerful approach for characterizing and classifying volatile metabolite profiles in colored onions. This method holds potential for quality assessment and breeding applications.

A Machine Learning-Based Approach for the Prediction of Anticoagulant Activity of Hypericum perforatum L. and Evaluation of Compound Activity

INTRODUCTION

Hypericum perforatum L. (HPL) is extensively researched domestically and internationally as a medicinal plant. However, no reports of studies related to the anticoagulant activity of HPL have been retrieved. The specific bioactive components are unknown.

OBJECTIVE

The aim of this study was to develop a machine learning (ML) method for rapid prediction of anticoagulant activity in HPL and evaluation of compound activity.

MATERIALS AND METHODS

First, an in vitro anticoagulant activity assay was developed for the determination of the bioactivity of various medicinal parts of HPL. Then, the peak areas of compounds in HPL were integrated using UPLC-Q-TOF-MS analysis. Subsequently, nine independent ML methods and two ensemble learning methods have been established to predict the anticoagulant activity of HPL and to evaluate the contribution of compounds. Feature importance scores were used for models visualization.

RESULTS

A total of 24 compounds were shown to exhibited superior anticoagulant activity. Molecular docking experiments likewise confirmed this result. The results show that the branches of HPL have excellent anticoagulant activity, which has been previously overlooked. The established ML model demonstrated good performance in the prediction of the activity of HPL.

CONCLUSION

The results were accurate and reliable, which significantly improved the efficiency of active compounds screening, and further exploration in this area is warranted.

Advances in High-Resolution Mass Spectrometry-Based Metabolomics: Applications in Food Analysis and Biomarker Discovery

Consumer concerns regarding food nutrition and quality are becoming increasingly prevalent. High-resolution mass spectrometry (HRMS)-based metabolomics stands as a cutting-edge and widely embraced technique in the realm of food component analysis and detection. It boasts the capability to identify character metabolites at exceedingly low abundances, which remain undetectable by conventional platforms. It can also enable real-time monitoring of the flux of targeted compounds in metabolic synthesis and decomposition. With the emergence of artificial intelligence and machine learning, it has become more convenient to process the vast data sets of metabolomics and identify biomarkers. The review summarizes the latest applications of HRMS-based metabolomics platforms in traditional foods, novel foods, and pharmaceutical-food homologous matrices. It compares the suitability of HRMS to nuclear magnetic resonance (NMR) in metabolomics across three dimensions and discusses the principles and application scenarios of various mass spectrometry technologies.

Germplasm Resources and Genetic Breeding of Huang-Qi (Astragali Radix): A Systematic Review

Huang-Qi (Astragali radix) is one of the most widely used herbs in traditional Chinese medicine, derived from the dried roots of Astragalus membranaceus or Astragalus membranaceus var. mongholicus. To date, more than 200 compounds have been reported to be isolated and identified in Huang-Qi. However, information pertaining to Huang-Qi breeding is considerably fragmented, with fundamental gaps in knowledge, creating a bottleneck in effective breeding strategies. This review systematically introduces Huang-Qi germplasm resources, genetic diversity, and genetic breeding, including wild species and cultivars, and summarizes the breeding strategy for cultivars and the results thereof as well as recent progress in the functional characterization of the structural and regulatory genes related to horticultural traits. Perspectives about the resource protection and utilization, breeding, and industrialization of Huang-Qi in the future are also briefly discussed.

Development of an ultra-high-performance liquid chromatography-tandem mass spectrometry method for the simultaneous determination of crassicauline A, fuziline, karacoline, and songorine in rat plasma and application in their pharmacokinetics

In this paper, an ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed for quantifying the levels of crassicauline A, fuziline, karacoline, and songorine in rat plasma. After processing the rat plasma, the proteins in the plasma were separated by extracting the analytes with acetonitrile-methanol (9:1, v/v). The chromatographic column used was the UPLC HSS T3 column, and the mobile phase (methanol-water with 0.1% formic acid) under a gradient elution profile was used to separate the four compounds, with elution times for each analyte being less than 5 min. Electrospray ionization in positive-ion mode and operating in multiple reaction monitoring mode was used for quantitative analysis. Crassicauline A, fuziline, karacoline, and songorine were administered to 48 rats (n = 6 per group) orally (5 mg/kg) and intravenously (0.5 mg/kg). The standard curves demonstrated excellent linearity in the range of 1-2500 ng/mL, wherein all r values were greater than 0.99. The UPLC-MS/MS method for the determination of crassicauline A, fuziline, karacoline, and songorine in rat plasma was successfully applied in determining their pharmacokinetics parameters, from which their oral bioavailabilities were calculated to be 18.7%, 4.3%, 6.0%, and 8.4%, respectively.