ArticleMolecules (Basel, Switzerland)2024
Geographical Origin Traceability of Atractylodis Macrocephalae Rhizoma Based on Chemical Composition, Chromaticity, and Electronic Nose.
Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comprehensive Quality Assessment of Atractylodis macrocephalae Rhizoma Between Standardized Bases and Retail Market Samples in Pan'an County.ACS omega · 2026Article
- Learning Compact Multispectral Signatures for Geographical-Origin Authentication ofMolecules (Basel, Switzerland) · 2026Article
- Pharmacological Effects and Proposed Mechanisms ofLife (Basel, Switzerland) · 2026Review
- Geographic transplantation promotes the growth ofiScience · 2026Article
- Chemical Pattern Recognition and Color-Chromaticity Correlation Analysis for Quality Control of Stir-Fried Perillae Fructus.Molecules (Basel, Switzerland) · 2026Article
- A Novel Strategy for Rapid Quantification of Multiple Quality Indicators and Grade Discrimination ofMolecules (Basel, Switzerland) · 2026Article
- Development and application of artificial intelligence in traditional Chinese medicine research and development.Chinese medicine · 2026Review
- Fungal diversity and network analysis in rhizosphere soil of Atractylodes macrocephala across different cultivation regions.Scientific reports · 2025Article
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Authors and funding
9 authors.
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Abstract
Atractylodis Macrocephalae Rhizoma (AMR) is a traditional Chinese medicine used for gastrointestinal diseases. With increased demand, there are more and more places of cultivation for AMR. However, the quality of AMR varies from place to place, and there is no good way to distinguish AMR from different origins at present. In this paper, we determined the content of eight chemical components including 60% ethanol extracts, essential oil, polysaccharides, atractylenolides, and atractylone, obtained the color parameters of AMR powder by colorimetry, and odor information was captured by the electronic nose, all of which were combined with machine learning to establish a rapid origin traceability method. The results of the principal component analysis of the chemical components revealed that Zhejiang AMR has a high comprehensive score and overall better quality. The Kruskal-Wallis test demonstrated that there are varying degrees of differences in chemical composition and color parameters across the different origin. However, the accuracy of the classification model is low (less than 80%), making it difficult to distinguish between different origins of AMR. The electronic nose demonstrated excellent classification performance in the traceability of AMR from different origins, with accuracy reaching more than 90% (PLS-DA: 96.88%, BPNN: 96.88%, PSO-SVM: 100%). Overall, this study clarified the quality differences of AMR among different origins, and a rapid and precise method combining machine learning was developed to trace the origin of AMR.
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