{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T05:54:10Z","timestamp":1792475650817,"version":"4.5.0"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Atractylodes macrocephala Rhizoma (AMR) is a frequently used medicinal herb for treating gastrointestinal disorders, with its quality influenced by factors such as origin and cultivation duration. Traditional quality control methods for AMR are time-consuming and invasive, making the development of faster and more efficient alternatives urgently needed. This study aims to utilize electronic nose (E-nose) and electronic tongue (E-tongue) to achieve the acquisition of odor\u2013taste two-dimensional information of AMR. Integrating this approach with machine learning (ML) enables intelligent transformation from \u201cexperience-driven\u201d to \u201cdata-driven\u201d quality assessment, thereby developing a rapid and cost-effective quality control strategy for AMR. Feature-extraction and feature-selection techniques were employed to optimize back-propagation neural network (BPNN) classification and regression models for eight key quality markers, selecting the optimal feature subset. Additionally, nine machine-learning algorithms were applied with the optimal feature subset to establish classification models for different AMR grades and quantitative regression models for eight components based on E-nose and E-tongue data. The results demonstrated that the E-tongue combined with the k-nearest neighbors (KNN) algorithm could achieve a rapid classification of AMR grades with an accuracy of 95.56%. It also successfully predicted the contents of the extract, volatile oil, polysaccharides, atractylenolide I, atractylenolide II, atractylenolide III, bis-atractylenolide, and atractylone, with the test set\u2019s coefficient of determination (R2) values of 0.8874, 0.8313, 0.9628, 0.8406, 0.8736, 0.8532, 0.7758, and 0.8101, respectively. In conclusion, this study provides a comprehensive and rapid solution for AMR grade classification and quality evaluation, significantly improving efficiency compared with traditional methods. This strategy holds substantial promise for real-world applications, as it enables a high-throughput, non-destructive screening of AMR in settings such as post-harvest processing and market quality surveillance, thereby supporting the sustainable and intelligent development of the herbal medicine industry.<\/jats:p>","DOI":"10.3390\/molecules31050881","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:05:44Z","timestamp":1772795144000},"page":"881","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Strategy for Rapid Quantification of Multiple Quality Indicators and Grade Discrimination of Atractylodis\u00a0macrocephalae Rhizoma Based on Electronic Nose, Electronic Tongue and Machine-Learning Algorithms"],"prefix":"10.3390","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1552-1603","authenticated-orcid":false,"given":"Ruiqi","family":"Yang","sequence":"first","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"},{"name":"School of Traditional Chinese Materia Medica, Shanxi University of Chinese Medicine, Taiyuan 030619, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1093-4093","authenticated-orcid":false,"given":"Yushi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunqi","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyue","family":"Song","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4782-3788","authenticated-orcid":false,"given":"Keyao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghong","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gu, S., Li, L., Huang, H., Wang, B., and Zhang, T. 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