{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T08:06:24Z","timestamp":1793261184822,"version":"4.5.0"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Chem."],"abstract":"<jats:p><jats:bold>Introduction:<\/jats:bold> We here describe a new method for distinguishing authentic <jats:italic>Bletilla striata<\/jats:italic> from similar decoctions (namely, <jats:italic>Gastrodia elata<\/jats:italic>, <jats:italic>Polygonatum odoratum<\/jats:italic>, and <jats:italic>Bletilla ochracea schltr<\/jats:italic>).<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> Preliminary identification and analysis of four types of decoction pieces were conducted following the <jats:italic>Chinese Pharmacopoeia<\/jats:italic> and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of <jats:italic>B. striata<\/jats:italic> vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared.<\/jats:p><jats:p><jats:bold>Results and Discussion:<\/jats:bold> Gas chromatography\u2013ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.<\/jats:p>","DOI":"10.3389\/fchem.2023.1342311","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T04:18:49Z","timestamp":1704860329000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data"],"prefix":"10.3389","volume":"11","author":[{"given":"Han","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan-Pan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao-Zhou","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin-Jing","family":"Gui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue-Hua","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng-Yu","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan-Pan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue-Lin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui-Xin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"3790","DOI":"10.1039\/c3ay40582f","article-title":"Classification tools in chemistry. 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