{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T06:27:19Z","timestamp":1787120839322,"version":"build-2736575974"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"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:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      The flower of\n                      <jats:italic>Camellia petelotii<\/jats:italic>\n                      (Merr.) Sealy is highly valued in Traditional Chinese Medicine, making it susceptible to adulteration with its leaves or other lower-cost adulterants. This study aims to develop a robust authentication approach to distinguish different plant parts and identify adulterated mixtures using ATR-FTIR spectroscopy integrated with chemometrics and artificial intelligence.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The flowers, leaves, seeds, and mixed samples of\n                      <jats:italic>C. petelotii<\/jats:italic>\n                      were subjected to ATR-FTIR spectroscopy. The spectral data of pure plant parts were analyzed with Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). In the machine learning phase, a Convolutional Neural Network (CNN) model was trained on spectra of pure plant parts and laboratory-prepared mixed samples. The Synthetic Minority Oversampling Technique (SMOTE) was employed to address class imbalance and sample scarcity. Model robustness was validated via Repeated Random Subsampling Validation (RRSV).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      ATR-FTIR analysis clearly differentiated the leaves from other plant parts, revealing distinct spectral characteristics. The PCA and OPLS-DA effectively classified the three distinct plant parts with high accuracy, sensitivity, and specificity scores exceeding 95%. The OPLS-DA model achieved high internal validity (R\n                      <jats:sup>2<\/jats:sup>\n                      X, R\n                      <jats:sup>2<\/jats:sup>\n                      Y, and Q\n                      <jats:sup>2<\/jats:sup>\n                      Y \u2265 0.738). In the machine learning workflow, the baseline CNN models suffered from minority-class collapse. Implementing SMOTE effectively resolved this issue. In multi-class configurations, the SMOTE-trained models showed high predictive precision and achieved high F1-scores for Seed (0.846) and Flower (0.931) classes, but moderate performance on Mix (0.593) and Leaf (0.657) classes. The binary classification model resolved the ambiguity, increasing the average F1-scores of the Mix and Leaf classes to 0.674 and 0.932, respectively. When validated against non-augmented data across both multi-class and binary-class configurations, SMOTE-trained models showed high stability for pure plant parts but remained sensitive to the Mix class across both multi-class (F1-score: 0.361) and binary-class (F1-score: 0.249) configurations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>While AI-driven data augmentation mitigates sample-size constraints, classification performance remains heavily influenced by spectral characteristics. The binary classification architecture offered distinct advantages when analyzing samples with heterogeneous spectral complexity. This integrated approach may serve as a reference for quality control and authentication of botanical products.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fchem.2026.1830739","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T05:44:58Z","timestamp":1787118298000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An integrative approach for rapid authentication of different parts of Camellia petelotii (Merr.) Sealy: combining ATR-FTIR spectroscopy with conventional chemometric analysis and a convolutional neural network"],"prefix":"10.3389","volume":"14","author":[{"given":"Chen","family":"Jingying","sequence":"first","affiliation":[{"name":"Crop Research Institute, Fujian Academy of Agricultural Sciences","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Yunqing","sequence":"additional","affiliation":[{"name":"Crop Research Institute, Fujian Academy of Agricultural Sciences","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Wujun","sequence":"additional","affiliation":[{"name":"Crop Research Institute, Fujian Academy of Agricultural Sciences","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huang","family":"Yingzhen","sequence":"additional","affiliation":[{"name":"Crop Research Institute, Fujian Academy of Agricultural Sciences","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan Yin","family":"Tew","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences, Universiti Sains Malaysia","place":["Minden, Pulau Pinang, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingsong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences, Universiti Sains Malaysia","place":["Minden, Pulau Pinang, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyun","family":"Ouyang","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences, Universiti Sains Malaysia","place":["Minden, Pulau Pinang, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiyue","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences, Universiti Sains Malaysia","place":["Minden, Pulau Pinang, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Ying","sequence":"additional","affiliation":[{"name":"College of Pharmacy, Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiem Leong","family":"Yoon","sequence":"additional","affiliation":[{"name":"School of Physics, Universiti Sains Malaysia","place":["Minden, Pulau Pinang, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mun Fei","family":"Yam","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences, Universiti Sains Malaysia","place":["Minden, Pulau Pinang, Malaysia"]},{"name":"College of Pharmacy, Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, China"]},{"name":"Faculty of Business and Communications, INTI International University","place":["Nilai, Negeri Sembilan, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3389\/fdgth.2024.1377165","article-title":"A comparative study in class imbalance mitigation when working with physiological signals","volume":"6","author":"Abdulsadig","year":"2024","journal-title":"Front. 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