{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T03:17:09Z","timestamp":1792034229722,"version":"4.4.0"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>This study develops a comprehensive workflow integrating Headspace Solid-Phase Microextraction Gas Chromatography\u2013Mass Spectrometry (HS-SPME-GC-MS) with advanced supervised machine learning to authenticate the botanical origin of honeys from five distinct floral sources\u2014coriander, orange blossom, astragalus, rosemary, and chehelgiah. While HS-SPME-GC-MS combined with traditional chemometrics (e.g., PCA, LDA, OPLS-DA) is well-established for honey discrimination, the application and direct comparison of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Neural Network (NN) models represent a significant advancement in multiclass prediction accuracy and model robustness. A total of 57 honey samples were analyzed to generate detailed volatile organic compound (VOC) profiles. Key chemotaxonomic markers were identified: anethole in coriander and chehelgiah, thymoquinone in astragalus, p-menth-8-en-1-ol in orange blossom, and dill ester (3,6-dimethyl-2,3,3a,4,5,7a-hexahydrobenzofuran) in rosemary. Principal component analysis (PCA) revealed clear separation across botanical classes (PC1: 49.8%; PC2: 22.6%). Three classification models\u2014RF, XGBoost, and NN\u2014were trained on standardized, stratified data. The NN model achieved the highest accuracy (90.32%), followed by XGBoost (86.69%) and RF (83.47%), with superior per-class F1-scores and near-perfect specificity (&gt;0.95). Confusion matrices confirmed minimal misclassification, particularly in the NN model. This work establishes HS-SPME-GC-MS coupled with deep learning as a rapid, sensitive, and reliable tool for multiclass honey botanical authentication, offering strong potential for real-time quality control, fraud detection, and premium market certification.<\/jats:p>","DOI":"10.3390\/foods15020389","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T17:52:38Z","timestamp":1769190758000},"page":"389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Honey Botanical Origin Authentication Using HS-SPME-GC-MS Volatile Profiling and Advanced Machine Learning Models (Random Forest, XGBoost, and Neural Network)"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9191-8244","authenticated-orcid":false,"given":"Amir","family":"Pourmoradian","sequence":"first","affiliation":[{"name":"Department of Food Science and Technology, Faculty of Agriculture, Tarbiat Modares University, Tehran P.O. Box 14115-336, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6811-5566","authenticated-orcid":false,"given":"Mohsen","family":"Barzegar","sequence":"additional","affiliation":[{"name":"Department of Food Science and Technology, Faculty of Agriculture, Tarbiat Modares University, Tehran P.O. Box 14115-336, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7163-2975","authenticated-orcid":false,"given":"\u00c1ngel A.","family":"Carbonell-Barrachina","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n \u201cCalidad y Seguridad Alimentaria\u201d, Instituto de Investigaci\u00f3n e Innovaci\u00f3n Agroalimentaria y Agroambiental (CIAGRO-UMH), Universidad Miguel Hern\u00e1ndez de Elche, Carretera de Beniel, km 3.2, 03312 Alicante,  Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9269-1125","authenticated-orcid":false,"given":"Luis","family":"Noguera-Artiaga","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n \u201cCalidad y Seguridad Alimentaria\u201d, Instituto de Investigaci\u00f3n e Innovaci\u00f3n Agroalimentaria y Agroambiental (CIAGRO-UMH), Universidad Miguel Hern\u00e1ndez de Elche, Carretera de Beniel, km 3.2, 03312 Alicante,  Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143465","DOI":"10.1016\/j.foodchem.2025.143465","article-title":"Authenticity of Greek honey based on phenolic compounds and physicochemical characteristics","volume":"476","author":"Ntakoulas","year":"2025","journal-title":"Food Chem."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"M\u0103rg\u0103oan, R., Topal, E., Balkanska, R., Y\u00fccel, B., Oravecz, T., Cornea-Cipcigan, M., and Vodnar, D.C. 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