Lakshitha, RushanKantono, KevinChen, TonyLe, Thao TGannabathula, SwapnaHamid, Nazimah2026-03-242026-03-242026-03-24Food Research International, ISSN: 0963-9969 (Print), Elsevier BV, 118954-118954. doi: 10.1016/j.foodres.2026.1189540963-9969http://hdl.handle.net/10292/20804Authenticating monofloral honeys is essential for protecting premium markets and ensuring traceability. This study applied an integrated analytical and explainable machine-learning workflow to identify volatile biomarkers for four New Zealand monofloral honeys: thyme, mānuka, kānuka, and clover. Twenty-two samples were profiled using headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME/GC–MS), yielding 122 tentative volatiles across aldehydes, alcohols, acids, esters, terpenoids, and phenolics. Data were analysed using ANOVA, hierarchical clustering, Partial Least Squares Discriminant Analysis (PLS-DA), and Random Forest classification with SHapley Additive exPlanations (SHAP). ANOVA and heatmap analysis revealed honey-specific volatile modules, while PLS-DA confirmed clear supervised separation of floral types. A rule-based SHAP framework was implemented to select biomarkers that were not only influential in the model but also directionally consistent and chemically distinctive. Thyme honey was characterised by short-chain fatty acids and oxygenated terpenoids; mānuka by methoxyacetophenones and benzofuran/methoxylated benzoates; kānuka by anisole-type aromatics and bicyclic monoterpenes; and clover by phenylpropanoid-related aldehydes, fusel alcohols, and linalool-oxide derivatives. These panels achieved non-overlapping group separation and near-perfect cross-validated performance (micro-average ROC-AUC = 0.995). This combined HS-SPME/GC–MS and RF-SHAP approach provides a transparent, statistically supported route to defining interpretable volatile biomarkers, offering a scalable framework for honey authentication, quality assurance, and traceability, and helping safeguard the premium positioning of New Zealand monofloral honeys in global markets.© 2026 The Authors. Published by Elsevier Ltd. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.30 Agricultural, Veterinary and Food Sciences32 Biomedical and Clinical Sciences40 Engineering4004 Chemical Engineering3210 Nutrition and Dietetics3006 Food SciencesMachine Learning and Artificial Intelligence0904 Chemical Engineering0908 Food Sciences1111 Nutrition and DieteticsFood Science3006 Food sciences3210 Nutrition and dietetics4004 Chemical engineeringNew Zealand honeyVolatile biomarkersHS-SPME/GC–MSMachine learningRandom forestSHAPPLS-DAAuthenticityTraceabilityJournal ArticleOpenAccess10.1016/j.foodres.2026.118954