Volatile fingerprinting and interpretable machine learning for authenticating New Zealand monofloral honeys
Year: 2026
Type: article
Abstract: Authenticating 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.
Source: Food Research International
Institution: Auckland University of Technology
Language: English
Subfield: Insect Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $4,320
OpenAlex ID: W7138250047