Comparative Evaluation of Machine Learning Models for Discriminating Honey Geographic Origin Based on Altitude-Dependent Mineral Profiles

Abstract

Authenticating the geographical origin of honey is crucial for ensuring its quality and preventing fraudulent labeling. This study investigates the influence of altitude on the mineral composition of honey and comparatively evaluates the performance of chemometric and machine learning models for its geographic discrimination. Honey samples from three distinct altitude regions in Türkiye were analyzed for their mineral content using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Results revealed that Calcium (Ca), Potassium (K), and Sodium (Na) were the predominant minerals. A significant moderate negative correlation was found between altitude and Ca concentration (r = −0.483), alongside a weak negative correlation with Copper (Cu) (r = −0.371). Among the five supervised models tested (Partial Least Squares-Discriminant Analysis (PLS-DA), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)), PLS-DA achieved the highest classification accuracy (94.9%). Variable importance analysis consistently identified Ca as the most influential discriminator across all models, followed by Barium (Ba) and Cu. These minerals, therefore, represent key markers for differentiating honey by geographical origin. This research demonstrates that an integrated model utilizing mineral profiles provides a robust, practical, and reliable method for the geographical authentication of honey.