Identification and adulteration detection of Heterotrigona itama and Apis dorsata honey using differential scanning...

This study presents a simple approach for detecting honey adulteration by integrating calorimetric data from differential scanning calorimetry (DSC) with machine learning classification (MLC) techniques, specifically using convolutional neural network (CNN) model alongside the Synthetic Minority Over-sampling TEchnique (SMOTE) for data augmentation. The thermal profiles of different honey varieties, sugar adulterants, and adulterated samples were acquired using DSC. Shifts in glass transition temperatures were observed in adulterated honey. The DSC data were analyzed using principal component analysis and MLC workflow. CNN model applied to original dataset reported accuracy of 24-67 %. However, integrating CNN model with SMOTE algorithm resulted in a significant accuracy improvement to 60-91 %. The integration of DSC with MLC provides a rapid and accurate method for detecting honey adulteration, demonstrating strong generalization capability. The proposed approach could facilitate the development of a framework to detect fraudulent practices, safeguarding honey industry and consumers from sugar-based adulterations.