A Machine Learning Approach for Honey Adulteration Detection using Mineral Element Profiles
This addresses food safety and fraud detection for consumers and regulators, but it is incremental as it applies standard ML methods to a specific dataset.
The paper tackled honey adulteration detection by using mineral element profiles with machine learning models, achieving 98.37% accuracy with a random forest classifier.
This paper aims to develop a Machine Learning (ML)-based system for detecting honey adulteration utilizing honey mineral element profiles. The proposed system comprises two phases: preprocessing and classification. The preprocessing phase involves the treatment of missing-value attributes and normalization. In the classifica-tion phase, we use three supervised ML models: logistic regression, decision tree, and random forest, to dis-criminate between authentic and adulterated honey. To evaluate the performance of the ML models, we use a public dataset comprising measurements of mineral element content of authentic honey, sugar syrups, and adul-terated honey. Experimental findings show that mineral element content in honey provides robust discriminative information for detecting honey adulteration. Results also demonstrate that the random forest-based classifier outperforms other classifiers on this dataset, achieving the highest cross-validation accuracy of 98.37%.