A Machine Learning Approach for Honey Adulteration Detection using Mineral Element Profiles

• URL: http://arxiv.org/abs/2507.23412v1
• Date: Thu, 31 Jul 2025 10:36:58 GMT
• Title: A Machine Learning Approach for Honey Adulteration Detection using Mineral Element Profiles
• Authors: Mokhtar A. Al-Awadhi, Ratnadeep R. Deshmukh,
• Abstract summary: This paper aims to develop a Machine Learning-based system for detecting honey adulteration utilizing honey mineral element profiles.<n>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.<n> Experimental findings show that mineral element content in honey provides robust discriminative information for detecting honey adulteration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

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