Abstract
This research seeks to construct a Machine Learning (ML)-based method for identifying adulterated honey 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 classification phase, we use three supervised ML models: logistic regression, decision tree, and random forest, to discriminate 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 adulterated 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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Acknowledgements
This research was supported by the Department of Science and Technology’s Funds for Infrastructure through Science and Technology (DST-FIST) grant SR/FST/ETI-340/2013 to Dr. Babasaheb Ambedkar Marathwada University in Aurangabad, Maharashtra, India. The authors would like to express their gratitude to the department and university administrators for providing the research facilities and assistance.
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Al-Awadhi, M.A., Deshmukh, R.R. (2023). A Machine Learning Approach for Honey Adulteration Detection Using Mineral Element Profiles. In: Shukla, P.K., Singh, K.P., Tripathi, A.K., Engelbrecht, A. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7892-0_29
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DOI: https://doi.org/10.1007/978-981-19-7892-0_29
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