Enhancing Honey Quality Control: A Machine Learning-Based Approach Using Hyperspectral Imaging

Abstract

Food safety and quality control is an important component of sustainable healthcare in smart cities as it contributes to public health, preventive healthcare, sustainable agriculture, consumer empowerment, economic development, and regulatory compliance. The focus of this paper is on adulteration identification of honey. Honey is a natural sweetener that has been utilised for thousands of years for its different health advantage such as culinary sector, skincare, wound treatment, and as a natural cough suppressant. Adulteration of honey refers to the practice of introducing contaminants or diluting pure honey with other substances such as sugars, syrups, or water in order to increase volume and lower manufacturing costs. There are different mechanisms for identifying adulterated honey e.g physiocochemical properties, chromatography, spectroscopy, and hyperspectral imaging; each of which presents its own sets of challenges and limitation. The current study uses a publicly available dataset with different types of honey adulterated with sugar syrup. Hyperspectral imaging is used to extract spectral features of the honey samples. As the dataset represents an unbalanced representation of the adulterated samples. We propose to balance the samples and train the machine learning models across two validation strategies: k-fold crossvalidation and leave-oneout validation. Various models have been generated to extract different information from the dataset. The performance of the models across the different strategies has been reported. The current research study offers a viable way to maintain consumer trust and advance transparency in the honey sector, in addition to helping to protect the purity of honey products. Hence, by prioritizing food safety and quality, smart cities can create healthier, safer, and more resilient communities for their residents.