Honey Adulteration Detection through Hyperspectral Imaging and Machine Learning

Introduction/Importance of Study:The purity and authenticity of honey are paramount for ensuring consumer trust and maintaining the integrity of the honey industry. There is a pressing need for advanced and efficient detection methodsto increasethe prevalence of honey adulteration. Novelty statement:Our research providesa solution to thechallenge of predicting the change in adulterated honey properties through hyperspectral imaging and advanced machine learning algorithms, filling a critical gap in existing methodologies. Material and Method:A publicly available dataset with spectral features, extracted through hyperspectral imaging, across different classes of honey and adulteration levelshas been examined and various machine learning models were developed to identify honey adulteration concentration and type of honey. The dataset was balanced and a five-fold cross-validation technique was used to train the machine learning models. Result and Discussion:Random forest was found to perform better in three identified scenarios i.e. (a) type of honey (b) adulteration level (c) both (a,b); with a maximum average accuracy of 99.69% performing better than the one reported in the literature (95%). For both single-output and multiple-output ML models, the trend in feature importance was observed. The single model identifying the class of honey utilized low and mid-frequency spectrawhile the multi-model used mid-frequency spectrum only.Concluding Remarks:The proposed approach aims to provide an accurate and cost-effective solution to address the challenges associated with honey adulteration, contributing to the enhancement of honey quality assessment and consumer confidence.