Predicting Impurities in Mānuka Honey Using Machine Learning

Abstract

Mānuka honey is a high-value monofloral honey known for its unique medicinal properties, but its premium status makes it a prime target for adulteration and contamination. This article explores how machine learning (ML) techniques can predict and detect impurities in Mānuka honey, including adulterants (e.g., added sugars or cheaper honeys) and chemical contaminants. We provide an overview of recent research, highlighting both theoretical foundations and practical implementations of ML in honey authenticity testing. Various data sources – from spectral signatures (NIR, FTIR, Raman, NMR) to hyperspectral imaging and microscopic pollen analysis – are leveraged in combination with algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and deep learning models. The methodologies for data preprocessing, feature selection, model training, and validation are detailed. We discuss performance metrics reported in the literature, with many ML models achieving high accuracy in classifying authentic vs. adulterated honey and strong predictive power in quantifying adulterant levels.