Spectroscopic food adulteration detection using machine learning: Current challenges and future prospects

Abstract

BackgroundFood adulteration has emerged as a significant challenge in the food industry, impacting consumer health and trust in the market. Utilizing machine learning especially deep learning with spectroscopic methods has revolutionized food adulteration detection enabling the development of more sophisticated and automated solutions. Scope and approachThis review aims to provide a comprehensive overview of the challenges and opportunities in machine learning-based spectroscopic techniques for detecting food adulteration by exploring various spectroscopic techniques commonly employed in the food industry, such as infrared spectroscopy, Raman spectroscopy, NMR spectroscopy, fluorescence spectroscopy, multi-spectral imaging, and hyperspectral imaging. The article addresses data pre-processing, feature engineering, model complexity, interpretability and their performance, and the need for large-scale diverse datasets. Key findings and conclusionsTo develop a commercial spectroscopic adulteration detection system that uses machine learning, one needs to optimize not only the model, but also the dataset size, the combination of pre-processing methods, the feature selection and extraction methods, the model selection, the hyperparameters by validation and the performance criteria. In addition, new machine learning algorithms are growing rapidly but creating a specialized model for adulteration detection using spectroscopy is still an area of research.