Authors: Godse Samiksha Jagdish, Professor Bangar Abhishek Popat
Abstract: Milk is a highly perishable, high-value food whose quality is affected by natural compositional variation, economically motivated adulteration with water, sugars, starch, urea or melamine, microbial spoilage during storage and transport, and udder diseases such as mastitis. Reference methods, including laboratory Fourier-transform infrared (FTIR) analysis, microbiological culture and chromatography, are accurate but centralised, slow and costly, which restricts routine testing at farm and collection-centre level. Artificial intelligence (AI), combined with rapid sensing and the Internet of Things (IoT), is increasingly proposed as a route to fast, on-site and continuous milk quality assessment. This paper presents a focused narrative review of verified peer-reviewed studies, prioritising 2022–2026 publications, that use AI for milk composition prediction, adulteration detection, spoilage assessment and udder-health prediction. The review examines three connected themes: sensing technologies (mid-infrared, near-infrared and fluorescence spectroscopy, low-cost multispectral chips, electronic noses and smartphone-based vibration sensing), learning methods (partial least squares chemometrics, classical machine learning such as support vector machines, random forests and gradient boosting, and deep learning such as convolutional neural networks), and IoT architectures with edge and cloud inference. The literature shows that spectroscopic data with machine learning or deep learning can detect single adulterants with reported accuracies of about 95–100 %, and that inexpensive 18channel spectral sensors calibrated against laboratory FTIR can estimate fat and protein with mean errors of about 0.07–0.14 %. Simple, interpretable models often perform as well as complex ones. However, most studies use small, single-region datasets, laboratory conditions, single adulterants and random data splits, and few report field validation, sensor drift, uncertainty or long-term IoT operation. The review contributes a comparative analysis of representative studies, a proposed conceptual farmto-plant architecture that links multi-sensor acquisition, edge AI, IoT decision support and laboratory reference validation, and a prioritised agenda of research gaps and future directions.
