Quality Analysis and Detection of Adulterants and Contaminations in Milk/Milk Powder by Raman Spectroscopy

Milk and milk powder are central to global nutrition, yet remain vulnerable to adulteration and contamination. Adulteration using water, urea, ammonium sulfate, thiocyanates, detergents, melamine, or compositional changes with whey and carbohydrate fillers undermines nutritional quality, reduces consumer confidence, and challenges regulatory control, particularly in infant formula products. A field-ready analytical platform that is rapid, nondestructive, and capable of multi-adulterant surveillance is urgently needed across diverse dairy matrices. This review consolidates advances in Raman spectroscopy for milk and milk powder authentication reported from 2015 to early 2025, covering conventional Raman, surface-enhanced Raman spectroscopy (SERS), Fourier-transform Raman, hyperspectral Raman imaging, confocal/mapping approaches, and portable systems. We critically evaluate preprocessing and chemometrics such as principal component analysis, partial least squares regression, and partial least squares discriminant analysis, as well as machine-learning and deep-learning pipelines for classification and quantification. Species-specific applications including cow, buffalo, goat, camel, donkey, human breast milk (macronutrients, sex-linked profiles, microplastics, antibiotics), and milk powder workflows are compared with attention to matrix effects, fluorescence interference, and validation practices. Raman enables chemically specific fingerprints of proteins, lipids, and carbohydrates, whereas common adulterants present diagnostic bands. SERS substrates routinely extend sensitivity to ppm-ppb levels and suppress fluorescence, supporting rapid detection of melamine, urea, ammonium sulfate, thiocyanates, benzoate, and selected antibiotics. Hyperspectral imaging provides spatially resolved maps, differentiating multi-adulterant mixtures and thermo-structural behavior in powders. Chemometric models achieve high accuracy for classification and concentration prediction, whereas deep-learning architectures improve robustness under nonlinear matrix variation and instrument drift. Challenges persist in substrate reproducibility, calibration transfer, fluorescence in lipid-rich systems, and detection of emerging adulterants and trace preservatives under field conditions. Future progress will hinge on multi-excitation instruments with adaptive laser power control, universal SERS substrates integrating plasmonic metals, dielectric shells, and molecular recognition, and standard operating procedure grade preprocessing. Industrial reliability requires calibration-transfer strategies, rigorous validation, and explainable artificial intelligence to link decisions to chemically meaningful features, supporting regulatory acceptance and auditability. Portable Raman and SERS systems can aid nutritional profiling and contaminant surveillance in breast milk, whereas Fourier-transform Raman and hyperspectral imaging mitigate fluorescence and map heterogeneity in powders. Raman spectroscopy, augmented by SERS, hyperspectral imaging, and intelligent analytics, offers a rapid, nondestructive, label-free, and scalable platform for dairy authentication. Continued innovation will enable real-time, on-site detection of single and multiple adulterants, strengthening consumer confidence, industrial quality assurance, and regulatory compliance while advancing global food safety.