Raman, and related techniques, have the potential to provide field-based rapid and non-destructive testing for dairy products and powders.
This review (open access) consolidates advances 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.
The authors critically evaluate preprocessing and chemometrics as well as machine-learning and deep-learning pipelines for classification and quantification.
They compare species-specific applications including cow, buffalo, goat, camel, donkey, human breast milk (macronutrients, sex-linked profiles, microplastics, antibiotics), and milk powder workflows with respect to matrix effects, fluorescence interference, and validation practices.
They summarise that 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.
They conclude that 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. They highlight that 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.
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