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Published on: December 16, 2015
Quality Analysis and Detection of Adulterants and Contaminations in Milk/Milk Powder by Raman Spectroscopy
B Sudarshan Acharya1, Sreerag Nair1, Abdul Ajees Abdul Salam1
1Manipal Institute of Applied Physics, Manipal Academy of Higher Education, Manipal, Udupi, Karnataka, India.
Raman spectroscopy, enhanced by SERS and hyperspectral imaging, offers a rapid, nondestructive method for detecting single and multiple adulterants in milk and milk powder. This technology strengthens food safety and consumer confidence through advanced dairy authentication.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Food Science
Background:
- Milk and milk powder are vital global nutrition sources but are susceptible to adulteration and contamination.
- Adulteration compromises nutritional value, erodes consumer trust, and complicates regulatory oversight, especially for infant formulas.
- A need exists for rapid, non-destructive, field-deployable analytical platforms for multi-adulterant detection in dairy products.
Purpose of the Study:
- To review advancements in Raman spectroscopy for milk and milk powder authentication from 2015 to early 2025.
- To evaluate various Raman techniques, including conventional Raman, SERS, and hyperspectral imaging.
- To assess chemometric and machine/deep-learning approaches for data analysis in dairy authentication.
Main Methods:
- Review of literature on Raman spectroscopy applications in dairy authentication (2015-2025).
- Analysis of conventional Raman, SERS, Fourier-transform Raman, and hyperspectral Raman imaging.
- Evaluation of chemometrics (PCA, PLSR, PLSDA) and machine/deep-learning models for classification and quantification.
Main Results:
- Raman spectroscopy provides specific chemical fingerprints of milk components and adulterants.
- SERS enhances sensitivity to ppm-ppb levels and reduces fluorescence, enabling detection of melamine, urea, and antibiotics.
- Hyperspectral imaging maps adulterant distribution and powder characteristics; chemometric and deep-learning models achieve high accuracy in classification and prediction.
Conclusions:
- Raman spectroscopy, augmented by SERS and hyperspectral imaging, is a scalable, label-free platform for rapid, non-destructive dairy authentication.
- Portable systems can aid nutritional profiling and contaminant surveillance in breast milk.
- Further innovation in instrumentation, substrates, and data analysis is needed for reliable field detection of adulterants and preservatives, enhancing food safety and regulatory compliance.
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