Use of Raman spectroscopy as a rapid tool to discriminate milk deriving from different pasture-based diets and breeds in a seasonal, spring-calving dairy production system
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View abstract on PubMed
Summary
This summary is machine-generated.Raman spectroscopy effectively distinguishes bovine milk based on pasture diets and cow breeds. This technology can predict milk
Area Of Science
- Agricultural Science
- Analytical Chemistry
- Spectroscopy
Background
- Pasture-based feeding systems significantly influence bovine milk composition.
- Understanding milk's chemical profile is crucial for quality assessment and traceability.
Purpose Of The Study
- To evaluate Raman spectroscopy for discriminating milk based on pasture diets (perennial ryegrass vs. multispecies swards) and cow breeds (Holstein-Friesian vs. Jersey × Holstein-Friesian).
- To correlate spectral data with specific milk compositional parameters and predict the unsaturation index (UI).
Main Methods
- Raman spectroscopy was applied to milk samples from 144 cows across different lactation stages.
- Multivariate analyses, including principal component analysis (PCA) and support vector machine discriminant analysis, were used for classification.
- Wet chemistry techniques analyzed fatty acid profile, β-carotene, and cholesterol; partial least squares regression predicted UI.
Main Results
- Raman spectroscopy coupled with multivariate analysis successfully discriminated and classified milk based on diet, breed, and lactation stage.
- Specific spectral regions correlated with compositional differences (fatty acids, carotenoids, cholesterol).
- Partial least squares regression accurately predicted the unsaturation index (R² = 0.789) from Raman spectral data.
Conclusions
- Raman spectroscopy and chemometrics offer a powerful, non-destructive tool for raw milk analysis.
- This approach enables discrimination and authentication of milk based on farm-level factors like diet and breed.
- The technology can predict key milk quality indicators, such as the unsaturation index.
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