Monitoring diet-induced variations in cow milk by employing multivariate methods for accurate quantitative profiling

Area of Science:

• Dairy Science
• Analytical Chemistry
• Biosensor Technology

Background:

• Milk quality is significantly influenced by cow diet.
• Conventional analysis of milk composition is time-consuming.
• Developing rapid, non-invasive methods for milk analysis is crucial for dairy production.

Purpose of the Study:

• To evaluate the impact of different feeding regimens on raw milk composition.
• To compare the efficacy of a potentiometric bioelectronic tongue (bioET) with conventional methods.
• To assess the bioET's ability to predict key milk compositional parameters.

Main Methods:

• Milk samples from cows on standard, essential fatty acid-supplemented, tannin-supplemented, or 3-nitrooxypropanol-supplemented diets were analyzed.
• Conventional chemical analysis and a custom-built bioelectronic tongue with specific enzyme-based membranes were employed.
• Multivariate analysis and Support Vector Machines with a radial basis function kernel were used for data classification and prediction.

Main Results:

• Dietary supplementation with essential fatty acids (EFAF) and tannins (TA) significantly altered fat-related parameters (total fatty acids, saturated fatty acids, short-chain fatty acids).
• The bioelectronic tongue achieved better classification accuracy for EFAF and TA diets compared to conventional methods.
• The bioET demonstrated strong correlations with fat and lactose content, with high prediction accuracy for fat (R² = 0.96) and saturated fatty acids (R² = 0.97).

Conclusions:

• The optimized bioelectronic tongue is a promising rapid, non-invasive tool for detecting diet-induced changes in raw milk.
• The bioET can reliably predict major milk compositional parameters, aiding nutritional monitoring in dairy production.
• This sensor technology offers an efficient alternative to conventional methods for assessing milk quality influenced by animal feed.