Using Fluorescence Spectroscopy To Identify Milk from Grass-Fed Dairy Cows and To Monitor Its Photodegradation

Synchronous Front-Face Fluorescence Spectra: A Review of Milk Fluorophores

Milk is subjected to different industrial processes, provoking significant physicochemical modifications that impact milk's functional properties. As a rapid and in-line method, front-face fluorescence can be used to characterize milk instead of conventional analytical tests. However, when applying fluorescence spectroscopy for any application, it is not always necessary to determine which compound is responsible for each fluorescent response. In complex matrixes such as milk where several variables are interdependent, the unique identification of compounds can be challenging. Thus, few efforts have been made on the chemical characterization of milk' fluorescent spectrum and the current information is dispersed. This review aims to organize research findings by dividing the milk spectra into areas and concatenating each area with at least one fluorophore. Designations are discussed by providing specific information on the fluorescent properties of each compound. In addition, a summary table of all fluorophores and references cited in this work by area is provided. This review provides a solid foundation for further research and could serve as a central reference.

Evaluation and quantification of associations between commonly suggested milk biomarkers and the proportion of grassland-based feeds in the diets of dairy cows

This study is a first step approach towards the prediction of the proportion of grassland-based feeds (%GB) in dairy cow diets with the aid of three different groups of milk biomarkers. We aimed to evaluate and quantify the associations between biomarkers commonly suggested in the literature and %GB in individual cows as a hypothesis-generating stage for the prospective establishment of accurate %GB prediction models. Consumers and governments financially encourage sustainable, local milk production making grass-based feeding, in grassland-dominated regions, of major interest. Milk from grassland-fed cows differs from that of other feeding systems by inferential fatty acids (FA), β-carotene content and yellow color; however, these biomarkers have not been evaluated together for their association with %GB. Using approved methods of parametric regression analysis, gas chromatography (GC), mid-infrared spectra (MIR) and color spectroscopy, we aimed to develop a first step towards an easy-to-implement, cost-effective milk-based control to estimate %GB in dairy cow diets. The underlying database was generated with 24 cows each fed one of 24 different diets gradually increasing in grass silage and decreasing in corn silage. Our results indicate that GC-measured α-linolenic acid, total n-3 FA and the n-6:n-3 ratio, MIR-estimated PUFA and milk red-green color index a* are robust milk biomarkers for constructing accurate prediction models to determine %GB. Based on simplified regression analysis, diets containing 75% GB should contain ≥ 0.669 and 0.852 g α-linolenic acid and total n-3 FA per 100 g total FA, respectively, and an n-6:n-3 FA ratio of < 2.02 measured with GC; estimated with MIR, polyunsaturated FA should be ≥ 3.13 g/100 g total FA. β-carotene was not a good predictor for estimating %GB. Unexpectedly, the milk became greener with increasing %GB (negative a* values, ‒6.416 for 75% GB), suggesting the red-green color index, not yellow-blue, as a suitable biomarker.

Spectroscopic technologies and data fusion: Applications for the dairy industry

Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.

The degradation of chlorophyll pigments in dairy silage: the timeline of anaerobic fermentation

BACKGROUND

Detection of chlorophyll metabolites in milk has recently been suggested to be an indicator of a grass-fed diet fed for cattle. Such a means of detection, however, is complicated when the grazing season is over because cattle can be fed fermented silage ingredients, such as alfalfa and corn silage. During fermentation, chlorophyll compounds and other pigments undergo degradation due to the accumulation of lactic acid and the resultant decline in pH.

RESULTS

We monitored degradation of chlorophyll compounds by measuring the fluorescence and absorption spectra of silage extracts. The spectroscopic evidence supports the hypothesis that chlorophylls are converted into fluorescent products, such as pheophytin, and further cleaved into pheophorbide. The degradation starts with dechelation and removal of the magnesium ion to produce pheophytin. Further removal of the phytol chain from pheophytin results in the production of pheophorbide.

CONCLUSIONS

The fluorescence intensity of these degradation products is reduced compared to that of the parent molecule. These findings are important in understanding the fluorescent signal in milk when cows consume silage rather than fresh pasture grass. © 2020 Society of Chemical Industry.