From lab-based to in-line: Analytical tools for the characterization of whey protein denaturation and aggregation-A review.

Whey protein denaturation and aggregation have long been areas of research interest to the dairy industry, having significant implications for process performance and final product functionality and quality. As such, a significant number of analytical techniques have been developed or adapted to assess and characterize levels of whey protein denaturation and aggregation, to either maximize processing efficiency or create products with enhanced functionality (both technological and biological). This review aims to collate and critique these approaches based on their analytical principles and outline their application for the assessment of denaturation and aggregation. This review also provides insights into recent developments in process analytical technologies relating to whey protein denaturation and aggregation, whereby some of the analytical methods have been adapted to enable measurements in-line. Developments in this area will enable more live, in-process data to be generated, which will subsequently allow more adaptive processing, enabling improved product quality and processing efficiency. Along with the applicability of these techniques for the assessment of whey protein denaturation and aggregation, limitations are also presented to help assess the suitability of each analytical technique for specific areas of interest.

Application of machine-learning methods to milk mid-infrared spectra for discrimination of cow milk from pasture or total mixed ration diets.

The prevalence of "grass-fed" labeled food products on the market has increased in recent years, often commanding a premium price. To date, the majority of methods used for the authentication of grass-fed source products are driven by auditing and inspection of farm records. As such, the ability to verify grass-fed source claims to ensure consumer confidence will be important in the future. Mid-infrared (MIR) spectroscopy is widely used in the dairy industry as a rapid method for the routine monitoring of individual herd milk composition and quality. Further harnessing the data from individual spectra offers a promising and readily implementable strategy to authenticate the milk source at both farm and processor levels. Herein, a comprehensive comparison of the robustness, specificity, and accuracy of 11 machine-learning statistical analysis methods were tested for the discrimination of grass-fed versus non-grass-fed milks based on the MIR spectra of 4,320 milk samples collected from cows on pasture or indoor total mixed ration-based feeding systems over a 3-yr period. Linear discriminant analysis and partial least squares discriminant analysis (PLS-DA) were demonstrated to offer the greatest level of accuracy for the prediction of cow diet from MIR spectra. Parsimonious strategies for the selection of the most discriminating wavelengths within the spectra are also highlighted.

Effect of bovine feeding system (pasture or concentrate) on the oxidative and sensory shelf life of whole milk powder.

Correlating volatile compounds with the sensory attributes of whole milk powder (WMP) is fundamental for appreciating the effect of lipid oxidation (LO) on sensory perception. LO compounds can adversely affect the sensory perception of WMP by imparting rancid, metallic, and painty notes. Whole milk powders derived from milk produced by cows maintained on a pasture diet (grass and grass-clover mix) versus a nonpasture diet [total mixed ration (TMR); concentrates and silage] were stored at room temperature 21°C (ambient storage) and 37°C (accelerated storage) and analyzed for volatile compounds and sensory attributes every 2 mo for a total of 6 mo. Thirteen volatile compounds originating from LO were chosen to track the volatile profile of the WMP during storage. Color, composition, total fatty acid, and free fatty acid profiling were also carried out. Significant variations in the concentrations of 14 fatty acids were observed in WMP based on diet. Concentrations of free fatty acids increased in all sample types during storage. Similar trends in sensory attributes were observed with an increase in painty attributes, corresponding to an increase in hexanal. Buttery/toffee attributes were found to be more closely correlated with TMR WMP. Those WMP derived from pasture diets were found to be more susceptible to LO from a volatile perspective, particularly in relation to aldehyde development, which is likely due to increased concentrations of conjugated linoleic acid and α-linolenic acid found in these samples.

Raman spectroscopy: A rapid method to assess the effects of pasture feeding on the nutritional quality of butter.

The animal diet is a critical variable affecting the composition and functionality of dairy products. As "Grass-Fed" labeling becomes more prominent on the market, rapid and label-free methods for verification of feeding systems are required. This work proposes the use of Raman spectroscopy to study the effects of 3 different experimental cow feeding systems-perennial ryegrass pasture, perennial ryegrass with white clover pasture, and an indoor total mixed ration diet (TMR)-on the nutritional quality of sweet cream butter. The results demonstrate that Raman spectroscopy coupled with multivariate analysis is a promising approach to distinguish butter derived from pasture or conventional TMR feeding systems. A Pearson correlation analysis confirmed high positive correlations between the spectral bin at 1,657 cm-1, ascribed to the stretching vibrations of C=C bonds, and concentrations of α-linolenic acid and conjugated linolenic acid (CLA) in butter, and in general with the concentration of n-3 and n-3+CLA fatty acids and polyunsaturated fatty acids in the samples. The yellow color indicative of the presence of carotenoids in butter, which has previously been suggested as a biomarker of pasture or "Grass-Fed" feeding, was also positively correlated with the data obtained from the Raman spectra. Raman spectroscopy could also be used to accurately predict indicators of the nutritional quality of butter, such as the thrombogenic index, which showed a strong negative correlation with the spectral bin at 3,023 cm-1.

The gut microbiome as a virtual endocrine organ with implications for farm and domestic animal endocrinology.

The gut microbiome exerts a marked influence on host physiology, and manipulation of its composition has repeatedly been shown to influence host metabolism and body composition. This virtual endocrine organ also has a role in the regulation of the plasma concentrations of tryptophan, an essential amino acid and precursor to serotonin, a key neurotransmitter within both the enteric and central nervous systems. Control over the hypothalamic-pituitary-adrenal axis also appears to be under the influence of the gut microbiota. This is clear from studies in microbiota-deficient germ-free animals with exaggerated responses to psychological stress that can be normalized by monocolonization with certain bacterial species including Bifidobacterium infantis. Therapeutic targeting of the gut microbiota may thus be useful in treating or preventing stress-related microbiome-gut-brain axis disorders and metabolic diseases, much the same way as redirections of metabolopathies can be achieved through more traditional endocrine hormone-based interventions. Moreover, the implications of these findings need to be considered in the context of farm and domestic animal physiology, behavior, and food safety.