Development of Infrared Prediction Models for Diffusible and Micellar Minerals in Bovine Milk

Simple Summary

Minerals are distributed in milk in two main forms: The diffusible (or soluble) fraction, composed of free ions and inorganic salts, and the micellar fraction, composed of mineral elements located on the surface or in the inner part of casein micelles. The ratio between diffusible and micellar minerals strongly affects milk coagulation ability. The objective of this study was to investigate the ability of mid-infrared spectroscopy to predict diffusible and micellar Ca, P, K, Mg and Na in individual milk samples of Holstein Friesian cows. Overall, the accuracy of mid-infrared prediction models was moderate for Ca, P and Mg, and low for micellar K, micellar Na and diffusible Na.

Abstract

Milk and dairy products are major sources of minerals in human diet. Minerals influence milk technological properties; in particular, micellar and diffusible minerals differentially influence rennet clotting time, curd firmness and curd formation rate. The aim of the present study was to investigate the ability of mid-infrared spectroscopy to predict the content of micellar and diffusible mineral fractions in bovine milk. Spectra of reference milk samples (n = 93) were collected using Milkoscan™ 7 (Foss Electric A/S, Hillerød, Denmark) and total, diffusible and micellar content of minerals were quantified using inductively coupled plasma optical emission spectrometry. Backward interval partial least squares algorithm was applied to exclude uninformative spectral regions and build prediction models for total, diffusible and micellar minerals content. Results showed that backward interval partial least squares analysis improved the predictive ability of the models for the studied traits compared with traditional partial least squares approach. Overall, the predictive ability of mid-infrared prediction models was moderate to low, with a ratio of performance to deviation in cross-validation that ranged from 1.15 for micellar K to 2.73 for total P.

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.

Bovine whey peptides transit the intestinal barrier to reduce oxidative stress in muscle cells

Health benefits are routinely attributed to whey proteins, their hydrolysates and peptides based on in vitro chemical and cellular assays. The objective of this study was to track the fate of whey proteins through the upper gastrointestinal tract, their uptake across the intestinal barrier and then assess the physiological impact to downstream target cells. Simulated gastrointestinal digestion (SGID) released a selection of whey peptides some of which were transported across a Caco-2/HT-29 intestinal barrier, inhibited free radical formation in muscle and liver cells. In addition, SGID of β-lactoglobulin resulted in the highest concentration of free amino acids (176 nM) arriving on the basolateral side of the co-culture with notable levels of branched chain and sulphur-containing amino acids. In vitro results indicate that consumption of whey proteins will deliver bioactive peptides to target cells.

Quantitative analysis of amino acids in human and bovine colostrum milk samples through iTRAQ labeling

BACKGROUND

The types and quantity of proteins vary widely between bovine and human milk, with corresponding differences in free and hydrolytic amino acids. In this study, the free and hydrolytic amino acids of bovine and human colostrum were for the first time qualitatively and quantitatively determined using isobaric tags for relative and absolute quantification technology combined with liquid chromatography tandem mass spectrometry detection.

RESULTS

Total free amino acid content was 0.32 g L^-1 and 0.63 g L^-1 in bovine and human colostrum respectively, with free amino acid content in human colostrum twice that of bovine colostrum. However, total hydrolytic amino acid content was 4.2 g L^-1 and 2.2 g L^-1 in bovine and human colostrum respectively. We found that the hydrolytic amino acid content in bovine colostrum was higher than that in human colostrum; however, the amount of free amino acids and the overall amino acid content in human colostrum were respectively substantially higher and more varied than in bovine colostrum.

CONCLUSION

Our findings revealed differences between bovine and human colostrum, with these data providing the basis for further research into amino acid metabolomics and infant formula. © 2018 Society of Chemical Industry.

Evaluation of the Potential of Lactobacillus paracasei Adjuncts for Flavor Compounds Development and Diversification in Short-Aged Cheddar Cheese

The non-starter microbiota of Cheddar cheese mostly comprises mesophilic lactobacilli, such as Lactobacillus casei, Lactobacillus paracasei, Lactobacillus rhamnosus, and Lactobacillus plantarum. These bacteria are recognized for their potential to improve Cheddar cheese flavor when used as adjunct cultures. In this study, three strains of L. paracasei (DPC2071, DPC4206, and DPC4536) were evaluated for their contribution to the enhancement and diversification of flavor in short-aged Cheddar cheese. The strains were selected based on their previously determined genomic diversity, variability in proteolytic enzyme activities and metabolic capability in cheese model systems. The addition of adjunct cultures did not affect the gross composition or levels of lipolysis of the cheeses. The levels of free amino acids (FAA) in cheeses showed a significant increase after 28 days of ripening. However, the concentrations of individual amino acids in the cheeses did not significantly differ except for some amino acids (aspartic acid, threonine, serine, and tryptophan) at Day 14. Volatile profile analysis revealed that the main compounds that differentiated the cheeses were of lipid origin, such as long chain aldehydes, acids, ketones, and lactones. This study demonstrated that the adjunct L. paracasei strains contributed to the development and diversification of compounds related to flavor in short-aged Cheddar cheeses.

Omics-Based Insights into Flavor Development and Microbial Succession within Surface-Ripened Cheese

In this study, a young Cheddar curd was used to produce two types of surface-ripened cheese, using two commercial smear-culture mixes of yeasts and bacteria. Whole-metagenome shotgun sequencing was used to screen the microbial population within the smear-culture mixes and on the cheese surface, with comparisons of microorganisms at both the species and the strain level. The use of two smear mixes resulted in the development of distinct microbiotas on the surfaces of the two test cheeses. In one case, most of the species inoculated on the cheese established themselves successfully on the surface during ripening, while in the other, some of the species inoculated were not detected during ripening and the most dominant bacterial species, Glutamicibacter arilaitensis, was not a constituent of the culture mix. Generally, yeast species, such as Debaryomyces hansenii and Geotrichum candidum, were dominant during the first stage of ripening but were overtaken by bacterial species, such as Brevibacterium linens and G. arilaitensis, in the later stages. Using correlation analysis, it was possible to associate individual microorganisms with volatile compounds detected by gas chromatography-mass spectrometry in the cheese surface. Specifically, D. hansenii correlated with the production of alcohols and carboxylic acids, G. arilaitensis with alcohols, carboxylic acids and ketones, and B. linens and G. candidum with sulfur compounds. In addition, metagenomic sequencing was used to analyze the metabolic potential of the microbial populations on the surfaces of the test cheeses, revealing a high relative abundance of metagenomic clusters associated with the modification of color, variation of pH, and flavor development. IMPORTANCE Fermented foods, in particular, surface-ripened cheese, represent a model to explain the metabolic interactions which regulate microbial succession in complex environments. This study explains the role of individual species in a heterogeneous microbial environment, i.e., the exterior of surface-ripened cheese. Through whole-metagenome shotgun sequencing, it was possible to investigate the metabolic potential of the resident microorganisms and show how variations in the microbial populations influence important aspects of cheese ripening, especially flavor development. Overall, in addition to providing fundamental insights, this research has considerable industrial relevance relating to the production of fermented food with specific qualities.

Atlantic salmon (Salmo salar) co-product-derived protein hydrolysates: A source of antidiabetic peptides

Large quantities of low-value protein rich co-products, such as salmon skin and trimmings, are generated annually. These co-products can be upgraded to high-value functional ingredients. The aim of this study was to assess the antidiabetic potential of salmon skin gelatin and trimmings-derived protein hydrolysates in vitro. The gelatin hydrolysate generated with Alcalase 2.4L and Flavourzyme 500L exhibited significantly higher (p < 0.001) insulin and GLP-1 secretory activity from pancreatic BRIN-BD11 and enteroendocrine GLUTag cells, respectively, when tested at 2.5 mg/mL compared to hydrolysates generated with Alcalase 2.4L or Promod 144MG. The gelatin hydrolysate generated with Alcalase 2.4L and Flavourzyme 500L showed significantly more potent (p < 0.01) DPP-IV inhibitory activity than those generated with Alcalase 2.4L or Promod 144MG. No significant difference was observed in the insulinotropic activity mediated by any of the trimmings-derived hydrolysates when tested at 2.5 mg/mL. However, the trimmings hydrolysate generated with Alcalase 2.4L and Flavourzyme 500L exhibited significantly higher DPP-IV inhibitory (p < 0.05:Alcalase 2.4L and p < 0.01:Promod 144MG) and GLP-1 (p < 0.001, 2.5 mg/mL) secretory activity than those generated with Alcalase 2.4L or Promod 144MG. The salmon trimmings hydrolysate generated with Alcalase 2.4L and Flavourzyme 500L when subjected to simulated gastrointestinal digestion (SGID) was shown to retain its GLP-1 secretory and DPP-IV inhibitory activities, in addition to improving its insulin secretory activity. However, the gelatin hydrolysate generated with Alcalase 2.4L and Flavourzyme 500L was shown to lose GLP-1 secretory activity following SGID. A significant increase in membrane potential (p < 0.001) and intracellular calcium (p < 0.001) by both co-product hydrolysates generated with Alcalase 2.4L and Flavourzyme 500L suggest that both hydrolysates mediate their insulinotropic activity through the K_ATP channel-dependent pathway. Additionally, by stimulating a significant increase in intracellular cAMP release (p < 0.05) it is likely that the trimmings-derived hydrolysate may also mediate insulin secretion through the protein kinase A pathway. The results presented herein demonstrate that salmon co-product hydrolysates exhibit promising in vitro antidiabetic activity.

Characterization of protein hydrolysates from blue whiting (Micromesistius poutassou) and their application in beverage fortification

Enzymatic hydrolysis of fish proteins has been employed as a principle method for converting under-utilised fish into valuable products for the pharmaceutical and health food industries. In this study, six commercial enzymes were tested for their ability to make fish protein hydrolysate powders from whole blue whiting. The chemical and functional properties of these powders were compared. The powders all had high solubility (>80%) across a wide pH range in water and their solubility improved further within a vitamin-tea beverage matrix (>85%). Varying degrees of anti-oxidant activities were recorded for the powders using three model systems (DPPH, ferrous chelating and reducing power). This study demonstrates that commercial enzymes are useful for the extraction and alteration of fish protein from a low value source to produce highly digestible, low molecular weight peptide powders that could be used as a fortifying health ingredient, especially in beverages.

Cow and environmental factors associated with protein fractions and free amino acids predicted using mid-infrared spectroscopy in bovine milk

The objective of the present study was to identify the factors associated with both the protein composition and free amino acid (FAA) composition of bovine milk predicted using mid-infrared spectroscopy. Milk samples were available from 7 research herds and 69 commercial herds. The spectral data from the research herds comprised 94,286 separate morning and evening milk samples; the spectral data from the commercial herds comprised 40,260 milk samples representing a composite sample of both the morning and evening milkings. Mid-infrared spectroscopy prediction models developed in a previous study were applied to all spectra. Factors associated with the predicted protein and FAA composition were quantified using linear mixed models. Factors considered in the model included the fixed effects of calendar month of the test, milking time (i.e., morning, evening, or both combined), parity (1, 2, 3, 4, 5, and ≥6), stage of lactation, the interaction between parity and stage of lactation, breed proportion of the cow (Friesian, Jersey, Norwegian Red, Montbéliarde, and other), and both the general heterosis and recombination coefficients of the cow. Contemporary group as well as both within- and across-lactation permanent environmental effects were included in all models as random effects. Total proteins (i.e., total casein, CN; total whey; and total β-lactoglobulin) and protein fractions (with the exception of α-lactalbumin) decreased postcalving until 36 to 65 days in milk and increased thereafter. After adjusting the statistical model for differences in crude protein content and milk yield separately, irrespective of stage of lactation, younger animals produced more total proteins (i.e., total CN, total whey, and total β-lactoglobulin) as well as more total FAA, Glu, and Asp than their older contemporaries. The concentration of all protein fractions (except β-CN) in milk was greatest in the evening milk, even after adjusting for differences in the crude protein content of the milk. Relative to a purebred Holstein cow, Jersey cows, on average, produced a greater concentration of all CN fractions but less total FAA, Glu, Gly, Asp, and Val in milk. Relative to their respective purebred parental average, first-cross cows produced more total CN and more β-CN. Results from the present study indicate that many cow-level factors, as well as other factors, are associated with protein composition and FAA composition of bovine milk.

The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows

Knowledge of animal-level and herd-level energy intake, energy balance, and feed efficiency affect day-to-day herd management strategies; information on these traits at an individual animal level is also useful in animal breeding programs. A paucity of data (especially at the individual cow level), of feed intake in particular, hinders the inclusion of such attributes in herd management decision-support tools and breeding programs. Dairy producers have access to an individual cow milk sample at least once daily during lactation, and consequently any low-cost phenotyping strategy should consider exploiting measureable properties in this biological sample, reflecting the physiological status and performance of the cow. Infrared spectroscopy is the study of the interaction of an electromagnetic wave with matter and it is used globally to predict milk quality parameters on routinely acquired individual cow milk samples and bulk tank samples. Thus, exploiting infrared spectroscopy in next-generation phenotyping will ensure potentially rapid application globally with a negligible additional implementation cost as the infrastructure already exists. Fourier-transform infrared spectroscopy (FTIRS) analysis is already used to predict milk fat and protein concentrations, the ratio of which has been proposed as an indicator of energy balance. Milk FTIRS is also able to predict the concentration of various fatty acids in milk, the composition of which is known to change when body tissue is mobilized; that is, when the cow is in negative energy balance. Energy balance is mathematically very similar to residual energy intake (REI), a suggested measure of feed efficiency. Therefore, the prediction of energy intake, energy balance, and feed efficiency (i.e., REI) from milk FTIRS seems logical. In fact, the accuracy of predicting (i.e., correlation between predicted and actual values; root mean square error in parentheses) energy intake, energy balance, and REI from milk FTIRS in dairy cows was 0.88 (20.0MJ), 0.78 (18.6MJ), and 0.63 (22.0MJ), respectively, based on cross-validation. These studies, however, are limited to results from one research group based on data from 2 contrasting production systems in the United Kingdom and Ireland and would need to be replicated, especially in a range of production systems because the prediction equations are not accurate when the variability used in validation is not represented in the calibration data set. Heritable genetic variation exists for all predicted traits. Phenotypic differences in energy intake also exists among animals stratified based on genetic merit for energy intake predicted from milk FTIRS, substantiating the usefulness of such FTIR-predicted phenotypes not only for day-to-day herd management, but also as part of a breeding strategy to improve cow performance.

Predictive ability of mid-infrared spectroscopy for major mineral composition and coagulation traits of bovine milk by using the uninformative variable selection algorithm

Milk minerals and coagulation properties are important for both consumers and processors, and they can aid in increasing milk added value. However, large-scale monitoring of these traits is hampered by expensive and time-consuming reference analyses. The objective of the present study was to develop prediction models for major mineral contents (Ca, K, Mg, Na, and P) and milk coagulation properties (MCP: rennet coagulation time, curd-firming time, and curd firmness) using mid-infrared spectroscopy. Individual milk samples (n=923) of Holstein-Friesian, Brown Swiss, Alpine Grey, and Simmental cows were collected from single-breed herds between January and December 2014. Reference analysis for the determination of both mineral contents and MCP was undertaken with standardized methods. For each milk sample, the mid-infrared spectrum in the range from 900 to 5,000cm(-1) was stored. Prediction models were calibrated using partial least squares regression coupled with a wavenumber selection technique called uninformative variable elimination, to improve model accuracy, and validated both internally and externally. The average reduction of wavenumbers used in partial least squares regression was 80%, which was accompanied by an average increment of 20% of the explained variance in external validation. The proportion of explained variance in external validation was about 70% for P, K, Ca, and Mg, and it was lower (40%) for Na. Milk coagulation properties prediction models explained between 54% (rennet coagulation time) and 56% (curd-firming time) of the total variance in external validation. The ratio of standard deviation of each trait to the respective root mean square error of prediction, which is an indicator of the predictive ability of an equation, suggested that the developed models might be effective for screening and collection of milk minerals and coagulation properties at the population level. Although prediction equations were not accurate enough to be proposed for analytic purposes, mid-infrared spectroscopy predictions could be evaluated as phenotypic information to genetically improve milk minerals and MCP on a large scale.