Heat Stability Assessment of Milk: A Review of Traditional and Innovative Methods

It is important to differentiate milk with different thermostabilities for diverse applications in food products and for the appropriate selection of processing and maintenance of manufacturing facilities. In this review, an overview of the chemical changes in milk subjected to high-temperature heating is given. An emphasis is given to the studies of traditional and state-of-the-art strategies for assessing the milk thermostability, as well as their influencing factors. Traditional subjective and objective techniques have been used extensively in many studies for evaluating thermostability, whereas recent research has been focused on novel approaches with greater objectivity and accuracy, including innovative physical, spectroscopic, and predictive tools.

The Ninhydrin Reaction Revisited: Optimisation and Application for Quantification of Free Amino Acids

The ninhydrin reaction is commonly used for the detection of amino acids. However, in the literature, different conditions with respect to the buffer system, its pH and concentration, type of organic solvent, incubation time, and temperature, as well as the concentrations of the reagents, are described. To identify the most suitable conditions, colour development with reagents of varying compositions and different reaction temperatures and times were investigated using asparagine as a model amino acid. Asparagine was selected since it is one of the most abundant free amino acids in many types of samples. The optimal reaction mixture consisted of 0.8 mol L-1 potassium acetate, 1.6 mol L-1 acetic acid, 20 mg mL-1 ninhydrin and 0.8 mg mL-1 hydrindantin in DMSO/acetate buffer 40/60 (v/v) (final concentrations). The best reaction condition was heating the samples in 1.5 mL reaction tubes to 90 °C for 45 min. Afterwards, the samples were diluted with 2-propanol/water 50/50 (v/v) and the absorbance was measured at 570 nm. The proteinogenic amino acids showed a similar response except for cysteine and proline. The method was highly sensitive and showed excellent linearity as well as intra-day and inter-day reproducibility.

The ability of a handheld near-infrared spectrometer to do a rapid quality assessment of bovine colostrum, including the immunoglobulin G concentration

Portable infrared-based instruments have made important contributions in different research fields. Within the dairy supply chain, for example, most of portable devices are based on near-infrared spectroscopy (NIRS) and are nowadays an important support for farmers and operators of the dairy sector, allowing fast and real-time decision-making, particularly for feed and milk quality evaluation and animal health and welfare monitoring. The affordability, portability, and ease of use of these instruments have been pivotal factors for their implementation on farm. In fact, pocket-sized devices enable nonexpert users to perform quick, low-cost, and nondestructive analysis on various matrixes without complex preparation. Because bovine colostrum (BC) quality is mostly given by the IgG level, evaluating the ability of portable NIRS tools to measure antibody concentration is advisable. In this study we used the wireless device SCiO manufactured by Consumer Physics Inc. (Tel Aviv, Israel) to collect BC spectra and then attempt to predict IgG concentration and gross and fine composition in individual samples collected immediately after calving (<6 h) in primiparous and pluriparous Holstein cows on 9 Italian farms. Chemometric analyses revealed that SCiO has promising predictive performance for colostral IgG concentration, total Ig concentration, fat, and AA. The coefficient of determination of cross-validation (R2CV) was in fact ≥0.75). Excellent accuracy was observed for dry matter, protein, and S prediction in cross-validation and good prediction ability in external validation (R2CV ≥ 0.93; the coefficient of determination of external validation, R2V, was ≥0.82). Nonetheless, SCiO's ability to discriminate between good- and low-quality samples (IgG ≥ vs. < 50 g/L) was satisfactory. The affordable cost, the accurate predictions, and the user-friendly design, coupled with the increased interest in BC within the dairy sector, may boost the collection of extensive BC data for management and genetic purposes in the near future.

Assessing the relationship between somatic cell count and the milk mid-infrared spectrum in Chinese Holstein cows

BACKGROUND

Milk produced by dairy cows is a complex combination of many components, but the effect of mastitis has only been investigated for a few of these components. Milk mid-infrared (MIR) spectra can reflect the global composition of milk, and this study aimed to detect the relationships between milk MIR spectral wavenumbers and milk somatic cell count (SCC)-a sensitive biomarker for mastitis.

METHODS

Pearson correlation analysis was used to calculate the correlation coefficient between somatic count score (SCS) and spectral wavenumbers. A general linear mixed model was applied to investigate the effect of three different classes of SCC (low, middle and high) on spectral wavenumbers.

RESULTS

The mean correlation coefficient between the 'fingerprint region' (wavenumbers 925-1582 cm-1 ) and the SCS was higher than that for other regions of the MIR spectrum, and the specific wavenumber with the strongest correlation with the SCS was within the 'fingerprint region'. SCC class had a significant (p < 0.05) effect on 639 spectral wavenumbers. In particular, some spectral wavenumbers within the 'fingerprint region' were highly affected by the SCC class.

LIMITATION

The data were collected from only one province in China, so the generalisability of the findings may be limited.

CONCLUSION

SCC had close relationships with milk spectral wavenumbers related to important milk components or chemical bonds.

Minerals and essential amino acids of bovine colostrum: Phenotypic variability and predictive ability of mid- and near-infrared spectroscopy

Colostrum quality and volume are fundamental for calves because it is the primary supplier of antibodies and the first source of energy, carbohydrates, lipids, proteins, minerals, and vitamins for the newborn. Assessing the detailed composition (i.e., AA and mineral content) of bovine colostrum (BC) on-line and at a reasonable cost would help dairy stakeholders such as farmers or veterinarians for precision feeding purposes and industries producing preparations containing BC such as foodstuff, supplements, and medicaments. In the present study we evaluated mid- (MIRS) and near-infrared spectroscopy (NIRS) prediction ability for AA and mineral composition of individual BC. Second, we the investigated the major factors affecting the phenotypic variability of such traits also evaluating the correlations with the Ig concentration. Results demonstrated that MIRS and NIRS were able to provide sufficiently accurate predictions for all the AA. The coefficient of determination in external validation (R2V) fell, in fact, within the range of 0.70 to 0.86, with the exception of Ile, His, and Met. Only some minerals reached a sufficient accuracy (i.e., Ca, P, S, and Mg; R2V ≥ 0.66) using MIRS, and also S (R2V = 0.87) using NIRS. Phenotypically, both parity and calving season affected the variability of these BC composition traits. Heifers' colostrum was the one with the greatest concentration of Ca and P, the 2 most abundant minerals. These minerals were however very low in cows calving in summer compared with the rest of the year. The pattern of AA across parities and calving season was not linear, likely because their variability was scarcely (or not) affected by these effects. Finally, samples characterized by high IgG concentration were those presenting on average greater concentration of AA. Findings suggest that infrared spectroscopy has the potential to be used to predict certain AA and minerals, outlining the possibility of implementing on-site analyses for the evaluation of the broad-sense BC quality.

Impact of varying levels of pasture allowance on the nutritional quality and functionality of milk throughout lactation

The objective of this study was to examine the impact of increasing proportions of grazed pasture in the diet on the composition, quality, and functionality of bovine milk across a full lactation. Fifty-four spring-calving cows were randomly assigned to 1 of 3 groups (n = 18), blocked on the basis of mean calving date (February 15, 2020 ± 0.8 d), pre-experimental daily milk yield (24.70 ± 3.70 kg), milk solids yield (2.30 ± 0.27 kg), lactation number (3.10 ± 0.13), and economic breeding index (182 ± 19). Raw milk samples were obtained weekly from each group between March and November 2020. Group 1 (GRS) consumed perennial ryegrass and was supplemented with 5% concentrates (dry matter basis); group 2 was maintained indoors and consumed a total mixed ration (TMR) diet consisting of maize silage, grass silage, and concentrates; and group 3 consumed a partial mixed ration diet (PMR), rotating between perennial ryegrass during the day and indoor TMR feeding at night. Raw milk samples consisted of a pooled morning and evening milking and were analyzed for gross composition, free amino acids, fatty acid composition, heat coagulation time, color, fat globule size, and pH. The TMR milks had a significantly higher total solids, lactose, protein, and whey protein as a proportion of protein content compared with both GRS and PMR milks. The GRS milks demonstrated a significantly lower somatic cell count (SCC), but a significantly higher pH and b*-value than both TMR and PMR milks. The PMR milks exhibited significantly lower total solids and fat content, but also demonstrated significantly higher SCC and total free amino acid content compared with GRS and TMR. Partial least squares discriminant analysis of fatty acid profiles displayed a distinct separation between GRS and TMR samples, while PMR displayed an overlap between both GRS and TMR groupings. Variable importance in projection analysis identified conjugated linoleic acid cis-9,trans-11, C18:2n-6 cis, C18:3n-3, C11:0, and C18:2n-6 trans as the largest contributors to the variation between the diets. Milk fats derived from GRS diets exhibited the highest proportion of unsaturated fats and higher unsaturation, health-promoting, and desaturase indices. The lowest proportions of saturated fats and the lowest atherogenic index were also exhibited by GRS-derived milk fats. This work highlights the positive influence of grass-fed milk for human consumption through its more nutritionally beneficial fatty acid profile, despite the highest milk solid percentages derived from TMR feeding systems. Furthermore, this study demonstrates the proportional response of previously highlighted biomarkers of pasture feeding to the proportion of pasture in the cow's diet.

Dairy and plant based protein beverages: In vitro digestion behaviour and effect on intestinal barrier biomarkers

The consumer demand for protein-enriched food products continues to grow, in parallel with consumers' interest in plant based alternatives. The replacement of milk protein by plant protein is likely to be occur predominantly in prepared consumer foods such as nutritional beverages. This study aimed to compare and contrast powder beverages formulated with commercially available dairy versus plant ingredients in terms of protein digestion and gut barrier health. After simulated static in vitro gastrointestinal digestion, the release of free amino acids increased for all model beverages. In addition, the majority of peptides present in digested beverages were < 0.8 kDa in size. Gastrointestinal digestion did not increase the degree of protein hydrolysis in beverages formulated with prehydrolysed milk protein, whey or pea ingredients. A 2 h permeability assessment of digested beverages across the intestinal barrier, using Caco-2/HT-29/MTX co-cultures, revealed reduced transcription of tight junction protein 1, claudin-1 and mucus protein 2 albeit gut barrier impedance was unchanged. IL-8 mRNA levels in cell monolayers was significantly increased with digested fluids treatment but even more so with digesta from hydrolysed milk protein beverage. Overall, the response observed on intestinal biomarkers with digested plant beverages was similar to dairy based beverages supporting the replacement of dairy with plant proteins in powder beverage formulations.

Effectiveness of mid-infrared spectroscopy for the prediction of cow milk metabolites

Proton nuclear magnetic resonance (¹H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of ¹H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through ¹H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional ¹H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk.

The Protein Composition and In Vitro Digestive Characteristics of Animal- versus Plant-Based Infant Nutritional Products

The protein composition and digestive characteristics of four commercially available infant formulae (IF) manufactured using bovine (B-IF), caprine (C-IF), soy (S-IF), and rice (R-IF) as a protein source were examined in this study. Plant-based formulae had significantly higher crude protein and non-protein nitrogen (NPN) concentrations. Static in vitro gastrointestinal digestion of these formulae, and subsequent analysis of their digestates, revealed significantly higher proteolysis of B-IF at the end of gastrointestinal digestion compared to the other formulae, as indicated by the significantly higher concentration of free amine groups. Furthermore, differences in structure formation during the gastric phase of digestion were observed, with formation of a more continuous, firmer coagulum by C-IF, while R-IF demonstrated no curd formation likely due to the extensive hydrolysis of these proteins during manufacture. Differences in digestive characteristics between formulae manufactured from these different protein sources may influence the bio-accessibility and bioavailability of nutrients, warranting additional study.

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.

Comparison of Mobile Phone and CCD Cameras for Electrochemiluminescent Detection of Biogenic Amines

Biogenic amines are an important and widely studied class of molecules due to their link to the physiological processes of food-related illnesses and histamine poisoning. Electrochemiluminescent (ECL) detection offers an inexpensive and portable analytical method of detection for biogenic amines when coupled with recent advancements in low-cost carbon-based electrodes and a smartphone camera. In this work, a mobile phone camera was evaluated against a piece of conventional instrumentation, the charge-coupled device, for the detection of ECL from the reaction of biogenic amines with the luminescent compound tris(2,2'-bipyridyl)ruthenium(II). Assisted by a 3D-printed light-tight housing, the mobile phone achieved limits of detection of 127, 425 and 421 μM for spermidine, putrescine, and histamine, respectively. The mobile phone's analytical figures of merit were lesser than the CCD camera but were still within the range to detect contamination. In an exploration of real-world samples, the mobile phone was able to determine the contents of amines in skim milk on par with that of a CCD camera.

Breeding for improved protein fractions and free amino acids concentration in bovine milk

Considerable resources are required to routinely measure detailed milk compositional traits. Hence, an insufficient volume of phenotypic data can hinder genetic progress in these traits within dairy cow breeding programmes. The objective of the present study was to quantify the opportunities for breeding for improved milk protein and free amino acid (FAA) composition by exploiting mid‐infrared spectroscopy (MIRS) predictions routinely recorded from milk samples. Genetic parameters for protein fractions and FAA composition were estimated using 134,546 test‐day records from 16,166 lactations on 9,572 cows using linear mixed models. Heritability of MIRS‐predicted protein fractions ranged from 0.19 (α‐lactalbumin) to 0.55 (β‐lactoglobulin A), while heritability of MIRS‐predicted FAA ranged from 0.08 for glycine to 0.29 for glutamic acid. Genetic correlations among the MIRS‐predicted FAA were moderate to strong ranging from −0.44 (aspartic acid and lysine) to 0.97 (glutamic acid and total FAA). Adjustment of the genetic correlations for the genetic merit of 24‐h milk yield did not greatly affect the correlations. Results from the current study highlight the presence of exploitable genetic variation for both protein fractions and FAA in dairy cow milk. Besides, the direction of genetic correlations reveals that breeding programmes directly selecting for greater milk protein concentration carry with them favourable improvement in casein and whey fractions.

Identification and Activity Characterization of γ-Glutamyltransferase from Bovine Milk

It is well known that bovine milk contains γ-glutamyltransferase (GGT) activity. To verify the identity of the GGT and further to characterize the generation of γ-glutamyl peptides, identification of GGT from bovine milk and quantification of kokumi peptides and free amino acids were performed. GGT was purified from skim milk and identified as the bovine protein (G3N2D8), and it reveals that it is composed of two subunits. Sequence alignment with human GGT and molecular mass determination showed that the bovine GGT was glycosylated and contained an N-terminal transmembrane part. Further activity characterization was performed in comparison with GGT from Bacillus amyloliquefaciens in terms of the ability to generate γ-glutamyl peptides from casein hydrolysates. During the transpeptidation reaction catalyzed by both GGT, γ-glutamyl peptides significantly (P < 0.05) increased after γ-glutamylation; addition of glutamine contributed to the generation of γ-glutamyl peptides, suggesting that glutamine could act as a γ-glutamyl donor. This study reveals that the GGT of skim milk membranes is a glycosylated membrane protein that can generate γ-glutamyl peptides.

Potential of Thermolysin-like Protease A69 in Preparation of Bovine Collagen Peptides with Moisture-Retention Ability and Antioxidative Activity

Bovine bone is rich in collagen and is a good material for collagen peptide preparation. Although thermolysin-like proteases (TLPs) have been applied in different fields, the potential of TLPs in preparing bioactive collagen peptides has rarely been evaluated. Here, we characterized a thermophilic TLP, A69, from a hydrothermal bacterium Anoxybacillus caldiproteolyticus 1A02591, and evaluated its potential in preparing bioactive collagen peptides. A69 showed the highest activity at 60 °C and pH 7.0. We optimized the conditions for bovine bone collagen hydrolysis and set up a process with high hydrolysis efficiency (99.4%) to prepare bovine bone collagen peptides, in which bovine bone collagen was hydrolyzed at 60 °C for 2 h with an enzyme-substrate ratio of 25 U/g. The hydrolysate contained 96.5% peptides that have a broad molecular weight distribution below 10000 Da. The hydrolysate showed good moisture-retention ability and a high hydroxyl radical (•OH) scavenging ratio of 73.2%, suggesting that the prepared collagen peptides have good antioxidative activity. Altogether, these results indicate that the thermophilic TLP A69 has promising potential in the preparation of bioactive collagen peptides, which may have potentials in cosmetics, food and pharmaceutical industries. This study lays a foundation for the high-valued utilization of bovine bone collagen.

Influence of Chymosin on Physicochemical and Hydrolysis Characteristics of Casein Micelles and Individual Caseins

The effects of chymosin on the physicochemical and hydrolysis characteristics of casein micelles and individual caseins were investigated. Adding 0.03 units of chymosin/mL led to the casein micelles in skim milk coagulating after a 3 h incubation period at 30 °C. SDS–PAGE investigation showed that β-CN, κ-CN, α_s-CN, and a portion of β-lactoglobulin (β-LG) in the milk supernatant fraction (MSF) were precipitated into the milk pellet fraction (MPF). The mean particle size of the MSF with chymosin decreased from 254.4 nm to 179.2 nm after a 3 h incubation period. Mass spectrometry and SDS–PAGE analysis suggested that chymosin hydrolyzed individual β-CN, κ-CN, and α_s-CN, but not β-LG. Chymosin hydrolysis led to a decrease in the molecular weights of the hydrolyzed β-CN, κ-CN, and α_s-CN. Particle size analysis indicated that there was no difference in the particle size distribution of hydrolyzed β-CN and α_s-CN. Moreover, our outcomes demonstrated that the hydrolysis of κ-CN by chymosin occurs before that of β-CN and α_s-CN.

Milk metabolome reveals variations on enteric methane emissions from dairy cows fed a specific inhibitor of the methanogenesis pathway

Metabolome profiling in biological fluids is an interesting approach for exploring markers of methane emissions in ruminants. In this study, a multiplatform metabolomics approach was used for investigating changes in milk metabolic profiles related to methanogenesis in dairy cows. For this purpose, 25 primiparous Holstein cows at similar lactation stage were fed the same diet supplemented with (treated, n = 12) or without (control, n = 13) a specific antimethanogenic additive that reduced enteric methane production by 23% with no changes in intake, milk production, and health status. The study lasted 6 wk, with sampling and measures performed in wk 5 and 6. Milk samples were analyzed using 4 complementary analytical methods, including 2 untargeted (nuclear magnetic resonance and liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer) and 2 targeted (liquid chromatography-tandem mass spectrometry and gas chromatography coupled to a flame ionization detector) approaches. After filtration, variable selection and normalization data from each analytical platform were then analyzed using multivariate orthogonal partial least square discriminant analysis. All 4 analytical methods were able to differentiate cows from treated and control groups. Overall, 38 discriminant metabolites were identified, which affected 10 metabolic pathways including methane metabolism. Some of these metabolites such as dimethylsulfoxide, dimethylsulfone, and citramalic acid, detected by nuclear magnetic resonance or liquid chromatography-mass spectrometry methods, originated from the rumen microbiota or had a microbial-host animal co-metabolism that could be associated with methanogenesis. Also, discriminant milk fatty acids detected by targeted gas chromatography were mostly of ruminal microbial origin. Other metabolites and metabolic pathways significantly affected were associated with AA metabolism. These findings provide new insight on the potential role of milk metabolites as indicators of enteric methane modifications in dairy cows.

Ultrasound-Assisted Transglutaminase Catalysis of the Cross-Linking and Microstructure of αs-Casein, β-Casein and κ-Casein

The effects of ultrasonic treatment (UT)-assisted transglutaminase (TGase) catalysis on the physicochemical properties of individual αs-casein (αs-CN), β-casein (β-CN), and κ-casein (κ-CN) were investigated. After 60 min of incubation at 30 °C, αs-CN, β-CN, and κ-CN were cross-linked with TGase (6.0 units/mL), and high molecular weight polymers (>200 kDa) were formed. The use of TGase in conjunction with UT (20 kHz, power of 400 W, and amplitude 20%) led to an increase in the rate of αs-CN, β-CN, and κ-CN polymerization compared to the individual casein that contained TGase but did not undergo UT. SDS-PAGE scrutiny showed that the intensities of αs-CN, β-CN, and κ-CN incubation with regard to TGase and UT at 30 °C for 60 min noticeably decreased to 5.66 ± 0.39, 3.97 ± 0.43, and 26.07 ± 1.18%, respectively (p < 0.05). Particle size analysis results indicated that the molecule size appropriation for the cross-linking of αs-CN, β-CN, and κ-CN ranged from 6000 to 10,000 nm after 60 min incubation with TGase and UT. Transmission electron microscopy investigation showed network structures of cross-linking αs-CN, β-CN, and κ-CN were formed from αs-CN, β-CN, and κ-CN, respectively. As our results show, the comprehensive utilization of TGase and UT will be a superior method for the polymerization of αs-CN, β-CN, and κ-CN.

Influence of Estrus on the Milk Characteristics and Mid-Infrared Spectra of Dairy Cows

Simple Summary

Some studies have confirmed the variation in milk profiles when dairy cows show estrus. However, only a few milk components, such as fat, protein, and lactose, have been investigated so far, and thus any changes in the many other parts of milk’s composition due to estrus are unknown. Milk mid-infrared (MIR) spectra consist of wavenumbers, which provide insight into the chemical composition of milk. The MIR spectrum reflects the global composition of milk, but this information is currently underused. In this study, we considered MIR wavenumbers as traits, and directly studied the spectral information as a way to study the estrus of dairy cows linked to milk composition. This research provides a deeper understanding of the milk MIR spectrum and may lead to new approaches for estrus detection in dairy cows from routine milk analysis, thereby guiding an opportune insemination time.

Abstract

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm⁻¹, of 371 records from 162 Holstein cows on the days before (day −3, day −2, and day −1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day −3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods

Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated [curd firmness at 60 min, α_S1-casein (CN), α_S2-CN, κ-CN, α-lactalbumin, and β-lactoglobulin B], whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, β-CN, and β-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits.

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.

Dairy Cows' Health during Alpine Summer Grazing as Assessed by Milk Traits, Including Differential Somatic Cell Count: A Case Study from Italy

Simple Summary

Dairy herds in alpine areas usually adopt summer grazing, mainly to reduce feeding costs. This practice is related to the maintenance of local traditions and to the manufacturing of niche dairy products. However, it is important to assess the impact of this practice on cattle health. This case study investigated how milk-related health traits vary across extensive grazing during the summer period, using data collected in a dairy herd whose cows were repeatedly controlled for individual milk samples. Although the transition from barn farming to pasture led to a reduction in milk production, proper grazing management can make dairy cows more resilient in terms of udder health and metabolic efficiency. Findings of the present research report suggested that pasture can be adopted to maintain dairy herd sustainability without impairing animal health.

Abstract

Extensive summer grazing is a dairy herd management practice frequently adopted in mountainous areas. Nowadays, this activity is threatened by its high labour demand, but it is fundamental for environmental, touristic and economic implications, as well as for the preservation of social and cultural traditions. Scarce information on the effects of such low-input farming systems on cattle health is available. Therefore, the present case study aimed at investigating how grazing may affect the health status of dairy cows by using milk traits routinely available from the national milk recording scheme. The research involved a dairy herd of 52 Simmental and 19 Holstein × Simmental crossbred cows. The herd had access to the pasture according to a rotational grazing scheme from late spring up to the end of summer. A total of 616 test day records collected immediately before and during the grazing season were used. Individual milk yield was registered during the milking procedure. Milk samples were analysed for composition (fat, protein, casein and lactose contents) and health-related milk indicators (electrical conductivity, urea and β-hydroxybutyrate) using mid-infrared spectroscopy. Somatic cell count (SCC) and differential SCC were also determined. Data were analysed with a linear mixed model, which included the fixed effects of the period of sampling, cow breed, stage of lactation and parity, and the random effects of cow nested within breed and the residual. The transition from barn farming to pasture had a negative effect on milk yield, together with a small deterioration of fat and protein percentages. Health-related milk indicators showed a minor deterioration of the fat to protein ratio, differential SCC and electrical conductivity, particularly towards the end of the grazing season, whereas the somatic cell score and β-hydroxybutyrate were relatively constant. Overall, the study showed that, when properly managed, pasture grazing does not have detrimental effects on dairy cows in terms of udder health and efficiency. Therefore, the proper management of cows on pasture can be a valuable solution to preserve the economic, social and environmental sustainability of small dairy farms in the alpine regions, without impairing cows’ health.

Bulk milk quality as affected by cattle breed composition of the herd in mountain area

The aim of this study was to investigate the variation of gross composition, somatic cell count, urea content, and fatty acids (FA) composition of bulk milk from single-breed and multi-breed farms in alpine area, keeping either Brown Swiss (BS), Holstein Friesian (HF), Simmental (SI), or their combinations. Gross milk composition, urea content, and FA composition were predicted using mid-infrared spectroscopy. Observations were grouped in 7 combinations consisting of 3 singlebreed and 4 multi-breed types of herd. A mixed linear model was used for data analysis, accounting for the fixed effects of herd composition (7 combinations), month of sampling, year of sampling, and the interactions between herd composition and month of sampling, and between herd composition and year of sampling. Farm was included as random effect. Results highlighted that about two thirds of South Tyrolean farms were single-breed and herds with more than 20 lactating cows changed herd structure over time, switching from multi- to single-breed. Single-breed BS farms produced milk with greater fat, protein, casein, lactose, and FA content than single-breed HF and SI farms. Further, multi-breed herds including BS cows produced milk with greater fat, protein, casein, and polyunsaturated FA content than multi-breed HF+SI herds. Overall, single-breed SI farms produced milk with lower somatic cell count than other herd combinations. Despite the number of BS cows in South Tyrol has decreased in favor of SI in the last years, this breed is still the most interesting for alpine dairy farming to achieve optimal milk quality in both single- and multi-breed scenarios. The tendency to move to SI is mainly related to good milk performance of SI cows coupled with their robustness, high carcass value, high market value of calves, and adaptability to mountain farming system.

A rapid method to quantify casein in fluid milk by front-face fluorescence spectroscopy combined with chemometrics

Casein in fluid milk determines cheese yield and affects cheese quality. Traditional methods of measuring casein in milk involve lengthy sample preparations with labor-intensive nitrogen-based protein quantifications. The objective of this study was to quantify casein in fluid milk with different casein-to-crude-protein ratios using front-face fluorescence spectroscopy (FFFS) and chemometrics. We constructed calibration samples by mixing microfiltration and ultrafiltration retentate and permeate in different ratios to obtain different casein concentrations and casein-to-crude-protein ratios. We developed partial least squares regression and elastic net regression models for casein prediction in fluid milk using FFFS tryptophan emission spectra and reference casein contents. We used a set of 20 validation samples (including raw, skim, and ultrafiltered milk) to optimize and validate model performance. We externally tested another independent set of 20 test samples (including raw, skim, and ultrafiltered milk) by root mean square error of prediction (RMSEP), residual prediction deviation (RPD), and relative prediction error (RPE). The RMSEP for casein content quantification in raw, skim, and ultrafiltered milk ranged from 0.12 to 0.13%, and the RPD ranged from 3.2 to 3.4. The externally validated error of prediction was comparable to the existing rapid method and showed practical model performance for quality-control purposes. This FFFS-based method can be implemented as a routine quality-control tool in the dairy industry, providing rapid quantification of casein content in fluid milk intended for cheese manufacturing.

Influence of Microbial Transglutaminase on Physicochemical and Cross-Linking Characteristics of Individual Caseins

The effects of microbial transglutaminase (MTGase) cross-linking on the physicochemical characteristics of individual caseins were investigated. MTGase was used to modify three major individual caseins, namely, κ-casein (κ-CN), α_S-casein (α_S-CN) and β-casein (β-CN). The SDS-PAGE analysis revealed that MTGase-induced cross-linking occurred during the reaction and that some components with high molecular weights (>130 kDa) were formed from the individual proteins κ-CN, α_S-CN and β-CN. Scanning electron microscopy (SEM) and particle size analysis respectively demonstrated that the κ-CN, α_S-CN and β-CN particle diameters and protein microstructures were larger and polymerized after MTGase cross-linking. The polymerized κ-CN (~749.9 nm) was smaller than that of β-CN (~7909.3 nm) and α_S-CN (~7909.3 nm). The enzyme kinetics results showed K_M values of 3.04 × 10⁻⁶, 2.37 × 10⁻⁴ and 8.90 × 10⁻³ M for κ-CN, α_S-CN and β-CN, respectively, and, furthermore, k_cat values of 5.17 × 10⁻⁴, 1.92 × 10⁻³ and 4.76 × 10⁻² 1/s, for κ-CN, α_S-CN and β-CN, respectively. Our results revealed that the cross-linking of β-CN catalyzed by MTGase was faster than that of α_S-CN or κ-CN. Overall, the polymers that formed in the individual caseins in the presence of MTGase presented a higher molecular weight and larger particles.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

Microbiological assessment of liquid feed for finisher pigs on commercial pig units

AIMS

To investigate the microbiological and nutritional quality of liquid finisher pig feed on commercial production units and the factors influencing this.

METHODS AND RESULTS

Microbiological and physio-chemical analyses were performed on liquid feed sampled from the mixing tank and troughs of the finisher section of eight commercial pig units. Lactic acid bacteria, yeast and Escherichia coli counts, as well as lactic acid, ethanol and acetate concentrations were higher in residual feed sampled from the troughs compared with mixing tank samples (P < 0·001). Feed pH, as well as lysine, methionine and threonine concentrations and gross energy were all lower in the residual trough samples (P < 0·001). Liquid co-products reduced E. coli counts in the residual trough samples (P < 0·05), pH in the mixing tank (P < 0·01) and fresh trough samples (P < 0·05) and mould counts at all three sampling locations (P < 0·01) but sanitation practices had no impact.

CONCLUSIONS

Even when considered unfermented, a considerable degree of spontaneous fermentation occurs in liquid feed, with resultant negative effects on nutritional quality.

SIGNIFICANCE AND IMPACT OF THE STUDY

This is one of the first studies showing that uncontrolled fermentation of fresh liquid pig feed is commonplace on commercial units, highlighting the need for implementation of suitable control strategies.

Effect of water-to-feed ratio on feed disappearance, growth rate, feed efficiency, and carcass traits in growing-finishing pigs

The optimum proportion of water for preparing liquid feed to maximize growth and optimize feed efficiency (FE) in growing-finishing pigs is not known. The aim of the current study was, using an automatic short-trough sensor liquid feeding system, to identify the water-to-feed ratio at which growth was maximized and feed was most efficiently converted to live-weight. Two experiments were conducted in which four commercially used water-to-feed ratios were fed: 2.4:1, 3.0:1, 3.5:1, and 4.1:1 on a dry matter (DM) basis (the equivalent of 2:1, 2.5:1, 3.0:1, and 3.5:1 on a fresh matter basis). Each experiment comprised 216 pigs, penned in groups of 6 same sex (entire male and female) pigs/pen with a total of 9 pen replicates per treatment. The first experiment lasted 62 days (from 40.6 to 102.2 kg at slaughter) and the second experiment was for 76 days (from 31.8 to 119.6 kg at slaughter). Overall, in Exp. 1, FE was 0.421, 0.420, 0.453, and 0.448 (s.e. 0.0081 g/g; P < 0.01) for pigs fed at 2.4:1, 3.0:1, 3.5:1, and 4.1:1, respectively. Overall, in Exp. 2, average daily gain was 1,233, 1,206, 1,211, and 1,177 (s.e. 12.7 g/day; P < 0.05) for pigs fed at 2.4:1, 3.0:1, 3.5:1, and 4.1:1, respectively. At slaughter, in Exp. 1, dressing percentage was 76.7, 76.6, 76.7, and 75.8 (s.e. 0.17%; P < 0.01) for 2.4:1, 3.0:1, 3.5:1, and 4.1:1, respectively. There were no differences between treatment groups for DM, organic matter, nitrogen, gross energy, or ash digestibilities. These findings indicate that liquid feeding a diet prepared at a water-to-feed ratio of 3.5:1 maximizes FE of growing-finishing pigs without negatively affecting dressing percentage. Therefore, preparing liquid feed for growing-finishing pigs at a water-to-feed ratio of 3.5:1 DM is our recommendation for a short-trough liquid feeding system.

A Systems-Wide Analysis of Proteolytic and Lipolytic Pathways Uncovers The Flavor-Forming Potential of The Gram-Positive Bacterium Macrococcus caseolyticus subsp. caseolyticus

Macrococcus caseolyticus subsp. caseolyticus is a Gram-positive, commensal organism documented to be present as a component of the secondary microflora in fermented foods such as Ragusano and Fontina cheeses and Cantonese sausage. In these products, the organism appears to play a role in ripening and the development of the final organoleptic qualities. However, the role of this organism in flavor generation is not well understood. Therefore, the objective of this study was to investigate the role of M. caseolyticus subsp. caseolyticus in flavor compound formation through an examination of enzymatic, metabolomic and genomic data. A bank of M. caseolyticus subsp. caseolyticus strains derived from a variety of niches were examined. Enzyme activities analyzed comprised those of the proteolytic and lipolytic cascades including cell-envelope proteinase (CEP), peptidases, esterases, lipases, aminotransferases and glutamate dehydrogenase (GDH). Strain to strain variation was observed, often associated with niche. All strains, except those isolated from non-dairy sources, demonstrated high CEP activity. Such high CEP activity associated with dairy strains implies the importance of this characteristic in the adaptation of these strains to a dairy-specific niche. However, limited downstream peptidolytic activity, in addition to a limited ability to generate free amino acids (FAA) was observed across all strains, indicating weak ability of this organism to generate amino-acid derived flavor compounds. Interestingly, the strains with high CEP activity also demonstrated high esterase activity and gas chromatography-mass spectrometry (GC-MS) analysis of the volatile compounds produced when these strains were grown in lactose-free milk demonstrated differences in the range and types of volatiles produced. In contrast to this metabolic versatility, comparative genome analysis revealed the distribution of components of the proteolytic and lipolytic system in these strains to be conserved. Overall, this study demonstrates the potential of M. caseolyticus subsp. caseolyticus to generate diverse volatile flavor compounds. Additionally, the identification of the highly active strain-specific cell wall bound caseolytic proteases deriving extensive casein hydrolysis, serves as a promising avenue which can be potentially harnessed in the future to produce greater and more diverse flavor compounds.

The effect of feed form and delivery method on feed microbiology and growth performance in grow-finisher pigs

There is no generally accepted optimal feed form and delivery method for feeding finisher pigs. The objective of this study was to compare the effect of feed form (meal and pellet) and delivery method (liquid, dry, and wet/dry) on feed microbiology and growth, gain-to-feed ratio (G:F), and carcass quality of finisher pigs. Two batches of pigs were used, each with six pen replicates per treatment. In each batch 216 pigs (32.7 kg; ± 0.48 SE) housed in same-sex (entire male or female) pens of six pigs per pen were on treatment for ~62 d prior to slaughter. The experiment was a 2 × 3 factorial arrangement with two factors for diet form (meal and pellets) and three factors for feed delivery (dry, wet/dry, liquid). The treatments were 1) meal from dry feeder, 2) meal from wet/dry feeder, 3) meal from liquid system, 4) pellet from dry feeder, 5) pellet from wet/dry feeder, and 6) pellet from liquid system. Pig growth performance was determined, blood samples collected at slaughter for hematological analysis and microbiological and proximate analysis of feed performed. A significant feed form × delivery interaction was found for G:F. During the overall period G:F was 0.446, 0.433, 0.423, 0.474, 0.459, and 0.418 g/g (SE = 0.0080; P < 0.01) for treatments 1 through 6, respectively. When feed was pelleted, G:F was improved when feed delivery was dry or wet/dry compared to meal but when the delivery was liquid, pelleting did not affect G:F. There were no interactive effects for overall average daily gain (ADG). Overall ADG was 1,114 and 1,156 g/d (SE = 16.9; P < 0.01) for pigs fed diets in meal and pellet form, respectively and 1,080, 1,114, and 1,210 g/d (SE = 18.4; P < 0.001) for dry-, wet/dry-, and liquid-fed pigs, respectively. Carcass weight was 76.6 and 79.0 kg (SE = 0.55; P < 0.001) for pigs fed in meal and pellet form, respectively, while it was 74.7, 77.3, and 81.5 kg (SE = 0.60; P < 0.001) for pigs delivered dry, wet/dry, and liquid diets, respectively. Lactic acid bacteria (P < 0.05) and yeast (P < 0.01) counts in troughs were greater for the liquid than the dry diet in both meal and pelleted form. There was also evidence of lysine degradation in the liquid diet but this did not impact pig growth. Feeding the diet in pelleted vs. meal form led to lower hemoglobin and greater white blood cell and neutrophil counts (P < 0.05). To conclude, wet/dry feeding of a pelleted diet is recommended to maximize growth rate while optimizing G:F in grow-finisher pigs.

Short communication: Potential prediction of vitamin B12 concentration based on mid-infrared spectral data using Holstein Dairy Herd Improvement milk samples

The purpose of this study was (1) to predict the quantitative concentration of vitamin B₁₂ in milk using mid-infrared (MIR) spectrometry, and (2) to evaluate the potential of MIR spectra to discriminate different clusters of records based on their B₁₂ concentration. Milk samples were collected from 4,340 Holstein cows between 3 and 592 d in milk and located in 100 herds. Samples were taken using in-line milk meters and divided into 2 aliquots: one for MIR spectrometry and the other for B₁₂ concentration reference analyses by radioassay. Analyses were performed on 311 selected spectral wavelengths. A partial least squares regression model was built to quantify B₁₂ concentration. Discriminant analysis was used to isolate B₁₂ concentration clusters. A B₁₂ concentration threshold was set at 442 ng/dL, because this represents the cutoff value for a 250-mL glass of milk to fulfill 46% of the daily vitamin B₁₂ recommended dietary allowance for individuals 14 yr or older. For each analysis, records coming from two-thirds of herds were used to calibrate prediction equations, and the remaining records (one-third of herds for validation) were used to assess the prediction performance. In the case of discriminant analysis, validation sets were divided into evaluation sets (one-third of herds) to obtain alternate probability cutoffs and in test sets (two-thirds of herds) to validate equations. Spectral and B₁₂ concentration outliers were identified by calculating standardized Mahalanobis distance and with a residual analysis, respectively (n = 3,154). Regarding quantitative B₁₂ concentration, cross-validation and validation coefficients of determination averaged 0.51 and 0.46, respectively, which are relatively low, which would limit the potential use of the developed quantitative equations. In addition, root mean square errors of prediction of cross validation and validation sets averaged 88.9 and 94.7 ng/dL, respectively. Area under the receiver operating characteristic curve of test sets averaged 0.81 based on the 442 ng/dL threshold, which could be considered to represent good accuracy of classification. However, the false discovery rate averaged 36%. In summary, models predicting quantitative B₁₂ concentration had low cross-validation and validation coefficients of determination, limiting their use, but the proposed discriminant models could be used to identify milk samples with naturally high B12.

Effect of wet/dry, fresh liquid, fermented whole diet liquid, and fermented cereal liquid feeding on feed microbial quality and growth in grow-finisher pigs

Fermented liquid feeding has proved beneficial for weaner pigs; however, there is limited research on its effect on the growth and feed conversion efficiency (FCE) of grow-finisher pigs. Microbial decarboxylation of amino acids is associated with whole diet fermentation, while wet/dry and liquid feeding reportedly improve growth compared with dry feeding. The objective of this study was to determine the effect of wet/dry feeding and fresh, fermented whole diet, and fermented cereal liquid feeding on pig growth, feed efficiency, and carcass quality in grow-finisher pigs. Pigs were allocated to one of four dietary treatments in two experiments: 1) Single-space wet/dry feeders (WET/DRY), 2) Fresh liquid feeding (FRESH), 3) Fermented cereal liquid feeding where the cereal fraction (38% barley, 40% wheat) of the diet was fermented prior to feeding (FERM-CER), and 4) Fermented whole diet liquid feeding where the whole diet was fermented prior to feeding (FERM-WH). In exp. 1, pigs were fed the experimental diets for 68 d prior to slaughter (29.8 kg ± 0.92 SE to 102.3 kg ± 0.76 SE). Overall, average daily gain (ADG) was 1,094, 1,088, 1,110, and 955 g/d (SE = 13.0; P < 0.001) and FCE was 2.26, 2.37, 2.40, and 2.88 (SE = 0.031; P < 0.001) for treatments one through four, respectively. Pigs fed FERM-WH were lighter at slaughter than pigs fed the other three treatments (P < 0.001). In exp. 2, pigs were on treatment for 26 d prior to slaughter (85.3 kg ± 1.69 SE to 117.5 kg ± 0.72 SE). Overall, ADG in exp. 2 was 1,103, 1,217, 1,284, and 1,140 g/d (SE = 27.9; P < 0.01) and FCE was 2.78, 2.99, 2.95, and 3.09 g/g (SE = 0.071; P = 0.05), for treatments one through four, respectively. There were no significant differences observed between treatments for apparent total tract digestibility of dry matter, organic matter, nitrogen, gross energy, or ash. Higher lactic acid bacteria counts and lower Enterobacteriaceae counts and pH were observed in FERM-CER and FERM-WH compared with WET/DRY and FRESH. Ethanol concentrations were almost 4-fold higher in FERM-CER troughs than FRESH troughs and 5-fold higher in FERM-WH than FRESH troughs. To conclude, FERM-WH resulted in poorer growth and FCE compared with WET/DRY, FRESH, and FERM-CER, probably due to amino acid degradation and a loss in gross energy found in FERM-WH.

Opportunities and limitations of milk mid-infrared spectra-based estimation of acetone and β-hydroxybutyrate for the prediction of metabolic stress and ketosis in dairy cows

Subclinical (SCK) and clinical (CK) ketosis are metabolic disorders responsible for big losses in dairy production. Although Fourier-transform mid-infrared spectrometry (FTIR) to predict ketosis in cows exposed to great metabolic stress was studied extensively, little is known about its suitability in predicting hyperketonemia using individual samples, e.g. in small dairy herds or when only few animals are at risk of ketosis. The objective of the present research was to determine the applicability of milk metabolites predicted by FTIR spectrometry in the individual screening for ketosis. In experiment 1, blood and milk samples were taken every two weeks after calving from Holstein (n = 80), Brown Swiss (n = 72) and Swiss Fleckvieh (n = 58) cows. In experiment 2, cows diagnosed with CK (n = 474) and 420 samples with blood β-hydroxybutyrate [BHB] <1.0 mmol/l were used to investigate if CK could be detected by FTIR-predicted BHB and acetone from a preceding milk control. In experiment 3, correlations between data from an in farm automatic milk analyser and FTIR-predicted BHB and acetone from the monthly milk controls were evaluated. Hyperketonemia occurred in majority during the first eight weeks of lactation. Correlations between blood BHB and FTIR-predicted BHB and acetone were low (r = 0.37 and 0.12, respectively, P < 0.0001), as well as the percentage of true positive values (11.9 and 16.6%, respectively). No association of FTIR predicted ketone bodies with the interval of milk sampling relative to CK diagnosis was found. Data obtained from the automatic milk analyser were moderately correlated with the same day FTIR-predicted BHB analysis (r = 0.61). In conclusion, the low correlations with blood BHB and the small number of true positive samples discourage the use of milk mid-infrared spectrometry analyses as the only method to predict hyperketonemia at the individual cow level.

The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle

Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform mid-infrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.

FTIR spectroscopy for quality evaluation of sports supplements on the Polish market

Introduction. Our study aimed to apply medium infrared (MIR/FTIR) spectroscopy to evaluate the quality of various sports supplements available in the Polish shops and gyms. Study objects and methods. The study objects included forty-eight sports supplements: whey (15 samples), branched-chain amino acids (12 samples), creatine (3 samples), mass gainers (6 samples), and pre-workouts (12 samples). First, we determined the protein quantity in individual whey supplements by the Kjeldahl method and then correlated the results with the measured FTIR spectra by chemometric methods. The principal component analysis (PCA) was used to distinguish the samples based on the measured spectra. The samples were grouped according to their chemical composition. Further, we correlated the spectra with the protein contents using the partial least squares (PLS) regression method and mathematic transformations of the FTIR spectral data. Results and discussion. The analysis of the regression models confirmed that we could use FTIR spectra to estimate the content of proteins in protein supplements. The best result was obtained in a spectrum region between 1160 and 2205 cm–1 and after the standard normal variate normalization. R2 for the calibration and validation models reached 0.85 and 0.76, respectively, meaning that the models had a good capability to predict protein content in whey supplements. The RMSE for the calibration and validation models was low (2.7% and 3.7%, respectively). Conclusion. Finally, we proved that the FTIR spectra applied together with the chemometric analysis could be used to quickly evaluate the studied products.

Milk somatic cell count and its relationship with milk yield and quality traits in Italian water buffaloes

In Southern Italy, buffalo (Bubalus bubalis) milk is mostly intended for the manufacture of Mozzarella di Bufala Campana Protected Denomination of Origin (PDO) cheese. Despite the economic boost of the last 2 decades, the buffalo farming system should be improved to maximize the efficiency of the dairy industry, improve yield and quality of milk and cheese, and work toward better animal welfare. Milk somatic cell count (SCC) is used worldwide as an indicator of udder health in individual milk and is useful for monitoring farm hygiene in bulk milk. Mastitis data are currently not available on a large scale in Italy; thus, SCC is essential for identifying animals with suspected udder infection and inflammation. Moreover, high milk SCC is associated with altered composition and acidity, and poor technological properties of milk. However, payment systems of the PDO area are based simply on the delivered volume of milk rather than on quality characteristics. Hence, currently there are no penalties for elevated SCC in bulk milk in the Italian buffalo dairy industry. In addition, SCC for buffalo milk is not mentioned by either the European Community regulations or the PDO protocol, evidencing a lack of rules for the maximum SCC limit. To provide a phenotypic characterization of SCC at the population level and to improve knowledge on buffalo milk quality, 876,299 test-day records of 70,156 buffaloes reared in the PDO area were analyzed. Data revealed that around 11% of herd-test-dates (≥5 animals sampled each) showed average milk SCC ≥400,000 cells/mL (i.e., above the threshold fixed by the European Community for bovine milk). This suggests that there is room to improve SCC at both the farm and individual level. Within first parity, more than 28 and 15% of lactations had average SCC ≥200,000 and ≥300,000 cells/mL, respectively. Both percentages increased with parity and were 39 and 25% in sixth parity, respectively. Supporting this, the proportion of lactations with average SCC ≥500,000 cells/mL increased from 6% in first parity to 12% in sixth parity. Milk yield and SCC were negatively correlated with each other, especially when SCC level was high. An ANOVA was carried out on test-day record milk yield and composition traits, with fixed effects of parity, lactation stage, class of somatic cell score (n = 6), month of calving, and their interactions; buffalo, herd-test-date, and residual were considered random effects. Significantly lower milk yield and lactose percentage were estimated in progressively higher classes of somatic cell score, whereas no significant differences were observed for fat and protein percentages. This is the first attempt to investigate milk SCC in a large data set of Italian dairy buffaloes. These findings may be helpful for defining reliable and effective SCC thresholds to be adopted whenever specific penalties for high SCC are included in milk payment systems. Finally, these results could be used in mastitis monitoring plans aiming to reduce SCC and udder issues at both the individual and farm levels in the Italian buffalo population.

Replacing fishmeal with plant protein in Atlantic salmon (Salmo salar) diets by supplementation with fish protein hydrolysate

The effects of feeding an 80% plant protein diet, with and without fish protein hydrolysate (FPH) supplementation, on the growth and gut health of Atlantic salmon were investigated. Fish were fed either (A) a control diet containing 35% fishmeal, (B) an 80% plant protein diet with 15% fishmeal, (C) an 80% plant protein diet with 5% fishmeal and 10% partly hydrolysed protein, or (D) an 80% plant protein diet with 5% fishmeal and 10% soluble protein hydrolysate. Fish on the 80% plant- 15% fishmeal diet were significantly smaller than fish in the other dietary groups. However, partly-hydrolysed protein supplementation allowed fish to grow as well as fish fed the control 35% fishmeal diet. Fish fed the FPH diets (diets C and D) had significantly higher levels of amino acids in their blood, including 48% and 27% more branched chain amino acids compared to fish on the 35% fishmeal diet, respectively. Plant protein significantly altered gut microbial composition, significantly decreasing α-diversity. Spirochaetes and the families Moritellaceae, Psychromonadaceae, Helicobacteraceae and Bacteroidaceae were all found at significantly lower abundances in the groups fed 80% plant protein diets compared to the control fishmeal diet.

Genetic Analysis of Milk Production Traits and Mid-Infrared Spectra in Chinese Holstein Population

Simple Summary

Usually, spectral data are used as predictors to predict milk components, animal characteristics, and even reproductive status. Another innovative way to use spectral data involves considering spectral wavenumbers as traits and then analyzing from the genetic perspective. In this study, we considered milk spectral data directly as traits, then detected the influence of some non-genetic factors on spectral wavenumbers and estimated the genetic parameters of spectral points. The result of the present study could be used as a management tool for dairy farm and also provides a further understanding of genetic background of milk mid-infrared (MIR) spectra. In future, milk spectral data could be applied more effective. For example, some sub-clinical diseases might be detected based on the difference between the expected and observed values of the spectral traits. In addition, we could also use genetic correlation between wavenumbers and a trait of interest, which are difficult and expensive to measure, to apply for the genetic improvement of dairy species.

Abstract

Milk composition always serves as an indicator for the cow’s health status and body condition. Some non-genetic factors such as parity, days in milk (DIM), and calving season, which obviously affect milk performance, therefore, need to be considered in dairy farm management. However, only a few milk compositions are used in the current animal selection programs. The mid-infrared (MIR) spectroscopy can reflect the global composition of milk, but this information is currently underused. The objectives of this study were to detect the effect of some non-genetic factors on milk production traits as well as 1060 individual spectral points covering from 925.92 cm⁻¹ to 5011.54 cm⁻¹, estimate heritabilities of milk production traits and MIR spectral wavenumbers, and explore the genetic correlations between milk production traits and 1060 individual spectral points in a Chinese Holstein population. The mixed models procedure of SAS software was used to test the non-genetic factors. Single-trait animal models were used to estimate heritabilities and bivariate animal models were used to estimate genetic correlations using the package of ASReml in R software. The results showed that herd, parity, calving season, and lactation stage had significant effects on the percentages of protein and lactose, whereas herd and lactation stage had significant effects on fat percentage. Moreover, the herd showed a significant effect on all of the 1060 individual wavenumbers, whereas lactation stage, parity, and calving season had significant effect on most of the wavenumbers of the lactose-region (925 cm⁻¹ to 1200 cm⁻¹), protein-region (1240 cm⁻¹ to 1600 cm⁻¹), and fat-regions (1680 cm⁻¹ to 1770 cm⁻¹ and 2800 cm⁻¹ to 3015 cm⁻¹). The estimated heritabilities for protein percentage (PP), fat percentage (FP), and lactose percentage (LP) were 0.08, 0.05, and 0.09, respectively. Further, the milk spectrum was heritable but low for most individual points. Heritabilities of 1060 individual spectral points were 0.04 on average, ranging from 0 to 0.11. In particular, heritabilities for wavenumbers of spectral regions related to water absorption were very low and even null, and heritabilities for wavenumbers of specific MIR regions associated with fat-I, fat-II, protein, and lactose were 0.04, 0.06, 0.05, and 0.06 on average, respectively. The genetic correlations between PP and FP, PP and LP, FP, and LP were 0.78, −0.29, and −0.14, respectively. In addition, PP, FP, and LP shared the similar patterns of genetic correlations with the spectral wavenumbers. The genetic correlations between milk production traits and spectral regions related to important milk components varied from weak to very strong (0.01 to 0.94, and −0.01 to −0.96). The current study could be used as a management tool for dairy farms and also provides a further understanding of the genetic background of milk MIR spectra.

Impact of Bovine Diet on Metabolomic Profile of Skim Milk and Whey Protein Ingredients

The influence of bovine diet on the metabolome of reconstituted skim milk powder (SMP) and protein ingredients produced from the milk of cows fed on pasture or concentrate-based diets was investigated. Cows were randomly assigned to diets consisting of perennial ryegrass only (GRS), perennial ryegrass/white clover sward (CLV), or indoor total mixed ration (TMR) for an entire lactation. Raw milk obtained from each group was processed at pilot scale, to produce SMP and sweet whey, and SMP was further processed at laboratory scale, to yield ideal whey and acid whey. The total amino acid composition and metabolome of each sample were analyzed, using high-performance cation exchange and a targeted combination of direct-injection mass spectrometry and reverse-phase liquid chromatography–tandem mass spectrometry (LC–MS/MS), respectively. The nitrogen composition of the products from each of the diets was similar, with one exception being the significantly higher nonprotein nitrogen content in TMR-derived skim milk powder than that from the GRS system. Total amino acid analysis showed significantly higher concentrations of glycine in GRS- and CLV-derived sweet whey and acid whey than in those from TMR. The cysteine contents of CLV-derived ideal whey and acid whey were significantly higher than for TMR, while the valine content of GRS-derived acid whey was significantly higher than TMR. The phenylalanine content of GRS-derived ideal whey was significantly higher than that from CLV. Metabolomic analysis showed significantly higher concentrations of the metabolites glutamine, valine, and phosphocreatine in each ingredient type derived from TMR than those from GRS or CLV, while the serine content of each GRS-derived ingredient type was significantly higher than that in TMR-derived ingredients. These results demonstrate that the type of bovine feeding system used can have a significant effect on the amino acid composition and metabolome of skim milk and whey powders and may aid in the selection of raw materials for product manufacture, while the clear separation between the samples gives further evidence for distinguishing milk products produced from different feeding systems based on LC–MS/MS.

Effects of somatic cell score on milk yield and mid-infrared predicted composition and technological traits of Brown Swiss, Holstein Friesian, and Simmental cattle breeds

High milk somatic cell count (SCC) influences milk production and quality; however, very little is known about the effect of low SCC on milk quality, especially in terms of mineral content and coagulation properties. Thus, the present study aimed to investigate the effects of somatic cell score (SCS), calculated as log₂(SCC/100) + 3, on milk yield, composition (fat, crude protein, casein, lactose, milk urea nitrogen, protein fractions, and mineral contents), and coagulation properties of Brown Swiss, Holstein Friesian, and Simmental cows from multibreed herds. Milk composition and coagulation traits were predicted using mid-infrared spectroscopy. The data set comprised 95,591 observations of 6,940 cows in 313 multibreed herds, collected from January 2011 to December 2017. Observations were divided into 8 classes based on SCS. Statistical analysis was performed using a linear mixed model, which included breed, parity, stage of lactation, SCS class, and their interactions as fixed effects, and herd test day, cow, and residual as random effects. The probability that cows experienced SCS > 4.00 at 30 ± 5, 60 ± 5, or 90 ± 5 d after the observation test day was calculated for each SCS class, and odds ratios to the reference class (-1.00 < SCS ≤ 0.00) were reported. Results showed that the relationship between SCS and milk traits followed a third-order polynomial regression. The average loss of milk, fat, and crude protein yields were 0.43, 0.01, and 0.01 kg/d, respectively, for each SCS unit higher than 1.00. Very low SCS (<-1.00) had detrimental effects on milk yield and quality traits similar to or even stronger than high SCS (>4.00). Moreover, cows with SCS lower than -1.00 on a test day were about 7 times more likely to present high SCS within the following 90 ± 5 d than cows with SCS between -1.00 and 0.00. Breeds responded similarly to the increase of SCS, but the overall loss of fat and crude protein yields, and several minerals among Holstein Friesian were lower with increasing SCS. The best milk yield and quality were observed between SCS 0.00 and 1.00, but milk quality of Holstein Friesians started to decrease at lower SCS compared with milk quality of Brown Swiss and Simmental cows. Results suggest a breed-dependent optimum of SCS, and highlighted that very low SCS can be an indicator of udder health problems and, thus, may be used for early detection of mastitis.

Factors associated with the weight of individual primal cuts and their inter-relationship in cattle

Input parameters for decision support tools are comprised of, amongst others, knowledge of the associated factors and the extent of those associations with the animal-level feature of interest. The objective of the present study was to quantify the association between animal-level factors with primal cut yields in cattle and to understand the extent of the variability in primal cut yields independent carcass weight. The data used consisted of the weight of 14 primal carcass cuts (as well as carcass weight, conformation, and fat score) on up to 54,250 young cattle slaughtered between the years 2013 and 2017. Linear mixed models, with contemporary group of herd-sex-season of slaughter as a random effect, were used to quantify the associations between a range of model fixed effects with each primal cut separately. Fixed effects in the model were dam parity, heterosis coefficient, recombination loss, a covariate per breed representing the proportion of Angus, Belgian Blue, Charolais, Jersey, Hereford, Limousin, Simmental, and Holstein-Friesian and a three-way interaction between whether the animal was born in a dairy or beef herd, sex, and age at slaughter, with or without carcass weight as a covariate in the mixed model. The raw correlations among all cuts were all positive varying from 0.33 (between the bavette and the striploin) to 0.93 (between the topside and knuckle). The partial correlation among cuts, following adjustment for differences in carcass weight, varied from -0.36 to 0.74. Age at slaughter, sex, dam parity, and breed were all associated (P < 0.05) with the primal cut weight. Knowledge of the relationship between the individual primal cuts, and the solutions from the models developed in the study, could prove useful inputs for decision support systems to increase performance.