Effectiveness of mid-infrared spectroscopy for prediction of the contents of calcium and phosphorus, and titratable acidity of milk and their relationship with milk quality and coagulation properties

Extraction of protein from apricot kernel oil press cake (AKOPC) through innovative techniques and the formulation of supplemented yogurt

The apricot kernel oil press cake (AKOPC) is a high protein natural by-product of oil mechanical expression with potential uses in cosmetics, medicines, and food. The purpose of this research was to improve the protein extraction process from apricot kernel oil press cake by using enzymatic extraction (EEP), aqueous extraction (AEP), and ultrasound extraction (UEP) process. Protein extraction by AEP was facilitated by a low solid-liquid ratio (SLR) (1:15.97) and prolonged reaction durations (3.30 h), resulting in extraction yields of 68%. When compared to the AEP by similar reaction time, increased enzyme utilization (0.90%) in the EEP resulted in greater protein extraction yields (70%) in a shorter reaction time. In addition to AEP and EEP, ultrasound extraction was also used to improve protein extractability. Temperature (50°C), power density (225 W/L), and extraction duration (20 min) were shown to be the best extraction points. Protein yield was found to be 56.47% at ideal UEP conditions. The experimental values for these reactions were found to be equivalent to the predicted values formed by the mathematical models. When supplementary skimmed milk powder (SMP) was substituted with apricot kernel protein (AKP) in the yogurt manufacturing process, the total solids, average titratable acidity, total protein, and fat contents of the yogurt were increased. In contrast, pH and syneresis values decreased as AKP increased in the resulting yogurt, whether fresh or after 7 days of cold storage. Substitution of additional SMP with AKP in yogurt production might be recommended up to 35%.

Correlations of milk and serum element concentrations with production and management traits in dairy cows

The present study investigated the potential consequences, positive or negative, that selection for favorable production-related traits may have on concentrations of vitamin B₁₂ and key chemical elements in dairy cow milk and serum and the possible impact on milk healthiness, and associated benefits, for the dairy product consumer. Milk and serum samples (950 and 755, respectively) were collected from Holstein-Friesian dairy cows (n = 479) on 19 occasions over a 59-mo period, generating 34,258 individual records, and analyzed for concentrations of key trace and quantity elements, heavy metals, and milk vitamin B₁₂. These data were then matched to economically important production data (milk, fat, and protein yield) and management data (dry matter intake, liveweight, and body condition score). Multivariate animal models, including full pedigree information, were used to analyze data and investigate relationships between traits of interest. Results highlighted negative genetic correlations between many quantity and trace elements in both milk and serum with production and management traits. Milk yield was strongly negatively correlated with the milk quantity elements Mg and Ca (genetic correlation between traits, r_a = -0.58 and -0.63, respectively) as well as the trace elements Mn, Fe, Ni, Cu, Zn, and Mo (r_a = -0.32, -0.58, -0.52, -0.40, -0.34, and -0.96, respectively); and in serum, Mg, Ca, Co, Fe, and Zn (r_a = -0.50, -0.36, -0.68, -0.54, and -0.90, respectively). Strong genetic correlations were noted between dry matter intake with V (r_a = 0.97), Fe (r_a = -0.69), Ni (r_a = -0.81), and Zn (r_a = -0.75), and in serum, strong negative genetic correlations were observed between dry matter intake with Ca and Se (r_a = -0.95 and -0.88, respectively). Body condition score was negatively correlated with serum P, Cu, Se, and Pb (r_a = -0.45, -0.35, -0.51, and -0.64, respectively) and positively correlated with Mn, Fe, and Zn (r_a = 0.40, 0.71, and 0.55, respectively). Our results suggest that breeding strategies aimed at improving economically important production-related traits would most likely result in a negative impact on levels of beneficial nutrients within milk for human consumption (such as Mg, Ca, Fe, Zn, and Se).

Impact of Recombinant Bovine Somatotropin on Bovine Milk Composition and Fatty Acidome: A Multidose Longitudinal Study

Somatotropin is a species-specific polypeptide hormone produced in the pituitary gland of vertebrates. When administered exogenously to cattle, it can increase milk yield. However, the trade and administration of recombinant bovine somatotropin (rbST) to farm animals have been banned in the European Union (EU). Aside from food safety issues, very little is known about the effects of this hormone on milk composition and quality. In this work, a wide profile of fatty acids (the so-called fatty acidome) was determined by GC-FID in raw milk collected from control and rbST-treated lactating cows in a multidose longitudinal study. Milk composition (lactose, protein, fat, dry matter), including minerals (Ca, K, Mg, Na, P), was also determined, and milk yield was recorded. A tendency toward a less saturated profile was observed in the milk collected from animals treated with rbST, with higher concentrations of monounsaturated fatty acids. In addition, less calcium and potassium and more lactose and protein content were observed in milk from treated animals than in regular milk. As a result of this multicomponent profiling of milk, a clear impact of somatotropin treatment on milk quality was observed. The obtained results should be particularly interesting for those countries that permit the use of this hormone in dairy production.

Sleep duration of lactating mothers and its relationship with feeding pattern, milk macronutrients and related serum factors: A combined longitudinal cohort and cross-sectional study

Objective

Insufficient sleep is common in postpartum mothers. The main objectives of this study are to explore the sleep duration among Chinese lactating mothers and preliminarily investigate the relationship between sleep duration and feeding pattern. The secondary objectives are to investigate the relationships between sleep duration and milk macronutrients and between maternal-related indicators, including melatonin (MT), growth hormone (GH), ghrelin (GHRL), glucagon-like peptide-1 (GLP-1), prolactin (PRL), and cholecystokinin (CCK).

Methods

The present study comprises a longitudinal and a cross-sectional cohort from December 2019 to December 2021. Postpartum lactating women living in Shanghai were recruited through online and offline recruitment. The subjects were included in the longitudinal cohort or cross-sectional study based on their lactation period at the time of recruitment. The longitudinal cohort included a total of 115 mothers. Human milk and feeding pattern were measured and collected at 2–4 months and 5–7 months postpartum. At four predetermined follow-up time points, data on sleep duration was collected (at the time of recruitment, 2–4 months postpartum, 5–7 months postpartum, and 12–17 months postpartum). The cross-sectional study included 35 lactating mothers (2–12 months postpartum) who reported their sleep duration and provided blood samples. Mid-infrared spectroscopy (MIRS) method was used to analyze the macronutrients of breast milk, while MT, GH, GHRL, GLP-1, PRL, and CCK in maternal blood were determined by ELISA.

Results

The maternal sleep duration before pregnancy was 8.14 ± 1.18 h/d (n = 115), 7.27 ± 1.31 h/d (n = 113) for 2–4 months postpartum, 7.02 ± 1.05 h/d (n = 105) for 5–7 months postpartum, and 7.45 ± 1.05 h/d (n = 115) for 12–17 months postpartum. The incidence of insufficient sleep (<7 h/d) before pregnancy (12.17%) was significantly less than at any follow-up time after delivery (vs. 2–4 months postpartum, χ² = 10.101, p = 0.001; vs. 5–7 months postpartum, χ² = 15.281, p < 0.0001; vs. 12–17 months postpartum, χ² = 6.426, p = 0.011). The percentage of insufficient maternal sleep was highest at 5–7 months postpartum (34.29%). No significant difference was found between the incidence of insufficient sleep at 5–7 months postpartum, 2–4 months postpartum (29.20%, χ² = 0.650, p = 0.420), and 12–17 months postpartum (25.22%, χ² = 2.168, p = 0.141). At 2–4 months postpartum, the frequency of formula feeding per day is related to reduced maternal sleep duration (Standardization coefficient β = −0.265, p = 0.005, Adjusted R² = 0.061). At 2–4 months and 5–7 months postpartum, the relationship between macronutrients in breast milk and the mother's sleep duration was insignificant (all p > 0.05). Other than the positive correlation found between maternal GHRL and sleep duration (r = 0.3661, p = 0.0305), no significant relationship was observed between sleep duration and other indexes (all p > 0.05).

Conclusions

Postpartum mothers generally sleep less, but there is no correlation between insufficient sleep and the macronutrient content of breast milk. Formula feeding may be related to the mother's sleep loss, while breastfeeding (especially direct breastfeeding) may be related to increased maternal sleep duration. The findings suggest that sleep duration is related to maternal serum GHRL. More high-quality studies are needed to clarify the mechanism of these findings and provide a solid theoretical basis and support references for breastfeeding.

Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting

OBJECTIVE

Fat, carbohydrates (mainly lactose) and protein in breast milk all provide indispensable benefits for the growth of newborns. The only source of nutrition in early infancy is breast milk, so the energy of breast milk is also crucial to the growth of infants. Some macronutrients composition in human breast milk varies greatly, which could affect its nutritional fulfillment to preterm infant needs. Therefore, rapid analysis of macronutrients (including lactose, fat and protein) and milk energy in breast milk is of clinical importance. This study compared the macronutrients results of a mid-infrared (MIR) analyzer and an ultrasound-based breast milk analyzer and unified the results by machine learning.

METHODS

This cross-sectional study included breastfeeding mothers aged 22-40 enrolled between November 2019 and February 2021. Breast milk samples (n = 546) were collected from 244 mothers (from Day 1 to Day 1086 postpartum). A MIR milk analyzer (BETTERREN Co., HMIR-05, SH, CHINA) and an ultrasonic milk analyzer (Honɡyanɡ Co,. HMA 3000, Hebei, CHINA) were used to determine the human milk macronutrient composition. A total of 465 samples completed the tests in both analyzers. The results of the ultrasonic method were mathematically converted using machine learning, while the Bland-Altman method was used to determine the limits of agreement (LOA) between the adjusted results of the ultrasonic method and MIR results.

RESULTS

The MIR and ultrasonic milk analyzer results were significantly different. The protein, fat, and energy determined using the MIR method were higher than those determined by the ultrasonic method, while lactose determined by the MIR method were lower (all p < 0.05). The consistency between the measured MIR and the adjusted ultrasound values was evaluated using the Bland-Altman analysis and the scatter diagram was generated to calculate the 95% LOA. After adjustments, 93.96% protein points (436 out of 465), 94.41% fat points (439 out of 465), 95.91% lactose points (446 out of 465) and 94.62% energy points (440 out of 465) were within the LOA range. The 95% LOA of protein, fat, lactose and energy were - 0.6 to 0.6 g/dl, -0.92 to 0.92 g/dl, -0.88 to 0.88 g/dl and - 40.2 to 40.4 kj/dl, respectively and clinically acceptable. The adjusted ultrasonic results were consistent with the MIR results, and LOA results were high (close to 95%).

CONCLUSIONS

While the results of the breast milk rapid analyzers using the two methods varied significantly, they could still be considered comparable after data adjustments using linear regression algorithm in machine learning. Machine learning methods can play a role in data fitting using different analyzers.

Genome Wide Scan to Identify Potential Genomic Regions Associated With Milk Protein and Minerals in Vrindavani Cattle

In this study, genome-wide association study (GWAS) was conducted for identifying significantly associated genomic regions/SNPs with milk protein and minerals in the 96 taurine-indicine crossbred (Vrindavani) cows using 50K SNP Chip. After quality control, a total of 41,427 SNPs were retained and were further analyzed using a single-SNP additive linear model. Lactation stage, parity, test day milk yield and proportion of exotic inheritance were included as fixed effects in GWAS model. Across all traits, 13 genome-wide significant (p < 1.20 x 10⁻⁰⁶) and 49 suggestive significant (p < 2.41 x 10⁻⁰⁵) SNPs were identified which were located on 18 different autosomes. The strongest association for protein percentage, calcium (Ca), phosphorus (P), copper (Cu), zinc (Zn), and iron (Fe) were found on BTA 18, 7, 2, 3, 14, and 2, respectively. No significant SNP was detected for manganese (Mn). Several significant SNPs identified were within or close proximity to CDH13, BHLHE40, EDIL3, HAPLN1, INHBB, USP24, ZFAT, and IKZF2 gene, respectively. Enrichment analysis of the identified candidate genes elucidated biological processes, cellular components, and molecular functions involved in metal ion binding, ion transportation, transmembrane protein, and signaling pathways. This study provided a groundwork to characterize the molecular mechanism for the phenotypic variation in milk protein percentage and minerals in crossbred cattle. Further work is required on a larger sample size with fine mapping of identified QTL to validate potential candidate regions.

Calcium: A comprehensive review on quantification, interaction with milk proteins and implications for processing of dairy products

Calcium (Ca) is a key micronutrient of high relevance for human nutrition that also influences the texture and taste of dairy products and their processability. In bovine milk, Ca is presented in several speciation forms, such as complexed with other milk components or free as ionic calcium while being distributed between colloidal and serum phases of milk. Partitioning of Ca between these phases is highly dynamic and influenced by factors, such as temperature, ionic strength, pH, and milk composition. Processing steps used during the manufacture of dairy products, such as preconditioning, concentration, acidification, salting, cooling, and heating, all contribute to modify Ca speciation and partition, thereby influencing product functionality, product yield, and fouling of equipment. This review aims to provide a comprehensive understanding of the influence of Ca partition on dairy products properties to support the development of kinetics models to reduce product losses and develop added-value products with improved functionality. To achieve this objective, approaches to separate milk phases, analytical approaches to determine Ca partition and speciation, the role of Ca on protein-protein interactions, and their influence on processing of dairy products are discussed.

Multiple Breeds and Countries' Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry

Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals' variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.

Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle

BACKGROUND

Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk.

RESULTS

Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.

CONCLUSIONS

This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.

Genetic analysis on infrared-predicted milk minerals for Danish dairy cattle

A group of milk components that has shown potential to be predicted with milk spectra is milk minerals. Milk minerals are important for human health and cow health. Having an inexpensive and fast way to measure milk mineral concentrations would open doors for research, herd management, and selective breeding. The first aim of this study was to predict milk minerals with infrared milk spectra. Additionally, milk minerals were predicted with infrared-predicted fat, protein, and lactose content. The second aim was to perform a genetic analysis on infrared-predicted milk minerals, to identify QTL, and estimate variance components. For training and validating a multibreed prediction model for individual milk minerals, 264 Danish Jersey cows and 254 Danish Holstein cows were used. Partial least square regression prediction models were built for Ca, Cu, Fe, K, Mg, Mn, Na, P, Se, and Zn based on 80% of the cows, selected randomly. Prediction models were externally validated with 8 herds based on the remaining 20% of the cows. The prediction models were applied on a population of approximately 1,400 Danish Holstein cows with 5,600 infrared spectral records and 1,700 Danish Jersey cows with 7,200 infrared spectral records. Cows from this population had 50k imputed genotypes. Prediction accuracy was good for P and Ca, with external R² ≥ 0.80 and a relative prediction error of 5.4% for P and 6.3% for Ca. Prediction was moderately good for Na with an external R² of 0.63, and a relative error of 18.8%. Prediction accuracies of milk minerals based on infrared-predicted fat, protein, and lactose content were considerably lower than those based on the infrared milk spectra. This shows that the milk infrared spectrum contains valuable information on milk minerals, which is currently not used. Heritability for infrared-predicted Ca, Na, and P varied from low (0.13) to moderate (0.36). Several QTL for infrared-predicted milk minerals were observed that have been associated with gold standard milk minerals previously. In conclusion, this study has shown infrared milk spectra were good at predicting Ca, Na, and P in milk. Infrared-predicted Ca, Na, and P had low to moderate heritability estimates.

Sequence-based GWAS and post-GWAS analyses reveal a key role of SLC37A1, ANKH, and regulatory regions on bovine milk mineral content

The mineral composition of bovine milk plays an important role in determining its nutritional and cheese-making value. Concentrations of the main minerals predicted from mid-infrared spectra produced during milk recording, combined with cow genotypes, provide a unique opportunity to decipher the genetic determinism of these traits. The present study included 1 million test-day predictions of Ca, Mg, P, K, Na, and citrate content from 126,876 Montbéliarde cows, of which 19,586 had genotype data available. All investigated traits were highly heritable (0.50-0.58), with the exception of Na (0.32). A sequence-based genome-wide association study (GWAS) detected 50 QTL (18 affecting two to five traits) and positional candidate genes and variants, mostly located in non-coding sequences. In silico post-GWAS analyses highlighted 877 variants that could be regulatory SNPs altering transcription factor (TF) binding sites or located in non-coding RNA (mainly lncRNA). Furthermore, we found 47 positional candidate genes and 45 TFs highly expressed in mammary gland compared to 90 other bovine tissues. Among the mammary-specific genes, SLC37A1 and ANKH, encoding proteins involved in ion transport were located in the most significant QTL. This study therefore highlights a comprehensive set of functional candidate genes and variants that affect milk mineral content.

Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.

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.

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.

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.

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.

Phenotypic investigation of fine milk components in bovine milk and their prediction using mid-infrared spectroscopy

This study aimed to examine the phenotypic variation observed in fine milk components, the use of mid-infrared (MIR) spectroscopy to predict these components, and the correlations with other milk production traits. A total of 4116 milk samples were collected from 414 dairy cows from 44 Canadian herds. Overall 3117 samples were analyzed for casein micelle size, 986 and 937 for total and soluble calcium, respectively, and 2054 for lactoferrin. Individual milk MIR spectra were obtained. Linear mixed models with repeated records were used to test the effects of herd nested within breed, breed, days in milk class, parity, and season on milk components. Lactoferrin content in milk increased with stage of lactation and parity number. Prediction equations were developed by partial least squares regression to predict each milk component using MIR spectroscopy, with the greatest coefficient of determination achieved for lactoferrin (0.55). Pearson correlation coefficients for lactoferrin with somatic cell score and protein percentage were 0.49% and 0.40%, respectively. The estimated correlation increased to 0.53% for predicted lactoferrin and protein percentage. The examined fine milk components exhibited variation, but low accuracies for their MIR prediction may limit the utility of this technology for their phenotyping.

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.

Investigation on the effectiveness of mid-infrared spectroscopy to predict detailed mineral composition of bulk milk

This Research Communication investigated the potential of mid-infrared spectroscopy to predict detailed mineral composition of bovine milk. A total of 153 bulk milk samples were analysed for contents of Ca, Cl, Cu, Fe, K, Mg, Na, P and Zn. Also, soluble and colloidal fractions of Ca, Mg and P were quantified. For each milk sample the mid-infrared spectrum was captured and stored. Prediction models were developed using partial least squares regression and the accuracy of prediction was evaluated using both cross- and external validation. The proportion of variance explained by the prediction models in cross-validation ranged from 34% (Na) to 77% (total P), and it ranged from 13% (soluble Mg) to 54% (Cl−) in external validation. The ratio of the standard deviation of each trait to the standard error of prediction in external validation, which is an indicator of the practical utility of the prediction model, was low and never greater than 2. Results from the current study supported the limited usefulness of mid-infrared spectroscopy to predict minerals present in low concentration in bulk milk. For major mineral components, results from the present research did not match previous findings demonstrating the need for further studies using larger reference datasets.

Single nucleotide polymorphism (g.2786 A>G) of DGAT1 gene associated with milk yield and fat percentage in crossbred cattle

In present study, association of five single nucleotide polymorphisms (SNPs) of database rs109421300, rs43691049, rs109727821, rs109047657 and rs135678421 was investigated with milk minerals including calcium (Ca), phosphorus (P), copper (Cu), zinc (Zn), manganese (Mn), iron (Fe), sodium (Na), potassium (K) and constituents (fat and protein percentage) after adjusting the records with various significant non-genetic factors in crossbred (Vrindavani) cattle. The effect of rs109421300 pertaining to Diacylglycerol Acyltransferase (DGAT) gene located on BTA 14, was highly significant on fat percentage and significant on test day milk yield and non-significant on Ca, Cu, Mn, Zn, Fe, Na, K, test day milk yield and protein percentage. The AA genotypes of rs109421300 locus had significantly lower (2.96±0.17%) fat percentage than AG (4.64±0.22%), GG (4.62±0.27%) genotypes but simultaneously cows with AA genotypes had significantly highest (11.59±0.51 kg) test day milk yield than other two genotypes. At locus rs109727821, the manganese concentration was significantly highest (1.64±0.16 mg/l) for AG followed by AA (0.98±0.15 mg/l) and GG (0.77±0.24 mg/l) genotypes. The other SNPs had no significant association with traits under investigation. The existing association suggested possibilities to select animals for specific minerals and constituent traits in crossbred population.

Rapid and non-invasive multivariate approach for the quality control of raw milk from mountain areas based on proton transfer reaction mass spectrometry data

RATIONALE

A rapid and non-invasive method for the control of milk by proton transfer reaction mass spectrometry was developed. The approach has the potential to verify the geographic origin and altitude of dairy farms, provided that the cows have been extensively grazed with forage that reflects the botanical composition of the mountain environment.

METHODS

Over a 1-month period, a total of 116 samples were analysed by proton transfer reaction mass spectrometry (PTRMS). A multivariate control chart based on the Hotelling T² statistic was built with PTRMS data and, for comparison, with the chemical parameters obtained by infrared spectroscopy (FTIR, MilkoScan).

RESULTS

The headspace analysis of the samples led to characteristic volatile profiles. Farms located in different mountain areas were discriminated by the protonated molecules m/z 45 (acetaldehyde), 59 (acetone), 73 (2-butanone) and 89 (butyric acid, ethyl acetate, pentanol). Milk samples were also discriminated according to the altitude of the farms according to m/z 45, 59, 63 (dimethyl sulfide), 73 (propionic acid, methyl acetate) and 81 (terpenes).

CONCLUSIONS

A multivariate control chart based on PTRMS data was used for the quality control of milk. Milk samples from farms located at different mountain areas and altitudes were successfully discriminated.

Phenotypic analysis of milk coagulation properties and mineral content of Pinzgauer cattle breed

Abstract. This study aimed to characterize milk coagulation properties (rennet coagulation time, curd-firming time and curd firmness 30 min after rennet addition to milk) and major mineral contents (Ca, Mg, P, K and Na) in Pinzgauer dual-purpose cattle breed. The edited dataset consisted of 7763 milk observations from 851 cows reared in 60 herds in the Alpine area of Bolzano province (Italy). Data were analysed through a linear mixed model which included stage of lactation, parity and their interaction as fixed effects, and cow and herd test date as random effects. Rennet coagulation time, curd-firming time and curd firmness 30 min after rennet addition to milk averaged 22.66 min, 5.53 min and 16.79 mm, respectively. The most abundant minerals were P (1495 mg kg−1) and Ca (1344 mg kg−1), and the least abundant Mg (141 mg kg−1). Compared to their older contemporaries, early-lactating younger animals yielded milk that was more favourable for cheese production (i.e. with shorter coagulation time and stronger curd firmness). Mineral contents were lower in milk of primiparous than multiparous cows, except for Na. Moreover, Ca, Mg, P and Na contents decreased from parturition to peak of lactation and increased thereafter, except for K, which exhibited an opposite trend. Our results showed that Pinzgauer breed produced milk with better coagulation properties and mineral content, from a technological point of view, in first than later parities and in early than late lactation. The characterization of milk coagulation properties and mineral content in autochthonous breeds is important to increase their value and marketability of their products.

Factors affecting milk minerals and constituents in indigenous vis-à-vis crossbred cattle and buffaloes

The present investigation was conducted to compare milk minerals and constituents in different bovine speciesand breeds. The genetic and non-genetic factors affecting milk fat, protein and milk mineral composition in crossbredcattle were investigated. The effect of breeds was highly significant on copper (Cu), zinc (Zn), manganese (Mn), sodium (Na), potassium (K), fat and protein, significant (p≤0.05) on calcium (Ca) and non-significant on phosphorus (P) and iron (Fe) content. The Ca, Cu, fat and protein were significantly higher in Murrah buffalo followed by Tharparkar cattle and crossbred cattle. However, Mn and Zn were higher in Tharparkar cattle followed by Murrah buffalo and crossbred cattle. Tharparkar milk was also rich in Na and K followed by crossbred cattle and Murrah buffalo. The effect of parity was highly significant only on Mn, whereas, effect of lactation stage (LS) was significant on Fe and Cu in milk of crossbred cattle. The effect of Test Day Milk Yield (TDMY) was significant on Fe and Cu. The product moment correlation was highest (0.414) between Zn and Fe and lowest (-0.347) between Cu and Zn. Among milk minerals, the estimates of heritability for Fe, Ca and Mn were moderate, where as it was low for otherminerals in crossbred cattle.

Rennet-induced coagulation of raw and heated camel and cow milk gels determined by instrumental techniques: effects of added calcium and phosphate

BACKGROUND

The potentiality of the front-face fluorescence spectroscopy and rheological measurements combined with chemometric tools to characterise the structure evolution during coagulation of raw and preheated camel and cow milk at 50 and 70 °C with/without added calcium and phosphate was evaluated. Tryptophan and vitamin A fluorescence spectra were collected during the gelation of milk at 0, 10, 20, 30, 40, and 45 min after the addition of rennet-induced coagulation. In parallel, the storage modulus (G'), loss modulus (G″) and tan delta (tan δ) were determined using low amplitude oscillation shear analysis.

RESULTS

The principal component analysis (PCA) applied to the normalised tryptophan spectra allowed the gels made with camel milk to be differentiated from those of cow milk on the one hand, and to monitor protein structure modifications during the gelation, on the other hand. The common components and specific weights analysis (CCSWA) applied jointly to the fluorescence and rheological data sets permitted a clear separation of raw milk gels from those preheated at 50 and 70 °C.

CONCLUSION

The front-face fluorescence spectroscopy method coupled with multi-variate statistical analyses showed a high capacity for studying changes in the micelle structure throughout the rennet-induced coagulation process. © 2017 Society of Chemical Industry.

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.