Short communication: Prediction of milk coagulation and acidity traits in Mediterranean buffalo milk using Fourier-transform mid-infrared spectroscopy

Milk coagulation and acidity traits are important factors to inform the cheesemaking process. Those traits have been deeply studied in bovine milk, whereas scarce information is available for buffalo milk. However, the dairy industry is interested in a method to determine milk coagulation and acidity features quickly and in a cost-effective manner, which could be provided by Fourier-transform mid-infrared (FT-MIR) spectroscopy. The aim of this study was to evaluate the potential of FT-MIR to predict coagulation and acidity traits of Mediterranean buffalo milk. A total of 654 records from 36 herds located in central Italy with information on milk yield, somatic cell score, milk chemical composition, milk acidity [pH, titratable acidity (TA)], and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness) were available for statistical analysis. Reference measures of milk acidity and coagulation properties were matched with milk spectral information, and FT-MIR prediction models were built using partial least squares regression. The data set was divided into a calibration set (75%) and a validation set (25%). The capacity of FT-MIR spectroscopy to correctly classify milk samples based on their renneting ability was evaluated by a canonical discriminant analysis. Average values for milk coagulation traits were 13.32 min, 3.24 min, and 39.27 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. Milk acidity traits averaged 6.66 (pH) and 7.22 Soxhlet-Henkel degrees/100 mL (TA). All milk coagulation and acidity traits, except for pH, had high variability (17 to 46%). Prediction models of coagulation traits were moderately to scarcely accurate, whereas the coefficients of determination of external validation were 0.76 and 0.66 for pH and TA, respectively. Canonical discriminant analysis indicated that information on milk coagulating ability is present in the MIR spectra, and the model correctly classified as noncoagulating the 91.57 and 67.86% of milk samples in the calibration and validation sets, respectively. In conclusion, our results can be relevant to the dairy industry to classify buffalo milk samples before processing.     

Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy

The objectives of the study were to estimate the reproducibility and repeatability of milk coagulation properties (MCP) measured by a computerized renneting meter (CRM) and to evaluate the predictive ability of mid-infrared spectroscopy (MIRS) as an innovative technology for the assessment of rennet coagulation time (RCT, min) and curd firmness (a₃₀, mm). Four samples without addition of preservative (NP) and 4 samples with Bronopol addition (PS) were collected from each of 83 Holstein-Friesian cows. Six hours after collection, 2 replicated measures of MCP were obtained with CRM using 1 NP and 1 PS sample from each cow. Mid-infrared spectra of the remaining NP and PS samples from each animal were recorded after 6 h, 4 d, and 8 d after sampling. Two groups of calibration equations were developed using MIRS spectra and CRM measures of MCP as reference data obtained from analysis of NP and PS, respectively. Reproducibility and repeatability of CRM measures were obtained from REML estimation of variance components on the basis of a linear model including the fixed effects of herd and days in milk class and the random effects of cows, sample treatment (addition or no addition of preservative), and the interaction between cow and sample treatment. Coefficient of reproducibility is an indicator of the agreement between 2 measurements of MCP for the same milk sample preserved with or without addition of Bronopol. Coefficient of repeatability is an indicator of the agreement between repeated measures of MCP. Pearson correlations between MCP measures for NP and PS were 0.97 and 0.83 for RCT and a₃₀, respectively. Reproducibility of CRM measures under different preserving conditions of milk was 93.5% for RCT and 64.6% for a₃₀. Repeatabilities of RCT and a₃₀ measures were 95.7 and 77.3%, respectively. Based on the estimated cross-validation standard errors and coefficients of determination and ratios of standard errors of cross-validation to standard deviation of reference data, the predictive ability of MIRS calibration equations was moderate for RCT and unsatisfactory for a_30. Predictive ability of equations based on spectra and MCP measures of PS was greater than that of equations based on data of NP. The study did not provide conclusive evidence on the effectiveness of MIRS as a predictive tool for MCP and it requires an enlargement of the variability of milk sampling circumstances. Because the relevance of MIRS predictions in relation to breeding programs for MCP based on indicator traits relies on the genetic variation of MIRS predictions and on phenotypic and genetic correlations between MIRS predictions and MCP measures, additional specific investigations on these topics are needed.     

Variation in fat globule size in bovine milk and its prediction using mid-infrared spectroscopy

The objectives of this study were to investigate the sources of variation in milk fat globule (MFG) size in bovine milk and its prediction using mid-infrared (MIR) spectroscopy. Mean MFG size was measured in 2,076 milk samples from 399 Ayrshire, Brown Swiss, Holstein, and Jersey cows, and expressed as volume moment mean (D[4,3]) and surface moment mean (D[3,2]). The mid-infrared spectra of the samples and milk performance data were also recorded during routine milk recording and testing. The effects of breed, herd nested within breed, days in milk, season, milking period, age at calving, parity, and individual animal on the variation observed in MFG size were investigated. Breed, herd nested within breed, days in milk, season, and milking period significantly affected mean MFG size. Milk fat globule size was the largest at the beginning of lactation and subsequently decreased. Milk samples with the smallest MFG on average came from Holstein cows, and those with the largest were from Jersey and Brown Swiss cows. Partial least squares regression was used to predict MFG size from MIR spectra of samples with a calibration data set containing 2,034 and 2,032 samples for D[4,3] and D[3,2], respectively. Coefficients of determination of cross validation for D[4,3] and D[3,2] prediction models were 0.51 and 0.54, respectively. The associated ratio of performance deviation values were 1.43 and 1.48 for D[4,3] and D[3,2], respectively. With these models, individual mean MFG size could not be accurately predicted, but results may be sufficient to screen samples for having either small or large MFG on average. Significant but low correlations of D[4,3] and D[3,2] with milk fat yield were estimated (0.16 and 0.21, respectively). Significant and moderate Pearson correlation coefficients for fat percent with D[4,3] and D[3,2] were assessed (0.34 and 0.36, respectively). This correlation was greater between milk fat percentage and predicted MFG size than with measured MFG size with coefficients of 0.47 and 0.49 for D[4,3] and D[3,2], respectively. The MIR prediction equations are potentially overusing the correlation between fat and MFG size and exploiting the strong relationship between the MIR spectra and total milk fat. However, the predictions of MFG size are able to determine variation in mean globule size beyond what would be achieved just by looking at the correlation with fat production.     

Prediction and repeatability of milk coagulation properties and curd-firming modeling parameters of ovine milk using Fourier-transform infrared spectroscopy and Bayesian models

The aim of this study was to apply Bayesian models to the Fourier-transform infrared spectroscopy spectra of individual sheep milk samples to derive calibration equations to predict traditional and modeled milk coagulation properties (MCP), and to assess the repeatability of MCP measures and their predictions. Data consisted of 1,002 individual milk samples collected from Sarda ewes reared in 22 farms in the region of Sardinia (Italy) for which MCP and modeled curd-firming parameters were available. Two milk samples were taken from 87 ewes and analyzed with the aim of estimating repeatability, whereas a single sample was taken from the other 915 ewes. Therefore, a total of 1,089 analyses were performed. For each sample, 2 spectra in the infrared region 5,011 to 925 cm^?1 were available and averaged before data analysis. BayesB models were used to calibrate equations for each of the traits. Prediction accuracy was estimated for each trait and model using 20 replicates of a training-testing validation procedure. The repeatability of MCP measures and their predictions were also compared. The correlations between measured and predicted traits, in the external validation, were always higher than 0.5 (0.88 for rennet coagulation time). We confirmed that the most important element for finding the prediction accuracy is the repeatability of the gold standard analyses used for building calibration equations. Repeatability measures of the predicted traits were generally high (≥95%), even for those traits with moderate analytical repeatability. Our results show that Bayesian models applied to Fourier-transform infrared spectra are powerful tools for cheap and rapid prediction of important traits in ovine milk and, compared with other methods, could help in the interpretation of results.     

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a₃₀, mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900 cm⁻¹. Prediction models for RCT and a₃₀ based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a₃₀, respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a₃₀ were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a₃₀ ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.     

Association between the bovine milk metabolome and rennet-induced coagulation properties of milk

The milk metabolomes of 407 individual Swedish Red dairy cows were analyzed by nuclear magnetic resonance spectroscopy as part of the Danish-Swedish Milk Genomics Initiative. By relating these metabolite profiles to total milk protein concentration and rheological measurements of rennet-induced milk coagulation together using multivariate data analysis techniques, we were able to identify several different associations of the milk metabolome to technological properties of milk. Several novel correlations of milk metabolites to protein content and rennet-induced coagulation properties were demonstrated. Metabolites associated with the prediction of total protein content included choline, N-acetyl hexosamines, creatinine, glycerophosphocholine, glutamate, glucose 1-phosphate, galactose 1-phosphate, and orotate. In addition, levels of lactate, acetate, glutamate, creatinine, choline, carnitine, galactose 1-phosphate, and glycerophosphocholine were significantly different when comparing noncoagulating and well-coagulating milks. These findings suggest that the mentioned metabolites are associated with milk protein content and rennet-induced coagulation properties and may act as quality markers for cheese milk.     

Prediction of indicators of cow diet composition and authentication of feeding specifications of Protected Designation of Origin cheese using mid-infrared spectroscopy on milk

The ability of mid-infrared spectroscopy (MIR) to predict indicators (1) of diet composition in dairy herds and (2) for the authentication of the cow feeding restrictions included in the specification of 2 Protected Designation of Origin (PDO) cheeses (Cantal and Laguiole) was tested on 7,607 bulk milk spectra from 1,355 farms located in the Massif Central area of France. For each milk sample, the corresponding cow diet composition data were obtained through on-farm surveys. The cow diet compositions varied largely (i.e., from full grazing for extensive farming systems to corn silage-based diets, which are typical of more intensive farming systems). Partial least square regression and discriminant analysis were used to predict the proportion of different feedstuffs in the cows' diets and to authenticate the cow feeding restrictions for the PDO cheese specifications, respectively. The groups for the discriminant analysis were created by dividing the data set according to the threshold of a specific feedstuff. They were issued based on the specifications of the restriction of the PDO cheese. The pasture proportion in the cows' diets was predicted by MIR with an coefficient of determination in external validation (R²V) = 0.81 and a standard error of prediction of 11.7% dry matter. Pasture + hay, corn silage, conserved herbage, fermented forage, and total herbage proportion in the cows' diets were predicted with a R²V >0.61 and a standard error of prediction <14.8. The discrimination models for pasture presence, pasture ≥50%, and pasture ≥57% in the cows' diets achieved an accuracy and specificity ≥90%. A sensitivity and precision ≥85% were also observed for the pasture proportion discrimination models, but both of these indexes decreased at increasing thresholds from 0 to 50, and 57% pasture in the cows' diets. An accuracy ≥80% was also observed for pasture + hay ≥72%, herbage ≥50%, pasture + hay ≥25%, absence of fermented herbage, absence of corn silage, and corn silage ≤30% in the cows' diets, but for several models, either the sensitivity or precision was lower than the accuracy. Models built on the simultaneous respect of all the criteria of the feeding restrictions of PDO cheese specifications achieved an accuracy, specificity, sensitivity, and precision >90%. Both the regression and discriminant MIR models for bulk milk can provide useful indicators of cow diet composition and PDO cheese specifications to producers and consumers (farmers, dairy plants).     

Effects of heating on the secondary structure of proteins in milk powders using mid-infrared spectroscopy

Milk powder is an important source of protein for adults and children. Protein is very sensitive to heat, which may influence people’s usage of nutrients in milk powder. In this study, we describe the temperature-induced secondary structure of protein in milk powders. In this study, whole milk powder containing 24% protein and infant formula containing 11% protein were heated from 25 to 100°C. Attenuated total reflectance (ATR) spectra in the mid-infrared range 400–4,000 cm^?1 were used to evaluate the heat effect on the secondary structure of protein in these 2 milk powders. The spectral changes as a function of temperature were maintained by difference spectra, second-derivative spectra and Gauss curve-fitted spectra. The secondary structures of protein in the whole milk powder began to change at 70°C and in the infant formula at 50°C. The β-sheet and β-turn structures in the whole milk powder both decreased in the range of 70 to 85°C, whereas α-helix structures increased. The loss of β-sheet and β-turn may contribute to the formation of α-helix in the whole milk powder. In infant formula powder, the β-sheet structure showed a decrease and then increase, whereas the β-turn structure showed an increase and then decrease in the range of 50 to 75°C, and no change was found for α-helix structures. This implies that heating may induce the transformation from β-sheet to β-turn. Overall, whole milk powder had better temperature stability than infant formula powder, probably because of the lower content of lipid in the former than in the latter. These results help us understand the thermal stability of protein in milk powder.     

Selection for milk coagulation properties predicted by Fourier transform infrared spectroscopy in the Italian Holstein-Friesian breed

Milk coagulation is based on a series of physicochemical changes at the casein micelle level, resulting in formation of a gel. Milk coagulation properties (MCP) are relevant for cheese quality and yield, important factors for the dairy industry. They are also evaluated in herd bulk milk to reward or penalize producers of Protected Designation of Origin cheeses. The economic importance of improving MCP justifies the need to account for this trait in the selection process. A pilot study was carried out to determine the feasibility of including MCP in the selection schemes of the Italian Holstein. The MCP were predicted in 1,055 individual milk samples collected in 16 herds (66 ± 24 cows per herd) located in Brescia province (northeastern Italy) by means of Fourier transform infrared (FTIR) spectroscopy. The coefficient of determination of prediction models indicated moderate predictions for milk rennet coagulation time (RCT = 0.65) and curd firmness (a₃₀ = 0.68), and poor predictions for curd-firming time (k₂₀ = 0.49), whereas the range error ratio (8.9, 6.9, and 9.5 for RCT, k₂₀, and a₃₀, respectively) indicated good practical utility of the predictive models for all parameters. Milk proteins were genotyped and casein haplotypes (α_S1-, β-, α_S2-, and κ-casein) were reconstructed. Data from 51 half-sib families (19.9 ± 16.4 daughters per sire) were analyzed by an animal model to estimate (1) the genetic parameters of predicted RCT, k₂₀, and a₃₀; (2) the breeding values for these predicted clotting variables; and (3) the effect of milk protein genotypes and casein haplotypes on predicted MCP (pMCP). This is the first study to estimate both genetic parameters and breeding values of pMCP, together with the effects of milk protein genotypes and casein haplotypes, that also considered k₂₀, probably the most important parameter for the dairy industry (because it indicates the time for the beginning of curd-cutting). Heritability of predicted RCT (0.26) and k₂₀ (0.31) were close to the average heritability described in literature, whereas the heritability of a₃₀ was higher (0.52 vs. 0.27). The effects of milk proteins were statistically significant and similar to those obtained on measured MCP. In particular, haplotypes including uncommon variants showed positive (B-I-A-B) or negative (B-A¹-A-E) effects. Based on these findings, FTIR spectroscopy-pMCP is proposed as a potential selection criterion for the Italian Holstein.     

Invited review: Genetics and modeling of milk coagulation properties

Milk coagulation properties (MCP) are conventionally measured using computerized renneting meters, mechanical or optical devices that record curd firmness over time (CF_t). The traditional MCP are rennet coagulation time (RCT, min), curd firmness (a₃₀, mm), and curd-firming time (k₂₀, min). The milk of different ruminant species varies in terms of CF_t pattern. Milk from Holstein-Friesian and some Scandinavian cattle breeds yields higher proportions of noncoagulating samples, samples with longer RCT and lower a₃₀, and samples for which k₂₀ is not estimable, than does milk from Brown Swiss, Simmental, and other local Alpine breeds. The amount, proportion, and genetic variants (especially κ-casein) of milk protein fractions strongly influence MCP and explain variable proportions of the observed differences among breeds and among individuals of the same breed. In addition, other major genes have been shown to affect MCP. Individual repeatability of MCP is high, whereas any herd effect is low; thus, the improvement of MCP should be based principally on selection. Exploitable additive genetic variation in MCP exists and has been assessed using different breeds in various countries. Several models have been formulated that either handle noncoagulating samples or not. The heritability of MCP is similar to that of other milk quality traits and is higher than the heritability of milk yield. Rennet coagulation time and a₃₀ are highly correlated, both phenotypically and genetically. This means that the use of a₃₀ data does not add valuable information to that obtainable from RCT; both traits are genetically correlated mainly with milk acidity. Moreover, a₃₀ is correlated with casein content. The major limitations of traditional MCP can be overcome by prolonging the observation period and by using a novel CF_t modeling, which uses all available information provided by computerized renneting meters and allows the estimation of RCT, the potential asymptotic curd firmness, the curd-firming rate, and the syneresis rate. Direct measurements of RCT obtained from both mechanical and optical devices show similar heritabilities and exhibit high phenotypic and genetic correlations. Moreover, mid-infrared reflectance spectroscopy can predict MCP. The heritabilities of predicted MCP are higher than those of measured MCP, and the 2 sets of values are strongly correlated. Therefore, mid-infrared reflectance spectroscopy is a reliable and cheap method whereby MCP can be improved at the population level; this is because such spectra are already routinely acquired from the milk of cows enrolled in milk recording schemes.     

Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets

The fatty acid profile of milk is a prevailing issue due to the potential negative or positive effects of different fatty acids to human health and nutrition. Mid-infrared spectroscopy can be used to obtain predictions of otherwise costly fatty acid phenotypes in a widespread and rapid manner. The objective of this study was to evaluate the prediction of fatty acid content for the Canadian dairy cattle population from mid-infrared spectral data and to compare the results produced by altering the partial least squares (PLS) model development set used. The PLS model development sets used to develop the predictions were reference fatty acids expressed as (1) grams per 100 g of fatty acid, (2) grams per 100 g of milk, (3) the natural logarithmic transform of grams per 100 g of milk, and (4) subsets of samples randomly selected by removing excess records around the mean to present a more uniform distribution, repeated 10 times. Gas chromatography measured fatty acid concentration and spectral data for 2,023 milk samples of 373 cows from 4 breeds and 44 herds were used in the model development. The coefficient of determination of cross-validation $\left({\text{R}}_{\text{cv}}^2\right)$ increased when fatty acids were expressed on a per 100 g of milk basis compared with on a per 100 g of fat basis for all examined fatty acids. The logarithmic transformation used to create a more Gaussian distribution in the development set had little effect on the prediction accuracy. The individual fatty acids C12:0, C14:0, C16:0, C18:0, C18:1n-9 cis, and saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain fatty acid groups had $\left({\text{R}}_{\text{cv}}^2\right)$ greater than 0.70. When model development was performed with subsets of the original samples, slight increases in $\left({\text{R}}_{\text{cv}}^2\right)$ values were observed for the majority of fatty acids. The difference in $\left({\text{R}}_{\text{cv}}^2\right)$ between the top- and bottom-performing prediction equation across the different subsets for a single predicted fatty acid was on average 0.055 depending on which samples were randomly selected to be used in the PLS model development set. Predictions for fatty acids with high accuracies can be used to monitor fatty acid contents for cows in milk recording programs and possibly for genetic evaluation.     

Genetic analysis of coagulation properties,curd firming modeling,milk yield,composition, and acidity in Sarda dairy sheep

Sheep milk is an important source of food, especially in Mediterranean countries, and is used in large part for cheese production. Milk technological traits are important for the sheep dairy industry, but research is lacking into the genetic variation of such traits. Therefore the aim of this study was to estimate the heritability of traditional milk coagulation properties and curd firmness modeled on time t (CF_t) parameters, and their genetic relationships with test-day milk yield, composition (fat, protein, and casein content), and acidity in Sarda dairy sheep. Milk samples from 1,121 Sarda ewes from 23 flocks were analyzed for 5 traditional coagulation properties by lactodynamographic tests conducted for up to 60 min: rennet coagulation time (min), curd-firming time (k₂₀, min), and 3 measures of curd firmness (a₃₀, a₄₅, and a₆₀, mm). The 240 curd firmness observations (1 every 15 s) from each milk sample were recorded, and 4 parameters for each individual sample equation were estimated: rennet coagulation time estimated from the equation (RCT_eq), the asymptotic potential curd firmness (CF_P), the curd firming instant rate constant (k_CF), and the syneresis instant rate constant (k_SR). Two other derived traits were also calculated (CF_max, the maximum curd firmness value; and t_max, the attainment time). Multivariate analyses using Bayesian methodology were performed to estimate the genetic relationships of milk coagulation properties and CF_t with the other traits; statistical inference was based on the marginal posterior distributions of the parameters of concern. The marginal posterior distribution of heritability estimates of milk yield (0.16 ± 0.07) and composition (0.21 ± 0.11 to 0.28 ± 0.10) of Sarda ewes was similar to those often obtained for bovine species. The heritability of rennet coagulation time as a single point trait was also similar to that frequently obtained for cow milk (0.19 ± 0.09), whereas the same trait calculated as an individual equation parameter exhibited larger genetic variation and a higher heritability estimate (0.32 ± 0.11). The other curd firming and syneresis traits, whether as traditional single point observations or as individual equation parameters and derived traits, were characterized by heritability estimates lower than for coagulation time and for the corresponding bovine milk traits (0.06 to 0.14). Phenotypic and additive genetic correlations among the 11 technological traits contribute to describing the interdependencies and meanings of different traits. The additive genetic relationships of these technological traits with the single test-day milk yield and composition were variable and showed milk yield to have unfavorable effects on all measures of curd firmness (a₃₀, a₄₅, a₆₀, CF_P, and CF_max) and t_max, but favorable effects on both instant rate constants (k_CF and k_SR). Milk fat content had a positive effect on curd firmness traits, especially on those obtained from CF_t equations, whereas the negative effects on both coagulation time traits were attributed to the milk protein and casein contents. Finally, in view of the estimated heritabilities and additive genetic correlations, enhancement of technological traits of sheep milk through selective breeding could be feasible in this population.     

Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows

The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a₃₀) and their genetic correlations with test-day milk yield, composition (fat, protein, and casein content), somatic cell score, and acidity (pH and titratable acidity) using coagulating and noncoagulating (NC) milk information. Data were from 1,025 Holstein-Friesian (HF) and 1,234 Brown Swiss (BS) cows, which were progeny of 54 HF and 58 BS artificial insemination sires, respectively. Milk coagulation properties (MCP) of each cow were measured once using a computerized renneting meter and samples not exhibiting coagulation within 31 min after rennet addition were classified as NC milk. For NC samples, RCT was unobserved. Multivariate analyses, using Bayesian methodology, were performed to estimate the genetic relationships of RCT or a₃₀ with the other traits and statistical inference was based on the marginal posterior distributions of parameters of concern. For analyses involving RCT, a right-censored Gaussian linear model was used and records of NC milk samples, being censored records, were included as unknown parameters in the model implementing a data augmentation procedure. Rennet coagulation time was more heritable [heritability (h²) = 0.240 and h² = 0.210 for HF and BS, respectively] than a₃₀ (h² = 0.148 and h² = 0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h² = 0.103 and h² = 0.097 for HF and BS, respectively) and less heritable than milk composition traits whose heritability ranged from 0.275 to 0.275, with the only exception of fat content of BS milk (h² = 0.108). A negative genetic correlation, lower than −0.85, was estimated between RCT and a₃₀ for both breeds. Genetic relationships of MCP with yield and composition were low or moderate and favorable. The genetic correlation of somatic cell score with RCT in BS cows was large and positive and even more positive were those of RCT with pH and titratable acidity in both breeds, ranging from 0.80 to 0.94. Including NC milk information in the data affected the estimated correlations and decreased the uncertainty associated with the estimation process. On the basis of the estimated heritabilities and genetic correlations, enhancement of MCP through selective breeding with no detrimental effects on yield and composition seems feasible in both breeds. Milk acidity may play a role as an indicator trait for indirect enhancement of MCP.     

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

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

Prediction of fatty acid profiles in cow,ewe, and goat milk by mid-infrared spectrometry

Mid-infrared (MIR) spectrometry was used to estimate the fatty acid (FA) composition in cow, ewe, and goat milk. The objectives were to compare different statistical approaches with wavelength selection to predict the milk FA composition from MIR spectra, and to develop equations for FA in cow, goat, and ewe milk. In total, a set of 349 cow milk samples, 200 ewe milk samples, and 332 goat milk samples were both analyzed by MIR and by gas chromatography, the reference method. A broad FA variability was ensured by using milk from different breeds and feeding systems. The methods studied were partial least squares regression (PLS), first-derivative pretreatment + PLS, genetic algorithm + PLS, wavelets + PLS, least absolute shrinkage and selection operator method (LASSO), and elastic net. The best results were obtained with PLS, genetic algorithm + PLS and first derivative + PLS. The residual standard deviation and the coefficient of determination in external validation were used to characterize the equations and to retain the best for each FA in each species. In all cases, the predictions were of better quality for FA found at medium to high concentrations (i.e., for saturated FA and some monounsaturated FA with a coefficient of determination in external validation >0.90). The conversion of the FA expressed in grams per 100 mL of milk to grams per 100 g of FA was possible with a small loss of accuracy for some FA.     

Short communication: Fourier-transform mid-infrared spectroscopy to predict coagulation and acidity traits of sheep bulk milk

Sheep milk is mainly transformed into cheese; thus, the dairy industry seeks more rapid and cost-effective methods of analysis to determine milk coagulation and acidity traits. This study aimed to assess the feasibility of Fourier-transform mid-infrared spectroscopy to determine milk coagulation and acidity traits of sheep bulk milk and to classify milk samples according to their renneting capacity. A total of 465 bulk milk samples collected in 140 single-breed flocks of Comisana (84 samples, 24 flocks) and Sarda (381 samples, 116 flocks) breeds located in Central Italy were analyzed for coagulation properties (rennet coagulation time, curd firming time, and curd firmness) and acidity traits (pH and titratable acidity) using standard laboratory procedures. Fourier-transform mid-infrared spectroscopy prediction models for these traits were built using partial least squares regression analysis and were externally validated by randomly dividing the full data set into a calibration set (75%) and a validation set (25%). The discriminant capacity of the rennet coagulation time prediction model was determined using partial least squares discriminant analysis. Prediction models were more accurate for acidity traits than for milk coagulation properties, and the ratio of prediction to deviation ranged from 1.01 (curd firmness) to 2.14 (pH). Moreover, the discriminant analysis led to an overall accuracy of 74 and 66% for the calibration and validation sets, respectively, with greater sensitivity for samples that coagulated between 10 and 20 min and greater specificity to detect early-coagulating (<10 min) and late-coagulating (20–30 min) samples. Results suggest that Fourier-transform mid-infrared spectroscopy has the potential to help the dairy sheep industry identify milk with better coagulation ability for cheese production and thus improve milk transformation efficiency. However, further research is needed before this information can be exploited at the industry level.     

Genetic variability of lactoferrin content estimated by mid-infrared spectrometry in bovine milk

The effects of lactoferrin (LF) on the immune system have already been shown by many studies. Unfortunately, the current methods used to measure LF levels in milk do not permit the study of the genetic variability of lactoferrin or the performance of routine genetic evaluations. The first aim of this research was to derive a calibration equation permitting the prediction of LF in milk by mid-infrared spectrometry (MIR). The calibration with partial least squares on 69 samples showed a ratio of standard error of cross-validation to standard deviation equal to 1.98. Based on this value, the calibration equation was used to establish an LF indicator trait (predicted LF; pLF) on a large number of milk samples (n = 7,690). A subsequent study of its variability was conducted, which confirmed that stage of lactation and lactation number influence the overall pLF level. Small differences in mean pLF among 7 dairy breeds were also observed. The pLF content of Jersey milk was significantly higher than that in Holstein milk. Therefore, the choice of breed could change the expected LF level. Heritability estimated for pLF was 19.7%. The genetic and phenotypic correlations between somatic cell score and pLF were 0.04 and 0.26, respectively. As somatic cell score increases in presence of mastitis, this observation seems to indicate that pLF, or a function of observed pLF, compared with expected LF might have potential as an indicator of mastitis. The negative genetic correlation (−0.36) between milk yield and pLF could indicate an undesirable effect of selection for high milk production on the overall LF level.     

Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer

It has recently been shown that Fourier transform infrared spectroscopy has potential for the prediction of detailed milk fat composition, even based on a limited number of observations. Therefore, there seems to be an opportunity for improvement by means of using more observations. The objective of this study was to verify whether the use of more data would add to the accuracy of predicting milk fat composition. In addition, the effect of season on modeling was quantified because large differences in milk fat composition between winter and summer samples exist. We concluded that the use of 3,622 observations does increase predictability of milk fat composition based on infrared spectroscopy. However, for fatty acids with low concentrations, the use of many observations does not increase predictability to a level at which application of the model becomes obvious. Furthermore, the effect of season on validation r-square was limited but was occasionally large on prediction bias. For fatty acids that show large differences in level and standard deviation between winter and summer, a representative sample that includes observations collected in various seasons is critical for unbiased prediction. This research shows that all major fatty acids, combined groups of fatty acids, and the ratio of saturated to unsaturated fatty acids can be predicted accurately.     

Genetic and phenotypic correlations between milk coagulation properties, milk production traits, somatic cell count, casein content, and pH of milk

Genetic and phenotypic correlations between milk coagulation properties (MCP: coagulation time and curd firmness), milk yield, fat content, protein content, ln(somatic cell count) (SCS), casein content, and pH of milk and heritability of these traits were estimated from data consisting of milk samples of 4664 Finnish Ayrshire cows sired by 91 bulls. In addition, differences in average estimated breeding values (EBV) for the above traits between the cows with noncoagulating (NC) milk and those with milk that coagulated (CO samples) were examined. The estimations were carried out to study the possibilities of indirect genetic improvement of MCP by use of the above characteristics. The genetic and phenotypic correlations between MCP and the milk production traits were low or negligible. The genetic associations between desirable MCP and low SCS were rather strong (-0.45 to 0.29). Desirable MCP correlated both genetically and phenotypically with low pH of milk (-0.51 to 0.50). The rather high heritability estimates for curd firmness in different forms (0.22 to 0.39), and the wide variation in the proportion of daughters producing NC milk between the sires (0 to 47%) suggested that noncoagulation of milk is partly caused by additive genetic factors. Based on the genetic correlations between curd firmness and SCS and the high EBV for SCS obtained for the cows with NC-milk, it is possible that the loci causing noncoagulation of milk and increasing somatic cell count of milk are closely linked or partly the same. One means to genetically improve MCP and to reduce the occurrence of NC milk could thus be selection for low somatic cell count of milk.     

Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows

Mid-infrared (MIR) spectroscopy was used to predict the detailed protein composition of 1,517 milk samples of Simmental cows. Contents of milk protein fractions and genetic variants were quantified by reversed-phase HPLC. The most accurate predictions were those obtained for total protein, casein (CN), α_S1-CN, β-lactoglobulin (LG), glycosylated κ-CN, and whey protein content, which exhibited coefficients of determination between predicted and measured values in cross-validation (1-VR) ranging from 0.61 to 0.78. Less favorable were results for β-CN (1-VR = 0.53), α_S2-CN, and κ-CN (1-VR = 0.49). Neither the content of α-LA nor that of γ-CN was accurately predicted by MIR. Predicting the content of the most common milk protein genetic variants (κ-CN A and B; β-CN A¹, A², and B; and β-LG A and B) was unfeasible (1-VR <0.15 for the content of κ-CN genetic variants and 1-VR <0.01 for the content of β-CN variants). The best predictions were obtained for β-LG A and β-LG B contents (1-VR of 0.60 and 0.44, respectively). Results indicated that MIR is not applicable for predicting individual milk protein composition with high accuracy. However, MIR spectroscopy predictions may play a role as indicator traits in selective breeding to enhance milk protein composition. The genetic correlation between MIR spectroscopy predictions and measures of milk protein composition needs to be investigated, as it affects the suitability of MIR spectroscopy predictions as indicator traits in selective breeding.