The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese.

The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.

Genomic inbreeding coefficients using imputation genotypes: assessing the effect of ancestral genotyping in Holstein-Friesian dairy cows.

The objective of this study was to assess the effect of using or not the genotypes of the parents of a cow for imputing single nucleotide polymorphisms (SNP), on the estimation of genomic inbreeding coefficients of cows. Imputation (i.e., genotyped plus imputed) genotypes from 68,127 Italian Holstein dairy cows registered in the Italian National Association of Holstein, Brown and Jersey Breeders (ANAFIBJ) were analyzed. Cows were genotyped with the HD Illumina Infinium BovineHD BeadChip and GeneSeek Genomic Profiler HD-150K, and the MD GeneSeek Genomic Profiler 3, GeneSeek Genomic Profiler 4, GeneSeek MD and the Labogena MD. To assess differences among estimators genomic inbreeding coefficients were estimated with 4 PLINK v1.9 estimators (F, Fhat1, 2, 3), 2 genomic relationship matrix (grm) based estimators (Fgrm and Fgrm2; with the latter including also pedigree information) and one estimator of runs of homozygosity (ROH; FROH). Assuming that the correct genomic inbreeding coefficients should be those estimated from genotyped SNP, a comparison of the genomic inbreeding coefficients estimated either with the genotyped SNP or the SNP after imputation was made. Information on the presence or absence of genotypic information from sire, dam and maternal grandsire during the imputation was investigated. Genomic inbreeding coefficients estimated with genotyped SNP or SNP after imputation were consistent for F, Fhat3, Fgrm2 and FROH, when at least one of the parents was genotyped. Biased (mainly higher) genomic inbreeding coefficients of imputation SNP were observed in cows that were genotyped with MD SNP panels whose SNP were poorly represented in the selected imputation SNP data set and also did not have their parents genotyped compared with what expected based on actual genotype data. For cows genotyped with MD the estimators Fhat1, Fhat2 and Fgrm provided higher genomic inbreeding coefficients of imputation SNP even with both parents and the maternal grandsire genotyped. Overall, FROH was the most robust estimator, followed by F and Fhat3. Our findings suggest that SNP selection, parental genotyping and estimator should be considered for designing imputation strategies in dairy cattle for estimating genomic inbreeding with imputation SNP. For computing genomic inbreeding coefficients, it is recommendable to have at least one parent genotyped and use an ROH based estimator.

Microsatellite imputation using SNP data for parentage verification in four Italian sheep breeds.

Microsatellite markers (MS) have been widely used for parentage verification in most of the livestock species over the past decades mainly due to their high polymorphic information content. In the genomic era, the spread of genotype information as single-nucleotide polymorphism (SNP) has raised the question to effectively use SNPs also for parentage testing. Despite the clear advantages of SNP panels in terms of cost, accuracy, and automation, the transition from MS to SNP markers for parentage verification is still very slow and, so far, only routinely applied in cattle. A major difficulty during this transition period is the need of SNP data for parents and offspring, which in most cases is not yet feasible due to the genotyping cost. To overcome the unavailability of same genotyping platform during the transition period, in this study we aimed to assess the feasibility of a MS imputation pipeline from SNPs in four native sheep dairy breeds: Comisana (N = 331), Massese (N = 210), Delle Langhe (N = 59) and Sarda (N = 1003). Those sheep were genotyped for 11 MS and with the Ovine SNP50 Bead Chip. Prior to imputation, a quality control (QC) was performed, and SNPs located within a window of 2 Mb from each MS were selected. The core of the developed pipeline was made up of three steps: (a) storing of both MS and SNP data in a Variant Call Format file, (b) masking MS information in a random sample of individuals (10%), (c) imputing masked MS based on non-missing individuals (90%) using an imputation program. The feasability of the proposed methodology was assessed also among different training - testing split ratio, population size, number of flanking SNPs as well as within and among breeds. The accuracy of the MS imputation was assessed based on the genotype concordance as well as at parentage verification level in a subset of animals in which assigned parents' MS were available. A total of 8 MS passed the QC, and 505 SNPs were located within the ±2 Mb window from each MS, with an average of 63 SNPs per MS. The results were encouraging since when excluding the worst imputed MS (OARAE129), and regardless on the analyses performed (within and across breeds) for all breeds, we achieved an overall concordance rate over 94%. In addition, on average, the imputed offspring MS resulted in equivalent parentage outcome in 94% of the cases when compared to verification using original MS, highlighting both the feasibility and the eventual practical advantage of using this imputation pipeline.

Heritability and genetic correlations of total and differential somatic cell count with milk yield and composition traits in Italian Simmental cows.

Costs of production have deeply increased each year in the last decades, breeders are continuously looking for more cost effective and more efficient ways to produce milk. Despite the major signs of progress in productivity, it is fundamental to optimize rather than maximize the performances of the dairy cows. Mastitis is still a highly prevalent disease in the dairy sector which causes several economic losses and environmental effect. Its accurate and early diagnosis is crucial to improve profitability of dairy cows and contribute to a more sustainable dairy industry. Among mastitis reduction strategies, there is the urgent need to implement breeding objectives to select cows displaying mastitis resistance by investigating the genetic mechanisms at the base of the inflammatory response. Therefore, in this study we aimed to further understand the genetic background of the differential somatic cell count (DSCC), which provides thorough insights on the actual inflammatory status of the mammary glands. The objectives of this study were to estimate on a cohort of 20,215 Italian Simmental cows over a 3-yr period: (1) the heritability and repeatability values of somatic cell score (SCS) and DSCC, (2) the genetic and phenotypic correlations between these 2 traits and milk production and milk composition traits, (3) the heritability and repeatability values of SCS and DSCC within class of udder health status. Heritability was low both for SCS (0.06) and DSCC (0.08), whereas the repeatability values for these traits were 0.43 and 0.36, suggesting that the magnitude of cow permanent environmental effect for these traits is remarkable. The genetic and phenotypic correlation of SCS with DSCC was 0.612 and 0.605, respectively. Because both significantly differed from the unit, we must consider those traits as different ones. This latter aspect corroborates the need to consider the DSCC as a further indicator of inflammatory status which might be implemented in the Simmental breed genetic evaluation. It is worthy to mention that heritability estimates for SCS and DSCC were the highest in healthy cows compared with the other udder health classes. This implies that when the udder health status changes, it is most likely due to environmental factors rather than aspects related to the animal's genetics. In contrast, the highest additive genetic variance and heritability found for SCS and DSCC in the healthy group might reveal the potential to further implement breeding strategies to select for healthier animals.

Predictive formulas for different measures of cheese yield using milk composition from individual goat samples.

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CYCURD), total solids (%CYSOLIDS), and water retained (%CYWATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R2VAL) and the root mean square error of validation (RMSEVAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R2VAL = 0.65, 0.96, and 0.23 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively). The WAV procedure showed R2VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CYCURD and %CYWATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production.

Genomic inbreeding coefficients using imputed genotypes: Assessing different estimators in Holstein-Friesian dairy cows.

The objective of this study was to estimate inbreeding coefficients in Holstein dairy cattle using imputed SNPs data. A data set of 95,540 Italian Holstein dairy cows from the routine genomic evaluations of the Italian National Association of Holstein, Brown, and Jersey Breeders were analyzed, with 84,445 imputed SNP. Ten widely used genomic inbreeding estimators were tested, including 4 PLINK v1.9 estimators (F, FHAT1, FHAT2, FHAT3), 3 genomic relationship matrix (GRM)-based methods [VanRaden's first method with observed allele frequencies (FGRM) or with fixed frequencies at 0.5 (FGRM05), VanRaden's third method, allelic frequency free and pedigree regressed (FGRM2)], runs of homozygosity (ROH)-based estimators in a complete (FROH) and simplified version (FROH2), and proportion of homozygous SNP (FPH). Pairwise comparisons among them were made, including the comparison with traditional pedigree-based inbreeding coefficients (FPED). Our results showed variability among the genomic inbreeding estimators. Coefficients of FGRM and FHAT3 were >1, meaning that more variability has been lost than the variability that existed in the base population. Regarding the remaining ones, FGRM05, FROH, FROH2, and FPH provided coefficients within the [0,1] space and are considered comparable to FPED. Not comparable to FPED, yet with an interpretable value, can be considered the coefficients of F, FHAT2, and FGRM2. Estimators based on ROH had the highest correlation with pedigree-based coefficients (0.59-0.66), among all estimators tested. In this study, Spearman correlations were shown to possibly provide a clearer estimation of the strength of the relationship between estimators. We hypothesize that imputation might cause extreme genomic inbreeding values that deserves further investigation.

The mineral profile affects the coagulation pattern and cheese-making efficiency of bovine milk.

Natural variations in milk minerals, their relationships, and their associations with the coagulation process and cheese-making traits present an opportunity for the differentiation of milk destined for high-quality natural products, such as traditional specialties or Protected Designation of Origin (PDO) cheeses. The aim of this study was to quantify the effects of the native contents of Ca, P, Na, K, and Mg on 18 traits describing traditional milk coagulation properties (MCP), curd firming over time (CFt) equation parameters, cheese yield (CY) measures, and nutrient recoveries in the curd (REC) using models that either included or omitted the simultaneous effects of milk fat and casein contents. The results showed that, by including milk fat and casein and the minerals in the statistical model, we were able to determine the specific effects of each mineral on coagulation and cheese-making efficiency. In general, about two-thirds of the apparent effects of the minerals on MCP and the CFt equation parameters are actually mediated by their association with milk composition, especially casein content, whereas only one-third of the effects are direct and independent of milk composition. In the case of cheese-making traits, the effects of the minerals were mediated only negligibly by their association with milk composition. High Ca content had a positive effect on the coagulation pattern and cheese-making traits, favoring water retention in the curd in particular. Phosphorus positively affected the cheese-making traits in that it was associated with an increase in CY in terms of curd solids, and in all the nutrient recovery traits. However, a very high P content in milk was associated with lower fat recovery in the curd. The variation in the Na content in milk only mildly affected coagulation, whereas with regard to cheese-making, protein recovery was negatively associated with high concentrations of this mineral. Potassium seemed not to be actively involved in coagulation and the cheese-making process. Magnesium content tended to slow coagulation and reduce CY measures. Further studies on the relationships of minerals with casein and protein fractions could deepen our knowledge of the role of all minerals in coagulation and the cheese-making process.

Occurrence, biological properties and potential effects on human health of ß-casomorphin 7: Current knowledge and concerns.

The genetic variant A1 of bovine ß-casein (ß-Cn) presents a His residue at a position 67 of the mature protein. This feature makes the Ile66-His67 bond more vulnerable to enzymatic cleavage, determining the release of the peptide ß-Cn f(60-66), named ß-casomorphin 7 (BCM7). BCM7 is an opioid-agonist for µ receptors, and it has been hypothesized to be involved in the development of different non-transmissible diseases in humans. In the last decade, studies have provided additional results on the potential health impact of ß-Cn A1 and BCM7. These studies, here reviewed, highlighted a relation between the consumption of ß-Cn A1 (and its derivative BCM7) and the increase of inflammatory response as well as discomfort at the gastrointestinal level. Conversely, the role of BCM7 and the effects of ingestion of ß-Cn A1 on the onset or worsening of other non-transmissible diseases as caused or favored by still need proof of evidence. Overall, the reviewed literature demonstrates that the "ß-Cn A1/BCM7 issue" remains an intriguing but not exhaustively explained topic in human nutrition. On this basis, policies in favor of breeding for ß-Cn variants not releasing BCM7 and consumption of "A1-like" milk appear not yet sound for a healthier and safer nutrition.

Modeling weight loss of cheese during ripening and the influence of dairy system, parity, stage of lactation, and composition of processed milk.

The yield, flavor, and texture of ripened cheese result from numerous interrelated microbiological, biochemical, and physical reactions that take place during ripening. The aims of the present study were to propose a 2-compartment first-order kinetic model of cheese weight loss over the ripening period; to test the variation in new informative phenotypes describing this process; and to assess the effects on these traits of dairy farming system, individual farms within dairy system, animal factors, and milk composition. A total of 1,211 model cheeses were produced in the laboratory using individual 1.5-L milk samples from Brown Swiss cows reared on 83 farms located in Trento Province. During ripening (60 d; temperature 15°C, relative humidity 85%), the weight of all model cheeses was measured, and cheese yield (cheese weight/processed milk weight, %CY) was calculated at 7 intervals from cheese-making (0, 1, 7, 14, 28, 42, and 60 d). Using these measures, a 2-compartment first-order kinetic model (3-parameter equation) was developed for modeling %CY during the ripening period, as follows: [Formula: see text] , where %CYt is the %CY at ripening time t; %CYi and %CYf are the modeled %CY traits at time 0 d (%CYi = initial %CY) and at the end of a ripening period sufficient to reach a constant wheel weight (%CYf = final %CY after 60 d ripening in the case of small model cheeses); kCY is the instant rate constant for cheese weight loss (%/d). Cheese weight and protein and fat losses were calculated as the % difference between the model cheeses at 0 and after 60 d of ripening. The variation in cheese pH was calculated as the % difference between pH at 0 and after 60 d. Dairy system, individual herd within dairy system, and the cow's parity and lactation stage (tested with a linear mixed model) strongly affected almost all the traits collected during model cheese ripening. Milk fat, protein, lactose, pH, and somatic cell score also greatly affected almost all the traits, although kCY was affected only by milk protein. After including milk composition in the linear mixed model, the importance of all the herd and animal sources of variation was greatly reduced for all traits. The proposed model and novel traits could be tested, first, with the aim of establishing new monitoring procedures enabling the dairy industry to improve milk quality-based payment systems at the herd level and, second, with a view to exploring possible genetic improvements to dairy cow populations.

Genetic parameters estimation in an Italian horse native breed to support the conversion from agricultural uses to riding purposes.

Horses are nowadays mainly used for sport and leisure purposes, and several local breeds, traditionally used in agriculture, are exposed to the risk of extinction. The long-term survival of local horse breeds depends on strategies to both monitor their genetic diversity and to find their sustainable role in the equine market. Thus, several local horse breeds need to adapt their breeding objective to allow a modernization process. The Bardigiano is an example of such horse breeds; we, therefore, studied the existing evaluation protocol from a genetic standpoint to assess the protocol's suitability to convert the Bardigiano from an agricultural to a riding horse. To this end, we estimated genetic parameters for four conformation measurements, ten grading traits and 23 linear traits. For conformation measurements, the heritabilities ranged from 0.31 for cannon bone circumference to 0.63 for height at withers. For conformation and attitude grading traits, the highest heritability (0.34) was estimated for development and the lowest (0.09) for gaits. The heritabilities for linear traits ranged from 0.05 for the leg straightness to 0.32 for the coat colour. Genetic correlations between linear traits and corresponding grading traits varied considerably, ranging from -0.42 to 0.98. This study showed that the current evaluation protocol in the Bardigiano horse is appropriate for genetic evaluation. Genetic parameters estimation can, in turn, be used to develop novel breeding values to help this conversion. Our study paves the way to optimize the Bardigiano horse breeding programme, and it may help several other local horse breeds experiencing similar issues.

New Insights in Cheese Yield Capacity of the Milk of Italian Brown and Italian Friesian Cattle in the Production of High-Moisture Mozzarella.

The aim of the present study is to investigate the effect of κ-casein B content in milk on the yield of high-moisture mozzarella cheese. The study was carried out by monitoring the production of eight mozzarella cheese batches at four cheese making factories. At each factory, two cheese making trials were performed in parallel: one using bulk milk from Italian Brown cattle and the other using bulk milk from Italian Friesian cattle. The average κ-casein B content was 0.04 g per 100 g in the Italian Friesian cows' milk, whereas it was four time higher in the Italian Brown cows' milk, reaching values of 0.16 g per 100 g. Both the κ-casein content and κ-casein B to casein ratio were positively correlated with actual cheese yield. Both parameters showed correlation coefficient values over 0.9, higher than for any other protein fraction. The influence of the level of κ-casein on the increase of the yield is probably due to smaller and more homogeneous micelles, with more efficient rennet coagulation. Consequently, milk with higher κ-casein B content produces a more elastic curd that withstands better the technological treatments and limits losses during curd mincing and stretching. In conclusion, the Italian Brown cows' milk used, characterized by higher κ-casein content than the Italian Friesian's one, allowed a yield increase of about 2.65%, which is a very relevant result for both farms and cheese making factories.

Milk protein fractions strongly affect the patterns of coagulation, curd firming, and syneresis.

The aim of this study was to assess the role of milk protein fractions in the coagulation, curd firming, and syneresis of bovine milk. Analyses were performed on 1,271 individual milk samples from Brown Swiss cows reared in 85 herds classified into 4 types of farming systems, from the very traditional (tied cows, feed manually distributed, summer highland pasture) to the most modern (loose cows, use of total mixed rations with or without silage). Fractions αS1-casein (CN), αS2-CN, ß-CN, κ-CN, ß-lactoglobulin (LG), and α-lactalbumin (LA) and genotypes at CSN2, CSN3, and BLG were obtained by reversed-phase HPLC. The following milk coagulation properties were measured with a lactodynamograph, with the testing time extended to 60 min: rennet coagulation time (RCT, min), curd firming time (min), and curd firmness at 30 and 45 min (mm). All the curd firmness measures recorded over time (total of 240 observations/sample) were used in a 4-parameter nonlinear model to obtain parameters of coagulation, curd firming, and syneresis: RCT estimated from the equation (min), asymptotic potential curd firmness (mm), the curd firming and syneresis instant rate constants (%/min), and the maximum curd firmness value (CFmax, mm) and the time taken to reach it (min). All the aforementioned traits were analyzed with 2 linear mixed models, which tested the effects of the protein fractions expressed in different ways: in the first, quantitative model, each protein fraction was expressed as content in milk; in the second, qualitative model, each protein fraction was expressed as a percentage of total casein content. Besides proteins, additional nuisance parameters were herd (included as a random effect), daily milk production (only for the quantitative model), casein content (only for the qualitative model), dairy system, parity, days in milk, the pendulum of the lactodynamograph, and the CSN2, CSN3, and BLG genotypes. Both αS1-CN and ß-CN showed a clear and favorable effect on CFmax, where the former effect was almost double the latter. Milk coagulation ability was favorably affected by κ-CN, which reduced both the RCT and RCT estimated from the equation, increased the curd firming and syneresis instant rate constants, and allowed a higher CFmax to be reached. In contrast, αS2-CN delayed gelation time and ß-LG worsened curd firming, both resulting in a low CFmax. The results of this study suggest that modification of the relative contents of specific protein fractions can have an enormous effect on the technological behavior of bovine milk.

Effects of indirect indicators of udder health on nutrient recovery and cheese yield traits in goat milk.

In dairy goats, very little is known about the effect of the 2 most important indirect indicators of udder health [somatic cell count (SCC) and total bacterial count (TBC)] on milk composition and cheese yield, and no information is available regarding the effects of lactose levels, pH, and NaCl content on the recovery of nutrients in the curd, cheese yield traits, and daily cheese yields. Because large differences exist among dairy species, conclusions from the most studied species (i.e., bovine) cannot be drawn for all types of dairy-producing animals. The aims of this study were to quantify, using milk samples from 560 dairy goats, the contemporary effects of a pool of udder health indirect indicators (lactose level, pH, SCC, TBC, and NaCl content) on the recovery of nutrients in the curd (%REC), cheese yield (%CY), and daily cheese yields (dCY). Cheese-making traits were analyzed using a mixed model, with parity, days in milk (DIM), lactose level, pH, SCC, TBC, and NaCl content as fixed effects, and farm, breed, glass tube, and animal as random effects. Results indicated that high levels of milk lactose were associated with reduced total solids recovery in the curd and lower cheese yields, because of the lower milk fat and protein contents in samples rich in lactose. Higher pH correlated with higher recovery of nutrients in the curd and higher cheese yield traits. These results may be explained by the positive correlation between pH and milk fat, protein, and casein in goat milk. High SCC were associated with higher recovery of solids and energy in the curd but lower recovery of protein. The higher cheese yield obtained from milk with high SCC was due to both increased recovery of lactose in the curd and water retention. Bacterial count proved to be the least important factor affecting cheese-making traits, but it decreased daily cheese yields, suggesting that, even if below the legal limits, TBC should be considered in order to monitor flock management and avoid economic losses. The effect of NaCl content on milk composition was linked with lower recovery of all nutrients in the curd during cheese-making. In addition, high milk NaCl content led to reductions in fresh cheese yield and cheese solids. The indirect indicators of the present study significantly affected the cheese-making process. Such information should be considered, to adjust the milk-to-cheese economic value and the milk payment system.

Variations in milk protein fractions affect the efficiency of the cheese-making process.

The aim of this study was to assess the influence of the amounts of the αS1-, αS2-, ß-, and κ-casein (CN) and the α-lactalbumin and ß-lactoglobulin protein fractions on the efficiency of the cheese-making process independently of their genetic polymorphisms. The study was carried out on milk samples from 1,271 Brown Swiss cows from 85 herds classified into 4 categories according to management, feeding, and housing characteristics (traditional and modern systems). To assess the efficiency of the cheese-making process, we processed the milk samples according to a laboratory cheese-making procedure (1,500 mL/sample) and obtained the following measures: (1) 3 percentage cheese yields (%CYcurd, %CYsolids, %CYwater), (2) 2 daily cheese yields obtained by multiplying %CY (curd and total solids) by daily milk yields (dCYcurd, dCYsolids), (3) 4 measures of nutrient recovery in the curd (RECfat, RECprotein, RECsolids, RECenergy), and (4) 2 measures of cheese-making efficiency in terms of the ratio between the observed and theoretical %CY (Ef-%CYcurd, Ef-%CYsolids). All the aforementioned traits were analyzed by fitting 2 linear mixed models with protein fractions as fixed effects expressed as percentage in the milk (model M-%milk) and as percentage of the total casein content (model M-%cas) together with the effects of total casein content (only in model M-%cas), daily milk yield (only in model M-%milk; not for dCY traits), dairy system, herd (random effect), days in milk, parity, and vat. The efficiency of overall cheese yield (Ef-%CYcurd) was mostly positively associated with ß-CN content in the milk, whereas Ef-%CYsolids was greater with higher amounts of κ-CN and αS1-CN (M-%milk) due to the strong influence of both fractions on the recovery rate of milk components in the curd (fat and total solids, protein with αS1-CN only) when expressed as percentage of milk and of total casein; only ß-CN was more important for RECprotein. In contrast, we found ß-lactoglobulin to be highly negatively related to all the traits related to the cheese-making process and to the daily cheese yield per cow, whereas α-lactalbumin was positively associated with the latter traits. Additional research on this topic is needed, with particular focus on the genetic and genomic aspects of the role of protein fractions in the cheese-making process and on the associations between genetic polymorphisms in milk protein and milk nutrient recovery in the curd.

Cheese yield, cheesemaking efficiency, and daily production of 6 breeds of goats.

Little is known about the complex process of cheesemaking at the individual level of dairy goats because of the difficulties of producing a high number of model cheeses. The objectives of this work were (1) to study the cheesemaking ability of goat milk; (2) to investigate the variability of cheesemaking-related traits among different farms; (3) to assess the effects of stage of lactation and parity; and (4) to compare 6 breeds of goat (Saanen and Camosciata delle Alpi for the Alpine type; Murciano-Granadina, Maltese, Sarda and Sarda Primitiva for the Mediterranean type) for their cheesemaking ability. For each goat (n = 560) we studied (1) 8 milk quality traits (fat, protein, total solids, casein, lactose, pH, somatic cell score, and bacterial count); (2) 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in curd; (3) 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water); (4) 2 theoretical cheese yield values (fresh cheese and cheese solids) and the related cheesemaking efficiencies; and (5) daily milk yield and 3 daily cheese yield traits (fresh cheese, cheese solids, and water retained in the curd). With respect to individual animal factors, farm was not particularly important for recovery traits or actual and theoretical cheese yield and estimates of efficiency, whereas it highly influenced daily productions. Parity of goats influenced daily cheese production, whereas DIM slightly affected recovery as well as percent and daily cheese yield traits. Breed was the most important source of variation for almost all cheesemaking traits. Compared with those of Alpine type, the 4 Mediterranean breeds had, on average, lower daily milk and cheese productions, greater actual and theoretical cheese yield, and higher recovery of nutrients in the curd. Among Alpine type, Camosciata delle Alpi was characterized by greater nutrients recovery than Saanen. Within the 4 Mediterranean types, the 3 Italians produced much less milk per day, with much more fat and protein and greater recovery traits than the Murciano-Granadina, resulting in greater actual cheese yield. Within the Italian breeds, milk from Sarda and Sarda Primitiva was characterized by lower daily yields, higher protein and fat content, and greater recoveries of nutrients than Maltese goats. These results confirmed the potential of goat milk for cheese production and could be useful to give new possibilities and direction in breeding programs.

Cheese as Functional Food: The Example of Parmigiano Reggiano and Grana Padano.

Italian hard cooked types of cheese, like Parmigiano Reggiano and Grana Padano, are characterised by positive nutritional qualities. In fact, they contain substances that have particular biological activities, and therefore they can be fully considered, according to the definition given by the European Unit, as 'functional' foods. This short review concisely describes these components and the beneficial effects related to their activities. The description of the biologically active components has been organised in the following paragraphs: protein and peptides, fat and lipids, carbohydrates and prebiotics, probiotic bacteria, vitamins, mineral salts, and components of dairy products active in disease prevention. In particular, several known bioactive peptides were found in Parmigiano Reggiano cheese samples: for example, phosphopeptides, which are known for their mineral-binding capacity and vehiculation activity, peptides with immunomodulatory activity, and angiotensin-converting enzyme-inhibitory peptides with anti-hypertensive effects. Among lipids, the role of conjugated linoleic acid and other fatty acids present in these cheese types was taken into consideration. The presence of oligosaccharides with prebiotic properties and probiotic bacteria was also described. Finally, particular emphasis was given to highly available calcium and its impact on bone health.

Feasibility of developing a pediatric telehealth network in Honduras with international consultation support.

INTRODUCTION: Honduras is the second poorest country in Central America, and roughly 50% of the population lives in rural areas. A telehealth network linking these areas to larger health centers may improve patient access to care, and physician access to educational opportunities. This pilot study assessed the feasibility of establishing a pediatric telehealth network between underserved clinics in Honduras and the Medical University of South Carolina (MUSC). METHODS: Two underserved Honduran clinics were identified and invited to participate in the telehealth network. Providers from these clinics connected remotely to educational conferences at MUSC, and received teleconsults from MUSC physicians and physicians from the other Honduran site. Honduran providers completed five-point Likert scale satisfaction surveys following participation in the conferences and teleconsults. RESULTS: Survey feedback was positive, with 100% of respondents stating they would utilize telemedicine in the future. Dissatisfaction was expressed subjectively in the survey comments with regards to poor Internet connectivity and unreliable electrical power. CONCLUSIONS: The establishment of a telehealth network between Honduras and MUSC is feasible, and rural providers were receptive to the clinical and educational opportunities this network provides. Future projects will expand telehealth capabilities to other Honduran sites and focus on intra-country collaboration to ensure sustainability.

The complex of hypericin with ß-lactoglobulin has antimicrobial activity with potential applications in dairy industry.

Using a combination of molecular modeling and spectroscopic experiments, the naturally occurring, pharmacologically active hypericin compound is shown to form a stable complex with the dimeric form of ß-lactoglobulin (ß-LG). Binding is predicted to occur at the narrowest cleft found at the interface between monomers in the dimeric ß-LG. The complex is able to preserve the fluorescence and singlet oxygen photosensitizing properties of the dye. The equilibrium constant for hypericin binding has been determined as Ka=1.40±0.07µM(-1), equivalent to a dissociation constant, Kd=0.71±0.03µM. The complex is active against Staphylococcus aureus bacteria. Overall, the results are encouraging for pursuing the potential application of the complex between hypericin and ß-LG as a nanodevice with bactericidal properties for disinfection.

Influence of milk somatic cell content on Parmigiano-Reggiano cheese yield.

The aim of this study was to determine the influence of the somatic cell content (SCC) of milk on Parmigiano-Reggiano cheese yield, produced in commercial cheese factories under field conditions. The study was carried out following the production of 56 batches of Parmigiano-Reggiano in 13 commercial cheese factories by processing milk collected from Italian Friesian cattle herds. The vat-milk (V-milk) used for making each cheese batch was obtained by mixing evening milk (partially skimmed following spontaneous separation of fat overnight, natural creaming) and morning milk. The batches of cheese produced were divided into 5 classes according to the SCC value of the evening milk determined prior to natural creaming (class 1, from 0 to 200,000; 2, 201,000-300,000; 3, 301,000-400,000; 4, 401,000-500,000; 5, over 501,000 cells/ml). The cheese yield was calculated as the amount of 24-h cheese, expressed in kilograms, obtained from 100 kg of V-milk (24 h ACY). The values of fat, crude protein, true protein, casein and 24 h ACY of V-milk were negatively correlated with the somatic cell score (SCS) of the evening milk. Conversely, a positive correlation was observed between chloride and SCS. Fat, protein fractions (crude protein, casein and whey proteins), P and titratable acidity of V-milk were positively correlated with its 24 h ACY, while chloride, pH and SCS showed a negative correlation. A significant drop in 24 h ACY was observed in classes 3, 4 and 5, therefore when the SCC of the evening milk exceeded 300,000 cells/ml. Finally a lower recovery of milk fat in cheese was observed as SCC of evening milk increase.

Influence of micellar calcium and phosphorus on rennet coagulation properties of cows milk.

The main requirement for milk processed in most cheese typologies is its rennet coagulation ability. Despite the increasing number of studies, the causes for abnormal coagulation of milk are not fully understood. The aim of this study was to ascertain relationships between milk characteristics and its rennet coagulation ability, focusing on the influence of calcium (Ca) and phosphorus (P). Ca and P are essential constituents of the micelles. Micellar P can be present as part of colloidal calcium phosphate (inorganic-P) or covalently bound to caseins as phosphate groups (casein-P). Eighty one herd milk samples (SCC<400 000 cell/ml) were classified as Optimal (8), Suboptimal (39) Poor (29) and Non-coagulating milk (5), according to their rennet coagulation parameters as assessed by lactodynamographic test. Samples were analysed for their chemical composition (basic composition, protein fractions, minerals and salt equilibria), physicochemical parameters (pH and titratable acidity) and rheological properties. Optimal milk was characterised by the highest contents of major constituents, protein fractions and minerals, lowest content of chloride and highest values of titratable acidity. Non-coagulating milk was characterised by the highest values of pH and the lowest of titratable acidity. At micellar level, Optimal milk showed the highest values of colloidal Ca, casein-P and colloidal Mg (g/100 g casein), while Non-coagulating milk showed the lowest values. Interestingly, there was no statistical difference regarding the content of colloidal inorganic-P (g/100 g casein) between Optimal and Non-coagulating milks. Overall, high mineralisation of the micelle (expressed as g inorganic-P/100 g casein) positively affect its rennetability. However, excessive mineralisation could lead to a reduction of the phosphate groups (g casein-P/100 g casein) available for curd formation.