Mark A. Fenelon

The Prevalence and Control of Bacillus and Related Spore-Forming Bacteria in the Dairy Industry.

Milk produced in udder cells is sterile but due to its high nutrient content, it can be a good growth substrate for contaminating bacteria. The quality of milk is monitored via somatic cell counts and total bacterial counts, with prescribed regulatory limits to ensure quality and safety. Bacterial contaminants can cause disease, or spoilage of milk and its secondary products. Aerobic spore-forming bacteria, such as those from the genera Sporosarcina, Paenisporosarcina, Brevibacillus, Paenibacillus, Geobacillus and Bacillus, are a particular concern in this regard as they are able to survive industrial pasteurization and form biofilms within pipes and stainless steel equipment. These single or multiple-species biofilms become a reservoir of spoilage microorganisms and a cycle of contamination can be initiated. Indeed, previous studies have highlighted that these microorganisms are highly prevalent in dead ends, corners, cracks, crevices, gaskets, valves and the joints of stainless steel equipment used in the dairy manufacturing plants. Hence, adequate monitoring and control measures are essential to prevent spoilage and ensure consumer safety. Common controlling approaches include specific cleaning-in-place processes, chemical and biological biocides and other novel methods. In this review, we highlight the problems caused by these microorganisms, and discuss issues relating to their prevalence, monitoring thereof and control with respect to the dairy industry.

Evaluation of two food grade proliposomes to encapsulate an extract of a commercial enzyme preparation by microfluidization.

The entrapment by microfluidization of a commercial enzyme extract (Debitrase DBP20) in liposomes using two food grade proliposome (C and S) preparations was studied. Liposomes obtained at a low microfluidization pressure (4000 psi) were distributed in a bimodal population of small (30-40 nm) and large vesicles (300-700 nm). The composition of the proliposome influenced entrapment efficiency and the repartition of the enzyme between the core and the surface of the liposome. More enzyme was associated with the liposomal surface and greater entrapment efficiencies (64%) were obtained for liposomes with the highest negative zeta potential (proliposome C). Increasing microfluidization pressure and increasing the number of passes through the microfluidizer resulted in losses in entrapment efficiency and enzyme activity, due to decreasing liposome size and enzyme denaturation. Entrapment efficiency was not influenced by external pH and enzyme activity was not adversely affected over storage for 18 days under the conditions evaluated.

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n=713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000cm(-1) were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation.

Prediction of individual milk proteins including free amino acids in bovine milk using mid-infrared spectroscopy and their correlations with milk processing characteristics

The aim of this study was to evaluate the effectiveness of mid-infrared spectroscopy in predicting milk protein and free amino acid (FAA) composition in bovine milk. Milk samples were collected from 7 Irish research herds and represented cows from a range of breeds, parities, and stages of lactation. Mid-infrared spectral data in the range of 900 to 5,000 cm(-1) were available for 730 milk samples; gold standard methods were used to quantify individual protein fractions and FAA of these samples with a view to predicting these gold standard protein fractions and FAA levels with available mid-infrared spectroscopy data. Separate prediction equations were developed for each trait using partial least squares regression; accuracy of prediction was assessed using both cross validation on a calibration data set (n=400 to 591 samples) and external validation on an independent data set (n=143 to 294 samples). The accuracy of prediction in external validation was the same irrespective of whether undertaken on the entire external validation data set or just within the Holstein-Friesian breed. The strongest coefficient of correlation obtained for protein fractions in external validation was 0.74, 0.69, and 0.67 for total casein, total β-lactoglobulin, and β-casein, respectively. Total proteins (i.e., total casein, total whey, and total lactoglobulin) were predicted with greater accuracy then their respective component traits; prediction accuracy using the infrared spectrum was superior to prediction using just milk protein concentration. Weak to moderate prediction accuracies were observed for FAA. The greatest coefficient of correlation in both cross validation and external validation was for Gly (0.75), indicating a moderate accuracy of prediction. Overall, the FAA prediction models overpredicted the gold standard values. Near-unity correlations existed between total casein and β-casein irrespective of whether the traits were based on the gold standard (0.92) or mid-infrared spectroscopy predictions (0.95). Weaker correlations among FAA were observed than the correlations among the protein fractions. Pearson correlations between gold standard protein fractions and the milk processing characteristics of rennet coagulation time, curd firming time, curd firmness, heat coagulating time, pH, and casein micelle size were weak to moderate and ranged from -0.48 (protein and pH) to 0.50 (total casein and a30). Pearson correlations between gold standard FAA and these milk processing characteristics were also weak to moderate and ranged from -0.60 (Val and pH) to 0.49 (Val and K20). Results from this study indicate that mid-infrared spectroscopy has the potential to predict protein fractions and some FAA in milk at a population level.

Recent advances in spray drying relevant to the dairy industry: A comprehensive critical review

ABSTRACT Milk is extremely perishable, and yet it has to be preserved for later consumption. In this view, membrane filtration, vacuum concentration lactose crystallization, homogenization, and spray-drying dehydration are valuable techniques to stabilize most dairy ingredients. Considering the increasing development of dairy trade, there is a need for the dairy industry to improve its understanding of how these concentration and spray-drying processes affect the quality of the resulting dairy powders, so to control it. However, the residence time of the droplet and the powder in the spray dryer is so short that it is very difficult to implement studies on the mechanisms of the structural changes in the protein without fundamental research into the process/product interactions. Moreover, several authors have reported the crucial and specific role of dairy components in the mechanisms of water transfer during drying and rehydration. The aim of this paper is to review the present and recent advances in knowledge and innovations, on the properties of spray-dried dairy products, on the modeling and simulation of water transfer processes (drying and rehydration), and on spray-drying equipment and energy consumption.

The physical characteristics and emulsification properties of partially dephosphorylated bovine β-casein.

Bovine β-casein was purified from phosphocasein by rennet coagulation and cold solubilisation from the resultant curd. β-Casein was then dephosphorylated using potato acid phosphatase. Urea-polyacrylamide gel electrophoresis (PAGE) of partially dephosphorylated β-casein showed a number of bands, depending on the final level of phosphorylation. Dephosphorylating β-casein increased its pH of minimum solubility from ∼pH 5 to 5.5 and reduced its net negative charge from -30.8 to -27.0 mV. During the acidification of β-casein solutions, partially dephosphorylated β-casein failed to form a gel, unlike the phosphorylated (i.e., control) β-casein. Use of partially dephosphorylated β-casein to stabilise oil-in-water emulsions resulted in larger fat globules compared to control β-casein, but such globules were less susceptible to aggregation in the presence of 15 or 30 mM CaCl(2). Overall, the dephosphorylation of β-casein resulted in a protein similar to human β-casein in terms of physicochemical functionality, with increased stability against calcium-induced aggregation.

The effect of dietary crude protein and phosphorus on grass-fed dairy cow production, nutrient status, and milk heat stability

Dietary crude protein (CP) and phosphorus (P) have the potential to alter dairy cow production, nutrient status, and milk heat stability, specifically in early lactation. This study examined the effect of supplementary concentrates with different CP and P concentrations on blood N and P status and on milk yield, composition, and heat stability. The concentrates [4kg of dry matter (DM) concentrate per cow daily] were fed to grazing dairy cows (13kg DM grass) during early lactation. Forty-eight spring-calving dairy cows were allocated to 4 treatments: high CP, high P (HPrHP; 302g/kg DM CP, 6.8g/kg DM P), medium CP, high P (MPrHP; 202g/kg DM CP, 4.7g/kg DM P), low CP, high P (LPrHP; 101g/kg DM CP, 5.1g/kg DM P), and low CP, low P (LPrLP; 101g/kg DM CP, 0.058g/kg DM P), for 8wk. Levels of N excretion were significantly higher in animals fed the HPrHP and MPrHP concentrates; P excretion was significantly lower in animals fed the LPrLP concentrate. Reducing the level of P in the diet (LPrLP concentrate) resulted in a significantly lower blood P concentration, whereas milk yield and composition (fat and protein) were not affected by either CP or P in the diet. The effect of the interaction between treatment and time on milk urea N was significant, reflecting the positive correlation between dietary CP and milk nonprotein N. Increasing supplementary CP and P (HPrHP) in the diet resulted in significantly lower milk heat stability at pH 6.8. The findings show that increasing dietary CP caused a decrease in milk heat stability, which reduced the suitability of milk for processing. The study also found that increasing dietary CP increased milk urea N and milk nonprotein N. Increasing dietary P increased fecal P excretion. These are important considerations for milk processors and producers for control of milk processing and environmental parameters.

Effectiveness of mid-infrared spectroscopy to predict the color of bovine milk and the relationship between milk color and traditional milk quality traits

The color of milk affects the subsequent color features of the resulting dairy products; milk color is also related to milk fat concentration. The objective of the present study was to quantify the ability of mid-infrared spectroscopy (MIRS) to predict color-related traits in milk samples and to estimate the correlations between these color-related characteristics and traditional milk quality traits. Mid-infrared spectral data were available on 601 milk samples from 529 cows, all of which had corresponding gold standard milk color measures determined using a Chroma Meter (Konica Minolta Sensing Europe, Nieuwegein, the Netherlands); milk color was expressed using the CIELAB uniform color space. Separate prediction equations were developed for each of the 3 color parameters (L*=lightness, a*=greenness, b*=yellowness) using partial least squares regression. Accuracy of prediction was determined using both cross validation on a calibration data set (n=422 to 457 samples) and external validation on a data set of 144 to 152 samples. Moderate accuracy of prediction was achieved for the b* index (coefficient of correlation for external validation=0.72), although poor predictive ability was obtained for both a* and L* indices (coefficient of correlation for external validation of 0.30 and 0.55, respectively). The linear regression coefficient of the gold standard values on the respective MIRS-predicted values of a*, L*, and b* was 0.81, 0.88, and 0.96, respectively; only the regression coefficient on L* was different from 1. The mean bias of prediction (i.e., the average difference between the MIRS-predicted values and gold standard values in external validation) was not different from zero for any of 3 parameters evaluated. A moderate correlation (0.56) existed between the MIRS-predicted L* and b* indices, both of which were weakly correlated with the a* index. Milk fat, protein, and casein were moderately correlated with both the gold standard and MIRS-predicted values for b*. Results from the present study indicate that MIRS data provides an efficient, low-cost screening method to determine the b* color of milk at a population level.

Decoupling macronutrient interactions during heating of model infant milk formulas.

Understanding macronutrient interactions during heating is important for controlling viscosity during infant milk formula (IMF) manufacture. Thermal behavior of macronutrients (casein, whey, lactose, fat) was studied, in isolation and combination, over a range of concentrations. Addition of phosphocasein to whey protein solutions elevated denaturation temperature (Td) of β-lactoglobulin and the temperature at which viscosity started to increase upon heating (Tv). Secondary structural changes in whey proteins occurred at higher temperatures in dispersions containing phosphocasein; the final extent of viscosity increase was similar to that of whey protein alone. Addition of lactose to whey protein solutions delayed secondary structural changes, increased Td and Tv, and reduced post heat treatment viscosity. This study demonstrated that heat-induced changes in IMF associated with whey protein (denaturation, viscosity) are not only a function of concentration but are also dependent on interactions between macronutrients.

Processing characteristics of dairy cow milk are moderately heritable

Milk processing attributes represent a group of milk quality traits that are important to the dairy industry to inform product portfolio. However, because of the resources required to routinely measure such quality traits, precise genetic parameter estimates from a large population of animals are lacking for these traits. Milk processing characteristics considered in the present study-rennet coagulation time, curd-firming time, curd firmness at 30 and 60 min after rennet addition, heat coagulation time, casein micelle size, and milk pH-were all estimated using mid-infrared spectroscopy prediction equations. Variance components for these traits were estimated using 136,807 test-day records from 5 to 305 d in milk (DIM) from 9,824 cows using random regressions to model the additive genetic and within-lactation permanent environmental variances. Heritability estimates ranged from 0.18 ± 0.01 (26 DIM) to 0.38 ± 0.02 (180 DIM) for rennet coagulation time; from 0.26 ± 0.02 (5 DIM) to 0.57 ± 0.02 (174 DIM) for curd-firming time; from 0.16 ± 0.01 (30 DIM) to 0.56 ± 0.02 (271 DIM) for curd firmness at 30 min; from 0.13 ± 0.01 (30 DIM) to 0.48 ± 0.02 (271 DIM) for curd firmness at 60 min; from 0.08 ± 0.01 (17 DIM) to 0.24 ± 0.01 (180 DIM) for heat coagulation time; from 0.23 ± 0.02 (30 DIM) to 0.43 ± 0.02 (261 DIM) for casein micelle size; and from 0.20 ± 0.01 (30 DIM) to 0.36 ± 0.02 (151 DIM) for milk pH. Within-trait genetic correlations across DIM weakened as the number of days between compared intervals increased but were mostly >0.4 except between the peripheries of the lactation. Eigenvalues and associated eigenfunctions of the additive genetic covariance matrix for all traits revealed that at least the 80% of the genetic variation among animals in lactation profiles was associated with the height of the lactation profile. Curd-firming time and curd firmness at 30 min were weakly to moderately genetically correlated with milk yield (from 0.33 ± 0.05 to 0.59 ± 0.05 for curd-firming time, and from -0.62 ± 0.03 to -0.21 ± 0.06 for curd firmness at 30 min). Milk protein concentration was strongly genetically correlated with curd firmness at 30 min (0.84 ± 0.02 to 0.94 ± 0.01) but only weakly genetically correlated with milk heat coagulation time (-0.27 ± 0.07 to 0.19 ± 0.06). Results from the present study indicate the existence of exploitable genetic variation for milk processing characteristics. Because of possible indirect deterioration in milk processing characteristics due to selection for greater milk yield, emphasis on milk processing characteristics is advised.