Heat stability (HS) is substantial technology property of raw milk. Analysis of sources of HS variation and its regular monitoring can contribute to creating higher added value in the dairy industry. The goal of this analysis was to assess the practice sources of raw cow milk HS variability on the results of an extensive data set of bulk tank milk samples. There was implemented neither a compositional technology modification nor acidity adjustment of milk, just original raw milk was used for the analysis. A total 2634 HS analyses were performed, including other milk indicators, during three years of an experimental period. The log HS mean and standard deviation were 1.273654 ± 0.144189, equal to the HS geometric mean of 18.8 min. Explanation of the HS variability through the linear model used was 41.1% (p < 0.0001). According to the results of the variance analysis, the milk HS was influenced (p = 0.0033 and mostly <0.0001) by all the farm factors such as year; season; calendar month; altitude; total annual rainfall; herd size by the number of cows; milk yield; cow breed; type of milking; litter type in the stable; summer grazing application; farm effect. During the calendar months (p < 0.0001), milk HS values suggest similar seasonal dynamics with the somatic cell count, total count of mesophilic microorganisms, coli bacteria count and urea and lactose concentration and opposite configuration pattern to fat, crude protein, solids-not-fat and total solids content and milk freezing point depression. Here performed quantification of these effects by analyzing the variance may allow efficient raw milk selection to be processed into specific dairy products.

In a return to tradition, the popularity of caprine milk is on the rise. However, particularly in countries with developed dairy industries based on bovine milk, there is the risk of adulteration with bovine milk, which is a cheaper alternative. Thus, a rapid, robust, and simple method for the detection of bovine milk added to caprine milk is necessary, and 1 H nuclear magnetic resonance spectroscopy appears to provide a solution. A matrix of 115 pure and artificially adulterated pasteurized milk samples was prepared and used to discover biomarkers of bovine milk that are independent of chemical and biological variation caused by factors such as genetics, diet, or seasonality. Principal component analysis and orthogonal projections to latent structures discriminant analysis of pure bovine milk and pure caprine milk revealed spectral features that were assigned to the resonances of 4 molecules. Of these, the peaks corresponding to protons in the N-acetylglucosamine and N-acetylgalactosamine acetyl moieties showed significant applicability for our method. Receiver operating characteristic curve analysis was used to evaluate the performance of the peak integrals as biomarkers of adulteration. This approach was able to distinguish caprine milk adulterated with 5% of bovine milk with 84.78% accuracy and with 10% of bovine milk an excellent 95.65% accuracy. This study demonstrates that N-acetyl carbohydrates could be used as biomarkers for the detection of bovine milk in caprine milk and could help in protecting caprine milk authenticity.

Detection of adulteration of small ruminant milk is very important for health and commercial reasons. New analytical and cost-effective methods need to be developed to detect new adulteration practices. In this work, we aimed to explore the ability of the MALDI-TOF mass spectrometry to detect bovine milk in caprine and ovine milk using samples from 18 dairy farms. Different levels of adulteration (0.5, 1, 5, 10, 20, 40, 60, and 80%) were analyzed during the lactation period of goat and sheep (in May, from 60 to 90 d in milk, and in August, from 150 to 180 d in milk). Two different ranges of peptide-protein spectra (500-4,000 Da; 4-20 kDa) were used to establish a calibration model for predicting the concentration of adulterant using partial least squares and generalized linear model with lasso regularization. The low molecular weight part of the spectra together with the generalized linear model with lasso regularization regression model appeared to have greater potential for our aim of detection of adulteration of small ruminants' milk. The subsequent prediction model was able to predict the concentration of bovine milk in caprine milk with a root mean square error of 11.4 and 17.0% in ovine milk. The results offer compelling evidence that MALDI-TOF can detect the adulteration of small ruminants' milk. However, the method is severely limited by (1) the complexity of the milk proteome resulting from the adulteration technique, (2) the potential degradation of thermolabile proteins, and (3) the genetic variability of tested samples. Additionally, the root mean square error of prediction based only on one individual sample adulteration series can drop down to 6.34% for quantification of adulterated caprine milk and 6.28% for adulterated ovine milk for the full set of concentrations or down to 2.33 and 4.00%, respectively, if we restrict only to low concentrations of adulteration (0, 0.5, 1, 5, 10%).

This study aimed to monitor milk parameters on three different dairy farms in the Czech Republic to describe their readiness for implementing selective dry cow therapy. Fat, protein, casein, lactose, solids-not-fat content, total solids content, freezing point, titratable acidity, and somatic cell count of quarter milk samples collected from tested Holstein cows were evaluated. Associations between the tested parameters, as well as the effects of parity, farm, day of calving, and time of evaluation at dry-off and after calving, were assessed. Values of the leading milk components dynamically changed between dry-off and after calving, but only protein content was significantly affected. The most important parameter of our research, the somatic cell count of quarter milk samples, was also not affected by the time of evaluation. Even though a slight increase in the mean of somatic cell count is expected before the dry period and after calving, at dry-off, we observed 30%, 42%, and 24% of quarters with somatic cell counts above 200,000 cells per mL, while after calving, we observed 27%, 16%, and 18% of quarters with somatic cell counts above 200,000 cells per mL on Farm 1, Farm 2, and Farm 3, respectively. High somatic cell counts (>200,000 cells per mL) indicate bacterial infection, as confirmed by the significant negative correlation between this parameter and lactose content. In addition, a deficient milk fat-to-protein ratio was observed on two farms, which may indicate metabolic disorders, as well as the occurrence of intramammary infections. Despite the above, we concluded that according to the thresholds of somatic cell counts for selective dry cow therapy taken from foreign studies, a large part of the udder quarters could be dried off without the administration of antibiotics. However, it is necessary to set up more effective mechanisms for mastitis prevention.

The study aimed to explore the relationship between teat structure dimensions and their short-term reaction to milking, to find the optimal dimensions of teat structures in relation to milking-induced teat tissue changes. Teat structures (teat length, canal length, thickness at barrel and apex, wall and cistern width) were measured by ultrasonography before and after milking for 38 Holstein cows at the beginning, middle, and end of lactation. We found that milking-induced changes in teat structures significantly depended on their pre-milking size. Furthermore, we observed that some teat structures and their changes were interconnected, and some did not affect each other. For example, changes in the barrel thickness and cistern width were affected by all structures, while the canal and apex did not influence each other. We deduced that more favorable changes were observed for teats of medium length, medium barrel and apex thickness, with teat canals of medium length, but with wider cisterns and thinner walls. The results of this study may help improve research in the area of milking-induced changes in teat morphology. Our findings could help understand potential health risks to animals in relation to teat morphology, milking equipment, and machine settings.