Journal of Dairy Research最新文献

This study evaluated the effects of replacing ground corn with cassava root silage (CRS) in the supplement of grazing dairy cows on production yield, physicochemical characteristics, sensory attributes, and profitability of Minas Frescal cheese. Ten primiparous Girolando cows, with a mean weight of 373.45 ± 63.55 kg, a mean milk production of 12.48 ± 1.58 kg/d, and 76 days of lactation, were distributed into two 5 × 5 Latin squares. The animals were placed in the following five treatments: I, grazing without supplementation (WOS); II to V, grazing receiving 5 kg of dry matter (DM) of supplement without CRS (0 g/kg DM CRS) and with 260, 520 and 780 g/kg DM of CRS. Inclusion level of CRS did not affect (P > 0.067) physicochemical characteristics, sensory attributes, and production yield of cheese. However, cheese produced from supplemented animals had greater levels of protein (P = 0.025) and individual cheese production (kg of cheese/animal/day; P < 0.001) compared to WOS animals. Finally, the inclusion of CRS at up to 520 g/kg DM maximized cheese production by 0.73 kg of cheese/animal/day and gross revenue by 3.49 US$/animal/day, compared to WOS animals. In conclusion, replacement of ground corn with CRS in the supplement of dairy cows did not impact physicochemical characteristics and sensory attributes of Minas Frescal cheese. In addition, inclusion of CRS at up to 520 g/kg DM replacing ground corn in the supplement may be a suitable strategy for enhancing the profitability of Minas Frescal cheese production.

This research article addresses the hypothesis that vehicles used for cattle transport are contaminated with Escherichia coli, a potential foodborne pathogen, despite current regulations on sanitation practices. Dairy cattle and calves are regularly transported to auction markets, calf rearers and slaughterhouses. UK Government guidelines require livestock transport vehicles to be cleaned and disinfected within 24 hours of use or before re-use within that period. It is feasible, however, that if cleaning fails to eradicate bacteria, then transport vehicles can act as a fomite in the spread of antimicrobial resistance (AMR) pathogens. In this study, 13 trailer-loads (TLs) of calves were transported for 40-60 minutes. Trailers were then cleaned and disinfected within 20 minutes of unloading. Five sites within the trailer were swabbed after pressure washing and again 30 minutes after application of disinfectant. A bacterial count for E. coli was performed through growth on selective agar, and species identification was confirmed by MALDI-TOF. A subset of 30 isolates was selected for antibiotic susceptibility screening to a panel of veterinary and human antibiotics. E. coli were recovered from all TLs and sites; however, not all sites were contaminated in each TL. E. coli count was significantly reduced, but not eliminated, following application of disinfectant. Furthermore, high prevalence of resistance to sulphonamides, first-generation cephalosporins, and tetracyclines was observed. Forty percent of screened isolates were also classified as multidrug-resistant (MDR) (i.e. resistant to at least one antibiotic from three or more antibiotic classes). Application of disinfectant did not increase the risk of recovering an MDR isolate. This study demonstrates that livestock trailers can harbour potential zoonotic pathogens with AMR properties. Disinfection in accordance with current guidelines is an important step in reducing, but not eradicating, bacterial populations in these vehicles. Improved cleaning and/or disinfection policies are required to mitigate the potential for AMR transmission.

Yogurt acid whey (YAW) contains significant amounts of calcium as well as small amounts of protein, thus the idea of its reintroduction, especially of its calcium content, to the food chain is attractive. Calcium in milk is mainly complexed with casein micelles, whereas YAW contains only small amounts of protein, with no caseins at all, differing substantially from milk in the form in which calcium occurs. Therefore, the objective of the present research paper was to evaluate whether calcium bioavailability differs between YAW and milk. Following the INFOGEST protocol for simulated digestion and by coupling it with the Caco-2 model for intestinal absorption, calcium in YAW had higher bioaccessibility than calcium in milk. However, there were no differences in calcium transport by the intestinal cells and the transcription level of calcium absorption-related genes (VDR, TRPV6, S100G and PMCA1). Lastly, there were no differences in calcium bioaccessibility and the transcription of the calcium absorption-related genes between YAW samples of bovine, ovine or caprine origin obtained from Greek dairy products enterprises. In conclusion, despite the major differences in the protein profile between YAW and milk, there were no differences in calcium transport by the cells, but YAW was associated with higher calcium bioaccessibility, which ultimately may result in higher amount of absorbed calcium.

This study aimed to identify and quantify the various stem-like cell types in dairy cows' colostrum and milk at the onset of lactation. Five second parity Holstein cows were monitored from calving until the seventh-day postpartum. Mammary secretions were collected immediately after calving, then every 3 h until 12 h during day (d) 0, and during morning milking on d 1, d 2, d 4 and d 7. Cells were prepared from mammary secretions and analysed by flow cytometry using relevant cellular markers. The highest total and viable cell concentrations were observed in colostrum collected at calving and up to 6 h, with these concentrations decreasing substantially in samples collected later at d 0. Then, the concentrations of both total and viable cell populations continued to slowly decrease until d 7, the kinetic curves reaching a baseline plateau. Flow cytometry showed that the CD49f^posCD24^pos population, which identifies mammary epithelial stem cells, represented about 0.9% of viable cells at calving and about 0.1% 12 h later, the mammary epithelial stem cell concentration therefore being at its highest level in the very first colostrum. In contrast, the percentage of mesenchymal stem-like cells, defined as the population of CD34^negCD105^posCD90^posCD29^pos cells, was roughly constant (≈0.3%) during the first two milkings and decreased mainly during the first day to a basal level close to 0. Concerning haematopoietic stem-like cells, defined as the CD45^negCD34^posCD117^posCD90^pos cell population, they were only observed in the colostrum collected at calving. All the types of stem cells studied here were therefore only present in substantial quantities in the colostrum of the very first hours after calving, a period during which the calf's intestine is permeable, possibly allowing the transfer and integration of these cells in the tissues of the newborn calf.

Exceeding 10°C during raw milk transportation leads to its degradation, making it unsuitable for consumption. Maintaining a stable low temperature is therefore crucial to preserving milk quality over long distances. Traditional insulation methods have proven inadequate, particularly under extreme climatic conditions. This study proposes a novel approach incorporating an immersed evaporator within the milk tank to significantly extend the cooling duration and ensure product safety. Using unsteady laminar flow and heat transfer simulations, we evaluated the performance of the immersed evaporator system under various operating conditions. Results demonstrate a substantial improvement in cooling efficiency compared to conventional insulation. Key findings include: (1) An immersed evaporator can extend the cooling time by up to 3 h and 19 min (a 69% improvement) compared to insulation alone (1 h and 36 min). (2) Increasing the length of the evaporator by approximately 46% further extends the cooling period by 91%, while maintaining the same temperature of 2°C in both cases. (3) Increasing the evaporator length and decreasing its temperature to 0°C further enhance cooling performance, with potential improvements of up to 137% (8 h 48 min) in cooling time. A well-designed immersed evaporator system can maintain milk temperature below 10°C for an entire day, even in harsh environments. These findings offer a promising solution for the safe and efficient transportation of milk over long distances. By integrating immersed evaporators into milk tankers, we can ensure product quality and minimize spoilage, thereby contributing to a more sustainable and reliable food supply chain.

Sanitation Standard Operating Procedures (SSOP) are critical in key stages of food production and processing. After manufacturing, slicing process can serve as a point of contamination, potentially compromising the quality and shelf life of mozzarella. The objective of this study was to determine the effect of SSOP on the quantification and diversity of psychrotrophic bacteria with proteolytic and lipolytic potential in mozzarella before and after industrial slicing. Psychrotrophic bacteria were isolated, phenotypically assessed for spoilage potential under mesophilic and psychrotrophic conditions, analysed for diversity using dendrograms of genetic similarity and identified by partial sequencing of the 16S rRNA gene. The mean psychrotrophic counts were 3.77 (±0.83) log CFU/mL before slicing and 3.58 (±0.51) log CFU/mL in the sliced product, indicating a non-significant reduction (p < 0.05). Regarding spoilage potential, none of the 233 isolates evaluated exhibited proteolytic activity under psychrotrophic conditions. However, psychrotrophic lipolytic activity was predominant both before and after slicing. The species Lactobacillus delbrueckii, which is part of the saccharolytic inoculum used to reduce the pH of the curd during cheese production, was the main proteolytic bacteria under mesophilic conditions (35°C) in both before and after sliced samples. Although the bacterial counts indicated the full efficiency of the slicer's SSOP, the microbial diversity analysis revealed the inclusion of Staphylococcus succinus, Staphylococcus hominis, Enterococcus faecalis and Klebsiella pneumoniae during the slicing process, albeit at low levels. Therefore, relying solely on psychrotrophic quantification may not be sufficient to attest the efficiency of the slicer's SSOP. Even under controlled industrial conditions, spoilage bacteria from handling and environmental sources may be introduced into sliced mozzarella. Methods for improving the microbiological quality of the mozzarella pieces prior to slicing, as well as the intensification of sanitary procedures, must be reviewed and implemented to improve the shelf life and commercial potential of sliced mozzarella.

Mastitis is a major health problem in dairy industry as well as a major threat to profitability of dairy farms. Mastitis is also the main reason for the application of antibiotic treatment during lactation or at dry period. The aim of our study was to determine the prevalence of the most common mastitis pathogens in dairy cows and the antibiotic resistance under the conditions of Slovak dairy farms in 2017-2023. The samples came from 52 samplings in 2017 (47 farms), from 32 samplings in 2018 (29 farms), from 31 samplings in 2019 (29 farms), from 44 samplings in 2020 (41 farms), from 40 samplings in 2021 (35 farms), from 33 samplings in 2022 (31 farms) and form 38 samplings in 2023 (35 farms). A total of 2236 quarter udder milk samples were collected. The milk samples were taken from dairy cows based on high somatic cell count or California mastitis test or visible abnormalities in milk. Up to 88.62% of the identified isolates were the Coagulase-negative staphylococci (36.89%) followed by Escherichia coli (24.26%), Streptococcus uberis (16.21%), Staphylococcus aureus (8.41%) and Streptococcus agalactiae (2.86%). The most effective antibiotic was amoxicillin/clavulanic acid and antibiotic with the highest resistance was streptomycin. In conclusion, identification of mastitis pathogens in dairy cows and detection of antibiotic resistance is very important for the mastitis treatment and prevention of antibiotic resistance.

This research paper aimed to develop a supervised machine learning (ML) approach that learns and predicts data-based culling from farm information that reflects the criteria of the decisions taken to cull a cow by a farm manager. Data containing the features of milk yield, days in milk, lactation number, pregnancy status, days open and days pregnant were obtained from January to December 2020 from dairy cows on a large dairy farm in northern Mexico. The cows were labelled as those that were data-based culled (Cull) and those that were not culled (Stay). Six supervised ML algorithms were evaluated in a binary classification including logistic regression (LR), Gaussian naïve Bayes (GNB), k-nearest neighbors (k-NN), support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). Each model was subjected to hyperparameter optimization using a grid search approach combined with tenfold stratified cross-validation. This ensured that the class imbalance (Cull vs. Stay) was accounted during model evaluation. The best-performing model for each algorithm was selected on cross-validated accuracy. To evaluate the prediction performance of the ML algorithms on both labels from learned data, the metrics accuracy, precision, recall, F₁-score and the Matthews correlation coefficient (MCC) were employed. Accuracy among all classifiers was >0.90. The poorest prediction performance was observed in GNB (MCC = 0.50) and LR (MCC = 0.72). Conversely, the rest of the classifiers achieved superior prediction performance in learning the specific culling criteria, reaching an MCC score >0.91. Overall, culling criteria can be learned and predicted by ML algorithms and their performance varies among classifiers. This study identified RF as the best performing algorithm, but k-NN, SVM and MLP are possible candidates to be used in on-farm conditions. To increase their reliability, these approaches need to be tested in several farms, under different scenarios and varieties of features.

This study explores Turkish yoghurt varieties, emphasizing their cultural significance, historical roots, and regional diversity. Traditionally made with animal- and plant-based coagulants, Turkish yoghurt has evolved from its origins in Central Asia and the Ottoman Empire into a dynamic industry. Variations are classified by texture, fat content, and flavour, shaped by Türkiye's geographical diversity. Unique regional types exhibit distinct sensory and nutritional traits. The study also highlights the integration of traditional methods into modern production, ensuring the preservation and innovation of Turkish yoghurt in today's markets. Turkish yoghurt varieties have rich diversity and cultural significance, offering valuable insights into the gastronomy field. The findings emphasize the unique sensory and nutritional characteristics of regional yoghurts, which can inspire innovative culinary applications. For instance, chefs and product developers can incorporate traditional yoghurts like Antakya Salted Yoghurt or Tavas Smoked Sheep Yoghurt into modern recipes, creating novel dishes that balance authenticity and creativity. The study also sheds light on the potential of yoghurt-based products such as ayran and keş to serve as sustainable and nutritious alternatives in contemporary gastronomy. Additionally, understanding traditional production methods opens avenues for preserving and reviving artisanal techniques, fostering a deeper appreciation for local food heritage. By bridging tradition and innovation, this research contributes to the development of gastronomy as both an art and a science, enhancing the global visibility of Turkish cuisine.

This study aimed to identify the best automatic milking system (AMS) parameters and monitoring data for early detection of clinical mastitis in dairy cows and to determine the earliest possible detection within 30 days with the highest predictive accuracy. From August 2021 to February 2022, 55 Holstein cows were monitored for mastitis using physical examination, positive California mastitis test (CMT) and the AMS manufacturer's software (Delpro®) criteria: milk electrical conductivity ≥ 5.37 mS/cm, milk yield ≤ 80%, somatic cell count (SCC) > 200,000 cells/mL and Mastitis Detection Index (MDi) ≥ 2.0. For every cow suspected of mastitis, two other lactating cows were randomly chosen for evaluation to provide a comparison with healthy herd companions. In total, 129 inspections were evaluated: 39 with clinical mastitis and 90 without. Data on milking, milk composition and production from the AMS, and behavioural data from monitoring collars were summarized for the 30 days leading up to the mastitis diagnosis. Thirty measurement parameters were analysed using generalized linear models. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated. In the final model, significant parameters included: milk production per day (kg), SCC (cells/mL), average flow mean (kg/min), average conductivity (mS/cm), average flow peak (kg/min), average production per milking (kg), milking duration (s), rumination (min/day), panting (min/day) and feeding activity (min/day). From -30 to -10 days, accuracy, sensitivity and specificity varied without a defined pattern. However, from day -9, there was stabilization of the evaluated parameters. Results showed an average accuracy of 79.2%, a sensitivity of 82.5%, a specificity of 78.7%, a positive predictive value of 41.5% and a negative predictive value of 92.2% in predicting mastitis occurrence. In conclusion, using AMS parameters and behavioural data from monitoring collars, it was possible to predict clinical mastitis in dairy cows in an AMS with a 9-day advance notice.