Milk Quality Research Articles

Combined Metabolomics and Biochemical Analyses of Serum and Milk Revealed Parity-Related Metabolic Differences in Sanhe Dairy Cattle

The production performance of dairy cattle is closely related to their metabolic state. This study aims to provide a comprehensive understanding of the production performance and metabolic features of Sanhe dairy cattle across different parities, with a specific focus on evaluating variations in milk traits and metabolites in both milk and serum. Sanhe dairy cattle from parities 1 to 4 (S1, n = 10; S2, n = 9; S3, n = 10; and S4, n = 10) at mid-lactation were maintained under the same feeding and management conditions. The milk traits, hydrolyzed milk amino acid levels, serum biochemical parameters, and serum free amino acid levels of the Sanhe dairy cattle were determined. Multiparous Sanhe dairy cattle (S2, S3, and S4) had a greater milk protein content, lower milk lactose content, and lower solids-not-fat content than primiparous Sanhe dairy cattle (S1). Moreover, S1 had a higher ratio of essential to total amino acids (EAAs/TAAs) in both the serum and milk. The serum biochemical results showed the lower glucose and total protein levels in S1 cattle were associated with milk quality. Furthermore, ultra-high-resolution high-performance liquid chromatography with tandem MS analysis (UPLC-MS/MS) identified 86 and 105 differential metabolites in the serum and milk, respectively, and these were mainly involved in amino acid, carbohydrate, and lipid metabolism. S1 and S2/S3/S4 had significantly different metabolic patterns in the serum and milk, and more vitamin B-related metabolites were significantly higher identified in S1 than in multiparous cattle. Among 36 shared differential metabolites in the serum and milk, 10 and 7 metabolites were significantly and strongly correlated with differential physiological indices, respectively. The differential metabolites identified were enriched in key metabolic pathways, illustrating the metabolic characteristics of the serum and milk from Sanhe dairy cattle of different parities. L-phenylalanine, dehydroepiandrosterone, and linoleic acid in the milk and N-acetylornithine in the serum could be used as potential marker metabolites to distinguish between Sanhe dairy cattle with parities of 1–4. In addition, a metabolic map of the serum and milk from the three aspects of carbohydrates, amino acids, and lipids was created for the further analysis and exploration of their relationships. These results reveal significant variations in milk traits and metabolites across different parities of Sanhe dairy cattle, highlighting the influence of parity on the metabolic profiles and production performance. Tailored nutritional strategies based on parity-specific metabolic profiles are recommended to optimize milk production and quality in Sanhe cattle.

Milk yield and quality at udder quarter level are influenced by quarter position, pathogen, and somatic cell score

There is a paucity of information about milk composition and quality at the quarter level from complete milking of the udder, mainly because of the difficulties of sampling procedures which imply the adoption of a vacuum system connected to four independent buckets. The objective of this study was to evaluate the effect of quarter position, intramammary infection status, and somatic cells on milk yield and composition at the mammary quarter level of Simmental cows. Milk samples were collected during three consecutive days, after milking individual mammary quarters of 10 lactating cows separately, for a total of 120 quarter milk samples. Samples were analysed for composition, somatic cell count (SCC), differential SCC, and bacterial culture. Somatic cell count was log-transformed to somatic cell score (SCS) to achieve normality of distribution. Sources of variation of milk yield and quality traits were investigated using a linear mixed model which included the fixed effects of quarter position (1 to 4), pathogen presence (positive or negative), and SCS class (low, medium-low, medium-high, and high). The interaction between cow and quarter position was included as random effect. Quarter position significantly affected milk yield, with rear udder quarters yielding more milk compared to their front counterparts, but with negligible effects on milk composition. Classes of SCS influenced milk composition, in a way that quarters with high SCS had lower lactose content and higher differential SCC. This study is a contribution towards the possibility to better understand physiological processes at cow udder quarter level.

Specificity of the AMP-6000 Method for Enumerating Clostridium Endospores in Milk

Enumeration of endospores of butyric acid-forming clostridia in cheese milk is an essential part of milk quality monitoring for cheese producers to avoid late blowing, severe spoilage caused by clostridia during ripening. However, due to the lack of an internationally standardized method, different methods are used and it is important to consider how the choice of method affects the results. This is particularly relevant when clostridial spore counts in milk are considered for quality payments. The aim of this study was to evaluate the specificity of the AMP-6000 method for the enumeration of endospores of cheese spoiling clostridia in milk. First, to assess the prevalence of Clostridium diversity and to determine potential non-target species, we identified isolates from positive reactions of the AMP-6000 method used to quantify clostridial endospores in raw milk and teat skin samples by MALDI-TOF MS. Based on these results, a strain library was designed to evaluate method inclusivity and exclusivity using pure cultures of target and non-target strains according to ISO 16140-2:2016. Most target Clostridium tyrobutyricum strains, as well as all tested C. butyricum and C. sporogenes strains were inclusive. However, C. beijerinckii may be underestimated as only some strains gave positive results. All non-target strains of bacilli and lysinibacilli, but not all paenibacilli, were confirmed to be exclusive. This study provides performance data to better understand the results of microbiological enumeration of butyric acid-forming clostridia in milk and serves as a basis for future methodological considerations and improvements.

Exploring the Impact of Varying Fat Content on Milk Properties During Ohmic Heating: A PCA Study of Viscosity Changes Alongside pH Fluctuations

Background: Some of the factors that contribute to the quality of milk include fat content, pH, viscosity and other rheological aspects. This research extensively investigated the effects of ohmic heating (OH) on milk viscosity, along with other rheological attributes and pH. Methods: Milk samples with varied fat constituents were subjected to OH at a constant voltage of 140V until they reached a temperature of 90°C. The pH of raw milk and heated milk was determined using an auto-pH meter, whereas the viscosity of the same was measured using a Brookfield viscometer that includes a wide range of characteristics, such as viscosity, temperature, shear stress and torque at various spindle rotation speeds. The current investigation used Principal Component Analysis (PCA) as a key statistical approach. Result: The pH values exhibited little fluctuations during OH across several milk kinds, indicating mild acidification throughout the process, but the observed alterations did not reach statistical significance (p greater than 0.05), suggesting that OH does not significantly affect milk pH. Viscosity measurements showed that the application of OH resulted in a decrease in the average viscosity of milk, suggesting changes in its ability to withstand shear forces. The correlation study revealed robust positive associations between viscosity and fat content (PC2 - 26.77%), as well as between rotational speed, shear rate and torque (PC1 - 53.72%) and a negative correlation with temperature, offering valuable insights into the dynamic properties of milk when subjected to thermal heat treatment.

Distribution of mycotoxins during manufacture and storage of cheeses – A review

The distribution of mycotoxins during cheese production and storage is a complex process that depends on several factors, including milk quality, cheese type, storage conditions, and the presence of competing microorganisms. This paper investigates the occurrence and distribution of mycotoxins in curd/cheese/whey during different cheese processes and the effect of ripening/storage time on mycotoxin levels in the final product using a review approach. The paper covers the major mycotoxins that can contaminate cheese, including aflatoxins, ochratoxin‐A, roquefortine‐C, mycophenolic acid, and beauvericin. Overall, it synthesises the existing understanding of the spread of mycotoxins during cheese production and storage.

Effect of electron beam irradiation on raw goat milk: microbiological, physicochemical and protein structural analysis.

Goat milk is considered a nutritionally superior resource, owing to its advantageous nutritional attributes. Nevertheless, it is susceptible to spoilage and the persistence of pathogens. Electron beam irradiation stands as a promising non-thermal processing technique capable of prolonging shelf life with minimal residue and a high degree of automation. The effects of electron beam irradiation (2, 3, 5, and 7 kGy) on microorganisms, physicochemical properties, and protein structure and compared with conventional pasteurized goat milk (PGM). 2 kGy electron beam irradiation reduces the total microbial count of goat milk by 6-logs, and the irradiated goat milk protein secondary structure showed a significant decrease in ɑ-helix content. Low irradiation doses led to microaggregation and cross-linking. In contrast, high doses (≥ 5 kGy) slightly disrupted the aggregates and decreased the particle size, disrupting the microscopic surface structure of goat milk, verified by scanning electron and confocal laser scanning microscopies. The irradiation of goat milk with 2 kGy of electron beam irradiation of goat milk may effectively inactivate harmful microorganisms in the milk and maintain/or improve the physicochemical quality and protein structure of goat milk compared to thermal pasteurization. This article is protected by copyright. All rights reserved.

Potencial alimenticio de ensilados híbridos de maíz Puma incluidos en la dieta de ovejas gestantes.

Objective. The nutritional potential of maize´s Puma hybrids was evaluated as silage forage in 50% of the pregnant ewe’s diet. Materials and methods. Sixty-one pregnant ewes were used, assigned to the groups: Centli Puma (N = 21), Tsiri Puma (N = 20) and Tlaoli Puma (N = 20) and their weight, body condition (CC), FAMACHA and metabolic profile in gestation were measured, as well as milk production and quality at 15 and 30 days of lactation. In lambs, birth temperature and weight were measured. Results. The weight and CC of the ewes during pregnancy and lactation were not affected by the group (p>0.05). On the other hand, the estimated feed intake was higher in the ewes of the Centli group (p<0.0001), while the FAMACHA values were better in the Centli and Tlaoli groups (p<0.05). Proteins, glucose, cholesterol and β hydroxybutyrate during pregnancy and milk production and composition during lactation were not affected by the group (p>0.05). Neither did the weight of the lambs and their body temperature vary among the three groups (p>0.05). Conclusions. The use of Puma corn hybrid silages developed for grain production has good and similar response in the productive and metabolic parameters of sheep, as well in the lambs.

Enhancing Milk Quality Detection with Machine Learning: A Comparative Analysis of KNN and Distance-Weighted KNN Algorithms

Ensuring the quality of milk is paramount for consumer health and industry standards. This study introduces a comparative analysis of two machine learning approaches, the k-Nearest Neighbors (KNN) algorithm and its variant, the Distance-Weighted KNN (DW-KNN), for the detection of milk quality. While the traditional KNN algorithm has been widely applied across various sectors for its simplicity and effectiveness, our research proposes an enhanced methodology through the implementation of the DW-KNN algorithm, which incorporates distance weighting to improve prediction accuracy. Through the analysis of a comprehensive dataset encompassing multiple milk quality indicators, we demonstrate that the DW-KNN algorithm significantly outperforms the standard KNN approach, achieving an exceptional accuracy of 99.53% compared to 98.58% by KNN. This substantial improvement highlights the potential of distance weighting in enhancing classification performance, particularly in applications requiring high precision in quality assessment. Our findings advocate for the adoption of the DW-KNN algorithm in the dairy industry and related fields, offering a robust tool for ensuring product quality and safety.

Antimicrobial resistance profiling of coagulase negative staphylococci isolated from bovine mastitis

Mastitis plays a crucial role in the economics of dairy industry by deteriorating the quality and quantity of milk produced, as well as endangering the animal health and welfare. The objective of this study was to investigate the antimicrobial resistance (AMR) profile of coagulase negative staphylococci (CNS) isolated from bovine mastitis. Microbiological evaluation by morphological, cultural and biochemical characterisation as well as by monoplex polymerase chain reaction (PCR) of the 83 quarter milk samples revealed that CNS were the most predominant bacteria (32.53%). It was also concluded that most of the CNS were resistant to multiple antibiotics. The phenotypic and genotypic AMR profiling was done using in vitro disc diffusion assay and PCR, respectively, to identify the resistance pattern towards penicillin, methicillin, tetracycline and enrofloxacin. The results depicted a significant difference between the phenotypic and genotypic resistance of CNS against penicillin, methicillin and tetracycline. This outcome on the interaction of phenotypic and genotypic AMR profiling is intriguing and opens a huge scope for future studies on the transcriptomic and proteomic aspects of drug resistance. A better knowledge of the AMR profile guides the dairy producers in developing suitable timely intervention strategies for the economic management of mastitis, which in turn helps in tackling AMR and reduces the threat of its zoonotic transmission.

Performance and economics of fall grazing cover crops compared to a traditional drylot system of fall-calving cow-calf pairs

The objective of this study was to evaluate cow and calf performance while strip grazing a cover crop mix consisting of radishes, turnips, oats, and common vetch (CC) compared to a traditional, drylot management system (CON) of the Iowa State University Beef Teaching Farm fall-calving herd. The study was repeated for 3 consecutive years to account for weather variation.  Each growing season presented a unique situation. With a targeted seeding date of August 1st each year, yields ranged from 2,750 – 4,830 lbs of dry matter per acre, averaging 3,952 lbs. The greatest yield was observed in 2019 while the lowest yield occurred in 2020 with drought conditions restricting growth. Growth in 2021 was intermediate, but peak yield occurred after grazing began due to a delayed killing frost. Turnout dates ranged from early-November to mid-December, and fall pairs grazed for an average of 32 days, ranging from 17 – 45 days.  Despite fluctuation in cow body weights between treatments over the years, calves on CC had equal to or greater average daily gain and final body weights compared to calves on CON. Over the 3 years, CC calves gained an average of 0.36 lbs more per head per day than their CON counterparts. This advantage may be a reflection of CC dams producing a higher quality milk due to the high-quality forage diet and the ability of calves to graze alongside their dam.  An economic analysis of the 3-year project revealed a 29% average reduction in calf cost of gain of cow-calf pairs grazing cover crops ($1.11/lbs of gain) compared to cow-calf pairs fed in a drylot setting ($1.57/lb of gain). Compared to the standardized drylot in this scenario, this efficiency boost emphasizes the value of extending the grazing season, especially in times of high feed costs.

Assessing the Microbial Contamination Levels in Milk Samples from Rural and Urban Areas: A Focus on Raisen and Bhopal Districts

AbstractThis study aimed to assess the level of microbial contamination in raw cow milk samples collected from farms in rural and urban regions of Raisen and Bhopal districts, Madhya Pradesh, India. Recent data are gathered to investigate the microbial quality of the milk samples directly, rather than relying on reported findings from other sources. The results reveal that both rural and urban milk samples exhibit significantly higher bacterial colony counts compared to acceptable limits for total viable count. The average total viable count for urban samples is 8.699 log CFU mL−1, with a standard deviation of 0.821 log CFU mL−1, using a sample size of 1 mL. Moreover, urban samples show a higher initial incubation count (8.728 ± 0.431 log CFU mL‐1), indicating potential unsanitary milk handling practices in urban areas. In contrast, rural milk samples demonstrate significantly lower bacterial counts (8.0778 ± 0.079 log CFU mL−1) and exhibit superior laboratory sanitization measures. Enteric bacteria such as Escherichia coli, Enterobacter species, Klebsiella species, and Shigella are found in both urban and rural milk samples after isolation. However, strains of bacterial culture specific to Pseudomonas and Salmonella species are exclusively detected in urban samples, suggesting a higher contamination level in urban areas compared to rural regions. These findings highlight the urgent need for improved milk production and handling practices in both rural and urban areas, particularly in urban settings where the contamination levels are found to be significantly higher. The results of this study contribute to the understanding of microbial quality in raw cow milk and emphasize the importance of implementing appropriate interventions to ensure the safety and quality of milk consumed by the population.

XAI-empowered IoT multi-sensor system for real-time milk adulteration detection

In response to the critical issue of milk adulteration jeopardizing both the nutritional integrity of milk and the health of consumers, this paper presents an innovative Artificial Intelligence (AI) enabled Internet of Things (IoT) based multi-sensor system. The escalating consumption of milk as a pivotal nutritional source necessitates robust measures to ensure its safety and quality. Traditional methods of detecting adulteration have shown limitations, prompting the development of an automated and advanced approach. The proposed system integrates various sensors capable of real-time measurement, including pH, electrical conductivity (EC), temperature, gas parameters, and Volatile Organic Compounds (VOC) parameters. This comprehensive approach extends to measuring key constituents of milk samples like Fat, Protein, Solids Not Fat (SNF), Lactose, and Gravity values. To address specific adulterants—Urea, Starch, Sodium Bicarbonate, Maltodextrin, and Formaldehyde—a machine learning-based ensemble technique is employed for classification. This ensemble method outperforms conventional algorithms like RF, Light GBM, and Extra Trees Classifiers, achieving an impressive 96% accuracy rate in detecting adulterants within the milk dataset. The pivotal contribution of this study lies in the development of an IoT-based data acquisition device that seamlessly integrates with the sensor system, enabling efficient and precise measurements. Additionally, XAI is used to analyse the results obtained by the proposed model. For this, a framework called SHAP (SHapley Additive exPlanations) analysis is employed to elucidate the decision-making process of the ensemble model, enhancing the interpretability of results. By virtue of its real-time monitoring capabilities and accurate classification, the AI-enabled IoT-based multi-sensor system emerges as a promising solution for addressing milk adulteration. This innovation holds the potential to bolster milk quality control measures in the dairy industry. The system's ability to swiftly detect and categorize adulterants underscores its significance in combating the pervasive issue of compromised milk quality, thereby ensuring consumer safety and fostering industry integrity.

Mining key circRNA-associated-ceRNA networks for milk fat metabolism in cows with varying milk fat percentages

BackgroundCow milk fat is an essential indicator for evaluating and measuring milk quality and cow performance. Growing research has identified the molecular functions of circular RNAs (circRNAs) necessary for mammary gland development and lactation in mammals.MethodThe present study analyzed circRNA expression profiling data in mammary epithelial cells (MECs) from cows with highly variable milk fat percentage (MFP) using differential expression analysis and weighted gene co-expression network analysis (WGCNA).ResultsA total of 309 differentially expressed circRNAs (DE-circRNAs) were identified in the high and low MFP groups. WGCNA analysis revealed that the pink module was significantly associated with MFP (r = − 0.85, P = 0.007). Parental genes of circRNAs in this module were enriched mainly in lipid metabolism-related signaling pathways, such as focal adhesion, ECM-receptor interaction, adherens junction and AMPK. Finally, six DE-circRNAs were screened from the pink module: circ_0010571, circ_0007797, circ_0002746, circ_0003052, circ_0004319, and circ_0012840. Among them, circ_0002746, circ_0003052, circ_0004319, and circ_0012840 had circular structures and were highly expressed in mammary tissues. Subcellular localization revealed that these four DE-circRNAs may play a regulatory role in the mammary glands of dairy cows, mainly as competitive endogenous RNAs (ceRNAs). Seven hub target genes (GNB1, GNG2, PLCB1, PLCG1, ATP6V0C, NDUFS4, and PIGH) were obtained by constructing the regulatory network of their ceRNAs and then analyzed by CytoHubba and MCODE plugins in Cytoscape. Functional enrichment analysis revealed that these genes are crucial and most probable ceRNA regulators in milk fat metabolism.ConclusionsOur study identified several vital circRNAs and ceRNAs affecting milk fat synthesis, providing new research ideas and a theoretical basis for cow lactation, milk quality, and breed improvement.

A smartphone-based diagnostic analyzer for point-of-care milk somatic cell counting

BackgroundMastitis, a pervasive and detrimental disease in dairy farming, poses a significant challenge to the global dairy industry. Monitoring the milk somatic cell count (SCC) is vital for assessing the incidence of mastitis and the quality of raw cow's milk. However, existing SCC detection methods typically require large-scale instruments and specialized operators, limiting their application in resource-constrained settings such as dairy farms and small-scale labs. To address these limitations, this study introduces a novel, smartphone-based, on-site SCC testing method that leverages smartphone capabilities for milk somatic cell identification and enumeration, offering a portable and user-friendly testing platform. ResultsThe central findings of our study demonstrate the effectiveness of the proposed method for counting milk somatic cells. Its on-site applicability, facilitated by the microfluidic chip, optical system, and smartphone integration, heralds a paradigm shift in point-of-care testing (POCT) for dairy farms and smaller laboratories. This approach bypasses complex processing and presents a user-friendly solution for real-time SCC monitoring in resource-limited settings. This device boasts several unique features: small size, low cost (<$1,000 total manufacturing cost and <$1 per test), and high accuracy. Remarkably, it delivers test results within just 2 min. Actual-sample testing confirmed its consistency with results from the commercial Bentley FTS/FCM cytometer, affirming the reliability of the proposed method. Overall, these results underscore the potential for transformative change in dairy farm management and laboratory testing practices. SignificanceIn summary, this study concludes that the proposed smartphone-based method significantly contributes to the accessibility and ease of SCC testing in resource-limited environments. By fostering the use of POCT technology in food safety control, particularly in the dairy industry, this innovative approach has the potential to revolutionize the monitoring and management of mastitis, ultimately benefiting the global dairy sector.

Plasma and milk metabolomics profiles in dairy cows with subclinical and clinical ketosis

Ketosis, a commonly observed energy metabolism disorder in dairy cows during the peripartal period, is distinguished by increased concentrations of β-hydroxybutyrate (BHB) in blood. This condition has a negative impact on milk production and quality, causing financial losses. An untargeted metabolomics approach was performed on plasma samples from cows between 5 and 7 DIM diagnosed as controls (CON, BHB <1.2 mM, n = 30), subclinically ketotic (SCK, 1.2 < BHB <3.0 mM, n = 30), or clinically ketotic (CK, BHB >3.0 mM, n = 30). Cows were selected from a commercial farm of 214 Holstein cows (average 305-d yield in the previous lactation of 35.42 ± 7.23 kg/d; parity, 2.41 ± 1.12; body condition score, 3.1 ± 0.45). All plasma and milk samples (n = 90) were subjected to Liquid Chromatography-Mass Spectrometry (LC-MS)-based metabolomic analysis. Statistical analyses was performed using the Graph Pad Prism 8.0, MetaboAnalyst 4.0 and R packages (version 4.1.3). Compared with the CON group, both SCK and CK groups had greater milk fat, freezing point, and fat-to-protein ratio and lower milk protein, lactose, solids-nonfat, and milk density. Within 21 d after calving, compared with CON, the SCK group experienced a reduction of 2.65 kg/d in milk yield, while the CK group experienced a decrease of 7.7 kg/d. Untargeted metabolomics analysis facilitated the annotation of a total of 5,259 and 8,423 metabolites in plasma and milk. Differentially affected metabolites were screened in CON vs. SCK, CON vs. CK, and SCK vs. CK (unpaired t-test, False discovery rate <0.05; and absolute value of log(2)-fold change >1.5). A total of 1,544 and 1,888 differentially affected metabolites were detected in plasma and milk. In plasma, glycerophospholipid metabolism, pyrimidine metabolism, tryptophan metabolism, sphingolipid metabolism, amino sugar and nucleotide sugar metabolism, phenylalanine metabolism, steroid hormone biosynthesis were identified as significant pathways. Weighted gene co-expression network analysis (WGCNA) indicated that tryptophan metabolism is a key pathway associated with the occurrence and development of ketosis. Increases in 5-Hydroxytryptophan and decreases in kynurenine and 3-indoleacetic acid in SCK and CK were suggestive of an impact at the gut level. The decrease of most glycerophospholipids indicated that ketosis is associated with disordered lipid metabolism. For milk, pyrimidine metabolism, purine metabolism, pantothenate and CoA biosynthesis, amino sugar and nucleotide sugar metabolism, nicotinate and nicotinamide metabolism, sphingolipid metabolism, fatty acid degradation were identified as significant pathways. The WGCNA indicated that purine and pyrimidine metabolism in plasma was highly correlated with milk yield during the peripartal period. Alterations in purine and pyrimidine metabolism characterized ketosis, with lower levels of these metabolites in both milk and blood underscoring reduced efficiency in nitrogen metabolism. Our results may help to establish a foundation for future research investigating mechanisms responsible for the occurrence and development of ketosis in peripartal cows.

Understanding the causes and consequences of variability in the compositional quality of milk

Understanding the causes and consequences of variability in the compositional quality of milk

Influence of Magnetic Induction Power on Physico-Chemical and Microbial Quality of Milk

Influence of Magnetic Induction Power on Physico-Chemical and Microbial Quality of Milk

Total bacterial count and somatic cell count in bulk and individual goat milk around kidding: two longitudinal observational studies.

Total bacterial count (TBC) and somatic cell count (SCC) are important quality parameters in goat milk. Exceeding the bulk milk TBC (BMTBC) thresholds leads to price penalties for Dutch dairy goat farmers. Controlling these milk quality parameters can be challenging, especially around kidding. First, we describe the variation and the peaks around kidding of TBC and SCC in census data on Dutch bulk milk over the last 22 years. Second, to explore causes of these elevations, we studied the variation of TBC and SCC in individual goat milk from 3 weeks before to 5 weeks after kidding and their association with systemic response markers interferon-γ (IFN-γ), calprotectin, β-hydroxybutyrate (BHB), body condition score (BCS) and fecal consistency. We visited 4 Dutch dairy goat farms weekly for 10 to 16 weeks around kidding. Some of the goats had been dried off, other goats were milked continuously throughout pregnancy. A total of 1,886 milk samples from 141 goats were collected for automated flowcytometric quantification of TBC and SCC measurement. IFN-γ, calprotectin and BHB were determined twice in blood of the same goats, most samples were collected after kidding. The BCS and fecal consistency were scored visually before and after kidding. We found a strong correlation between TBC and SCC (Spearman's rho = 0.87) around kidding. Furthermore, in the third week before kidding, the average TBC (5.67 log10 cfu/mL) and SCC (6.70 log10 cells/mL) were significantly higher compared with the fifth week after kidding, where the average TBC decreased to 4.20 log10 cfu/mL and the average SCC decreased to 5.92 log10 cells/mL. In multivariable linear regression models, farm and stage of lactation were significantly associated with TBC and SCC, but none of the systemic response markers correlated with TBC or SCC. In conclusion, TBC and SCC in dairy goats were high in late lactation and decreased shortly after parturition. For SCC, the dilution effect might have caused the decrease, but this was not plausible for TBC. Moreover, the excretion of bacteria and cells in goat milk was not associated with the selected systemic response markers that were chosen as a read out for general immunity status, intestinal health and metabolic diseases. Therefore, we assume that the TBC increase before kidding and the decrease after parturition is caused by other systemic, possibly hormonal, processes. To reduce BMTBC and BMSCC, it would be advisable to keep milk of goats with highest numbers of bacteria and cells in their milk out of the bulk milk during end lactation. Further studies are needed to investigate the effects of withholding this end lactation milk from the bulk tank.

Enhancing Dairy Production through Virtual Reality: A Comparative Study of Milk Quality and Quantity in Open and Closed Environments

Abstract: In dairy production, understanding the factors influencing the milk production is crucial for enhancing productivity and ensuring quality. The research revolves around the concept of usage of Virtual Reality (VR) Technology for stimulating milk extraction by contrasting the result of Open and Close cow farming in India. A comparative analysis was conducted on milk quality and quantity between Open and Close cows, with a focus on the impact of pre and post production VR exposure on Close cow farms. This study comprises a summation of (no of cows) samples gathered from diverse geographical areas engulfing both (Open and Closed) settings. The study exhibits the effectiveness of VR bridging the gap between Close and Open environments. Findings suggest that VR has the potential to harmonize the milk production environment and enhance overall production.The dairy milk production is based on changing environment and geographical area

An automated fluorescent immunoassay for on-site screening of AFM1 in raw milk at the ppt level

In the dairy industry, the quality of raw milk as it is collected from dairy farmers must be strictly ensured. Therefore, when on-site screening of typical carcinogens in raw milk, this technology must simultaneously be convenient to operate and highly sensitive. Here, an automated and fluorescent immunoassay system for screening trace amounts of aflatoxin M1 (AFM1) in raw milk was developed. The whole immunoassay procedure can be processed in a true “sample-to-results” paradigm, avoiding the tedious manual operation of the traditional indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) method. In addition, we designed an integrated fluorescent spectrometer that can quantitatively measure fluorescent signals with high sensitivity. The automated and fluorescent immunoassay system can screen AFM1 in raw milk samples with an ultra low limit of detection (4.7 pg/mL in raw milk). The half maximal inhibitory concentration (IC50) was 8.3 pg/mL, which is approximately 4-fold lower than that of traditional ic-ELISA. When the system was applied to screen actual raw milk samples, the recovery rates ranged from 79.4% to 104.6%, with a coefficient of variation ranging from 8.9% to 15.2%. Overall, we believe that the automated and fluorescent immunoassay system can provide a one-stop solution that is user-friendly and highly sensitive for screening trace AFM1 contaminants in raw milk.