Influence of lactic acid fermentation on the microbiological parameters, biogenic amines, and volatile compounds of bovine colostrum

In this study we hypothesized that the relations between the bovine colostrum (BC) microbiota, biogenic amine (BA) as well as volatile compound (VC) profiles can lead to new deeper insights concerning the BC changes during the biological preservation. To implement such an aim, BC samples were collected from 5 farms located in Lithuania and fermented with Lactiplantibacillus plantarum and Lacticaseibacillus paracasei strains. Nonfermented and fermented BC were subjected to microbiological [lactic acid bacteria (LAB), Escherichia coli, and total bacteria (TBC), total Enterobacteriaceae (TEC) and total mold and yeast (M-Y) viable counts] and physicochemical (pH, color coordinates, BA content and VC profile) parameters evaluation, and the relationship between the tested parameters were also further analyzed. In comparison pH and dry matter (DM) of nonfermented samples, significant differences were not found, and pH of BC was, on average, 6.30, and DM, on average, 27.5%. The pH of fermented samples decreased, on average, until 4.40 in Lp. plantarum fermented group, and, on average, until 4.37 in Lc. paracasei fermented group. Comparing color characteristics among nonfermented BC groups, significant differences between lightness (L*) and yellowness (b*) were not detected, however, the origin (i.e., agricultural company), LAB strain used for fermentation and the interaction between these factors were statistically significant on BC redness (a*) coordinate. The microbial contamination among all the tested BC groups was similar. However, different LAB strains used for BC fermentation showed different effects toward the microbial contamination reduction, and specifically Lc. paracasei was more effective than Lp. plantarum strain. Predominant BA in BC were putrescine and cadaverine. The main VC in nonfermented and fermented BC were decane, 2-ethyl-1-hexanol, dodecane, 1,3-di-tert-butylbenzene, 3,6-dimethyldecane and tetradecane. Moreover, this study showed worrying trends with respect to the frozen colostrum storage, because most of the dominant VC in BC were contaminants from the packaging material. Additionally, significant correlations between separate VC and microbial contamination were obtained. Finally, these experimental results showed that the separate VC in BC can be an important marker for biological as well as chemical contamination of BC. Also, it should be pointed out that despite the fermentation with LAB is usually described as a safe and natural process with many advantages, control of BA in the end product is necessary.

Importance of Mid-Infrared Spectra Regions for the Prediction of Mastitis and Ketosis in Dairy Cows

Mid-infrared (MIR) spectroscopy is routinely applied to determine major milk components, such as fat and protein. Moreover, it is used to predict fine milk composition and various traits pertinent to animal health. MIR spectra indicate an absorbance value of infrared light at 1060 specific wavenumbers from 926 to 5010 cm−1. According to research, certain parts of the spectrum do not contain sufficient information on traits of dairy cows. Hence, the objective of the present study was to identify specific regions of the MIR spectra of particular importance for the prediction of mastitis and ketosis, performing variable selection analysis. Partial least squares discriminant analysis (PLS-DA) along with three other statistical methods, support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and random forest (RF), were compared. Data originated from the Austrian milk recording and associated health monitoring system (GMON). Test-day data and corresponding MIR spectra were linked to respective clinical mastitis and ketosis diagnoses. Certain wavenumbers were identified as particularly relevant for the prediction models of clinical mastitis (23) and ketosis (61). Wavenumbers varied across four distinct statistical methods as well as concerning different traits. The results indicate that variable selection analysis could potentially be beneficial in the process of modeling.

Relation between Color and Chemical Composition of Dromedary Camel Colostrum

Camel milk industrialization faces technological problems related to the presence of colostrum in milk. The determination of color parameters may serve to differentiate between colostrum and milk. This work aimed to study the relationship between the chemical composition of camel colostrum and milk and their colors. Samples of colostrum were collected at 2, 12, 24, 48, 72, 96, 120, 144, 168, and 360 h postpartum (n = 16), and their physicochemical properties (pH, acidity, viscosity, color, dry matter, ash, protein, and fat) were analyzed. The results show that all the components decreased during the first 3 days except fat. The content of this later increased from zero in the three sampling on the first day (2, 12, and 24 h) to 1.92 ± 0.61% at 48 h postpartum. The amount of total dry matter and protein decreased from 20.95 ± 3.63% and 17.43 ± 4.28% to 13.05 ± 0.81% and 3.71 ± 0.46%, respectively, during the first 7 days postpartum. There was a weak correlation between the brightness (L*) of the camel milk and its contents of dry matter, protein, and fat; however, these parameters were strongly correlated with redness (a*) and yellowness (b*). Ash content was poorly correlated with the color parameters. Hence, the measurement of the color parameters of camel colostrum and milk can be a new tool to evaluate their quality.

Electrochemical Biosensor Designed to Distinguish Tetracyclines Derivatives by ssDNA Aptamer Labelled with Ferrocene

Controlling food safety and preventing the growing spread of antibiotics into food products have been challenging problems for the protection of human health. Hence, the development of easy-to-use, fast, and sensitive analytical methods for the detection of antibiotics in food products has become one of the priorities in the food industry. In this paper, an electrochemical platform based on the ssDNA aptamer for the selective detection of tetracycline has been proposed. The aptasensor is based on a thiolated aptamer, labelled with ferrocene, which has been covalently co-immobilized onto a gold electrode surface with 6-mercaptohexan-1-ol. The changes in the redox activity of ferrocene observed on the aptamer-antibiotics interactions have been the basis of analytical signal generation registered by square-wave voltammetry. Furthermore, the detection of tetracycline-spiked cow milk samples has been successfully demonstrated. The limits of detection (LODs) have been obtained of 0.16 nM and 0.20 nM in the buffer and spiked cow milk, respectively, which exceed the maximum residue level (225 nM) more than 1000 times. The proposed aptasensor offers high selectivity for tetracycline against other structurally related tetracycline derivatives. The developed biosensor characterized by simplicity, a low detection limit, and high reliability shows practical potential for the detection of tetracycline in animal-origin milk.

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%−72.8%)), GEA (75.9% (62.4%−86.5%)), Lely (78.2% (67.4%−86.8%)), and Lemmer-Fullwood (67.6% (50.2%−82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%−90.7%)), GEA (79.2% (77.1%−81.2%)), Lely (86.2% (84.6%−87.7%)), and Lemmer-Fullwood (92.2% (90.8%−93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm.

Escherichia coli Mastitis in Dairy Cattle: Etiology, Diagnosis, and Treatment Challenges

Bovine mastitis is an inflammation of the udder tissue parenchyma that causes pathological changes in the glandular tissue and abnormalities in milk leading to significant economic losses to the dairy industry across the world. Mammary pathogenic Escherichia (E.) coli (MPEC) is one of the main etiologic agents of acute clinical mastitis in dairy cattle. MPEC strains have virulence attributes to resist the host innate defenses and thrive in the mammary gland environment. The association between specific virulence factors of MPEC with the severity of mastitis in cattle is not fully understood. Furthermore, the indiscriminate use of antibiotics to treat mastitis has resulted in antimicrobial resistance to all major antibiotic classes in MPEC. A thorough understanding of MPEC’s pathogenesis and antimicrobial susceptibility pattern is required to develop better interventions to reduce mastitis incidence and prevalence in cattle and the environment. This review compiles important information on mastitis caused by MPEC (e.g., types of mastitis, host immune response, diagnosis, treatment, and control of the disease) as well as the current knowledge on MPEC virulence factors, antimicrobial resistance, and the dilemma of MPEC as a new pathotype. The information provided in this review is critical to identifying gaps in knowledge that will guide future studies to better design diagnostic, prevent, and develop therapeutic interventions for this significant dairy disease.

Productional data of primiparous dairy cows reared in different social environments during the first 8 weeks after birth

This paper is composed of 5 datasets describing primiparous milk production, reproduction, body weight, activity and whole life longevity and reproductional data in dairy cows that had been reared either with or without mother for the first four days after birth and either in single housing or housing in groups of four between 1 and 8 weeks of age. The datasets contain the following variables- survival to the first lactation, date of first successful insemination, milk parameters per day (such as sum of milk yield, milk electrical conductivity and milking time), activity and body weight, all these collected during the first standardized lactation of 305 days. Cows’ longevity, reproduction and other management events were recorded during the whole life of experimental animals (such as inseminations, pregnancy diagnostics, group changes etc.). Calves’ body weight was measured first 12 weeks of life of the experimental animals. The data include the information about the type of housing (with or without mother, individual vs group housing) in the early ontogeny period and two different breeds (Holstein and Czech Fleckvieh). Data on the milk parameters, body weight and activity were collected twice a day by commercially used precision dairy monitoring technologies. Data on survival to the first lactation, longevity, first successful insemination and other events were recorded by farm managers on farm basis. Data on body weight of animals during early ontogeny were taken after birth, at 4 d of age, at 7 d of age, and then weekly until 12 weeks of age. The data can be used for further analyses of the influence of parameters from early ontogeny on cow performance, especially during the first lactation. This information can be useful for researchers and other stakeholders investigating the influence of early ontogenetic social environment on the dairy cattle performance and welfare.

Revisiting the Relationships between Fat-to-Protein Ratio in Milk and Energy Balance in Dairy Cows of Different Parities, and at Different Stages of Lactation

Simple Summary

Data from 840 Holstein-Friesian cows (1321 lactations) were used to evaluate trends in fat-to-protein ratios in milk (FPR), and the use of FPR as an indicator of energy balance (EB). The fat-to-protein ratio was negatively related to EB, and this relationship became more negative with increased parity. Regression slopes describing linear relationships between FPR and EB differed over time, although trends were inconsistent. Similarly, ‘High’ FPR scores in milk (≥1.5) were consistently associated with a greater negative energy balance, milk yields, body weight loss, and plasma non-esterified fatty acid concentrations; however, their relationships with dry matter intake did not follow a clear trend. Although FPR can provide an indication of EB at a herd level, this analysis suggests that FPR cannot accurately predict the EB of individual cows.

Abstract

A statistical re-assessment of aggregated individual cow data was conducted to examine trends in fat-to-protein ratio in milk (FPR), and relationships between FPR and energy balance (EB, MJ of ME/day) in Holstein-Friesian dairy cows of different parities, and at different stages of lactation. The data were collected from 27 long-term production trials conducted between 1996 and 2016 at the Agri-Food and Biosciences Institute (AFBI) in Hillsborough, Northern Ireland. In total, 1321 lactations (1 to 20 weeks in milk; WIM), derived from 840 individual cows fed mainly grass silage-based diets, were included in the analysis. The energy balance was calculated daily and then averaged weekly for statistical analyses. Data were further split in 4 wk. intervals, namely, 1–4, 5–8, 9–12, 13–16, and 17–20 WIM, and both partial correlations and linear regressions (mixed models) established between the mean FPR and EB during these periods. Three FPR score categories (‘Low’ FPR, <1.0; ‘Normal’ FPR, 1.0–1.5; ‘High’ FPR, >1.5) were adopted and the performance and EB indicators within each category were compared. As expected, multiparous cows experienced a greater negative EB compared to primiparous cows, due to their higher milk production relative to DMI. Relatively minor differences in milk fat and protein content resulted in large differences in FPR curves. Second lactation cows displayed the lowest weekly FPR, and this trend was aligned with smaller BW losses and lower concentrations of non-esterified fatty acids (NEFA) until at least 8 WIM. Partial correlations between FPR and EB were negative, and ‘greatest’ in early lactation (1–4 WIM; r = −0.38 on average), and gradually decreased as lactation progressed across all parities (17–20 WIM; r = −0.14 on average). With increasing parity, daily EB values tended to become more negative per unit of FPR. In primiparous cows, regression slopes between FPR and EB differed between 1–4 and 5–8 WIM (−54.6 vs. −47.5 MJ of ME/day), while differences in second lactation cows tended towards significance (−57.2 vs. −64.4 MJ of ME/day). Irrespective of the lactation number, after 9–12 WIM, there was a consistent trend for the slope of the linear relationships between FPR and EB to decrease as lactation progressed, with this likely reflecting the decreasing milk nutrient demands of the growing calf. The incidence of ‘High’ FPR scores was greatest during 1–4 WIM, and decreased as lactation progressed. ‘High’ FPR scores were associated with increased energy-corrected milk (ECM) yields across all parities and stages of lactation, and with smaller BW gains and increasing concentrations (log transformed) of blood metabolites (non-esterified fatty acid, NEFA; beta-hydroxybutyrate, BHB) until 8 WIM. Results from the present study highlight the strong relationships between FPR in milk, physiological changes, and EB profiles during early lactation. However, while FPR can provide an indication of EB at a herd level, the large cow-to-cow variation indicates that FPR cannot be used as a robust indicator of EB at an individual cow level.

Unraveling the Relationship between Milk Yield and Quality at the Test Day with Rumination Time Recorded by a PLF Technology

Simple Summary

Precision livestock farming, by real time monitoring of dairy cows, has the potential to generate a huge amount of data to be used for farm management purposes, as well as in breeding programs. Daily rumination time (RT) recorded by commercial systems is promising in this context because it may be related to individual milk yield and composition. However, it is necessary to assess the ability of sensor data to be used in a predictive model, but also to evaluate and standardize the correct phenotypes, and how they are related to individual variability rather than from other sources. RT data and milk test day (TD) records collected from 691 cows, monitored for thirteen months, were analyzed for the already mentioned goals and to better characterize the effect of high-, medium- and low-level daily RT on milk yield and composition. Our results showed that “animal” in a farm major contributed to the RT total variability, confirming a possible use in breeding program. The higher RT class reported the best productive performance for milk and each solid yield, in spite of a small reduction in their contents, and appears to be related to a higher degree of saturation in the fatty acid profile.

Abstract

The study aimed to estimate the components of rumination time (RT) variability recorded by a neck collar sensor and the relationship between RT and milk composition. Milk test day (TD) and RT data were collected from 691 cows in three farms. Daily RT data of each animal were averaged for 3, 7, and 10 days preceding the TD date (RT_D). Variance component analysis of RT_D, considering the effects of farm, cow, parity, TD date, and lactation phase, showed that a farm, followed by a cow, had major contributions to the total variability. The RT₁₀ variable best performed on TD milk yield and quality records across models by a multi-model inference approach and was adopted to study its relationship with milk traits, by linear mixed models, through a 3-level stratification: low (LRT₁₀ ≤ 8 h/day), medium (8 h/day < MRT₁₀ ≤ 9 h/day), and high (HRT₁₀ > 9 h/day) RT. Cows with HRT₁₀ had greater milk, fat, protein, casein, and lactose daily yield, and lower fat, protein, casein contents, and fat to protein ratio compared to MRT₁₀ and LRT₁₀. Higher percentages of saturated fatty acid and lower unsaturated and monounsaturated fatty acid were found in HRT₁₀, with respect to LRT₁₀ and MRT₁₀ observations.

Associations between Mammary Gland Echotexture and Milk Composition in Cows

Simple Summary

Transcutaneous ultrasonography of the four quarters of the cow’s mammary gland with the 5-MHz ultrasound transducer combined with computer-assisted analysis of the resultant grey-scale images have the makings of an inexpensive and rapid technique to determine certain physicochemical properties of the pooled milk. The latter include crude protein, casein and lactose content. The relative ease and practically unlimited frequency with which this method can be used in farm settings makes it an attractive alternative to laboratory testing of milk samples. More studies are needed to determine the suitability of this approach for detecting changes in milk chemical composition in animals with mastitis.

Abstract

Thirty clinically healthy Holstein-Friesian cows underwent twice daily machine milking and ultrasonographic examinations of the udder just prior to and after milking. Digital ultrasonographic images of each udder quarter were subjected to computer-assisted echotextural analyses to obtain mean numerical pixel values (NPVs) and pixel heterogeneity (PSD) of the mammary gland parenchyma. The average milk yield and pH were higher (p < 0.05) in the morning, whereas crude fat, total solids, solids non-fat and citric acid content were higher (p < 0.05) during the evening milking period. Mean NPVs and PSDs of the mammary gland parenchyma were greater (p < 0.05) after than before milking. There were significant correlations among echotextural characteristics of the udder and protein percentage, lactose content and freezing point depression determined in the milk samples collected in the morning and crude protein, casein, lactose and solids non-fat in the evening. Our results can be interpreted to suggest that computerized analysis of the mammary gland ultrasonograms has the makings of a technique for estimating non-fat milk constituents in cows. However, future validating studies are necessary before this method can be employed in commercial settings and research. Moreover, significant inter-quarter differences in udder echogenicity may necessitate further echotextural studies of separate quarters.

The Internet of Things enhancing animal welfare and farm operational efficiency

The growth in wirelessly enabled sensor network technologies has enabled the low cost deployment of sensor platforms with applications in a range of sectors and communities. In the agricultural domain such sensors have been the foundation for the creation of decision support tools that enhance farm operational efficiency. This Research Reflection illustrates how these advances are assisting dairy farmers to optimise performance and illustrates where emerging sensor technology can offer additional benefits. One of the early applications for sensor technology at an individual animal level was the accurate identification of cattle entering into heat (oestrus) to increase the rate of successful pregnancies and thus optimise milk yield per animal. This was achieved through the use of activity monitoring collars and leg tags. Additional information relating to the behaviour of the cattle, namely the time spent eating and ruminating, was subsequently derived from collars giving further insights of economic value into the wellbeing of the animal, thus an enhanced range of welfare related services have been provisioned. The integration of the information from neck-mounted collars with the compositional analysis data of milk measured at a robotic milking station facilitates the early diagnosis of specific illnesses such as mastitis. The combination of different data streams also serves to eliminate the generation of false alarms, improving the decision making capability. The principle of integrating more data streams from deployed on-farm systems, for example, with feed composition data measured at the point of delivery using instrumented feeding wagons, supports the optimisation of feeding strategies and identification of the most productive animals. Optimised feeding strategies reduce operational costs and minimise waste whilst ensuring high welfare standards. These IoT-inspired solutions, made possible through Internet-enabled cloud data exchange, have the potential to make a major impact within farming practices. This paper gives illustrative examples and considers where new sensor technology from the automotive industry may also have a role.

Genetic relationships of lactose and freezing point with minerals and coagulation traits predicted from milk mid-infrared spectra in Holstein cows

The aim of the present study was to assess the relationships of lactose percentage (LP), lactose yield (LY), and freezing point (FRP) with minerals and coagulation properties predicted from mid-infrared spectra in bovine milk. To achieve this purpose, we analyzed 54,263 test-day records of 4,297 Holstein cows to compute (co)variance components with a linear repeatability animal model. Parity, stage of lactation, season of calving, and herd-test-date were included as fixed effects in the model, and additive genetic animal, within- and across-lactation permanent environment, and residual were included as random effects. Lactose percentage was more heritable (0.405 ± 0.027) than LY (0.121 ± 0.021) and FRP (0.132 ± 0.014). Heritabilities (± standard error) of predicted milk minerals varied from 0.375 ± 0.027 for Na to 0.531 ± 0.028 for P, and those of milk coagulation properties ranged from 0.348 ± 0.052 for rennet coagulation time to 0.430 ± 0.026 for curd firming time. Lactose percentage showed favorable (negative) genetic correlations with milk somatic cell score (SCS) and FRP, and it was almost uncorrelated with casein-related minerals (Ca and P) and coagulation properties. Moreover, LP was strongly correlated with Na (-0.783 ± 0.022), a mineral known to increase in the presence of intramammary infection (IMI) and high somatic cell count. Indeed, Na is the main osmotic replacer of lactose in mastitic milk when the blood-milk barrier is altered during IMI. Being strongly associated with milk yield, LY did not favorably correlate with coagulation properties, likely because of the negative correlation of this trait with protein and casein percentages. Milk FRP presented moderate and null genetic associations with Na and SCS, respectively. Results of the present study suggest that the moderate heritability of LP and its genetic correlations with IMI-related traits (Na and SCS) could be exploited for genetic selection against mastitis. Moreover, selection for LP would not impair milk coagulation characteristics or Ca and P content, which are important for cheesemaking.

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data 'as is', further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

Discrimination of Milks with a Multisensor System Based on Layer-by-Layer Films

A nanostructured electrochemical bi-sensor system for the analysis of milks has been developed using the layer-by-layer technique. The non-enzymatic sensor [CHI+IL/CuPcS]₂, is a layered material containing a negative film of the anionic sulfonated copper phthalocyanine (CuPcS) acting as electrocatalytic material, and a cationic layer containing a mixture of an ionic liquid (IL) (1-butyl-3-methylimidazolium tetrafluoroborate) that enhances the conductivity, and chitosan (CHI), that facilitates the enzyme immobilization. The biosensor ([CHI+IL/CuPcS]₂-GAO) results from the immobilization of galactose oxidase on the top of the LbL layers. FTIR, UV⁻vis, and AFM have confirmed the proposed structure and cyclic voltammetry has demonstrated the amplification caused by the combination of materials in the film. Sensors have been combined to form an electronic tongue for milk analysis. Principal component analysis has revealed the ability of the sensor system to discriminate between milk samples with different lactose content. Using a PLS-1 calibration models, correlations have been found between the voltammetric signals and chemical parameters measured by classical methods. PLS-1 models provide excellent correlations with lactose content. Additional information about other components, such as fats, proteins, and acidity, can also be obtained. The method developed is simple, and the short response time permits its use in assaying milk samples online.

The Effect of Lipopolysaccharide-Induced Experimental Bovine Mastitis on Clinical Parameters, Inflammatory Markers, and the Metabolome: A Kinetic Approach

Mastitis is an inflammatory condition of the mammary tissue and represents a major problem for the dairy industry worldwide. The present study was undertaken to study how experimentally induced acute bovine mastitis affects inflammatory parameters and changes in the metabolome. To this end, we induced experimental mastitis in nine cows by intramammary infusion of 100 µg purified Escherichia coli lipopolysaccharide (LPS) followed by kinetic assessments of cytokine responses (by enzyme-linked immunosorbent assay), changes in the metabolome (assessed by nuclear magnetic resonance), clinical parameters (heat, local pain perception, redness, swelling, rectal temperature, clot formation, and color changes in the milk), and milk somatic cell counts, at several time points post LPS infusion. Intramammary LPS infusion induced clinical signs of mastitis, which started from 2 h post infusion and had returned to normal levels within 24–72 h. Milk changes were seen with a delay compared with the clinical signs and persisted for a longer time. In parallel, induction of IL-6 and TNF-α were seen in milk, and there was also a transient elevation of plasma IL-6 whereas plasma TNF-α was not significantly elevated. In addition, a robust increase in CCL2 was seen in the milk of LPS-infused cows, whereas G-CSF, CXCL1, and histamine in milk were unaffected. By using a metabolomics approach, a transient increase of plasma lactose was seen in LPS-induced cows. In plasma, significant reductions in ketone bodies (3-hydroxybutyrate and acetoacetate) and decreased levels of short-chain fatty acids, known to be major products released from the gut microbiota, were observed after LPS infusion; a profound reduction of plasma citrate was also seen. Intramammary LPS infusion also caused major changes in the milk metabolome, although with a delay in comparison with plasma, including a reduction of lactose. We conclude that the LPS-induced acute mastitis rapidly affects the plasma metabolome and cytokine induction with similar kinetics as the development of the clinical signs, whereas the corresponding effects in milk occurred with a delay.

Microwave dielectric spectroscopy study of water dynamics in normal and contaminated raw bovine milk

The role of water in bovine milk is more complicated than that of a background solvent. To understand the interaction between water and the constituents of milk, an extensive dielectric study of the γ-dispersion of raw bovine milk was carried out over the frequency range 0.1-50GHz and the interval of temperatures (10°C-42°C). Samples were provided by utilizing an extended donor pool. The results reveal that the temperature dependence of the characteristic relaxation times is described by the Arrhenius law. Furthermore, it conforms to a Meyer-Neldel compensation, whereby the pre-factor of the relaxation times is dependent on the activation energy. This entropy/enthalpy compensation is traced to the interaction between bulk water dynamic clusters and other milk constituents. A statistical correlation between the Somatic Cell Count, a traditional measure of milk quality, and the relaxation times is provided as well, opening new vistas for the industrial classification of milk.

Quarter vs. composite colostrum composition assessed by Brix refractometry, specific gravity and visual color appearance in primiparous and multiparous dairy cows

The control of colostrum quality is essential for successful calf rearing. Instruments for on-farm colostrum quality determination are mostly utilized for testing composite colostrum samples, but do not take potential variation between quarters into account. In cases of low composite colostrum quality, feeding of better quality colostrum from individual quarters might be beneficial. The objective of the present study was to identify relationships between colostrum color, colostrum quality and composition. Besides laboratory methods, a colostrometer and a Brix refractometer were used to assess colostrum quality at quarter levels. Quarter and composite colostrum samples from 17 primiparous and 11 multiparous Holstein cows were analyzed for total IgG, fat, protein and lactose content; color was measured by a spectrophotometer. In the present study, an IgG concentration below 50 g/L as determined by ELISA was found in 14.3% of the analyzed quarter samples. Concentration and mass of IgG in composite colostrum samples were greater in multiparous compared with primiparous cows. Specific gravity (SG) of colostrum of individual and composite samples was lower in primiparous compared with multiparous cows. Milk fat content was greater in quarter and composite colostrum samples of primiparous compared with multiparous dairy cows. No clear relationships between IgG content and SG, Brix, and the color space coordinates L*, a*, and b* were detected. Interestingly, results indicate that despite a similar range of the variables investigated, correlations between those parameters can differ at quarter compared to composite level. Not only for SG and Brix determination, but also for the color space coordinates measured, correlation coefficients with IgG concentration of the respective samples were greater at a composite compared with the individual quarter level. In conclusion, accuracy and limitations of on-farm instruments estimating colostrum quality apply to both quarter colostrum samples and composite evaluations. Identification of quarters with superior colostrum quality would possibly be a way to improve the immunization of newborn calves. However, the potential on-farm methods validated in the present study to estimate quarter colostrum quality are not sufficiently sensitive to distinguish between quarters. This is due to the variation of gross colostrum composition between individual quarters of a cow.

Capitalizing on fine milk composition for breeding and management of dairy cows

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest.

Calibration of an automated California mastitis test with focus on the device-dependent variation

The aim of the paper was to estimate the accuracy of the metrology of an installed indirect on-line sensor system based on the automated California Mastitis Test (CMT) with focus on the prior established device-dependent variation. A sensor calibration was implemented. Therefore, seven sensors were tested with similar trials on the dairy research farm Karkendamm (Germany) on two days in July 2011 and January 2012. Thereby, 18 mixed milk samples from serial dilutions were fourfold recorded at every sensor. For the validation, independent sensor records with corresponding lab somatic cell score records (LSCS) in the period between both trials were used (n = 1,357). From these records for each sensor a polynomial regression function was calculated. The predicted SCS (PSCS) was obtained for each sensor with the previously determined regression coefficients. Pearson correlation coefficients between PSCS and LSCS were established for each sensor and ranged between r = 0.57 and r = 0.67. Comparing the results with the correlation coefficients between the on-line SCS (OSCS) and the LSCS (r = 0.20 to 0.57) for every sensor, the calibration showed the tendency to improve the installed sensor system.

Colour measurement of colostrum for estimation of colostral IgG and colostrum composition in dairy cows

Instruments for on-farm determination of colostrum quality such as refractometers and densimeters are increasingly used in dairy farms. The colour of colostrum is also supposed to reflect its quality. A paler or mature milk-like colour is associated with a lower colostrum value in terms of its general composition compared with a more yellowish and darker colour. The objective of this study was to investigate the relationships between colour measurement of colostrum using the CIELAB colour space (CIE L*=from white to black, a*=from red to green, b*=from yellow to blue, chroma value G=visual perceived colourfulness) and its composition. Dairy cow colostrum samples (n=117) obtained at 4·7±1·5 h after parturition were analysed for immunoglobulin G (IgG) by ELISA and for fat, protein and lactose by infrared spectroscopy. For colour measurements, a calibrated spectrophotometer was used. At a cut-off value of 50 mg IgG/ml, colour measurement had a sensitivity of 50·0%, a specificity of 49·5%, and a negative predictive value of 87·9%. Colostral IgG concentration was not correlated with the chroma value G, but with relative lightness L*. While milk fat content showed a relationship to the parameters L*, a*, b* and G from the colour measurement, milk protein content was not correlated with a*, but with L*, b*, and G. Lactose concentration in colostrum showed only a relationship with b* and G. In conclusion, parameters of the colour measurement showed clear relationships to colostral IgG, fat, protein and lactose concentration in dairy cows. Implementation of colour measuring devices in automatic milking systems and milking parlours might be a potential instrument to access colostrum quality as well as detecting abnormal milk.

Implementation of multivariate cumulative sum control charts in mastitis and lameness monitoring

This study analyzed the methodology and applicability of multivariate cumulative sum (MCUSUM) charts for early mastitis and lameness detection. Data used were recorded on the Karkendamm dairy research farm, Germany, between August 2008 and December 2010. Data of 328 and 315 cows in their first 200 d in milk were analyzed for mastitis and lameness detection, respectively. Mastitis as well as lameness was specified according to veterinary treatments. Both diseases were defined as disease blocks. Different disease definitions for mastitis and lameness (2 for mastitis and 3 for lameness) varied solely in the sequence length of the blocks. Only the days before the treatment were included in the disease blocks. Milk electrical conductivity, milk yield, and feeding patterns (feed intake, number of trough visits, and feeding time) were used for the recognition of mastitis. Pedometer activity and feeding patterns were used for lameness detection. To exclude biological trends and obtain independent observations, the values of each input variable were either preprocessed by wavelet filters or a multivariate vector autoregressive model. The residuals generated between the observed and filtered or observed and forecast values, respectively, were then transferred to a classic or self-starting MCUSUM chart. The combination of the 2 preprocessing methods with each of the 2 MCUSUM sum charts resulted in 4 combined monitoring systems. For mastitis as well as lameness detection requiring a block sensitivity of at least 70%, all 4 of the combined monitoring systems used revealed similar results within each of the disease definitions. Specificities of 73 to 80% and error rates of 99.6% were achieved for mastitis. The results for lameness showed that the definitions used obtained specificities of up to 81% and error rates of 99.1%. The results indicate that the monitoring systems with these study characteristics have appealing features for mastitis and lameness detection. However, they are not yet directly applicable for practical implementations.

Principal component analysis for the early detection of mastitis and lameness in dairy cows

This investigation analysed the applicability of principal component analysis (PCA), a latent variable method, for the early detection of mastitis and lameness. Data used were recorded on the Karkendamm dairy research farm between August 2008 and December 2010. For mastitis and lameness detection, data of 338 and 315 cows in their first 200 d in milk were analysed, respectively. Mastitis as well as lameness were specified according to veterinary treatments. Diseases were defined as disease blocks. The different definitions used (two for mastitis, three for lameness) varied solely in the sequence length of the blocks. Only the days before the treatment were included in the blocks. Milk electrical conductivity, milk yield and feeding patterns (feed intake, number of feeding visits and time at the trough) were used for recognition of mastitis. Pedometer activity and feeding patterns were utilised for lameness detection. To develop and verify the PCA model, the mastitis and the lameness datasets were divided into training and test datasets. PCA extracted uncorrelated principle components (PC) by linear transformations of the raw data so that the first few PCs captured most of the variations in the original dataset. For process monitoring and disease detection, these resulting PCs were applied to the Hotelling's T2 chart and to the residual control chart. The results show that block sensitivity of mastitis detection ranged from 77·4 to 83·3%, whilst specificity was around 76·7%. The error rates were around 98·9%. For lameness detection, the block sensitivity ranged from 73·8 to 87·8% while the obtained specificities were between 54·8 and 61·9%. The error rates varied from 87·8 to 89·2%. In conclusion, PCA seems to be not yet transferable into practical usage. Results could probably be improved if different traits and more informative sensor data are included in the analysis.

Invited review: sensors to support health management on dairy farms

Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process

The robustness of in-line raw milk analysis with near-infrared spectroscopy (NIRS) was tested with respect to the prediction of the raw milk contents fat, protein and lactose. Near-infrared (NIR) spectra of raw milk (n = 3119) were acquired on three different farms during the milking process of 354 milkings over a period of six months. Calibration models were calculated for: a random data set of each farm (fully random internal calibration); first two thirds of the visits per farm (internal calibration); whole datasets of two of the three farms (external calibration), and combinations of external and internal datasets. Validation was done either on the remaining data set per farm (internal validation) or on data of the remaining farms (external validation). Excellent calibration results were obtained when fully randomised internal calibration sets were used for milk analysis. In this case, RPD values of around ten, five and three for the prediction of fat, protein and lactose content, respectively, were achieved. Farm internal calibrations achieved much poorer prediction results especially for the prediction of protein and lactose with RPD values of around two and one respectively. The prediction accuracy improved when validation was done on spectra of an external farm, mainly due to the higher sample variation in external calibration sets in terms of feeding diets and individual cow effects. The results showed that further improvements were achieved when additional farm information was added to the calibration set. One of the main requirements towards a robust calibration model is the ability to predict milk constituents in unknown future milk samples. The robustness and quality of prediction increases with increasing variation of, e.g., feeding and cow individual milk composition in the calibration model.

Sensors and clinical mastitis--the quest for the perfect alert

When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models.