Effect of intramammary infection on milk electrical conductivity in Murciano-Granadina goats

Effect of the Onset of Intramammary Infection on the Electrical Conductivity of Ewe's Milk and Study of Various Algorithms for Its On-Line Detection

The aim of this study was to determine the effect of the onset of intramammary infection (IMI) on the electrical conductivity (EC) of ewe milk and assess the detection capability of various algorithms based on daily glandular milk EC measurement. An experiment was carried out with 26 Manchega sheep located at the farm of the Miguel Hernández University, Elche, Spain. The variables in milk from the gland (production, EC) were monitored daily for 2 weeks during the morning and evening milking; once infection was established in the gland, the variables were measured for a further 4 weeks. In addition, the SCC, sodium, potassium, chloride and milk macro-compositions were analysed. The sensitivity, specificity and positive and negative predictive values for IMI detection of different algorithms were calculated using the EC variable. It was observed that the onset of IMI resulted in an increase in SCC and a significant decrease in yield, and EC rose significantly when infection occurred bilaterally. The best results for IMI detection were obtained with the algorithm that detected deviations greater than 3σ of the conductivity ratio between collateral glands with respect to a moving average calculated with a time horizon of 10 days (50% sensitivity and 100% specificity).

Relationship among Milk Conductivity, Production Traits, and Somatic Cell Score in the Italian Mediterranean Buffalo

The measurement of milk electrical conductivity (EC) is a relatively simple and inexpensive technique that has been evaluated as a routine method for the diagnosis of mastitis in dairy farms. The aim of this study was to obtain further knowledge on relationships between EC, production traits and somatic cell count (SCC) in Italian Mediterranean Buffalo. The original dataset included 5411 records collected from 808 buffalo cows. Two mixed models were used to evaluate both the effect of EC on MY, PP and FP and EC at test-day, and the effect of EC on somatic cell score (SCS) by using five different parameters (EC_param), namely: EC collected at the official milk recording test day (EC_day0), EC collected 3 days before official milk recording (EC_day3), and three statistics calculated from EC collected 1, 3 and 5 days before each test-day, respectively. All effects included in the model were significant for all traits, with the only exception of the effect of EC nested within parity for FP. The relationship between EC and SCS was always positive, but of different magnitude according to the parity. The regression of EC on SCS at test-day using different EC parameters was always significant except when the regression parameter was the slope obtained from a linear regression of EC collected over the 5-day period. Moreover, in order to evaluate how well the different models fit the data, three parameters were used: the Average Information Criteria (AIC), the marginal R2 and the conditional R2. According to AIC and to both the Marginal and Conditional R2, the best results were obtained when the regression parameter was the mean EC estimated over the 5-day period.

The Impact of Mastitis on the Biochemical Parameters, Oxidative and Nitrosative Stress Markers in Goat's Milk: A Review

Goat mastitis has become one of the most frequently diagnosed conditions in goat farms, with significant economic impact on the dairy industry. Inflammation of the mammary gland poses serious consequences on milk composition, with changes regarding biochemical parameters and oxidative stress markers. The aim of this paper is to present the most recent knowledge on the main biochemical changes that occur in the mastitic milk, as well as the overall effect of the oxidative and nitrosative stress on milk components, focusing on both enzymatic and nonenzymatic antioxidant markers. Mastitis in goats is responsible for a decrease in milk production, change in protein content with pronounced casein hydrolysis, and reduction in lactose concentration and milk fat. Milk enzymatic activity also undergoes changes, regarding indigenous enzymes and those involved in milk synthesis. Furthermore, during mastitis, both the electrical conductivity and the milk somatic cell count are increased. Intramammary infections are associated with a reduced milk antioxidant capacity and changes in catalase, lactoperoxidase, glutathione peroxidase or superoxide dismutase activity, as well as reduced antioxidant vitamin content. Mastitis is also correlated with an increase in the concentration of nitric oxide, nitrite, nitrate and other oxidation compounds, leading to the occurrence of nitrosative stress.

Machine milking parameters for Murciano-Granadina breed goats

In dairy ruminants, the combination of milking parameters must ensure good milking performance without harming udder conditions. Commonly, milking conditions for goats are established without having checked the admissible limits for optimal and fast milking. The aim of this study was to establish a limit combination of machine milking parameters that improves machine milking performance without altering milkability or udder status. To this end, we studied the effect of 2 combinations (42 kPa, 120 cpm, 60% vs. 44 kPa, 120 cpm, 60% in terms of kilopascals of vacuum level, cycles per minute of pulsator rate, and percentage of pulsator ratio, respectively) on milk production and composition, milk fractioning during milking, SCC, teat tissue thickness variation after milking, and the milk emission kinetics parameters throughout 1 lactation period (6 mo). The 42 and 44 kPa measured at the vacuum gauge level became average values of 37.5 and 39.3 kPa, respectively, measured at the teat sphincter level during milking. Milk flow significantly increased and total milking time decreased 25 s with the elevation of the vacuum level from 42 to 44 kPa without any adverse effect on milk fractioning at milking. However, the use of 44 kPa also showed an increase in tissue thickness above 5%, and we observed a tendency of average conductivity of milk to increase, although without any adverse effect on SCC. It seems that 44 kPa, 120 cpm, 60% is a possible limit combination of parameters to improve milking performance without altering milkability or udder conditions. We concluded that this combination can be used for milking Murciano-Granadina breed goats in conditions similar to those of this study (mid-level milking system and 1 milking/d), although further studies are necessary to verify its application in the case of 2 milkings/d.

Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models

Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical mastitis leads to the use of antibiotics with consequent increased risk of the emergence of antibiotic-resistant bacteria. Therefore, early detection of infected cows is of great importance. The Somatic Cell Count (SCC) day-test used for mastitis surveillance, gives data that fluctuate widely between days, creating questions about its reliability and early prediction power. The recent identification of risk parameters of sub-clinical mastitis based on milking parameters by machine learning models is emerging as a promising new tool to enhance early prediction of mastitis occurrence. To develop the optimal approach for early sub-clinical mastitis prediction, we implemented 2 steps: (1) Finding the best statistical models to accurately link patterns of risk factors to sub-clinical mastitis, and (2) Extending this application from the farms tested to new farms (method generalization). Herein, we applied various machine learning-based prediction systems on a big milking dataset to uncover the best predictive models of sub-clinical mastitis. Data from 364,249 milking instances were collected by an electronic automated in-line monitoring system where milk volume, lactose concentration, electrical conductivity (EC), protein concentration, peak flow and milking time for each sample were measured. To provide a platform for the application of the models developed to other farms, the Z transformation approach was employed. Following this, various prediction systems [Deep Learning (DL), Naïve Bayes, Generalized Liner Model, Logistic Regression, Decision Tree, Gradient-Boosted Tree (GBT) and Random Forest] were applied to the non-transformed milking dataset and to a Z-standardized dataset. ROC (Receiver Operating Characteristics Curve), AUC (Area Under The Curve), and high accuracy demonstrated the high sensitivity of GBT and DL in detecting sub-clinical mastitis. GBT was the most accurate model (accuracy of 84.9%) in prediction of sub-clinical bovine mastitis. These data demonstrate how these models could be applied for prediction of sub-clinical mastitis in multiple bovine herds regardless of the size and sampling techniques.

Relationship of mammary gland health status and other noninfectious factors with electrical conductivity of milk in Manchega ewes

Measuring the electrical conductivity (EC) of milk during milking has been extensively studied in cattle as a low-cost mastitis detection method that can be easily automated. The aim of this work was to study the effect of the health status of the glands and several noninfectious factors (lactation stage, milking session, and lactation number) that affect the use of EC measurement of milk to detect mastitis in dairy sheep livestock. Likewise, we studied the relation between EC and milk composition (macrocomposition and mineral content) and between EC and somatic cell count (SCC). Finally, we evaluated the use of EC thresholds as a mastitis detection method. To this end, we monitored the glandular milk EC throughout 2 consecutive lactations, during which 42 and 40 ewes were controlled, respectively. We carried out 7 biweekly checks, analyzing the EC, SCC, composition, and mineral content of glandular milk at morning and evening milkings. Before the morning milking, samples were aseptically collected for bacteriological analysis, and the results along with the SCC were used to classify the glands according to their sanitary status (healthy, latently infected, or infected). Lactation stage, parity, milking (morning or evening), health status, and the interactions of parity with health status, lactation stage with health status, and parity with lactation stage all had a significant effect on SCC and EC of the milk. The correlation between EC and SCC was only significant when all the data were analyzed jointly (r = 0.33) and for SCC ≥ 600.000 cells/mL (r = 0.25). The changes in milk composition, mainly in fat content, largely explained the variation in EC (R² = 0.69). For the same EC threshold, the specificity and sensitivity varied depending on the parity or the milking, with the negative predictive value obtained being higher than the positive predictive value at all times. We concluded that developing methods of detecting mastitis in sheep by milk EC readings would require consideration of noninfectious factors that also affect the gauging of EC. One option to consider would be individualized daily monitoring of the glands, as demonstrated in other species such as cattle and goat.

Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis

The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors.

Evaluation of the Fourier Frequency Spectrum Peaks of Milk Electrical Conductivity Signals as Indexes to Monitor the Dairy Goats' Health Status by On-Line Sensors

The aim of this study is a further characterization of the electrical conductivity (EC) signal of goat milk, acquired on-line by EC sensors, to identify new indexes representative of the EC variations that can be observed during milking, when considering not healthy (NH) glands. Two foremilk gland samples from 42 Saanen goats, were collected for three consecutive weeks and for three different lactation stages (LS: 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total amount of 1512 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define the health status of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as NH. For each milk EC signal, acquired on-line and for each gland considered, the Fourier frequency spectrum of the signal was calculated and three representative frequency peaks were identified. To evaluate data acquired a MIXED procedure was used considering the HS, LS and LS × HS as explanatory variables in the statistical model.Results showed that the studied frequency peaks had a significant relationship with the gland’s health status. Results also explained how the milk EC signals’ pattern change in case of NH glands. In fact, it is characterized by slower fluctuations (due to the lower frequencies of the peaks) and by an irregular trend (due to the higher amplitudes of all the main frequency peaks). Therefore, these frequency peaks could be used as new indexes to improve the performances of algorithms based on multivariate models which evaluate the health status of dairy goats through the use of gland milk EC sensors.

Short communication: Goat colostrum quality: litter size and lactation number effects

The quality of colostrum of Murciano-Granadina goats was studied to establish the transition period and the time when milk can be marketed. Forty-three dairy goats were used: 19 primiparous (15 single births; 4 multiple births) and 24 multiparous (10 single births; 14 multiple births). Samples were collected every 12h during the first week postpartum. Physicochemical parameters and somatic cell count were determined. Analysis of variance with repeated measures was used to study the effect of different factors: postpartum time, litter size, lactation number, their interactions, and production level on colostrum. Postpartum time had a significant effect on all parameters studied, which decreased along the first week of lactation, whereas lactose, pH, and conductivity increased. Based on these results, colostrum secretion takes place until 36 h postpartum (hpp). In relation to other factors of variation studied, the lactation number influenced most colostrum components, whereas the litter size only affected the pH value, protein and lactose content. The production level influenced only the protein and dry matter contents, with an inverse relationship. Milk produced during the period between 36 and 96 hpp is considered transition milk, which should not be commercialized. Milk collected after 4d postpartum (96 hpp) could be marketed, ensuring that its composition does not present a risk in the dairy industry.

Evaluation of commercial probes for on-line electrical conductivity measurements during goat gland milking process

The measurement of the milk electrical conductivity (EC) during mechanical milking has been widely studied for mastitis detection on cows because its improving of welfare and animal health, although research about small ruminants is scarce. The aim of this study was to evaluate the performance of three commercial conductimeters to be used during mechanical milking of small ruminant halves, especially Murciano-Granadina goats. The objective of this research was to integrate the probes on the milking unit and to check the suitability of the probe selected. The results presented in this research have guided authors to discard the commercial probes and to establish the requirements of a new probe design that is briefly outlined in the conclusions of this contribution.