The potential value of electrical conductivity of milk as a screening test for subclinical mastitis was evaluated. Conductivity of foremilk and of postmilking strippings from 368 quarters of 92 cows was measured. Infection status of quarters was determined by bacteriological analysis of strict foremilk samples. Infections were classified as by primary or secondary pathogens, depending on the importance of the isolated organism as a mastitis pathogen. Somatic cells were counted on foremilk samples. Milk conductivity increased with infection. Conductivity of postmilking strippings was higher than that of foremilk in samples from quarters infected by primary pathogens. By thresholds which correctly classified at least 90% of normal quarters, accuracy of identifying primary pathogen infections by absolute conductivity was 62.8 and 96.2% with foremilk and postmilking strippings. Differential conductivity and combination of absolute and differential methods also were evaluated with the latter being the most effective. Number of quarters with elevated conductivity of postmilking strippings tended to be higher when somatic cell count was greater than 500,000/ml in both normal and infected groups. Conductivity of milk seems to hold promise as an indicator of subclinical mastitis.
"As such, there is considerable interest in developing onfarm tests which are simple, fast and potentially amenable to in-line monitoring as part of milking systems. Current on-farm tests include the California mastitis test (CMT) (Schalm and Noorlander, 1957) or measuring the electrical conductivity of the milk (Fernando et al., 1982). Both * Corresponding author. "
[Show abstract][Hide abstract] ABSTRACT: Current on-farm methods for detecting mastitis in dairy cows have limitations with their specificity and sensitivity, particularly at an early stage of infection. There is therefore a need to explore new approaches for detecting early and subclinical mastitis. This study examined the expression of a group of neutrophil-specific proteins, the cathelicidins, in milk samples from naturally occurring as well as experimentally induced mastitis infections. Immunoblot analysis indicated that cathelicidin proteins are only observed in infected quarters and demonstrate a high correlation with somatic cell count (SCC) during the onset of infection. In most of the infections examined, cathelicidin was detected prior to the observation of clinical symptoms and at SCC counts as low as 6.2 × 10(3)cells/mL. In naturally occurring mastitis the correlation between cathelicidin and infection status is not as strong, with 25% of pathogen-positive milk samples containing no detectable cathelicidin. This may reflect the varying levels of neutrophil concentration and activity at different stages or severities of infection. Our results indicate that milk cathelicidin levels increase following intramammary infection and cathelicidin-based biomarkers may assist in the detection of preclinical mastitis or determining the stage of infection.
[Show abstract][Hide abstract] ABSTRACT: Two types of artificial neural networks, Multilayer Perceptron (MLP) and Self-organizing Feature Map (SOM), were employed to detect mastitis for robotic milking stations using the preprocessed data relating to the electrical conductivity and milk yield. The SOM was developed to classify the health status into three categories: healthy, moderately ill and severely ill. The clustering results were successfully evaluated and validated by using statistical techniques such as K-means clustering, ANOVA and Least Significant Difference. The result shows that the SOM could be used in the robotic milking stations as a detection model for mastitis. For developing MLP models, a new mastitis definition based on higher EC and lower quarter yield was created and Principle Components Analysis technique was adopted for addressing the problem of multi-colinearity existed in the data. Four MLPs with four combined datasets were developed and the results manifested that the PCA-based MLP model is superior to other non-PCA-based models in many respects such as less complexity, higher predictive accuracy. The overall correct classification rate (CCR), sensitivity and specificity of the model was 90.74 %, 86.90 and 91.36, respectively. We conclude that the PCA-based model developed here can improve the accuracy of prediction of mastitis by robotic milking stations.
[Show abstract][Hide abstract] ABSTRACT: In the present exposition different chemical-physical parameters were evaluated for their sensor-controlled use to detect udder diseases in dairy cows having a special regard on spectral photometry. The appraisal of all results was done based upon the laboratory determined quarter specific somatic cell count (SCC) and the corresponding classification. First of all the expressiveness of the simple methods to describe the composition of foremilk (visual inspection by law and CMT) was evaluated under excellent artificial light conditions. For that altogether 1,510 milkings were sampled and evaluated in two herds over a period of 12 days each. These foremilkings were analysed considering the milk consistence, milk colour and the CMT-value. It was found that the sole use of visual inspection (milk consistence and -colour), stipulated by law, allows no accurate statement about the udder health. In merely 15.1 % (29.7 %) of all foremilkings with more than 500,000 cells/ml a change in the milk-colour (-consistence) could be detected. Only the additional use of the CMT made a detection rate of 88.8 % of all foremilkings with more than 500,000 cells/ml possible. By changing the threshold up to 1 Mio. cells/ml the sensitivity went up to 94.7 %. In order to investigate the possibility to detect udder diseases by using the spectral photometry in milk, 1,033 quarter first milk samples (13 cows, 280 milkings) were evaluated. This examination resulted an increased correlation between milk ingredients like fat and protein and spectral reflectance (SR) in wave lengths with more than 530 nm. The highest correlation between SR and SCC and also to the lactose content was found in the band of 400 - 520 nm. Moreover there was a high significant difference between the average value of SR in the band of 430 - 510 nm of quarter foremilk with raised SCC (>100,000 cells/ml) and samples with less than 100,000 cells/ml. Therefore that range was defined as mastitis-band and was solely used for all further analyses. Due to the possibility of using an AMS (automatic milking system) to record relevant parameters during the milking, the optimal point in time for sampling is important to get the most expressive values of the recorded parameters. Consequently the quarter specific milk fractions from 64 milkings were collected. They were analysed on electrical conductivity (EC), spectral reflectance in the mastitis band(SR) and the Na+ and Cl- content (NA, CL). In addition the significance of the SRmastitis of the quarter first milk and the quarter composite milk was compared. The results revealed that the best values for all tested chemical-physical parameters regarding the udder health status were found in the quarter first milk. There was almost no information about the udder health in the quarter composite milk, even though no significant difference between SCC in the quarter foremilk and the quarter composite milk could be detected. The main part of this study was to verify an improved detection of udder diseases by combining the chemical-physical parameters (EC, SR, NA, CL). For this research the quarter first milk samples of 280 milkings were collected from 16 cows, which were automatically milked. The informational value of the parameters was calculated in diagnostic tests (sensitivity and specificity). The sensitivities and the associated cut-off points for the individual and combined parameters were determined at a specificity of at least 95 %. By measuring the values of SRmastitis resp. EC and the calculated values to the reference quarters (inter quarter comparison), 55.6 % resp. 61.4 % of all quarter first milk samples with more than 500,000 cells/ml were detected (specificity=95 %). With the combination of both parameters it was possible to increase the sensitivity up to 73.3 % at the same specificity and threshold. This parameter combination enabled an identification of 85.2 % of all quarter milkings with both, more than 500,000 cells/ml and a positive bacteriological finding. In this case a marginal increased detection rate up to 89.0 % could be reached using an additional combination with direct ion measurement (NA, CL). Altogether it was not possible to detect udder diseases, i.e. subclinical mastitis, prematurely by solely checking the visual appearance of foremilk without any sensor system. In case of using a sensor system, based on chemical-physical parameters, the most meaningful values concerning the udder health status can be gained in the quarter first milk ideally before milk ejection starts. The measurement of milk colour based on spectral reflectance gives some useful information and improves the detection rate of udder diseases in combination with the measurement of EC.
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