Analysis of the electrical conductivity in milking fractions as a mean for detecting and characterizing mastitis in goats

Romero, Gema; Pantoja, José Carlos de Figueiredo; Sendra, Esther; Peris, C.; Díaz, J.R.

doi:10.1016/j.smallrumres.2012.05.001

Cited by 19 publications

(38 citation statements)

References 24 publications

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“…Results obtained do not match with the findings of Romero et al (2012), who found a lack of significant effect of HS on EC. However, in this study a higher prevalence of bilateral infections was observed (47.9 vs 32.3%).…”

Section: Experimental Data Evaluationcontrasting

confidence: 99%

“…Coagulase-negative staphylococci significantly increased during lactation (5.26 to 5.51 log SCC) and were significantly higher in milk samples from non-healthy glands (5.33 vs 5.45 log SCC). The same trends were recently reported by other studies (Díaz et al, 2011Paape et al, 2007;Romero et al, 2012;Tangorra et al, 2010). Romero et al (2012), in a study on 84 Murciano-Granadina goats at the fourth mouth of lactation, analysed three different milking fractions and found that the highest SCC levels were observed in infected glands associated with all the considered fractions.…”

Section: Experimental Data Evaluationsupporting

confidence: 88%

“…The same trends were recently reported by other studies (Díaz et al, 2011Paape et al, 2007;Romero et al, 2012;Tangorra et al, 2010). Romero et al (2012), in a study on 84 Murciano-Granadina goats at the fourth mouth of lactation, analysed three different milking fractions and found that the highest SCC levels were observed in infected glands associated with all the considered fractions. Furthermore, SCC in infected glands was approximately twice as high as in healthy glands (from 5.03 for the first fraction of milking to 5.31 log SCC for the third fraction of milking in healthy glands, compared with 5.47 for the first milking fraction and 5.66 log SCC for the third fraction of milking in cases of infected glands).…”

Section: Experimental Data Evaluationsupporting

confidence: 88%

“…In another study based on daily EC measurements , a remarkable increase in EC was observed, when major pathogens were causing an infection. Romero et al (2012) reported low sensitivity and specificity of mastitis detection with EC, when different milking fractions and thresholds were considered. Furthermore, the authors highlighted that such low performance was in line with the results obtained by Díaz et al (2011) and highlighted that, in order to develop a better mastitis detection system using EC, the intrinsic variation in the animal must be considered, the use of simple thresholds have to be avoided (as in cows), and other significant factors need to be included.…”

Section: Introductionmentioning

confidence: 99%

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On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Zaninelli

Rossi

Tangorra

et al. 2014

Italian Journal of Animal Science

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Intramammary infection affects the quality and quantity of dairy goat milk. Health status (HS) and milk quality can be monitored by electrical conductivity (EC). The aim of the study was to determine the detection potential of EC when measured on-line on a daily basis and compared with readings from previous milkings. Milk yields (MYs) were investigated with the same approach. To evaluate these relative traits, a multivariate model based on fuzzy logic technology -which provided interesting results in cows -was used. Two foremilk samples from 8 healthy Saanen goats were measured daily over the course of six months. Bacteriological tests and somatic cells counts were used to define the HS. On-line EC measurements for each gland and MYs were also considered. Predicted deviations of EC and MY were calculated using a moving-average model and entered in the fuzzy logic model. The reported accuracy has a sensitivity of 81% and a specificity of 69%. Conclusions show that fuzzy logic is an interesting approach for dairy goats, since it offered better accuracy than other methods previously published. Nevertheless, specificity was lower than in dairy cows, probably due to the lack of a significant decrease of MY in diseased glands. Still, results show that the detection of the HS characteristics with EC is improved, when measured on-line, daily and compared with the readings from previous milkings.

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Section: Experimental Data Evaluationcontrasting

confidence: 99%

Section: Experimental Data Evaluationsupporting

confidence: 88%

Section: Experimental Data Evaluationsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Zaninelli

Rossi

Tangorra

et al. 2014

Italian Journal of Animal Science

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“…There are several factors other than mastitis related to EC, including differences between animals, so that, in dairy cows, methods that employ an absolute EC threshold for all the animals for mastitis detection are not accepted, even if EC is measured in the complete milking or at gland level. Studies in dairy goats (Ying et al 2002;Diaz et al, 2011Diaz et al, , 2012Romero et al 2012) have shown significant effects of parity, lactation stage, farm, and the analyzed fraction, in addition to mammary infection.…”

Section: Introductionmentioning

confidence: 99%

Goat mastitis detection using daily records of milk conductivity: comparative results of different algorithms

Romero¹,

Reinemann²,

Alejandro³

et al. 2014

CZECH J ANIM SCI

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Milk electrical conductivity is employed for mastitis detection in cows due to its automation, low cost, and infection detectability at early stage. Nevertheless, the number of publications about its use in dairy goats is scarce. The aim of this study was to check and compare the detectability of goat mastitis (sensitivity and specificity) using different algorithms, constructed with individual daily conductivity data from glands, in order to improve the know how about the potential of this variable for goat mastitis detection. A total of 18 goats (8 primiparous and 10 multiparous) free of mastitis were used, and gland milk conductivity was daily monitored. After 16 days of monitoring, some unfavourable situations for gland health were simulated in order to increase the cases of infection. Once infection was established (9 goats and 12 glands got infected), the experiment continued for further 16 days. A total of 19 different algorithms that employed conductivity data from gland were designed; they were tested using gland milk conductivity (EC) and ratio of EC of collateral glands in the same goat (RAT EC ). The algorithms were tested in all the animals and intramammary infection detection ability characteristics (sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), and negative predictive value (NPV)) were recorded. All clinical cases were detected (n = 2, 100% SENS) with all the algorithms. Best global SENS (clinical and subclinical, 33.3-58.3%) and SPEC (77.8-100%) were similar to results reported in previous studies in cows, and obtained with algorithms ARIMA and Rule 1 (3 standard deviations of data). The best algorithms to use in mastitis detection depend on the prevalence and type of mastitis. EC ARIMA and Rule 1 algorithms detected the most severe cases on-line and quickly, with a low proportion of false positives.

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Management of Dairy Animals: Goat: Milking Management

Billon¹,

Romero²,

Díaz³

2016

Encyclopedia of Dairy Sciences

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Analysis of the electrical conductivity in milking fractions as a mean for detecting and characterizing mastitis in goats

Cited by 19 publications

References 24 publications

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Goat mastitis detection using daily records of milk conductivity: comparative results of different algorithms

Management of Dairy Animals: Goat: Milking Management

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