Analysis of the influence of variation factors on electrical conductivity of milk in Murciano-Granadina goats

Díaz, J.R.; Romero, Gema; Muelas, R.; Sendra, Esther; Pantoja, J.C.F.; Paredes, C.

doi:10.3168/jds.2011-4187

Cited by 29 publications

(60 citation statements)

References 17 publications

(23 reference statements)

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“…Tangorra et al (2010) found a significantly higher level of EC in infected glands, but only in different lactation stages (LSs) and types of EC traits. Diaz et al (2011) observed a remarkable effect with unspecific mastitis (negative bacterial culture and SCC>1,000,000 cells/mL), which they related to EC variations arising from other factors, such as farm, parity, month of lactation, and milk composition. In another study based on daily EC measurements , a remarkable increase in EC was observed, when major pathogens were causing an infection.…”

Section: Introductionmentioning

confidence: 91%

“…CE n 1662/2006 sets at 1,500,000 cells/mL the legal limit for somatic cells in raw goat milk, whereas this limit was set at 1,000,000 cells/mL according to the Pasteurized Milk Ordinance regulation (FDA, 2007). In the literature, some recent studies on dairy goats (Diaz et al, 2011(Diaz et al, , 2012 reported that the persistence of 1,000,000 somatic cells/mL which is not due to physiological causes is indicative of nonhealthy glands, even if bacteriological analysis are negative for IMI. In the present study, the threshold of SSC was selected being the lower level among those reported above.…”

Section: Collection and Analyses Of Milk Samplesmentioning

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: Introductionmentioning

confidence: 91%

Section: Collection and Analyses Of Milk Samplesmentioning

confidence: 99%

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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“…To determine the health status of the glands, both bacteriological analysis and SCC results were considered in addition to clinical observation, according to Diaz et al (2011). A gland was defined as having bacterial mastitis (positive for IMI) when bacteriological analyses were positive.…”

Section: Experimental Design and Analyzed Variablesmentioning

confidence: 99%

“…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%

“…Tangorra et al (2010) compared daily EC between healthy and infected glands with a mixed model in a pilot study, showing significant differences, and our research group has reported significant effects of mammary infection and mastitis at studies carried out monthly (Diaz et al, 2011), in an individual sampling day , and daily ) with mixed models.…”

Section: Introductionmentioning

confidence: 99%

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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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Effect of a 60-day organic selenium-supplemented diet on the decrease of somatic cell counts in goat milk

Vasconcelos

Martins

Souza

et al. 2023

Trop Anim Health Prod

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Analysis of the influence of variation factors on electrical conductivity of milk in Murciano-Granadina goats

Cited by 29 publications

References 17 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

Effect of a 60-day organic selenium-supplemented diet on the decrease of somatic cell counts in goat milk

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