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

Romero, Gema; Reinemann, Douglas J.; Alejandro, Manuel; Díaz, J.R.

doi:10.17221/7654-cjas

Cited by 7 publications

(13 citation statements)

References 24 publications

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“…Mean values of milk EC, found in the present study, were higher than those reported by other authors [ 1 , 29 , 32 , 33 , 34 , 39 ]. These different results may be explained by the characteristics of the measuring system used.…”

Section: Discussioncontrasting

confidence: 89%

“…In a study on the use of time series evaluation of the milk EC to detect health status of dairy goats, and in another study that applied fuzzy logic technology to a multivariate model with the same aim, we confirmed that a better accuracy can be reached considering the intrinsic variation of animals and that simple thresholds have to be avoided (as in cows). However, all the studies on this topic suggest that a better knowledge of the relationship between the milk EC signal and the animal HS, in order to find more informative indexes, could lead to better results in detecting HS of dairy goats [ 26 , 27 , 28 , 29 , 32 , 33 , 34 , 35 ]. With this in mind, results obtained in the present study have to be evaluated.…”

Section: Discussionmentioning

confidence: 99%

“…The average value of the 20 highest measurements of the gland’s milk EC recorded online during a milking and in case of early lactation, may be used for the monitoring of the animals’ HS [ 28 ]. However, many of these studies suggest the use of more informative indexes built with a better knowledge of the relationship between the milk EC’ signal and the animals’ HS, along with improvements on the algorithms that analyze these data (based on multivariate models), as a possible way to reach good performance detection also in dairy goats [ 26 , 27 , 28 , 29 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

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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

Zaninelli

Agazzi

Costa

et al. 2015

Sensors

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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.

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Section: Discussioncontrasting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Zaninelli

Agazzi

Costa

et al. 2015

Sensors

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“…Nevertheless, for dairy goats, good performance in detecting udder health status (HS) by the use of milk EC has not been possible. Published studies on this topic are scare and although some authors have demonstrated that milk EC signals can increase in infected goats [ 10 , 14 , 15 ], when major pathogens are the cause of the infection [ 16 ], algorithms and multivariate models that are able to achieve detection performance during milkings, and equal to those already reached for dairy cows, are still not reported [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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

Zaninelli

Tangorra

Costa

et al. 2016

Sensors

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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.

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“…Cl − and Na + concentrations of milk represent EC more accurately to predict the inflammatory status of the mammary gland than other parameters, such as K + , lactose and bovine serum albumin (Fernando et al., ). Romero, Reinemann, Alejandro, and Diaz (). proposed a series of algorithms for mastitis detection in goats based on daily measurement of the EC of glandular milk.…”

Section: Discussionmentioning

confidence: 99%

S100 calcium‐binding protein A12 as a diagnostic index for subclinical mastitis in cows

Zhong

Zhang

Zha

et al. 2018

Reprod Domestic Animals

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Diagnosis of subclinical mastitis is very important in management of the dairy industry and improvement of dairy cow productivity. S100A12, that is found in related tissues of mammals, is considered as an index for diagnosing inflammatory reaction. To evaluate whether S100A12 is involved in subclinical mastitis, milk somatic cell mRNA from 276 dairy cows was used to detect the transcriptional level of S100A12 by real-time quantitative polymerase chain reaction. A predictive analysis for mastitis was performed, and the correlation between S100A12 and other subclinical mastitis indicators was also assessed. The transcriptional levels of S100A12 in the milk of cows with mastitis were significantly higher than those in the milk of healthy cows (p < 0.05). The correlation analysis showed that S100A12 was positively associated with the somatic cell count and the sodium and chloride concentrations of milk. In contrast, a negative correlation was found between S100A12 and the potassium concentration and pH of milk. However, no significant correlation was detected between S100A12 and the other parameters, such as protein, lactose, ash, fat, density, Ca and SNF. These results suggested that the S100A12 level in milk may serve as a diagnostic tool for subclinical mastitis in cows without obvious clinical signs.

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

Cited by 7 publications

References 24 publications

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

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

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

S100 calcium‐binding protein A12 as a diagnostic index for subclinical mastitis in cows

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