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

Abstract

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.

First Results of a Detection Sensor for the Monitoring of Laying Hens Reared in a Commercial Organic Egg Production Farm Based on the Use of Infrared Technology

“…Snapshots and video recordings were acquired every day from the start to the end of a monitoring window. Dates and time stamps were added to each image and used to name each file [32]. Images and video recordings were used in a preliminary phase of the experiment to set up the detection sensor and analyzed in the following phases to evaluate the accuracy of the monitoring system.…”

Development and test of a portable device to monitor the health status of Sarda breed sheep by the measurement of the milk electrical conductivity

“…A high SCC is also linked to a deterioration of milk quality and frequently to a loss in milk production (Gonzalo et al 1994(Gonzalo et al , 2002Ying et al 2002Ying et al , 2004Nudda et al 2003;Leitner et al 2004aLeitner et al , 2004bD€ urr et al 2008;Hagnestam-Nielsen et al 2009;Hand et al 2012). EC is generally used to detect health status, studies in dairy goats showed that daily measurements of EC may represent a useful method to detect intra-mammary infections (D ıaz et al 2012;Zaninelli et al 2014). The electrical conductivity (EC) is one of the indirect systems for determining the quantity of somatic cells in milk (Peris et al 1991;Barth et al 2008;Tangorra et al 2010;Romero et al 2012a).…”

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

“…Although many factors, other than the health status of the mammary gland, can introduce some interpretation errors such as parity, lactation stage, and milk composition [ 22 , 23 , 24 ], the use of this parameter is a well consolidated practice with dairy cows [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] and many devices that evaluate the EC of milk measured in constant current (DC) or alternating current (AC—in a range of frequency between 50–70 kHz) [ 25 ] are available. On the contrary in dairy goats, the use of algorithms and sensors based on the milk EC and included in the milking systems in order to monitor online health status (HS) of the animals is not a widespread practice because of the low performances achieved to date [ 26 , 27 , 28 , 29 ].…”

“…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 ].…”