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. 2016 Jul 13;16(7):1079.
doi: 10.3390/s16071079.

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

Mauro Zaninelli  1 , Francesco Maria Tangorra  2 , Annamaria Costa  3 , Luciana Rossi  4 , Vittorio Dell'Orto  5 , Giovanni Savoini  6
Affiliations

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

Mauro Zaninelli et al. Sensors (Basel). .

Abstract

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.

Keywords: dairy goats; electrical conductivity; fuzzy logic; mastitis.

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Figures

Figure 1
Figure 1
Dimensions of the EC sensor head placed at the base of each individual teatcup.
Figure 2
Figure 2
Example of gauges obtained from the milk electrical conductivity (EC) signals acquired within a milking. Furthermore, the following graphs report: (A) the sequences without the signal samples related to the start and the end of milking; (B) the sequences where the mean value of each sequence have been subtracted to each signal sample acquired; (C) the Fourier frequency spectra of the previous sequences of signal samples and the three main frequency peaks and (D) the bandwidth length of the signal (also colored to be easily highlighted).
Figure 3
Figure 3
Example of a rule used in the fuzzy inference.
See this image and copyright information in PMC

References

    1. Lansbergen L.M.T.E., Nielen M., Lam T.J.G.M., Pengov A., Schukken Y.H., Maatje K. Evaluation of a prototype on-line electrical conductivity system for detection of subclinical mastitis. J. Dairy Sci. 1994;77:1132–1140. doi: 10.3168/jds.S0022-0302(94)77049-1. - DOI - PubMed
    1. Maatje K., Huijsmans P.J.M., Rossing W., Hogewerf P.H. The efficacy of in-line measurement of quarter milk electrical conductivity, milk yield and milk temperature for the detection of clinical and subclinical mastitis. Livest. Prod. Sci. 1992;30:239–249. doi: 10.1016/S0301-6226(06)80013-8. - DOI
    1. Nielen M., Schukken Y.H., Brand A., Deluyker H.A., Maatje K. Detection of subclinical mastitis from on-line milking parlor data. J. Dairy Sci. 1995;78:1039–1049. doi: 10.3168/jds.S0022-0302(95)76720-0. - DOI - PubMed
    1. Nielen M., Spigt M.H., Schukken Y.H., Deluyker H.A., Maatje K., Brand A. Application of a neural network to analyse on-line milking parlour data for the detection of clinical mastitis in dairy cows. Prev. Vet. Med. 1995;22:15–28. doi: 10.1016/0167-5877(94)00405-8. - DOI
    1. Zecconi A., Piccinini R., Giovannini G., Casirani G., Panzeri R. Clinical mastitis detection by on-line measurements of milk yield, electrical conductivity and milking duration in commercial dairy farms. Milchwiss. Milk Sci. Int. 2004;59:240–244.