Signal spectral analysis to characterize gland milk electrical conductivity in dairy goats

Intramammary infection affects quality and quantity of milk. Having as final target the improving of animal health' monitoring, this research studied the gland milk electrical conductivity (EC) signal in order to identify a possible parameter more representative of the EC variations that can be observed, during a milking, when not healthy (NH) glands are considered. Two foremilk gland samples, from 40 Saanen goats, were acquired for three weeks and lactation stages (LS:0-60 Days In Milking; 61-120 DIM; => 120 DIM), for a total amount of 1440 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define glands health status. In case of negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy; alternatively, when bacteriological analyses were positive or SCC higher than 1,000,000 cells/mL, for two or more consecutive days, glands were classified as NH. A spectral analysis, to calculate the frequency spectrum and the bandwidth length of the milk EC signal, was performed. To validate data acquired, A MIXED procedure was used considering the HS, LS and the LS x HS as explanatory variables of the statistical model. Results showed that spectral analysis allows characterizing the milk EC variations thorough the bandwidth length parameter. This parameter has a significant relationship with the gland health status and it provides more accurate information than other traits, like the statistical variance of the signal. Therefore, it could be useful to improve the performances of multivariate models/algorithms that detect dairy goat health status.