Exploratory data inference for detecting mastitis in dairy cattle (Acta Scientiarum. Animal Sciences, 2020)
The aim of this study was to employ the principal component technique to physiological data and environmental thermohygrometric variables correlated with detection of clinical and subclinical mastitis in dairy cattle. A total of 24 lactating Girolando cows with different clinical conditions were selected (healthy, and with clinical or subclinical mastitis). The following physiological variables were recorded: udder surface temperature, ST (°C); eyeball temperature, ET (°C); rectum temperature, RT (°C); respiratory frequency, RF (mov. min-1). Thermohygrometric variables included air temperature, AirT (°C), and relative humidity, RU (%). ST was determined by means of thermal images, with four images per animal, on these quarters: front left side (FL), front right side (FR), rear right side (RR) and rear left side (RL), totaling 96 images. Exploratory data analysis was run through multivariate statistical technique with the employment of principal components, comprehending nine variables: ST on the FL, FR, RL and RR quarters; ET, RT; RF, AirT and RU. The representative quarters of the animals with clinical and subclinical mastitis showed udder temperatures 8.55 and 2.46° C higher than those of healthy animals, respectively. The ETs of the animals with subclinical and clinical mastitis were, respectively, 7.9 and 8.0% higher than those of healthy animals. Rectum temperatures were 2.9% (subclinical mastitis) and 5.5% (clinical mastitis) higher compared to those of healthy animals. Respiratory frequencies were 40.3% (subclinical mastitis) and 61.6% (clinical mastitis) higher compared to those of healthy animals. The first component explained 91% of the total variance for the variables analyzed. The principal component technique allowed verifying the variables correlated with the animals' clinical condition and the degree of dependence between the study variables.