Adaptive Spectral Model for abnormality detection based on physiological status monitoring of dairy cows

This research study developed milk spectral data-driven approach, called Adaptive Spectral Model for Abnormality Detection - ASMAD, for detection of physiological abnormalities of individual dairy cows. The algorithm is based on the logic that milk spectra of each individual cow is highly animal-specific, which means it could be used as a respective individual marker for identification. When the algorithm fails to recognize the milk spectra as coming from a certain animal, instead of being treated as a mistake, this outcome is accepted as a deviation of the respective individual marker, and a potential indicator of abnormal physiological state. For the purpose of ASMAD development, near infrared spectra of milk of seven dairy cows have been collected daily during 1-year period. The abnormality detection model is built using supervised recognition method Soft Independent Modeling of Class Analogies - SIMCA, and optimized with respect to spectral pre-processing, choice of the wavelength region and size of the time-window when developing the adaptive model. The sensitivity and specificity of ASMAD were dependent on the animal, and in the ranges 40.00-64.29% and 87.23-98.86%, respectively. Considering significant level of day-to-day spectral variation and multitude of physiological and environmental factors influence on milk constituents and spectra, these results represent a significant potential for creating a health-status monitoring and detection of abnormal physiological states in dairy animals. The adaptive modeling based on the time series of spectral data collected from the individual organism utilized in this work for monitoring physiological status and abnormality detection in dairy cows, has a good potential to be used for similar purposes in other animals and humans.