Passive breath monitoring of livestock: Using factor analysis to deconvolve the cattle shed

Abstract

Respiratory and metabolic diseases in livestock cost the agriculture sector billions each year, with delayed diagnosis a key exacerbating factor. Previous studies have shown the potential for breath analysis to successfully identify incidence of disease in a range of livestock. However, these techniques typically involve animal handling, the use of nasal swabs or fixing a mask to individual animals to obtain a sample of breath. Using a cohort of 26 cattle as an example, we show how the breath of individual animals within a herd can be monitored using a passive sampling system, where no such handling is required. These benefits come at the cost of the desired breath samples unavoidably mixed with the complex cocktail of odours that are present within the cattle shed. Data were analysed using positive matrix factorisation (PMF) to identify and remove non-breath related sources of VOC. In total three breath factors were identified (endogenous-, non-endogenous breath and rumen) and seven factors related to other sources within and around the cattle shed (e.g. cattle feed, traffic, urine and faeces). Simulation of a respiratory disease within the herd showed that the abnormal change in breath composition were captured in the residuals of the 10 factor PMF solution, highlighting the importance of their inclusion as part of the breath fraction. Increasing the number of PMF factors to 17 saw the identification of a “diseased” factor, which coincided with the visits of the three “diseased” cattle to the breath monitor platform. This work highlights the important role that factor analysis techniques can play in analysing passive breath monitoring data.