Method for detecting poor meat quality in groups of live animals

Abstract

The invention provides methods of detecting poor meat quality in live animals using infrared thermography. Animals from a group of live domestic animals such as cattle or swine are scanned to produce thermographic images. The images are then statistically analyzed to determine a measure of central tendency such as the mean temperature for each animal's image and for all of the images in the group. A measure of dispersion from the measure of central tendency, such as standard deviation is determined. Then, animals are rejected as having a high probability of producing poor meat quality if the measure of central tendency for that animal's temperature differs from the measure of central tendency for the group by more than 0.9 standard deviations. Alternatively a set percent of animals are rejected, preferably up to 20%, these being animals whose measures of central tendency differ the most from the measure of central tendency for the group. When mean temperature is used as a measure of central tendency, the method is preferably practised by rejecting animals whose mean temperature differs from the group mean temperature by more than 1.28 times the standard deviation for the group. The method is particularly useful in detecting high probability of poor meat quality in groups of animals in an antemortem environment which have mean temperatures significantly above or below the normal surface temperatures for unstressed animals.