Correlating colour to moisture content of large cooked beef joints by computer vision

Thirty-six tumbled samples from triceps brachii (beef) were water-immersion cooked and cooled by vacuum, air blast, and cold room cooling. A computer vision system was set up to obtain images of the samples. Colour features including the mean and the standard deviation in two colour spaces, Red, Green, Blue (RGB) and Hue, Saturation, Intensity (HSI) were extracted. Moisture content of the samples was determined by chemical analysis. A partial least square regression (PLSR) model and a neural network (NN) model were proposed for correlating the colour to the moisture content of the beef joints. Correlation coefficients (r2) of the models were 0.56 (PLSR) and 0.75 (NN). A stepwise selection together with the analysis of the regression coefficients of the PLSR model both showed that among the 12 colour features analysed, saturation was the one that had the largest contribution to the results of the prediction model. However, only saturation itself was not sufficient for establishing the correlation between meat colour and its moisture content.