Predicting shrinkage of ellipsoid beef joints as affected by water immersion cooking using image analysis and neural network

Images were acquired from 25 beef samples before and after cooking and shrinkages were measured from the images in four different ways: volume, surface area, major axis, and minor axis. A total of 15 factors relevant to the samples, which included weight, moisture content, volume, surface area, major and minor axes, cooking temperature, projected area and perimeter, as well as the mean, standard deviation, and Fourier descriptors of both radiuses and lengths of power curves, were trained in an error backpropagation network in order to correlate them to the shrinkages. The correlation coefficients (r2) were 0.684, 0.674, and 0.745 for the shrinkage of volume, surface area, and major axis, respectively, indicating that the method worked with sufficient confidence in predicting the shrinkage of these three parameters. However, the correlation coefficient for minor axis was only 0.42, showing the limitation of the method in predicting the shrinkage of minor axis. The difference of shrinkage between major and minor axis was possibly caused by different heat transfer behaviour along the axes. Sensitivity analyses were conducted to further explore the ability of the above 15 factors in predicting the shrinkage. Results showed that among these 15 factors, mean length of power curves, projected perimeter, and cooking temperature played the most important role in determining all the four kinds of shrinkage.