Prediction of temperature and moisture content of frankfurters during thermal processing using neural network

An artificial neural network (ANN) was developed to predict temperature and moisture content of frankfurters during smokehouse cooking. Fat protein ratio (FP), initial moisture content, initial temperature, radius of frankfurter, ambient temperature, relative humidity and process time were input variables. Temperature at the frankfurter centre, average temperature of the frankfurter and average moisture content (d.b) of the frankfurter were outputs. Network training data were obtained from validated heat and mass transfer models simulating temperature and moisture profiles of a frankfurter. Backpropagation method was used for ANN training. Selection of hidden nodes, learning rate, momentum and range of input variables were important to ANN prediction. The FP was not an important factor in predictions.