Abstract: Prediction of sensory texture evaluation of smoked-and-cooked sausage was obtained and improved with back propagation neural network(BPNN) ,which had been analyzed on the basis of multiple regressions by Dong and Luo previously. It was found that the accuracy and goodness-of-fit of BPNN are higher significantly than those of multiple regressions with lower root-mean-squares error(RMSE) and standard error of prediction(SEP) ,and accuracy factor(Af) and bias factor(Bf) in acceptable range. Therefore,BPNN provides a useful and accurate method for predicting sensory texture evaluation in meat industry,and is meaningful for the fast,on-time and convenient detection of instrumental texture measurement instead of sensory evaluation.

Key words: BPNN, smoked-and-cooked sausage, texture, prediction