Application of Artificial Neural Network in Prediction of Salmonella Incidence in Chicken Breast Chilling Process

The prediction of Salmonella incidence in chicken breast chilling process was performed through the combination of response surface methodology(RSM) and general regression neural network(GRNN). RSM was utilized to collect the experimental data and the GRNN model was established to predict the changes of Salmonella incidence in chicken breast chilling process. In the GRNN model,the initial contamination level,pre-chill incidence and sodium hypochlorite(NaClO) concentration were considered as the input variable while post-chill incidence was regarded as the output variable. Furthermore,the training set was used for model fitting and the test set was used to evaluate the prediction ability of the model. The results showed that the post-chill incidence in chicken breast chilling process increased significantly with the initial contamination level and pre-chill incidence increased. On the contrary,post-chill incidence in broiler chilling process decreased significantly with the NaClO concentration increased(P<0.05). The GRNN model showed the best fit as indicated by the r(0.93) and SEP(10.8%) values. Besides,the model had a SEP value of 13% for new data,suggesting accurate prediction of the Salmonella incidence in chicken breast chilling process using GRNN. This study could use to predict Salmonella incidence in chicken breast chilling process at slaughter house and for quantitative microbial risk assessment as well.