A general regression neural network (GRNN) model was established to predict the change of Salmonella incidence in broiler chilling process based on the data collected from the laboratory experiments. The initial contamination level, pre-chill incidence and sodium hypochlorite (NaClO) concentration were considered as the input variables and the post-chill incidence was regarded as the output variable. The training set was used for model fitting and the test set was used to evaluate the prediction ability of the model. The study was the preparation on constructing the dynamic risk assessment model using the artificial neural network method. The research titled with “The application of arti?cial neural network in prediction of Salmonella incidence in chicken breast chilling process” has been published the Chinese Journal of Science and Technology of Food Industry.

GRNN prediction model of Salmonella incidence