Prediction model of PSO-BP neural network on coliform amount in special food

Abstract EN

Special food safety supervision by means of intelligent models and methods is of great significance for the health of local people and tourists.

Models like BP neural network have the problems of low accuracy and poor robustness in food safety prediction.

So, firstly, the principal component analysis was used to extract the key factors that influenced the amount of coliform communities, which was applied to reduce the dimension of this model as the input variable of BP neural network.

Secondly, both the particle swarm optimization (PSO) and BP neural network were implemented to optimize initial weights and threshold to obtain the optimal parameter, and a model was constructed to predict the amount of coliform bacteria in Dai Special Snacks, Sa pie, based on PSO-BP neural network model.

Finally, the predicted value of the model is verified.

The results show that MSE is 0.0097, MAPE is 0.3198 and MAE is 0.0079, respectively.

It was clear that PSO-BP model was better accuracy and robustness.

That means, this model can effectively predict the amount of coliform.

The research has important guiding significance for the quality and the production of Sa pie