Food safety pre-warning system based on Robust Principal Component Analysis and Improved Apriori Algorithm

In response to the frequent food safety incidents in recent years, a risk pre-warning system for food supply chain is proposed to ensure the food quality, This papers builds the food security information pre-warning system use association rules mining technology against the security problems of food production and processing, Monitor the detection data timely and give pre-warn automatically in the whole supply chain. we combines a Robust Principal Component Analysis (RPCA) to obtain better clustering performance and an improved Apriori algorithm to reduces the memory consumption and I/O operations and to shortens the running time. We study of a case of meat producer and the results shows the proposed pre-warning method can identify safety risks efficiently and report the exact warning, when an abnormality is detected by the expert analysis. Experiments verify the correctness of the model and the effectiveness of the algorithm.