Knowledge Acquisition Approach Based on SVM in an Online Aided Decision System for Food Processing Quality and Safety

In connection with the problem that the food processing information system is poor due to the absence of knowledge acquisition and a knowledge selfupdating function, a knowledge acquisition approach, based on a Support Vector Machine (SVM) is proposed. First, the approach establishes a set of predicted samples for the relationship between the food processing parameters and product quality; then it uses discretization of the continuous attributes, attributes reduction and a rule extraction algorithm of SVM to automatically acquire predicted knowledge from a large number of predicted sample sets. After that it saves the predicted knowledge in the knowledge base of an expert system; finally, the method realizes extraction of the knowledge about the food processing process based on the inference engine, which greatly enhances the efficiency and applicability of the acquired knowledge in an online aided decision system of food processing quality and safety.