APPLICATION OF ARTIFICIAL NEURAL NETWORK TO PREDICTION OF CANTONESE SOY SAUCE BREWING AND CHANGING PATTERN CONCERNING TOTAL NITROGEN AND α‐AMINO ACID NITROGEN

Abstract

ABSTRACTIn the present study, the artificial neural network was used to predict the brewing of traditional Cantonese soy sauce and its changing pattern with regard to total nitrogen (TN) andα‐amino acidic nitrogen (AN). The effects of parameters during brewing on the contents of TN and AN were modeled by various networks. After the 7‐40‐2 network was determined statistically, it was used to extract the possible correlations between TN and AN, and the relative significance of some factors. It was found that the change of TN and AN contents with the change of time and acidic protease activity was strongly correlated with an R value of 0.9848 and 0.9916, respectively. As for the relative significances of inputs, aging time is the key factor for both outputs during the moromi aging. And for other inputs, it was temperature > pH > neutral protease activity > acid protease activity for TN, and temperature > acidic protease activity > neutral protease activity > pH for AN, respectively.PRACTICAL APPLICATIONSBecause of its open‐air brewing and lengthy brewing time of Cantonese soy sauce, it was difficult to predict its quality indices, the contents of total nitrogen and α‐amino acidic nitrogen, which would change with outside factors (time and temperature) and inside factors (protease activity and pH values). So the study on brewing‐state predication and changing pattern of quality indices were performed by using the artificial neural network on an experimental scale. And results from this preliminary study would help manufacturers to foresee the change and contents of quality indices accurately in the brewing process on an industrial scale. Furthermore, it was anticipated that more effective control and improvement on the quality of traditional Cantonese would be achieved in the future study. Finally, the trained network also can be used in the research of online prediction for other time‐consuming natural fermentation.