Modeling and Optimization of Electrodialytic Desalination of Fish Sauce Using Artificial Neural Networks and Genetic Algorithm

• Published in: Food and bioprocess technology Vol. 6; no. 10; pp. 2695 - 2707
• Main Authors: Chindapan, Nathamol, Sablani, Shyam S., Chiewchan, Naphaporn, Devahastin, Sakamon
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.10.2013; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Amino acids; Aroma compounds; Artificial neural networks; Biotechnology; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Desalination; Electrodialysis; Ethnic foods; Fish; fish sauce; Flavors; Food Science; Genetic algorithms; Ion concentration; Multiple objective analysis; Neural networks; odor compounds; Optimization; Original Paper; Performance prediction; Quality management; salt concentration; Saltiness; salts; Sauces; sodium; Low-sodium product; Multi-objective optimization; Process performance; Quality
• ISSN: 1935-5130; 1935-5149
• DOI: 10.1007/s11947-012-0914-6

Electrodialysis (ED) has been proposed as a means to reduce sodium ion concentration in fish sauce. However, no information is so far available on the optimum condition to operate the ED process. Artificial neural network (ANN)-based models were therefore developed to predict the ED performance and changes in selected quality attributes of ED-treated fish sauce; optimum operating condition of the process was then determined via multi-objective optimization using genetic algorithm (MOGA). The optimal ANN models were able to predict the ED performance with R 2  = 0.995, fish sauce basic characteristics with R 2  = 0.992, and the concentrations of total aroma compounds and total amino acids, flavor difference, and saltiness of the treated fish sauce with R 2  = 0.999. Through the use of MOGA, the optimum condition of the ED process was the use of an applied voltage of 6.3 V and the maintenance of the residual salt concentration of the treated fish sauce of 14.3 % ( w / w ).