A hybrid of back propagation neural network and genetic algorithm for optimization of collagen extraction from Malaysian cultured catfish (Hybrid Clarias sp.)

作者：Kiew Peck Loo^*; Ahmad Zainal; Don Mashitah Mat

来源：Biotechnology and Bioprocess Engineering, 2013, 18(2): 257-265.

DOI：10.1007/s12257-012-0552-x

• collagen
• fractional factorial
• artificial neural network
• genetic algorithm
• extraction

Fractional factorial design (FFD) was applied to evaluate the effects of various process parameters in influencing the extraction efficiency of pepsin soluble collagen (PSC) from muscles of cultured catfish (Clarias gariepinusxC. macrocephalus). Result of the first order factorial design showed that acetic acid concentration, acid extraction time, acetic acid to muscles ratio, and stirring speed posed significant effect (P %26lt; 0.05) on the yield of PSC obtained at the end of the extraction process. Two different artificial intelligence techniques namely artificial neural network (ANN) and genetic algorithm (GA) were then integrated for optimizing the extraction conditions to obtain the highest yield of PSC. The ANN was trained using the back propagation algorithm. A model was successfully generated with R (2) value of 0.9527 and MSE value of 0.1672 for unseen data set, implying a good generalization of the network. Input parameters of the established ANN model were subsequently optimized using GA. The hybrid of ANN-GA model predicted a maximum extraction yield of PSC at 238.25 mg/g under the following conditions: an acetic acid concentration of 0.70 M, the acetic acid to muscles ratio of 25.78 mL/g, and the stirring speed of 432.50 rpm. Verification of the optimization showed the percentage error differences between the experimental and predicted values were less than 5%, indicating excellent modeling, predicting ability and optimization by the ANN-GA model.

• 出版日期2013-4