Comparison of response surface methodology and artificial neural network to enhance the release of reducing sugars from non‑edible seed cake by autoclave assisted HCl hydrolysis

Shet, Vinayaka B and Palan, Anusha M and Rao, Shama U and Varun, C and Aishwarya, Uday and Raja, Selvaraj and Goveas, Louella Concepta and Rao, Vaman and Ujwal, P (2018) Comparison of response surface methodology and artificial neural network to enhance the release of reducing sugars from non‑edible seed cake by autoclave assisted HCl hydrolysis. 3 Biotech, 8 (2). pp. 1-8. ISSN 2190-5738

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Abstract

In the current investigation, statistical approaches were adopted to hydrolyse non-edible seed cake (NESC) of Pongamia and optimize the hydrolysis process by response surface methodology (RSM). Through the RSM approach, the optimized conditions were found to be 1.17%v/v of HCl concentration at 54.12 min for hydrolysis. Under optimized conditions, the release of reducing sugars was found to be 53.03 g/L. The RSM data were used to train the artificial neural network (ANN) and the predictive ability of both models was compared by calculating various statistical parameters. A three-layered ANN model consisting of 2:12:1 topology was developed; the response of the ANN model indicates that it is precise when compared with the RSM model. The fit of the models was expressed with the regression coefficient R2, which was found to be 0.975 and 0.888, respectively, for the ANN and RSM models. This further demonstrated that the performance of ANN was better than that of RSM.

Item Type: Article
Uncontrolled Keywords: Hydrolysis · Hydrochloric acid · Non-edible seed cake · Artificial neural network
Subjects: Engineering > MIT Manipal > Biotechnology
Depositing User: MIT Library
Date Deposited: 08 Mar 2018 04:20
Last Modified: 29 Jun 2018 04:56
URI: http://eprints.manipal.edu/id/eprint/150701

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