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.

Autor: Shet VB; Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110 India., Palan AM; Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110 India., Rao SU; Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110 India., Varun C; Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110 India., Aishwarya U; Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110 India., Raja S; 2Department of Biotechnology, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, Karnataka 576104 India., Goveas LC; Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110 India., Vaman Rao C; Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110 India., Ujwal P; Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110 India.
Jazyk: angličtina
Zdroj: 3 Biotech [3 Biotech] 2018 Feb; Vol. 8 (2), pp. 127. Date of Electronic Publication: 2018 Feb 13.
DOI: 10.1007/s13205-018-1163-9
Abstrakt: 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 R 2 , 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.
Competing Interests: Compliance with ethical standardsThe authors declare that they have no conflict of interest.
Databáze: MEDLINE
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