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.; Palan, Anusha M.; Rao, Shama U.; Varun, C.; Aishwarya, Uday; Raja, Selvaraj; Goveas, Louella Concepta; Vaman Rao, C.; Ujwal, P.

doi:10.1007/s13205-018-1163-9

Original Article
Published: 13 February 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

Vinayaka B. Shet¹,
Anusha M. Palan¹,
Shama U. Rao¹,
C. Varun¹,
Uday Aishwarya¹,
Selvaraj Raja²,
Louella Concepta Goveas¹,
C. Vaman Rao¹ &
P. Ujwal¹

3 Biotech volume 8, Article number: 127 (2018) Cite this article

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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.

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Department of Biotechnology Engineering, NMAM Institute of Technology (V.T.U Belagavi), Nitte, Udupi District, Udupi, 574110, India
Vinayaka B. Shet, Anusha M. Palan, Shama U. Rao, C. Varun, Uday Aishwarya, Louella Concepta Goveas, C. Vaman Rao & P. Ujwal
Department of Biotechnology, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
Selvaraj Raja

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Vinayaka B. Shet
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Anusha M. Palan
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Shama U. Rao
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C. Varun
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Uday Aishwarya
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Selvaraj Raja
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Louella Concepta Goveas
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C. Vaman Rao
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P. Ujwal
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Correspondence to Vinayaka B. Shet.

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Shet, V.B., Palan, A.M., Rao, S.U. et al. 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, 127 (2018). https://doi.org/10.1007/s13205-018-1163-9

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Received: 08 August 2017
Accepted: 06 February 2018
Published: 13 February 2018
DOI: https://doi.org/10.1007/s13205-018-1163-9

Keywords

Hydrolysis
Hydrochloric acid
Non-edible seed cake
Artificial neural network