Xylanase production from Thermomyces lanuginosus VAPS-24 using low cost agro-industrial residues via hybrid optimization tools and its potential use for saccharification

Vishal Kumar; Deepak Chhabra; Pratyoosh Shukla

doi:10.1016/j.biortech.2017.07.094

Xylanase production from Thermomyces lanuginosus VAPS-24 using low cost agro-industrial residues via hybrid optimization tools and its potential use for saccharification

Bioresour Technol. 2017 Nov:243:1009-1019. doi: 10.1016/j.biortech.2017.07.094. Epub 2017 Jul 19.

Authors

Vishal Kumar¹, Deepak Chhabra², Pratyoosh Shukla³

Affiliations

¹ Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak 124001, Haryana, India.
² Optimization and Mechatronics Laboratory, Department of Mechanical Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, Haryana, India.
³ Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak 124001, Haryana, India. Electronic address: pratyoosh.shukla@gmail.com.

PMID: 28764103
DOI: 10.1016/j.biortech.2017.07.094

Abstract

The xylanase production from Thermomyces lanuginosus VAPS-24 has been optimized using OFAT (One factor at a time) approach using agro-industrial substrates. Further, central composite design (CCD) has been employed to optimize various process parameters such as temperature (45-55°C), carbon source concentration (1.5-2.5%), fermentation time (72-120h) and production medium pH (6-8). Maximum xylanase yield after RSM optimization was approximately double (119.91±2.53UmL^-1) than un-optimized conditions (61.09±0.91UmL^-1). Several hybrid statistical tools such as Genetic Algorithm-Response Surface Methodology (GA-RSM), Artificial Neural Network (ANN), Genetic Algorithm-Artificial Neural Network (GA-ANN) were employed to obtain more optimized process parameters to maximize the xylanase production and observed an increase of 10.50% xylanase production (132.51±3.27UmL^-1) as compared to RSM response (119.91±2.53UmL^-1). The various pretreated and untreated agricultural residues were subjected to saccharification by using crude xylanase in which the pretreated rice straw yielded maximum fermentable sugars 126.89mgg^-1.

Keywords: Agro-industrial residues; Artificial neural network; Central composite design; Genetic algorithm; Thermomyces lanuginosus; Xylanase.

MeSH terms

Ascomycota*
Carbon*
Fermentation
Neural Networks, Computer
Oryza*
Temperature

Substances

Carbon