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利用低成本农业工业废弃物通过杂交优化工具生产Thermomyces lanuginosus VAPS-24 木聚糖酶及其在糖化中的潜在应用。

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

机构信息

Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak 124001, Haryana, India.

Optimization and Mechatronics Laboratory, Department of Mechanical Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, Haryana, India.

出版信息

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

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) than un-optimized conditions (61.09±0.91UmL). 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) as compared to RSM response (119.91±2.53UmL). 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.

摘要

从Thermomyces lanuginosus VAPS-24 生产木聚糖酶已采用分批实验(One factor at a time,OFAT)方法在农业工业底物上进行了优化。此外,还采用中心复合设计(Central Composite Design,CCD)对各种工艺参数(如温度(45-55°C)、碳源浓度(1.5-2.5%)、发酵时间(72-120h)和生产培养基 pH(6-8))进行了优化。通过 RSM 优化后,木聚糖酶的最大产量约为未优化条件(61.09±0.91UmL)的两倍(119.91±2.53UmL)。采用遗传算法-响应面法(GA-RSM)、人工神经网络(ANN)、遗传算法-人工神经网络(GA-ANN)等几种混合统计工具,以获得更优化的工艺参数,从而最大限度地提高木聚糖酶的产量,与 RSM 响应(119.91±2.53UmL)相比,木聚糖酶产量提高了 10.50%(132.51±3.27UmL)。各种预处理和未处理的农业残余物都用粗木聚糖酶进行糖化,其中预处理的稻草产生了最大的可发酵糖 126.89mgg。

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