Extraction and optimization of exopolysaccharide from Lactobacillus sp. using response surface methodology and artificial neural networks

N. Suryawanshi, Sweta H. Naik, J. Eswari
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引用次数: 21

Abstract

Abstract The microbial polysaccharides secreted and produced from various microbes into their extracellular environment is known as exopolysaccharide. These polysaccharides can be secreted from the microbes either in a soluble or insoluble form.Lactobacillus sp. is one of the organisms that have been found to produce exopolysaccharide. Exo-polysaccharides (EPS) have various applications such as drug delivery, antimicrobial activity, surgical implants and many more in different fields. Medium composition is one of the major aspects for the production of EPS from Lactobacillus sp., optimization of medium components can help to enhance the synthesis of EPS . In the present work, the production of exopolysaccharide with different medium composition was optimized by response surface methodology (RSM) followed by tested for fitting with artificial neural networks (ANN). Three algorithms of ANN were compared to investigate the highest yeild of EPS. The highest yeild of EPS production in RSM was achieved by the medium composition that consists of (g/L) dextrose 15, sodium dihydrogen phosphate 3, potassium dihydrogen phosphate 2.5, triammonium citrate 1.5, and, magnesium sulfate 0.25. The output of 32 sets of RSM experiments were tested for fitting with ANN with three algorithms viz. Levenberg–Marquardt Algorithm (LMA), Bayesian Regularization Algorithm (BRA) and Scaled Conjugate Gradient Algorithm (SCGA) among them LMA found to have best fit with the experiments as compared to the SCGA and BRA.
利用响应面法和人工神经网络对乳酸菌胞外多糖的提取及优化
各种微生物分泌并产生的微生物多糖被称为胞外多糖。这些多糖可以以可溶性或不可溶性的形式从微生物中分泌出来。乳酸菌是一种已发现能产生外多糖的生物。外显多糖(EPS)具有多种应用,如药物传递、抗菌活性、外科植入物等许多不同领域。培养基组成是乳酸菌生产EPS的主要方面之一,优化培养基组成有助于提高EPS的合成。采用响应面法(RSM)对不同培养基组成的胞外多糖的生产工艺进行了优化,并进行了人工神经网络(ANN)的拟合检验。比较了三种人工神经网络算法,探讨了EPS的最高产率。当培养基组成为(g/L)葡萄糖15、磷酸二氢钠3、磷酸二氢钾2.5、柠檬酸三铵1.5和硫酸镁0.25时,在RSM中EPS的产率最高。采用Levenberg-Marquardt算法(LMA)、贝叶斯正则化算法(BRA)和缩放共轭梯度算法(SCGA)三种算法对32组RSM实验的输出进行了与人工神经网络的拟合测试,其中LMA与SCGA和BRA相比,与实验的拟合效果最好。
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