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Strategies for Fermentation Medium Optimization: An In-Depth Review

机译:发酵培养基优化策略:深入研究

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摘要

Optimization of production medium is required to maximize the metabolite yield. This can be achieved by using a wide range of techniques from classical “one-factor-at-a-time” to modern statistical and mathematical techniques, viz. artificial neural network (ANN), genetic algorithm (GA) etc. Every technique comes with its own advantages and disadvantages, and despite drawbacks some techniques are applied to obtain best results. Use of various optimization techniques in combination also provides the desirable results. In this article an attempt has been made to review the currently used media optimization techniques applied during fermentation process of metabolite production. Comparative analysis of the merits and demerits of various conventional as well as modern optimization techniques have been done and logical selection basis for the designing of fermentation medium has been given in the present review. Overall, this review will provide the rationale for the selection of suitable optimization technique for media designing employed during the fermentation process of metabolite production.
机译:需要优化生产培养基以最大化代谢产物的产量。这可以通过使用从经典的“一次一因素”到现代统计和数学技术等多种技术来实现。人工神经网络(ANN),遗传算法(GA)等。每种技术都有其自身的优点和缺点,尽管存在缺点,但仍应用某些技术来获得最佳效果。各种优化技术的组合使用也可提供理想的结果。在本文中,已尝试审查在代谢物生产的发酵过程中应用的当前使用的培养基优化技术。本文对各种传统技术和现代优化技术的优缺点进行了比较分析,并为发酵培养基的设计提供了合理的选择依据。总体而言,本综述将为选择代谢物生产发酵过程中所用培养基设计的最佳优化技术提供依据。

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