The L-lysine is one of the most important essential amino acid used in food and pharmaceutical industries. The present investigation was conducted to optimize the L-lysine production by Corynebacterium glutamicum (NCIM 2168). The production parameters such as the temperature, pH and glucose concentration (g/l) were optimised and evaluated by simulation method to develop a suitable model. The experimental design was done using central composite design (CCD). Total 20 set of experiments were performed according to the CCD. The factors and their responses were analysed by using the statistical tools: response surface methodology (RSM) and artificial neural network (ANN) linked with genetic algorithm (GA). The predicted optimum production of L-lysine was 19.003 g/l and 28.363 g/l by CCD-RSM and ANN-GA respectively. During validation by GA under optimized conditions, the L-lysine production was found to be 27.25 ± 1.15 g/l, which was significantly high than that obtained using CCD-RSM optimization method. The ANN coupled with GA was found to be a powerful tool for optimizing production parameters with high level of accuracy. This technique may be used for other fermentation products to optimize the important process parameters before scaling up the process to industrial level.

中文翻译：

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谷氨酸棒杆菌NCIM 2168生产L-赖氨酸的工艺设计的响应面和人工神经网络仿真

L-赖氨酸是食品和制药工业中最重要的必需氨基酸之一。进行本研究以优化谷氨酸棒杆菌的L-赖氨酸生产（NCIM 2168）。优化并通过模拟方法评估了生产参数，例如温度，pH和葡萄糖浓度（g / l），以建立合适的模型。实验设计使用中央复合设计（CCD）完成。根据CCD进行总共20组实验。使用统计工具对因素及其响应进行了分析：响应面方法（RSM）和与遗传算法（GA）关联的人工神经网络（ANN）。CCD-RSM和ANN-GA预测的L-赖氨酸的最佳产量分别为19.003 g / l和28.363 g / l。在优化条件下通过GA验证的过程中，发现L-赖氨酸的产量为27.25±1.15 g / l，明显高于使用CCD-RSM优化方法获得的产量。人们发现，将人工神经网络与遗传算法相结合是一种用于以高精确度优化生产参数的强大工具。该技术可用于其他发酵产品，以在将工艺规模扩大到工业水平之前优化重要的工艺参数。