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为实现混合底物的高效定向转化,以产絮菌根癌农杆菌(Agrobactrium tumefaciens)F2为研究对象,考察不同单一碳源及不同初始浓度对菌体生长、絮凝效能及絮凝剂产量的变化规律,采用BP算法构建絮凝效能及产量预测神经网络.产絮菌F2利用葡萄糖时的絮凝效能和产量分别为88.98%和2.20 g·L-1,过低的初始浓度将影响产量,不低于7.5 g·L-1为佳.以D-(+)-葡萄糖、D-半乳糖和D-甘露糖3种单糖为混合碳源,构建网络结构为3-5-2的产絮效能及絮凝剂产量预测模型,对两个输出层的预测误差范围均在4%以内,预测葡萄糖、半乳糖、甘露糖浓度的最优解为6.59 g·L-1、1.32 g·L-1、3.57 g·L-1,经验证混合碳源发酵产絮可使絮凝效能和产量比单一葡萄糖发酵时分别提高6.87%和26.82%,本文为产絮菌F2利用含糖有机质废液发酵产絮凝剂提供数据参考.
In order to achieve efficient directional transformation of the mixed substrate, Agrobactrium tumefaciens F2 was used as the research object to investigate the variation of cell growth, flocculation efficiency and flocculant yield of different single carbon sources and different initial concentrations , The BP algorithm was used to construct the flocculation efficiency and yield prediction neural network.The flocculation efficiency and yield of flocculation F2 with glucose were 88.98% and 2.20 g · L-1, respectively, too low initial concentration would affect the yield no less than 7.5 g · L-1 was better than that of the control.Under the mixed carbon source of D - (+) - glucose, D-galactose and D-mannose as the mixed carbon source, the flocculating efficiency and flocculation The prediction model for the yield of the two output layers is within 4% of the prediction error range. The optimal solution of glucose, galactose and mannose concentrations is 6.59 g · L-1, 1.32 g · L-1, and 3.57 g · L-1. It was verified that the flocculation efficiency and yield of mixed carbon source fermented flocculation increased by 6.87% and 26.82% respectively compared with the single glucose fermentation. In this paper, flocculation agent F2 reference.