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使用原位中红外光谱法监测和控制木醋杆菌分批补料培养过程

Monitoring and control of Gluconacetobacter xylinus fed-batch cultures using in situ mid-IR spectroscopy.

作者信息

Kornmann Henri, Valentinotti Sergio, Duboc Philippe, Marison Ian, von Stockar Urs

机构信息

Serono Biotech Center, route de Fenil, ZI B, CH-1809 Fenil-sur-Corsier, Switzerland.

出版信息

J Biotechnol. 2004 Sep 30;113(1-3):231-45. doi: 10.1016/j.jbiotec.2004.03.029.

DOI:10.1016/j.jbiotec.2004.03.029
PMID:15380658
Abstract

A partial least-squares calibration model, relating mid-infrared spectral features with fructose, ethanol, acetate, gluconacetan, phosphate and ammonium concentrations has been designed to monitor and control cultivations of Gluconacetobacter xylinus and production of gluconacetan, a food grade exopolysaccharide (EPS). Only synthetic solutions containing a mixture of the major components of culture media have been used to calibrate the spectrometer. A factorial design has been applied to determine the composition and concentration in the calibration matrix. This approach guarantees a complete and intelligent scan of the calibration space using only 55 standards. This calibration model allowed standard errors of validation (SEV) for fructose, ethanol, acetate, gluconacetan, ammonium and phosphate concentrations of 1.16 g/l, 0.36 g/l, 0.22 g/l, 1.54 g/l, 0.24 g/l and 0.18 g/l, respectively. With G. xylinus, ethanol is directly oxidized to acetate, which is subsequently metabolized to form biomass. However, residual ethanol in the culture medium prevents bacterial growth. On-line spectroscopic data were implemented in a closed-loop control strategy for fed-batch fermentation. Acetate concentration was controlled at a constant value by feeding ethanol into the bioreactor. The designed fed-batch process allowed biomass production on ethanol. This was not possible in a batch process due to ethanol inhibition of bacterial growth. In this way, the productivity of gluconacetan was increased from 1.8 x 10(-3) [C-mol/C-mol substrate/h] in the batch process to 2.9 x 10(-3) [C-mol/C-mol substrate/h] in the fed-batch process described in this study.

摘要

设计了一种偏最小二乘校准模型,将中红外光谱特征与果糖、乙醇、乙酸盐、葡糖醛酸聚糖、磷酸盐和铵浓度相关联,以监测和控制木醋杆菌的培养以及葡糖醛酸聚糖(一种食品级胞外多糖,EPS)的生产。仅使用含有培养基主要成分混合物的合成溶液来校准光谱仪。采用析因设计来确定校准矩阵中的组成和浓度。这种方法仅使用55个标准品就能保证对校准空间进行完整且智能的扫描。该校准模型对果糖、乙醇、乙酸盐、葡糖醛酸聚糖、铵和磷酸盐浓度的验证标准误差(SEV)分别为1.16 g/l、0.36 g/l、0.22 g/l、1.54 g/l、0.24 g/l和0.18 g/l。对于木醋杆菌,乙醇直接氧化为乙酸盐,随后乙酸盐被代谢以形成生物质。然而,培养基中的残留乙醇会抑制细菌生长。在线光谱数据被应用于分批补料发酵的闭环控制策略中。通过向生物反应器中加入乙醇,将乙酸盐浓度控制在恒定值。所设计的分批补料工艺能够利用乙醇生产生物质。由于乙醇对细菌生长的抑制作用,在分批工艺中这是不可能实现的。通过这种方式,葡糖醛酸聚糖的生产率从分批工艺中的1.8×10⁻³ [C - mol/C - mol底物/h]提高到了本研究中分批补料工艺的2.9×10⁻³ [C - mol/C - mol底物/h]。

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