Kishimoto M, Sawano T, Yoshida T, Taguchi H
International Center of Cooperative Research and Development in Microbial Engineering, Japan, and Faculty of Engineering, Osaka University, Suita-shi, Osaka 565, Japan.
Biotechnol Bioeng. 1984 Aug;26(8):871-6. doi: 10.1002/bit.260260809.
In many cases of fermentation processes, it is difficult to measure all of the state variables needed to indicate the culture state and to predict state changes. We propose the use of time-series data of measurable state variables instead of unmeasurable to describe the fermenatation process. We also employ a discrimination method of independent variables of regression analysis using statistical measures, PSS and AIC. These statistical procedure were applied to fed batch culture for yeast production under aerobic conditions, and the effectiveness was ascertained experimentally.
在许多发酵过程的情况下,很难测量所有指示培养状态并预测状态变化所需的状态变量。我们建议使用可测量状态变量的时间序列数据而非不可测量的变量来描述发酵过程。我们还采用了一种使用统计量PSS和AIC的回归分析自变量判别方法。这些统计程序应用于需氧条件下酵母生产的分批补料培养,并通过实验确定了其有效性。