[关键词]
[摘要]
丙氨酸是谷氨酸发酵过程中的副产物之一,对谷氨酸产量及转化率有显著影响,因此及时准确监测丙氨酸浓度变化对谷氨酸发酵过程控制有重要意义。为实现谷氨酸发酵过程中丙氨酸浓度的快速检测,采用近红外光谱技术结合偏最小二乘的方法,通过不同光谱预处理和波长范围,建立并优化谷氨酸温度敏感突变株强制发酵过程中丙氨酸浓度预测模型。优化后的模型交叉验证误差均方根、决定系数和剩余预测偏差分别为0.21 g/L、0.97和5.55。以谷氨酸温度敏感突变株强制发酵作为外部检验进一步验证模型的准确性和可靠性,并将预测值与实际值进行对比,经分析其决定系数和平均相对误差分别为0.97和5.83%,表明该模型具有很好的预测能力。本文所建预测模型能够准确快速地对发酵过程中丙氨酸进行预测,可为谷氨酸温度敏感突变株强制发酵过程的实时控制及其优化提供理论基础和借鉴。
[Key word]
[Abstract]
Alanine is one of main by-products produced during process of glutamate fermentation. High concentration of alanine significantly affects glutamate production and yield, so accurately and rapidly monitoring alanine concentration is rather important for controlling fermentation process. In this study, to realize the rapid detection of alanine during glutamate fermentation, the calibration models for monitoring concentrations of alanine in the temperature-triggered glutamate fermentation process were developed by the near infrared (NIR). The NIR measurements of samples were analyzed by partial least-squares (PLS) regression with selecting spectral pre-processing methods and different wavelength. The root-mean square error of cross-validation (RMSECV), determination coefficient (R2) and residual predictive deviation (RPD) of the model was 0.21 g/L, 0.97 and 5.55, respectively, indicating that the model had good predictive ability. New batch fermentations as external validation were used to check the model. Compared with concentrations of predict value and measured value, the determination coefficient and average relative error of the model was 0.97 and 5.83%, respectively. These results showed that prediction model could accurately and quickly predict and monitor alanine concentration during the temperature-triggered glutamate fermentation process, and the study could provide theoretical basis for the real-time control and optimization of the temperature-triggered glutamate fermentation.
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[基金项目]
国家高技术研究发展计划(2013AA102106);天津市应用基础与前沿技术研究计划(14JCYBJC23500)