摘要
以L-谷氨酸（L-Glu）为底物经谷氨酸脱羧酶（GAD）酶法转化制备γ-氨基丁酸（γ-GABA）,经固定化后的GAD可以连续使用.为了在线监测酶促转化反应过程,引入近红外光谱分析技术（NIRS）结合最小偏二乘法（PLS）建立定量分析模型,对GAD酶法转化制备γ-GABA的过程进行在线监测.建模所使用的数据来自4个批次发酵过程不同时间收集得到的148个样品,其中3组数据用于建模,组内数据用于内部验证,最后1组数据用于外部验证.采用OPUS 7.0处理数据优化模型,结果显示,选用一阶导数光谱预处理方法,当选定波长为1567~1789 nm时,对于L-Glu外部验证的预测标准偏差为1.70 g/L,决定系数为95.67;对于γ-GABA外部验证的预测标准偏差为5.14 g/L,决定系数为86.32.实验表明建立的L-Glu和γ-GABA多元校准模型可用于预测监控酶促转化过程中底物与产物的相对含量的变化,从而为GAD酶法转化制备γ-GABA的生产在线监控提供理论基础.
The purpose of this study was to develop models for NIRS monitoring glutamate that was catalyzed into γ-GABA by immobilized Glutamate decarboxylase, Catalyzed reaction by immobilized glutamate decarboxylase could be used continuously for two times. In order to achieve the online monitoring of enzymatic reaction process, introducing the near infrared spectroscopy （NIRS） combined with partial least squares （PLS） method to establish the quantitative analysis models to monitor the process of the preparation of γ-GABA by enzymatic conversion. The data for establishing Models collecting from 4 batches of 148 samples in different fermentation time. The three groups of data were used for modeling, data in the group was for internal validation, the last data was for the external validation. Using OPUS 7.0 dealed with data and optimized the model. The results showed that the model were good accuracy and stability in the wave number 1 567 - 1 789 nm after first derivative pretreatment. The standard errors of prediction for 1.70 g/L, the coefficient of determination （R2） was 95.67 in L-Glu external validation; The standard errors of prediction for 5.14 g/L, the coefficient of determination （Ra） was 86.32 in L- Glu external validation. The results showed that NIRS can determine the concentration of substrate and product online, that can providing fundamental basis for real-time controling the process of fermentation.
出处
《南开大学学报（自然科学版）》
CAS
CSCD
北大核心
2015年第6期86-91,共6页
Acta Scientiarum Naturalium Universitatis Nankaiensis
