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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.
Γ-aminobutyric acid (γ-GABA) was prepared by enzymatic conversion of glutamic acid decarboxylase (GAD) using L-glutamic acid (L-Glu) as a substrate and the immobilized GAD could be continuously used. The process of enzymatic conversion was monitored, NIRS and PLS were used to establish the quantitative analysis model, and the process of enzymatic conversion of γ-GABA by GAD was monitored online.The data used in modeling 148 samples collected from 4 batches of fermentation at different times, of which 3 groups were used for modeling, the data in group were used for internal verification, and the last group of data was used for external verification.Using OPUS 7.0 to process data optimization model, The results showed that the predictive standard deviation for L-Glu external validation was 1.70 g / L at the selected wavelength of 1567-1789 nm and the determination coefficient was 95.67 for the first-order derivative spectral pretreatment method. For γ-GABA external validation The predicted standard deviation was 5.14 g / L with a determination coefficient of 86.32. The experimental results show that the established L-Glu and γ-GABA multivariate calibration models can be used to predict the change of the relative content of substrate and product in the process of monitoring enzymatic conversion, GAD enzymatic conversion preparation of γ-GABA production monitoring online mention For theoretical basis.