摘要
Near-infrared spectroscopy （NIRS） with its fast and nondestructive advantages can be qualified for the real-time quantitative analysis. This paper demonstrates that NIRS combined with partial least squares （PLS） regression can be used as a rapid analytical method to simultaneously quantify L-glutamic acid （L- GIu） and γ-aminobutyric acid （GABA） in a biotransformation process and to guide the optimization of production conditions when the merits of NIRS are combined with response surface methodology. The high performance liquid chromatography （HPLC） reference analysis was performed by the o-phthaldialdehyde pre-column derivatization. NIRS measurements of two batches of 141 samples were firstly analyzed by PLS with several spectral pre-processing methods. Compared with those of the HPLC reference analysis, the resulting determination coefficients （R2）, root mean square error of prediction （RMSEP） and residual predictive deviation （RPD） of the external validation for the L-GIu concentration were 99.5%, 1.62 g/L, and 11.3, respectively. For the GABA concentration, R2, RMSEP, and RPD were 99.8%, 4.00 g/L, and 16.4, respectively. This NIRS model was then used to optimize the biotransformation process through a Box- Behnken experimental design. Under the optimal conditions without pH adjustment, 200 gjL L-GIu could be catalyzed by 7148 U/L glutamate decarboxylase （GAD） to GABA, reaching 99% conversion at the fifth hour. NIRS analysis provided timely information on the conversion from L-GIu to GABA. The results suggest that the NIRS model can not only be used for the routine profiling of enzymatic conversion, providing a simple and effective method of monitoring the biotransformation process of GABA, but also be considered to be an optimal tool to guide the optimization of production conditions.
Near-infrared spectroscopy （NIRS） with its fast and nondestructive advantages can be qualified for the real-time quantitative analysis. This paper demonstrates that NIRS combined with partial least squares （PLS） regression can be used as a rapid analytical method to simultaneously quantify L-glutamic acid （L- GIu） and γ-aminobutyric acid （GABA） in a biotransformation process and to guide the optimization of production conditions when the merits of NIRS are combined with response surface methodology. The high performance liquid chromatography （HPLC） reference analysis was performed by the o-phthaldialdehyde pre-column derivatization. NIRS measurements of two batches of 141 samples were firstly analyzed by PLS with several spectral pre-processing methods. Compared with those of the HPLC reference analysis, the resulting determination coefficients （R2）, root mean square error of prediction （RMSEP） and residual predictive deviation （RPD） of the external validation for the L-GIu concentration were 99.5%, 1.62 g/L, and 11.3, respectively. For the GABA concentration, R2, RMSEP, and RPD were 99.8%, 4.00 g/L, and 16.4, respectively. This NIRS model was then used to optimize the biotransformation process through a Box- Behnken experimental design. Under the optimal conditions without pH adjustment, 200 gjL L-GIu could be catalyzed by 7148 U/L glutamate decarboxylase （GAD） to GABA, reaching 99% conversion at the fifth hour. NIRS analysis provided timely information on the conversion from L-GIu to GABA. The results suggest that the NIRS model can not only be used for the routine profiling of enzymatic conversion, providing a simple and effective method of monitoring the biotransformation process of GABA, but also be considered to be an optimal tool to guide the optimization of production conditions.
基金
supported by the National Natural Science Foundation of China (Nos. 81374046 and 81373506)
