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摘要
Rapid development in the glutamate fermentation industry has dictated the need for effective fermentation monitoring by rapid and precise methods that provide real-time information for quality control of the end-product. In recent years, near-infrared (NIR) spectroscopy and multivariate calibration have been developed as fast, inexpensive, non-destructive and environmentally safe techniques for industrial applications. The purpose of this study was to develop models for monitoring glutamate, glucose, lactate and alanine concentrations in the temperature-triggered process of glutamate fermentation. NIR measurements of eight batches of samples were analyzed by partial least-squares regression with several spectral pre-processing methods. The coefficient of determination (R (2)), model root-mean square error of calibration (RMSEC), root-mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of the test calibration for the glutamate concentration were 0.997, 3.11 g/L, 2.56 g/L and 19.81, respectively. For the glucose concentration, R (2), RMSEC, RMSEP and RPD were 0.989, 1.37 g/L, 1.29 g/L and 9.72, respectively. For the lactate concentration, R (2), RMSEC, RMSEP and RPD were 0.975, 0.078 g/L, 0.062 g/L and 6.29, respectively. For the alanine concentration, R (2), RMSEC, RMSEP and RPD were 0.964, 0.213 g/L, 0.243 g/L and 5.29, respectively. New batch fermentation as an external validation was used to check the models, and the results suggested that the predictive capacity of the models for the glutamate fermentation process was good.
