Monitoring Glutamate and Glucose Concentration During the Temperature Triggered Glutamate Fermentation by Near-Infrared Spectroscopy

Abstract

In this study, the calibration models for monitoring concentrations of glutamate and glucose in the temperature-triggered glutamate fermentation process were developed by near infrared (NIR) spectroscopy. The NIR measurements of samples were analyzed by partial least-squares (PLS) regression with selecting spectral pre-processing methods and different wavelengths. The root-mean square errors of cross-validation (RMSECV) of glutamate and glucose were 2.73 and 1.92 g/L, respectively. The determination coefficients (R2) were 0.996 and 0.982, respectively. The residual predictive deviation (RPD) was 17.8 and 8.37, respectively. These results showed that all models had good predictive ability. New batch fermentation as an external validation was used to check the models. Compared with concentrations of predict value and measured value, the determination coefficient was 0.992 and 0.951, respectively. The average relative errors were 5.79 and 7.38 %, respectively. These results showed that prediction model could predict and monitor the temperature-triggered glutamate fermentation process accurately and quickly, and thus theoretical basis for the real-time control and optimization in the temperature-triggered glutamate fermentation process was provided.