Alanine is one of main by-products produced during process of glutamate fermentation. High concentration of alanine significantly affects glutamate production and yield, so accurately and rapidly monitoring alanine concentration is rather important for controlling fermentation process. In this study, to realize the rapid detection of alanine during glutamate fermentation, the calibration models for monitoring concentrations of alanine in the temperature-triggered glutamate fermentation process were developed by the near infrared (NIR). The NIR measurements of samples were analyzed by partial least-squares (PLS) regression with selecting spectral pre-processing methods and different wavelength. The root-mean square error of cross-validation (RMSECV), determination coefficient (R2) and residual predictive deviation (RPD) of the model was 0.21 g/L, 0.97 and 5.55, respectively, indicating that the model had good predictive ability. New batch fermentations as external validation were used to check the model. Compared with concentrations of predict value and measured value, the determination coefficient and average relative error of the model was 0.97 and 5.83%, respectively. These results showed that prediction model could accurately and quickly predict and monitor alanine concentration during the temperature-triggered glutamate fermentation process, and the study could provide theoretical basis for the real-time control and optimization of the temperature-triggered glutamate fermentation.