Lactate is a main by-product during glutamate fermentation and is important for control and optimization of the process. In this study,the calibration models for monitoring concentration of lactate in the temperature-triggered glutamate fermentation process were developed by the near infrared spectroscopy and partial least-squares regression. The models were developed and optimized by selecting different spectral pretreatment methods and wave number regions. The spectral pretreatment method was first derivative + vector normalization and the wave number region was5 500 ~ 7 500 cm- 1+ 4 200 ~ 4 900 cm- 1. The root-mean square error of cross-validation( RMSECV) of lactate,determination coefficient( R2) and predicted residual deviation( RPD) were 0.482 g/L,0.912 and 5.98,respectively. These results showed that the lactate model had good predictive ability. Fermentation liquor samples from normal fermentation and low dissolved oxygen fermentation were used as external validations to check the model. Compared with the predict values,the determination coefficient were 0. 914 and 0. 923,respectively. And the average relative errors were 7. 86% and 6. 58%,respectively. These results showed that the model could predict and monitor lactate concentration during glutamate fermentation process accurately and quickly,and it will provide theoretical basis for the real-time control of lactate concentration during the temperature-triggered glutamate fermentation process.