Real-time and on-line parallel detection of key fermentation process parameters by near-infrared spectroscopy in different environments

Abstract The fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids and sodium gluconate were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R2 is greater than 0.90, exhibiting a good linear relationship. The root mean square error shows that the model has high credibility. This research provides a basis for the application of NIR spectroscopy in complex fermentation systems.