Real-time estimation of glucose concentration in algae cultivation system using Raman spectroscopy.

This work proposes a soft-sensor design for real-time estimation of glucose concentration under mixotrophic conditions using Raman spectroscopy. The suggested approach applies a Rolling-Circle Filter (RCF), Partial Least Squares (PLS), and a successive Savitzky-Golay (SG) smoothing filter. RCF is used to remove the background effects of Raman spectrum in the pre-processing step. PLS is used to reduce the dimensionality of spectrum data and relate them to the concentration. The SG filter is further employed as a post-processing step in a successive manner to adjust predicted glucose concentrations. Two sets of experiments using artificial assays and samples from a microalgae cultivation system were performed for verification. The proposed approach showed improved prediction performances compared to other data processing and regression techniques.