Assessing the Application of Near-Infrared Spectroscopy to Determine Saccharification Efficiency of Corn Biomass

Nowadays, in the bioethanol production process, improving the simplicity and yield of cell wall saccharification procedure represent the main technical hurdles to overcome. This work evaluated the application of a rapid and cost-effective technology such as near -infrared spectroscopy (NIRS) for easily predict saccharification efficiency from corn stover biomass. Calibration process focussing on the number of samples and the genetic background of the maize inbred lines were tested; while Modified Partial Least Squares Regression (MPLS) and Multiple Linear Regression (MLR) were assessed in predictions. The predictive capacity of the NIRS models was mainly determined by the coefficient of determination (r2ev) and the index of prediction to deviation (RPDev) in external validation. Overall, we could check a better efficiency of the NIRS calibration process for saccharification using larger number of observations (1500 sample set) and genetic backgrounds; while MPLS regression provided better prediction statistics (r2ev = 0.80; RPDev = 2.21) compared to MLR (r2ev = 0.68; RPDev = 1.75). These results indicate that NIRS could be successfully implemented as a large-phenotyping tool in order to test the saccharification potential of corn biomass. • NIRS could be successfully implemented as a large-phenotyping tool in order to test the saccharification potential of corn biomass. • NIRS wavelengths noted provide information about associated chemical components interfering in the saccharification potential. • The best efficiency in the NIRS calibration process was obtained using larger number of observations (1500 samples) and genetic backgrounds. • MPLS regression model is the most reliable for NIRS prediction of corn saccharification.