Application of Near Infrared Reflectance Spectroscopy to Determine Saccharification Efficiency of Corn Biomass

Abstract

Abstract 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 Reflectante Spectroscopy (NIRS) for predicting saccharification efficiency from corn stover biomass. For the development of the calibration model, 1150 dried and ground samples of corn biomass were included, while for external validation, 350 samples were utilized. Modified Partial Least Squares Regression (MPLS) and Multiple Linear Regression (MLR) were used to develop prediction models. The predictive capacity of the NIRS models was assessed by the coefficient of determination value in external validation (r2) and the ratio between the standard deviation and the error of prediction in external validation (RPDev). Overall, MPLS regression provided better prediction statistics for saccharification efficiency (r2 = 0.80; RPDev = 2.21) compared to MLR regression (r2 = 0.68; RPDev = 1.75). These results indicated that predictive ability of MPLS model is more appropriate for screening purposes and NIR spectroscopy can be implemented as a large-phenotyping tool to test the saccharification yield of the corn biomass.