Near infrared spectroscopy model for analyzing chemical composition of biomass subjected to Fenton oxidation and hydrothermal treatment

Herein, near infrared (NIR) spectroscopy, rapid, accurate, and non-destructive method, was employed to analyze biomass composition. Calibration and prediction models for various types of biomass were developed from NIR data by applying the partial least squares method. Cellulose, hemicellulose, and lignin in a total of 75 samples were analyzed by a wet chemical method and NIR spectroscopy. The NIR model developed for hardwood accurately predicted the lignin content with a particle size of 20-80 mesh with a correlation coefficient (R-2) of >0.95, low root mean square error (0.68), high ratio of error range (22.23), and high residual predictive deviation (6.07). On the other hand, the models for other compositions exhibited relatively low prediction accuracy. Different biomass particle sizes (20-80 mesh, >40 mesh, and <40 mesh) led to statistically significant differences in NIR spectra based on the root mean square value. Although preprocessing (via smoothing, first and second derivatives) was performed to improve the prediction accuracy and reduce differences based on biomass particle size, a significant improvement was not achieved. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Near infrared (NIR) spectroscopy is a rapid, accurate, and non-destructive method for analyzing biomass composition, but prediction accuracy varies for different biomass compositions. Different biomass particle sizes have statistically significant differences in NIR spectra based on root mean square values, and preprocessing methods do not significantly improve prediction accuracy.