Rapid determination of lignocellulose in corn stover based on near-infrared reflectance spectroscopy and chemometrics methods.

In this study, a rapid detection method based on near-infrared reflectance spectroscopy was proposed for measuring the contents of cellulose, hemicellulose and lignin in corn stover. In the basis of strategies of variable selection, feature extraction and nonlinear modeling, BiPLS-PCA-SVM was constructed using backward interval partial least squares combined with principal component analysis and support vector machine, which was used to improve the performance of spectral regression calibration model. For BiPLS-PCA-SVM model, the determination coefficients, root mean squared error and residual predictive deviation for the validation set were 0.906, 0.900% and 3.213 for cellulose; 0.987, 0.797% and 9.071 for hemicellulose; and 0.936, 0.264% and 4.024 for lignin, correspondingly. The results indicate that near-infrared reflectance spectroscopy combined with BiPLS-PCA-SVM can provide a reliable alternative strategy to detect contents of lignocellulosic components for pretreated corn stover in the anaerobic digestion process.