Determination of hemicellulose, cellulose and lignin content using visible and near infrared spectroscopy in Miscanthus sinensis. (October 2017)

• Record Type: Journal Article
• Authors: Jin, Xiaoli; Chen, Xiaoling; Shi, Chunhai; Li, Mei; Guan, Yajing; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua
• Is Part Of: Bioresource technology. Volume 241(2017)
• Page Start: 603
• Page End: 609
• Publication Date: 2017-10
• Subjects: VIS/NIR spectroscopy -- Miscanthus sinensis -- Hemicellulose -- Cellulose -- Lignin -- Bioenergy crop; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2017.05.047
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: Statistical models were developed for determination of lignocellulosic components. The models were constructed based on whole wavelengths. The characteristic wavelengths contributed largely to lignocellulosic components. Models by PLS, LS-SVM and ANN were developed based on characteristic wavelengths. Abstract: Lignocellulosic components including hemicellulose, cellulose and lignin are the three major components of plant cell walls, and their proportions in biomass crops, such as Miscanthus sinensis, greatly impact feed stock conversion to liquid fuels or bio-products. In this study, the feasibility of using visible and near infrared (VIS/NIR) spectroscopy to rapidly quantify hemicellulose, cellulose and lignin in M. sinensis was investigated. Initially, prediction models were established using partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function neural network (RBF_NN) based on whole wavelengths. Subsequently, 23, 25 and 27 characteristic wavelengths for hemicellulose, cellulose and lignin, respectively, were found to show significant contribution to calibration models. Three determination models were eventually built by PLS, LS-SVM and ANN based on the characteristic wavelengths. Calibration models for lignocellulosic components were successfully developed, and can now be applied to assessment of lignocellulose contents in M. sinensis .