期刊
CARBOHYDRATE POLYMERS
卷 121, 期 -, 页码 336-341出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.carbpol.2014.11.062
关键词
Chemical composition; Fast prediction; ATR-FTIR; FT-NIR; Hardwood
资金
- Agriculture and Food Research Initiative (AFRI) CAP-Southeast Partnership for Integrated Biomass Supply Systems
- Department of Energy grant titled, High Tonnage Forest Biomass Production Systems from Southern Pine Energy Plantations
- National Natural Science Foundation of China [50973048]
- National Natural Science Foundation of Shandong Province [ZR2012EM002]
- Special Foundation of Taishan Scholar Construction and National Science & Technology Pillar Program [2012BAD32806]
This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Fourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) and partial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysis coupled with high performance liquid chromatography (HPLC) was employed to obtain the chemical composition of these samples. Spectra loadings were studied to identify key wavenumber in the prediction of chemical composition. NIR-PLS and FTIR-PLS performed the best for extractives, lignin and xylose, whose residual predictive deviation (RPD) values were all over 3 and indicates the potential for either instrument to provide superior prediction models with NM performing slightly better. During testing, it was found that more accurate determination of holocellulose content was possible when HPLC was used. Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groups responsible for the prediction of chemical composition and suggested that coupling the two techniques could strengthen interpretation and prediction. (C) 2015 Elsevier Ltd. All rights reserved.
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