期刊
JOURNAL OF NEAR INFRARED SPECTROSCOPY
卷 21, 期 6, 页码 485-493出版社
N I R PUBLICATIONS
DOI: 10.1255/jnirs.1085
关键词
Klason lignin; near infrared; precision; prediction model
资金
- Donghua University [BC2010121]
- Agriculture and Food Research Initiative (AFRI) CAP-Southeast Partnership for Integrated Biomass Supply Systems
- Department of Energy
- Regions Bank
- National Natural Science Foundation of China [50973048]
- National Natural Science Foundation of Shandong Province [ZR2012EM002]
- Special Foundation of Taishan Scholar Construction
- National Science & Technology Pillar Program [2012BAD32B06]
Delignification pretreatments were applied on southern pine (Pinus spp.) to obtain low lignin content samples, and then near infrared (NIR) spectroscopy was coupled with partial least squares regression (PLS-R) to monitor the lignin content in both delignified and untreated wood. Extractives removal and particle size reduction was also applied to further improve calibration equations. The PLS-R results showed that NIR was a useful tool for monitoring the wood during delignification with an R-2=0.99 and a residual predictive deviation (RPM = 14.34. Furthermore, the lignin content range was expanded by combining untreated with delignified wood samples. It was found that calibrations could be combined to monitor simultaneously delignified and raw biomass. NIR was found to be sensitive to changes in woody biomass during delignification while particle size reduction coincided to play a significant role. Extractives removal prior to NIR scanning provided minimal improvement and does not appear to be necessary for monitoring wood before or during delignification.
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