Journal
CELLULOSE
Volume 16, Issue 4, Pages 567-576Publisher
SPRINGER
DOI: 10.1007/s10570-009-9320-2
Keywords
Near-infrared; Biomass; Compositional analysis; Chemometrics; Multivariate; Calibration model; Corn stover
Funding
- US Department of Energy [DE-AC36-99GO10337]
Ask authors/readers for more resources
We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available