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dc.creatorVia, Brian
dc.creatorFasina, Oladiran
dc.creatorPan, Hui
dc.date.accessioned2013-04-04T21:56:27Z
dc.date.available2013-04-04T21:56:27Z
dc.date.created2011
dc.date.issued2013-04-04
dc.identifier.urihttp://hdl.handle.net/11200/44161
dc.description.abstractThe assessment of wood biomass density through multivariate modeling of mid-infrared spectra can be useful for interpreting the relationship between feedstock density and functional groups. This study looked at predicting feedstock density from mid-infrared spectra and interpreting the multivariate models. The wood samples possessed a random cell wall orientation, which would be typical of wood chips in a feedstock process. Principal component regression and multiple linear regression models were compared both before and after conversion of the raw spectra into the 1st derivative. A principal component regression model from 1st derivative spectra exhibited the best calibration statistics, while a multiple linear regression model from the 1st derivative spectra yielded nearly similar performance. Earlywood and latewood based spectra exhibited significant differences in carbohydrate-associated bands (1000 and 1060 cm-1). Only statistically significant principal component terms (alpha less than 0.05) were chosen for regression; likewise, band assignments only originated from statistically significant principal components. Cellulose, lignin, and hemicelllose associated bands were found to be important in the prediction of wood density.en_US
dc.relation.ispartofBioresourcesen_US
dc.subjectmid infrared, spectroscopy, FT-IT, multivariate, chemometrics, wood, chemistry, lignin, cellulose, hemicellulose, densityen_US
dc.titleASSESSMENT OF PINE BIOMASS DENSITY THROUGH MID-INFRARED SPECTROSCOPY AND MULTIVARIATE MODELINGen_US
dc.typeCollectionen_US
dc.type.genreJournal Articleen_US
dc.citation.volume6en_US
dc.citation.issue1en_US
dc.citation.spage807en_US
dc.citation.epage822en_US


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