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.