This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy andFourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) andpartial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysiscoupled with high performance liquid chromatography (HPLC) was employed to obtain the chemicalcomposition of these samples.
Spectra loadings were studied to identify key wavenumber in the predic-tion 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 eitherinstrument to provide superior prediction models with NIR performing slightly better.
During testing,it was found that more accurate determination of holocellulose content was possible when HPLC wasused.
Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groupsresponsible for the prediction of chemical composition and suggested that coupling the two techniquescould strengthen interpretation and prediction.