Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR

• Hardwood prediction models were built using ATR-FTIR and FT-NIR.
• ATR-FTIR modeling analysis could be used for screening purposes.
• FT-NIR–PLS models can be used for quantitative analysis of specific samples.
• PCR performed better for interpretation while PLS performed better for prediction.

This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Fourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) and partial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysis coupled with high performance liquid chromatography (HPLC) was employed to obtain the chemical composition of these samples. Spectra loadings were studied to identify key wavenumber in the prediction 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 either instrument 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 was used. Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groups responsible for the prediction of chemical composition and suggested that coupling the two techniques could strengthen interpretation and prediction.