Using FTIR to predict saccharification from enzymatic hydrolysis of alkali‐pretreated biomasses

Abstract

Fourier transform infrared, attenuated total reflectance (FTIR-ATR) spectroscopy combined with partial least squares (PLS) regression accurately predicted 72-h glucose and xylose conversions (g sugars/100 g potential sugars) and yields (g sugars/100 g dry solids) from cellulase-mediated hydrolysis of alkali-pretreated lignocellulose. Six plant biomasses that represent a variety of potential biofuel feedstocks--two switchgrass cultivars, big bluestem grass, a low-impact, high-diversity mixture of 32 species of prairie biomasses, mixed hardwood, and corn stover--were subjected to four levels of low-temperature NaOH pretreatment to produce 24 samples with a wide range of potential digestibility. PLS models were constructed by correlating FTIR spectra of pretreated samples to measured values of gluose and xylose conversions and yields. Variable selection, based on 90% confidence intervals of regression-coefficient matrices, improved the predictive ability of the models, while simplifying them considerably. Final models predicted sugar conversions with coefficient of determination for cross-validation (Q(2)) values of 0.90 for glucose and 0.89 for xylose, and sugar yields with Q(2) values of 0.92 for glucose and 0.91 for xylose. The sugar-yield models are noteworthy for their ability to predict enzymatic saccharification per mass dry solids without a priori knowledge of the composition of the solids. All peaks retained in the final regression coefficient matrices were previously assigned to chemical bonds and functional groups in lignocellulose, demonstrating that the models were based on real chemical information. This study demonstrates that FTIR spectroscopy combined with PLS regression can be used to rapidly estimate sugar conversions and yields from enzymatic hydrolysis of pretreated plant biomass.