ABSTRACT
For a sustainable economy, biorefineries that use second-generation feedstocks to produce biochemicals and biofuels are essential. However, the exact composition of renewable feedstocks depends on the natural raw materials used and is therefore highly variable. In this contribution, a process analytical technique (PAT) strategy for determining the sugar composition of lignocellulosic process streams in real-time to enable better control of bioprocesses is presented. An in-line mid-IR probe was used to acquire spectra of ultra-filtered spent sulfite liquor (UF-SSL). Independent partial least squares models were developed for the most abundant sugars in the UF-SSL. Up to 5 sugars were quantified simultaneously to determine the sugar concentration and composition of the UF-SSL. The lowest root mean square errors of the predicted values obtained per analyte were 1.02 g/L arabinose, 1.25 g/L galactose, 0.50 g/L glucose, 1.60 g/L mannose, and 0.85 g/L xylose. Equipped with this novel PAT tool, new bioprocessing strategies can be developed for UF-SSL.
