Determining Bio-Oil Composition via Chemometric Tools Based on Infrared Spectroscopy

The development of rapid and accurate techniques to predict the composition of crude bio-oils obtained via the pyrolysis of lignocellulosic biomass is a prerequisite for their industrial implementation. Here, we demonstrate the potential of Fourier transform infrared spectroscopy to replace gas chromatography-mass spectrometry (GC-MS) in determining the compositional groups of bio-oils. Using the mid-infrared spectroscopic technique as a predictor, chemometric tools based on partial least squares regression models were contrasted with GC-MS results to foresee the various families of organic compounds. A broad data set consisting of more than 100 samples obtained from the thermal and catalytic pyrolysis of woody biomass and from the upgrading of bio-oil vapors by catalytic cracking over zeolites and metal oxides was used. The applicability of the developed model was assessed by external validation using the Kennard-Stone algorithm, showing that more than 90 wt % of the bio-oil composition was accurately determined. These results pave the path for the online monitoring of the forthcoming manufacture system of second-generation biofuels through rapid and cost-effective characterization of the pyrolysis bio-oils, thus enabling industrial producers to make timely decisions. KEYWORDS: Chemometric tools, Bio-oil composition, Fourier transform infrared spectroscopy,