Using FTIR spectroscopy to model alkaline pretreatment and enzymatic saccharification of six lignocellulosic biomasses

• Published in: Biotechnology and bioengineering 2012-04, Vol.109 (4), p.894-903
• Main Authors: Sills, Deborah L., Gossett, James M.
• Format: Article
• Language: English
• Subjects: beta-Glucosidase - pharmacology; Biofuels; Biomass; Carbohydrates; Cell Wall - drug effects; Cellulase - pharmacology; Enzymes; Fourier transforms; FTIR; Glucans - metabolism; Glucose; Glucose - biosynthesis; Hydrogen-Ion Concentration; Hydrolysis; Least-Squares Analysis; Lignin - metabolism; lignocellulose; Models, Chemical; PLS regression; Poaceae - drug effects; pretreatment; Sodium Hydroxide - pharmacology; Spectroscopy, Fourier Transform Infrared; Spectrum analysis; Wood - drug effects; Xylans - metabolism; Xylose - biosynthesis; Zea mays - drug effects
• ISSN: 0006-3592; 1097-0290
• DOI: 10.1002/bit.24376

Fourier transform infrared, attenuated total reflectance (FTIR‐ATR) spectroscopy, combined with partial least squares (PLS) regression, accurately predicted solubilization of plant cell wall constituents and NaOH consumption through pretreatment, and overall sugar productions from combined pretreatment and enzymatic hydrolysis. PLS regression models were constructed by correlating FTIR spectra of six raw biomasses (two switchgrass cultivars, big bluestem grass, a low‐impact, high‐diversity mixture of prairie biomasses, mixed hardwood, and corn stover), plus alkali loading in pretreatment, to nine dependent variables: glucose, xylose, lignin, and total solids solubilized in pretreatment; NaOH consumed in pretreatment; and overall glucose and xylose conversions and yields from combined pretreatment and enzymatic hydrolysis. PLS models predicted the dependent variables with the following values of coefficient of determination for cross‐validation (Q2): 0.86 for glucose, 0.90 for xylose, 0.79 for lignin, and 0.85 for total solids solubilized in pretreatment; 0.83 for alkali consumption; 0.93 for glucose conversion, 0.94 for xylose conversion, and 0.88 for glucose and xylose yields. The sugar yield models are noteworthy for their ability to predict overall saccharification through combined pretreatment and enzymatic hydrolysis per mass dry untreated solids without a priori knowledge of the composition of solids. All wavenumbers with significant variable‐important‐for‐projection (VIP) scores have been attributed to chemical features of lignocellulose, demonstrating the models were based on real chemical information. These models suggest that PLS regression can be applied to FTIR‐ATR spectra of raw biomasses to rapidly predict effects of pretreatment on solids and on subsequent enzymatic hydrolysis. Biotechnol. Bioeng. 2012; 109:894–903. © 2011 Wiley Periodicals, Inc. PLS regression models applied to FTIR spectra accurately predicted overall sugar conversions (g sugar per 100 g potential sugar) and yields (g sugar per 100 g dry solids) from pretreatment and enzymatic hydrolysis of six plant biomasses (two switchgrass cultivars, big bluestem grass, a mixture of prairie biomasses, mixed hardwood, and corn stover). The sugar yield models are noteworthy for their ability to predict overall saccharification per mass dry untreated solids without a priori knowledge of the composition of solids.