Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

Journal Article · Thu Mar 12 00:00:00 EDT 2015 · Biotechnology for Biofuels

DOI:https://doi.org/10.1186/s13068-015-0222-2· OSTI ID:1220630

Payne, Courtney E. ^[1]; Wolfrum, Edward J. ^[1]

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1. National Renewable Energy Lab. (NREL), Golden, CO (United States). National Bioenergy Center

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. Here are the results: We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. In conclusion, it is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO)
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 1220630
• Report Number(s): NREL/JA-5100-62999
• Journal Information: Biotechnology for Biofuels, Vol. 8; ISSN 1754-6834
• Publisher: BioMed Central
• Country of Publication: United States
• Language: English

Related Subjects

09 BIOMASS FUELS
59 BASIC BIOLOGICAL SCIENCES
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
FT-NIR
NIR spectroscopy
biomass conversion
pretreatment
enzymatic hydrolysis
high-throughput assay
compositional analysis
cellulosic biomass
herbaceous feedstocks
PLS
reactivity
biofuels
multivariate analysis