Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy
Abstract
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,more »
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States). National Bioenergy Center
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO)
- OSTI Identifier:
- 1220630
- Report Number(s):
- NREL/JA-5100-62999
Journal ID: ISSN 1754-6834
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Biotechnology for Biofuels
- Additional Journal Information:
- Journal Volume: 8; Journal ID: ISSN 1754-6834
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 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
Citation Formats
Payne, Courtney E., and Wolfrum, Edward J. Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy. United States: N. p., 2015.
Web. doi:10.1186/s13068-015-0222-2.
Payne, Courtney E., & Wolfrum, Edward J. Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy. United States. https://doi.org/10.1186/s13068-015-0222-2
Payne, Courtney E., and Wolfrum, Edward J. Thu .
"Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy". United States. https://doi.org/10.1186/s13068-015-0222-2. https://www.osti.gov/servlets/purl/1220630.
@article{osti_1220630,
title = {Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy},
author = {Payne, Courtney E. and Wolfrum, Edward J.},
abstractNote = {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.},
doi = {10.1186/s13068-015-0222-2},
journal = {Biotechnology for Biofuels},
number = ,
volume = 8,
place = {United States},
year = {Thu Mar 12 00:00:00 EDT 2015},
month = {Thu Mar 12 00:00:00 EDT 2015}
}
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