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Cited by 82Spectroscopy and Chemometric AnalysesBiofuel production and bioconversionSpectroscopy Techniques in Biomedical and Chemical Research

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

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Payne, Courtney E, Wolfrum, Edward J·2015·Biotechnology for Biofuels
DOI 10.1186/s13068-015-0222-2PMC PMC4381445
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Abstract

BACKGROUND: 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. 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. 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.

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  • The NREL mixed feedstocks model was validated with an external set of 25 samples including two of switchgrass and eight of miscanthus.

    “The NREL mixed feedstocks model was validated with an external set of 25 samples including two of switchgrass and eight of miscanthus, and we therefore considered it to be a standard against which to judge the suitability of the NIRSC switchgrass model for analysis of both species.”

    From Switchgrass and Giant Miscanthus Biomass and Theoretical Ethanol Production from Reclaimed Mine Lands (2018) · cites this paper as [30]

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Cited by

Sorted by citations

Authors
Payne, C. E., & Wolfrum, E. J.
Affiliations
National Laboratory of the Rockies (US)
Corresponding author
Payne, Courtney E
ORCID
Venue
Biotechnology for Biofuels
Publisher
Springer Science and Business Media LLC
Published
2015-03-12
Volume
8
Issue
1
Pages
43-43
ISSN
1754-6834 (ISSN-L)
DOI
10.1186/s13068-015-0222-2
PMC
PMC4381445
OpenAlex
W2148574659
Cited by
82
References
1
Citations by year
2026: 2 · 2025: 9 · 2024: 7 · 2023: 6 · 2022: 8 · 2021: 8 · 2020: 10 · 2019: 6 · 2018: 9 · 2017: 11 · 2016: 5 · 2015: 1
FWCI
6.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Biotechnology for Biofuels (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • U.S. Department of Energy (DE -AC36-08GO28308, No. DE-AC36-08GO28308, 08GO28308, DE-AC36, Contract No. DE-AC36-08GO28308, AC36‐08GO28308)
  • Office of Energy Efficiency and Renewable Energy (Contract No. DE-AC36-08GO28308, DE-AC36-08-GO28308., AC36-08-GO28308, No. DE-AC36-08GO28308)
  • Office of Energy Efficiency
  • National Renewable Energy Laboratory (Contract No. DE-AC36-08GO28308, DE-AC36-08-GO28308, AC36-08GO28308)
Topics
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
  • Physical Sciences › Engineering › Biomedical Engineering › Biofuel production and bioconversion
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
Keywords
cellulosic biomass, near-infrared spectroscopy, partial least squares, calibration models, dilute acid pretreatment, enzymatic hydrolysis, carbohydrate release, lignin, ash content, corn stover, switchgrass, sorghum, rice straw, Miscanthus, biofuel feedstock, rapid screening

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