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Near-Infrared Spect...
Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover
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- Cousins, Dylan S. (författare)
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, MT, United States
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- Otto, William G. (författare)
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, MT, United States
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- Rony, Asif Hasan (författare)
- Idaho National Laboratory, Idaho Falls, ID, United States
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- Pedersen, Kristian P. (författare)
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, MT, United States
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- Aston, John E. (författare)
- Idaho National Laboratory, Idaho Falls, ID, United States
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- Hodge, David B. (författare)
- Luleå tekniska universitet,Kemiteknik,Department of Chemical and Biological Engineering, Montana State University, Bozeman, MT, United States
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(creator_code:org_t)
- 2022-02-17
- 2022
- Engelska.
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Ingår i: Frontiers in Energy Research. - : Frontiers Media S.A.. - 2296-598X. ; 10
- Relaterad länk:
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https://doi.org/10.3...
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https://www.frontier...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R2v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R2v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Industriell bioteknik -- Bioprocessteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Industrial Biotechnology -- Bioprocess Technology (hsv//eng)
Nyckelord
- near-infrared spectrocopy
- corn stover
- bioenergy
- biomass pre-processing
- biomass characterization
- Biokemisk processteknik
- Biochemical Process Engineering
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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