Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Cousins, Dylan S.; Otto, William G.; Rony, Asif Hasan; Pedersen, Kristian P.; Aston, John E.; Hodge, David B.

doi:10.3389/fenrg.2022.836690

Title: Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Journal Article · 16 February 2022 · Frontiers in Energy Research

DOI: https://doi.org/10.3389/fenrg.2022.836690 · OSTI ID:1845721

Cousins, Dylan S.; Otto, William G.; Rony, Asif Hasan; Pedersen, Kristian P.; Aston, John E.; Hodge, David B.

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 R ² _v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R ² _v = 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.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States); Montana State Univ., Bozeman, MT (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Bioenergy Technologies Office; USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517; EE0008907

OSTI ID:: 1845721

Report Number(s):: INL/JOU-21-65460-Rev000; 836690

Journal Information:: Frontiers in Energy Research, Journal Name: Frontiers in Energy Research Vol. 10; ISSN 2296-598X

Publisher:: Frontiers Media SACopyright Statement

Country of Publication:: Switzerland

Language:: English

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Title: Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

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