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dc.contributor.authorCousins, Dylan S.
dc.contributor.authorOtto, William G.
dc.contributor.authorRony, Asif Hasan
dc.contributor.authorPedersen, Kristian P.
dc.contributor.authorAston, John E.
dc.contributor.authorHodge, David B.
dc.identifier.citationCousins DS, Otto WG, Rony AH, Pedersen KP, Aston JE and Hodge DB (2022) Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover. Front. Energy Res. 10:836690. doi: 10.3389/fenrg.2022.836690en_US
dc.description.abstractFeedstock 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.en_US
dc.publisherFrontiers Media SAen_US
dc.subjectnear-infrared spectrocopyen_US
dc.subjectcorn stoveren_US
dc.subjectbiomass pre-processingen_US
dc.subjectbiomass characterizationen_US
dc.titleNear-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stoveren_US
mus.citation.journaltitleFrontiers in Energy Researchen_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.departmentChemical & Biological Engineering.en_US
mus.relation.universityMontana State University - Bozemanen_US

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