Abstract
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.
Highlights
Lignocellulosic biomass offers enormous potential as a renewable feedstock for biorefining processes that can yield sustainable fuels, chemicals, and materials (Sharma et al, 2020)
While NIRS has been used in a number of studies and applications in the past to predict the composition of corn stover, the three objectives of the present study differentiate this work from the prior literature
Husk, and cob show the most improvement in glucose yields between untreated and pretreated samples, while the sheath is recalcitrant. Since this recalcitrance is fundamentally rooted in cell wall structure and chemical composition, we investigate later whether yields can be correlated to NIR spectra
Summary
Lignocellulosic biomass offers enormous potential as a renewable feedstock for biorefining processes that can yield sustainable fuels, chemicals, and materials (Sharma et al, 2020). Corn stover, like other gramineous feedstocks for biorefining processes, exhibits significant withinplant heterogeneity as a consequence of the differences in the cell wall composition and higher order structures between different cell types, tissues, or anatomical fractions (e.g., cob, leaf, husk, stem). In addition to this heterogeneity, variability within a single feedstock can arise from differences in feedstock biological origin, agronomic practices, local environment during growth, harvest time and approach, and biomass storage time and conditions (Morrison et al, 1998)
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