Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

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

Automated summary

Heterogeneity in feedstock poses a challenge in the conversion of herbaceous lignocellulosic biomass to biobased products. Different anatomical tissues in corn stover respond differently to pretreatment and enzymatic hydrolysis, highlighting the need for tailored processing methods. Predictive correlation of near-infrared spectra to biomass chemical composition shows potential for robust correlations, with Gaussian process regression demonstrating stronger performance compared to partial least squares regression.