Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

Abstract

In this work, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant's response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These were compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models for p-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.

Accurate determination of genotypic variance of cell wall characteristics of a Populus trichocarpa pedigree using high-throughput pyrolysis-molecular beam mass spectrometry

“…In particular, py-MBMS has been used as a high-throughput technique to estimate relative lignin content, lignin monomeric syringyl (S) to guaiacyl (G), (S/G) ratios, sugar composition and terpenoid content of lignocellulosic biomass [ 9 , 10 , 21 – 25 ]. The source of ions present in mass spectra derived from the pyrolysis of lignocellulosic biomass has been the focus of many investigations and has been studied on many types of instruments and scales [ 26 – 29 ].…”

Impact of dilute acid pretreatment conditions on p-coumarate removal in diverse maize lines

“…It is known that pCA is acylated to the lignin polymer at the γcarbon of the side chain region primarily in syringyl moieties and to a lesser degree on arabinosyl side chains of arabinoxylan (Hatfield et al, 2008). The pCA content of grasses is known to exhibit substantial diversity with respect to genotype, anatomical fraction, and maturity (Li et al, 2015;Li et al, 2017) with the content in some fractions in maize reaching more than 150 mg pCA / g Klason lignin (Hatfield et al, 2008) or ~13% of the total aromatic content of the biomass. The prior processing history of the biomass (i.e., pretreatment) as well as any lignin recovery and purification steps will also impact the pCA content of the lignin as the ester bond is labile to acid-or base-catalyzed hydrolysis.…”

Accurate Determination of Genotypic Variance of Cell Wall Characteristics of a Populus trichocarpa Pedigree Using High-Throughput Pyrolysis-Molecular Beam Mass Spectrometry

“…In particular, py-MBMS has been used as a high-throughput technique to estimate relative lignin content, lignin monomeric syringyl (S) to guaiacyl (G), (S/G) ratios, sugar composition and terpenoid content of lignocellulosic biomass (9,10,(21)(22)(23)(24)(25). The source of ions present in mass spectra derived from the pyrolysis of lignocellulosic biomass has been the focus of many investigations and has been studied on many types of instruments and scales (26)(27)(28)(29).…”