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Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR
Department of Plant Biology, Michigan State University, East Lansing, .
DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, .
Department of Agronomy, University of Wisconsin, Madison.
Department of Agronomy, University of Wisconsin, Madison.
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Number of Authors: 7
2017 (English)In: Bioenergy Research, ISSN 1939-1234, E-ISSN 1939-1242, Vol. 10, no 2, 329-343 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 10, no 2, 329-343 p.
National Category
Bioprocess Technology
Research subject
Biochemical Process Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-60047DOI: 10.1007/s12155-016-9798-zISI: 000400861200003OAI: oai:DiVA.org:ltu-60047DiVA: diva2:1042886
Note

Validerad; 2017; Nivå 2; 2017-05-12 (andbra)

Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2017-06-02Bibliographically approved

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CiteExportLink to record
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