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

Abstract

In this paper, 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 MLRmore » 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.« less

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1328560
Alternate Identifier(s):
OSTI ID: 1364052
Report Number(s):
NREL/JA-5100-68694
Journal ID: ISSN 1939-1234; PII: 9798
Grant/Contract Number:  
FC02-07ER64494; AC36-08GO28308
Resource Type:
Journal Article: Published Article
Journal Name:
BioEnergy Research
Additional Journal Information:
Journal Name: BioEnergy Research Journal Volume: 10 Journal Issue: 2; Journal ID: ISSN 1939-1234
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; analytical pyrolysis; plant cell wall property prediction; Py-MBMS; NIR chemometrics; PLS regression

Citation Formats

Li, Muyang, Williams, Daniel L., Heckwolf, Marlies, de Leon, Natalia, Kaeppler, Shawn, Sykes, Robert W., and Hodge, David. Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR. United States: N. p., 2016. Web. doi:10.1007/s12155-016-9798-z.
Li, Muyang, Williams, Daniel L., Heckwolf, Marlies, de Leon, Natalia, Kaeppler, Shawn, Sykes, Robert W., & Hodge, David. Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR. United States. doi:10.1007/s12155-016-9798-z.
Li, Muyang, Williams, Daniel L., Heckwolf, Marlies, de Leon, Natalia, Kaeppler, Shawn, Sykes, Robert W., and Hodge, David. Tue . "Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR". United States. doi:10.1007/s12155-016-9798-z.
@article{osti_1328560,
title = {Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR},
author = {Li, Muyang and Williams, Daniel L. and Heckwolf, Marlies and de Leon, Natalia and Kaeppler, Shawn and Sykes, Robert W. and Hodge, David},
abstractNote = {In this paper, 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.},
doi = {10.1007/s12155-016-9798-z},
journal = {BioEnergy Research},
issn = {1939-1234},
number = 2,
volume = 10,
place = {United States},
year = {2016},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
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