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Improved Multivariate Calibration Models For Corn Stover Feedstock And Dilute-acid Pretreated Corn Stover

E. Wolfrum, Amie D. Sluiter
Published 2009 · Materials Science

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We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.
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This paper is referenced by
10.3389/fpls.2014.00388
NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review
Li Xiao (2014)
10.1366/13-07003
Advancing Energy Cane Cell Wall Digestibility Screening by Near-Infrared Spectroscopy
B. F. Chong (2013)
10.1186/1754-6834-6-186
A holistic high-throughput screening framework for biofuel feedstock assessment that characterises variations in soluble sugars and cell wall composition in Sorghum bicolor
A. Martin (2013)
10.3389/fenrg.2018.00120
High Throughput Screening Technologies in Biomass Characterization
Stephen R Decker (2018)
10.4155/bfs.12.13
Developing sugarcane lignocellulosic biorefineries: opportunities and challenges
Barrie Fong Chong (2012)
10.1016/J.BEJ.2015.01.003
Line monitoring by near-infrared chemometric technique for potential ethanol production from hydrothermally treated Eucalyptus globulus
Yoshiki Horikawa (2015)
10.1016/J.SOILBIO.2011.04.002
Impact of plant cell wall network on biodegradation in soil: Role of lignin composition and phenolic acids in roots from 16 maize genotypes
G. Machinet (2011)
10.1021/jf100807b
Compositional Analysis of Lignocellulosic Feedstocks. 2. Method Uncertainties
D. W. Templeton (2010)
Compositional analysis and classification of Miscanthus using Fourier transform near infrared spectroscopy
Daniel Williams (2014)
10.1007/s12010-011-9460-3
Chemometric Analysis with Near-Infrared Spectroscopy for Chemically Pretreated Erianthus toward Efficient Bioethanol Production
Yoshiki Horikawa (2011)
10.1007/978-3-319-16298-0_30
Analytical methods for lignocellulosic biomass structural polysaccharides
Jason S. Lupoi (2014)
10.1007/s12155-015-9610-5
High-Throughput Method for Determining the Sugar Content in Biomass with Pyrolysis Molecular Beam Mass Spectrometry
Robert W. Sykes (2015)
10.3390/AGRICULTURE4040274
Vertical Distribution of Structural Components in Corn Stover
Jane M. F. Johnson (2014)
Mathématiques appliquées et traitement du signal pour l’évaluation de la dégradation de la biomasse lignocellulosique
Abbas Rammal (2016)
10.1007/s12155-016-9801-8
High-Throughput Profiling of the Fiber and Sugar Composition of Sugarcane Biomass
Nam V Hoang (2016)
10.1089/IND.2012.0018
Developing and Evaluating NIR Calibration Models for Multi-Species Herbaceous Perennials
Ewumbua M. Monono (2012)
Drought effec ts on composition and yield for corn stover, mixed grasses, and Miscanthusas bioenergy
Rachel M. Emerson (2014)
10.1016/J.RENENE.2017.03.020
Development of near-infrared spectroscopy models for quantitative determination of cellulose and hemicellulose contents of big bluestem
Ke Zhang (2017)
10.3389/fpls.2013.00218
Biomass for thermochemical conversion: targets and challenges
Paul Tanger (2013)
10.2172/1029015
Sorghum to Ethanol Research Initiative: Cooperative Research and Development Final Report, CRADA Number CRD-08-291
Ed Wolfrum (2011)
10.1007/s12155-010-9098-y
Feasibility of Spectroscopic Characterization of Algal Lipids: Chemometric Correlation of NIR and FTIR Spectra with Exogenous Lipids in Algal Biomass
L. Laurens (2010)
10.1016/B978-0-12-800080-9.00002-5
Analysis of Lignocellulosic Biomass Using Infrared Methodology
Feng Xu (2015)
10.1021/jf403086f
High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.
Lieve M L Laurens (2013)
10.1016/J.APENERGY.2018.06.020
System-level energy consumption modeling and optimization for cellulosic biofuel production
Y. Ge (2018)
10.2135/CROPSCI2009.04.0168
Genetic Analysis of Cell Wall Traits Relevant to Cellulosic Ethanol Production in Maize (Zea mays L.)
A. Lorenz (2010)
IB IN DEPTH—Special Section on Advances in Biomass Characterization Technology
B. Davison (2012)
Methods for Biomass Compositional Analysis
Amie D. Sluiter (2013)
10.1080/07388551.2017.1331336
Compositional analysis of lignocellulosic biomass: conventional methodologies and future outlook
Daniel J Krasznai (2018)
10.1016/J.APENERGY.2012.12.019
Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: A mini-review
F. Xu (2013)
Evaluating Standard Wet Chemistry Techniques and NIR Spectroscopic Models for Determining Composition and Potential Ethanol Yields of Multi-Species Herbaceous Bioenergy Crops
Scott W. Pryor (2018)
10.18174/518519
Quantitative mapping of lignin: Comprehensive insight into fungal delignification of plant biomass
Gijs van Erven (2020)
10.3934/BIOENG.2016.1.1
Investigating the impact of biomass quality on near-infrared models for switchgrass feedstocks
Lindsey M. Kline (2015)
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