High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

Jason S Lupoi; Seema Singh; Mark Davis; David J Lee; M Shepherd; Blake A Simmons; Robert J Henry

doi:10.1186/1754-6834-7-93

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High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

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High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

Jason S Lupoi, Seema Singh, Mark Davis, David J Lee, M Shepherd, Blake A Simmons and Robert J Henry

Biotechnology for Biofuels, Vol.7, 93

2014

DOI: https://doi.org/10.1186/1754-6834-7-93

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Abstract

biomass

Raman spectroscopy

near-infrared spectroscopy

fourier-transform infrared spectroscopy

high-throughput

multivariate analysis

Lignin S/G

Background: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). Results: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. Conclusion Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Details

Title: High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development
Authors: Jason S Lupoi (Author) - University of Queensland
Seema Singh (Author) - Joint BioEnergy Institute, United States
Mark Davis (Author) - National Bioenergy Centre, United States
David J Lee (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
M Shepherd (Author) - Southern Cross University
Blake A Simmons (Author) - University of Queensland
Robert J Henry (Author) - University of Queensland
Publication details: Biotechnology for Biofuels, Vol.7, 93; 13
Publisher: BioMed Central Ltd.
Date published: 2014
DOI: 10.1186/1754-6834-7-93
ISSN: 1754-6834
Copyright note: Copyright © 2014 Lupoi et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Organisation Unit: Tropical Forests and People Research Centre; University of the Sunshine Coast, Queensland; Forest Industries Research Centre; Forest Research Institute
Language: English
Record Identifier: 99448946402621
Output Type: Journal article

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Web Of Science research areas: Biotechnology & Applied Microbiology; Energy & Fuels