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

Biotechnology for Biofuels, Jun 2014

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.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

http://www.biotechnologyforbiofuels.com/content/pdf/1754-6834-7-93.pdf

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

Biotechnology for Biofuels 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 0 1 Seema Singh 0 Mark Davis David J Lee Merv Shepherd Blake A Simmons 0 1 Robert J Henry 1 0 Joint BioEnergy Institute, Lawrence Berkeley National Laboratory , 5885 Hollis Street, Emeryville, CA 94608 , USA 1 Queensland Alliance for Agriculture and Food Innovation, University of Queensland , 306 Carmody Road, St. Lucia, QLD 4072 , Australia 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. Biomass; Raman spectroscopy; Near-infrared spectroscopy; Fourier-transform infrared spectroscopy; High-throughput; Multivariate analysis; Lignin S/G - Background Second-generation biofuels from lignocellulosic biomass have been progressively explored as plausible pathways to relinquishing global dependency on greenhouse gasemitting fossil fuels [1-3]. Given the multitude of possible plants, the development of high-throughput analytical techniques capable of screening large arrays of feedstocks is paramount to isolating ideal candidates for biofuel and biochemical research and production. The measurement of biomass phenotypic parameters such as chemical composition, enzymatic hydrolysis sugar release, and the ratio of syringyl (S)-to-guaiacyl (G) lignin moieties can aid in identifying biomass species possessing traits found to play important roles in diminishing biomass recalcitrance. The effects different feedstock traits have on cell wall deconstruction are multifaceted, and no one superlative characteristic has been identified. Therefore, the collective highthroughput measurements of various traits can rapidly illuminate plants of interest. For example, Sykes et al. used high-throughput pyrolysis molecular beam mass spectrometry (pyMBMS) to screen approximately 800 poplar trees based on lignin content and S/G ratio [4]. Lignin is a three-dimensional, structurally complex, biopolymer comprised of phenylpropanoid units, designated as S, G, and p-coumaryl (H) components [5,6]. The ratio of S- to G-lignin provides a pivotal parameter for gauging the expected chemical reactivity of delignifying plant cell walls and for determining the energy requirements for pulping and bleaching feedstocks [7-9]. The correlation of S/G ratios to monomeric sugar release following hydrolysis of biomass has been explored, revealing conflicting results [7,10,11]. A slight decrease in the S/G ratio of hybrid poplar was shown to improve the rate of dilute acid hydrolysis [7]. While counter to the authors original hypothesis, the results demonstrate the applicability of using S/G ratios as an indicator of monomeric sugar yield. When juxtaposed with other correlative studies of S/G ratio and saccharification yield, high S/G ratios have occ (...truncated)


This is a preview of a remote PDF: http://www.biotechnologyforbiofuels.com/content/pdf/1754-6834-7-93.pdf

Jason S Lupoi, Seema Singh, Mark Davis, David J Lee, Merv Shepherd, Blake A Simmons, Robert J Henry. 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, Biotechnology for Biofuels, 2014, pp. 93, 7, DOI: 10.1186/1754-6834-7-93