Dataset Information

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.

ABSTRACT: 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.

SUBMITTER: Lupoi JS

PROVIDER: S-EPMC4064109 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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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.

Lupoi Jason S JS Singh Seema S Davis Mark M Lee David J DJ Shepherd Merv M Simmons Blake A BA Henry Robert J RJ

Biotechnology for biofuels 20140617

<h4>Background</h4>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 ...[more]

PMID: 24955114

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Project description:New environmentally sound technologies are needed to derive valuable compounds from renewable resources. Lignin, an abundant polymer in terrestrial plants comprised predominantly of guaiacyl and syringyl monoaromatic phenylpropanoid units, is a potential natural source of aromatic compounds. In addition, the plant secondary metabolite tricin is a recently discovered and moderately abundant flavonoid in grasses. The most prevalent interunit linkage between guaiacyl, syringyl, and tricin units is the β-ether linkage. Previous studies have shown that bacterial β-etherase pathway enzymes catalyze glutathione-dependent cleavage of β-ether bonds in dimeric β-ether lignin model compounds. To date, however, it remains unclear whether the known β-etherase enzymes are active on lignin polymers. Here we report on enzymes that catalyze β-ether cleavage from bona fide lignin, under conditions that recycle the cosubstrates NAD+ and glutathione. Guaiacyl, syringyl, and tricin derivatives were identified as reaction products when different model compounds or lignin fractions were used as substrates. These results demonstrate an in vitro enzymatic system that can recycle cosubstrates while releasing aromatic monomers from model compounds as well as natural and engineered lignin oligomers. These findings can improve the ability to produce valuable aromatic compounds from a renewable resource like lignin.IMPORTANCE Many bacteria are predicted to contain enzymes that could convert renewable carbon sources into substitutes for compounds that are derived from petroleum. The β-etherase pathway present in sphingomonad bacteria could cleave the abundant β-O-4-aryl ether bonds in plant lignin, releasing a biobased source of aromatic compounds for the chemical industry. However, the activity of these enzymes on the complex aromatic oligomers found in plant lignin is unknown. Here we demonstrate biodegradation of lignin polymers using a minimal set of β-etherase pathway enzymes, the ability to recycle needed cofactors (glutathione and NAD+) in vitro, and the release of guaiacyl, syringyl, and tricin as depolymerized products from lignin. These observations provide critical evidence for the use and future optimization of these bacterial β-etherase pathway enzymes for industrial-level biotechnological applications designed to derive high-value monomeric aromatic compounds from lignin.

Dataset Information

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.

Publications

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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