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

Jason S Lupoi²  Seema Singh³  Mark Davis⁶  David J Lee⁵  Merv Shepherd¹  Blake A Simmons³  Robert J Henry⁴ 

[1] Southern Cross Plant Science, Southern Cross University, Military Road, East Lismore, NSW 2480, Australia

[2] Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, 5885 Hollis Street, Emeryville, CA 94608, USA

[3] Biological and Materials Science Center, Sandia National Laboratories, 7011 East Avenue, Livermore, CA 94551, USA

[4] Queensland Alliance for Agriculture and Food Innovation, University of Queensland, 306 Carmody Road, St. Lucia, QLD 4072, Australia

[5] Forest Industries Research Centre, University of the Sunshine Coast and Queensland Department of Agriculture, Fisheries and Forestry, Locked Bag 4, Maroochydore DC, QLD 4558, Australia

[6] National Bioenergy Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA

关键词: Lignin S/G;    Multivariate analysis;    High-throughput;    Fourier-transform infrared spectroscopy;    Near-infrared spectroscopy;    Raman spectroscopy;    Biomass;   

Others  :  791776
DOI  :  10.1186/1754-6834-7-93

 received in 2014-03-18, accepted in 2014-05-28,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Lupoi et al.; licensee BioMed Central Ltd.

【 图 表 】

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