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Title: Solvent-free spectroscopic method for high-throughput, quantitative screening of fatty acids in yeast biomass

Abstract

Sustainable biofuels and bioproducts technologies are being developed by fermentation of sugars present and released from pretreated cellulosic biomass to lipids using oleaginous yeasts. Detailed analytical characterization of lipid content through cultivation under different scenarios not only is a bottleneck that slows down development of improved strains and processes, this process also creates significant chemical waste. Since lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum, the use of multivariate linear regression of respective wavelengths can be used for the prediction of intracellular lipid content present in the yeast biomass. We present data on the multivariate quantitative correlation of NIR spectra with measured lipid content in different oleaginous yeast strains. This work is the first demonstration of the rapid, non-destructive, lipid quantification on as little as 10 mg of yeast biomass in a 96-well format, preventing significant chemical pollution by applying a real-time monitoring process. We demonstrate a distinct correlation of lipid content with the accumulation of select fatty acids of the lipids for 5 different yeast species, among which, for S. cerevisiae and L. starkeyi, in-depth calibration curves were developed from 65 and 154 unique samples, respectively. We demonstrate that NIR spectra can be used tomore » accurately predict intracellular lipid content using multivariate linear regression analysis in a manner of minutes, avoiding the need for lengthy chemical analyses that are resource intensive.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1]
  1. National Renewable Energy Laboratory; Golden; USA
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1487324
Alternate Identifier(s):
OSTI ID: 1484395
Report Number(s):
NREL/JA-5100-72973
Journal ID: ISSN 1759-9660; AMNECT
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Analytical Methods
Additional Journal Information:
Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 1759-9660
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; cellulosic biomass; NIR spectrum; yeast strains; lipid quantification

Citation Formats

Laurens, Lieve M. L., Knoshaug, Eric P., Rohrer, Holly, Van Wychen, Stefanie, Dowe, Nancy, and Zhang, Min. Solvent-free spectroscopic method for high-throughput, quantitative screening of fatty acids in yeast biomass. United States: N. p., 2019. Web. doi:10.1039/C8AY02416B.
Laurens, Lieve M. L., Knoshaug, Eric P., Rohrer, Holly, Van Wychen, Stefanie, Dowe, Nancy, & Zhang, Min. Solvent-free spectroscopic method for high-throughput, quantitative screening of fatty acids in yeast biomass. United States. doi:10.1039/C8AY02416B.
Laurens, Lieve M. L., Knoshaug, Eric P., Rohrer, Holly, Van Wychen, Stefanie, Dowe, Nancy, and Zhang, Min. Tue . "Solvent-free spectroscopic method for high-throughput, quantitative screening of fatty acids in yeast biomass". United States. doi:10.1039/C8AY02416B.
@article{osti_1487324,
title = {Solvent-free spectroscopic method for high-throughput, quantitative screening of fatty acids in yeast biomass},
author = {Laurens, Lieve M. L. and Knoshaug, Eric P. and Rohrer, Holly and Van Wychen, Stefanie and Dowe, Nancy and Zhang, Min},
abstractNote = {Sustainable biofuels and bioproducts technologies are being developed by fermentation of sugars present and released from pretreated cellulosic biomass to lipids using oleaginous yeasts. Detailed analytical characterization of lipid content through cultivation under different scenarios not only is a bottleneck that slows down development of improved strains and processes, this process also creates significant chemical waste. Since lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum, the use of multivariate linear regression of respective wavelengths can be used for the prediction of intracellular lipid content present in the yeast biomass. We present data on the multivariate quantitative correlation of NIR spectra with measured lipid content in different oleaginous yeast strains. This work is the first demonstration of the rapid, non-destructive, lipid quantification on as little as 10 mg of yeast biomass in a 96-well format, preventing significant chemical pollution by applying a real-time monitoring process. We demonstrate a distinct correlation of lipid content with the accumulation of select fatty acids of the lipids for 5 different yeast species, among which, for S. cerevisiae and L. starkeyi, in-depth calibration curves were developed from 65 and 154 unique samples, respectively. We demonstrate that NIR spectra can be used to accurately predict intracellular lipid content using multivariate linear regression analysis in a manner of minutes, avoiding the need for lengthy chemical analyses that are resource intensive.},
doi = {10.1039/C8AY02416B},
journal = {Analytical Methods},
number = 1,
volume = 11,
place = {United States},
year = {2019},
month = {1}
}

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This content will become publicly available on January 1, 2020
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