Journal of the American Oil Chemists' Society

, Volume 94, Issue 1, pp 69–76

Development of Rigorous Fatty Acid Near-Infrared Spectroscopy Quantitation Methods in Support of Soybean Oil Improvement

  • Avinash Karn
  • Crystal Heim
  • Sherry Flint-Garcia
  • Kristin Bilyeu
  • Jason Gillman
Original Paper

DOI: 10.1007/s11746-016-2916-4

Cite this article as:
Karn, A., Heim, C., Flint-Garcia, S. et al. J Am Oil Chem Soc (2017) 94: 69. doi:10.1007/s11746-016-2916-4

Abstract

The mature seeds of soybean (Glycine max L. Merr) are a valuable source of high-quality edible lipids and protein. Despite dramatic breeding gains over the past 80 years, soybean oil continues to be oxidatively unstable, due to a high proportion of polyunsaturated triacylglycerols. Until recently, the majority of soybean oil underwent partial chemical hydrogenation. Mounting health concerns over trans fats, however, has increased breeding efforts to introgress mutant and biotechnological genetic alterations of soybean oil composition into high-yielding lines. As a result, there is an ongoing need to characterize fatty acid composition in a rapid, inexpensive and accurate manner. Gas chromatography is the most commonly used method, but near-infrared reflectance spectroscopy (NIRS) can be calibrated to non-destructively phenotype various seed compositions accurately and at a high throughput. Here we detail development of NIRS calibrations using intact seeds for every major soybean fatty acid breeding goal over an unprecedented range of oil composition. The NIRS calibrations were shown to be equivalent to destructive chemical analysis, and incorporation into a soybean phenotyping operation has the potential to dramatically reduce cost and accelerate phenotypic analysis.

Keywords

Oilseeds Crop production and agronomy Genetics/breeding Lipid chemistry/lipid analysis 

Abbreviations

NIRS

Near-infrared reflectance spectroscopy

GC

Gas chromatography

MS

Multiple scatter correction

SEP

Standard error of performance

RMSEP

Root mean square error of prediction

RPD

Relative prediction deviation

FAs

Fatty acid species

PLS

Partial least squares

Supplementary material

11746_2016_2916_MOESM1_ESM.docx (25 kb)
Supplementary material 1 (DOCX 25 kb)

Copyright information

© AOCS (outside the USA) 2016

Authors and Affiliations

  • Avinash Karn
    • 1
  • Crystal Heim
    • 1
  • Sherry Flint-Garcia
    • 2
  • Kristin Bilyeu
    • 2
  • Jason Gillman
    • 2
  1. 1.Division of Plant SciencesUniversity of MissouriColumbiaUSA
  2. 2.U.S. Department of Agriculture-Agricultural Research ServiceColumbiaUSA

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