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


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.

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Fig. 1
Fig. 2



Near-infrared reflectance spectroscopy


Gas chromatography


Multiple scatter correction


Standard error of performance


Root mean square error of prediction


Relative prediction deviation


Fatty acid species


Partial least squares


  1. Rotundo JL, Westgate ME (2009) Meta-analysis of environmental effects on soybean seed composition. Field Crop Res 110:147–156

    Article  Google Scholar 

  2. Harwood JL (1980) 1—Plant acyl lipids: structure, distribution, and analysis A2. In: Stumpf PK (ed) Lipids: structure and function. Academic Press, New York, pp 1–55

    Chapter  Google Scholar 

  3. Wilson RF (2004) Seed composition. In: Boerma HR, Specht J (eds) Soybeans: improvement, production, and uses. American Society of Agronomy, Madison, pp 621–677

    Google Scholar 

  4. Remig V, Franklin B, Margolis S, Kostas G, Nece T, Street JC (2010) Trans fats in America: a review of their use, consumption, health implications, and regulation. J Acad Nutr Diet. 110:585–592

    CAS  Google Scholar 

  5. Hunter JE, Zhang J, Kris-Etherton PM (2010) Cardiovascular disease risk of dietary stearic acid compared with trans, other saturated, and unsaturated fatty acids: a systematic review. Am J Clin Nutr 91:46–63

    CAS  Article  Google Scholar 

  6. Yu S, Derr J, Etherton TD, Kris-Etherton PM (1995) Plasma cholesterol-predictive equations demonstrate that stearic acid is neutral and monounsaturated fatty acids are hypocholesterolemic. Am J Clin Nutr 61:1129–1139

    CAS  Google Scholar 

  7. Gillman JD, Bilyeu K (2012) Genes and alleles for quality traits on the soybean genetic/physical map. In: Willson RF (ed) Designing soybeans for 21st century markets. AOCS Press, Urbana, pp 67–96

    Chapter  Google Scholar 

  8. Medic J, Atkinson C, Hurburgh CR (2014) Current knowledge in soybean composition. J Am Oil Chem Soc 91:363–384

    CAS  Article  Google Scholar 

  9. Osborne BG (2006) Near-Infrared Spectroscopy in food analysis. Encyclopedia of analytical chemistry. Wiley, New York

    Google Scholar 

  10. Williams P (2007) Grains and seeds. In: Ozaki Y, McClure WF, Christy AA (eds) Near-infrared spectroscopy in food science and technology. Wiley, New York, pp 165–217

    Google Scholar 

  11. Hacisalihoglu G, Gustin JL, Louisma J, Armstrong P, Peter GF, Walker AR, Settles AM (2016) Enhanced single seed trait predictions in soybean (Glycine max) and robust calibration model transfer with near-infrared reflectance spectroscopy. J Agric Food Chem 64:1079–1086

    CAS  Article  Google Scholar 

  12. Orman BA, Schumann RA (1991) Comparison of near-infrared spectroscopy calibration methods for the prediction of protein, oil, and starch in maize grain. J Agric Food Chem 39:883–886

    CAS  Article  Google Scholar 

  13. Han S-I, Chae J-H, Bilyeu K, Shannon JG, Lee J-D (2014) Non-destructive determination of high oleic acid content in single soybean seeds by near infrared reflectance spectroscopy. J Am Oil Chem Soc 91:229–234

    CAS  Article  Google Scholar 

  14. Pazdernik DL, Killam AS, Orf JH (1997) Analysis of amino and fatty acid composition in soybean seed, using near infrared reflectance spectroscopy. Agron J 89:679–685

    CAS  Article  Google Scholar 

  15. Roberts CA, Ren C, Beuselinck PR, Benedict HR, Bilyeu K (2006) Fatty acid profiling of soybean cotyledons by near-infrared spectroscopy. Appl Spectrosc 60:1328–1333

    CAS  Article  Google Scholar 

  16. Patil AG, Oak MD, Taware SP, Tamhankar SA, Rao VS (2010) Nondestructive estimation of fatty acid composition in soybean [Glycine max (L.) Merrill] seeds using near-infrared transmittance spectroscopy. Food Chem 120:1210–1217

    CAS  Article  Google Scholar 

  17. Tillman BL, Gorbet DW, Person G (2006) Predicting oleic and linoleic acid content of single peanut seeds using near-infrared reflectance spectroscopy. Crop Sci 46:2121–2126

    CAS  Article  Google Scholar 

  18. Hammond EG, Fehr WR (1983) Registration of A6 germplasm line of soybean. Crop Sci 23:192–193

    Google Scholar 

  19. Gillman JD, Stacey MG, Cui Y, Berg HR, Stacey G (2014) Deletions of the SACPD-C locus elevate seed stearic acid levels but also result in fatty acid and morphological alterations in nitrogen fixing nodules. BMC Plant Biol 14:143

    Article  Google Scholar 

  20. Cantor SL, Hoag SW, Ellison CD, Khan MA, Lyon RC (2011) NIR Spectroscopy applications in the development of a compacted multiparticulate system for modified release. AAPS PharmSciTech 12:262–278

    CAS  Article  Google Scholar 

  21. Martens M, Martens H (1986) Partial least squares regression. Elsevier Applied Science, London

    Google Scholar 

  22. Brown SD (1995) The Unscrambler®, Version 5.5. Multivariate analysis software for MS-DOS. J Chemom 9:527–529

    CAS  Article  Google Scholar 

  23. Geladi P, MacDougall D, Martens H (1985) Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl Spectrosc 39:491–500

    Article  Google Scholar 

  24. Spielbauer G, Armstrong P, Baier JW, Allen WB, Richardson K, Shen B, Settles AM (2009) High-throughput near-infrared reflectance spectroscopy for predicting quantitative and qualitative composition phenotypes of individual maize kernels. Cereal Chem 86:556–564

    CAS  Article  Google Scholar 

  25. Baye TM, Pearson TC, Settles AM (2006) Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy. J Cereal Sci 43:236–243

    CAS  Article  Google Scholar 

  26. Martens H, Naes T (1989) Assessment, validation and choice of calibration method. Wiley, New York

    Google Scholar 

  27. Williams PC, Norris KH (2001) Near-infrared technology in the agricultural and food industries. American Association of Cereal Chemists, Saint Paul

    Google Scholar 

  28. Igne B, Rippke GR, Hurburgh CR (2008) Measurement of whole soybean fatty acids by Near Infrared Spectroscopy. J Am Oil Chem Soc 85:1105–1113

    CAS  Article  Google Scholar 

  29. Igne B, Roger J-M, Roussel S, Bellon-Maurel V, Hurburgh CR (2009) Improving the transfer of near infrared prediction models by orthogonal methods. Chemometr Intel Lab 99:57–65

    CAS  Article  Google Scholar 

  30. Porep JU, Kammerer DR, Carle R (2015) On-line application of near infrared (NIR) spectroscopy in food production. Trends Food Sci Tech. 46:211–230

    CAS  Article  Google Scholar 

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We acknowledge the excellent technical assistance of two USDA-ARS technicians Alexandria Berghaus and Jeremy Mullis who were primarily responsible for all field work and gas chromatography in support of this work.

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Correspondence to Jason Gillman.

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The authors declare that they have no pertinent conflict of interest related to this study, which was partially supported by a grant from United Soybean Board (Project No. 1420-632-6605), and by USDA-Agricultural Research Service internal funding. Mention of any trademarks, vendors, or proprietary products does not constitute a guarantee or warranty of the product by the USDA and does not imply its approval to the exclusion of other products or vendors that may also be suitable. The USDA, Agricultural Research Service, Midwest Area, is an equal opportunity, affirmative action employer and all agency services are available without discrimination.

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Karn, A., Heim, C., Flint-Garcia, S. et al. Development of Rigorous Fatty Acid Near-Infrared Spectroscopy Quantitation Methods in Support of Soybean Oil Improvement. J Am Oil Chem Soc 94, 69–76 (2017).

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