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Non-destructive determination of high oleci acid content in single soybean seeds by near infrared reflectance spectroscopy

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Soybean with increased oleic acid is desirable to improve oxidative stability and functionality of soybean seed oil. Recently, soybean genotypes with high oleic acid (= 70%) were developed by conventional breeding and molecular genetic selection of mutant fatty acid desaturase alleles. Determination of soybean genotypes with high oleic acid content has been done by gas chromatography and using DNA markers. Efficient and effective identification of high oleic acid soybean genotypes using non-destructive near infrared reflectance spectroscopy (NIRs) on whole seed would greatly enhance progress in development of high oleic acid soybean varieties. The objective of this study was to develop a calibration equation for NIRs determination of high oleic acid from single soybean seeds. A total of 600 intact, single F2 seeds were scanned by NIRs. Spectral data were collected between 400 and 2,500 nm at 2nm intervals. The relationship between NIRs spectral patterns of each soybean seed and its oleic acid content was examined. The best predicted equations for oleic acid were selected on the basis of minimizing the standard error of cross validation and increasing the coefficient of determination. Validation demonstrated that the equations for determining seed oleic acid and high oleic acid content (over 50%) had high predictive ability (r2=0.91 and r2=0.99, respectively). To validate the newly developed equation, F2 seeds from several different genetic backgrounds were tested. Again, high oleic acid from single soybean seeds was accurately predicted from various genetic backgrounds. Therefore, applying the calibration equation to NIRs will be useful to rapidly and efficiently select high oleic acid soybean genotypes in breeding programs.
Kristin Bilyeu
Glycine max , breeding , equations , genetic background , near-infrared reflectance spectroscopy , oleic acid , oxidative stability , reflectance , seed oils , seeds , soybeans , spectral analysis
USDA Scientist Submission
Journal of the American Oil Chemists' Society 2013 10 25 v.91
Journal Articles, USDA Authors, Peer-Reviewed
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