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Measurement of Whole Soybean Fatty Acids by Near Infrared Spectroscopy

Igne, Benoit, Rippke, Glen R., Hurburgh, Charles R. Jr.
journal of the American Oil Chemists' Society 2008 v.85 no.12 pp. 1105-1113
lipid content, linoleic acid, Glycine max, linear models, linolenic acid, fatty acid composition, calibration, neural networks, soybeans, stearic acid, chemical analysis, near-infrared spectroscopy, regression analysis, palmitic acid, oleic acid, algorithms, United States
Whole soybean fatty acid contents were measured by near infrared spectroscopy. Three calibration algorithms--partial least squares (PLS), artificial neural networks (ANN), and least squares support vector machines (LS-SVM)--were implemented. Three different validation strategies using independent sets and part of calibration samples as validation sets were created. There was a significant improvement of the prediction precision of all fatty acids measured on relative concentration of oil compared with previous literature using PLS (standard error of prediction of 0.85, 0.42, 1.64, 1.67, and 0.90% for palmitic, stearic, oleic, linoleic and linolenic acids respectively). ANN and LS-SVM methods performed significantly better than PLS for palmitic, oleic and linolenic acids. Calibration models developed on relative concentrations (% of oil) were compared to prediction models created on absolute fatty acid concentration (% of weight) and corrected to relative concentration by multiplying by the predicted oil content. While models were easier to develop in absolute concentration (higher coefficients of determination), the multiplication of errors with the total oil content model resulted in no net precision improvement.