Measurement of Whole Soybean Fatty Acids by Near Infrared Spectroscopy |
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Authors: | Benoit Igne Glen R Rippke Charles R Hurburgh Jr |
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Affiliation: | (1) Department of Agricultural and Biosystems Engineering, Iowa State University, 1551 Food Sciences Building, Ames IA, 50011, USA |
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Abstract: | 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. |
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Keywords: | Partial least squares Artificial neural networks Least squares support vector machines Near infrared spectroscopy Fatty acids Soybeans |
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