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| - Determination of Amino Acid Composition of Soybeans(Glycine max) by Near-Infrared Spectroscopy
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| - Calibration equations for the estimation of amino acid composition in whole soybeans were developedusing partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM)regression methods for five models of near-infrared (NIR) spectrometers. The effects of amino acid/protein correlation, calibration method, and type of spectrometer on predictive ability of the equationswere analyzed. Validation of prediction models resulted in r 2 values from 0.04 (tryptophan) to 0.91(leucine and lysine). Most of the models were usable for research purposes and sample screening.Concentrations of cysteine and tryptophan had no useful correlation with spectral information.Predictive ability of calibrations was dependent on the respective amino acid correlations to referenceprotein. Calibration samples with nontypical amino acid profiles relative to protein would be neededto overcome this limitation. The performance of PLS and SVM was significantly better than that ofANN. Choice of preferred modeling method was spectrometer-dependent. Keywords: Near-infrared (NIR) spectroscopy; soybeans; Glycine max; amino acids; chemometrics;partial least squares (PLS); artificial neural networks (ANN); support vector machines (SVM)
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