Determination of Amino Acid Composition of Soybeans (Glycine max) by Near-Infrared Spectroscopy

Kovalenko, I. V. and Rippke, G. R. and Hurburgh, C. R. (2006) Determination of Amino Acid Composition of Soybeans (Glycine max) by Near-Infrared Spectroscopy. Journal of Agricultural and Food Chemistry, 54 (10). pp. 3485-3491.

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Calibration equations for the estimation of amino acid composition in whole soybeans were developed using 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 equations were 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 reference protein. Calibration samples with nontypical amino acid profiles relative to protein would be needed to overcome this limitation. The performance of PLS and SVM was significantly better than that of ANN. Choice of preferred modeling method was spectrometer-dependent.

Item Type: Article
Uncontrolled Keywords: Near-infrared (NIR) spectroscopy; soybeans; Glycine max; amino acids; chemometrics; partial least squares (PLS); artificial neural networks (ANN); support vector machines (SVM)
Author Affiliation: Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa 50011
Subjects: Crop Improvement
Divisions: Soyabean
Depositing User: Mr Siva Shankar
Date Deposited: 12 Sep 2012 04:00
Last Modified: 12 Sep 2012 04:02
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