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Article
Determination of Amino Acid Composition of Soybeans (Glycine max) by Near-Infrared Spectroscopy
Journal of Agricultural and Food Chemistry
  • Igor V. Kovalenko, Iowa State University
  • Glen R. Rippke, Iowa State University
  • Charles R. Hurburgh, Jr., Iowa State University
Document Type
Article
Publication Date
5-17-2006
DOI
10.1021/jf052570u
Abstract
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.
Comments

Posted with permission from Journal of Agricultural and Food Chemistry 54 (2006): 3485–3491, doi:10.1021/jf052570u. Copyright 2006 American Chemical Society.

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Open
Copyright Owner
American Chemical Society
Language
en
File Format
application/pdf
Citation Information
Igor V. Kovalenko, Glen R. Rippke and Charles R. Hurburgh. "Determination of Amino Acid Composition of Soybeans (Glycine max) by Near-Infrared Spectroscopy" Journal of Agricultural and Food Chemistry Vol. 54 Iss. 10 (2006) p. 3485 - 3491
Available at: http://works.bepress.com/charles_hurburgh/62/