Determination of amino acid composition of soybeans (Glycine max) by near-infrared spectroscopy.
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 r2 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.
Kovalenko
Igor V
IV
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa 50011, USA. igorkov@iastate.edu
Rippke
Glen R
GR
Hurburgh
Charles R
CR
D000596:Amino Acids / Q000032:analysis*; D002138:Calibration; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D011506:Proteins / Q000032:analysis; D012044:Regression Analysis; D013025:Glycine max / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
D000596:Amino Acids; D011506:Proteins