Agricultural and Biosystems Engineering Publications

Document Type

Article

Publication Date

5-17-2006

Journal or Book Title

Journal of Agricultural and Food Chemistry

Volume

54

Issue

10

First Page

3485

Last Page

3491

Research Focus Area(s)

Biological and Process Engineering and Technology

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.

Copyright Owner

American Chemical Society

Language

en

Date Available

2013-10-18

File Format

application/pdf

Share

COinS