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

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 spectrometerdependent. 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.

Meta-analysis of the amino acid digestibility of oilseed meal in growing pigs

“…However, although our model takes into account some variability between laboratories for the analysis of NDF, this analysis of NDF should be validated considering its inter-laboratory variability (Mertens, 2003). For dEAA contents, although the content NDF provides a valid estimate of dEAA, if the total essential amino acid (tEAA) content is available easily, for example by analysis by NIRS (near-infrared spectroscopy) (Kovalenko et al, 2006), the dEAA prediction model using the tEAA is more accurate with lower RMSE and a higher R 2 .…”

Development of polyherbal granules and its nutritional characterization

“…Eschun and He (2004) also reported that 98.4 per cent water in raw pulp of A. vera while discussing the use of A. vera in connection with pharmaceutical and cosmetic industries. Though the protein content of soybean whey in the present study is low (1.61%), soya whey is considered as a good source of nutrients because of the presence of several essential amino acids which are in balanced amount resulting in high protein efficiency ratio (Kovalenko et al, 2006). The roots of C. borivilianum and the stem of T. cordifolia exhibited better values for protein (8.41 and 12.91, respectively) which are in conformity as reported by Singh et al (2003).…”

Development of Poly Herbal Granules and its Anti-Fatigue Efficacy in Swiss Mice

“…Eschun and He (2004) also reported 98.4 per cent water in raw pulp of A. vera while discussing the use of A. vera in connection with pharmaceutical and cosmetic industries. Although the protein content of the soybean whey in the present study is low (1.61%), soya whey is considered as a good source of nutrient because of the presence of several essential amino acids which are in balanced amount resulting in high protein efficiency ratio (Kovalenko et al, 2006). The roots of C. borivilianum and the stem of T. cordifolia exhibited better values for protein (8.41 and 12.91, respectively) which are in conformity as reported by Singh et al, (2003).…”