Nondestructive estimation of fatty acid composition in soybean [Glycine max (L.) Merrill] seeds using Near-Infrared Transmittance Spectroscopy

The potential of Near-Infrared Transmittance (NIT) Spectroscopy for estimation of fatty acid composition in soybean seed samples was studied. Total 612 whole seed samples with wide range of variability for major fatty acids were used to develop calibration equations by applying SNV de-trend and first derivative mathematical treatment in the range of 850–1048 nm. Useful chemometric models for most important fatty acids present in soybean seed oil were developed using Modified Partial Least Squares (MPLS) regression method. In external validation oleic (r2 = 0.89, SEP = 1.61), linoleic (r2 = 0.86, SEP = 1.50) and palmitic (r2 = 0.89, SEP = 0.17) acids were predicted with good accuracy, while the predictions for linolenic acid (r2 = 0.78, SEP = 0.36) and stearic acid (r2 = 0.63, SEP = 0.11) had relatively poor accuracy. The whole-seed NIT spectroscopy equations for fatty acid estimation would be useful for improving efficiency of breeding programs aimed at altering fatty acid composition in soybean.