Enhanced Single Seed Trait Predictions in Soybean (Glycine max) and Robust Calibration Model Transfer with Near-Infrared Reflectance Spectroscopy

Single seed near-infrared reflectance (NIR) spectroscopy predicts soybean (Glycine max) seed quality traits of moisture, oil, and protein. We tested the accuracy of transferring calibrations between different single seed NIR analyzers of the same design by collecting NIR spectra and analytical trait data for globally diverse soybean germplasm. X-ray microcomputed tomography (μCT) was used to collect seed density and shape traits to enhance the number of soybean traits that can be predicted from single seed NIR. Partial least-squares (PLS) regression gave accurate predictive models for oil, weight, volume, protein, and maximal cross-sectional area of the seed. PLS models for width, length, and density were not predictive. Although principal component analysis (PCA) of the NIR spectra showed that black seed coat color had significant signal, excluding black seeds from the calibrations did not impact model accuracies. Calibrations for oil and protein developed in this study as well as earlier calibrations fo...