Enhanced single seed trait predictions in soybean (Glycine max) and robust calibration model transfer with near infrared reflectance spectroscopy

Authors: HACISALIHOGLU, GOKHAN - Florida A & M University; GUSTIN, JEFFERY - University Of Florida; LOUISMA, JEAN - Florida A & M University; Armstrong, Paul; PETER, GARY - University Of Florida; WALKER, ALEJANDRO - University Of Florida; SETTLES, A. MARK - University Of Florida

Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/15/2016
Publication Date: 2/15/2016
Publication URL: https://handle.nal.usda.gov/10113/62719
Citation: Hacisalihoglu, G., Gustin, J.L., Louisma, J., Armstrong, P.R., Peter, G.F., Walker, A.R., Settles, A. 2016. Enhanced single seed trait predictions in soybean (Glycine max) and robust calibration model transfer with near infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry. 64(5): 1079-1056. doi: 10.1021/acs.jafc.5b05508.

Interpretive Summary: Single seed near infrared reflectance (NIR) spectroscopy can be used to measure soybean seed quality such as moisture, oil, and protein and other characteristics. It is thus a useful tool in studying how genetics, growing conditions and field management influence these seed traits. The difficulty with NIR is making one instrument perform to the same standards as a duplicate instrument. This is important when trying to compare seed traits collected by different research groups and instruments. In this study the accuracy between two instruments was tested by measuring several seed traits on a diverse set of soybean samples. Results show that accurate measurements were obtained by both instruments for oil and protein content, weight, volume and maximal cross-sectional area of the seed. Accurate measurements were not obtainable for width, length and density. The ability to directly compare results between similarly designed single seed NIR spectrometer systems makes this technology have broader adaption to high-throughput, non-destructive seed analysis.

Technical Abstract: 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 micro-computed 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 for a separate NIR analyzer of the same design were used to test the ability to transfer PLS regressions between platforms. PLS models built from data collected on one NIR analyzer had minimal differences in accuracy when applied to spectra collected from a sister device. Model transfer was more robust when spectra were trimmed from 910-1679 nm to 955-1635 nm due to divergence of edge wavelengths between the two devices. The ability to transfer calibrations between similar single seed NIR spectrometer systems facilitates broader adoption of this high-throughput, non-destructive, seed phenotyping technology.