title: Enhanced Single Seed Trait Predictions in Soybean (Glycine max) and Robust Calibration Model Transfer with Near-Infrared Reflectance Spectroscopy.
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 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 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 to 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 spectrometers facilitates broader adoption of this high-throughput, nondestructive, seed phenotyping technology.
authors.affiliation: Biology Department, Florida A&M University , Tallahassee, Florida 32307, United States.
authors.forename: Gokhan
authors.initials: G
authors.lastname: Hacisalihoglu
authors.affiliation: Horticultural Sciences Department, University of Florida , Gainesville, Florida 32611, United States.
authors.forename: Jeffery L
authors.initials: JL
authors.lastname: Gustin
authors.affiliation: Biology Department, Florida A&M University , Tallahassee, Florida 32307, United States.
authors.forename: Jean
authors.initials: J
authors.lastname: Louisma
authors.affiliation: Center for Grain and Animal Health Research, USDA-ARS , Manhattan, Kansas 66502, United States.
authors.forename: Paul
authors.initials: P
authors.lastname: Armstrong
authors.affiliation: School of Forest Resources and Conservation, University of Florida , Gainesville, Florida 32611, United States.
authors.forename: Gary F
authors.initials: GF
authors.lastname: Peter
authors.affiliation: School of Forest Resources and Conservation, University of Florida , Gainesville, Florida 32611, United States.
authors.forename: Alejandro R
authors.initials: AR
authors.lastname: Walker
authors.affiliation: Horticultural Sciences Department, University of Florida , Gainesville, Florida 32611, United States.
authors.forename: A Mark
authors.initials: AM
authors.lastname: Settles
mesh_terms: D010938:Plant Oils / Q000737:chemistry; D010940:Plant Proteins / Q000737:chemistry; D012639:Seeds / Q000737:chemistry*; D013025:Glycine max / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
chemical_list: D010938:Plant Oils; D010940:Plant Proteins
keywords: chemometrics; density; microcomputed tomography; near-infrared spectroscopy; oil; protein; seed phenotyping