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.

Quantitative trait loci associated with soybean seed weight and composition under different phosphorus levels

“…NIRS calibrations predicted seed oil, seed protein, and total seed volume, while average seed weight was determined using a microbalance. The oil and protein content of seeds were determined using a NIR‐256‐1‐1,7T1 Near Infrared Spectrometer by triple scanning them in a 1 nm intervals (from 907 to 1,688 nm), with calibrations equations developed and described in Hacisalihoglu et al (). The results were expressed as percentages…”

“…Seed composition traits were estimated with singleseed near-infrared spectroscopy (NIRS) (Hacisalihoglu et al 2010(Hacisalihoglu et al , 2016. Average seed traits were estimated per plant by individually weighing and scanning 20 seeds three times each.…”

“…While studies have demonstrated that P deficiency in soybean reduces yield, due to aborted seed pods and reduced seed size (Zhang et al ), the effect of P deficiency on seed quality is not known. Based on correlations between soybean seed size and composition traits, reduced seed size is expected to alter protein and oil composition under low‐P growth conditions (Hacisalihoglu et al ). Similar to low‐P tolerance, a number of QTL studies have examined seed weight, seed protein content, and yield traits in various recombinant inbred line (RIL) populations (Pathan et al ; Yesudas et al ; Ramamurthy et al ; Xie et al ).…”

Application of near-infrared spectroscopy for screening the potato flour content in Chinese steamed bread

“…Adding more PLS factors to the model improved its linearity, resulting in R CV 2 and RPD increasing and RMSECV decreasing as the number of PLS factors increased (Rinnan et al, 2009). However, too many PLS factors ([ 8) led to the model running the risk of over-fitting (Feng et al, 2015;Hacisalihoglu et al, 2016). The RPD values were relatively high (3.65-6.77) when there were 8-20 PLS factors.…”

Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy

“…PLS models for width and length were not predictive. Single seed NIRS facilitates broader adoption of this high-throughput, nondestructive, seed phenotyping technology [16]. Chinese yam samples were analyzed for sugar, polysaccharides, and flavonoids through NIR and mid-IR.…”