Paul R. Armstrong

High-Throughput Near-Infrared Reflectance Spectroscopy for Predicting Quantitative and Qualitative Composition Phenotypes of Individual Maize Kernels

ABSTRACT Near-infrared reflectance (NIR) spectroscopy can be used for fast and reliable prediction of organic compounds in complex biological samples. We used a recently developed NIR spectroscopy instrument to predict starch, protein, oil, and weight of individual maize (Zea mays) seeds. The starch, protein, and oil calibrations have reliability equal or better to bulk grain NIR analyzers. We also show that the instrument can differentiate quantitative and qualitative seed composition mutants from normal siblings without a specific calibration for the constituent affected. The analyzer does not require a specific kernel orientation to predict composition or to differentiate mutants. The instrument collects a seed weight and a spectrum in 4–6 sec and can collect NIR data alone at a 20-fold faster rate. The spectra are acquired while the kernel falls through a glass tube illuminated with broad spectrum light. These results show significant improvements over prior single-kernel NIR systems, making this inst...

Ionomic Screening of Field-Grown Soybean Identifies Mutants with Altered Seed Elemental Composition

Soybean [Glycine max (L.) Merr.] seeds contain high levels of mineral nutrients essential for human and animal nutrition. High throughput elemental profiling (ionomics) has identified mutants in model plant species grown in controlled environments. Here, we describe a method for identifying potential soybean ionomics mutants grown in a field setting and apply it to 975 N‐nitroso‐N‐methylurea (NMU) mutagenized lines. After performing a spatial correction, we identified mutants using either visual scoring of standard score (z‐score) plots or computational ranking of putative mutants followed by visual confirmation. Although there was a large degree of overlap between the methods, each method identified unique lines. The visual scoring approach identified 22 out of 427 (5%) potential mutants, 70% (16 out of 22) of which were confirmed when seeds from the same parent plant were regrown in the field. We also performed simulations to determine an optimal strategy for screening large populations. All data from the screen is available at the Ionomics Hub File Transfer Page (http://www.ionomicshub.org/home/PiiMS/dataexchange).

Detection of Fungus-Infected Corn Kernels using Near-Infrared Reflectance Spectroscopy and Color Imaging

Contamination of grain products by fungus can lead to economic losses and is deleterious to human and livestock health. Detection and quantification of fungus-infected corn kernels would be advantageous for producers and breeders in evaluating quality and in selecting hybrids with resistance to infection. This study evaluated the performance of single-kernel near-infrared reflectance spectroscopy (NIRS) and color imaging to discriminate corn kernels infected by eight fungus species at different levels of infection. Discrimination was done according to the level of infection and the mold species. NIR spectra (904 to 1685 nm) and color images were used to develop linear and nonlinear prediction models using linear discriminant analysis (LDA) and multi-layer perceptron (MLP) neural networks. NIRS was able to accurately detect 98% of the uninfected control kernels, compared to about 89% for the color imaging. Results for detecting all levels of infection using NIR were 89% and 79% for the uninfected control and infected kernels, respectively; color imaging was able to discriminate 75% of both the control and infected kernels. In general, there was better discrimination for control kernels than for infected kernels, and certain mold species had better classification accuracy than others when using NIR. The vision system was not able to classify mold species well. The use of principal component analysis on image data did not improve the classification results, while LDA performed almost as well as MLP models. LDA and mean centering NIR spectra gave better classification models. Compared to the results of NIR spectrometry, the classification accuracy of the color imaging system was less attractive, although the instrument has a lower cost and a higher throughput.

Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study

Four near-infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single-seed attributes: weight (g), protein (%), and oil (%). Using partial least-squares (PLS) and four preprocessing methods, the attribute that was significantly most easily predicted was seed weight (RPD > 3 on average) and protein the least. The performance of all instruments differed from each other. Performances for oil and protein predictions were correlated with the instrument sampling system, with the best predictions using spectra taken from more than one seed angle. This was facilitated by the seed spinning or tumbling during spectral collection as opposed to static sampling methods. From the preprocessing methods utilized, no single one gave the best overall performances but weight measurements were often more successful with raw spectra, whereas protein and oil predictions were often enhanced by SNV and SNV + detrending.

Analysis of maize ( Zea mays ) kernel density and volume using microcomputed tomography and single-kernel near-infrared spectroscopy.

Maize kernel density affects milling quality of the grain. Kernel density of bulk samples can be predicted by near-infrared reflectance (NIR) spectroscopy, but no accurate method to measure individual kernel density has been reported. This study demonstrates that individual kernel density and volume are accurately measured using X-ray microcomputed tomography (μCT). Kernel density was significantly correlated with kernel volume, air space within the kernel, and protein content. Embryo density and volume did not influence overall kernel density. Partial least-squares (PLS) regression of μCT traits with single-kernel NIR spectra gave stable predictive models for kernel density (R(2) = 0.78, SEP = 0.034 g/cm(3)) and volume (R(2) = 0.86, SEP = 2.88 cm(3)). Density and volume predictions were accurate for data collected over 10 months based on kernel weights calculated from predicted density and volume (R(2) = 0.83, SEP = 24.78 mg). Kernel density was significantly correlated with bulk test weight (r = 0.80), suggesting that selection of dense kernels can translate to improved agronomic performance.

DEVELOPMENT OF SINGLE-SEED NEAR-INFRARED SPECTROSCOPIC PREDICTIONS OF CORN AND SOYBEAN CONSTITUENTS USING BULK REFERENCE VALUES AND MEAN SPECTRA

Rapid, non-destructive single-seed compositional analyses are useful for many areas of crop science, including breeding and genetics. Seeds are sometimes unique and require preservation due to small samples, which necessitates development of methods for total non-destructive measurement. Near-infrared reflectance spectroscopy (NIRS) can be used for non-destructive single-seed composition prediction, but the reference methods used to develop prediction models are usually destructive. Reference methods are costly, and extensive sets of seeds must be used to obtain prediction models for multiple constituents. In this research, single-seed NIRS prediction models were developed for common constituents of soybeans and corn using composition values from bulk reference measurement and respective averaged single-seed spectra as opposed to single-seed reference and spectra. The bulk reference model and a true single-seed model for soybean protein were also compared to determine how well the bulk model performs in predicting single-seed protein. This provided a basis for evaluating bulk model performance for other constituents. Bulk model statistics indicated that bulk models should perform well for soybean protein and oil, but not well for fiber; corn bulk models should perform well for protein, oil, starch, and seed density. Bulk model predictions of single-seed soybean reference protein show, at best, that bulk models work reasonably well, with a standard error of prediction (SEP) = 1.82%) compared to an SEP of 0.97% for a true single-seed protein model. Bias correction may be needed, though, depending how the bulk model is developed. Overall, the bulk models should be useful for selecting single seeds in breeding programs targeting specific composition traits and segregating individual samples based on composition.

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 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.

Differences between conventional and glyphosate tolerant soybeans and moisture effect in their discrimination by near infrared spectroscopy

Previous studies showed that Near Infrared Spectroscopy (NIRS) could distinguish between Roundup Ready® (RR) and conventional soybeans at the bulk and single seed sample level, but it was not clear which compounds drove the classification. In this research the varieties used did not show significant differences in major compounds between RR and conventional beans, but moisture content had a big impact on classification accuracies. Four of the five RR samples had slightly higher moistures and had a higher water uptake than their conventional counterparts. This could be linked with differences in their hulls, being either compositional or morphological. Because water absorption occurs in the same region as main compounds in hulls (mainly carbohydrates) and water causes physical changes from swelling, variations in moisture cause a complex interaction resulting in a large impact on discrimination accuracies.

A Vernonia Diacylglycerol Acyltransferase Can Increase Renewable Oil Production.

Increasing the production of plant oils such as soybean oil as a renewable resource for food and fuel is valuable. Successful breeding for higher oil levels in soybean, however, usually results in reduced protein, a second valuable seed component. This study shows that by manipulating a highly active acyl-CoA:diacylglycerol acyltransferase (DGAT) the hydrocarbon flux to oil in oilseeds can be increased without reducing the protein component. Compared to other plant DGATs, a DGAT from Vernonia galamensis (VgDGAT1A) produces much higher oil synthesis and accumulation activity in yeast, insect cells, and soybean. Soybean lines expressing VgDGAT1A show a 4% increase in oil content without reductions in seed protein contents or yield per unit land area. Incorporation of this trait into 50% of soybeans worldwide could result in an increase of 850 million kg oil/year without new land use or inputs and be worth ∼U.S.

Development and Evaluation of a Near-Infrared Instrument for Single-Seed Compositional Measurement of Wheat Kernels

1 billion/year at 2012 production and market prices.