Determining Vitreous Subclasses of Hard Red Spring Wheat Using Visible/Near‐Infrared Spectroscopy

The percentage of dark hard vitreous (DHV) kernels in hard red spring wheat is an important grading factor that is associated with protein content, kernel hardness, milling properties, and baking quality. The current visual method of determining DHV and non‐DHV (NDHV) wheat kernels is time‐consuming, tedious, and subject to large errors. The objective of this research was to classify DHV and NDHV wheat kernels, including kernels that were checked, cracked, sprouted, or bleached using visible/near‐infrared (Vis/NIR) spectroscopy. Spectra from single DHV and NDHV kernels were collected using a diode‐array NIR spectrometer. The dorsal and crease sides of the kernels were viewed. Three wavelength regions, 500–750 nm, 750–1,700 nm, and 500–1700 nm were compared. Spectra were analyzed by using partial least squares (PLS) regression. Results suggest that the major contributors to classifying DHV and NDHV kernels are light scattering, protein content, kernel hardness, starch content, and kernel color effects on the absorption spectrum. Bleached kernels were the most difficult to classify because of high lightness values. The sample set with bleached kernels yielded lower classification accuracies of 91.1–97.1% compared with 97.5–100% for the sample set without bleached kernels. More than 75% of misclassified kernels were bleached. For sample sets without bleached kernels, the classification models that included the dorsal side gave the highest classification accuracies (99.6–100%) for the testing sample set. Wavelengths in both the Vis/NIR regions or the NIR region alone yielded better classification accuracies than those in the visible region only.     

An Automated Near‐Infrared System for Selecting Individual Kernels Based on Specific Quality Characteristics

An automated sorting system was developed that nondestructively measured quality characteristics of individual kernels using near‐infrared (NIR) spectra. This single‐kernel NIR system was applied to sorting wheat (Triticum aestivum L.) kernels by protein content and hardness, and proso millet (Panicum miliaceum L.) into amylose‐bearing and amylose‐free fractions. Single wheat kernels with high protein content could be sorted from pure lines so that the high‐protein content portion was 3.1 percentage points higher than the portion with the low‐protein kernels. Likewise, single wheat kernels with specific hardness indices could be removed from pure lines such that the hardness index in the sorted samples was 29.4 hardness units higher than the soft kernels. The system was able to increase the waxy, or amylose‐free, millet kernels in segregating samples from 94% in the unsorted samples to 98% in the sorted samples. The portion of waxy millet kernels in segregating samples was increased from 32% in the unsorted samples to 55% after sorting. Thus, this technology can be used to enrich the desirable class within segregating populations in breeding programs, to increase the purity of heterogeneous advanced or released lines, or to measure the distribution of quality within samples during the marketing process.     

Automated Color Classification of Single Wheat Kernels Using Visible and Near-Infrared Reflectance

Modification of an existing single kernel wheat characterization system allowed collection of visible and near-infrared (NIR) reflectance spectra (450–1,688 nm) at a rate of 1 kernel/4 sec. The spectral information was used to classify red and white wheats in an attempt to remove subjectivity from class determinations. Calibration, validation, and prediction results showed that calibrations using partial least squares regression and derived from the full wavelength profile correctly classed more kernels than either the visible region (450–700 nm) or the NIR region (700–1,688 nm). Most results showed >99% correct classification for single kernels when using the visible and NIR regions. Averaging of single kernel classifications resulted in 100% correct classification of bulk samples.     

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

A single‐kernel, near‐infrared reflectance instrument was designed, built, and tested for its ability to measure composition and traits in wheat kernels. The major objective of the work was targeted at improving an existing design concept of an instrument used for larger seeds such as soybeans and corn but in this case designed for small seeds. Increases in throughput were sought by using a vacuum to convey seeds without compromising measurement accuracy. Instrument performance was evaluated by examining measurement accuracy of wheat kernel moisture, protein content, and kernel mass. Spectral measurements were obtained on individual wheat kernels as they were conveyed by air through an illuminated tube. Partial least squares (PLS) prediction models for these constituents were then developed and evaluated. PLS single‐kernel moisture predictions had a root mean square error of prediction (RMSEP) around 0.5% MC wet basis; protein prediction models had an RMSEP near 0.70%. Prediction of mass was not as good but still provided a reasonable estimate of single‐kernel mass, with RMSEP values of 2.8–4 mg. Data showed that kernel mass and protein content were not correlated, in contrast to some previous research. Overall, results showed the instrument performed comparably to other single‐seed instruments or methods based on accuracy but with an increased throughput at a rate of at least 4 seeds/s.     

Selecting and Sorting Waxy Wheat Kernels Using Near‐Infrared Spectroscopy

An automated single kernel near‐infrared (NIR) sorting system was used to separate single wheat (Triticum aestivum L.) kernels with amylose‐free (waxy) starch from reduced‐amylose (partial waxy) or wild‐type wheat kernels. Waxy kernels of hexaploid wheat are null for the granule‐bound starch synthase alleles at all three Wx gene loci; partial waxy kernels have at least one null and one functional allele. Wild‐type kernels have three functional alleles. Our results demonstrate that automated single kernel NIR technology can be used to select waxy kernels from segregating breeding lines or to purify advanced breeding lines for the low‐amylose kernel trait. Calibrations based on either amylose content or the waxy trait performed similarly. Also, a calibration developed using the amylose content of waxy, partial waxy, and wild‐type durum (T. turgidum L. var durum) wheat enabled adequate sorting for hard red winter and hard red spring wheat with no modifications. Regression coefficients indicated that absorption by starch in the NIR region contributed to the classification models. Single kernel NIR technology offers significant benefits to breeding programs that are developing wheat with amylose‐free starches.     

Single‐Kernel Near‐Infrared Analysis for Evaluating Wheat Samples for Fusarium Head Blight Resistance

This report describes a method to estimate the bulk deoxynivalenol (DON) content of wheat grain samples with the single‐kernel DON levels estimated by a single‐kernel near‐infrared (SKNIR) system combined with single‐kernel weights. The described method estimated the bulk DON levels in 90% of 160 grain samples to within 6.7 ppm of DON when compared with the DON content determined with the gas chromatography–mass spectrometry method. The single‐kernel DON analysis showed that the DON content among DON‐containing kernels (DCKs) varied considerably. The analysis of the distribution of DON levels among all kernels and among the DCKs of grain samples is helpful for the in‐depth evaluation of the effect of varieties or fungicides on Fusarium head blight (FHB) reactions. The SKNIR DON analysis and estimation of the single‐kernel DON distribution patterns demonstrated in this study may be helpful for wheat breeders to evaluate the FHB resistance of varieties in relation to their resistance to the spread of the disease and resistance to DON accumulation.     

Detecting and Segregating Black Tip‐Damaged Wheat Kernels Using Visible and Near‐Infrared Spectroscopy

Detection of individual wheat kernels with black tip symptom (BTS) and black tip damage (BTD) was demonstrated with near‐infrared reflectance spectroscopy (NIRS) and silicon light‐emitting‐diode (LED) based instruments. The two instruments tested, a single‐kernel NIRS instrument (SKNIRS) and a silicon LED‐based single‐kernel high‐speed sorter (SiLED‐SKS) were both developed by the Stored Product Insect and Engineering Research Unit, Center for Grain and Animal Health Research, USDA Agricultural Research Service. BTD was classified into four levels for the study ranging from sound, symptomatic (BTS) at two levels, and damaged (BTD). Discriminant analysis models for the SKNIRS instrument could distinguish sound undamaged kernels well, correctly classifying kernels 80% of the time. Damaged kernels were classified with 67% accuracy and symptomatic kernels at about 44%. Higher classification accuracy (81–87%) was obtained by creating only two groupings: 1) combined sound and lightly symptomatic kernels and 2) combined heavily symptomatic and damaged kernels. A linear regression model was developed from the SiLED‐SKS sorted fractions to predict the percentage of combined BTS and BTD kernels in a sample. The model had an R² of 0.64 and a standard error of prediction of 7.4%, showing it had some measurement ability for BTS and BTD. The SiLED‐SKS correctly classified and sorted out 90% of BTD and 66% of BTS for all 28 samples after three passes through the sorter. These instruments can serve as important tools for plant breeders and grading facilities of the wheat industry that require timely and objective determination and sorting of different levels of black tip present in wheat samples.     

Hardness Measurement of Bulk Wheat by Single‐Kernel Visible and Near‐Infrared Reflectance Spectroscopy

Reflectance spectra (400 to 1700 nm) of single wheat kernels collected using the Single Kernel Characterization System (SKCS) 4170 were analyzed for wheat grain hardness using partial least squares (PLS) regression. The wavelengths (650 to 700, 1100, 1200, 1380, 1450, and 1670 nm) that contributed most to the ability of the model to predict hardness were related to protein, starch, and color differences. Slightly better prediction results were observed when the 550–1690 nm region was used compared with 950–1690 nm region across all sample sizes. For the 30‐kernel mass‐averaged model, the hardness prediction for 550–1690 nm spectra resulted in a coefficient of determination (R²) = 0.91, standard error of cross validation (SECV) = 7.70, and relative predictive determinant (RPD) = 3.3, while the 950–1690 nm had R² = 0.88, SECV = 8.67, and RPD = 2.9. Average hardness of hard and soft wheat validation samples based on mass‐averaged spectra of 30 kernels was predicted and compared with the SKCS 4100 reference method (R² = 0.88). Compared with the reference SKCS hardness classification, the 30‐kernel (550–1690 nm) prediction model correctly differentiated (97%) between hard and soft wheat. Monte Carlo simulation technique coupled with the SKCS 4100 hardness classification logic was used for classifying mixed wheat samples. Compared with the reference, the prediction model correctly classified mixed samples with 72–100% accuracy. Results confirmed the potential of using visible and near‐infrared reflectance spectroscopy of whole single kernels of wheat as a rapid and nondestructive measurement of bulk wheat grain hardness.     

Determining Weight and Moisture Properties of Sound and Fusarium‐Damaged Single Wheat Kernels by Near‐Infrared Spectroscopy

Single kernel moisture content (MC) is important in the measurement of other quality traits in single kernels because many traits are expressed on a dry weight basis. MC also affects viability, storage quality, and price. Also, if near‐infrared (NIR) spectroscopy is used to measure grain traits, the influence of water must be accounted for because water is a strong absorber throughout the NIR region. The feasibility of measurement of MC, fresh weight, dry weight, and water mass of single wheat kernels with or without Fusarium damage was investigated using two wheat cultivars with three visually selected classes of kernels with Fusarium damage and a range of MC. Calibration models were developed either from all kernel classes or from only undamaged kernels of one cultivar that were then validated using all spectra of the other cultivar. A calibration model developed for MC when using all kernels from the wheat cultivar Jagalene had a coefficient of determination (R²) of 0.77 and standard error of cross validation (SECV) of 1.03%. This model predicted the MC of the wheat cultivar 2137 with R² of 0.81 and a standard error of prediction (SEP) of 1.02% and RPD of 2.2. Calibration models developed using all kernels from both cultivars predicted MC, fresh weight, dry weight, or water mass in kernels better than models that used only undamaged kernels from both cultivars. Single kernel water mass was more accurately estimated using the actual fresh weight of kernels and MC predicted by calibrations that used all kernels or undamaged kernels. The necessity for evaluating and expressing constituent levels in single kernels on a mass/kernel basis rather than a percentage basis was elaborated. The need to overcome the effects of kernel size and water mass on single kernel spectra before using in calibration model development was also highlighted.     

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

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 instrument a practical tool to collect quantitative seed phenotypes at high throughput. This technology has multiple applications for studying the genetic and physiological influences on seed traits.     

Effect of Wheat Moisture Content on Meal Apparent Particle Size and Hardness Scores Determined by Near-Infrared Reflectance Spectroscopy

The AACC Approved Method for near-infrared reflectance (NIR) spectroscopy to produce a wheat hardness score for wheat market classification can be corrected for variation in wheat moisture content. The cause of the variation in NIR spectra resulting from variation in wheat moisture was investigated. Ten samples each of soft red winter, soft white winter, hard red winter, and hard red spring wheats were stored at 20, 40, 60, and 80 equilibrium relative humidity. Wheats were then ground on a cyclone grinder as required by the standard method. Variation in unground wheat kernel moisture content resulted in variation in NIR data. NIR log 1/reflectance values increased at all wavelengths as wheat moisture content increased. Spectral changes were related to changes in the apparent particle size of ground wheat meal as it was influenced by moisture content. Higher moisture contents produced slightly higher apparent particle size in meal, suggesting larger particles of pericarp that became more pliable at higher moisture (temper) levels. The apparent particle size of meal of high moisture wheats resulted in greater NIR radiation scattering and decreased reflectance. Meal moisture content itself had no effect on the two NIR wavelengths used to evaluate wheat hardness.     

High‐Speed NIR Segregation of High‐ and Low‐Protein Single Wheat Seeds

Wheat breeders need a nondestructive method to rapidly sort high‐ or low‐protein single kernels from samples for their breeding programs. For this reason, a commercial color sorter equipped with near‐infrared filters was evaluated for its potential to sort high‐ and low‐protein single wheat kernels. Hard red winter and hard white wheat cultivars with protein content >12.5% (classed as high‐protein, 12% moisture basis) or < 11.5% (classed as low‐protein) were blended in proportions of 50:50 and 95:5 (or 5:95) mass. These wheat blends were sorted using five passes that removed 10% of the mass for each pass. The bulk protein content of accepted kernels (accepts) and rejected kernels (rejects) were measured for each pass. For 50:50 blends, the protein in the first‐pass rejects changed as much as 1%. For the accepts, each pass changed the protein content of accepts by ≈0.1%, depending on wheat blends. At most, two re‐sorts of accepts would be required to move 95:5 blends in the direction of the dominant protein content. The 95:5 and 50:50 blends approximate the low‐ and high‐protein mixture range of early generation wheat populations, and thus the sorter has potential to aid breeders in purifying samples for developing high‐ or low‐protein wheat. Results indicate that sorting was partly driven by color and vitreousness differences between high‐ and low‐protein fractions. Development of a new background specific for high‐ or low‐protein and fabrication of better optical filters for protein might help improve the sorter performance.     

Detection of Scab‐Damaged Hard Red Spring Wheat Kernels by Near‐Infrared Reflectance

Scab (Fusarium head blight) is a fungal disease that has become increasingly prevalent in North American wheat during the past 15 years. It is of concern to growers, processors, and the consumers because of depressed yields, poor flour quality, and the potential for elevated concentrations of the mycotoxin, deoxynivalenol (DON). Both wheat breeder and wheat inspector must currently deal with the assessment of scab in harvested wheat by manual human inspection. The study described herein examined the accuracy of a semi‐automated wheat scab inspection system that is based on near‐infrared (NIR) reflectance (1,000–1,700 nm) of individual kernels. Using statistical classification techniques such as linear discriminant analysis and nonparametric (k‐nearest‐neighbor) classification, upper limits of accuracy for NIR‐based classification schemes of ≈88% (cross‐validation) and 97% (test) were determined. An exhaustive search of the most suitable wavelength pairs for the spectral difference, [log(1/R)_λ1 ‐ log(1/R)_λ2], revealed that the slope of the low‐wavelength side of a broad carbohydrate absorption band (centered at ≈1,200 nm) was very effective at discriminating between healthy and scab‐damaged kernels with test set accuracies of 95%. The achieved accuracy levels demonstrate the potential for the use of NIR spectroscopy in commercial sorting and inspection operations for wheat scab.     

Measurement of Hard Vitreous Kernels in Durum Wheat by Machine Vision

An imaging method that detects nonvitreous regions in sound kernels of durum wheat at high speed is described. Kernels are analyzed simultaneously for individual vitreousness and individual kernel size and shape are measured concurrently. The measurement of 500 kernels per sample is adequate for highly reproducible results. Significant agreement was found between inspector‐determined hard vitreous kernel percentages (HVK) and machine‐determined HVK scores for export cargo samples of Canadian Western Amber Durum (CWAD), with differences between the two methods of typically ±3%. For railcar samples of CWAD taken on delivery to the terminal, agreement between inspector‐determined and machine‐determined HVK scores were more variable. The variability between the two methods generally increased as the HVK score of the sample became lower. For inspector‐determined HVK scores of <50%, difference between inspector and machine HVK scores for some samples was substantial. Such large differences are partially attributable to the way in which weathered kernels are assessed. Weather‐damaged kernels were frequently classified as nonvitreous by the machine system due to disruption of the enveloping tissues, whereas inspector evaluations often classify weather‐damaged kernels as vitreous. The speed, accuracy, and reproducibility of the machine methodology gives it enormous potential as a replacement for visual inspection of CWAD for HVK in Canadian grain terminals.     

Effects of Sampling and Wheat Grade on Precision and Accuracy of Kernel Features Determined by Digital Image Analysis

The effect of sampling on the precision and accuracy of digital image analysis of different commercial sample grades of Canada Western Red Spring (CWRS) wheat was investigated. Kernel perimeter, length, width, and area measurements were used to determine mean and dispersion statistics for composite railcar CWRS samples of No. 1, 2, and 3 grades; the numbers of railcars sampled were 27, 40, and 36, respectively. Sample sizes ranged from 10 to 2,000 kernels. Instrumental measurement precision was routinely better than 0.1 mm for macroview images, with a resolution of 0.0054 cm² per pixel. Computed mean kernel feature measurements and dispersion statistics were highly dependent on sample size and grade. Comparative analysis of wheat samples by digital imaging of individual kernels required a sample of no less than 300–500 kernels, depending on sample grade, for accurate representation of the parent sample. This level of sampling resulted in detection of significant differences (P < 0.05) in mean kernel features that, on average, differed by <1%. Except for some samples containing low numbers of kernels, lower grade wheat had more variable kernel features compared with higher grade samples. In relative terms, for comparably sized samples (≥133 kernels), variance in No. 2 grade wheat was 6–11% higher that for No. 1 grade wheat, depending on kernel feature. Similarly, variance in No. 3 grade wheat was 13–23% higher than for No. 2 grade wheat and 20–37% higher than for No. 1 grade wheat, indicating that wheat grading has a predictable effect on and is influenced by the uniformity of kernel characteristics in a sample. The ability of digital image analysis to detect these effects reflects the potential of this technology for use in objective classification of wheat according to grade.     

Differentiating Vitreous and Nonvitreous Durum Wheat Kernels by Using Near‐Infrared Spectroscopy

The vitreousnss of durum wheat is used by the wheat industry as an indicator of milling and cooking quality. The current visual method of determining vitreousness is subjective, and classification results between inspectors and countries vary widely. Thus, the use of near‐infrared (NIR) spectroscopy to objectively classify vitreous and nonvitreous single kernels was investigated. Results showed that classification of obviously vitreous or nonvitreous kernels by the NIR procedure agreed almost perfectly with inspector classifications. However, when difficult‐to‐classify vitreous and nonvitreous kernels were included in the analysis, the NIR procedure agreed with inspectors on only 75% of kernels. While the classification of difficult kernels by NIR spectroscopy did not match well with inspector classifications, this NIR procedure quantifies vitreousness and thus may provide an objective classification means that could reduce inspector‐to‐inspector variability. Classifications appear to be due, at least in part, to scattering effects and to starch and protein differences between vitreous and nonvitreous kernels.     

Bulk Functionality Diversification by Unsupervised Single‐Kernel Near‐Infrared (SKNIR) Sorting of Wheat

We explored the effects of fractioning heterogeneous bulk wheat by fast unsupervised single‐kernel near‐infrared (SKNIR) sorting according to an internal complex NIR functionality trait using a fast prototype kernel sorter designed for postharvest bulk sorting. Sorting into three functionality fractions was performed on low quality lots from an organic field experiment from two growth years and two locations. Sorted lots were mixtures originally diversified by three different preceding catch crops. The resulting 12 fractions, as well as the 12 original wheat lots were characterized by 20 standard quality variables of grains and flours. The data was analyzed by principal component analysis (PCA) and analysis of variance (ANOVA). Within each year and location/cultivar, the SKNIR fractionation had significant positive effect on bulk grain density, protein, wet gluten content, Zeleny sedimentation volume, farinograph water absorption, farinograph softening, falling number, gelatinization temperature, and hardness index. Using the NIR fingerprint directly for sorting without calibration to a univariate reference showed that the resulting fractions were based on the major variance in the entire physicochemical quality trait within each lot as expressed by NIR. This novel unsupervised approach may become a powerful tool for sorting according to complex functionality traits, thus increasing overall quality, applicability, and value of the sorted crop.     

Puroindoline Genotype of the U.S. National Institute of Standards & Technology Reference Material 8441, Wheat Hardness

Grain hardness (kernel texture) is of central importance in the quality and utilization of wheat (Triticum aestivum L.) grain. Two major classes, soft and hard, are delineated in commerce and in the Official U.S. Standards for Grain. However, measures of grain hardness are empirical and require reference materials for instrument standardization. For AACC Approved Methods employing near‐infrared reflectance (NIR) and the Single Kernel Characterization System (39‐70A and 55‐31, respectively), such reference materials were prepared by the U.S. Dept. of Agriculture Federal Grain Inspection Service. The material was comprised of genetically pure commercial grain lots of five soft and five hard wheat cultivars and was made available through the National Institute of Standards and Technology (SRM 8441, Wheat Hardness). However, since their establishment, the molecular‐genetic basis of wheat grain hardness has been shown to result from puroindoline a and b. Consequently, we sought to define the puroindoline genotype of these 10 wheat cultivars and more fully characterize their kernel texture through Particle Size Index (PSI, Method 55‐30) and Quadrumat flour milling. NIR, SKCS, and Quadrumat break flour yield grouped the hard and soft cultivars into discrete texture classes; PSI did not separate completely the two classes. Although all four of these methods of texture measurement were highly intercorrelated, each was variably influenced by some minor, secondary factors. Among the hard wheats, the two hard red spring wheat cultivars that possess the Pina‐D1b (a‐null) hardness allele were harder than the hard red winter wheat cultivars that possess the Pinb‐D1b allele based on NIR, PSI, and break flour yield. Among the soft wheat samples, SKCS grouped the Eastern soft red winter cultivars separate from the Western soft white. A more complete understanding of texture‐related properties of these and future wheat samples is vital to the use and calibration of kernel texture‐measuring instruments.     

Invisible Coatings for Wheat Kernels

It is occasionally necessary to tag wheat kernels without altering their appearance. Coatings have potential applications to tag wheat of a particular color or protein class, diseased wheat such as Karnal bunt, or genetically modified wheat. This methodology will aid in development of calibrations for sorting instruments. Procedures were developed to coat wheat kernels with invisible ultraviolet (UV) fluorescent and near‐infrared (NIR) absorbing noncarcinogenic dyes. Wheat coated with UV‐fluorescent compounds were identified under black light. The NIR‐absorbing coating required lower concentrations of dye than the UV dyes and wheat coated with NIR‐absorbing dye were identified from their NIR spectrum.     

Molecular Weight Distribution of Proteins in Hard Red Spring Wheat: Relationship to Quality Parameters and Intrasample Uniformity

Molecular weight distribution (MWD) of proteins extracted from hard red spring wheat was analyzed by size‐exclusion HPLC to investigate associations with wheat and breadmaking quality characteristics. Certain protein fractions were related to associations between wheat and breadmaking parameters, specifically when effect of quantitative variation of protein on those parameters was statistically eliminated by partial correlation analysis. SDS‐unextractable high molecular weight polymeric proteins had positive partial correlations with percent vitreous kernel content and breadmaking parameters, including mix time and bread loaf volume. SDS‐extractable protein fractions that were eluted before the primary gliadin peak had positive partial correlations with kernel hardness and water absorption parameters. The proportion of main gliadin fractions in total protein had a negative partial correlation with bread loaf volume and positive correlations with kernel hardness and water absorption parameters. Intrasample uniformity in protein MWD and kernel characteristics was estimated from three kernel subsamples that were separated according to single kernel protein content within individual wheat samples by a single‐kernel near‐infrared sorter. Wheat subsamples were significantly different in protein MWD. Intrasample uniformity in protein MWD did not differ greatly among wheat samples.