Single Wheat Kernels Research Articles

A pin-based probe for electronic moisture meters to determine moisture content in a single wheat kernel

BackgroundOptimum moisture in straw and grain at maturity is important for timely harvesting of wheat. Grain harvested at the right time has reduced chance of being affected by adverse weather conditions which is important to maintain grain quality and end use functionality. Wheat varieties with a short dry down period could help in timely harvest of the crop. However, measuring single kernel moisture in wheat and other small grain crops is a phenotyping bottleneck which requires characterising moisture content of the developing kernel at physiological maturity.ResultsHere we report developing a pin-based probe to detect moisture in a developing wheat kernel required for determining physiological maturity. An in-house designed pin-based probe was used with different commercially available electronic moisture meters to assess the moisture content of the individual kernels in spikes with high accuracy (R2 = 0.73 to 0.94, P < 0.001) compared with a reference method of oven drying. The average moisture values varied among different electronic moisture meters and the oven-dry method and differences in values were minimized at low kernel moisture content (< 50%). The single kernel moisture probe was evaluated in the field to predict the physiological maturity in wheat using 38% moisture content as the reference and visible notes on kernel stage.ConclusionThe pin-based moisture probe is a reliable tool for wheat physiologists and breeders to conveniently and accurately measure moisture content in developing grain that will aid in identifying wheat germplasm with fast dry-down characteristics.

Time dependent changes in the compression behavior of single wheat kernels upon tempering

Time dependent changes in the compression behavior of single wheat kernels upon tempering

Seedscreener: A novel integrated wheat germplasm phenotyping platform based on NIR-feature detection and 3D-reconstruction

Crop seed phenomics provides a new operational basis for breeders. However, a scarcity of seed phenotypic characterization, especially of the single seed kernels, severely restricts their use in plant breeding. In this study, we focus on designing and deploying a prototype for automatic and high throughput seed phenotyping. The novel setup, Seedscreener, allows simultaneous RGB imaging and NIR spectrum analysis pipeline to evaluate single kernel 3D morphologies and internal biochemicals. With the assistance of a data acquisition device centered on a near-infrared spectrometer and an industrial camera, as well as an automated acquisition software under the QT development framework, Seedscreener has the ability to acquire both endophenotype and exophenotype traits of single wheat kernels simultaneously. The developed biochemical endophenotype traits prediction model can forecast the protein content (PC), starch content (SC) and gluten content (GC) of wheat kernel by the full-band absorbance spectra. The developed morphological exophenotype traits extraction model based on Marching Cubes (MC) algorithm can calculate the wheat grain length (GL), grain width (GW), grain thickness (GT), grain surface area (GSA) and convex hull volume (CHV) using the three-dimensional visual hull constructed by the lateral profile information of the wheat grain. The results show that the platform could reach 94% success rate of single wheat phenotypic data collection, the average accuracy of endophenotype traits prediction is 0.85, the average accuracy of exophenotype traits extraction is 0.93, and the working flux of the platform is about 90 grains/h. Compared with existing individual wheat kernels phenotyping equipment, Seedscreener could obtain both endophenotype and exophenotype characteristics of wheat seeds with high throughput and precision.

Near-infrared hyperspectral imaging evaluation of Fusarium damage and DON in single wheat kernels

Fusarium is a DON producing filamentous fungi which commonly infects small grain cereals. Near-Infrared Hyperspectral Imaging (HSI-NIR) is considered for its potential to manage this contamination, as it uses spatial recognition, which may be able to deal with the heterogeneity inside the batches for cereal sorting implementation. The focus of this study was the application of HSI-NIR for Fusarium Damaged Kernels (FDK) detection and DON prediction and discrimination of wheat kernels over EU limits. After the HSI scanning of 300 individual grains, the reference values were obtained attributing categories for typical fungal symptoms and analyzing DON from individual grains by HPLC. Several spectral preprocessing methods selected valuable information before model calibration. Externally validated PLS predictions showed RMSEP of 6.65 mg/kg, an R 2 of 0.88 and an RPD of 3.21. However, the classification models managed wheat contaminations more appropriately, obtaining discrimination accuracies of 85.8% and 76.9% for fungal symptoms and DON at the EU limit, respectively. These findings suggest that HSI-NIR can be a suitable tool to sort DON contaminated kernels at EU limits. • This study was developed for single wheat kernels hyperspectral image acquisition. • HSI-NIR was able to classify 85.8% of kernels according to fungal symptomatology. • HSI-NIR was able to classify 76.9% of kernels according to the DON EU limit (1250 μg/kg). • DON prediction performance presented an R 2 of 0.88 and an RMSEP of 6.65 mg/kg.

Standardization of near infrared hyperspectral imaging for wheat single kernel sorting according to deoxynivalenol level

The spatial recognition feature of near infrared hyperspectral imaging (HSI-NIR) makes it potentially suitable for Fusarium and deoxynivalenol (DON) management in single kernels to break with heterogeneity of contamination in wheat batches to move towards individual kernel sorting and provide more quick, environmental-friendly and non-destructive analysis than wet-chemistry techniques. The aim of this study was to standardize HSI-NIR for individual kernel analysis of Fusarium damage and DON presence, to predict the level of contamination and classify grains according to the EU maximum limit (1250 µg/kg). Visual inspection on Fusarium infection symptoms and HPLC analysis for DON determination were used as reference methods. The kernels were scanned in both crease-up and crease-down position and for different image captures. The spectra were pretreated by Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), 1st and 2nd derivatives and normalisation, and they were evaluated also by removing spectral tails. The best fitted predictive model was on SNV pretreated data (R2 0.88 and RMSECV 4.8 mg/kg) in which 7 characteristic wavelengths were used. Linear Discriminant Analysis (LDA), Naïve Bayes and K-nearest Neighbours models classified with 100% of accuracy 1st derivative and SNV pretreated spectra according to symptomatology and with 98.9 and 98.4% of correctness 1st derivative and SNV spectra, respectively. The starting point results are encouraging for future investigations on HSI-NIR technique application to Fusarium and DON management in single wheat kernels to overcome their contamination heterogeneity.

Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method.

Near-infrared (NIR) hyperspectroscopy becomes an emerging nondestructive sensing technology for inspection of crop seeds. A large spectral dataset of more than 140,000 wheat kernels in 30 varieties was prepared for classification. Feature selection is a critical segment in large spectral data analysis. A novel convolutional neural network-based feature selector (CNN-FS) was proposed to screen out deeply target-related spectral channels. A convolutional neural network with attention (CNN-ATT) framework was designed for one-dimension data classification. Popular machine learning models including support vector machine (SVM) and partial least square discrimination analysis were used as the benchmark classifiers. Features selected by conventional feature selection algorithms were considered for comparison. Results showed that the designed CNN-ATT produced a higher performance than the compared classifier. The proposed CNN-FS found a subset of features, which made a better representation of raw dataset than conventional selectors did. The CNN-ATT achieved an accuracy of 93.01% using the full spectra and keep its high precision (90.20%) by training on the 60-channel features obtained via the CNN-FS method. The proposed methods have great potential for handling the analyzing tasks on other large spectral datasets. The proposed feature selection structure can be extended to design other new model-based selectors. The combination of NIR hyperspectroscopic technology and the proposed models has great potential for automatic nondestructive classification of single wheat kernels.

Moisture conversion and migration in single-wheat kernel during isothermal drying process by LF-NMR

Conversion among different moisture-binding types of single Hebei wheat kernels during isothermal drying processing at 60 °C was studied by low field nuclear magnetic resonance (LF-NMR), and moisture migration was studied by MRI. Water inside wheat kernels exists in four types: chemically combined water (T21), strong bound water (T22), loosely bound water (T23), and free water (T24). A new method to obtain the contents of different moisture-binding types is forwarded according to the transverse relaxation time distribution curve of dried wheat kernels. Moisture conversion inside wheat kernels during drying occurs mainly through the following mechanisms: high-temperature drives T24 to diffuse to lower moisture areas and transform into other moisture-binding types, and moisture-gradient drives T22 to transform into T24. The drying process can be divided into two stages. Moisture migrates from endosperm to epidermis during drying. As the drying rate of the wheat kernel significantly decreases, the drying conditions and parameters need to be adjusted to improve the drying rate while ensuring the quality of wheat kernels.

Near-infrared hyperspectral imaging for following imbibition of single wheat kernel sections

The scope of the present investigation was to evaluate the diffusion of water into single wheat kernel sections over time using Near-Infrared hyperspectral imaging. Five wheat kernels were transversely cut at about 80% slightly underneath the embryo, which was exposed to water. NIR hyperspectral images of the five surfaces were acquired at regular time intervals. The nine most representative for each kernel (reference, zero, 30min, 1h, 5h, 9h, 24h, 28h and 33h) were then analyzed using Principal Component Analysis (PCA) and a supervised method based on the Non-Negative Least Squares (NNLS) algorithm. The results are linked to the morphological changes and the presence of the radicle which marks the end of the germination process. Two out of five kernels had germinated at the end of the experiment. The Non-Negative Least Squares algorithm enables the changes in the different tissues of the grains (endosperm, embryo, and pericarp…) to be followed as well as the diffusion of water and the progressive appearance of the coleoptile. These preliminary results show that the kinetics of propagation of water are grain-specific: very rapid (a few minutes), slow (more than 24h) and not observable in our study. They also demonstrate that when the seeds absorb water, this necessarily leads to germination. The aim of this study was to develop a new analytical method, based on imaging, which can be automated and enable the visualization of water uptake by different tissues of the wheat kernel.

Moisture Effects on the Prediction Performance of a Single‐Kernel Near‐Infrared Deoxynivalenol Calibration

Fusarium head blight (FHB) is a serious disease in wheat that affects grain quality owing to the accumulation of mycotoxins such as deoxynivalenol (DON) in grains. Near‐infrared (NIR) spectroscopy has been used to develop techniques to estimate DON levels in single wheat kernels to facilitate rapid, nondestructive screening of FHB resistance in wheat breeding lines. The effect of moisture content (MC) variation on the accuracy of single‐kernel DON prediction by NIR spectroscopy was investigated. Sample MC considerably affected accuracy of the current NIR DON calibration by underestimating or overestimating DON at higher or lower moisture levels, respectively. DON in single kernels was most accurately estimated at 13–14% MC. Major NIR absorptions related to Fusarium damage were found around 1,198–1,200, 1,418–1,430, 1,698, and 1,896–1,914 nm. Major moisture related absorptions were observed around 1,162, 1,337, 1,405–1,408, 1,892–1,924, and 2,202 nm. Fusarium damage and moisture related absorptions overlapped in the 1,380–1,460 and 1,870–1,970 nm regions. These results show that absorption regions associated with water are often close to absorption regions associated with Fusarium damage. Thus, care must be taken to develop DON calibrations that are independent of grain MC.

Effective moisture diffusivity in wheat kernels during adsorption

Wheat samples with low (4.97–6.08%), normal (10.11–11.04%), and high (20.7–22.72%) initial moisture content (IMC, wet basis) were used to determine the rate of moisture adsorption by the gravimetric method at 10, 20, 25, 30, and 35°C under 65%, 86%, and 100% relative humidity (RH), respectively. A moisture diffusion equation was modified to fit the relationship between the moisture ratio of samples and exposure time. From 65% to 100% RH, the IMC of wheat samples was inversely related to the moisture adsorption rate at temperatures from 10 to 35°C. Moisture adsorption rates of samples increased with increasing temperatures. The moisture adsorption rate of samples with the same IMC increased with increasing RH at a given temperature. A single wheat kernel was considered geometrically to be a finite homogeneous slab shape, and the analytical solution of the partial differential equation for moisture diffusion was given. The effective moisture diffusivity was calculated using the slope method by plotting the experimental data in terms of ln (MR) versus rewetting time. In the range of 10–35°C, the effective moisture adsorption diffusivity of wheat kernels with normal moisture was 1.681×10−8–1.516×10−7m2h−1, and their activation energy was 23.651–28.434kJmol−1. For the same IMC, the effective moisture diffusivity of wheat kernels tended to increase with increasing temperature at a given RH, but decreased with an increase in RH at a given temperature. Activation energy and the pre-exponential factor of Arrhenius equation (D0) tended to decrease with increasing RH. With similar initial moisture content, winter wheat and spring wheat exhibited similar effective moisture diffusivity.

Real-time 3D visualization and quantitative analysis of internal structure of wheat kernels

Food micro-structure plays a crucial role in determining the properties of its end products. Micro X-ray Computer Tomography (CT) provides an excellent tool to assess the internal components of food products non-destructively. This paper describes the 3D visualization and quantitative analysis of the internal features of Canadian Amber Durum Wheat kernel after damage caused by sprouting and insect infestation with Cryptolestes ferrugineus. Micro X-ray CT images were reconstructed and processed using image processing algorithms. Imaging techniques such as segmentation and thresholding were used to render visualization modules such as volume rendering, geometric reconstruction, skeletonization and absolute permeability experiment simulation. In addition to visualization, morphometric parameters such as porosity, anisotropy and absolute permeability were determined in a selected region of interest of the sprout damaged and insect infested wheat kernel. Our data revealed that sprouting damage is first evident in the middle section of the sprouted wheat kernel, and associated with greater porosity than the insect damaged kernel. Insect damage occurred on the bottom of the wheat kernel. Compared to sprout damaged kernels, insect damaged kernels had similar anisotropy. X-ray micro CT is a powerful tool for the non-invasive investigation of damage, and microstructure organization in biological materials such as single wheat kernels.

Convective Drying Analysis of a Single Wheat Kernel Based on an Irreversible Thermodynamics Model

The application of irreversible thermodynamics offers a formal treatment for drying analysis that allows the evaluation of intra-particle or intra-medium temperature and moisture profiles, and enthalpy, liquid, and vapor fluxes. However, researchers have claimed that its implementation is complex. This work presents a simple methodology for modeling, solving, and validating the drying equations, as applied to wheat kernels, and for obtaining the inherent and usually unavailable transport coefficients. To clarify and simplify the ensuing physical analysis, a spherical shape and isotropy were assumed. Additionally, solutions obtained with both Dirichlet and convective boundary conditions were analyzed and compared against experimental data. The thermal and hydro-stresses depend heavily on internal vapor and liquid fluxes and on the respective drying evaporation fronts, all of which were evaluated and compared.

A multispectral sorting device for isolating single wheat kernels with high protein content

Automated sorting of single wheat kernels according to protein content was demonstrated using two novel multispectral sorting devices with different spectral ranges: 470–1,070 nm (silicone based detector) and 910–1,550 nm (InGaAs based detector). The multispectral data were acquired by rapidly (~12 kHz) and sequentially pulsing LEDs at nine wavelengths and measuring the amount of light reflected from the wheat kernels at each wavelength as they drop off a feeder chute. A microcontroller was used to direct the LED pulses, digitize the analog signal from the photodiode, perform signal processing, and apply classifications. Sorting was accomplished through activation of an air valve. The instruments were tested by sorting 13 hard red winter samples to select high protein kernels. The resulting sample of high protein kernels was sorted a second and third time to further increase average sample protein content. For the silicone based instrument, the sorted samples had 0.99, 1.60, and 1.86 % higher protein than the original samples after each sort, respectively. Similarly, after sorting with the InGaAs based LED instrument, sorted samples had 0.60, 1.25, and 1.68 % higher protein than the original samples after one, two, and three sorts, respectively. The simple LED-based sorter is very economical as compared to most other small scale sorting systems, with material costs for the prototype of around $6,000. This sorter would likely be most effectively employed to select desired traits of small lots of seed (e.g., breeder size samples), assist in sample purification, and help breeders in selecting the kernels with higher protein.

Influence of variety on selected physical and mechanical properties of wheat

Data on physical properties of grains and seeds have significant importance for machinery and process equipments design. The study was performed to investigate selected geometric, physical and mechanical properties of the wheat spelt varieties in compare with the common wheat. The length, width and thickness of wheat varieties were in the range of 6.42 (Korweta) to 8.80 (Obelkulmer Rotkorn), 3.27 (Holsternkorn) to 3.49 (Oberkulmer Rotkorn) and 2.67 (Korweta) to 2.89 (Holsternkorn) mm, respectively. The bulk density ranged from 721.0 (Ceralio) to 749.6kg/m3 (Oberkulmer Rotkorn), the kernel apparent density from 1342.7 (Ceralio) to 1491.2kg/m3 (Korweta) and the porosity from 45.9 (Oberkulmer Rotkorn) to 48.1% (Holsternkorn). The equivalent diameter and volume of a single wheat kernel ranged between 4.04 (Holsternkorn) and 4.20mm (both Korweta and Oberkulmer Rotkorn), and 33.1 (Ceralio) and 42.2mm3(Oberkulmer Rotkorn). The shape of a single wheat kernel was confirmed to be ellipsoid. The sphericity ranged from 53.5 (Oberkulmer Rotkorn) to 60.6% (Korweta) while the average aspect ratio was 47.9%. This study showed that the linear dimensions of all spelt varieties kernels were higher than those observed for common wheat. Wheat kernels showed relatively high values of angle of repose ranged from 17.6° (Korweta) to 20.5° (both Holsternkorn and Oberkulmer Rotkorn). Static coefficient of friction of spelt varieties were higher than that of common wheat. Furthermore, kernels of Korweta and Oberkulmer Rotkorn varieties were characterized by the highest energy required for rupture with average of 11.3J, while Ceralio and Holsternkorn by the lowest one with average of 5.9J.

Yellow Pigment Determination for Single Kernels of Durum Wheat (Triticum durum Desf.)

ABSTRACTThe carotenoid concentration of durum wheat is a criterion for the assessment of semolina quality, and it is of particular importance in determining the color of pasta. Thus, the development of a rapid screening method for kernel and semolina color has facilitated increasing the yellow color in durum wheat cultivars. However, the distribution of the pigment might vary within the ear of the wheat. The micromethod for the determination of yellow pigment concentration (YPC) of single wheat kernels now allows the discovery of whether this variation is the same for cultivars with different carotenoid concentrations. All of the cultivars investigated showed similar trends in the arrangements of the kernels within the ears, with the central positions of the ears being the most stable for YPC. Indeed, the best combination of higher YPC and larger kernel size is seen for the basal‐central region of the ear, which can be used for the selection during wheat‐breeding programs of cultivars with a more intense yellow color.

Characterization of the Influence of Moisture Content on the Morphological Features of Single Wheat Kernels Using Machine Vision

The objective of this study was to quantify changes in morphological features of kernels of western Canadian wheat classes caused by moisture increase using a machine vision system. One hundred single wheat kernels for each of eight western Canadian wheat classes were successively conditioned from 12% to 20% (wet basis) moisture contents using potassium hydroxide (KOH) concentrations which regulated relative humidity. A digital camera of 7.4 7.4-m pixel resolution with an inter-line transfer charge-coupled device (CCD) image sensor was used to acquire images of single kernels. A machine vision algorithm developed at the Canadian Wheat Board Centre for Grain Storage Research, University of Manitoba, was implemented to extract seven morphological features (area, perimeter, major axis length, minor axis length, maximum radius, minimum radius, and mean radius) from the wheat kernel images. All the seven features of Canada Western Red Spring, Canada Western Amber Durum, Canada Prairie Spring White, Canada Prairie Spring Red, Canada Western Extra Strong, Canada Western Red Winter, Canada Western Hard White Spring, and Canada Western Soft White Spring wheat kernels were significantly (a = 0.05) different as the moisture content increased from 12% to 20%. All seven features showed a linearly increasing trend with an increase in moisture content.

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

ABSTRACTSingle 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.

Tracking diffusion of conditioning water in single wheat kernels of different hardnesses by near infrared hyperspectral imaging

The combination of near infrared (NIR) hyperspectral imaging and chemometrics was used to follow the diffusion of conditioning water over time in wheat kernels of different hardnesses. Conditioning was attempted with deionised water (dH 2O) and deuterium oxide (D 2O). The images were recorded at different conditioning times (0–36 h) from 1000 to 2498 nm with a line scan imaging system. After multivariate cleaning and spectral pre-processing (either multiplicative scatter correction or standard normal variate and Savitzky–Golay smoothing) six principal components (PCs) were calculated. These were studied visually interactively as score images and score plots. As no clear clusters were present in the score plots, changes in the score plots were investigated by means of classification gradients made within the respective PCs. Classes were selected in the direction of a PC (from positive to negative or negative to positive score values) in almost equal segments. Subsequently loading line plots were used to provide a spectroscopic explanation of the classification gradients. It was shown that the first PC explained kernel curvature. PC3 was shown to be related to a moisture-starch contrast and could explain the progress of water uptake. The positive influence of protein was also observed. The behaviour of soft, hard and very hard kernels was different in this respect, with the uptake of water observed much earlier in the soft kernels than in the harder ones. The harder kernels also showed a stronger influence of protein in the loading line plots. Difference spectra showed interpretable changes over time for water but not for D 2O which had a too low signal in the wavelength range used. NIR hyperspectral imaging together with exploratory chemometrics, as detailed in this paper, may have wider applications than merely conditioning studies.

Selecting and Sorting Waxy Wheat Kernels Using Near‐Infrared Spectroscopy

ABSTRACTAn 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.

Influence of High Molecular Weight Glutenins on Viscoelastic Properties of Intact Wheat Kernel and Relation to Functional Properties of Wheat Dough

ABSTRACTHigh and low molecular weight glutenin subunits (HMW‐GS and LMW‐GS, respectively) are the main factors determining the viscoelastic properties of wheat dough. The mechanical and viscoelastic properties of 29 samples of wheat kernels differing in HMW‐GS were evaluated with load‐compression tests. Samples were grouped by genotypes differing in HMW‐GS composition (allelic variants: Glu‐A1: null, 1, 2*; Glu‐B1: 7, 7+8, 7+9, 13+16, and 17+18; Glu‐D1: 5+10, 2+12). Groups representing Glu‐A1 1 and 2*; Glu‐B1 7, 7+9 and 17+18; and Glu‐D1 5+10 generally possessed hard grain and showed the largest kernel elasticity values, while those representing subunits Glu‐A1 null; Glu‐B1 7+8; and Glu‐D1 2+12 had soft kernels and showed lower elastic work values. Genotypes possessing HMW‐GS 1, 17+18 and 5+10 gave large SDS‐sedimentation values and better dough viscoelastic properties than those with allelels: null, 7+8, and 2+12. Kernel hardness showed significant correlation with the dough‐strength‐related parameters: SDS‐sedimentation; dough mixing time; and the alveographic parameters, W and P. There was a negative correlation between kernel plastic work and dough mixing time and the dough tenacity/extensibility parameters, P/L. The significant relationship between sedimentation tests and kernel elastic work seems to indicate that elastic work is related to genotype (protein composition). The general tendency was that higher values in kernel elastic work and size corresponded to better dough rheological quality. Mechanical properties of the kernel were significantly related to the elastic behavior measured in a single wheat kernel. The use of the compression test on individual kernels is easy, rapid and nondestructive and therefore seems to show potential use as a rapid tool in breeding to improve wheat quality.