Single Wheat Kernels Research Articles

NIR Absorbance Characteristics of Deoxynivalenol and of Sound and Fusarium-Damaged Wheat Kernels

The near infrared (NIR) absorption spectra of deoxynivalenol (DON) and single wheat kernels with or without DON were examined. The NIR absorption spectra of 0.5–2000 ppm of DON in acetonitrile were recorded in the 350–2500 nm range. Second derivative processing of the NIR spectra and spectral subtractions showed DON absorption bands at 1408 nm, 1904 nm and 1919 nm. NIR spectra of sound and Fusarium-damaged kernels were also acquired using two instruments. Subtraction of average absorption spectra and second derivative spectra were evaluated to identify different NIR signatures of the two types of kernel. Differences in peak height and positions of the NIR absorption bands of the kernels were noted. At 1204 nm, 1365 nm and 1700 nm, the differences were in the heights of the absorption peaks. Such differences may be attributed to changes in the levels of grain food reserves such as starches, proteins and lipids and other structural compounds. Shifts in absorption peak positions between the two types of kernels were observed at 1425–1440 nm and 1915–1930 nm. These differences may arise from other NIR active compounds, such as DON, which are not common for the two types of kernel. Since the NIR absorption of DON may have contributed to the shifts between sound and Fusarium-damaged kernels, this study indicates the potential for NIR spectrometry to evaluate Fusarium damage in single kernels based on the DON levels.

Exploring abiotic stress on asynchronous protein metabolism in single kernels of wheat studied by NMR spectroscopy and chemometrics

Extreme climate events are being recognized as important factors in the effects on crop growth and yield. Increased climatic variability leads to more frequent extreme conditions which may result in crops being exposed to more than one extreme event within a growing season. The aim of this study was to examine the implications of different drought treatments on the protein fractions in grains of winter wheat using 1H nuclear magnetic resonance spectroscopy followed by chemometric analysis. Triticum aestivum L. cv. Vinjett was studied in a semi-field experiment and subjected to drought episodes either at terminal spikelet, during grain-filling or at both stages. Principal component trajectories of the total protein content and the protein fractions of flour as well as the 1H NMR spectra of single wheat kernels, wheat flour, and wheat methanol extracts were analysed to elucidate the metabolic development during grain-filling. The results from both the 1H NMR spectra of methanol extracts and the 1H HR-MAS NMR of single kernels showed that a single drought event during the generative stage had as strong an influence on protein metabolism as two consecutive events of drought. By contrast, a drought event at the vegetative growth stage had little effect on the parameters investigated. For the first time, 1H HR-MAS NMR spectra of grains taken during grain-filling were analysed by an advanced multiway model. In addition to the results from the chemical protein analysis and the 1H HR-MAS NMR spectra of single kernels indicating that protein metabolism is influenced by multiple drought events, the 1H NMR spectra of the methanol extracts of flour from mature grains revealed that the amount of fumaric acid is particularly sensitive to water deficits.

Mathematical modelling of wheat kernel drying with input from moisture movement studies using magnetic resonance imaging (MRI), Part I: Model development and comparison with MRI observations

A three-dimensional (3D) simultaneous heat and moisture transfer drying model for a single wheat kernel was developed. The model was based on the information obtained from magnetic resonance imaging (MRI) of wheat drying. The model assumed a non-uniform initial moisture distribution and two different values of water diffusion coefficients in the germ and endosperm of a wheat kernel. The model was theoretically developed using finite element method and then solved using COMSOL® Multiphysics™, a commercial finite element software package. Model simulations were performed on the actual shaped 3D geometric objects obtained from the MR images of wheat kernels using image processing algorithms. Model-predicted moisture data were compared with the results obtained from MR images under similar drying conditions. Activation energies of the water removal process in the endosperm and germ were calculated to be 26.5 and 13.8 kJ mol−1, respectively, based on the range of drying temperatures under study.

Mathematical modelling of wheat kernel drying with input from moisture movement studies using magnetic resonance imaging (MRI), Part II: Model comparison with published studies

The predicted moisture data, using the three-dimensional (3D) single wheat drying model (Part I) considering actual kernel geometry, were compared with two published semi-empirical thin-layer wheat drying models, and one published axisymmetric heat and mass transfer model for a single wheat kernel. The published axisymmetric model assumed a uniform initial moisture distribution within the whole kernel of ellipsoidal geometry and a single value of the water diffusion coefficient. Our model predictions agreed well with the observations obtained from the published models. Further, our model can predict the moisture distribution pattern within individual wheat components with drying time. Moisture and temperature distributions inside the wheat kernel and the effect of drying temperature on wheat quality were explained by calculating the Biot and Lewis numbers. High Biot numbers for moisture transfer (0.89×106–3.04×106 for endosperm and 1.98×106–3.37×106 for germ) indicated that moisture transfer inside the wheat structural components was governed by high internal resistance, whereas low Biot numbers for heat transfer (0.12–0.20 for endosperm and 0.02–0.10 for germ) indicated that heat transfer was controlled by external resistance and, hence, temperature within the wheat kernels was uniform at any given time during drying. Lewis numbers indicated that the rate of heat transfer was very high compared to moisture transfer in both the endosperm and the germ. Sensitivity analysis was performed for selecting proper model parameters which indicated that values of water diffusion coefficients in the germ and endosperm are the most sensitive parameters for a grain drying process.

Measurement of the Protein Composition of Single Wheat Kernels Using near Infrared Spectroscopy

Near infrared (NIR) spectroscopy of single seeds of cereal grains would allow their quality testing to be performed at earlier stages in breeding programmes than at present and for seeds of individual lines to be sorted into fractions of contrasting quality. Previous single-kernel NIR research involving cereals has been directed at traits such as protein content and seed colour for which NIR analysis is well-established. However, the development of methods for more complex traits such as wheat protein composition, which relate directly to end product performance, would be of particular use in plant breeding applications. NIR spectra of 200 single wheat seeds representing varieties with different protein composition profiles were measured using both a dispersive and a diode array instrument together with specially-designed sample accessories. The same seeds were then subjected to biochemical analysis using HPLC methods to determine their protein compositions. Predictive models were developed which showed that NIR measurements of single seeds could be used to determine the uniformity of a wheat sample, or to sort seeds, with respect to the concentrations of protein components.

Relationship Between Single Wheat Kernel Particle‐Size Distribution and Perten SKCS 4100 Hardness Index

ABSTRACTThe Perten Single Kernel Characterization system is the current reference method for determination of single wheat kernel texture. However, the SKCS 4100 calibration method is based on bulk samples. The objective of this research was to develop a single‐kernel hardness reference based on single‐kernel particle‐size distributions (PSD). A total of 473 kernels, drawn from eight different classes, was studied. Material from single kernels that had been crushed on the SKCS 4100 system was collected, milled, then the PSD of each ground single kernel was measured. Wheat kernels from soft and hard classes with similar SKCS hardness indices (HI 40–60) typically had a PSD that was expected from their genetic class. That is, soft kernels tended to have more particles at <21 μm than hard kernels after milling. As such, a combination of HI and PSD gives better discrimination between genetically hard and soft classes than either parameter measured independently. Additionally, the use of SKCS‐predicted PSD, combined with other low level SKCS parameters, appears to reduce classification errors into genetic hardness classes by ≈50% over what is currently accomplished with HI alone.

A magnetic resonance imaging study of wheat drying kinetics

A spin-echo (SE) magnetic resonance imaging (MRI) technique was used to study non-invasively moisture levels as a function of time in single wheat kernels. Internal moisture distribution during drying of wheat at temperatures of 30, 40, and 50 °C for 4 h was analysed from the MR images. The influence of the individual wheat components on the drying process was observed using physically different wheat kernels: intact kernels, mechanically scarified kernels with incisions in the pericarp, and kernels with the embryo removed. Drying rate curves for these different kernels were obtained at three different temperatures and the effects of temperature on the drying curves were discussed. A calibration curve of MR image intensity versus the moisture content of the grain was obtained using nuclear magnetic resonance (NMR) spectra of wheat at different, known, moisture contents. Results obtained from this study provided details of the drying kinetics in wheat kernels.

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

ABSTRACTAn 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.

MAGNETIC RESONANCE IMAGE ANALYSIS TO EXPLAIN MOISTURE MOVEMENT DURING WHEAT DRYING

Two-dimensional (2D) spin-echo magnetic resonance imaging (MRI) was used to explain the moisture movementin single wheat kernels prior to and during drying. Drying was performed at temperatures of 40C and 50C using heatedN2 gas at a velocity of 0.23 m/s. Individual wheat kernels of 20% to 64% moisture content (wet mass basis) were dried to studythe moisture movement inside the kernel during drying. MR images were recorded at equal time intervals during drying.Moisture distribution and transfer were analyzed from MR images of wheat kernels obtained before and during drying.Moisture distribution was non-uniform at an equilibrium state prior to drying. Further, moisture loss from the seed partsdiffered significantly during drying. The MR images were used to obtain the drying rates and patterns of water in wheat kernelsduring the drying process. Influence of grain parts on the moisture distribution was also studied using mechanically scarifiedkernels and germ-removed kernels.

Detection of Fusarium in single wheat kernels using spectral imaging

Fusarium head blight (FHB) is a harmful fungal disease that occurs in small grains. Non-destructive detection of this disease is traditionally done using spectroscopy or image processing. In this paper the combination of these two in the form of spectral imaging is evaluated. Transmission spectral images are recorded, both in the visible and near-infrared range from FHB infected wheat kernels. These images are analyzed, using light absorption, the relation between two wavelength bands, unsupervised fuzzy c-means clustering and supervised partial least squares regression. The reference method for training and validation is TaqMan real-time PCR. Results show that nearinfrared spectral images perform much better than spectral images in the visible range. Kernels with more than 6000 pg Fusarium DNA could clearly be identified. Above 100 pg it was possible to predict the amount of Fusarium with a Q2 of 0.8. This was both for Partial Least Squares regression (PLS) and a simple wavelength ratio. Also fuzzy c-means clustering shows a relation between amount of Fusarium and spectra. Fusarium head blight (FHB) is a harmful fungal disease that occurs in small grains. Non-destructive detection of this disease is traditionally done using spectroscopy or image processing. In this paper the combination of these two in the form of spectral imaging is evaluated. Transmission spectral images are recorded, both in the visible and near-infrared range from FHB infected wheat kernels. These images are analyzed, using light absorption, the relation between two wavelength bands, unsupervised fuzzy c-means clustering and supervised partial least squares regression. The reference method for training and validation is TaqMan real-time PCR. Results show that near-infrared spectral images perform much better than spectral images in the visible range. Kernels with more than 6000 pg Fusarium DNA could clearly be identified. Above 100 pg it was possible to predict the amount of Fusarium with a Q(2) of 0.8. This was both for Partial Least Squares regression (PLS) and a simple wavelength ratio. Also fuzzy c-means clustering shows a relation between amount of Fusarium and spectra.

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

ABSTRACTWheat 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.

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

ABSTRACTReflectance 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.

Development of Nondestructive Screening Methods for Single Kernel Characterization of Wheat

ABSTRACTThe development of nondestructive screening methods for single seed protein, vitreousness, density, and hardness index has been studied for single kernels of European wheat. A single kernel procedure was applied involving, image analysis, near‐infrared transmittance (NIT) spectroscopy, laboratory density determination, single kernel characterization system (SKCS), and finally Kjeldahl protein determination on the crushed single kernels. Single kernel NIT spectroscopy showed excellent ability to determine protein content, and some ability for determination of single kernel vitreousness. Nondestructive determination of single kernel density, either based on NIT spectroscopy or based on image analysis and kernel weight, needs to be further improved for practical use. The use of SKCS hardness index as a true single kernel hardness reference in a NIT prediction model resulted in a poor predictability. However, by applying an averaging approach, in which single seed replicate measurements are mathematically simulated, a very good NIT prediction model was achieved. This suggests that the single seed NIT spectra contain hardness information, but that a single seed hardness method with higher accuracy is needed to achieve a good NIT prediction model for single kernel hardness.

Near-Infrared Absorption and Scattering Separated by Extended Inverted Signal Correction (EISC): Analysis of Near-Infrared Transmittance Spectra of Single Wheat Seeds

A new extended method for separating, e.g., scattering from absorbance in spectroscopic measurements, extended inverted signal correction (EISC), is presented and compared to multiplicative signal correction (MSC) and existing modifications of this. EISC preprocessing is applied to near-infrared transmittance (NIT) spectra of single wheat kernels with the aim of improving the multivariate calibration for protein content by partial least-squares regression (PLSR). The primary justification of the EISC method is to facilitate removal of spectral artifacts and interferences that are uncorrelated to target analyte concentration. In this study, EISC is applied in a general form, including additive terms, multiplicative terms, wavelength dependency of the light scatter coefficient, and simple polynomial terms. It is compared to conventional MSC and derivative methods for spectral preprocessing. Performance of the EISC was found to be comparable to a more complex dual-transformation model obtained by first calculating the second derivative NIT spectra followed by MSC. The calibration model based on EISC preprocessing performed better than models based on the raw data, second derivatives, MSC, and MSC followed by second derivatives.

MOISTURE DIFFUSION COEFFICIENTS OF SINGLE WHEAT KERNELS WITH ASSUMED SIMPLIFIED GEOMETRIES: ANALYTICAL APPROACH

Using a combination of soaking data and an analytical solution of the diffusion equation, moisture diffusion coefficients of single wheat kernels were determined for nine commercial varieties representing six market classes of U.S. wheat. Two geometric conditions, the whole kernel as a prolate spheroid, and the endosperm (also modeled as prolate spheroidal) and pericarp as separate components, were examined. Values from the analytical solution for a sphere were adjusted by a geometrical correction factor to more closely represent the response of a prolate spheroid. The ranges in diffusion coefficients were 0.39 ×10 –10 to 1.04 ×10 –10 m 2 /s for endosperm and 0.04 ×10 –10 to 0.28 ×10 –10 m 2 /s for pericarp. Compared to the pericarp, moisture diffused more rapidly in the endosperm. Soft wheats tended to have a more permeable pericarp layer than hard wheats, which resulted in a greater overall rate of diffusion, despite the endosperm of these two groups being nearly equivalent in diffusion coefficient value.

Detection of Grain Weevils inside Single Wheat Kernels by a Very near Infrared Two-Wavelength Model

Near infrared reflectance spectroscopy in the very near infrared region (700 nm to 1100 nm) has been investigated for the detection of grain weevil larvae and pupae inside single wheat kernels. Using a total of 80 samples, simple, two-wavelength classification models have been identified, based on either log 1/ R (982 nm)–log 1/ R (1014 nm) or log 1/ R (972 nm)–log 1/ R (1032 nm). Both models correctly classified over 96% of samples as uninfested or infested. Detection performance equalled that obtained using the full-spectrum approach of principal components analysis. In a separate experiment, repeatedly scanning samples over time demonstrated detection of younger larvae as well as later developmental stages. This experiment confirmed that the observed spectral differences arise from the actions of the developing insect, rather than from any feature specific to kernels selected by adult females for egg-laying. The origins of the spectral differences are almost certainly decreasing grain starch, for log 1/ R (982 nm)–log 1/ R (1014 nm), or increasing grain moisture, for log 1/ R (972 nm)–log 1/ R (1032 nm), with infestation. These results indicate that the future incorporation of the wavelength pair, 982 nm and 1014 nm, as camera lens filters in a very near infrared imaging system, could lead to an inexpensive, rapid and reliable machine-vision method for detecting internal insects in grain.

Predicting Scab, Vomitoxin, and Ergosterol in Single Wheat Kernels Using Near‐Infrared Spectroscopy

ABSTRACTNear‐infrared spectroscopy (NIRS) was used to detect scab damage and estimate deoxynivalenol (DON) and ergosterol levels in single wheat kernels. Results showed that all scab‐damaged kernels identified by official inspectors were correctly identified by NIRS. In addition, this system identified more kernels with DON than did a visual inspection. DON and ergosterol were predicted with standard errors of ≈40 and 100 ppm, respectively. All samples with visible scab had single kernels with DON levels >120 ppm, and some kernels contained >700 ppm of DON. This technology may provide a means of rapidly screening samples for potential food safety and quality problems related to scab damage.

Single Wheat Kernel Size Effects on Near‐Infrared Reflectance Spectra and Color Classification

ABSTRACTAn optical radiation measurement system was used to measure reflectance spectra of single wheat kernels from 400 to 2,000 nm. Five classes of wheat were used for this study. Three kernel sizes (large, medium, and small) were used to determine how wheat kernel size affects visible and near‐infrared (NIR) reflectance spectra and single wheat kernel color classification. Mean kernel weights ranged from 35.6 to 57.2 mg for large kernels, from 26.9 to 45.0 mg for medium kernels, and from 17.6 to 31.4 mg for small kernels. The results showed that wheat kernel size significantly affects visible and NIR reflectance spectra. Determination of the color class of red kernels increased in accuracy and that of white kernels decreased in accuracy as kernel size decreased. Data pretreatments such as multiplicative scatter correction (MSC), first or second derivatives, and first or second derivatives with MSC reduced the effect of kernel size on reflectance spectra and color classification.

Predicting the Number of Dominant R Alleles in Single Wheat Kernels Using Visible and Near‐Infrared Reflectance Spectra

ABSTRACTAn optical radiation measurement system was used to measure reflectance spectra of single wheat kernels from 400 to 2,000 nm. A total of 18 wheat samples with 0, 1, 2, or 3 R alleles for red grain color was used for this study. The results indicated a linear relationship between the degree of the red pigmentation and the number of R alleles. The highest coefficient of determination (R2) was 0.78 in the wavelength region of 500–1,700 nm when a four‐class partial least squares model was used. The highest classification of red genes was 78.4%. For two‐class models, differentiating samples with 0 R alleles and 1 R allele from samples with 3 R alleles had the highest success rate of 100 and 98.8%, respectively. The number and combination of R alleles had a significant effect on wheat kernel color. These relationships may be useful to wheat breeders in estimating the number and location of R alleles.

MOISTURE DIFFUSION MODELING OF WHEAT KERNELS DURING SOAKING

A finite element moisture diffusion model was refined for the purpose of examining the distribution of moisturewithin single mature kernels of wheat. Among the gross kernel components (endosperm, pericarp, and germ), moisturediffusion coefficients of the endosperm and pericarp were determined from soaking experiments and optimized with afinite element model. Nine commercial varieties of wheat, representing six U.S. market classes or subclasses, wereexamined. Through separate soaking experiments of pearled wheat, which represented the endosperm alone, and intactwheat, which represented the pericarp by mathematical subtraction (neglecting the contribution from the germ), theranges in diffusion coefficients across the nine varieties were as follows: 0.46 10 10 1.4 10 10 m2/s for endosperm,0.042 1010 0.42 10 10 m2/s for pericarp. During finite element modeling, spherical and ellipsoidal geometrieswere assumed for pearled and intact kernels, respectively. With the kernel moisture ratio defined as the overall moisturecontent normalized with respect to the level of moisture at equilibrium, the soft varieties (Rely, Penawawa, andVanna) demonstrated a higher ratio than the hard variety TAM107. Agreement between modeled and measured valuesfor overall moisture content was very good, as seen by the ranges in the standard deviation of differences which were0.014-0.041 and 0.008-0.029% d.b. for pearled and intact kernels, respectively. Knowledge gained from this study will beuseful in understanding how the tempering process affects the milling of hard and soft wheats.