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1.
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.  相似文献   

2.
《Cereal Chemistry》2017,94(3):458-463
Oats and groats can be discriminated from other grains such as barley, wheat, rye, and triticale (nonoats) with near‐infrared spectroscopy. The two instruments tested herein were the manual version of the United States Department of Agriculture–Agricultural Research Service single‐kernel near‐infrared (SKNIR) instrument and the automated QualySense QSorter Explorer high‐speed sorter, both used in similar near‐infrared spectral ranges. Three linear discriminate self‐prediction models were developed: 1) oats versus groats + nonoats, 2) oats + groats versus nonoats, and 3) groats versus nonoats. For all three models, the SKNIR instrument showed high correct classification of oats or groats (94.5–100%), which was similar to results of the QSorter Explorer at 95.0–99.4%. The amount of nonoats that were misclassified as oats or groats was low for both instruments at 0–0.2% for the SKNIR instrument and 0.8–3.7% for the QSorter Explorer. Linear discriminate models from independent prediction and validation sets yielded classification accuracies of 91.6–99.3% (SKNIR) and 90.5–97.8% (QSorter Explorer). Small differences in classification accuracy were attributed to processing speeds between the two instruments: 3 kernels/s for the SKNIR instrument and 35 kernels/s for the QSorter Explorer. This indicated that both instruments are useful for quantifying grain sample compositions of oat and groat samples and that both could be useful tools for meeting consumer demand for gluten‐free or low‐gluten products. Discrimination between grains will help producers and manufacturers meet various regulatory requirements. Examples include requirements such as those from the U.S. Food and Drug Administration and the Commission of European Communities, in which gluten‐free oats or other products can only be labeled as nongluten if they contain gluten at less than 20 ppm, the established safe consumption limit for people with celiac disease. The QSorter Explorer is currently being used to meet these requirements.  相似文献   

3.
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.  相似文献   

4.
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.  相似文献   

5.
A high‐speed dual‐wavelength sorter was tested for removing corn contaminated in the field with aflatoxin and fumonisin. To achieve accurate sorting, single kernel reflectance spectra (500–1,700 nm) were analyzed to select the optimal pair of optical filters to detect mycotoxin‐contaminated corn during high‐speed sorting. A routine, based on discriminant analysis, was developed to select the two absorbance bands in the spectra that would give the greatest classification accuracy. In a laboratory setting, and with the kernels stationary, absorbances at 750 and 1,200 nm could correctly identify >99% of the kernels as aflatoxin‐contaminated (>100 ppb) or uncontaminated. A high‐speed sorter was tested using the selected filter pair for corn samples inoculated with Aspergillus flavus; naturally infested corn grown in central Illinois; and naturally infested, commercially grown and harvested corn from eastern Kansas (2002 harvest). For the Kansas corn, the sorter was able to reduce aflatoxin levels by 81% from an initial average of 53 ppb, while fumonisin levels in the same grain samples were reduced an average of 85% from an initial level of 17 ppm. Similar reductions in mycotoxin levels were observed after high‐speed sorting of A. flavus inoculated and naturally mold‐infested corn grown in Illinois.  相似文献   

6.
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.  相似文献   

7.
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.  相似文献   

8.
The accuracy of using near‐infrared spectroscopy (NIRS) for predicting 186 grain, milling, flour, dough, and breadmaking quality parameters of 100 hard red winter (HRW) and 98 hard red spring (HRS) wheat and flour samples was evaluated. NIRS shows the potential for predicting protein content, moisture content, and flour color b* values with accuracies suitable for process control (R2 > 0.97). Many other parameters were predicted with accuracies suitable for rough screening including test weight, average single kernel diameter and moisture content, SDS sedimentation volume, color a* values, total gluten content, mixograph, farinograph, and alveograph parameters, loaf volume, specific loaf volume, baking water absorption and mix time, gliadin and glutenin content, flour particle size, and the percentage of dark hard and vitreous kernels. Similar results were seen when analyzing data from either HRW or HRS wheat, and when predicting quality using spectra from either grain or flour. However, many attributes were correlated to protein content and this relationship influenced classification accuracies. When the influence of protein content was removed from the analyses, the only factors that could be predicted by NIRS with R2 > 0.70 were moisture content, test weight, flour color, free lipids, flour particle size, and the percentage of dark hard and vitreous kernels. Thus, NIRS can be used to predict many grain quality and functionality traits, but mainly because of the high correlations of these traits to protein content.  相似文献   

9.
Fusarium Head Blight (FHB), or scab, can result in significant crop yield losses and contaminated grain in wheat (Triticum aestivum L.). Growing less susceptible cultivars is one of the most effective methods for managing FHB and for reducing deoxynivalenol (DON) levels in grain, but breeding programs lack a rapid and objective method for identifying the fungi and toxins. It is important to estimate proportions of sound kernels and Fusarium‐damaged kernels (FDK) in grain and to estimate DON levels of FDK to objectively assess the resistance of a cultivar. An automated single kernel near‐infrared (SKNIR) spectroscopic method for identification of FDK and for estimating DON levels was evaluated. The SKNIR system classified visually sound and FDK with an accuracy of 98.8 and 99.9%, respectively. The sound fraction had no or very little accumulation of DON. The FDK fraction was sorted into fractions with high or low DON content. The kernels identified as FDK by the SKNIR system had better correlation with other FHB assessment indices such as FHB severity, FHB incidence and kernels/g than visual FDK%. This technique can be successfully employed to nondestructively sort kernels with Fusarium damage and to estimate DON levels of those kernels. Single kernels could be predicted as having low (<60 ppm) or high (>60 ppm) DON with ≈96% accuracy. Single kernel DON levels of the high DON kernels could be estimated with R2 = 0.87 and standard error of prediction (SEP) of 60.8 ppm. Because the method is nondestructive, seeds may be saved for generation advancement. The automated method is rapid (1 kernel/sec) and sorting grains into several fractions depending on DON levels will provide breeders with more information than techniques that deliver average DON levels from bulk seed samples.  相似文献   

10.
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.  相似文献   

11.
A nondestructive protocol for maize kernel starch sampling was developed, enabling starch preparation from a single kernel for analysis of starch structure while also maintaining the vitality of the seed. To develop the single kernel sampling (SKS) method, maize genotypes varying in starch structure including ae, wx, su2, du and normal in the W64A inbred line were used. Crude endosperm material was removed from the kernel crown, soaked, ground, washed, and dissolved in 90% DMSO. The sample represented ≈10% of the total kernel. Endosperm starch was also isolated from the same genotypes by a standard multikernel isolation (MKI) method. Starches isolated by the two methods were debranched and analyzed by high‐performance size‐exclusion chromatography (HPSEC) and fluorophore‐assisted carbohydrate electrophoresis (FACE). HPSEC and FACE showed similar results for the two sampling methods for degree of polymerization (DP) ≤ 50. We concluded that the material obtained by SKS could be used for identifying amylopectin structural differences among genotypes. Kernel sampling for SKS had no effect on germination, thus plants could be grown for subsequent genetic crosses and analysis. The SKS method may be useful for the screening of populations of maize kernels from genotypes producing novel amylopectin structure, and allow the growth of novel genotypes for further analysis.  相似文献   

12.
The purpose of this study was to examine the drying and grinding characteristics of sprouted and crushed wheat. The four‐day‐germinated wheat kernels were crushed, dried, and ground in a micro hammer mill. The drying kinetics of sprouts were best described by the Page and two‐factor models. The crushing of wheat sprouts before drying decreased the drying time by about half. Sprouting and crushing of wheat sprouts have a significant influence on the grinding process, both on the particle size distribution and on the grinding energy requirements. It was observed that the ground sprouts showed significantly lower values of average particle size compared with the samples of sound kernels. Sprouting caused an increase in the amount of fine particles (<0.2 mm) and a decrease in the mass fraction of coarse particles (>1.0 mm). All values of grinding indices showed that sprouting and crushing significantly reduced the grinding energy requirements. Moreover, sprouting significantly increased the total phenolics content (from 26 to 31%) and antioxidant activity (from 33 to 72%) of wheat kernels. The results showed that sprouting and crushing of sprouts followed by their drying and grinding may provide a practical method for preparing sprouted flour.  相似文献   

13.
Hydration kinetics for sound maize kernels in liquid water, determined by single‐kernel measurements for three different Mexican maize types, yielded water diffusion coefficients ordered as Celaya corn > Toluca corn > Palomero corn, at all temperatures examined. These diffusion coefficients are lower than those reported earlier for maize grains, possibly due to the fact that in the present study damaged kernels were rigorously excluded. The energies of activation determined from the Arrhenius plots were ordered as Palomero corn > Celaya corn = Toluca corn and were similar in value to those reported earlier for other maize types. Damage to the surface of the maize kernels during the hydration experiments occurs at a significant frequency. Even minor surface lacerations can strongly affect the rate of hydration of the kernels. Experiments with maize grains selectively varnished in various parts of their surface show that the entry of water into the kernels occurs predominantly through the pericarp, not through the tip cap, though the tip cap has a higher water inflow per unit area.  相似文献   

14.
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.  相似文献   

15.
Protein content of wheat by near‐infrared (NIR) reflectance of bulk samples is routinely practiced. New instrumentation that permits automated NIR analysis of individual kernels is now available, with the potential for rapid NIR‐based determinations of color, disease, and protein content, all on a single kernel (sk) basis. In the event that the protein content of the bulk sample is needed rather than that of the individual kernels, the present study examines the feasibility of estimating bulk sample protein from sk spectral readings. On the basis of 318 wheat samples of 10 kernels per sample, encompassing five U.S. wheat classes, the study demonstrates that with as few as 300 kernels bulk sample protein content may be estimated by sk NIR reflectance spectra at an accuracy equivalent to conventional bulk kernel NIR instrumentation.  相似文献   

16.
To investigate possible co-occurrences of type B trichothecenes and zearalenone within a Fusarium culmorum-infected wheat harvest lot, kernels were fractionated into six groups by visual criteria. The Fusarium-damaged kernels were subdivided into white, shrunken, and red kernel groups, and the remaining kernels were sorted into healthy, black spotted, and nonspecific groups. The distribution patterns of nivalenol, deoxynivalenol, zearalenone, and ergosterol were determined for possible correlations. Significant correlations between the distribution patterns were found for the mycotoxins and ergosterol for the grouped kernels (r = 0.997-0.999, p < 0.0001). Additionally, remarkably outstanding levels of nivalenol (24-fold more than the mean at 1.16 mg/kg), deoxynivalenol (27-fold more than the mean at 0.16 mg/kg), zearalenone (25-fold more than the mean at 77 microg/kg), and ergosterol (17-fold more than the mean at 13.4 mg/kg) were found in the red kernel group. Further, detailed mycotoxin and ergosterol analyses were carried out on various segments (kernel surface, conidia, bran, and flour) of the red kernels. However, the mycotoxin and ergosterol distribution profiles revealed nonsignificant correlations for these kernel segments, with the exception of deoxynivalenol and nivalenol, which were moderately correlated (r = 0.948, p = 0.035).  相似文献   

17.
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.  相似文献   

18.
An automated single kernel near‐infrared system was used to select kernels to enhance the end‐use quality of hard red wheat breeder samples. Twenty breeding populations and advanced lines were sorted for hardness index, protein content, and kernel color. To determine whether the phenotypic sorting was based upon genetic or environmental differences, the progeny of the unsorted control and sorted samples were planted at two locations two years later to determine whether differences in the sorted samples were transmitted to the progeny (e.g., based on genetic differences). The average hardness index of the harvested wheat samples for segregating populations improved significantly by seven hardness units. For the advanced lines, hardness index was not affected by sorting, indicating little genetic variation within these lines. When sorting by protein content, a significant increase from 12.1 to 12.6% was observed at one location. Purity of the red samples was improved from ≈78% (unsorted control) to ≈92% (sorted samples), while the purity of the white samples improved from 22% (control) to ≈62% (sorted samples). Similar positive results were found for sorting red and blue kernel samples. Sorting for kernel hardness, color, and protein content is effective and based upon genetic variation.  相似文献   

19.
A diode-array system, which measures spectral reflectance from 400 to 700 nm, was used to quantify single wheat kernel color before and after soaking in NaOH as a means of determining color class. Wheat color classification is currently a subjective determination and important in determining the end-use of the wheat. Soaking kernels in NaOH and classifying the soaked kernels with the diode-array system resulted in more difficult-to-classify kernels correctly classified (98.1%) than the visual method of classifying kernels (74.8%). Kernel orientation had a slight effect on correct classification, with the side view correctly classifying more kernels than the dorsal or crease view. The diode-array system provided a means of quantifying kernel color and eliminated inspector subjectivity when determining color class.  相似文献   

20.
Small kernels of soft wheat are sometimes considered to be harder than larger kernels and to have inferior milling and baking characteristics. This study distinguished between kernel size and kernel shriveling. Nine cultivars were separated into large, medium, and small kernels that had no shriveling. Eleven cultivars were separated into sound, moderate, and severely shriveled kernels. Shriveling greatly decreased the amount of flour produced during milling. It adversely affected all other milling quality characteristics (ash content, endosperm separation index, and friability). Shriveled kernels produced flour that had inferior soft wheat baking qualities (smaller cookie diameter and higher alkaline water retention capacity). In contrast, test weight and milling qualities were independent of kernel size. Small, nonshriveled kernels had slightly better baking quality (larger cookie diameter) than larger nonshriveled kernels. Small kernels were softer than large kernels (measured by break flour yield, particle size index, and flour particle size). Small nonshriveled kernels did not have diminished total flour yield potential or other reduced flour milling characteristics. Those observations suggest a possibility of separating small sound kernels from small shriveled kernels to improve flour yield and the need to improve dockage testing estimation techniques to distinguish between small shriveled and small nonshriveled kernels.  相似文献   

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