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1.
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.  相似文献   

2.
Fusarium head blight (FHB) symptoms, single‐kernel deoxynivalenol (DON) levels, and distribution of DON levels among kernels in response to artificial FHB inoculation were investigated in three selected wheat cultivars that had different reported levels of FHB resistance. DON levels were estimated with near‐infrared spectroscopy. The percentages of DON‐containing spikelets per spike of 15.2, 49.7, and 89.1% were significantly different among point‐inoculated spikes of Everest, Karl 92, and Overley, respectively. The percentage of visually Fusarium‐damaged kernels in point‐inoculated Karl 92 and Overley spikes was significantly higher than for Everest. However, the DON‐containing spikelets per spike and visually Fusarium‐damaged kernels values for spray‐inoculated spikes were not significantly different among the three cultivars. In spray‐inoculated spikes, DON levels in kernels ranged from 0 to 291.3 ppm, whereas the variation of DON levels in spikelet positions was random. In contrast, DON levels in spikelets below the inoculated spikelet in point‐inoculated spikes showed marked differences among the three cultivars. Overley had the highest DON accumulation in kernels. This near‐infrared spectroscopy method may be used as a novel way to evaluate wheat cultivars for FHB resistance to toxin accumulation. Other resistance components such as resistance to pathogen infection and resistance to pathogen spread may also be evaluated.  相似文献   

3.
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   

4.
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   

5.
Proteolytic degradation of 50% 1-propanol insoluble (50PI) glutenin of six common wheat cultivars by wheat bug (Eurygaster maura) protease was investigated using reversed-phase HPLC. Wheat at the milk-ripe stage was manually infested with adult bugs. After harvest, bug-damaged kernels were blended (2:1, kernel basis) with undamaged grain of the same cultivar. Samples of ground wheat were incubated in distilled water for different times (0, 30, 60, and 120 min). The incubated whole meal samples were subsequently freeze-dried and stored until analysis. The degree of proteolytic degradation of 50PI glutenin was determined based on the quantity of total glutenin subunits (GS), high molecular weight GS (HMW-GS), and low molecular weight GS (LMW-GS). For ground wheat samples incubated for ≥30 min, 50PI glutenin was substantially degraded as evidenced by a >80% decrease on average in total GS, HMW-GS, and LMW-GS. Some cultivars showed different patterns of glutenin proteolysis as revealed by differences in the ratios of HMW-GS to LMW-GS between sound and bug-damaged samples; a significant decrease in this ratio was found for four cultivars. This evidence, combined with other observations, indicated that there were intercultivar differences in polymeric glutenin resistance to the protease of the wheat bug Eurygaster maura. While the nature of this resistance is unknown, it should be possible to select and develop wheat cultivars with improved tolerance for wheat bug damage. Propanol insoluble glutenin, which corresponds to relatively large glutenin polymers, appears to be an excellent quantitative marker for this purpose.  相似文献   

6.
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   

7.
春季施氮方式对小麦子粒灌浆的调控及其生理机制   总被引：1，自引：0，他引：1  
以河北平原区主栽品种石新733和石麦12为材料,研究了春季节水灌溉条件下,等氮量下春季不同追施方式对小麦子粒灌浆特性的影响及其生理机制。结果表明,在适宜追氮量条件下,与拔节初期一次施氮处理（SF）相比,拔节初期和挑旗期两次施氮（DF）使灌浆期间强、弱势子粒的玉米素（Z）+玉米素核苷（ZR）含量、体积、鲜重和干重增加,但以弱势花子粒的增加幅度较大。DF提高了灌浆期间植株上位叶的可溶蛋白含量、可溶性糖含量和叶绿素含量,增加了灌浆期间的单茎绿叶面积和叶／粒比值;使成熟期供试品种的千粒重、单株穗粒重和抗旱性强的品种石麦12 产量均显著增加,表明春季分次施氮具有改善小麦子粒灌浆和增产的作用。研究还表明,分次施氮增大子粒库容和改善子粒灌浆特性与氮素后移增加子粒的Z+ZR含量有关。植株光合和群体质量的改善是分次施氮下供试品种强弱势子粒,尤其是弱势子粒粒重增加的重要生理基础;施氮方式对不同抗旱性小麦品种光合特性和子粒灌浆的调控效应有所不同。  相似文献   

8.
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.  相似文献   

9.
The feasibility of hyperspectral imaging (HSI) to detect deoxynivalenol (DON) content and Fusarium damage in single oat kernels was investigated. Hyperspectral images of oat kernels from a Fusarium‐inoculated nursery were used after visual classification as asymptomatic, mildly damaged, and severely damaged. Uninoculated kernels were included as controls. The average spectrum from each kernel was paired with the reference DON value for the same kernel, and a calibration model was fitted by partial least squares regression (PLSR). To correct for the skewed distribution of DON values and avoid nonlinearities in the model, the DON values were transformed as DON* = [log(DON)]3. The model was optimized by cross‐validation, and its prediction performance was validated by predicting DON* values for a separate set of validation kernels. The PLSR model and linear discriminant analysis classification were further used on single‐pixel spectra to investigate the spatial distribution of infection in the kernels. There were clear differences between the kernel classes. The first component separated the uninoculated and asymptomatic from the severely damaged kernels. Infected kernels showed higher intensities at 1,925, 2,070, and 2,140 nm, whereas noninfected kernels were dominated by signals at 1,400, 1,626, and 1,850 nm. The DON* values of the validation kernels were estimated by using their average spectra, and the correlation (R) between predicted and measured DON* was 0.8. Our results show that HSI has great potential in detecting Fusarium damage and predicting DON in oats, but it needs more work to develop a model for routine application.  相似文献   

10.
Test weight or bulk density in durum wheat is a physical quality characteristic considered by semolina millers. High test weight values are desirable because they positively influence market grade and price. This study reports data on kernel size features, determined on a sample size of only 25 kernels, replicated three-times, of 16 commercial durum wheat cultivars grown in two locations in southern Italy, to ascertain whether some kernel traits could be related to test weight. For each year and cultivar, the analysis of variance for all of characteristics showed that sample size effect was not significant, enabling the use such a small sample for further investigations. The kernel trait with the highest variation for year was kernel width. The absolute variation of 1994 with respect to 1993 growing season was 30.9%. While kernel weight or volume did not correlate with test weight, a negative association with kernel length (r = -0.61, P = 0.05) and perimeter (r = -0.57, P = 0.05) was found. The kernel shape factors, rectangular aspect ratio (RAR) and circularity shape factor (CSF), showed a positive correlation with test weight (r = 0.51, P = 0.05 and r = 0.59, P = 0.05, respectively). The shape factors were negatively correlated both with kernel length and perimeter and positively with kernel width. A predictive model for test weight (TW = 38.7 + 5.1·ETW, where the estimated test weight [ETW] was computed as individual kernel weight/estimated kernel volume [EKV] ratio), was highly correlated with actual test weight values (r = 0.82, P = 0.001). The effectiveness of the linear model was confirmed when a set of 10 advanced lines of durum wheat were considered, although a slightly lower correlation (r = 0.73, P = 0.05) between actual and predicted test weight values was found. Because the extent of predictability of this approach might be more effective in early-generation lines, the application of the findings in a durum wheat breeding program would be advisable.  相似文献   

11.
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.  相似文献   

12.
The Single Kernel Characterization System (SKCS 4100) measures single kernel weight, width, moisture content, and hardness in wheat grain with greater speed than existing methods and can be calibrated to predict flour starch damage and milling yield. The SKCS 4100 is potentially useful for testing applications in a durum improvement program. The mean SKCS 4100 kernel weight and moisture values from the analysis of 300 individual kernels gave good correlations with 1,000 kernel weight (r2 = 0.956) and oven moisture (r2 = 0.987), respectively. Although significant correlations were obtained between semolina mill yield and SKCS 4100 weight, diameter, and peak force, they were all very low and would be of little use for prediction purposes. Similarly, although there were significant correlations between some SKCS 4100 parameters and test weight and farinograph parameters, they too were small. The SKCS 4100 has been calibrated using either the single kernel hardness index or crush force profile to objectively measure the percentage vitreous grains in a sample with reasonable accuracy, and it correlates well with visual determination. The speed and accuracy of the test would be of interest to grain traders. An imprecise but potentially useful calibration was obtained for the prediction of semolina mill yield using the SKCS 4100 measurements on durum wheat. The SKCS 4100 is useful for some traits such as hardness, grain size and moisture for early‐generation (F3) selection in a durum improvement program.  相似文献   

13.
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   

14.
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   

15.
A better understanding of the impact of fertilizer nitrogen (N) on biomass and N accumulation, and their partitioning into different plant components is needed to optimize crop yield and quality. A field experiment with spring wheat (Triticum aestivum), hulless (Avena nuda), and hulled (Avena sativa) oats was conducted for 3 years in Ottawa, ON, Canada, to determine the crop responses to N addition (0, 75, and 150 kg N ha–1). Biomass, N, and phosphorus (P) accumulation and partitioning into different plant components were examined during the growth season. Lodging score was determined for all crops when it occurred and again at harvest. During the growth season, both hulless and hulled oats and the wheat cultivar showed almost similar patterns of N and P accumulation with maximum contents at late grain filling or at harvest. Plant N concentration was up to 60 g kg–1 during the seedling stage, decreased gradually with advancing growth stages, and was lowest at harvest. Nitrogen treatments significantly increased plant N and P contents. At heading stage, N treatments enhanced dry matter (24%–45%), N (35%–135%), and P (27%–45%) contents in plant components (i.e., culm, leaf, and head), but also enhanced crop lodging, especially in oats. Both hulled and hulless oats had higher total plant N (5%–35%), N : P ratio, and dry‐matter content in leaf (6%–43%) and head (0%–129%) along with higher P (up to 27%) in culm than the wheat cultivar. The wheat cultivar accumulated greater dry matter and higher N content in kernels than both hulled and hulless oats at harvest. Both hulled and hulless oat cultivars exhibited similar lodging susceptibility to N addition (75 or 150 kg N ha–1), produced lower dry weight and lower kernel N, and hence lower grain yield than the wheat cultivar. The larger vegetative dry‐matter accumulation at heading coupled with higher P content in culms under high‐N‐supply conditions may be related to severe lodging in oat cultivars.  相似文献   

16.
Wheat product quality is related to its physicochemical properties and to the viscoelastic properties of the kernel. The aim of this work was to evaluate the viscoelastic properties of individual wheat kernels using the uniaxial compression test under small strain (3%) to create experimental conditions that allow the use of the elasticity theory to explain the wheat kernel viscoelasticity and its relationships to physicochemical characteristics, such as weight tests, size, and ash and protein contents. The following viscoelastic properties of the kernels of hard and soft wheat cultivars at two different moisture contents (original and tempered at 15%) were evaluated: total work (Wt), elastic work (We), plastic work (Wp), and modulus of elasticity (E). There was a significant decrease in Wt as the moisture content increased. In the soft wheat Saturno, Wt decreased 80% (from 0.217 to 0.044 N·mm) as the moisture content increased. Individual wheat kernels at their original moisture content showed higher We than under the tempered condition. Wp increased as the moisture content increased. E decreased as the moisture content increased. The soft wheat Saturno showed the highest decline (54.9%) in E (from 14.18 to 6.39 MPa) as the moisture content increased. There were significant negative relationships between the viscoelastic properties and the 1,000‐kernel weight and kernel thickness. The uniaxial compression test under small strain can be applied to evaluate the viscoelastic properties of individual wheat kernels from different classes and cultivars.  相似文献   

17.
The effect of moisture content (MC) on the glass transition temperature (Tg) of individual brown rice kernels of Bengal, a medium‐grain cultivar, and Cypress, a long‐grain cultivar, was studied. Three methods were investigated for measuring Tg: differential scanning calorimetry (DSC), thermomechanical analysis (TMA), and dynamic mechanical analysis (DMA). Among these methods, TMA was chosen, because it can also measure changes in the thermal volumetric coefficient (β) of the kernel during glass transition. TMA‐measured Tg at similar MC levels for both cultivars were not significantly different and were combined to generate a brown rice state diagram. Individual kernel Tg for both cultivars increased from 22 to 58°C as MC decreased from 27 to 3% wb. Linear and sigmoid models were derived to relate Tg to MC. The linear model was sufficient to describe the property changes in the MC range encountered during rice drying. Mean β values across both cultivars in the rubbery state was 4.62 × 10‐4/°C and was higher than the mean β value of 0.87 × 10‐4/°C in the glassy state. A hypothetical rice drying process was mapped onto the combined state diagram generated for Bengal and Cypress.  相似文献   

18.
When grown with mixtures of nitrate‐nitrogen (NO3‐N) and ammonium‐nitrogen (NH4‐N) (mixed N) spring wheat (Triticum aestivum L.) plants develop higher order tillers and produce more grain than when grown with only NO3. Because similar work is lacking for winter wheat, the objective of this study was to examine the effect of N form on tillering, nutrient acquisition, partitioning, and yield of winter wheat. Plants of three cultivars were grown to maturity hydroponically with nutrient solutions containing N as either all NO3, all NH4, or an equal mixture of both forms. At maturity, plants were harvested; separated into shoots, roots, and grain; and each part analyzed for dry matter and chemical composition. While the three cultivars varied in all parameters, mixed N plants always produced more tillers (by a range of 16 to 35%), accumulated more N (28 to 61%), phosphorus (P) (22 to 80%), and potassium (K) (11 to 89%) and produced more grain (33 to 60%) than those grown with either form alone. Although mixed N‐induced yield increases were mainly the result of an increase in grain bearing tillers, there was cultivar specific variation in individual yield components (i.e., tiller number, kernels per tiller, and kernel weight) which responded to N form. The presence of NH4 (either alone or in the mixed N treatment), increased the concentration of reduced N in the shoots, roots, and grain of all cultivars. The effect of NH4 in either treatment on the concentrations of P and K was variable and depended on the cultivar and plant part. In most cases, partitioning of dry matter, P, and K to the root decreased when NH4 was present, while partitioning of N was relatively unaffected. Changes in partitioning between the shoot and grain were affected by N treatment, but varied according to cultivar. Based on these data, the changes in partitioning induced by NH4 and the additional macronutrient accumulation with mixed N are at least partially responsible for mixed‐N‐induced increases in tillering and yield of winter wheat.  相似文献   

19.
Growing conditions, kernel characteristics, and genetics affect wheat kernel color. As a result, red and white wheats sometimes cannot be differentiated by visual examination. Soaking wheat kernels in a sodium hydroxide solution enhances the difference in color; red wheat turns a darker red, and white wheat turns straw‐yellow. Previously, when NaOH was used for wheat determination of color class, only a visual assessment was made under arbitrary conditions, many times not suitable for field work. In the present work, visible reflectance spectroscopy and visual assessments were used to optimize NaOH (2 mL/g of wheat) soak time (10 min), concentration (5M or 20%), and temperature (60°C). The optimal procedure will provide users who are not laboratory trained with inexpensive, safe procedures to definitively assign wheat color class in the shortest time in field locations. Calibration and prediction of several wheat cultivars using partial least square regression were used to validate the optimal test procedure. The test differentiated even rain‐bleached wheat and cultivars that were difficult to classify visually. No distinct correlation occurred between predicted color value and the number of red genes.  相似文献   

20.
《Cereal Chemistry》2017,94(4):670-676
Wheat grain may be attacked by different insect species. Among them, some Heteroptera species (e.g., Aelia spp. and Eurygaster spp.) reduce wheat breadmaking quality; others, such as Nysius simulans , commonly extract water and nutrients from soy plants. The aim of this study was to assess the effect of N. simulans infestation on breadmaking quality of different bread wheat cultivars. Twelve wheat cultivars (damaged and undamaged by N. simulans ) were studied. Infested grain percentage varied between 51 and 78%, depending on cultivar. Protein and gluten quantity and quality were significantly reduced in damaged flours, as shown by gluten index, solvent retention capacity, and SDS sedimentation index. SDS‐PAGE from water‐extractable proteins evidenced an important proteolytic activity in damaged samples. Dough rheological properties showed a reduced dough viscoelasticity in damaged samples. Microbread specific volume changed from 3.26 cm3/g for samples made with undamaged flour to 2.77 cm3/g for bread made with damaged flour. No evidence for modification in starch properties was found. The infestation by N. simulans reduced wheat breadmaking quality in all cultivars studied, as a result of proteolytic activity occurring after dough hydration. Results suggest that the presence of N. simulans should be considered as a factor affecting wheat crops, mainly those located next to soy crop areas, which is the usual host for this insect.  相似文献   

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