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1.
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   

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) 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   

4.
《Cereal Chemistry》2017,94(4):677-682
Deoxynivalenol (DON) levels in harvested grain samples are used to evaluate the Fusarium head blight (FHB) resistance of wheat cultivars and breeding lines. Fourier transform near‐infrared (FT‐NIR) calibrations were developed to estimate the DON level and moisture content (MC) of bulk wheat grain samples harvested from FHB screening trials. Grains in a rotating glass petri dish were scanned in the 10,000–4,000 cm−1 (1,000–2,500 nm) spectral range using a Perkin Elmer Spectrum 400 FT‐IR/FT‐NIR spectrometer. The DON calibration predicted the DON levels in test samples with R 2 = 0.62 and root mean square error of prediction (RMSEP) = 8.01 ppm. When 5–25 ppm of DON was used as the cut‐off to classify samples into low‐ and high‐DON groups, 60.8–82.3% of the low‐DON samples were correctly classified, whereas the classification accuracy of the high‐DON group was 82.3–94.0%. The MC calibration predicted the MC in grain samples with R 2 = 0.98 and RMSEP = 0.19%. Therefore, these FT‐NIR calibrations can be used to rapidly prescreen wheat lines to identify low‐DON lines for further evaluation using standard laboratory methods, thereby reducing the time and costs of analyzing samples from FHB screening trials.  相似文献   

5.
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   

6.
Single kernel moisture content (MC) is important in the measurement of other quality traits in single kernels because many traits are expressed on a dry weight basis. MC also affects viability, storage quality, and price. Also, if near‐infrared (NIR) spectroscopy is used to measure grain traits, the influence of water must be accounted for because water is a strong absorber throughout the NIR region. The feasibility of measurement of MC, fresh weight, dry weight, and water mass of single wheat kernels with or without Fusarium damage was investigated using two wheat cultivars with three visually selected classes of kernels with Fusarium damage and a range of MC. Calibration models were developed either from all kernel classes or from only undamaged kernels of one cultivar that were then validated using all spectra of the other cultivar. A calibration model developed for MC when using all kernels from the wheat cultivar Jagalene had a coefficient of determination (R2) of 0.77 and standard error of cross validation (SECV) of 1.03%. This model predicted the MC of the wheat cultivar 2137 with R2 of 0.81 and a standard error of prediction (SEP) of 1.02% and RPD of 2.2. Calibration models developed using all kernels from both cultivars predicted MC, fresh weight, dry weight, or water mass in kernels better than models that used only undamaged kernels from both cultivars. Single kernel water mass was more accurately estimated using the actual fresh weight of kernels and MC predicted by calibrations that used all kernels or undamaged kernels. The necessity for evaluating and expressing constituent levels in single kernels on a mass/kernel basis rather than a percentage basis was elaborated. The need to overcome the effects of kernel size and water mass on single kernel spectra before using in calibration model development was also highlighted.  相似文献   

7.
Fusarium head blight, induced primarily by Fusarium graminearum, resulted in widespread damage to the Manitoba barley crops of 1993 and 1994, with contamination by deoxynivalenol (DON) and other 8-keto-trichothecenes. Visible Fusarium mold in samples of 1994 barley had little relationship to DON levels in the kernel as determined by gas chromatography-mass spectrometry (GC-MS). While samples of 1993 and 1994 barley showed a weak correlation between the logarithm of DON level and percentage of kernels infected by Fusarium graminearum (r = 0.79 and 0.71, respectively), the latter method is too lengthy and requires too much training for commercial application. A commercial enzyme immunoassay for DON gave results that correlated well with GC-MS methods (r = 0.95 and 0.89, respectively) in samples of 1993 and 1994 barley and afforded a rapid and convenient method for screening. In barley samples from 1994, DON, 15-acetylDON, 3-acetylDON and 3,15-diacetylDON were detected in the approximate ratio of 47:4:1:1. In view of the higher oral toxicities of 15-acetylDON and 3-acetylDON relative to DON, and the unknown oral toxicity of 3,15-diacetylDON, GC-MS assays might be advisable in samples positive for DON from enzyme immunoassay screening.  相似文献   

8.
The effect of proteolytic enzymes, associated with Fusarium head blight, on wheat storage proteins and dough functionality was studied. Fusarium damaged kernels (FDK) and sound kernels were hand-picked from F. graminearum Schwabe and F. avenaceum (Fr.) Sacc. infected samples of bread and durum wheat. Scanning electron microscopy revealed significant degradation of endosperm protein in FDK. Storage proteins from FDK and sound kernels were analyzed by SDS-PAGE, RP-HPLC, and SE-HPLC. Total storage protein was lower in FDK but no significant qualitative differences in protein were detected by either RP-HPLC or SDS-PAGE. SE-HPLC was used to follow the hydrolysis of wheat storage protein by proteolytic enzymes found in FDK and a pure culture of F. graminearum. Selective inhibition of proteolytic activity by p-chloromercuribenzoate, and not soybean trypsin inhibitor or iodoacetic acid, suggests that the F. graminearum protease is an alkaline protease. Farinograph and extensigraph curves showed that the presence of FDK decreased dough consistency and resistance to extension. The presence of FDK in flour resulted in a substantial reduction in loaf volume. The loss of dough functionality and loaf volume potential was attributed to the presence of fungal proteases.  相似文献   

9.
Near-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.  相似文献   

10.
《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   

11.
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   

12.
The potential of VIS‐NIR spectroscopy as a rapid screening method for resistance of Fusarium‐inoculated oats to replace the costly chemical measurements of deoxynivalenol (DON) was investigated. Partial least squares (PLS) regression was conducted on second‐derivative spectra (400–2,350 nm) of 166 DON‐contaminated samples (0.05–28.1 ppm, mean = 13.06 ppm) with separate calibration and test set samples. The calibration set had 111 samples, and the test set had 55 samples. The best model developed had three PLS components and a root mean square error of prediction (RMSEP) of 3.16 ppm. The residual predictive deviation (RPD) value of the prediction model was 2.63, an acceptable value for the purpose of rough screening. Visual inspection and the VIS spectra of the samples revealed that high‐DON samples tended to be darker in color and coarser in texture compared with low‐DON samples. The second‐derivative spectra showed that low‐DON samples tended to have more water and fat content than high‐DON samples. With an RMSEP value of 3.16 and RPD of value of 2.63, it seems possible to use VIS‐NIR spectroscopy to semiquantitatively estimate DON content of oats and discard the worst genotypes during the early stages of screening.  相似文献   

13.
When conservation tillage is practised in agriculture, plant residues remain on the soil surface for soil protection purposes. These residues should be widely decomposed within the following vegetation period as microbial plant pathogens surviving on plant litter may endanger the currently cultivated crop. Important soil-borne fungal pathogens that preferably infect small grain cereals belong to the genus Fusarium. These pathogens produce the mycotoxin deoxynivalenol (DON), a cytotoxic agent, in infected cereal organs. This toxin frequently occurs in cereal residues like straw. So far it is unclear if DON degradation is affected by members of the soil food web within decomposing processes in the soil system. For this purpose, a microcosm study was conducted under controlled laboratory conditions to investigate the degradation activity of the earthworm species Lumbricus terrestris when exposed to Fusarium-infected wheat straw being contaminated with DON.Highly Fusarium-infected and DON-contaminated straw seemed to be more attractive to L. terrestris because it was incorporated faster into the soil compared with straw infected and contaminated at low levels. This is supported by a greater body weight gain (exposure time 5 weeks) and smaller body weight loss (exposure time 11 weeks) of L. terrestris, respectively, when highly contaminated straw was offered for different time periods.Furthermore, L. terrestris takes part in the efficient degradation of both Fusarium biomass and DON occurring in straw in close interaction with soil microorganisms. Consequently, earthworm activity contributes to the elimination of potentially infectious plant material from the soil surface.  相似文献   

14.
Fusarium head blight (FHB) is one of the major diseases of wheat (both common and durum wheat) caused by various fungi including Microdochium nivale and different Fusarium species. Most of the Fusarium species associated with FHB (mainly F. graminearum, F. culmorum and F. sporotrichioides), under favourable environmental conditions, can produce various toxic secondary metabolites (mycotoxins) that can contaminate grains. The major Fusarium mycotoxins that can occur in wheat and derived products are deoxynivalenol, nivalenol, T‐2 and HT‐2 toxins, and zearalenone. Processing has generally significant effects on the levels of mycotoxins in the final products. Deoxynivalenol is typically concentrated in the bran coat which is removed in the production of semolina; consequently, a consistent reduction of deoxynivalenol levels has been observed during each of the processing steps, from raw durum wheat to pasta production. To allow monitoring programs and protect consumers' health, several analytical methods have been developed for Fusarium mycotoxins, based on chromatographic or immunometric techniques. The European Union has established maximum permitted levels for some Fusarium mycotoxins in cereals and cereal‐based products (including unprocessed durum wheat, bran, wheat flour, and pasta). Recommendations for the prevention and reduction of Fusarium mycotoxins contamination in cereals based on identification of critical risk factors and crop management strategies have been published by the Codex Alimentarius and the European Commission.  相似文献   

15.
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   

16.
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   

17.
The Fusarium mycotoxins deoxynivalenol (DON) and 3-acetyl-deoxynivalenol (3-acDON) were determined in grain samples from naturally infected and Fusarium culmorum inoculated plants in field experiments in Norway during 1992–1996. The mean DON content in trials with inoculated plants was 11.8 μg/g in spring oats, 11.3μg/g in winter wheat, 28.9 μg/g in spring wheat and 31.4 μg/g in spring barley. In the natural infection trials the mean DON content was 0.32 μg/g in spring oats, 0.22μg/g in winter wheat, 1.48μg/g in spring wheat and 0.54 μg/g in spring barley. Only small differences in DON content were observed among cultivars, and significant differences were found only in winter wheat in the inoculation trials, and in spring wheat in the natural infection trials. A significant correlation was observed between the 3-acDON and DON contents in the inoculated trials in all grain species, the mean ratio of 3-acDON to DON ranging from 0.011 in wheat to 0.071 in oats.  相似文献   

18.
施氮对不同种植密度下夏玉米产量及子粒灌浆的影响   总被引：36，自引：5，他引：36  
以夏玉米杂交种郑单958为材料,在不同种植密度(52500、67500、82500株/hm2)及不同供氮水平(0、120、240、360kg/hm2)下对玉米子粒产量、产量构成、植株地上部干物质积累、子粒灌浆动态及灌浆过程中的物质代谢状况进行了研究。结果表明,不同施氮水平及种植密度下子粒产量的差异主要是由穗粒数所决定。产量及穗粒数的形成与植株地上部干物质积累密切相关,施氮可明显促进植株地上部干物质积累量的增加。穗顶部与中下部子粒的灌浆动态及物质代谢具有明显的不同,授粉后5～20d,顶部子粒灌浆体积、干重、灌浆速率、总可溶性糖、蔗糖、淀粉含量均明显低于中下部子粒;同化物供应的差异是导致顶部与中下部子粒发育差异的一个重要原因。顶部子粒灌浆体积、干重、总糖、淀粉含量施氮处理高于不施氮处理;施氮可明显促进同化物的积累及向顶部子粒的供应,促进顶部子粒灌浆,减少败育,增加有效粒数,提高产量。  相似文献   

19.

Samples of winter wheat (n =84), winter rye (46) and barley (29) were collected from the larger family farms and from partnerships in Lithuania just after the 1998 harvest. The number of samples collected from each region was proportional to the amount of grain produced in it. The levels of the Fusarium toxins deoxynivalenol (DON), 3-acetyl-DON, 15-acetyl-DON, nivalenol (NIV), fusarenon-X (4-acetyl-NIV), T-2 toxin, HT-2 toxin, 4,5-diacetoxyscirpenol (DAS), 1,5-monoacetoxyscirpenol (MAS) and scirpentriol in the grain were determined by gas chromatography with mass-selective detection (GC-MS). DON was most often detected in the wheat and rye samples and NIV in the barley samples. The concentrations found were lower than those causing acute or chronic toxic effects in livestock or humans. No fusarenon-X or 15-acetyl-DON was detected, and only small amounts of other trichothecenes were present. Climatic conditions in Lithuania in the summer of 1998 were slightly cooler and wetter than the average for the 1992-1996 but were close to the norm. Because the samples analysed were representative of grain produced for the market in seasons with normal weather, trichothecene contamination of grain from large family farms and partnerships would not be expected to be a problem in most years.  相似文献   

20.
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   

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