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1.
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   

2.
Single-kernel characterization system (SKCS) 4100 measurements on wheat were reproducible and stable and gave good correlations with relevant reference data, e.g., kernel weight vs. 1,000 kernel weight, kernel hardness vs. particle size index, and kernel moisture vs. oven moisture. Under field conditions at a receiving station in Coleambally (NSW, Australia), the SKCS 4100 operated faultlessly and the reproducibility of the results was as good as in the laboratory. The measurements were completed within the time taken for the normal testing sequence, and the histograms were shown to provide valuable information about the samples that would not otherwise be available. For example, the distribution of moisture contents of individual kernels provides additional information about the samples' potential storage stability. Data on the uniformity of hardness could be interpreted in terms of the potential of the wheat to provide a consistent milling performance. An imprecise (r2 = 0.44) but potentially useful calibration was obtained for the prediction of flour yield under test milling conditions using SKCS 4100 measurements on wheat. A much stronger correlation (r2 = 0.83) was obtained between SKCS data on wheat and the starch damage contents of flours produced on a pilot mill. Thus, the SKCS 4100 has the potential for early generation screening of wheat lines for flour yield and starch damage.  相似文献   

3.
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   

4.
The three major classes of endosperm texture (grain hardness) of soft and hard common, and durum wheat represent and define one of the leading determinants of the milling and end‐use quality of wheat. Although these three genetic classes are directly related to the Hardness locus and puroindoline gene function, much less is known about the kernel‐to‐kernel variation within pure varietal grain lots. Measurement of this variation is of considerable interest. The objective of this research was to compare kernel texture as determined by compression failure testing using endosperm bricks with results of whole‐kernel hardness obtained with the Single Kernel Characterization System 4100 hardness index (SKCS HI). In general terms, the variation obtained with the SKCS HI was of similar magnitude to that obtained using failure strain and failure energy of endosperm brick compression. Objective comparisons included frequency distribution plots, normalized frequency distribution plots, ANOVA model R2, and coefficients of variation. Results indicated that compression testing and SKCS HI similarly captured the main features of texture classes but also reflected notable differences in texture properties among and within soft, hard, and durum classes. Neither brick compression testing nor the SKCS HI may be reasonably expected to correctly classify all individual kernels as to genetic texture class. However, modest improvements in correct classification rate or, more importantly, better classification related to end‐use quality may still be achievable.  相似文献   

5.
This study investigated the suitability of mid‐infrared diffuse reflectance Fourier transform (MIR‐DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties typical of Italian Mediterranean off‐shore environments. Pianosa, Elba and Sardinia are typical of islands from this environment, but developed on different geological substrates. Principal components analysis (PCA) showed that spectra could be grouped according to the soil composition of the islands. PLS full cross‐validation of soil property predictions was assessed by the coefficient of determination (R2), the root mean square error of cross‐validation and prediction (RMSECV and RMSEP), the standard error (SECV for cross‐validation and SEP for prediction), and the residual predictive deviation (RPD). Although full cross‐validation appeared to be the most accurate (R2 = 0.95 for organic carbon (OC), 0.96 for inorganic carbon (IC), 0.87 for CEC, 0.72 for pH and 0.74 for clay; RPD = 4.4, 6.0, 2.7, 1.9 and 2.0, respectively), the prediction errors were considered to be optimistic and so alternative calibrations considered to be more similar to ‘true’ predictions were tested. Predictions using individual calibrations from each island were the least efficient, while predictions using calibration selection based on a Euclidian distance ranking method, using as few as 10 samples selected from each island, were almost as accurate as full cross‐validation for OC and IC (R2 = 0.93 for OC and 0.96 for IC; RPD = 3.9 and 4.7, respectively). Prediction accuracy for CEC, pH and clay was less accurate than expected, especially for clay (R2 = 0.73 for CEC, 0.50 for pH and 0.41 for clay; RPD = 1.8, 1.5 and 1.4, respectively). This study confirmed that the DRIFT PLS method was suitable for characterizing important properties for soils typical of islands in a Mediterranean environment and capable of discriminating between the variations in soil properties from different parent materials.  相似文献   

6.
The 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.  相似文献   

7.
Solvent retention capacity (SRC) was investigated in assessing the end use quality of hard winter wheat (HWW). The four SRC values of 116 HWW flours were determined using 5% lactic acid, 50% sucrose, 5% sodium carbonate, and distilled water. The SRC values were greatly affected by wheat and flour protein contents, and showed significant linear correlations with 1,000‐kernel weight and single kernel weight, size, and hardness. The 5% lactic acid SRC value showed the highest correlation (r = 0.83, P < 0.0001) with straight‐dough bread volume, followed by 50% sucrose, and least by distilled water. We found that the 5% lactic acid SRC value differentiated the quality of protein relating to loaf volume. When we selected a set of flours that had a narrow range of protein content of 12–13% (n = 37) from the 116 flours, flour protein content was not significantly correlated with loaf volume. The 5% lactic acid SRC value, however, showed a significant correlation (r = 0.84, P < 0.0001) with loaf volume. The 5% lactic acid SRC value was significantly correlated with SDS‐sedimentation volume (r = 0.83, P < 0.0001). The SDS‐sedimentation test showed a similar capability to 5% lactic acid SRC, correlating significantly with loaf volume for flours with similar protein content (r = 0.72, P < 0.0001). Prediction models for loaf volume were derived from a series of wheat and flour quality parameters. The inclusion of 5% lactic acid SRC values in the prediction model improved R2 = 0.778 and root mean square error (RMSE) of 57.2 from R2 = 0.609 and RMSE = 75.6, respectively, from the prediction model developed with the single kernel characterization system (SKCS) and near‐infrared reflectance (NIR) spectroscopy data. The prediction models were tested with three validation sets with different protein ranges and confirmed that the 5% lactic acid SRC test is valuable in predicting the loaf volume of bread from a HWW flour, especially for flours with similar protein contents.  相似文献   

8.
The objectives of this study were to investigate the relationship between milling yield and grain hardness. A preliminary study was carried out with 20 samples (both hard and soft wheats) using the Brabender hardness tester (BHT) with two grind settings: one‐step grind (0‐10) and two‐step grind (2‐12: coarse; 0‐8: fine). The two‐step grind was correlated with particle size index, single‐kernel characterization system (SKCS) hardness, break yield, and reduction yield (P < 0.05), whereas there was no correlation with the one‐step grind method. An additional 64 samples were ground with the two‐step grind setting to further validate this method. In terms of the BHT crush profile, no discernible differences were observed between varieties for the coarse grind, whereas for the fine grind, hard wheat gave a higher BHT maximum peak height and shorter grinding time compared with soft wheat. The break and reduction yields were significantly correlated with both BHT and SKCS hardness (P < 0.05). The findings indicated that the BHT method could be used to differentiate for milling yield among the different varieties. Based on the results, two milling yield models were developed, and both gave highly significant correlations between the predicted and Buhler mill break (R2 = 0.791, P < 0.05) and reduction yield (R2 = 0.896, P < 0.05).  相似文献   

9.
Kernel hardness is not a well‐characterized food quality trait in barley. Unlike wheat, not much is known about the effect of barley kernel hardness on food processing. Ten barley genotypes differing in single kernel characterization system hardness index (SKCS‐HI) (30.1–91.2) of dehulled kernels were used to determine the association of barley HI with other physical grain traits and food processing parameters. Thousand kernel weight (TKW) values of 10 genotypes were 29.7–38.1 g. Values for bulk density of grains were 721.1–758.9 kg/m3. Crease width and depth values were 0.9–1.3 mm and 0.4–0.7 mm, respectively. Barley HI showed no significant association with TKW, bulk density, or kernel crease dimensions. Kernel loss due to pearling after 325 sec of abrasion was 28.8–38.4% and showed significant negative correlation with HI (r = –0.87, P < 0.01). Proportion of barley flour particles >106 μm had values of 34.5–42.0%, and starch damage values were 1.8–4.5% among those 10 barley genotypes. HI showed significant positive correlations with both proportion of barley flour particles >106 μm (r = 0.93, P < 0.01) and starch damage (r = 0.93, P < 0.01). Water imbibition of barley kernels and cooked kernel hardness did not show significant correlation with HI.  相似文献   

10.
The single kernel characterization system (SKCS) has been widely used in the wheat industry, and SKCS parameters have been linked to end‐use quality in wheat. The SKCS has promise as a tool for evaluating sorghum grain quality. However, the SKCS was designed to analyze wheat, which has a different kernel structure from sorghum. To gain a better understanding of the meaning of SKCS predictions for grain sorghum, individual sorghum grains were measured for length, width, thickness (diameter), and weight by laboratory methods and by the SKCS. SKCS predictions for kernel weight and thickness were highly correlated to laboratory measurements. However, SKCS predictions for kernel thickness were underestimated by ≈20%. The SKCS moisture prediction for sorghum was evaluated by tempering seven samples with varying hardness values to four moisture levels. The moisture contents predicted by SKCS were compared with a standard oven method and, while correlated, SKCS moisture predictions were less than moisture measured by air oven, especially at low moisture content. Finally, SKCS hardness values were compared with hardness measured by abrasive decortication. A moderate (r = 0.67, P < 0.001) correlation was observed between the hardness measurements. The SKCS predictions of kernel weight and diameter were highly correlated with laboratory measurement. Moisture prediction, however, was substantially lower by the SKCS than as measured by an air oven method. The SKCS should be suitable for measuring sorghum grain attributes. Further research is needed to determine how SKCS hardness predictions are correlated to milling properties of sorghum grain.  相似文献   

11.
Scab (Fusarium head blight) is a fungal disease that has become increasingly prevalent in North American wheat during the past 15 years. It is of concern to growers, processors, and the consumers because of depressed yields, poor flour quality, and the potential for elevated concentrations of the mycotoxin, deoxynivalenol (DON). Both wheat breeder and wheat inspector must currently deal with the assessment of scab in harvested wheat by manual human inspection. The study described herein examined the accuracy of a semi‐automated wheat scab inspection system that is based on near‐infrared (NIR) reflectance (1,000–1,700 nm) of individual kernels. Using statistical classification techniques such as linear discriminant analysis and nonparametric (k‐nearest‐neighbor) classification, upper limits of accuracy for NIR‐based classification schemes of ≈88% (cross‐validation) and 97% (test) were determined. An exhaustive search of the most suitable wavelength pairs for the spectral difference, [log(1/R)λ1 ‐ log(1/R)λ2], revealed that the slope of the low‐wavelength side of a broad carbohydrate absorption band (centered at ≈1,200 nm) was very effective at discriminating between healthy and scab‐damaged kernels with test set accuracies of 95%. The achieved accuracy levels demonstrate the potential for the use of NIR spectroscopy in commercial sorting and inspection operations for wheat scab.  相似文献   

12.
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   

13.
The level of grain hardness of wheat (Triticum aestivum) cultivars profoundly affects milling properties and end-use. We examined grain hardness among a genetically defined set of 83 chromosome 5D homozygous recombinant substitution lines derived from soft wheat cv. Chinese Spring and hard wheat cv. Cheyenne and compared four common methods of measuring wheat grain hardness. Measures of grain hardness included a modified particle size index, Brabender Quadrumat flour milling, near-infrared reflectance (NIR) spectroscopy, and the single-kernel characterization system (SKCS). Duncan's multiple range test was used to group recombinant lines according to parental classes. Quadrumat milling fractions, percent bran and middlings, were well correlated to NIR and SKCS grain hardness, whereas break flour, a traditional measure of grain hardness, was poorly correlated to other hardness measures. NIR and SKCS grain hardness measures provided the greatest and similar mean separations. Both methods identified recombinant lines as being significantly outside either parental class and significantly different from and in between the two parental classes. Between two divergent environments, correlations (r) for Quadrumat bran and middlings percents and NIR and SKCS hardness ranged from 0.83 to 0.94. Analysis of variance indicated that lines differed substantially for hardness, and hardness was highly influenced by environment, albeit consistently, as indicated by low line-location model interaction terms. The results confirmed the presence of major allelic differences assignable to chromosome 5D and suggested the action of minor gene(s). Break flour, in particular, showed strong indications of transgressive segregation independent of the Hardness (Ha) locus. The Perten 4100 SKCS provided the best (most discriminating) measure of the material properties of the wheat endosperm manifested by the action of the Ha locus.  相似文献   

14.
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   

15.
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   

16.
The authentication of rice (Korean domestic rice vs. foreign rice) has been attempted using near‐infrared spectroscopy (NIRS). Two sample sets (n1 = 280 and n2 = 200) were used to obtain calibration equations and the spectral regions used for this study were 500–600 nm, 700–900nm, and 980–2,498 nm. Modified partial least square (MPLS) regression was used to develop the prediction model. The standard error of cross validation (SECV) and the r2 were 0.165 and 0.91 respectively for 1st calibration set and 0.165 and 0.93 for 2nd calibration set respectively. The results of the independent validation (n3 = 80) showed that all of 80 samples were identified correctly. Even though authentication of rice was performed successfully using NIRS, the calibration statistics in this study showed that further effort is needed for implementation of NIRS for authentication of rice for industry purposes.  相似文献   

17.
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18.
Kernel hardness is an important trait influencing postharvest handling, processing, and food product quality in cereal grains. Though well‐characterized in wheat, the basis of kernel hardness is still not completely understood in barley. Kernels of 959 barley breeding lines were evaluated for hardness using the Single Kernel Characterization System (SKCS). Barley lines exhibited a broad range of hardness index (HI) values at 30.1–91.9. Distribution of kernel diameter and weight were 1.7–2.9 mm and 24.9–53.7 mg, respectively. The proportion of hull was 10.2–20.7%. From the 959 breeding lines, 10 hulled spring barley lines differing in HI values (30.1–91.2) were selected to study the associations of HI with proportion of hull, kernel weight, diameter, vitreousness, protein, β‐glucan, and amylose content. Vitreousness, evaluated visually using a light box, showed a clear distinction between hard and soft kernels. Hard kernels appeared translucent, while soft kernels appeared opaque when illuminated from below on the light box. Kernel brightness (L*), determined as an indicator of kernel vitreousness, showed a significant negative correlation (r = –0.83, P < 0.01) with HI. Protein, β‐glucan, amylose content, proportion of hull, kernel weight, and diameter did not show any significant association with HI.  相似文献   

19.
The objective of this research was to analyze the antioxidant capacity directly of water‐extractable nonstarch polysaccharides (NSP) and feruloylated arabinoxylans (WEAX) following their characterization. NSP were isolated from barley, wheat, and wheat fractions (germ, bran, and aleurone). WEAX were extracted only from wheat fractions. Antioxidant capacity of NSP measured with the 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH), 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid (ABTS), and oxygen radical absorbance capacity (ORAC) assays was 24.0–99.0, 40.0–122.0, and 140.0–286.0μM Trolox equivalents (TE)/g, respectively. The antioxidant capacity of WEAX was 75.7–84.0, 58.0–105.0, and 110.0–235.0μM TE/g for those three assays. DPPH and ABTS were highly correlated to xylose content (R2 = 0.85), degree of substitution (R2 = −0.99), total phenolic acids (R2 = >0.73), total phenolic content (TPC) (R2 = >0.78), and ferulic acid content (R2 = >0.86). ORAC was only influenced by TPC (R2 = 0.63). By taking yield and antioxidant capacity into account, NSP would provide about 0.4–4.2, 0.6–5.1, and 2.8–12.0μM TE/g of flour of radical scavenging activity as measured by DPPH, ABTS, and ORAC, respectively, compared with WEAX (0.4–1.0, 0.3–1.3, and 0.6–2.8μM TE/g). Our results suggest that NSP or WEAX may play a role in protection against free radicals in a food matrix and likely in the gastrointestinal tract.  相似文献   

20.
A simple, rapid method that uses a small mechanical rotary device (entoleter) was developed for estimating insect fragment counts in flour caused by hidden, internal‐feeding insects in whole grains of hard red winter and soft red winter wheat. Known counts of preemergent adults, pupae, and larvae of lesser grain borers and rice weevils were blended with 500 g samples of uninfested wheat. The entoleter impeller speed was adjusted based on grain hardness and moisture content to obtain about ≈98% intact and ≈2–2.5% broken kernels in an uninfested sample. The entoleter flung the wheat kernels against a surrounding steel ring. Approximately 70–90% of the insect‐infested kernels, being weaker, released internal insect pieces upon impact. The broken kernels were sieved with number 10 and number 20 sieves to obtain large‐sieved and small‐sieved fractions, respectively. Insect pieces in sieved fractions were counted. The insect piece counts were correlated with the estimated flour fragments (R2 = 0.94). The entoleter method can distinguish samples of grain containing 0, 25, or 75 fragments in 50 g of flour, with greater than 95% confidence. The method can be performed in approximately 5 min per 500 g sample and could potentially be a cost‐effective method that grain handlers can use to inspect wheat loads for detecting insect damage and estimating insect fragments in flour.  相似文献   

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