Application of the Single-Kernel Characterization System to Wheat Receiving Testing and Quality Prediction

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.     

Automated Single‐Kernel Sorting to Select for Quality Traits in Wheat Breeding Lines

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.     

Effect of Single‐Pass and Multipass Milling Systems on Whole Wheat Durum Flour and Whole Wheat Pasta Quality

Single‐pass and multipass milling systems were evaluated for the quality of whole wheat durum flour (WWF) and the subsequent whole wheat (WW) spaghetti they produced. The multipass system used a roller mill with two purifiers to produce semolina and bran/germ and shorts (bran fraction). The single‐pass system used an ultracentrifugal mill with two configurations (fine grind, 15,000 rpm with 250 μm mill screen aperture; and coarse grind, 12,000 rpm with 1,000 μm mill screen aperture) to direct grind durum wheat grain into WWF or to regrind the bran fraction, which was blended with semolina to produce a reconstituted WWF. Particle size, starch damage, and pasting properties were similar for direct finely ground WWF and multipass reconstituted durum flour/fine bran blend and for direct coarsely ground WWF and multipass reconstituted semolina/coarse bran blend. The semolina/fine bran blend had low starch damage and had desirable pasting properties for pasta cooking. WW spaghetti was better when made with WWF produced using the multipass than single‐pass milling system. Mechanical strength was greatest with spaghetti made from the semolina/fine bran or durum flour/fine bran blends. The semolina/fine bran and semolina/coarse bran blends made spaghetti with high cooked firmness and low cooking loss.     

Measurement of Wheat Hardness by Seed Scarifier and Barley Pearler and Comparison with Single‐Kernel Characterization System

A new procedure based on a seed scarifier (SS) for measuring wheat hardness was described and investigated along with methods using a barley pearler (BP) and the single kernel characterization system (SKCS). Hardness measured by SS and BP is expressed as a percentage of kernel weight remaining after abrading and defined as abrasion resistance index (ARI). For a given sample weight, increased abrading time decreased ARI but improved the ability to differentiate variation among samples. The effect of sample moisture was also statistically significant. For improved performance of SS and BP, based on distinct patterns of relationships between surface removal rates and surface removal levels among soft and hard wheats, a combination of parameters that produces ARI values in the range of 80–20, and a run for a set of reference material are recommended. Differences in measured hardness values from SS, BP, and SKCS existed within a wheat group, but they were very much method‐dependent. Nevertheless, all methods were able to differentiate variations between soft and hard wheat groups. Because of low cost, durability, simplicity, repeatability, and aforementioned ability, SS and BP, although limited by lack of standardization and calibration procedures, can still be useful for grain hardness measurement, particularly when and where instruments for contemporary popular methods such as SKCS and near‐infrared reflectance (NIR) spectroscopy are not readily available.     

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

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.     

Moisture Effects on the Prediction Performance of a Single‐Kernel Near‐Infrared Deoxynivalenol Calibration

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.     

Impacts of SSIIa‐A Null Allele on Durum Wheat Noodle Quality

A small increase in amylose content may impact end‐product quality of wheat. The effect of elevated amylose content in durum wheat is not known. We surveyed 255 durum wheat accessions and found two genotypes that lacked the SGP‐A1 protein. These genotypes were crossed to Mountrail, an adapted durum genotype, to create populations segregating for the SSIIa‐Ab null allele. Our goal was to determine the influence of allelic variation at the SSIIa‐A locus on semolina properties and end‐product quality with noodles as a test product. Amylose content increased 3% and cooked noodle firmness increased 2.8 g·cm for the SSIIa‐Ab class compared with the SSIIa‐Aa class for the PI 330546 source, but no change in either trait was detected between classes for the IG 86304 source. The SSIIa‐Ab class had a 10% reduction in flour swelling compared with the SSIIa‐Aa class for both crosses. Grain protein and semolina yield did not differ between SSIIa‐A classes. The relationship between flour swelling power and noodle firmness did not differ between SSIIa‐A allelic classes within a cross. The different results for amylose content and noodle firmness between these sources may be because the two sources of the SSIIa‐Ab null mutation contributed different linkages to the segregating populations. Results show that the SSIIa‐Ab allele could be used to produce durum‐based products that are slightly more firm in texture. However, the effect of the SSIIa‐Ab allele may depend on the source.     

Single Kernel Near‐Infrared Analysis of Tetraploid (Durum) Wheat for Classification of the Waxy Condition

Plant breeding programs are active worldwide in the development of waxy hexaploid (Triticum aestivum L.) and tetraploid (T. turgidum L. var. durum) wheats. Conventional breeding practices will produce waxy cultivars adapted to their intended geographical region that confer unique end use characteristics. Essential to waxy wheat development, a means to rapidly and, ideally, nondestructively identify the waxy condition is needed for point‐of‐sale use. The study described herein evaluated the effectiveness of near‐infrared (NIR) reflectance single‐kernel spectroscopy for classification of durum wheat into its four possible waxy alleles: wild type, waxy, and the two intermediate states in which a null allele occurs at either of the two homologous genes (Wx‐1A and Wx‐1B) that encodes for the production of the enzyme granule bound starch synthase (GBSS) that controls amylose synthesis. Two years of breeders' samples (2003 and 2004), corresponding to 47 unique lines subdivided about equally into the four GBSS genotypes, were scanned in reflectance (1,000–1,700 nm) on an individual kernel basis. Linear discriminant analysis models were developed using the best set of four wavelengths, best four wavelength differences, and best four principal components. Each model consistently demonstrated the high ability (typically >95% of the time) to classify the fully waxy genotype. However, correct classification among the three other genotypes (wild type, wx‐A1 null, and wx‐B1 null) was generally not possible.     

Single‐Kernel Near‐Infrared Analysis for Evaluating Wheat Samples for Fusarium Head Blight Resistance

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.     

Characterization of Durum Wheat Semolina by Means of a Rapid Shear‐Based Method

A rapid shear‐based test (the GlutoPeak test, recently proposed by Brabender) was used to investigate gluten aggregation properties of durum wheat semolina and to relate them to pasta cooking behavior. Thirty semolina samples were characterized by means of the conventional approaches used for pasta‐quality prediction (protein content, gluten index, and alveographic indices). All samples were also analyzed by the GlutoPeak test, obtaining three parameters: maximum peak torque, maximum peak time, and area under the peak. The GlutoPeak indices were significantly correlated with protein content, gluten index, and W alveographic parameter. The cooking quality of pasta obtained from the 30 semolina samples was evaluated by sensory analysis in terms of stickiness, bulkiness, firmness, and overall quality. The GlutoPeak indices were significantly correlated with the sensorial parameters. In comparison with the alveographic test, which is presently the most used rheological approach for semolina characterization, GlutoPeak analysis presents some advantages represented by a smaller amount of sample (9 g), a shorter time (less than 5 min), and the possibility that untrained analysts can carry it out. In addition, following testing with larger sample numbers, the GlutoPeak test has the potential to be used instead of the gluten index as a rapid and reliable approach for medium‐quality semolina characterization.     

Bulk Functionality Diversification by Unsupervised Single‐Kernel Near‐Infrared (SKNIR) Sorting of Wheat

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.     

Near‐Infrared Spectroscopic Evaluation of Single‐Kernel Deoxynivalenol Accumulation and Fusarium Head Blight Resistance Components in Wheat

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.     

Nutritional and Quality Traits of Pasta Made from SSIIa Null High‐Amylose Durum Wheat

Food products that are high in fiber and low in glycemic impact are healthier. Amylose is a form of resistant starch that mimics dietary fiber when consumed. A durum wheat (Triticum durum) line was created that lacks starch synthase IIa (SSIIa) activity, a key enzyme in amylopectin biosynthesis, by identifying a null mutation in ssIIa‐B following mutagenesis of a line that has a naturally occurring ssIIa‐A null mutation. Our objective here was to compare seed, milling, pasta, and nutritional characteristics of the SSIIa null line with a wild‐type control line. The SSIIa null line had increased amylose and grain protein with lower individual seed weight and semolina yield. Refined pasta prepared from the SSIIa null semolina absorbed less water, had increased cooking loss, had a shorter cook time, and was considerably firmer even after overcooking compared with the wild‐type line. Color of the SSIIa null cooked and uncooked pasta was diminished in brightness compared with the wild type. Nutritionally, the SSIIa null pasta had increased calories, fiber, fat, resistant starch, ash, and protein compared with the control line, along with reduced total and available carbohydrates. Pasta made from high‐amylose durum wheat provides a significant nutritional benefit along with enhanced end‐product quality via firmer pasta that resists overcooking.     

Effect of Soft Kernel Texture on the Milling Properties of Soft Durum Wheat

Worldwide, nearly 20 times more common wheat (Triticum aestivum) is produced than durum wheat (T. turgidum subsp. durum). Durum wheat is predominately milled into coarse semolina owing to the extreme hardness of the kernels. Semolina, lacking the versatility of traditional flour, is used primarily in the production of pasta. The puroindoline genes, responsible for kernel softness in wheat, have been introduced into durum via homoeologous recombination. The objective of this study was to determine what impact the introgression of the puroindoline genes, and subsequent expression of the soft kernel phenotype, had on the milling properties and flour characteristics of durum wheat. Three grain lots of Soft Svevo and one of Soft Alzada, two soft‐kernel back‐cross derived durum varieties, were milled into flour on the modified Quadrumat Senior laboratory mill at 13, 14, and 16% temper levels. Samples of Svevo (a durum wheat and recurrent parent of Soft Svevo), Xerpha (a soft white winter wheat), and Expresso (a hard red spring wheat) were included as comparisons. Soft Svevo and Soft Alzada exhibited dramatically lower single‐kernel characterization system kernel hardness than the other samples. Soft Svevo and Soft Alzada had high break flour yields, similar to the common wheat samples, especially the soft hexaploid wheat, and markedly greater than the durum samples. Overall, Soft Svevo and Soft Alzada exhibited milling properties and flour quality comparable, if not superior, to those of common wheat.     

Influence of Soft Kernel Texture on the Flour,Water Absorption,Rheology, and Baking Quality of Durum Wheat

Durum wheat (Triticum turgidum subsp. durum ) production worldwide is substantially less than that of common wheat (T. aestivum ). Durum kernels are extremely hard; thus, most durum wheat is milled into semolina, which has limited utilization. Soft kernel durum wheat was created by introgression of the puroindoline genes via homoeologous recombination. The objective of this study was to determine the effects of the puroindoline genes and soft kernel texture on flour, water absorption, rheology, and baking quality of durum wheat. Soft Svevo and Soft Alzada, back‐cross derivatives of the durum varieties Svevo and Alzada, were compared with Svevo, a hard durum wheat, Xerpha, a soft white winter wheat, and Expresso, a hard red spring wheat. Soft Svevo and Soft Alzada exhibited soft kernel texture; low water, sodium carbonate, and sucrose solvent retention capacities (SRCs); and reduced dough water absorptions similar to soft wheat. These results indicate a pronounced effect of the puroindolines. Conversely, SDS flour sedimentation volume and lactic acid SRC of the soft durum samples were more similar to the Svevo hard durum and Expresso samples, indicating much less effect of kernel softness on protein strength measurements. Alveograph results were influenced by the inherent differences in water absorption properties of the different flours and their genetic background (e.g., W and P were markedly reduced in the Soft Svevo samples compared with Svevo, whereas the puroindolines appeared to have little effect on L ). However, Soft Svevo and Soft Alzada differed markedly for W and L . Soft durum samples produced bread loaf volumes between the soft and hard common wheat samples but larger sugar‐snap cookie diameters than all comparison samples. The soft durum varieties exhibited new and unique flour and baking attributes as well as retaining the color and protein characteristics of their durum parents.     

Mechanical and Physicochemical Characterization of Vitreous and Mealy Durum Wheat Endosperm

The mechanical, physical, and biochemical characteristics of mealy and vitreous endosperm were investigated. Endosperm were obtained from four durum wheat cultivars grown under different nitrogen fertilization designs. The textural properties and the density of the endosperm were measured on hand‐shaped parallelepiped endosperm samples. Endosperm protein content and composition and also gliadin composition were investigated by HPLC. Mechanical tests showed that mealy and vitreous endosperm differed in hardness and vitreousness. Vitreousness increased with nitrogen fertilization supply whereas there was no variation among the different cultivars. Hardness seemed to be linked to genotype and insensitive to nitrogen supply. From this result, we concluded that hardness and vitreousness are not related. Endosperm protein content and gliadin‐to‐glutenin ratio were related to nitrogen supply and increased especially when nitrogen supply was applied at flowering. At the same time, endosperm vitreousness increased. Further biochemical analyses were performed on 270 kernels, mealy or vitreous, hand‐picked from 148 different crops. Results showed that protein content of vitreous endosperm exceeded 9.7% in >90% of the cases. The glia/glu ratio was a less accurate predictor of kernel vitreousness, indicating that, by itself, it cannot account for the change in kernel vitreousness. Endosperm vitreous texture would rise above a threshold content of 9.7% protein within the endosperm.     

Compressive Strength of Wheat Endosperm: Comparison of Endosperm Bricks to the Single Kernel Characterization System

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.     

Physical and Cooking Quality of Spaghetti Made from Whole Wheat Durum

The effects of cultivar on dough properties of ground whole wheat durum, and the effects of cultivar and drying temperature on the physical and cooking quality of spaghetti made from semolina and whole wheat were evaluated. Rankings of cultivars based on dough properties were similar for whole wheat and semolina. Dough made from whole wheat was weak and had poor stability. Whole wheat spaghetti had a rough reddish brown surface compared with the very smooth, translucent yellow color of spaghetti made from semolina. The reddish brown color of whole wheat spaghetti was enhanced by high‐temperature drying (70°C). Mechanical strength and cooking quality of spaghetti made from ground whole wheat or semolina varied with cultivar and with drying temperature. Compared with spaghetti made from semolina, whole wheat spaghetti had lower mechanical strength and cooked firmness and had greater cooking loss. Mechanical strength of whole wheat spaghetti was lower when dried at high temperature (70°C) than at low temperature (40°C). Conversely, the mechanical strength of spaghetti made from semolina was greater when dried at high temperature than at low temperature. Whole wheat and traditional spaghetti dried at high temperature had lower cooking losses than spaghetti dried at low temperature. When overcooked 6 min, firmness of spaghetti made from semolina or whole wheat was greater when dried at high temperature than at low temperature.