Single Kernel Characterization System Research Articles

Genetic analysis of grain protein content, grain hardness and dough rheology in a hard×hard bread wheat progeny

Abstract Kernel texture (hard vs soft grain) and more subtle within-class variations are known to have a large influence on end-use properties, mostly through the proportion of damaged starch and subsequent water requirement. In this study, quantitative trait loci (QTL) affecting kernel texture and dough rheology in a progeny from a cross between two ‘medium-hard’ wheat cultivars were identified and compared to the QTL locations for both traits. One hundred and sixty-five F 7 recombinant inbred lines were studied in three environments in 1999. Kernel texture was estimated by both near infrared reflectance (NIR: Hard NIR ) and the single-kernel characterisation system (SKCS: Hard SKCS ). Dough rheology, taken as a predictor of end-use quality, was estimated by the empirical parameters measured by Chopin alveograph. The genetic map for this population consisted of 254 loci quite evenly distributed over the wheat genome. Considering only the QTL which were stable (detected in the three locations) and robust (through bootstrap resampling), five genomic regions were found to influence Hard NIR , but only two of them are significant for Hard SKCS , which was probably due to a less representative measure of the phenomenon. Eight QTLs were found for rheological traits. Some QTLs for dough rheology co-located with those for hardness or grain protein content, particularly on chromosomes 3A and 5B and close to the unlinked marker Xgwm130 . According to trait, individual QTLs explained from 5.4 to 26.6% of the phenotypic variation and when taken together up to 46.0% of the variation.

Effect of Multiple Copies of Puroindoline Genes on Grain Softness

End use quality in wheat (Triticum aestivum L.) is primarily determined by grain hardness or texture. The puroindoline genes Pina and Pinb are the main components of the 15‐kDa friabilin protein, which is associated with kernel softness. Pina and Pinb are expressed in diploid wheat species but are silent in tetraploid wheat. The active puroindolines in hexaploid, or bread wheat, were derived from Aegilops tauschii Coss., the D genome diploid donor. The focus of this study was to incorporate active puroindoline genes into the A and B genomes of bread wheat and analyze their impact upon grain softness. Functional copies of Pina and Pinb in disomic substitution lines of T. monococcum L. chromosome 5Am for 5A of T. aestivum and 5Ss of Aegilops searsii Feldman & Kislev ex K. Hammer for 5B of T. aestivum were used to produce lines that contained four copies (5Am, 5D; 5Ss, 5D) and six copies (5Am, 5Ss, 5D) of the puroindolines. There was a direct correlation in grain softness with the increase in copy number of the puroindolines. Northern blots showed increased expression of both Pina and Pinb Extraction of TX114 soluble proteins indicated that levels of both proteins were also increased. Single kernel characterization system (SKCS) analysis showed a decrease in kernel hardness by approximately 10 points below the value of 71 for ‘Chinese Spring’ (CS) for each additional copy of Pina and Pinb added. These results indicate that increasing the functional copy number of the puroindolines can impact grain softness in bread wheat.

Effects of kernel hardness and moisture content on wheat breakage in the single kernel characterisation system

The particle size distribution (psd) produced by breakage of wheat in the Perten Single Kernel Characterisation System (SKCS) was measured using sonic sifting, for a range of wheat varieties, kernel sizes and moisture contents. At moisture contents of 16% wb, the psd produced by the SKCS was very evenly spread over the range 106–3350 μm, with the average particle size much greater than would result from roller milling. Hard wheats gave slightly smaller average particle sizes in the broken material (this was unexpected and contrasts with First Break roller milling, for which hard wheats give larger output particles than soft wheats), but the variation in psd among different wheat varieties was surprisingly small. This indicates that the SKCS exerts a very positive breakage action on wheat grains, giving similar degrees of breakage to kernels of different hardness. The reported hardness index therefore depends primarily on the crushing force profile or energy to grind, and is not confounded by differences in the extent of breakage achieved. Kernel size similarly gave little difference in the output psd. The effect of increasing the moisture content from 9 to 17% wb was to increase the average output particle size greatly; moist kernels do not break so readily in the SKCS.

Puroindoline Genotype of the U.S. National Institute of Standards & Technology Reference Material 8441, Wheat Hardness

ABSTRACTGrain hardness (kernel texture) is of central importance in the quality and utilization of wheat (Triticum aestivum L.) grain. Two major classes, soft and hard, are delineated in commerce and in the Official U.S. Standards for Grain. However, measures of grain hardness are empirical and require reference materials for instrument standardization. For AACC Approved Methods employing near‐infrared reflectance (NIR) and the Single Kernel Characterization System (39‐70A and 55‐31, respectively), such reference materials were prepared by the U.S. Dept. of Agriculture Federal Grain Inspection Service. The material was comprised of genetically pure commercial grain lots of five soft and five hard wheat cultivars and was made available through the National Institute of Standards and Technology (SRM 8441, Wheat Hardness). However, since their establishment, the molecular‐genetic basis of wheat grain hardness has been shown to result from puroindoline a and b. Consequently, we sought to define the puroindoline genotype of these 10 wheat cultivars and more fully characterize their kernel texture through Particle Size Index (PSI, Method 55‐30) and Quadrumat flour milling. NIR, SKCS, and Quadrumat break flour yield grouped the hard and soft cultivars into discrete texture classes; PSI did not separate completely the two classes. Although all four of these methods of texture measurement were highly intercorrelated, each was variably influenced by some minor, secondary factors. Among the hard wheats, the two hard red spring wheat cultivars that possess the Pina‐D1b (a‐null) hardness allele were harder than the hard red winter wheat cultivars that possess the Pinb‐D1b allele based on NIR, PSI, and break flour yield. Among the soft wheat samples, SKCS grouped the Eastern soft red winter cultivars separate from the Western soft white. A more complete understanding of texture‐related properties of these and future wheat samples is vital to the use and calibration of kernel texture‐measuring instruments.

Evaluation of malting barley quality using exploratory data analysis. II. The use of kernel hardness and image analysis as screening methods

This paper presents an exploratory investigation of the use of image analysis and hardness analysis of barley kernels for characterisation and prediction of malting quality. A sample set of fifty barley samples representing 15 spring barley and 10 winter barley varieties grown at two locations in Denmark was used. The samples were micro-malted and mashed and analysed for 13 quality parameters according to the official methods of the European Brewery Convention. A sub-sample of the barley samples was analysed on two different single kernel instruments: (1) Foss Tecator GrainCheck was applied for non-destructive recording of single kernel size and shape (width, length, roundness, area, volume and total light reflectance) and (2) Perten Single Kernel Characterization System 4100 was applied for single kernel hardness and weight determinations. The eight variables from these single seed analyses have been used in two different ways, either as means and standard deviations, or as appended histogram spectra representing 250 kernels from each bulk sample. By the two methods, it has been possible to obtain reasonable Partial Least Squares Regression (PLSR) models for the structural and physical part of the malting quality complex associated to malt modification, but it was as expected impossible to predict the biochemical parameters associated with nitrogen chemistry and enzymatic power. The best model was achieved for (1→3, 1→4)-β-D-glucan in barley. The hardness of the barley kernels is by far the most important variable for describing malting performance. The additional use of the morphological data as acquired by fast non-destructive image analysis, however, also reveals some malting quality information by improving the calibration models based on hardness alone. The brightness of the kernels is by far the most important GrainCheck variable but also kernel size and shape is associated to malting performance. In general, the utilisation of the single kernel readings (used as histogram spectra), compared to sample mean and standard deviation, did not provide additional information for an improved prediction of the malting quality parameters.

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

ABSTRACTReflectance 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.

Development of Nondestructive Screening Methods for Single Kernel Characterization of Wheat

ABSTRACTThe development of nondestructive screening methods for single seed protein, vitreousness, density, and hardness index has been studied for single kernels of European wheat. A single kernel procedure was applied involving, image analysis, near‐infrared transmittance (NIT) spectroscopy, laboratory density determination, single kernel characterization system (SKCS), and finally Kjeldahl protein determination on the crushed single kernels. Single kernel NIT spectroscopy showed excellent ability to determine protein content, and some ability for determination of single kernel vitreousness. Nondestructive determination of single kernel density, either based on NIT spectroscopy or based on image analysis and kernel weight, needs to be further improved for practical use. The use of SKCS hardness index as a true single kernel hardness reference in a NIT prediction model resulted in a poor predictability. However, by applying an averaging approach, in which single seed replicate measurements are mathematically simulated, a very good NIT prediction model was achieved. This suggests that the single seed NIT spectra contain hardness information, but that a single seed hardness method with higher accuracy is needed to achieve a good NIT prediction model for single kernel hardness.

Expression of the gamma-zein protein of maize in seeds of transgenic barley: effects on grain composition and properties.

A cDNA clone encoding the gamma-zein protein of maize was expressed in developing grain of barley using the starchy endosperm cell-specific promoter from the wheat Glu-1D-1 (HMW subunit 1Dx5) gene. Seven transgenic lines were recovered from 226 bombarded immature embryos, of which two were sterile and four tetraploid, while five were shown to express the gamma-zein protein based on western blotting. Southern blot analysis showed the presence of between about three and twelve transgene insertions. Detailed comparative studies of five null and five homozygous transformed sub-lines from transgenic line A showed that gamma-zein accounted for over 4% of the total prolamin fraction, corresponding to about 1.9% of the total grain N. Comparison of the proteins present in the gel protein fraction demonstrated that the gamma-zein was incorporated into polymers, as in maize. However, there was no effect on grain hardness measured using the Perten Single Kernel Characterisation System or on the vitreousness measured by visual inspection. This contrasts with the situation in maize where a clear association with vitreousness has been reported.

Estimating Barley Character for Shochu Using a Single Kernel Characterization System (SKCS)

The SKCS (Single Kernel Characterization System) is a device for measuring the physical properties of whole grain. Using the SKCS, the physical properties of barley produced in Australia in 2001 were examined in relation to their suitability for shochu production (shochu is a Japanese clear distilled beverage with an alcohol content of ˜25%). The hardness of whole grain showed a positive correlation with pearling, but negative correlations with both broken kernel ratio and conversion power. Softer endosperms facilitated crushing and fermentation (i.e. fermentation potential). From these correlations, quality characteristics of barley for shochu could be deduced. The measurement of barley hardness with the SKCS is important in the evaluation of barley for shochu production and the hardness of the whole grain could be used as one indicator in the evaluation of barley. The procedure can be carried out locally and rapidly.

AUTOMATED DETECTION OF INTERNAL INSECT INFESTATIONS IN WHOLE WHEAT KERNELS USING A PERTEN SKCS 4100

The wheat industry is in need of an automated, economical, and rapid means of detecting whole wheat kernelswith internal insect infestation. The feasibility of the Perten Single Kernel Characterization System (SKCS) to detect internalinsect infestations was studied. The SKCS monitors compression force and electrical conductance as individual kernels arecrushed. Samples of hard red winter (HRW) wheat and soft red winter (SRW) wheat infested with rice weevil [Sitophilus oryzae(L.)] and lesser grain borer [Rhyzopertha dominica (F.)] were run through the SKCS and the conductance/force signals savedfor post-run processing. Algorithms were developed to detect kernels with live internal insects, kernels with dead internalinsects, and kernels from which insects have emerged. The conductance signal was used to detect live infestations and theforce signal for dead and emerged infestations. Live insect detection rates were 24.5% for small-sized larvae, 62.2% formedium-sized larvae, 87.5% for large-sized larvae, and 88.4% for pupae. The predicted, and observed, false positive (soundkernels classified as infested) rate was 0.01%. Dead insect detection rates were 60.7% for large-sized larvae, 65.1% forpupae, and 72.6% for kernels where the insect emerged. The false positive rate of the dead insect detection algorithm rangedfrom 0.2% for SRW to 0.5% for HRW. In all cases, insect detection rates were higher for rice weevil than lesser grain borer.The classification algorithms were robust for a wide range of moisture contents.

Early expression of grain hardness in the developing wheat endosperm.

Seeds from near-isogenic hard and soft wheat lines were harvested at regular intervals from 5 days post-anthesis to maturity and examined for hardness using the single kernel characterisation system (SKCS). SKCS analysis revealed that hard and soft lines could be distinguished from 15 days post-anthesis (dpa). This trend continued until maturity where the difference between the hard and soft lines was most marked. SKCS could not be applied to the small 5- and 10-dpa wheat kernels. Fresh developing endosperm material was examined using light microscopy and no visible differences between the cultivars were detected. When air-dried material was examined using scanning electron microscopy (SEM) differences between soft and hard lines were visible from as early as 5 dpa. Accumulation of puroindoline a and puroindoline b was investigated in developing seeds using both Western blotting and ELISA. Low levels of puroindoline a could be detected in the soft cultivar from 10 dpa, reaching a maximum at 32 dpa. In the hard cultivar, puroindoline a levels were negligible throughout grain development. Puroindoline b accumulates in both the soft and hard cultivars from 15 dpa, but overall contents were higher in the soft cultivar. These findings indicate that endosperm hardness is expressed very early in developing grain when few starch granules and storage proteins were deposited in the endosperm cells. Further, the near-isogenic soft and hard Heron lines could be differentiated by SEM at a stage in development when the accumulation of puroindolines could not be detected by the methods used in this study.

Single Kernel Protein Variance Structure in Commercial Wheat Fields in Western Kansas

This research was undertaken to quantify the structure of protein variation in a commercial hard red winter (HRW) wheat (Triticum aestivum L.) production system. This information will augment our knowledge and practices of sampling, segregating, marketing, and varietal development to improve uniformity and end-use quality of HRW wheat. The allocation of kernel protein variance to specific components in southwestern Kansas was performed by a hierarchical sampling design. Sources of variability included Field, Plot (plots within a field), Row (rows within a plot), Plant (plants within a row), Head (heads within a plant), Position (spikelets at a specific position on a head), Spikelet (spikelets within a position), and Kernel (kernels within a spikelet). Individual kernels (10 152) were collected from 46 fields planted to one of four cultivars: Jagger, 2137, Ike, or TAM 107. Kernels were evaluated for protein concentration by a single kernel characterization system equipped with a diode array near-infrared (NIR) spectrometer. For the cultivars 2137 and Ike, all sources of variability except Spikelet were statistically significant (P < 0.05). For Jagger, all sources except Row were significant and for TAM 107, variation attributed to Field and Plant were not significant. Field and Plot sources of variability contributed the greatest amount of variance within the hierarchy for Jagger, 2137, and Ike. For TAM 107, Plot was the greatest source of variability. The least squares means were calculated for the fixed effect Position, Jagger, Ike, and 2137 showed a significant protein gradient in which the highest protein concentration occurred at the base of the head and the lowest protein content at the top. For TAM 107, the greatest protein content was found at the base. Results of this study provide a benchmark for future efforts to improve wheat consistency through breeding and crop management. The protein variance structure described during this study also defines practical limits for managing and marketing protein content in HRW.

Hordoindolines are associated with a major endosperm-texture QTL in barley (Hordeum vulgare).

Endosperm texture has a tremendous impact on the end-use quality of wheat (Triticum aestivum L.). Cultivars of barley (Hordeum vulgare L.), a close relative of wheat, also vary measurably in grain hardness. However, in contrast to wheat, little is known about the genetic control of barley grain hardness. Puroindolines are endosperm-specific proteins found in wheat and its relatives. In wheat, puroindoline sequence variation controls the majority of wheat grain texture variation. Hordoindolines, the puroindoline homologs of barley, have been identified and mapped. Recently, substantial allelic variation was found for hordoindolines among commercial barley cultivars. Our objective was to determine the influence of hordoindoline allelic variation upon grain hardness and dry matter digestibility in the 'Steptoe' x 'Morex' mapping population. This population is segregating for hordoindoline allele type, which was measured by a HinA/HinB/Gsp composite marker. One-hundred and fifty lines of the 'Steptoe' x 'Morex' population were grown in a replicated field trial. Grain hardness was estimated by near-infrared reflectance (NIR) and measured using the single kernel characterization system (SKCS). Variation attributable to the HinA/HinB/Gsp locus averaged 5.7 SKCS hardness units (SKCS U). QTL analysis revealed the presence of several areas of the genome associated with grain hardness. The largest QTL mapped to the HinA/HinB/Gsp region on the short arm of chomosome 7 (5H). This QTL explains 22% of the SKCS hardness difference observed in this study. The results indicate that the Hardness locus is present in barley and implicates the hordoindolines in endosperm texture control.

Identification and Characterization of Near‐Isogenic Hard and Soft Hexaploid Wheats

A complete understanding of the physical–chemical mechanism and underlying genetic control of wheat (Triticum aestivum L.) endosperm texture will contribute to defining optimal grain utilization while assisting the breeding and development of new cultivars. World trade in wheat grain primarily is based on the two main market classes, “soft” and “hard,” which are mostly determined by the expression of the puroindoline genes at the Hardness (Ha) locus. Here we identify and characterize new genetic stocks (near isogenic lines, NILs) in four different genetic backgrounds (20 NILs total, nine hard and 11 soft). Methods included identifying homogenous or mixed texture lines by Single Kernel Characterization System and Near‐Infrared Reflectance Spectroscopy. Puroindoline genes and Ha alleles were determined through nucleic acid sequence analysis. The four different genetic sources for NILs were (i) accessions of ‘Gamenya’ cultivar which were physical mixtures of hard and soft types, (ii) existing near‐isogenic lines from the cultivars Heron and Falcon, (iii) advanced‐generation backcross lines involving ‘Paha’ and ‘Early Blackhull,’ and (iv) ‘Nugaines’ and ‘Early Blackhull Derivative’. The NILs reported here provide new genetic materials for the study of wheat grain texture and the effect of puroindolines and the Hardness gene on end‐use quality. Two of the four sets of NILs possess the Gly‐46 to Ser‐46 Pinb‐D1b hardness allele which has not been previously available in NILs. The results corroborate a model of wheat grain texture that identifies two major hardness classes, as opposed to one that accommodates intermediate texture classes such as “semi‐hard” and “medium‐soft.” A direct role of the puroindoline proteins in conferring soft grain phenotype is supported; conversely, no genetic basis for intermediate hardness was found. Rather, intermediate hardness resulted from mixtures of the soft and hard classes.

Prevalence of Puroindoline Grain Hardness Genotypes among Historically Significant North American Spring and Winter Wheats

Grain hardness (“hard” or “soft” kernel texture) is the single most important trait in determining the utilization and marketing of wheat (Triticum aestivum L.). Puroindoline a and b proteins represent the molecular basis for this trait. This study surveyed the prevalence of puroindoline hardness mutations (alleles) among North American spring and winter wheat varieties with emphasis on those that are historically important. Each variety was assessed for kernel texture using the Single Kernel Characterization System; Hardness alleles were defined by puroindoline gene sequence and the presence or absence of puroindoline a protein on polyacrylamide gels. A total of 90 spring wheats were examined: nine were soft and possessed wild‐type (“soft”) puroindoline sequences, 10 were mixed hardness, and the remaining 71 were uniformly hard. Of these hard spring wheats, 18 carried the Pina‐D1b hardness allele (null for puroindoline a protein), 47 the Pinb‐D1b allele (Gly‐46–Ser‐46), and four the Pinb‐D1c allele (Leu‐60–Pro‐60). Two hard spring wheats possessed a new allele, designated Pinb‐D1e, which involves a single nucleotide change in Trp‐39 to a “stop” codon. Lastly, among the spring wheats, a new hardness allele was found in the hard component of the variety ‘Utac’ which was mixed. This allele, Pinb‐D1f, also involved a single nucleotide change such that Trp‐44 became a “stop” codon. A total of 62 winter wheat varieties were examined, of which five were soft and three were of mixed hardness. Of interest, the three mixed hardness wheats were ‘Turkey’, ‘Kharkof’, and ‘Weston’. The hard component of each carried the Pinb‐D1b allele. Of the 54 remaining wheats, all of which were hard, all but two carried this same Pinb‐D1b allele. ‘Chiefkan’ winter wheat carried the same Pinb‐D1e allele as ‘Canadian Red’ and ‘Gehun’ spring wheats. ‘Andrews’ hard red winter wheat possessed a new allele, designated Pinb‐D1g, which was a single nucleotide change in Cys‐56 to a “stop” codon. In conclusion, hard grain phenotype results from one of various mutations in either of the puroindoline proteins. To‐date, seven hardness alleles have been discovered and characterized in hexapoid wheat. All but one occur in the puroindoline b gene coding sequence and result from single nucleotide changes. These molecular markers are useful in characterizing lineages and analyzing ancestral relationships.

The hardness locus in Australian wheat lines

The genetic factors that determine grain hardness in Australian wheat (Triticum aestivum L.) germplasm were investigated by studying the grain from 4 crosses (160–180 lines per cross). Although not all the crosses were between hard and soft wheats, the doubled haploid lines derived from the crosses showed significant variation in hardness as assessed either by the Single-Kernel Characterisation System (SKCS 4100) or the scanning electron microscopy appearance of cut surfaces. The wheat cultivars used in the study were Cranbrook, Halberd, CD87, Katepwa, Sunco, Tasman, Egret, and Sunstar and of these only Egret is normally regarded as soft. The quantitative information from the SKCS 4100 was integrated into the genetic map information established for the Cranbrook Halberd, CD87 Katepwa, and Sunco Tasman crosses. For the Egret Sunstar cross, the limited information available from the positioning of genetic markers was used to specifically examine the linkage between the hardness trait and the Pina-D1 (puroindoline) genetic locus on 5DS, and very close linkage was established. The Egret Sunstar cross was also used to develop a more rigorous rheologically based analysis of the raw SKCS crush response data. In addition, the cut surface of the lines was analysed and most (98%) of the samples showed a genetic linkage between the appearance of the cut surface (related to vitreousness) and the SKCS hardness index. Among the other crosses only Cranbrook Halberd showed linkage of the hardness trait to the previously identified hardness locus (ha) located on 5DS as defined by DNA markers for the Pina-D1 locus, and the microsatellite wmc233. The statistical association was shown to be highly significant, with approximately 30% of the variation accounted for by the 5DS region. Another region on chromosome 4D showed a significant association in the Cranbrook Halberd cross. The CD87 Katepwa cross did not show any consistent associations between the SKCS measures and chromosome region, whereas in the Sunco Tasman cross a highly significant association only on chromosome 4B (accounting for 20% of the variation) was suggested. The Sunco Tasman cross showed an overlap of the chromosome region that accounted for variation in both grain weight and hardness and this influence of grain weight on hardness was independently confirmed by a detailed qualitative rheological analysis of the crush response profiles for the Egret Sunstar lines. It is evident from the study that, in Australian wheat lines, there are some major effects on grain hardness that are not associated with the classical ha locus located on 5DS.

Milling and Bread Baking Traits Associated with Puroindoline Sequence Type in Hard Red Spring Wheat

Recent results have shown that mutations in genes coding for puroindoline a and b (PinA and PinB) are associated with the expression of the hard texture of wheat (Triticum aestivum L.) grain. A majority of hard wheats have a glycine‐to‐serine mutation in puroindoline b (allele PinB‐D1b), or they are devoid of puroindoline a (allele PinA‐D1b). Hard wheats with PinA‐D1b tend to be harder than those with PinB‐D1b Grain hardness is known to affect milling and baking traits. Our objective was to determine the influence of allelic variation in PinA and PinB on milling and bread quality traits in a recombinant inbred population segregating for PinA‐D1b and PinB‐D1b One hundred thirty‐nine recombinant inbred lines from the cross ‘Butte 86’ (PinA‐D1b allele)/ND2603 (PinB‐D1b allele) and parents were grown in a field trial with two replications at two locations. Grain hardness was measured by near‐infrared reflectance (NIR) and the single‐kernel characterization system (SKCS). Grain was milled and baked for each line. Puroindoline allele type was determined for each line. The PinB‐D1b group had significantly softer grain, higher break flour yield, flour yield, milling score, and loaf volume, and lower flour ash and crumb grain score (low score being desirable) than the PinA‐D1b group. Significant genetic variability was detected within allelic classes for all traits. The proportion of variation among entry means attributed to puroindoline classes was 34% for break flour yield, 26% for NIR hardness, and 22% for SKCS harness index. Grain hardness was negatively correlated with break flour yield, flour yield, and mixing score and positively correlated with flour ash. Grain hardness was not correlated with loaf volume or crumb grain score. The PinB‐D1b allele was more desirable for milling and bread baking, although superior milling and bread quality genotypes could be selected within either class.

Application of the Single‐Kernel Characterization System to Durum Wheat Testing and Quality Prediction

ABSTRACTThe 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.

GENERAL PRINCIPLES

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Segregating Hard Red Winter Wheat into Dough Factor Groups Using Single Kernel Measurements and Whole Grain Protein Analysis

ABSTRACTIn accordance with the Grain Quality Acts of 1986 and 1990, scientists at Kansas State University are studying the feasibility of implementing a quality‐based marketing system for hard red winter (HRW) wheat in the Southern Plains. This research addresses the development of a segregation system that uses the single kernel characterization system and the whole grain near‐infrared analyzer to evaluate the milling and baking quality of wheat as a single value called “dough factor”. This single value represents the amount of flour‐water dough that can be produced from a given unit of wheat. Samples of HRW wheat (≈100 per location) were collected at five Kansas country elevators during the 1995 and 1996 harvests. After the dough factor was measured for individual samples, the samples were composited into seven dough factor groups to establish binning and segregation strategies and to explore the relationship between wheat quality measurements and dough factor groups. Results showed that dough factor groups were significantly different from each other and that dough factor groups were related (P &lt; 0.05) to increases in test weight, single kernel weight, single kernel size, flour yield, and mixing time. Although locations showed year‐to‐year variability for test weight, kernel weight, and kernel size, the differences among dough factor groups for these characteristics across locations were consistent, indicating that the mean values within dough factor groups are similar regardless of location. The lack of significant differences in protein content among dough factor groups was attributable to high variability within dough factor groups between years. High protein values were present in low and high dough factor groups, indicating that protein content alone is not a good indicator of wheat quality. Patterns of differences in wheat quality characteristics among dough factor groups suggest that the seven groups studied can be reduced to three groups: &lt;107, 107–112.9, ≥113. This study demonstrates that dough factor as a segregation and marketing tool is related to single kernel characteristics, differentiates wheats of varying quality, and reflects the end‐use value of wheat.