Application of Wheat Meal Solvent Retention Capacity Tests Within Soft Wheat Breeding Populations

The solvent retention capacity (SRC) test is a relatively new AACC Approved Method (56‐11) for evaluating soft wheat flour quality. The test measures the ability of flour to retain a set of four solvents (water, 50% sucrose, 5% sodium carbonate, and 5% lactic acid) after centrifugation. The objective of this study was to evaluate the utility of wheat meal sodium carbonate and lactic acid SRC tests and SDS sedimentation volume within three populations of soft spring wheat inbred lines as tools for selecting for improved flour SRC profiles, flour extraction, and cookie and pastry quality. The populations were derived from the crosses Vanna/Penawawa, Kanto 107/IDO488, and M2/IDO470 and were grown in replicated, irrigated trials in 2000 and 2001 near Aberdeen, Idaho. Within each of the three populations, wheat meal sodium carbonate SRC effectively predicted straight‐grade flour sodium carbonate (r = 0.69–0.81) and sucrose SRC (r = 0.74–0.84). Wheat meal sodium carbonate SRC also was negatively correlated with flour extraction and sugar snap cookie diameter. Wheat meal lactic acid SRC predicted straight‐grade flour lactic acid SRC in only one population. In contrast, SDS sedimentation volume predicted straight‐grade flour lactic acid SRC in all three populations (r = 0.74–0.93). Moreover, SDS sedimentation volume and wheat meal sodium carbonate SRC were independent in two of the three populations. This suggests that the SDS sedimentation and sodium carbonate SRC may measure different intrinsic characteristics. Therefore, a combination of sodium carbonate SRC and SDS sedimentation volume analyses of wheat meal may be an efficient approach to selecting toward target SRC profiles, increased flour extraction, and larger sugar snap cookie diameter in soft wheats.     

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.     

Modeling End‐Use Quality in U.S. Soft Wheat Germplasm

End‐use quality in soft wheat (Triticum aestivum L.) can be assessed by a wide array of measurements, generally categorized into grain, milling, and baking characteristics. Samples were obtained from four U.S. regional nurseries. Selected parameters included test weight, kernel hardness, kernel size, kernel diameter, wheat protein, polyphenol oxidase activity, flour yield, break flour yield, flour ash content, milling score, flour protein content, flour SDS sedimentation volume, flour swelling volume, Rapid Visco Analyzer peak paste viscosity, solvent retention capacity (SRC) parameters, total and water‐extractable arabinoxylan (TAX and WEAX, respectively), and cookie diameter. The objectives were to model cookie diameter and lactic acid SRC as well as to compare exceptionally performing varieties for each quality parameter. Cookie diameter and lactic acid SRC were modeled by using multiple regression analyses and all of the aforementioned quality parameters. Cookie diameter was positively associated with peak paste viscosity and was negatively associated with or modeled by kernel hardness, flour protein content, sodium carbonate SRC, lactic acid SRC, and water SRC. Lactic acid SRC was positively modeled by break flour yield, milling score, flour SDS sedimentation volume, and sucrose SRC and was negatively modeled by flour protein content. Exceptionally high‐ and low‐performing varieties were selected on the basis of their responses to the aforementioned characteristics in each nursery. High‐ and low‐performing varieties exhibited notably wide variation in kernel hardness, break flour yield, milling score, sodium carbonate SRC, sucrose SRC, water SRC, TAX content, and cookie diameter. This high level of variation in variety performance can facilitate selection for improved quality based on exceptional performance in one or more of these traits. The models described allow a more focused approach toward predicting soft wheat quality.     

Solvent Retention Capacity Values in Relation to Hard Winter Wheat and Flour Properties and Straight‐Dough Breadmaking Quality

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.     

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

Grain 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.     

Variation in Kernel Hardness and Associated Traits in U.S. Barley Breeding Lines

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.     

Association of Barley Kernel Hardness with Physical Grain Traits and Food Processing Parameters

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.     

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.     

Relationship Between Single Wheat Kernel Particle-Size Distribution and Perten SKCS 4100 Hardness Index

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.     

Adaptation of AACC Method 56‐11, Solvent Retention Capacity,for Use as an Early Generation Selection Tool for Cultivar Development

The solvent retention capacity (SRC) profile is useful for studying flour components contributing to end‐use functionality. The method tests four different solvents with 5 g of flour each. Because of the amount of grain (30–40 g) typically needed to produce 20 g of flour for the SRC test, the method is not well‐suited for assessing end‐use quality of early generation breeding material, where grain quantities are limited. The method was therefore modified to require only 0.2 g of ground wheat instead of 5 g of flour per SRC solvent. The small‐scale SRC results using whole meal had correlations of r = 0.86 for lactic acid, r = 0.85 for sodium carbonate, r = 0.78 for sucrose, r = 0.74 for sodium bicarbonate (the alkaline water retention capacity method) and r = 0.69 for water when compared with SRC values from full‐scale tests using 5 g of flour. Overall, cultivars with SRC values at the extremes of the distribution were in the same ranked order for the small‐ and large‐scale SRC test results. However, variation in ranked order of cultivars between test methods was detected among samples that were not at the extremes of the distribution. Traditionally, successful wheat breeding strategies involve eliminating or advancing lines from the extremes of the distribution to increase the proportion of desirable genotypes within breeding programs. Results indicated that advancing promising germplasm or eliminating germplasm with inferior end‐use quality potential is possible using the small‐scale SRC technique to evaluate early generation wheat breeding material, as a sort of breeding triage.     

A Comprehensive Survey of Soft Wheat Grain Quality in U.S. Germplasm

Wheat (Triticum aestivum L.) quality is dependent upon both genetic and environmental factors, which work in concert to produce specific grain, milling, flour, and baking characteristics. This study surveyed all of the 132 soft wheat varieties (cultivars and advanced breeding lines) grown in the U.S. regional nursery system, which encompassed the three main soft wheat producing regions of the United States (eastern and southern soft red winter and western soft white). The quality parameters included test weight, kernel hardness, weight, and diameter, wheat and flour protein, polyphenol oxidase, break flour yield, flour yield, flour ash, milling score, flour swelling volume, flour SDS sedimentation volume, solvent retention capacity (SRC) for water, sodium carbonate, sucrose, and lactic acid, Rapid Visco Analyzer peak pasting viscosity, and cookie diameter. High levels of variation were observed among varieties, regions, and specific environments, with environment being in general a much greater source of variation than varieties. Variety was observed to have a relatively stronger influence on wheat quality in the western nurseries, compared with the eastern and southern regions, where location effects had a stronger impact on overall wheat quality. The greater influence of variety was particularly notable for kernel hardness in the western nurseries. Kernel hardness also varied considerably as a result of environment. For the two soft red winter wheat nurseries, the western U.S. environment produced substantially harder kernels (37–40) compared with the same varieties grown in eastern U.S. locations (15–20). Intertrait quality relationships were observed to be unique to the specific nursery and germplasm in which they were studied, and these relationships were not consistent across nurseries. Nevertheless, on average, soft wheat quality was fairly similar across the United States, indicating that breeding and testing models have been successful in achieving a relatively uniform target for quality. However, many traits showed high levels of variability among varieties, suggesting that a greater level of selection for end‐use quality would benefit end users by increasing consistency and reducing variability. The often large role of environment (location) in quality indicates that end users must be assiduous in their origination and grain procurement. Clearly, “nursery mean” quality does not reflect the potential that can be obtained, as reflected by a few exceptional soft wheat varieties.     

Assessing Environmental Influences on Solvent Retention Capacities of Two Soft White Spring Wheat Cultivars

The solvent retention capacity test (SRC) (AACC Approved Method 56‐11) of flour is used to evaluate multiple aspects of wheat (Triticum aestivum L.) quality including pentosan content, starch damage, gluten strength, and general water retention based on the ability of flour to retain a range of solvents. The objectives of this study were to evaluate the effects of grain production environment in general and crop irrigation and fertility management in particular on SRC of soft wheat flour, and to evaluate the ability of SRC to predict end‐use quality across diverse environments. Two soft white spring wheat cultivars ‘Pomerelle’ and ‘Centennial’ were produced in a range of irrigated and rain‐fed production environments. SRC profiles and milling and baking quality parameters were measured. In a two‐year study at Aberdeen, ID, with two late‐season irrigation management regimes and two crop nitrogen fertility treatments, only wheat genotype significantly affected flour SRC. In two‐year studies at Tetonia, ID, one conducted under rain‐fed conditions and the other under irrigation, additional fertilizer applied at anthesis did not affect SRC. Correlations among quality parameters were determined using the Aberdeen and Tetonia flour samples, as well as samples of the same genotypes grown in fertility trials under rain‐fed conditions at Havre and Bozeman, MT, and under irrigation at Bozeman. Patterns of correlations among SRC values were similar for both genotypes. Grain test weight was negatively correlated with sodium carbonate and sucrose SRC of both genotypes. Flour protein was strongly positively correlated with sucrose and lactic acid SRC of both genotypes. The optimal regression models for predicting sugar snap cookie diameter (AACC Approved Method 10‐52) as a function of protein, SRC, flour extraction, and kernel hardness were different for the two cultivars. SRC evaluations of flours from these trials were consistent with large genotype and environment effects, yet minimal genotype × environment interaction. This suggests that selection among genotypes within an environment will produce a gain‐from‐selection observable in multiple and diverse environments.     

Solvent Retention Capacity (SRC) Testing of Wheat Flour: Principles and Value in Predicting Flour Functionality in Different Wheat‐Based Food Processes and in Wheat Breeding—A Review

Solvent retention capacity (SRC) technology, its history, principles, and applications are reviewed. Originally, SRC testing was created and developed for evaluating soft wheat flour functionality, but it has also been shown to be applicable to evaluating flour functionality for hard wheat products. SRC is a solvation test for flours that is based on the exaggerated swelling behavior of component polymer networks in selected individual diagnostic solvents. SRC provides a measure of solvent compatibility for the three functional polymeric components of flour—gluten, damaged starch, and pentosans—which in turn enables prediction of the functional contribution of each of these flour components to overall flour functionality and resulting finished‐product quality. The pattern of flour SRC values for the four diagnostic SRC solvents (water, dilute aqueous lactic acid, dilute aqueous sodium carbonate, and concentrated aqueous sucrose solutions), rather than any single individual SRC value, has been shown to be critical to various successful end‐use applications. Moreover, a new predictive SRC parameter, the gluten performance index (GPI), defined as GPI = lactic acid/(sodium carbonate + sucrose) SRC values, has been found to be an even better predictor of the overall performance of flour glutenin in the environment of other modulating networks of flour polymers. SRC technology is a unique diagnostic tool for predicting flour functionality, and its applications in soft wheat breeding, milling, and baking are increasing markedly as a consequence of many successful, recently published demonstrations of its extraordinary power and scope.     

Evaluation of the Single Kernel Characterization System (SKCS) for Measurement of Sorghum Grain Attributes

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.     

Developing Agreement Between Very Short Flow and Longer Flow Test Wheat Mills

Variations in soft wheat moisture content and kernel texture greatly affected the flour yield produced by a small (short flow) microtest mill (Quadrumat Jr.). An algorithm was developed that adjusted Quadrumat Jr. flour yield to 15% wheat moisture content, precluding the need to temper the wheat before milling. Another algorithm was developed to adjust Quadrumat flour yield relative to a constant softness equivalent (measurement of kernel texture) obtained during the micromilling procedure. Predicting the flour yield of the longer flow Allis‐Chalmers mill from Quadrumat Jr. unadjusted flour yield (R ² = 0.55) was compared with predicting Allis‐Chalmers flour yield from the Quadrumat Jr. adjusted flour yield (R ² = 0.90) across five diverse confirmation data sets. An algorithm to adjust flour yield for softness equivalent was individually developed for soft and hard wheats. Representative micromilling flour yield and softness equivalent data could be produced using as little as 10 g of untempered wheat and ≈3 min of operator time.     

Use of Solvent Retention Capacity Profile to Predict the Quality of Triticale Flours

The solvent retention capacity test (SRC) was used to evaluate flour functionality for end use applications and select wheat for production of flour with required functionality, but there is little information about SRC test application on triticale flour quality. The ability of flour to retain a set of four solvents produces a flour quality profile for predicting bakery performance. The objective of this study was to evaluate the capacity of SRC and its micro test to determine the potential quality of 25 triticale flours, as well as studying the relationship between the SRC parameters and flour chemical composition. The SRC parameters of triticale flours were correlated with the flour components that have been proposed by the method: sucrose SRC‐pentosan (r = 0.57), carbonate SRC‐damaged starch (r = 0.80), lactic SRC‐glutelin (r = 0.42), water SRC‐all hydrophilic constituents (damaged starch [r = 0.72], protein [r = 0.61], glutelin [r = 0.66], pentosan [r = 0.46]). Triticale flours have shown higher water and sodium carbonate SRC, similar sucrose SRC, and lower lactic SRC values than published results of typical flours used for cookie production. Summarizing, the high level of association found between SRC and micro SRC parameters with flour composition and quality flour tests evidence that either the SRC profile or the micro test SRC could be used to determine the potential quality of triticale flours.     

Solvent Retention Capacities of Indian Wheats and Their Relationship with Cookie‐Making Quality

Ninety‐two wheat genotypes including 50 cultivars released in India and 42 germplasm lines were subjected to solvent retention capacity (SRC) tests using 1 g of flour and 1 g of whole meal to see the relationship with cookie‐making quality and the utility in breeding programs. Very high negative correlations (P < 0.001) were observed between cookie diameter and spread factor and alkaline water retention capacity (AWRC), and solvent retention capacities of both flour and whole meal samples. Multiple regression analysis showed that AWRC explained 43.8%, sodium carbonate SRC 27.3%, lactic acid SRC 15.1%, and protein content 13.8% of the total variability (multiple r = 0.87) in cookie diameter. Total variability (multiple r = 0.85) in spread factor was explained 40.3% by AWRC, 27.4% by SODSRC, 14.5% by LASRC, and 17.8% by protein content. When the technique was further used to reduce the number of parameters contributing to cookie diameter, AWRC explained 67.2% of the total variability (multiple r = 0.85) and the rest by lactic acid SRC and protein content. Surprisingly, multiple regression analysis of whole meal samples exhibited that lactic acid SRC and sodium carbonate SRC explained 88 and 12%, respectively, of the total variability (multiple r = 0.76) in cookie diameter and 78 and 22%, respectively, of the total variability (multiple r = 0.71) in spread factor. Among the soft wheat flour samples selected based on W > 7.70 cm, pentosan content as revealed by sucrose SRC explained 87.7% of the total variability (multiple r = 0.54) of cookie diameter and 83.8% of total variability (multiple r = 0.52) in spread factor. Clustering of genotypes based on SRC profiles using both flour and whole meal produced clusters with similar average cookie diameter and spread factor. The data clearly demonstrate that whole meal tests can be used in screening the recombinant lines as well as in selecting desirable genotypes for making crosses to enhance cookie‐making quality.     

Relationship Between Soft Wheat Flour Physicochemical Composition and Cookie‐Making Performance

Nowadays in Argentina, cookies, crackers, and cakes are made of flour obtained from bread wheat with additives or enzymes that decrease the gluten strength but increase production costs. The present research work aims to study the relationship between flour physicochemical composition (particle size average [PSA], protein, damaged starch [DS], water soluble pentosans [WSP], total pentosans [TP], and gluten), alkaline water retention capacities behavior, solvent retention capacities profile (SRC) and cookie‐making performance in a set of 51 adapted soft wheat lines with diverse origin to identify better flour parameters for predicting cookie quality. Cookie factor (CF) values were 5.06–7.56. High and significant negative correlations between sucrose SRC (–0.68), water SRC (–0.65), carbonate SRC (–0.59), and CF were found, followed by lactic SRC that presented a low negative but significant correlation (r = –0.35). The flour components DS (r = –0.67), WSP (r = –0.49), and TP (r = –0.4) were negatively associated to CF. PSA showed a negative correlation with CF (r = –0.43). Protein and gluten were the flour components that affected cookie hardness, but no significant correlation were found with pentosan or DS content. A prediction equation for CF was developed. Sucrose SRC, PSA, and DS could be used to predict 68% of the variation in cookie diameter. The cluster analysis was conducted to assess differences in flour quality parameters among genotypes based on CF. Clusters 1 and 4 were typified by lower CF (5.70 and 5.23, respectively), higher DS, pentosan content, and SRC values. Cluster 2 with a relative good CF (6.47) and Cluster 3 with the best cookie quality, high CF (7.32) and low firmness, and the lowest DS, TP, WSP content, and sucrose SRC values.     

Mutagenesis‐Derived Puroindoline Alleles in Triticum aestivum and Their Impacts on Milling and Bread Quality

Wheat (Triticum aestivum) end‐product quality is impacted by grain hardness, which is determined by the Hardness locus consisting of the Puroindoline a and Puroindoline b genes, Pina and Pinb, respectively. Hard wheats commonly contain just one of two Pin mutations. We previously demonstrated the creation and preliminary hardness testing of 46 Pin missense alleles. In this study we examine the degree that individual Pin missense alleles confer unique milling and bread quality traits. Three Pina (PINA‐R103K, ‐G47S, and ‐P35S) and four Pinb (PINB‐D34N, ‐T38I, ‐G46D, and ‐E51K) missense alleles were chosen because they impart variable grain hardness levels, with one allele conferring soft seed texture, three conferring intermediate hardness (single‐kernel characterization system [SKCS] hardness approximately 50), and three conferring hard grain texture (SKCS hardness greater than 60). All but two of the alleles (PINA‐R103K and PINA‐G47S) resulted in higher total flour yield when compared with wild‐type controls. All hard and intermediate hardness alleles had decreased break flour yield, but intermediate hardness allele PINA‐P35S had higher break flour yield than common hard allele Pinb‐D1b. Intermediate and hard alleles resulted in increased abundance of larger and reduced levels of smaller flour particles. None of the missense alleles differed from their controls for loaf volume. The seven selected Pin alleles imparted defined levels of grain hardness and milling properties not previously available that may prove useful in wheat improvement.     

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

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.