Hard White Research Articles

Book Review: Hard White: The Mainstreaming of Racism in American Politics by Richard C. Fording and Sanford F. Schram

Book Review: <i>Hard White: The Mainstreaming of Racism in American Politics</i> by Richard C. Fording and Sanford F. Schram

AAC Whitehead hard white spring wheat

AAC Whitehead, an awned hard white spring wheat (Triticum aestivum L.) cultivar, combines high grain yield and good agronomic characteristics with excellent disease resistance. Based on 34 station-years of registration trial data from 2017 to 2019, AAC Whitehead had grain yield significantly higher than all of the check cultivars. AAC Whitehead had maturity similar to the checks, low lodging scores, and significantly shorter plant height than Snowstar and Whitehawk. AAC Whitehead had significantly lower test weight and higher kernel mass than than all the check cultivars. AAC Whitehead expressed resistance to the prevalent races of leaf, stripe and stem rust, and common bunt; and moderate resistance to Fusarium head blight (FHB). It also expressed tolerance to the orange wheat blossom midge. AAC Whitehead expresses quality attributes within the range of the check cultivars and is eligible for grades of Canada Western Hard White Spring wheat.

Novel Quantitative Trait Loci for Grain Cadmium Content Identified in Hard White Spring Wheat.

Cadmium (Cd) is a heavy metal that can cause a variety of adverse effects on human health, including cancer. Wheat comprises approximately 20% of the human diet worldwide; therefore, reducing the concentrations of Cd in wheat grain will have significant impacts on the intake of Cd in food products. The tests for measuring the Cd content in grain are costly, and the content is affected significantly by soil pH. To facilitate breeding for low Cd content, this study sought to identify quantitative trait loci (QTL) and associated molecular markers that can be used in molecular breeding. One spring wheat population of 181 doubled haploid lines (DHLs), which was derived from a cross between two hard white spring wheat cultivars “UI Platinum” (UIP) and “LCS Star” (LCS), was assessed for the Cd content in grain in multiple field trials in Southeast Idaho, United States. Three major QTL regions, namely, QCd.uia2-5B, QCd.uia2-7B, and QCd.uia2-7D, were identified on chromosomes 5B, 7B, and 7D, respectively. All genes in these three QTL regions were identified from the NCBI database. However, three genes related to the uptake and transport of Cd were used in the candidate gene analysis. The sequences of TraesCS5B02G388000 (TaHMA3) in the QCd.uia2-5B region and TraesCS7B02G320900 (TaHMA2) and TraesCS7B02G322900 (TaMSRMK3) in the QCd.uia2-7B region were compared between UIP and LCS. TaHMA2 on 7B is proposed for the first time as a candidate gene for grain Cd content in wheat. A KASP marker associated with this gene was developed and it will be further validated in near-isogenic lines via a gene-editing system in future studies.

The Characteristics of Steamed Bread from Reconstituted Whole Wheat Flour (WWF) of Different Hard Wheat Classes with Different Bran Particle Size Distributions.

The purpose of this study was to investigate the effects of reconstituted whole wheat flour (WWF) particle size on flour characteristics and northern-type steamed bread (NTSB) quality. In this study, hard white (HW), hard red winter (HRW), and hard red spring (HRS) wheat classes, and four different bran particle size distributions [D(50) values of 53 μm, 74 μm, 105 μm, and 125 μm] were blended at a ratio of 85% refined flour + 15% bran to create reconstituted WWF and make reconstituted WWF NTSB. Farinograph water absorption and water solvent retention capacity (SRC) increased as bran particle size decreased. Flour and dough strength tests such as lactic acid SRC and Farinograph and Mixolab development time and stability did not show any clear trends with bran particle size. HRW WWF tended to be the exception as Farinograph development time and stability generally increased as particle size increased. Resistance to extension increased as bran particle size decreased for HRW WWF and increased as particle size increased for HW and HRS. These differences in WWF dough rheology trends were likely due to differences in gluten characteristics between the classes. The results showed that larger particle sizes (105 μm and 125 μm) were more conducive to achieving desirable whole wheat NTSB specific volume, color, and texture.

Combined selection of Gpc-B1 and Glu-B1 locus encoding the Bx7OE subunit for improving end-use quality of hard white wheat

Wheat in Canada is classified into nine different market classes based on attributes that impact end-use functionality. The Canada Western Hard White Spring (CWHWS) class was created in 2001 to diversify wheat production. The quality requirements of the CWHWS class demand strong gluten properties but lower grain protein (GP) content levels compared to the premium CWRS class. Generally, GP and grain yield are negatively correlated, so a reduction in GP would allow CWHWS cultivars to express greater yield potential. However, lowering GP can result in weaker gluten strength. This study investigated the combined use of Gpc-B1 and Bx7OE to increase GP while maintaining the dough strength in hard white wheat lines. Additionally, the effects of these introgressions on agronomic traits (grain yield, days to heading, days to maturity, plant height, test weight, and thousand kernel weight) were evaluated. Seven populations (each comprising 40 RILs, all varying for presence of Bx7OE, and five populations carrying all possible combinations of Gpc-B1 and Bx7OE) were developed using marker-assisted selection and elite cultivars or advanced breeding lines as parents and tested in three field environments in Saskatchewan, Canada. End-use quality was measured on grain (protein content, kernel hardness), and flour (protein, ash, protein loss, color, and farinograph). Results from our study showed a 0.8–1.1% increase in GP content relative to CWHWS cultivars (used as checks in the study) and dough strength properties similar to CWRS cultivar ‘CDC Teal’ (representing uppermost limit of dough strength in CWRS class at the time). Except for a slight increase in plant height (still comparable to elite cultivars), no deleterious effects on agronomic or other quality traits were observed. We conclude that combined selection of Gpc-B1 DIC with Bx7OE is effective in developing CWHWS lines with a desirable end-use quality.

Hard White: The Mainstreaming of Racism in American Politics. By Richard C. Fording and Sanford F. Schram. New York: Oxford University Press, 2020. 288p. $99.00 cloth, $27.95 paper.

Hard White: The Mainstreaming of Racism in American Politics. By Richard C. Fording and Sanford F. Schram. New York: Oxford University Press, 2020. 288p. $99.00 cloth, $27.95 paper.

Response to David A. M. Peterson and Mark D. Ramirez’s Review of Hard White: The Mainstreaming of Racism in American Politics

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Reducing Visual Differences in Whole Grain Bread Prepared with Hard Red and Hard White Wheat: Application for Sensory Studies.

Consumer taste preference can be influenced by visual preference. To eliminate the influence of visual preference in the sensory evaluation of whole grain wheat, a reproducible method to eliminate color differences between Whole Grain breads prepared from hard white wheat (HWW) and hard red wheat (HRW) was evaluated. Response surface methodology (RSM) was used to match the color of HWW to HRW with the addition of commercially available dye solutions: McCormick black (red #40, yellow #5, blue #1), red (red #40, red #3, yellow #6), and yellow (yellow #5). Bread color was assessed by L* , a* , and b* color parameters according to the CIELAB international system of color measurement. Four replicates of the control and dye treated breads were analyzed. Initial color values for HRW were L* = 56.8 ± 0.40; a* = 8.04 ± 0.44; b* = 21.34 ± 0.46. RSM was used to predict dye addition levels to match color between HWW and HRW. With the addition of black (0.457 µL/mL), red (0.574 µL/mL), and yellow (1.165 µL/mL) dye to HWW, breads could be produced with L* , a* , and b* values of no statistical difference to the HRW (P < 0.05). A timed storage trail demonstrated the need to standardize the time between bread production and feeding studies. Visual bias can hinder assessment of wheat varieties in sensory studies. A reproducible method of dying wheat was developed that can be used to reduce this bias in sensory studies. PRACTICAL APPLICATIONS: The ability to control color variability is a critical tool in determining perceived quality in sensory analysis of breads. In this study, a method to reduce or eliminate visual bias between breads made from different varieties of wheat was developed. This method is applicable to any foods where dye could be added to reduce or eliminate color bias in sensory studies.

Starch digestibility and apparent metabolizable energy of western Canadian wheat market classes in broiler chickens.

Wheat is the primary grain fed to poultry in western Canada, but its nutritional quality, including the nature of its starch digestibility, may be affected by wheat market class. The objectives of this study were to determine the rate and extent of starch digestibility of wheat market classes in broiler chickens, and to determine the relationship between starch digestibility and wheat apparent metabolizable energy (AME). In vitro starch digestion was assessed using gastric and small intestinal phases mimicking the chicken digestive tract, while in vivo evaluation used 468 male broiler chickens randomly assigned to dietary treatments from 0 to 21 d of age. The study evaluated 2 wheat cultivars from each of 6 western Canadian wheat classes: Canadian Prairie Spring (CPS), Canadian Western Amber Durum (CWAD), CW General Purpose (CWGP), CW Hard White Spring (CWHWS), CW Red Spring (CWRS), and CW Soft White Spring (CWSWS). All samples were analyzed for relevant grain characteristics. Data were analyzed as a randomized complete block design and cultivars were nested within market class. Pearson correlation was used to determine relationships between measured characteristics. Significance level was P ≤ 0.05. The starch digestibility range and wheat class rankings were: proximal jejunum - 23.7 to 50.6% (CWHWSc, CPSbc, CWSWSbc, CWRSab, CWGPa, CWADa); distal jejunum - 63.5 to 76.4% (CWHWSc, CPSbc, CWSWSbc, CWRSab, CWGPa, CWADa); proximal ileum - 88.7 to 96.9% (CWSWSc, CPSbc, CWHWSbc, CWRSb, CWGPb, CWADa); distal ileum - 94.4 to 98.5% (CWSWSb, CWHWSb, CPSb, CWRSab, CWGPab, CWADa); excreta - 98.4 to 99.3% (CPSb, CWRSb, CWHWSb, CWSWSab, CWGPab, CWADa). Wheat class affected wheat AMEn with levels ranging from 3,203 to 3,411 kcal/kg at 90% DM (CWRSc, CWSWSc, CPSb, CWGPb, CWADa, CWHWSa). Significant and moderately strong positive correlations were observed between in vitro and in vivo starch digestibility, but no correlations were found between AME and starch digestibility. In conclusion, rate and extent of starch digestibility and AME were affected by western Canadian wheat class, but starch digestibility did not predict AME.

Effects of controlled germination on selected physicochemical and functional properties of whole-wheat flour and enhanced γ-aminobutyric acid accumulation by ultrasonication

Using hard red spring (HRS), hard white (HW), and soft white (SW) wheat, this study examined how germination time affected the functionality of whole-wheat flour (WWF) and enhancement of γ-aminobutyric acid (GABA) content through ultrasonication. The falling number values significantly decreased and the glucose content increased by 227–357% after 15h of controlled germination. The setback value of WWF paste decreased from 654 to 6cP (HW), 690 to 9cP (SW), and 698 to 7cP (HRS), respectively, showing significant decreases of starch retrogradation in an aqueous system. The gluten quality and dough mixing performance of WWF after 5–15h of controlled germination was enhanced since gluten is less weakened during the dough heating stage of Mixolab testing. After a 72h germination, the GABA content increased by 339% of the non-sprouting counterpart. Furthermore, the GABA content in the ultrasound-treated SW sample was 30.7% higher than that without ultrasound treatment.

Registration of ‘Sunshine’ Hard White Winter Wheat

‘Sunshine’ (Reg. No. CV‐1129, PI 674741) hard white winter wheat (Triticum aestivum L.) was developed by the Colorado Agricultural Experiment Station and released in August 2014 through a marketing agreement with the Colorado Wheat Research Foundation. In addition to researchers at Colorado State University, USDA‐ARS researchers at Manhattan, KS, St. Paul, MN, and Pullman, WA participated in its development. Sunshine was developed with the objective of making available a hard white winter wheat cultivar with improved grain yield and straw strength and similar milling and baking qualities to ‘Snowmass’ hard white winter wheat. Both Snowmass and Sunshine are grown in Colorado and adjacent areas of the west‐central Great Plains in an identity‐preserved marketing system whereby a grower premium is paid through a contract with a flour milling company. Sunshine was developed with a modified‐bulk breeding method from the cross KS01HW152‐6/HV9W02‐267W made in 2005 at Fort Collins, CO. Following two generations (F2 and F3) of bulk‐population evaluation, Sunshine was selected as an F3:4 line in July 2009, assigned experimental line number CO09W293, and evaluated in replicated yield trials from 2012 to 2014 in eastern Colorado.

Registration of ‘Earl’ Hard White Winter Wheat

End‐use quality and yield potential are the most important traits for hard white winter wheat (Triticum aestivum L.) cultivars produced in the Pacific Northwest region of the United States. The objective for the development of ‘Earl’ (Reg. No. CV‐1127, PI 680639) was to provide a hard white winter cultivar that meets end‐use quality expectations and performs well agronomically. Earl was developed and released in September 2014 by the Agricultural Research Center of Washington State University. It was tested under the experimental designations LasWA8061‐75, and WA8184, which were assigned through progressive generations of advancement. Earl is a semidwarf cultivar adapted for the intermediate‐ to high‐rainfall (&gt;400 mm of average annual precipitation), unirrigated wheat production regions of Washington. Earl has high‐temperature, adult‐plant resistance to stripe rust, above‐average grain protein concentration, and high grain volume weight with end‐use quality properties similar to those of hard red winter wheat cultivars ‘Bauermeister’, ‘Finley’, and ‘Whetstone’.

Taste Preferences of Whole Grain Bread and Tortilla Products in Young Children: A Comparison of Hard White Wheat Versus Hard Red Wheat

Taste Preferences of Whole Grain Bread and Tortilla Products in Young Children: A Comparison of Hard White Wheat Versus Hard Red Wheat

Eliminating Color Differences in Whole Grain Bread Prepared with Hard Red and Hard White Wheat: Application for Sensory Studies

Eliminating Color Differences in Whole Grain Bread Prepared with Hard Red and Hard White Wheat: Application for Sensory Studies

Registration of ‘Joe’ Hard White Winter Wheat

‘Joe’ (Reg. No. CV‐1119, PI 676270) hard white winter wheat (Triticum aestivum L.) was developed at the Agricultural Research Center‐Hays, Kansas State University, and released by the Kansas Agricultural Experiment Station in 2015. Joe was selected from a single cross of KS04HW101‐3/KS04HW119‐3 made in 2005 at Hays, KS. The objective of this cross was to develop a hard white winter wheat cultivar with adaptation to dryland production in western Kansas. Joe was developed using a modified bulk breeding method. Joe is an F6–derived line with experimental number KS11HW39‐5‐4, and it was tested in yield trials from 2010 to 2015. Joe is medium late and medium tall with a medium high test weight and medium protein concentration. Its milling and baking quality is acceptable to good. Joe was released because of its high grain yield potential in western Kansas and its good resistance to stripe rust (caused by Puccinia striiformis f. sp. tritici Westend.), leaf rust (caused by P. triticina f. sp. tritici Ericks.), and Wheat streak mosaic virus.

Use of fuzzy chromatography mass spectrometric (FCMS) fingerprinting and chemometric analysis for differentiation of whole-grain and refined wheat (T. aestivum) flour.

A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with chemometric analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80% WF of each cultivar. SIMCA was able to discriminate between 100%, 80%, 60%, 40%, and 20% WF and 100% RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6%. Graphical Abstract Workflow of targeting discriminatory compounds by use of FCMS and chemometric analysis.

Gluten Structural Evolution During Pasta Processing of Refined and Whole Wheat Pasta from Hard White Winter Wheat: The Influence of Mixing, Drying, and Cooking

An attempt was made to evaluate gluten structural changes in refined and whole wheat pasta from hard white winter wheat to elucidate the impact of whole wheat components on the formation and structure of the gluten network in pasta. Attenuated total reflectance–FTIR spectroscopy was used to track gluten secondary structure through most of the major steps in pasta processing: raw material, mixing, drying, and cooking. Protein solubility, accessible thiols, and SDS‐PAGE data were also collected to provide additional information on the nature of protein interactions and network composition. Few secondary structural differences were observed between refined and whole wheat flours from hard white wheat. However, mixing induced a significant shift to β‐sheet structures in refined dough that was not equally matched by whole wheat dough. Drying under both high temperature, short time (HT) and low temperature, long time (LT) conditions resulted in a reversion to structural distributions similar to those for flour in both pastas. However, greater protein denaturation in HT samples was indicated by lower protein solubility also in the presence of denaturants and disulfide reducing agents. Cooking generated a substantial increase in β‐sheet structures for both pasta systems. This structure was greatest in refined and LT samples. Thiol accessibility data indicate the presence of a highly aggregated, compact gluten network in refined pasta, mostly driven by hydrophobic association. Conversely, the network in whole wheat pasta was more loosely associated and dependent on disulfide bonding, both of which fit well with the secondary structural data.

Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat

The objective of this study was to compare the predictions of the protein contents and hardness values by partial least squares regression (PLSR) and principal components regression (PCR) models for bulk samples of Canadian wheat, which were obtained from different locations and crop years. Wheat samples of Canada Western Red Spring (CWRS), Canada Western Hard White Spring (CWHWS), Canada Western Soft White Spring (CWSWS), and Canada Prairie Spring Red (CPSR) classes were obtained from nearby agricultural farms in the main wheat growing locations in the Provinces of Alberta, Saskatchewan, and Manitoba from 2007, 2008, and 2009 crop years. Wheat samples were conditioned to moisture levels of 13, 16, and 19 % (wet basis) and pooled together for developing the regression models. A database of the near-infrared (NIR) hyperspectral image cubes of bulk samples of wheat classes was created in the wavelength region of 960–1,700 nm with 10 nm intervals. Reference protein contents and hardness values were determined using the Dumatherm method and single kernel characterization system (SKCS), respectively. A tenfold cross-validation was used for the ten-factor partial least squares regression (PLSR) and principal components regression (PCR) models for prediction purposes. Prediction performances of regression models were assessed by calculating the estimated mean square errors of prediction (MSEP), standard error of cross-validation (SECV), and correlation coefficient (r). Using the full data set in the protein prediction study, the ten-component PLSR model gave 1.76, 1.33, and 0.68 for the estimated MSEP, SECV, and r, respectively, which were better than the results for the ten-component PCR model (2.02, 1.42, and 0.62, respectively). For the hardness prediction, the estimated MSEP, SECV, and r values were 147.7, 12.15, and 0.82, respectively, for the ten-component PLSR model using the full data set. The PLSR models prediction performances outperformed the PCR models for predicting protein contents and hardness of wheat.

Association Mapping of Hagberg Falling Number in Hard White Spring Wheat

ABSTRACTHagberg falling number (FN) is an important quality trait used for grain grading. The FN test can only be measured in the late stages of variety development when progeny are homogeneous and sufficient grain is available for testing. The FN is strongly influenced by environmental conditions during reproductive growth stages, including excess moisture, extreme temperature, and biotic and abiotic stresses. The objective of this study was to identify potential genomic regions that influence FN in a subset of 110 hard white spring (HWS) wheat (Triticum aestivum L.) accessions from the National Small Grain Collection (NSGC) using genome‐wide association mapping. The FN tests were conducted using grain flour samples of these accessions grown in five environments. A total of 1740 single nucleotide polymorphism (SNP) markers were used to detect SNP–FN associations using both the general linear model (GLM) and the mixed linear model (MLM). Thirteen quantitative trait loci (QTL) located in nine chromosomal regions were identified in both GLM and MLM approaches. Pyramiding these QTL could explain up to 45% of the phenotypic variation of FN. The present study identified several potential markers for use in marker‐assisted selection for high FN lines in wheat breeding.

Comparing Two Statistical Discriminant Models with a Back-Propagation Neural Network Model for Pairwise Classification of Location and Crop Year Specific Wheat Classes at Three Selected Moisture Contents Using NIR Hyperspectral Images

<abstract><title><italic>Abstract.</italic></title> Knowledge of wheat classes and seed moisture contents not only determines the end use of wheat flour but also helps in developing effective storage systems for wheat. Samples of four classes of wheat, including Canada Western Red Spring (CWRS), Canada Western Hard White Spring (CWHWS), Canada Western Soft White Spring (CWSWS), and Canada Prairie Spring Red (CPSR), were obtained from at least five different locations for each class in Manitoba, Saskatchewan, and Alberta for the 2007, 2008, and 2009 crop years and conditioned to moisture contents of 13%, 16%, and 19%. Near-infrared (NIR) hyperspectral images were acquired from bulk samples in the 960-1700 nm wavelength region at 10 nm intervals. The first and second principal component score images were compared for the segmented images of all wheat classes. Pairwise wheat class identification was done using a non-parametric statistical model and a four-layer back-propagation neural network (BPNN) model. The NIR wavelengths of 1260 to 1380 nm had the highest factor loadings for the first principal component using principal component analysis (PCA). The four-layer BPNN model was used for two-class identification of wheat classes. Overall average pairwise classification accuracies of 83.7% were obtained for discriminating wheat samples based on their moisture contents. Average classification accuracies of 83.2%, 75.4%, and 73.1%, were obtained for identifying wheat classes for samples with 13%, 16%, and 19% moisture content (m.c.), respectively. In this study, discriminant models yielded better classification accuracies than BPNN models. Overall average classification accuracies of wheat classes using statistical models were 80.6% for the linear discriminant analysis (LDA) and 76.3% for the quadratic discriminant analysis (QDA). This work showed that NIR hyperspectral imaging can be used as a potential nondestructive tool for classifying moisture-specific wheat classes.