Wheat (Triticum aestivum L.) is primarily grown for human consumption. The ability of wheat flour to produce cohesive dough that retains gas has helped to make it the most widely used cereal ingredient for bread and other baked food products. In addition to superior bake quality, recent awareness on chemo-preventive benefits of wheat has encouraged breeders to develop cultivars with high phenolic compound levels that can potentially alleviate disease occurrence in humans. Bread loaf volume, one of the most important bread-making qualities is assessed by baking loaves of bread; however, this task is resource intensive, especially in a breeding program, where large numbers of genotypes are tested every year. Accurate loaf volume prediction methods can benefit both breeders and millers. At the same time wheat quality researchers are working to develop more useful systems to access different aspect of wheat quality more efficiently than before. Our objectives were i) to investigate phenolic and antioxidant properties of spring and winter wheat genotypes ii) to develop loaf volume prediction models through the use of Neural Network model utilizing Mixograph, Mixolab, and weather variables and, iii) to study relationships of creep and creep-recovery with other rheological properties. Variability in Phenolic compounds was greater in spring wheat genotypes and heritability estimates were moderate. It appears that focused breeding efforts could successfully increase wheat phenolic compound levels. Weather data collected at 20 days after heading showed the highest correlations with bread loaf volume. Neural Network model containing maximum, minimum and night time temperatures produced the highest coefficient of determination in predicting loaf volume. Creep, creep-recovery, and recovery strain percentage showed significant correlations with the Mixograph variables; envelope peak time, envelope width at 8 min, and envelope value at 8 min. Only recovery strain percentage was significantly correlated with four gluten properties, gluten index, wet gluten, dry gluten, and water binding capacity, whereas creep and creep-recovery was significantly correlated with only few gluten properties. Our results show that creep and creep-recovery measurements were not able to completely distinguish wheat genotypes for their end use quality. Nevertheless, valuable insight has been made available to wheat breeders that must make selections for various quality traits as well as growers, millers, and bakers that may be interested in end-use quality assessment and prediction.
","abstract_html":"<p>Wheat (<em>Triticum aestivum L.)</em> is primarily grown for human consumption. The ability of wheat flour to produce cohesive dough that retains gas has helped to make it the most widely used cereal ingredient for bread and other baked food products. In addition to superior bake quality, recent awareness on chemo-preventive benefits of wheat has encouraged breeders to develop cultivars with high phenolic compound levels that can potentially alleviate disease occurrence in humans. Bread loaf volume, one of the most important bread-making qualities is assessed by baking loaves of bread; however, this task is resource intensive, especially in a breeding program, where large numbers of genotypes are tested every year. Accurate loaf volume prediction methods can benefit both breeders and millers. At the same time wheat quality researchers are working to develop more useful systems to access different aspect of wheat quality more efficiently than before. Our objectives were i) to investigate phenolic and antioxidant properties of spring and winter wheat genotypes ii) to develop loaf volume prediction models through the use of Neural Network model utilizing Mixograph, Mixolab, and weather variables and, iii) to study relationships of creep and creep-recovery with other rheological properties. Variability in Phenolic compounds was greater in spring wheat genotypes and heritability estimates were moderate. It appears that focused breeding efforts could successfully increase wheat phenolic compound levels. Weather data collected at 20 days after heading showed the highest correlations with bread loaf volume. Neural Network model containing maximum, minimum and night time temperatures produced the highest coefficient of determination in predicting loaf volume. Creep, creep-recovery, and recovery strain percentage showed significant correlations with the Mixograph variables; envelope peak time, envelope width at 8 min, and envelope value at 8 min. Only recovery strain percentage was significantly correlated with four gluten properties, gluten index, wet gluten, dry gluten, and water binding capacity, whereas creep and creep-recovery was significantly correlated with only few gluten properties. Our results show that creep and creep-recovery measurements were not able to completely distinguish wheat genotypes for their end use quality. Nevertheless, valuable insight has been made available to wheat breeders that must make selections for various quality traits as well as growers, millers, and bakers that may be interested in end-use quality assessment and prediction.</p>","abstract_has_math":false,"creators":["Karki, David"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Thesis - University Access Only","degree_discipline":"Plant Science","degree_department":null,"school":null,"contributors":["Karl D. Glover"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-01-01T08:00:00Z","date_published":"2013-01-01T08:00:00Z","updated_at":"2026-07-24T04:29:08Z","subjects":["Plant Sciences"],"languages":["en"],"rights":["In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/
In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/
Wheat (Triticum aestivum L.) is primarily grown for human consumption. The ability of wheat flour to produce cohesive dough that retains gas has helped to make it the most widely used cereal ingredient for bread and other baked food products. In addition to superior bake quality, recent awareness on chemo-preventive benefits of wheat has encouraged breeders to develop cultivars with high phenolic compound levels that can potentially alleviate disease occurrence in humans. Bread loaf volume, one of the most important bread-making qualities is assessed by baking loaves of bread; however, this task is resource intensive, especially in a breeding program, where large numbers of genotypes are tested every year. Accurate loaf volume prediction methods can benefit both breeders and millers. At the same time wheat quality researchers are working to develop more useful systems to access different aspect of wheat quality more efficiently than before. Our objectives were i) to investigate phenolic and antioxidant properties of spring and winter wheat genotypes ii) to develop loaf volume prediction models through the use of Neural Network model utilizing Mixograph, Mixolab, and weather variables and, iii) to study relationships of creep and creep-recovery with other rheological properties. Variability in Phenolic compounds was greater in spring wheat genotypes and heritability estimates were moderate. It appears that focused breeding efforts could successfully increase wheat phenolic compound levels. Weather data collected at 20 days after heading showed the highest correlations with bread loaf volume. Neural Network model containing maximum, minimum and night time temperatures produced the highest coefficient of determination in predicting loaf volume. Creep, creep-recovery, and recovery strain percentage showed significant correlations with the Mixograph variables; envelope peak time, envelope width at 8 min, and envelope value at 8 min. Only recovery strain percentage was significantly correlated with four gluten properties, gluten index, wet gluten, dry gluten, and water binding capacity, whereas creep and creep-recovery was significantly correlated with only few gluten properties. Our results show that creep and creep-recovery measurements were not able to completely distinguish wheat genotypes for their end use quality. Nevertheless, valuable insight has been made available to wheat breeders that must make selections for various quality traits as well as growers, millers, and bakers that may be interested in end-use quality assessment and prediction.
"]},{"key":"dc:title","label":"Title","values":["Evaluation of Spring Wheat Genotypes for Nutritional and Bread-making Quality"]}]}],"canonical_facts":{"dc:contributor":["Karl D. Glover"],"dc:creator":["Karki, David"],"dc:date.available":["2017-08-04T07:00:00Z"],"dc:description.abstract":["Wheat (Triticum aestivum L.) is primarily grown for human consumption. The ability of wheat flour to produce cohesive dough that retains gas has helped to make it the most widely used cereal ingredient for bread and other baked food products. In addition to superior bake quality, recent awareness on chemo-preventive benefits of wheat has encouraged breeders to develop cultivars with high phenolic compound levels that can potentially alleviate disease occurrence in humans. Bread loaf volume, one of the most important bread-making qualities is assessed by baking loaves of bread; however, this task is resource intensive, especially in a breeding program, where large numbers of genotypes are tested every year. Accurate loaf volume prediction methods can benefit both breeders and millers. At the same time wheat quality researchers are working to develop more useful systems to access different aspect of wheat quality more efficiently than before. Our objectives were i) to investigate phenolic and antioxidant properties of spring and winter wheat genotypes ii) to develop loaf volume prediction models through the use of Neural Network model utilizing Mixograph, Mixolab, and weather variables and, iii) to study relationships of creep and creep-recovery with other rheological properties. Variability in Phenolic compounds was greater in spring wheat genotypes and heritability estimates were moderate. It appears that focused breeding efforts could successfully increase wheat phenolic compound levels. Weather data collected at 20 days after heading showed the highest correlations with bread loaf volume. Neural Network model containing maximum, minimum and night time temperatures produced the highest coefficient of determination in predicting loaf volume. Creep, creep-recovery, and recovery strain percentage showed significant correlations with the Mixograph variables; envelope peak time, envelope width at 8 min, and envelope value at 8 min. Only recovery strain percentage was significantly correlated with four gluten properties, gluten index, wet gluten, dry gluten, and water binding capacity, whereas creep and creep-recovery was significantly correlated with only few gluten properties. Our results show that creep and creep-recovery measurements were not able to completely distinguish wheat genotypes for their end use quality. Nevertheless, valuable insight has been made available to wheat breeders that must make selections for various quality traits as well as growers, millers, and bakers that may be interested in end-use quality assessment and prediction.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/1446"],"dc:language":["en"],"dc:rights":["In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/