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Developing Scalable Breeding Tools for Nixtamalization End-Product Quality
Developing Scalable Breeding Tools for Nixtamalization End-Product Quality
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103704
- ISBN
- 9798290909936
- DDC
- 580
- 서명/저자
- Developing Scalable Breeding Tools for Nixtamalization End-Product Quality
- 발행사항
- [Sl] : University of Minnesota, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 160 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Hirsch, Candice N.
- 학위논문주기
- Thesis (Ph.D.)--University of Minnesota, 2025.
- 초록/해제
- 요약Nixtamalization is a high-heat, high-pH cooking process that loosens pericarp and softens the endosperm of maize grain and is used to create a dough called masa. Several globally significant foods such as tortillas and tortilla chips are made of masa and make up significant portions of consumed calories and nutrients around the world. Relatively few acres of maize are grown for masa-based products in the United States compared to other uses of maize, leading to reduced resources for germplasm improvement in traits that impact masa-product quality. Two traits that have notable downstream impacts on masa quality include moisture absorption and pericarp retention, which can impact the taste, texture, appearance, and machinability of the final product. This thesis addresses the resource gap by developing scalable tools and biological knowledge to improve these traits. Specifically, machine learning models were created to predict nixtamalization moisture content in both inbred and hybrid maize based on the near-infrared spectra of raw maize kernels. These models were used to assess the relationship that nixtamalization moisture content has with kernel composition, the genetic architecture of nixtamalization moisture content, and develop breeding strategies to improve nixtamalization moisture content. This thesis also investigated the compositional and morphological characteristics of maize kernels that impact nixtamalization pericarp retention, providing key foundational knowledge for improving pericarp retention. Together, these studies provide a framework for improving masa-based product quality, reducing waste, and enhancing the sustainability of food-grade maize production in breeding, sourcing, and manufacturing systems.
- 일반주제명
- Plant sciences
- 일반주제명
- Food science
- 일반주제명
- Genetics
- 일반주제명
- Agriculture
- 일반주제명
- Agronomy
- 키워드
- Composition
- 키워드
- Machine learning
- 키워드
- Maize
- 키워드
- Nixtamalization
- 키워드
- Plant breeding
- 기타저자
- University of Minnesota Applied Plant Sciences
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202103704
■006m o d
■007cr#unu||||||||
■020 ▼a9798290909936
■035 ▼a(MiAaPQ)AAI32113306
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a580
■1001 ▼aBurns, Michael James.
■24510▼aDeveloping Scalable Breeding Tools for Nixtamalization End-Product Quality
■260 ▼a[Sl]▼bUniversity of Minnesota▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a160 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Hirsch, Candice N.
■5021 ▼aThesis (Ph.D.)--University of Minnesota, 2025.
■520 ▼aNixtamalization is a high-heat, high-pH cooking process that loosens pericarp and softens the endosperm of maize grain and is used to create a dough called masa. Several globally significant foods such as tortillas and tortilla chips are made of masa and make up significant portions of consumed calories and nutrients around the world. Relatively few acres of maize are grown for masa-based products in the United States compared to other uses of maize, leading to reduced resources for germplasm improvement in traits that impact masa-product quality. Two traits that have notable downstream impacts on masa quality include moisture absorption and pericarp retention, which can impact the taste, texture, appearance, and machinability of the final product. This thesis addresses the resource gap by developing scalable tools and biological knowledge to improve these traits. Specifically, machine learning models were created to predict nixtamalization moisture content in both inbred and hybrid maize based on the near-infrared spectra of raw maize kernels. These models were used to assess the relationship that nixtamalization moisture content has with kernel composition, the genetic architecture of nixtamalization moisture content, and develop breeding strategies to improve nixtamalization moisture content. This thesis also investigated the compositional and morphological characteristics of maize kernels that impact nixtamalization pericarp retention, providing key foundational knowledge for improving pericarp retention. Together, these studies provide a framework for improving masa-based product quality, reducing waste, and enhancing the sustainability of food-grade maize production in breeding, sourcing, and manufacturing systems.
■590 ▼aSchool code: 0130.
■650 4▼aPlant sciences
■650 4▼aFood science
■650 4▼aGenetics
■650 4▼aAgriculture
■650 4▼aAgronomy
■653 ▼aComposition
■653 ▼aGenome-wide association study
■653 ▼aMachine learning
■653 ▼aMaize
■653 ▼aNixtamalization
■653 ▼aPlant breeding
■690 ▼a0479
■690 ▼a0359
■690 ▼a0369
■690 ▼a0473
■690 ▼a0285
■71020▼aUniversity of Minnesota▼bApplied Plant Sciences.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0130
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358241▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
