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Improvement of tomato breeding selection capabilities using vibrational spectroscopy and prediction algorithms

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  1. Tez No: 622933
  2. Yazar: HACER AKPOLAT
  3. Danışmanlar: DR. LUİS E. RODRİGUEZ-SAONA
  4. Tez Türü: Doktora
  5. Konular: Besin Hijyeni ve Teknolojisi, Gıda Mühendisliği, Food Hygiene and Technology, Food Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2019
  8. Dil: İngilizce
  9. Üniversite: The Ohio State University
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 215

Özet

Tomatoes are one of the most consumed vegetables throughout the world delivering important nutrients as a widely available and cost-effective option. When 2010 Dietary guidelines were released with an effort to increase vegetable consumption, tomatoes were moved from“other vegetables”subgroup to the newly created“red-orange vegetables”subgroup as a nutritionally dense, widely available and affordable vegetable option for a healthy diet. Domestication and breeding for yield, size, disease resistance caused unexpected negative consequences, and the lack of aroma and taste has become a major complaint among consumers of modern tomato varieties. The overall objective of this research was to develop rapid and effective methods based on vibrational spectroscopy to improve tomato breeding selections in terms of improving tomato taste and flavor as well as color and nutritionally important components of tomatoes. In the first chapter, a background and literature review are given on tomatoes and breeding. A comprehensive review is given on vibrational spectroscopy and applications in food industry, and two independent experimental studies are presented. The first study focused on development of a rapid technique based on infrared spectroscopy to use in field applications in order to improve the selection of new tomato varieties with desired levels of chemical traits which are related to tomato flavor and aroma. In this part of the study, predictive regression algorithms were developed using portable FTIR devices with different sampling approaches to find the best application to utilize in breeding industry. Multiple quality traits were simultaneously determined by using a single drop of sample providing fast (

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