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Optimisation of the Computer Image Analysis Method for Evaluating the Quality of Bread With Added Acorn Flour

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Title: Optimisation of the Computer Image Analysis Method for Evaluating the Quality of Bread With Added Acorn Flour
Authors: Jukić, Marko, Koceva Komlenić, Daliborka, Lukinac Čačić, Jasmina, Šušak, Ana, Lončarić, Petra
Publisher Information: 2025.
Publication Year: 2025
Subject Terms: image analysis, segmentation algorithms, acorn flour, gluten-free bread, bread quality
Description: This study investigates the development of an automated image analysis method for assessing the structural quality of gluten-free bread enriched with acorn flour (Quercus rotundifolia L.). The unique techno-functional properties of acorn flour in bread dough pose significant challenges for bakery and pastry applications, making this research timely and relevant. The primary objective of this study is to evaluate key structural parameters of bread, including void count, average void size, and porosity, using various image segmentation algorithms. The algorithms tested include Default, IsoData, Li, Moments, and Otsu, with IsoData identified as the most stable and reliable for this application. The results demonstrate that increasing the acorn flour content leads to higher void count, larger void size, and increased porosity, with 100% acorn flour showing the most pronounced effects. Additionally, increasing the water content in the dough reduces void count while increasing void size and porosity, further highlighting the role of acorn flour in influencing bread texture and structure. These findings underscore the potential of acorn flour to improve the quality of gluten-free bread and contribute to the development of healthier, innovative bakery products. This study offers valuable insights into optimizing the use of acorn flour in bakery applications and promotes its potential as a sustainable ingredient in gluten-free and functional food products.
Document Type: Conference object
Access URL: https://acornfoodworkshop.itu.edu.tr/assets/presentations/Proceedings_978-975-561-571-4.pdf
Accession Number: edsair.dris...01492..ae82c0d2873feeabcff8c8ddd345c93c
Database: OpenAIRE
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PubType: Conference
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Optimisation of the Computer Image Analysis Method for Evaluating the Quality of Bread With Added Acorn Flour
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jukić%2C+Marko%22">Jukić, Marko</searchLink><br /><searchLink fieldCode="AR" term="%22Koceva+Komlenić%2C+Daliborka%22">Koceva Komlenić, Daliborka</searchLink><br /><searchLink fieldCode="AR" term="%22Lukinac+Čačić%2C+Jasmina%22">Lukinac Čačić, Jasmina</searchLink><br /><searchLink fieldCode="AR" term="%22Šušak%2C+Ana%22">Šušak, Ana</searchLink><br /><searchLink fieldCode="AR" term="%22Lončarić%2C+Petra%22">Lončarić, Petra</searchLink>
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: 2025.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2025
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22image+analysis%22">image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22segmentation+algorithms%22">segmentation algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22acorn+flour%22">acorn flour</searchLink><br /><searchLink fieldCode="DE" term="%22gluten-free+bread%22">gluten-free bread</searchLink><br /><searchLink fieldCode="DE" term="%22bread+quality%22">bread quality</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This study investigates the development of an automated image analysis method for assessing the structural quality of gluten-free bread enriched with acorn flour (Quercus rotundifolia L.). The unique techno-functional properties of acorn flour in bread dough pose significant challenges for bakery and pastry applications, making this research timely and relevant. The primary objective of this study is to evaluate key structural parameters of bread, including void count, average void size, and porosity, using various image segmentation algorithms. The algorithms tested include Default, IsoData, Li, Moments, and Otsu, with IsoData identified as the most stable and reliable for this application. The results demonstrate that increasing the acorn flour content leads to higher void count, larger void size, and increased porosity, with 100% acorn flour showing the most pronounced effects. Additionally, increasing the water content in the dough reduces void count while increasing void size and porosity, further highlighting the role of acorn flour in influencing bread texture and structure. These findings underscore the potential of acorn flour to improve the quality of gluten-free bread and contribute to the development of healthier, innovative bakery products. This study offers valuable insights into optimizing the use of acorn flour in bakery applications and promotes its potential as a sustainable ingredient in gluten-free and functional food products.
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  Data: Conference object
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  Data: <link linkTarget="URL" linkTerm="https://acornfoodworkshop.itu.edu.tr/assets/presentations/Proceedings_978-975-561-571-4.pdf" linkWindow="_blank">https://acornfoodworkshop.itu.edu.tr/assets/presentations/Proceedings_978-975-561-571-4.pdf</link>
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  Label: Accession Number
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  Data: edsair.dris...01492..ae82c0d2873feeabcff8c8ddd345c93c
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RecordInfo BibRecord:
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      – Text: Undetermined
    Subjects:
      – SubjectFull: image analysis
        Type: general
      – SubjectFull: segmentation algorithms
        Type: general
      – SubjectFull: acorn flour
        Type: general
      – SubjectFull: gluten-free bread
        Type: general
      – SubjectFull: bread quality
        Type: general
    Titles:
      – TitleFull: Optimisation of the Computer Image Analysis Method for Evaluating the Quality of Bread With Added Acorn Flour
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      – PersonEntity:
          Name:
            NameFull: Jukić, Marko
      – PersonEntity:
          Name:
            NameFull: Koceva Komlenić, Daliborka
      – PersonEntity:
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            NameFull: Lukinac Čačić, Jasmina
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            NameFull: Šušak, Ana
      – PersonEntity:
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            NameFull: Lončarić, Petra
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
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