Estimation of Subjective Internal Browning Severity Ratings for Scanned Images of Fuji Apples

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Author(s)

    • Matsubara Kazuya
    • Food Research Institute, National Agriculture and Food Research Organization|Ritsumeikan University
    • Wada Yuji
    • Food Research Institute, National Agriculture and Food Research Organization|Ritsumeikan University
    • Kasai Satoshi
    • Apple Research Institute, Aomori Prefectural Industrial Technology Research Center
    • Shoji Toshihiko
    • Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization
    • Hayakawa Fumiyo
    • Food Research Institute, National Agriculture and Food Research Organization
    • Kazami Yukari
    • Food Research Institute, National Agriculture and Food Research Organization
    • Ikehata Akifumi
    • Food Research Institute, National Agriculture and Food Research Organization
    • Kusakabe Yuko
    • Food Research Institute, National Agriculture and Food Research Organization

Abstract

<p>The severity of internal browning in apple cultivars is often evaluated subjectively, making it potentially unreliable, and a method for automatic evaluation is necessary in order to process many samples efficiently. The objective of this study was to propose a model for estimating subjective browning severity ratings (SBSRs) in scanned images of sliced apples that mimics mean expert judgments. We assessed SBSRs made by three expert observers for images of sliced apples. The results indicated that the experts' evaluations of internal browning were qualitatively similar, but not quantitatively equivalent. The proposed model estimates the mean SBSRs of experts as a percentage of the browning region of the total flesh. The browning regions were qualified using CIELAB color difference from the standard color. The model estimations were consistent with increasing browning during longer storage periods.</p>

Journal

  • Food Science and Technology Research

    Food Science and Technology Research 23(4), 545-549, 2017

    Japanese Society for Food Science and Technology

Codes

  • NII Article ID (NAID)
    130006069519
  • Text Lang
    ENG
  • ISSN
    1344-6606
  • Data Source
    J-STAGE 
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