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Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics

Received: 2 August 2017     Accepted: 26 August 2017     Published: 26 September 2017
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Abstract

In this study, changes in the apparent characteristics of abalone under a range of boiling temperatures and times were assessed. The trends of shape, color, and texture were statistically analyzed, while the Back Propagation (BP) neural network model was established by monitoring these 3 characteristics under different times and temperatures. This achieved a model of the characteristic parameters to predict the optimum boiling time and temperature, which can be used as a reference for abalone-processing technology. The results show that, although the model is acceptable, the BP neural network model with color feature offered the best predictor rate at 81.74%.

Published in International Journal of Nutrition and Food Sciences (Volume 6, Issue 6)
DOI 10.11648/j.ijnfs.20170606.12
Page(s) 221-227
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Abalone, BP Neural Network, Image Feature, RGB Color Model, Gray-Level, Co-occurrence Matrix

References
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Cite This Article
  • APA Style

    Xiaoyan Fang, Jiaxu Dong, Huihui Wang, Xu Zhang, Xueheng Tao. (2017). Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. International Journal of Nutrition and Food Sciences, 6(6), 221-227. https://doi.org/10.11648/j.ijnfs.20170606.12

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    ACS Style

    Xiaoyan Fang; Jiaxu Dong; Huihui Wang; Xu Zhang; Xueheng Tao. Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. Int. J. Nutr. Food Sci. 2017, 6(6), 221-227. doi: 10.11648/j.ijnfs.20170606.12

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    AMA Style

    Xiaoyan Fang, Jiaxu Dong, Huihui Wang, Xu Zhang, Xueheng Tao. Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. Int J Nutr Food Sci. 2017;6(6):221-227. doi: 10.11648/j.ijnfs.20170606.12

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  • @article{10.11648/j.ijnfs.20170606.12,
      author = {Xiaoyan Fang and Jiaxu Dong and Huihui Wang and Xu Zhang and Xueheng Tao},
      title = {Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics},
      journal = {International Journal of Nutrition and Food Sciences},
      volume = {6},
      number = {6},
      pages = {221-227},
      doi = {10.11648/j.ijnfs.20170606.12},
      url = {https://doi.org/10.11648/j.ijnfs.20170606.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijnfs.20170606.12},
      abstract = {In this study, changes in the apparent characteristics of abalone under a range of boiling temperatures and times were assessed. The trends of shape, color, and texture were statistically analyzed, while the Back Propagation (BP) neural network model was established by monitoring these 3 characteristics under different times and temperatures. This achieved a model of the characteristic parameters to predict the optimum boiling time and temperature, which can be used as a reference for abalone-processing technology. The results show that, although the model is acceptable, the BP neural network model with color feature offered the best predictor rate at 81.74%.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics
    AU  - Xiaoyan Fang
    AU  - Jiaxu Dong
    AU  - Huihui Wang
    AU  - Xu Zhang
    AU  - Xueheng Tao
    Y1  - 2017/09/26
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ijnfs.20170606.12
    DO  - 10.11648/j.ijnfs.20170606.12
    T2  - International Journal of Nutrition and Food Sciences
    JF  - International Journal of Nutrition and Food Sciences
    JO  - International Journal of Nutrition and Food Sciences
    SP  - 221
    EP  - 227
    PB  - Science Publishing Group
    SN  - 2327-2716
    UR  - https://doi.org/10.11648/j.ijnfs.20170606.12
    AB  - In this study, changes in the apparent characteristics of abalone under a range of boiling temperatures and times were assessed. The trends of shape, color, and texture were statistically analyzed, while the Back Propagation (BP) neural network model was established by monitoring these 3 characteristics under different times and temperatures. This achieved a model of the characteristic parameters to predict the optimum boiling time and temperature, which can be used as a reference for abalone-processing technology. The results show that, although the model is acceptable, the BP neural network model with color feature offered the best predictor rate at 81.74%.
    VL  - 6
    IS  - 6
    ER  - 

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Author Information
  • Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China

  • Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China

  • Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China

  • Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China

  • Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China

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