IDEAS home Printed from https://ideas.repec.org/a/caa/jnlcjf/v40y2022i1id109-2021-cjfs.html
   My bibliography  Save this article

Moisture content assessment of dried Hami jujube using image colour analysis

Author

Listed:
  • Benxue Ma

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, P.R. China
    Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, P.R. China)

  • Cong Li

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, P.R. China)

  • Yujie Li

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, P.R. China)

  • Wenxia Wang

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, P.R. China)

  • Guowei Yu

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, P.R. China)

  • Wancheng Dong

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, P.R. China)

  • Yuanjia Zhang

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, P.R. China)

Abstract

To investigate the feasibility of image colour information in predicting the moisture content of dried Hami jujube, the images were obtained under different colour space models, and the colour model component mean and chromaticity frequency sequences of R, G, B, H, S, V, L*, a* and b* were extracted through image analysis. After optimising the colour model component mean and chromaticity frequency sequence, the model was established and compared. The results showed that the GA-ELM (genetic algorithm - extreme learning machine) model established by CARS (competitive adaptive reweighted sampling) method to optimise 12 chromaticity features of S chromaticity frequency sequence had the best prediction effect, with Rc of 0.917, Rp of 0.934 and residual predictive deviation (RPD) of 2.507. Therefore, the colour image information can accurately predict the moisture content of dried Hami jujube.

Suggested Citation

  • Benxue Ma & Cong Li & Yujie Li & Wenxia Wang & Guowei Yu & Wancheng Dong & Yuanjia Zhang, 2022. "Moisture content assessment of dried Hami jujube using image colour analysis," Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 40(1), pages 33-41.
    • Handle: RePEc:caa:jnlcjf:v:40:y:2022:i:1:id:109-2021-cjfs
    DOI: 10.17221/109/2021-CJFS
    as

    Download full text from publisher

    File URL: http://cjfs.agriculturejournals.cz/doi/10.17221/109/2021-CJFS.html
    Download Restriction: free of charge
    File URL: http://cjfs.agriculturejournals.cz/doi/10.17221/109/2021-CJFS.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/109/2021-CJFS?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Iman Golpour & Jafar Amiri Parian & Reza Amiri Chayjan, 2014. "Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network," Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 32(3), pages 280-287.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlcjf:v:40:y:2022:i:1:id:109-2021-cjfs. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.