Moisture content assessment of dried Hami jujube using image colour analysis

Benxue Ma, Cong Li, Yujie Li, Wenxia Wang, Guowei Yu, Wancheng Dong and Yuanjia Zhang
Additional contact information
Benxue Ma: College of Mechanical and Electrical Engineering, Shihezi University, 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

Czech Journal of Food Sciences, 2022, vol. 40, issue 1, 33-41

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.

Keywords: chromaticity frequency sequence; colour mean; competitive adaptive reweighted sampling; extreme learning machine (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://cjfs.agriculturejournals.cz/doi/10.17221/109/2021-CJFS.html (text/html)
http://cjfs.agriculturejournals.cz/doi/10.17221/109/2021-CJFS.pdf (application/pdf)
free of charge

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjf:v:40:y:2022:i:1:id:109-2021-cjfs

DOI: 10.17221/109/2021-CJFS

Access Statistics for this article

Czech Journal of Food Sciences is currently edited by Ing. Zdeňka Náglová, Ph.D.

More articles in Czech Journal of Food Sciences from Czech Academy of Agricultural Sciences
Bibliographic data for series maintained by Ivo Andrle ().