Czech J. Food Sci., 2022, 40(1):33-41 | DOI: 10.17221/109/2021-CJFS

Moisture content assessment of dried Hami jujube using image colour analysisOriginal Paper

Benxue Ma*,1,2, Cong Li1, Yujie Li1, Wenxia Wang1, Guowei Yu1, Wancheng Dong1, Yuanjia Zhang1
1 College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, P.R. China
2 Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, P.R. China

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

Published: February 24, 2022  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Ma B, Li C, Li Y, Wang W, Yu G, Dong W, Zhang Y. Moisture content assessment of dried Hami jujube using image colour analysis. Czech J. Food Sci. 2022;40(1):33-41. doi: 10.17221/109/2021-CJFS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Barbedo J.G.A. (2016): A novel algorithm for semi-automatic segmentation of plant leaf disease symptoms using digital image processing, Tropical Plant Pathology, 41: 210-224. Go to original source...
    2. Dai Y.P., Wang Y.T., Xue J.R., Liu X.N., Liu B.H., Guo X.Y. (2017): Research of segmentation method on image of Lingwu Long Jujubes based on a new extraction model of Hue. IEEE Sensors Journal, 17: 6029-6036. Go to original source...
    3. Feng L., Li H.B., Gao Y.K., Zhang Y.K. (2020): A color image segmentation method based on region salient color and fuzzy c-means algorithm. Circuits, Systems & Signal Processing, 39: 586-610. Go to original source...
    4. Golpour I., Parian J.A., Chayjan R.A. (2014): Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network. Czech J. Food Sci., 32: 280-287. Go to original source...
    5. Guo W.C., Fang L.J., Liu D.Y., Wang Z.W. (2015): Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy. Computers and Electronics in Agriculture, 117: 226-233. Go to original source...
    6. Galvao R.K.H., Araujo M.C.U., Jose G.E., Pontes M.J.C., Silva E.C., Saldanha T.C.B. (2005): A method for calibration and validation subset partitioning. Talanta, 67: 736-740. Go to original source... Go to PubMed...
    7. Huang Y.F., Dong W.T, Sanaeifar A., Wang X.M., Luo W., Zhan B.S., Liu X.M., Li R.L., Zhang H.L., Li X.L. (2020): Development of simple identification models for four main catechins and caffeine in fresh green tea leaf based on visible and near-infrared spectroscopy. Computers and Electronics in Agriculture, 173: 1-8. Go to original source...
    8. Hong Y.S., Chen S.C., Chen Y.Y. (2020): Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest. Soil and Tillage Research, 199: 1-14. Go to original source...
    9. Keskin M., Karanlik S., Keskin S.G., Soysal Y. (2014): Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves. Turkish Journal of Agriculture and Forestry, 37: 604-612. Go to original source...
    10. Kreeger P.K. (2013): Using partial least squares regression to analyze cellular response data. Science Signaling. 6: 1-5. Go to original source... Go to PubMed...
    11. Li J.W., Ding S.D., Ding X.L. (2005): Comparison of antioxidant capacities of extracts from five cultivars of Chinese jujube. Process Biochemistry, 40: 3607-3613. Go to original source...
    12. Luo X.Z., Ma B.X., Wang W.X., Lei S.Y. Hu Y.Y., Yu G.W., Li X.Z. (2020): Evaluation of surface texture of dried Hami Jujube using optimised support vector machine based on visual features fusion. Food Science and Biotechnology, 29: 493-502. Go to original source... Go to PubMed...
    13. Manickavasagan A., Al-Mezeini N.K., Al-Shekaili H.N. (2014): RGB color imaging technique for grading of dates. Scientia Horticulturae, 175: 87-94. Go to original source...
    14. Mohebbi M., Akbarzadeh M.R., Shahidi F., Moussavi M., Ghoddusi H.B. (2009): Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp. Computers and Electronics in Agriculture, 69: 128-134. Go to original source...
    15. Nikmaram P., Mousavi S., Emam-Djomeh Z., Kiani H., Razavi S. (2015): Evaluation and prediction of metabolite production, antioxidant activities, and survival of Lactobacillus casei 431 in a pomegranate juice supplemented yogurt drink using support vector regression. Food Science and Biotechnology, 24: 2105-2112. Go to original source...
    16. Nicolaї B.M., Beullens K., Bobelyn E., Peirs A., Saeys W., Theron K.I., Lammertyn J. (2007): Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46: 99-118. Go to original source...
    17. Song G., Dai Q., Han X.M., Guo L. (2020): Two novel ELMbased stacking deep models focused on image recognition. Applied Intelligence, 50: 1345-1366. Go to original source...
    18. Shu M.Y., Shen M.Y., Zuo J.Y., Yin P.F., Ma Y.T. (2021): The application of UAV-based hyperspectral imaging to estimate crop traits in maize inbred lines. Plant Phenomics, 2021: 1-12. Go to original source... Go to PubMed...
    19. Wang W.X., Ma B.X., Luo X.Z., Li X.X., Lei S.Y., LI Y.J., SUN J.T. (2020): Study on moisture content of dried Hami big jujubes by near-infrared spectroscopy combined with variable preferred and GA-ELM model. Spectroscopy and Spectral Analysis, 40: 543-549.
    20. Wu L.G., He J.G., Liu G.S., Wang S.L., He X.G. (2016): Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging. Postharvest Biology and Technology, 112: 134-142. Go to original source...
    21. Wu C.D. (2020): Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method. Insight, 62: 15-21. Go to original source...
    22. Ying Y.B., Xu H.R., Xu Z.G. (2006): Non-destructive maturity evaluation of citrus by hue frequency sequence method. Journal of Biomathematics, 21: 306-312.
    23. Yuan L.M., Mao F., Huang G.Z., Chen X.J., Wu D., Li S.J., Zhou X.Q., Jiang Q.J., Lin D.P., He R. (2020): Models fused with successive CARS-PLS for measurement of the soluble solids content of Chinese bayberry by vis-NIRS technology, Postharvest Biology and Technology, 169: 1-9. Go to original source...
    24. Zhao H.X., Zhang H.S., Li Q., Xu S. (2014): Research progress of drying technology of jujube. Science and Technology of Food Industry, 35: 379-382.

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content