Quality grading of grapes coated with Farsi gum and hemp seed oil emulsion based on surface defects using RGB and binary images

مجله پژوهشهای علوم و صنایع غذایی ایران

Article 4, Volume 18, Issue 1 - Serial Number 73, March and April 2022, Pages 51-64   

Document Type: Research Article

DOI: 10.22067/ifstrj.v18i1.88305

Authors

Mohsen Zandi^{* 1}; Ali Ganjloo²; Mandana Bimakr²; Abolfazl Gharebaghi

¹Department of Food Science and Engineering, Faculty of Agriculture, Zanjan University, Iran.

²Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran.

Abstract

Introduction: Grape is a non-climacteric fruit with a low rate of physiological activity but is subject to serious physiological and parasitic disorders after harvest and during long term storage (Ciccarese et al., 2013). Currently, Edible coatings have been studied as potential substitutes for conventional plastics in food packaging. Edible coating is a thin layer of edible material formed as a coating on a food product. Edible coating can offer several advantages to the fresh fruit and vegetable industry such as improvement in the retention of color, acids, sugar and flavor components, the maintenance of quality during shipping and storage, the reduction of storage disorders and improved consumer appeal (Antoniou et al., 2015; Cazon et al., 2017; Fakhouri et al., 2015; Galus & Kadzińska, 2015). Farsi gum as a novel source of polysaccharides has drawn much attention in a wide range of various fields such as pharmaceutics, food and cosmetics industries. Functional properties of Farsi gum are influenced by its structure and molecular weight (Hadian et al., 2016; Joukar et al., 2017). By inclusion of bioactive compounds in the Farsi gum network the aforementioned impairments could be overcome and moreover, new protective and functional valences could be added. The inclusion of lipid-based component in Farsi gum gives it excellent light and moisture barrier properties. The benefic impact on human health of hemp seed oil is worldwide recognized. A recent study demonstrated the antimicrobial properties of hemp seed oil. Due to their abundance in biologically active compounds, hemp seed oil is promising natural alternatives that may extend the shelf-life, microbiological safety and nutritional values of food (Cozmuta et al., 2015; Leizer et al., 2000; Salarnia et al., 2018). Growing awareness of the quality of fruit has necessitated increasing effort to develop rapid and non-destructive methods for evaluating fruit quality (Bhargava & Bansal, 2020; Rachmawati et al., 2017; Tao & Zhou, 2017; Wu & Sun, 2013). The aim of this study was the consideration of image processing application for grape sorting based on visual surface characterize.
 
Materials and Methods: Coating emulsion was prepared using (Farsi gum (0%, 1.5% and 3%), hemp seed oil (0%, 0.075% and 0.15%) and glyceride (0.3%)). grape fruit were coated by immersion in coating dispersion for 5 min. Samples were then allowed to loss the excess coating dispersion. Coatings were developed at room temperature during an hour. Samples were refrigerated at 4± 1°C for 28 days and analyses were performed at days 0 and 28. Defect identification and maturity detection of grape fruits are challenging task for the computer vision to achieve near human levels of recognition. The image acquisition was performed in a homogenously controlled lighting condition. Considering the camera lens’s focal length, the samples were placed 25 cm under the camera’s lens to be under camera’s field of view. The images of grape were segmented from the background using thresholding of the high contrast images via MATLAB software (R2019a, image processing toolbox). The optimum threshold value was obtained to be 0.35, 0.45 and 0.30 for R, G and B channel, respectively.
 
Results and Discussion: The proposed techniques can separate between the defected and the healthy grape fruits, and then detect and classify the actual defected area. Classification is performed in two manners which in the first one, an input grape is classified with two different algorithms (RGB and binary). The Result showed that the accuracies for detecting the surface defects on grape were 97.73% and 96.30% using RGB and binary algorithms, respectively. Proposed method can be used to detect the visible defects of coated grape, and to grade the grape in high speed and precision.
 
Conclusions: The results of this research and similar ones can provide helpful recommendations in grading fruits for fresh consumption. The simplicity and the efficiency of the proposed techniques make them appropriate for designing a low-cost hardware kit that can be used for real applications.

Keywords

Coating; Grading; Image processing; Grape; Hemp seed oil; Surface defects

References

1. Antoniou, J., Liu, F., Majeed, H., & Zhong, F. (2015). Characterization of tara gum edible films incorporated with bulk chitosan and chitosan nanoparticles: A comparative study. Food Hydrocolloids, 44, 309-319. https://doi.org/10.1016/j.foodhyd.2014.09.023
2. Bhargava, A., & Bansal, A. (2020). Automatic Detection and Grading of Multiple Fruits by Machine Learning. Food Analytical Methods, 13(3), 751-761. https://doi.org/10.1007/s12161-019-01690-6
3. Cazon, P., Velazquez, G., Ramírez, J. A., & Vazquez, M. (2017). Polysaccharide-based films and coatings for food packaging: A review. Food Hydrocolloids, 68 136-148. https://doi.org/10.1016/j.foodhyd.2016.09.009
4. Chithra, P., & Henila, M. (2017). Defect identification in the fruit apple using k-means color image segmentation algorithm. International Journal of Advanced Research in Computer Science, 8(8).
5. Ciccarese, A., Stellacci, A. M., Gentilesco, G., & Rubino, P. (2013). Effectiveness of pre-and post-veraison calcium applications to control decay and maintain table grape fruit quality during storage. Postharvest Biology and Technology, 75, 135-141. https://doi.org/10.1016/j.postharvbio.2012.08.010
6. Cozmuta, A. M., Turila, A., Apjok, R., Ciocian, A., Cozmuta, L. M., Peter, A., Nicula, C., Galić, N., & Benković, T. (2015). Preparation and characterization of improved gelatin films incorporating hemp and sage oils. Food Hydrocolloids, 49, 144-155. https://doi.org/10.1016/j.foodhyd.2015.03.022
7. Dehghani, P., Hosseini, S. M. H., Golmakani, M.-T., Majdinasab, M., & Esteghlal, S. (2018). Shelf-life extension of refrigerated rainbow trout fillets using total Farsi gum-based coatings containing clove and thyme essential oils emulsions. Food Hydrocolloids, 77, 677-688. https://doi.org/10.1016/j.foodhyd.2017.11.009
8. Dorj, U.-O., Lee, M., & Yun, S.-s. (2017). An yield estimation in citrus orchards via fruit detection and counting using image processing. Computers and electronics in agriculture, 140, 103-112. https://doi.org/10.1016/j.compag.2017.05.019
9. Fakhouri, F. M., Martelli, S. M., Caon, T., Velasco, J. I., & Mei, L. H. I. (2015). Edible films and coatings based on starch/gelatin: Film properties and effect of coatings on quality of refrigerated Red Crimson grapes. Postharvest Biology and Technology, 109, 57-64. https://doi.org/10.1016/j.postharvbio.2015.05.015
10. Galus, S., & Kadzińska, J. (2015). Food applications of emulsion-based edible films and coatings. Trends in Food Science & Technology, 45(2), 273-283. https://doi.org/10.1016/j.tifs.2015.07.011
11. Ganjloo, A., Zandi, M., Bimakr, M., & Monajem, S. (2020). Ripening Stages Control of Cherry Tomato Coated with Aloe Vera Gel using Artificial Vision System (In persian). Food Science and Technology, 17(105), 135-149. DOI: 52547/fsct.17.105.135
12. Golzarian, M., Shamili, M., Doosti Irani, O., & Azarkish, P. (2017). Detection of surface defects caused by mechanical damages on Mangos of Kelk-e Sorkh variety with the use of color image processing. Iranian Food Science and Technology Research Journal (IFSTRJ), 12(5), 652-662.
13. Hadian, M., Hosseini, S. M. H., Farahnaky, A., Mesbahi, G. R., Yousefi, G. H., & Saboury, A. A. (2016). Isothermal titration calorimetric and spectroscopic studies of β-lactoglobulin-water-soluble fraction of Persian gum interaction in aqueous solution. Food Hydrocolloids, 55, 108-118. https://doi.org/10.1016/j.foodhyd.2015.11.006
14. Hashim, N., Adebayo, S. E., Abdan, K., & Hanafi, M. (2018). Comparative study of transform-based image texture analysis for the evaluation of banana quality using an optical backscattering system. Postharvest Biology and Technology, 135, 38-50. https://doi.org/10.1016/j.postharvbio.2017.08.021
15. Hassan, N. M. H., & Nashat, A. A. (2019). New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques. Multidimensional Systems and Signal Processing, 30(2), 571-589. https://doi.org/10.1007/s11045-018-0573-5
16. Joukar, F., Hosseini, S. M. H., Moosavi-Nasab, M., Mesbahi, G. R., & Behzadnia, A. (2017). Effect of Farsi gum-based antimicrobial adhesive coatings on the refrigeration shelf life of rainbow trout fillets. LWT, 80, 1-9. https://doi.org/10.1016/j.lwt.2017.01.074
17. Khalesi, H., Emadzadeh, B., Kadkhodaee, R., & Fang, Y. (2016). Whey protein isolate-Persian gum interaction at neutral pH. Food Hydrocolloids, 59, 45-49. https://doi.org/10.1016/j.foodhyd.2015.10.017
18. Khan, M. A., Lali, M. I. U., Sharif, M., Javed, K., Aurangzeb, K., Haider, S. I., Altamrah, A. S., & Akram, T. (2019). An optimized method for segmentation and classification of apple diseases based on strong correlation and genetic algorithm based feature selection. IEEE Access, 7, 46261-46277. DOI: 1109/ACCESS.2019.2908040
19. Khorram, F., Ramezanian, A., & Hosseini, S. M. H. (2017). Shellac, gelatin and Persian gum as alternative coating for orange fruit. Scientia Horticulturae, 225, 22-28. https://doi.org/10.1016/j.scienta.2017.06.045
20. Leizer, C., Ribnicky, D., Poulev, A., Dushenkov, S., & Raskin, I. (2000). The composition of hemp seed oil and its potential as an important source of nutrition. Journal of Nutraceuticals, functional & medical foods, 2(4), 35-53. https://doi.org/10.1300/J133v02n04_04
21. Moallem, P., Serajoddin, A., & Pourghassem, H. (2017). Computer vision-based apple grading for golden delicious apples based on surface features. Information processing in agriculture, 4(1), 33-40. https://doi.org/10.1016/j.inpa.2016.10.003
22. Pordarbani, R., Gasemzadeh, H. R., Golzadeh, A. A., & Behfar, H. (2009). Feasibility Study of Apple Quality Grading Using Image Processing. Journal of Food Research, 19(1), 75-85.
23. Rachmawati, E., Supriana, I., & Khodra, M. L. (2017, September). Toward a new approach in fruit recognition using hybrid RGBD features and fruit hierarchy property. In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 1-6). IEEE.
24. Sahu, D., & Potdar, R. M. (2017). Defect identification and maturity detection of mango fruits using image analysis. American Journal of Artificial Intelligence, 1(1), 5-14.
25. Sahu, U. K., & Patra, D. (2015, December). Shape features for image-based servo-control using image moments. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE.
26. Salarnia, M., Ganjloo, A., Emam-Djomeh, Z., & Bimakr, M. (2018). Physical, Barrier and Antimicrobial Properties of Sodium Caseinate-based Edible Film Containing Hemp Seed oil (in persian). journal of Innovative Food Technologies (JIFT), 5(3), 485-497. DOI: 22104/jift.2018.2607.1617
27. Salehi, F. (2019). Characterization of new biodegradable edible films and coatings based on seeds gum: A review. Journal of Packaging Technology and Research, 1-9. https://doi.org/10.1016/j.tifs.2019.11.011
28. Salehi, F. (2020). Effect of coatings made by new hydrocolloids on the oil uptake during deep‐fat frying: A review. Journal of Food Processing and Preservation, e14879. https://doi.org/10.1111/jfpp.14879
29. Shahbazi, Y., & Shavisi, N. (2020). Application of active Kurdi gum and Farsi gum-based coatings in banana fruits. Journal of Food Science and Technology, 1-11. https://doi.org/10.1007/s13197-020-04462-x
30. Tao, Y., & Zhou, J. (2017). Automatic apple recognition based on the fusion of color and 3D feature for robotic fruit picking. Computers and electronics in agriculture, 142, 388-396. https://doi.org/10.1016/j.compag.2017.09.019
31. Topalovic,, & Mikulic-Petkovsek, M. (2010). Changes in sugars, organic acids and phenolics of grape berries of cultivar Cardinal during ripening. J Food Agric Environ, 8(3&4), 223-227.
32. Vargas, M., & González-Martínez, C. (2010). Recent patents on food applications of chitosan. Recent patents on food, nutrition & agriculture, 2(2), 121-128.
33. Wu, D., & Sun, D.-W. (2013). Colour measurements by computer vision for food quality control–A review. Trends in Food Science & Technology, 29(1), 5-20. https://doi.org/10.1016/j.tifs.2012.08.004