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20 May 2024
Reseach Article

Non-destructive Detection for Irradiated Apple using Image Processing

by H.M. Nada, A.A. Arafa, I.F. Tarrad, M. Ashour
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 24
Year of Publication: 2021
Authors: H.M. Nada, A.A. Arafa, I.F. Tarrad, M. Ashour
10.5120/ijca2021921609

H.M. Nada, A.A. Arafa, I.F. Tarrad, M. Ashour . Non-destructive Detection for Irradiated Apple using Image Processing. International Journal of Computer Applications. 183, 24 ( Sep 2021), 20-24. DOI=10.5120/ijca2021921609

@article{ 10.5120/ijca2021921609,
author = { H.M. Nada, A.A. Arafa, I.F. Tarrad, M. Ashour },
title = { Non-destructive Detection for Irradiated Apple using Image Processing },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2021 },
volume = { 183 },
number = { 24 },
month = { Sep },
year = { 2021 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number24/32075-2021921609/ },
doi = { 10.5120/ijca2021921609 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:46.124058+05:30
%A H.M. Nada
%A A.A. Arafa
%A I.F. Tarrad
%A M. Ashour
%T Non-destructive Detection for Irradiated Apple using Image Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 24
%P 20-24
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a nondestructive method for detecting irradiated apple rather than the previous destructive method known before such as analytical methods; Chemical, Physical and Biological methods. Image processing technique was applied for rapid and nondestructive detection of irradiated apples. Color intensities, smoothness and uniformities were extracted and analyzed to correlate these color features of apple samples with its values before radiation. ANOVA analysis showed significant differences between both irradiated and un-irradiated apples sample. Linear discriminant analysis (LDA) was utilized for HSV data analysis. Results indicated that it was possible to detect irradiated food with good accuracy using imaging processing technique with an overall success rate of approximately 85%. The proposed method is cheap and less complicated which in turn saves time and effort. Consequently, it overcome the disadvantages of other analytical methods that are complex, costly and destructing the samples.

References
  1. (1992, Effects of Ionizing Radiation on Plants and Animals at Levels Implied by Current Radiation Protection Standards. Technical Reports Series No. 332INTERNATIONAL ATOMIC ENERGY AGENCY, Vienna. Available: https://www.iaea.org/publications/1436/effects-of-ionizing-radiation-on-plants-and-animals-at-levels-implied-by-current-radiation-protection-standards
  2. C.-J. Du and D.-W. Sun, "Recent developments in the applications of image processing techniques for food quality evaluation," Trends in food science & technology, vol. 15, pp. 230-249, 2004.
  3. M. T. Munir and M. Federighi, "Control of foodborne biological hazards by ionizing radiations," Foods, vol. 9, p. 878, 2020.
  4. S. Firouzi, A. Khorshidi, J. Soltani-Nabipour, S. M. Zia Barzi, M. Amani, and M. R. Ay, "Evaluation of gamma and electron radiations impact on vitamins for onion preservation," Appl Radiat Isot, vol. 167, p. 109442, Jan 2021.
  5. (2021). Food Facts for Consumers (Food and Drug Administration ed.). 2021. Available: https://www.fda.gov/media/81259/download
  6. E. Zanardi, A. Caligiani, and E. Novelli, "New Insights to Detect Irradiated Food: an Overview," Food Analytical Methods, vol. 11, pp. 224-235, 2018/01/01 2018.
  7. R. Stefanova, N. V. Vasilev, and S. L. Spassov, "Irradiation of Food, Current Legislation Framework, and Detection of Irradiated Foods," Food Analytical Methods, vol. 3, pp. 225-252, 2010/09/01 2010.
  8. D.-W. Sun, Computer vision technology for food quality evaluation: Academic Press, 2016.
  9. C. Garrido-Novell, D. Pérez-Marin, J. M. Amigo, J. Fernández-Novales, J. E. Guerrero, and A. Garrido-Varo, "Grading and color evolution of apples using RGB and hyperspectral imaging vision cameras," Journal of Food Engineering, vol. 113, pp. 281-288, 2012.
  10. M. Shahin, E. Tollner, R. McClendon, and H. Arabnia, "Apple classification based on surface bruises using image processing and neural networks," Transactions of the ASAE, vol. 45, p. 1619, 2002.
  11. F. Vesali, M. Gharibkhani, and M. H. Komarizadeh, "An approach to estimate moisture content of apple with image processing method," Australian journal of crop science, vol. 5, pp. 111-115, 2011.
  12. Y.-Y. Pu, M. Zhao, C. O’Donnell, and D.-W. Sun, "Nondestructive quality evaluation of banana slices during microwave vacuum drying using spectral and imaging techniques," Drying Technology, vol. 36, pp. 1542-1553, 2018/10/03 2018.
  13. H. M. A. Rahman, M. A. Ashour, I. F. Tarrad, and A. A. Arafa, "Irradiated Food Quality Evaluation Using Image Processing Technique," Master Thesis, Department of Electrical Engineering, Al-Azhar University, 2015.
  14. C. Xiong, C. Liu, W. Liu, W. Pan, F. Ma, W. Chen, et al., "Noninvasive discrimination and textural properties of E-beam irradiated shrimp," Journal of Food Engineering, vol. 175, pp. 85-92, 2016/04/01/ 2016.
  15. W. H. Organization, "Food irradiation: A technique for preserving and improving the safety of food," 1988.
  16. A. Salem, M. Naweto, and M. Mostafa, "Combined effect of gamma irradiation and chitosan coating on physical and chemical properties of plum fruits," Journal of Nuclear Technology in Applied Science, vol. 4, pp. 91-102, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Apples ANOVA Color evolution Color Intensity HSV imaging Imaging processing Linear Discriminant Analysis (LDA) RGB imaging.