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Journal of Food Measurement and Characterization

, Volume 13, Issue 3, pp 1730–1738 | Cite as

The assessment of fresh and spoiled beef meat using a prototype device based on GigE Vision camera and DSP

  • Assia ArsalaneEmail author
  • Noureddine El Barbri
  • Abdelmoumen Tabyaoui
  • Abdessamad Klilou
  • Karim Rhofir
Original Paper
  • 28 Downloads

Abstract

Beef meat freshness was evaluated using artificial vision technique and pattern recognition algorithms. Color and texture features were extracted from the saturation images. The wavelet transform was used to characterize texture and a range of features was used to better characterize color. Two classes of beef meat samples were obtained from the projection of color, texture, and color associated with texture datasets using Principal Component Analysis (PCA) method. The first class corresponds to fresh beef meat samples that have undergone 6 days of cold storage and the second class presents spoiled meat. Probabilistic Neural Network (PNN) and Linear Discriminant Analysis (LDA) algorithms were used to classify and predict beef meat samples into fresh or spoiled samples. Results show that the classification and identification rates obtained by PNN are superior to LDA algorithm using the datasets of color, texture, and color associated with texture. In addition, results show that texture features associated with color features give the best classification and identification rates. An implementation of all proposed algorithms was carried out on a real time embedded system.

Keywords

Beef meat Texture Wavelet analysis PCA PNN LDA 

Notes

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

References

  1. 1.
    Y. Peng, J. Zhang, W. Wang, Y. Li, J. Wu, H. Huang, X. Gao, W. Jiang, J. Food Eng. (2010).  https://doi.org/10.1016/j.jfoodeng.2010.08.014 Google Scholar
  2. 2.
    H. Li, X. Sun, W. Pan, F. Kutsanedzie, J. Zhao, Q. Chen, J. Meat Sci. (2016).  https://doi.org/10.1016/j.meatsci.2016.04.031 Google Scholar
  3. 3.
    L. Gram, L. Ravn, M. Rasch, J.B. Bruhn, A.B. Christensen, M. Givskov, Int. J. Food Microbiol. (2002).  https://doi.org/10.1016/S0168-1605(02)00233-7 Google Scholar
  4. 4.
    C.J. Du, D.W. Sun, J. Food Eng. (2006).  https://doi.org/10.1016/j.jfoodeng.2004.11.017 Google Scholar
  5. 5.
    N.E Barbri, A. Halimi, K. Rhofir, IJIREEICE (2014).  https://doi.org/10.17148/IJIREEICE.2014.0210001 Google Scholar
  6. 6.
    A. Arsalane, N.E. Barbri, A. Tabyaoui, A. Klilou, K. Rhofir, ICEMIS (2016).  https://doi.org/10.1109/ICEMIS.2016.7745327 Google Scholar
  7. 7.
    A. Arsalane, N.E. Barbri, K. Rhofir, A. Tabyaoui, A. Klilou, IJIE (2017).  https://doi.org/10.1504/IJIE.2017.087005 Google Scholar
  8. 8.
    A. Arsalane, N.E. Barbri, A. Tabyaoui, A. Klilou, A. Rhofir, Halimi, Comput. Electron. Agric. (2018)  https://doi.org/10.1016/j.compag.2018.07.031 Google Scholar
  9. 9.
    K. Shiranita, T. Miyajima, R. Takiyama, Pattern Recognit. Lett. (1998).  https://doi.org/10.1016/S0167-8655(98)00113-5 Google Scholar
  10. 10.
    J. Li, J. Tan, P. Shatadal, Meat Sci. (2001).  https://doi.org/10.1016/S03091740(00)00105 Google Scholar
  11. 11.
    P. Jackman, D.W. Sun, P. Allen, Meat Sci. (2008).  https://doi.org/10.1016/j.meatsci.2008.06.001 Google Scholar
  12. 12.
    P. Jackman, D.W. Sun, P. Allen, Pattern Recognit. (2009).  https://doi.org/10.1016/j.patcog.2008.09.009 Google Scholar
  13. 13.
    R. Quevedo, L.G. Carlos, J.M. Aguilera, L. Cadoche, J. Food Eng. (2002).  https://doi.org/10.1007/978-0-387-75430-7_16 Google Scholar
  14. 14.
    P. Jackman, D.W. Sun, P. Allen, P. Allen. Meat Sci. (2009).  https://doi.org/10.1016/j.meatsci.2009.04.003 Google Scholar
  15. 15.
    Z. Haddi, N.E. Barbri, K. Tahri, M. Bougrini, N.El Bari, E. Llobet, B. Bouchikhi, Anal. Methods (2015).  https://doi.org/10.1039/C5AY00572H Google Scholar
  16. 16.
    New Electronic Technology, EleGigEPRO Operational Manual. 2013. Rev. 1, 02-1409 (2013)Google Scholar
  17. 17.
    Automated Imaging Association, GigE Vision: Video Streaming and Device Control over Ethernt Standard, Version 1.2 (Automated Imaging Association, Ann Arbor, 2009)Google Scholar
  18. 18.
    TMDXEVM6678L, EVM Technical Reference, Manual Version 2.0 (Texas Instruments, Sherman, 2011), http://wfcache.advantech.com/support/ TMDXEVM6678L_Technical_Reference_Manual_2V00.pdf
  19. 19.
    TMS320C6678, Multicore Fixed and Floating-Point Digital Signal Processor (Texas Instruments, Sherman, 2014). http://www.ti.com/lit/ds/symlink/tms320c6678.pdf
  20. 20.
    A. Klilou, S. Belkouch, P. Elleaume, P. Le Gall, F. Bourzeix, M.M. Hassani, EURASIP J. Adv. Signal Process. (2014).  https://doi.org/10.1186/1687-6180-2014-161 Google Scholar
  21. 21.
    H. Chakib, B. Minaoui, M. Fakir, A. Salhi, I. Badi, IJACSA (2017).  https://doi.org/10.14569/IJACSA.2017.080959 Google Scholar
  22. 22.
    R.A. Mancini, M.C. Hunt, Meat Sci. (2005).  https://doi.org/10.1016/j.meatsci.2005.03.003 Google Scholar
  23. 23.
    O.S. Papadopoulou, E.Z. Panagou, F.R. Mohareb, G.J.E. Nychas, J. Food Res. (2013).  https://doi.org/10.1016/j.foodres.2012.10.020 Google Scholar
  24. 24.
    A. Doulgeraki, D. Ercolini, F. Villani, G.J.E. Nychas, Int. J. Food Microbiol. (2012).  https://doi.org/10.1016/j.ijfoodmicro.2012.05.020 Google Scholar
  25. 25.
    D. Dave, A.E. Ghaly, J. Agric. Biol. Sci. (2011).  https://doi.org/10.3844/ajabssp.2011.486.510 Google Scholar
  26. 26.
    D.I. Ellis, R. Goodacre, Trends Food Sci. Technol. (2001).  https://doi.org/10.1128/AEM.68.6.2822-2828.2002 Google Scholar
  27. 27.
    G. ElMasry, D.W. Sun, P. Allen, J. Food Res. (2011).  https://doi.org/10.1016/j.foodres.2011.05.001 Google Scholar
  28. 28.
    G.P. Zhang, IEEE Trans. Syst. Man Cybern. C (2000).  https://doi.org/10.1109/5326.897072 Google Scholar
  29. 29.
    D. Specht, ICNN (1988). https://  https://doi.org/10.1109/ICNN.1988.23887 Google Scholar
  30. 30.
    D. Specht, Probabilistic neural networks. Neural Netw. (1990).  https://doi.org/10.1016/0893-6080(90)90049-Q Google Scholar
  31. 31.
  32. 32.
    P.C. Mahalanobis, Proceeding of the National Institute of Science of India, 12(1936) pp. 49–55Google Scholar
  33. 33.
    Q. Chen, Z. Hui, Z. Zhao, J. Ouyang, LWT Food Sci. Technol. (2014) 502–507.  https://doi.org/10.1016/j.lwt.2014.02.031
  34. 34.
    N.E. Barbri, E. Llobet, N.El Bari, X. Correig, B. Bouchikhi, Sensors (2008).  https://doi.org/10.3390/s8010142 Google Scholar
  35. 35.
    A.A. Argyri, E.Z. Panagou, P.A. Tarantilis, M. Polysiou, G.-J.E. Nychas, Sens. Actuators B (2010).  https://doi.org/10.1016/j.snb.2009.11.052 Google Scholar
  36. 36.
    E.Z. Panagou, F.R. Mohareb, A.A. Argyri, C.M. Bessant, G.-J.E. Nychas, Food Microbiol. (2011).  https://doi.org/10.1016/j.fm.2010.05.014 Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory of Radiation, Material and InstrumentationFST, University of Hassan 1SettatMorocco
  2. 2.Laboratory of Informatics, Systems, Electrical, Networks and Telecommunications, LISERT-ENSAUniversity of Hassan 1KhouribgaMorocco
  3. 3.Laboratoire d’Automatique, de Conversion d’Energie et de Microélectronique, LACEM, FSTUniversity of Sultan Moulay SlimaneBeni MellalMorocco

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