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.
Similar content being viewed by others
References
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
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
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
C.J. Du, D.W. Sun, J. Food Eng. (2006). https://doi.org/10.1016/j.jfoodeng.2004.11.017
N.E Barbri, A. Halimi, K. Rhofir, IJIREEICE (2014). https://doi.org/10.17148/IJIREEICE.2014.0210001
A. Arsalane, N.E. Barbri, A. Tabyaoui, A. Klilou, K. Rhofir, ICEMIS (2016). https://doi.org/10.1109/ICEMIS.2016.7745327
A. Arsalane, N.E. Barbri, K. Rhofir, A. Tabyaoui, A. Klilou, IJIE (2017). https://doi.org/10.1504/IJIE.2017.087005
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
K. Shiranita, T. Miyajima, R. Takiyama, Pattern Recognit. Lett. (1998). https://doi.org/10.1016/S0167-8655(98)00113-5
J. Li, J. Tan, P. Shatadal, Meat Sci. (2001). https://doi.org/10.1016/S03091740(00)00105
P. Jackman, D.W. Sun, P. Allen, Meat Sci. (2008). https://doi.org/10.1016/j.meatsci.2008.06.001
P. Jackman, D.W. Sun, P. Allen, Pattern Recognit. (2009). https://doi.org/10.1016/j.patcog.2008.09.009
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
P. Jackman, D.W. Sun, P. Allen, P. Allen. Meat Sci. (2009). https://doi.org/10.1016/j.meatsci.2009.04.003
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
New Electronic Technology, EleGigEPRO Operational Manual. 2013. Rev. 1, 02-1409 (2013)
Automated Imaging Association, GigE Vision: Video Streaming and Device Control over Ethernt Standard, Version 1.2 (Automated Imaging Association, Ann Arbor, 2009)
TMDXEVM6678L, EVM Technical Reference, Manual Version 2.0 (Texas Instruments, Sherman, 2011), http://wfcache.advantech.com/support/ TMDXEVM6678L_Technical_Reference_Manual_2V00.pdf
TMS320C6678, Multicore Fixed and Floating-Point Digital Signal Processor (Texas Instruments, Sherman, 2014). http://www.ti.com/lit/ds/symlink/tms320c6678.pdf
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
H. Chakib, B. Minaoui, M. Fakir, A. Salhi, I. Badi, IJACSA (2017). https://doi.org/10.14569/IJACSA.2017.080959
R.A. Mancini, M.C. Hunt, Meat Sci. (2005). https://doi.org/10.1016/j.meatsci.2005.03.003
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
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
D. Dave, A.E. Ghaly, J. Agric. Biol. Sci. (2011). https://doi.org/10.3844/ajabssp.2011.486.510
D.I. Ellis, R. Goodacre, Trends Food Sci. Technol. (2001). https://doi.org/10.1128/AEM.68.6.2822-2828.2002
G. ElMasry, D.W. Sun, P. Allen, J. Food Res. (2011). https://doi.org/10.1016/j.foodres.2011.05.001
G.P. Zhang, IEEE Trans. Syst. Man Cybern. C (2000). https://doi.org/10.1109/5326.897072
D. Specht, ICNN (1988). https:// https://doi.org/10.1109/ICNN.1988.23887
D. Specht, Probabilistic neural networks. Neural Netw. (1990). https://doi.org/10.1016/0893-6080(90)90049-Q
R.A. Fisher, Ann. Eugen. (1936). https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
P.C. Mahalanobis, Proceeding of the National Institute of Science of India, 12(1936) pp. 49–55
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
N.E. Barbri, E. Llobet, N.El Bari, X. Correig, B. Bouchikhi, Sensors (2008). https://doi.org/10.3390/s8010142
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
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
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Arsalane, A., El Barbri, N., Tabyaoui, A. et al. The assessment of fresh and spoiled beef meat using a prototype device based on GigE Vision camera and DSP. Food Measure 13, 1730–1738 (2019). https://doi.org/10.1007/s11694-019-00090-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11694-019-00090-y