Estimate freshness of chicken meat using image processing and artificial intelligent techniques

Document Type : Research Paper

Authors

1 Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran

2 university of Lorestan

3 Lorestan University

Abstract

In the current study, new methods such as image processing and artificial intelligence have been used for the fast, easy and non-destructive evaluation of chicken meat freshness. After image acquisitions and pre-processing operations, the images were transferred to different color spaces and the statistical texture features of images were extracted. The feature selection operation was performed by combining particle swarm optimization (PSO) and artificial neural networks (ANNs) classifier to reduce the amount of calculations and improve the classification indicators. According to the number of selected features, the number of existing neurons in input layer were obtained 22 and the number of existing neurons in output layer were determined 5, according to classify the images as 5 classes. In the purpose of the classifier assessment operation to estimate the freshness of chicken meat, the statistical indicators such as precision, accuracy, sensitivity, specificity and area under the curve were calculated, which the values of these indicators for classification based on the selected features are 92, 80.02, 80.68, 94.89 and 87.83 percent, respectively. The obtained results of this study indicates that suggested system has the ability to diagnosis the chicken meat freshness with suitable accuracy.

Keywords

Main Subjects


Ameri, H., Alizade, S. & Barzegari, A. (2013). Knowledge Extraction of Diabetics Data by Decision Tree Method. Journal of Health Administration Iran University of Medical Sciences, 16(53), 58-72.
Banka, H. & Dara, S. (2014). Hamming distance based binary PSO for feature selection and classification from high dimensional gene expression data. In: Proceedings of 2nd International Work-Conference on Bioinformatics and Biomedical Engineering,7-9 April., Garanda, Spain, pp. 507-514.
Chaudhary, P., Chaudhari, A. K.,  Dr. Cheeran, A. N. & Godara., Sh. (2012). Color Transform Based Approach for Disease Spot Detection on Plant Leaf. International  Journal of computer science and telecommunications, 3(6), 65-70.
Chen, K., Sun, X., Qin, Ch.  & Tang, X. (2010). Color grading of beef fat by using computer vision and support vector machine. Computers and Electronics in Agriculture, 70, 27–32.
Chen, Q., Hui, Zh., Zhao, J. & Ouyang, Q. (2014).  Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBooste-OLDA classification algorithm. LWT - Food Science and Technology, 57(2), 502-507.
Chmiel, M., Sowinski, M. & Dasiewicz, K. (2011). Application of computer vision systems for estimation of fat content in poultry meat. Food Control, 22, 1424-1427.
Cozzolino, D. & Murray, I. (2012). A review on the application of infrared technologies to determine and monitor composition and other quality characteristics in raw fish, fish products, and seafood. Applied SpectroscopyReviews, 47(3), 207–218.
Dowlati, M., Mohtasebi, S. S., & de la Guardia, M. (2012). Application of machine vision techniques to fish-quality assessment. TrAC-Trends in Analytical Chemistry, 40, 168–179.
El Barbri, N., Halimi, A & Rhofir, K. (2014). A Nondestructive Method Based on an Artificial Vision for Beef Meat Quality Assesement. International journal of innovative research in electrical, electronics, instrumentation and control engineering. 2(10), 2060-2063.
 Ghiasi, H., Jebraeili, Sh., Jafari, S.M & Maghsoudlou, Y. (2014). Design and Calibration of a Software-based Food Colorimeter System, Iranian Food Science and Technology Research Journal, 9(4), 314-322.
Girolami, A., Napolitano, F., Faraone, D. & Braghieriv, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93, 111–118.
Goldberg, D. (1989). Genetic algorithms in optimization, search and machine learning. Reading: Addison-Wesley.
Gonzalez, R.C., Woods, R.E. & Eddins, S.L. (2002). Digital Image Processing (2nd ed.). Prentice Hall: New Jersey.
Gonzalez, R.C., Woods, R.E. & Eddins, S.L. (2004). Digital Image Processing Using MATLAB, Pearson Prentice Hall: New Jersey, USA.
Grau, R., Sanchez, A. J., Giron, J., Iborra, E., Fuentes, A., & Barat, J. M. (2011). Nondestructive assessment of freshness in packaged sliced chicken breastsusing SW-NIR spectroscopy. Food Research International, 44(1), 331–337.
Hosseinzadeh Samani, B  & Hourijafari, H. (2015).Modeling and forecasting of energy consumption in food and processing industry using artificial neural networks. Modares Mechanical Engineering, 15(6), 16-22. (In Farsi).
Huang, L., Zhao, J., Chen, Q. & Zhang, Y. (2014). Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat byintegrating near infrared spectroscopy, computer vision and electronic nosetechniques. Food Chemistry, 145, 228–236.
Jackman, P., Sun, D.-W. & Allen, P. (2011). Recent advances in the use of computer vision technology in the quality assessment of fresh meats. Trends in Food Science & Technology, 22(4), 185-197.
Karray, F.O.  & Silva, C.D. (2004). Soft Computing and Intelligent Systems Design: Theory, Tools and Applications. Addison Wesley Pearson Press, New York, USA.
Kennedy, J.&  Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. IEEE International Conference on Computational Cybernetics and Simulation.Volume 5. pp. 4104-4108.
Khazaee, M., Banakar, A., Ghobadian, B., Mirsalim, M., Minaei, S., Jafari, S. M. & Sharghi, P. (2016). Analysis of Timing Belt Vibrational Behavior During a Durability Test Using Artificial Neural Network (ANN). Modares Mechanical Engineering.16 (3), 311 -318. (In Farsi).
Kheiralipour, k. (2012). Implementation and construction of fungal contamination of kernel of pistachio detection system based on thermography and image processing technology, PhD. Thesis, Agriculture machinery  engineeringUniversity of Tehran, Iran,. (In Farsi).
Khulal, U., Zhao, J., Hu, W. & Chen, Q. (2016). Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chemistry, 197, 1191-1199.
Labatut, V. & Cheri, H. (2011). Accuracy Measures for the Comparison of Classifiers. Al-Dahoud Ali. The 5th International Conference on Information Technology, Amman, Jordan.
Leo´n, K., Mery, D., Pedreschi, F. & Leo´n, J. (2006). Color measurement in L*a*b* units from RGB digital images. Food research international, 39(10), 1084-1091.
Liu, F., He, Y., Wang, L., & Sun, G. (2011). Detection of organic acids and pH of fruit vinegars using near-infrared spectroscopy and multivariate calibration. Food and Bioprocess Technology, 4(8), 1331–1340.
    M. Goñi, S. & O. Salvadori, V. (2016). Color measurement: comparison of colorimeter vs. computer vision system. Food Measurement and Characterization, 1–10.
Ma, J., Sun, D.–W., Qu, J.–h., Liu, D., Pu, H., Gao, W.-h & Zeng, X.a. (2016). Applications of computer vision for assessing quality of agri-food products: a review of recent research advances. Critical Reviews in Food Science and Nutrition, 56(1), 113-127.
Nasrollahzade, M., Shahbazi Karami, J., Moslemi Naeini, H., Hashemi, S.J., Mohammadi Najafabadi, H. (2016). Multiobjective optimization of hot metal gas forming process to production of square parts. Modares Mechanical Engineering, 16(10), 364-374. (In Farsi).
Nezamabadi-pour, H., Rostami-sharbabaki, M. & Maghfoori-Farsangi, M. (2008). Binary Particle Swarm Optimization: Challenges and New Solutions. The Journal of Computer Society of Iran (CSI) On Computer Science and Engineering (JCSE), 6(1-A), 21-32. (In Farsi).
Nilsen, H., Esaiassen, M., Heia, K., & Sigernes, F. (2002). Visible/near-infrared spectroscopy: A new tool for the evaluation of fish freshness. Journal of FoodScience, 67(5), 1821–1826.
Pu, H., Xie, A., Sun, D.-W., Kamruzzaman, M. & Ma, J. (2015). Application of Wavelet Analysis to Spectral Data for Categorization of Lamb Muscles. Food Bioprocess Technol, 8(1), 1–16.
Salinas, Y., Ros-Lis, J. V., Vivancos, J. –L., Dolores Marcos, M., Aucejo, S., Herranz, N & Lorente, I. (2012). Monitoring of chicken meat freshness by means of a colorimetric sensor array. Analyst, 137(16), 3635–3643.
Salinas, Y., Ros-Lis, J. V., Vivancos, J. -L., Martínez-Máñez, R., Marcos, M. D., Aucejo, S., Herranz, N. & Lorente, I. (2014). A novel colorimetric sensor array for monitoring fresh porksausages spoilage, Food Control, 35(1), 166–176.
Shi, Z. & He, L. (2010). Application of neural networks in medical image processing, Proceedings of the Second International Symposium on Networking and Network Security, April 2-4., Jinggangshan, China.
Singh, V., Gupta, I. & Gupta, H. (2007). ANN-based estimator for distillation using Levenberg–Marquardt approach, Engineering Applications of Artificial Intelligence, 20)2(, 249-259.
 Sokolova, M. & Lapalme, Guy. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437.
Taheri-Garavand, A. (2015). Implementation and development fault diagnosis of cooling system radiator using combined thermography and artificial intelligence techniques, PhD. Thesis, University of Tehran, Iran,. (In Farsi).
Taheri-Garavand, A., Ahmadi, H., Omid, M., Mohtasebi, S.S., Mollazade, K., Russell Smith, A. J. & Carlomagno, G. M. (2015). An intelligent approach for cooling radiator fault diagnosis based oninfrared thermal image processing technique. Applied Thermal Engineering, 87, 434-443.
Taheri-Garavand, A., Omid, M., Ahmadi, H., Mohtasebi, S.S., Carlomagno, G. M. (2017). Intelligent fault diagnosis of cooling radiator based on thermal image processing and artificial intelligence techniques. Modares Mechanical Engineering, 17(2), 240-250. (In Farsi).
Tsai, Ch.-F., Eberle, W. & Chu, Ch.-Y. (2013). Genetic algorithms in feature and instance selection. Knowledge-Based Systems, 39, 240–247.
Vasconcelos, H., Saraiva, C., & de Almeida, J. M. M. M. (2014). Evaluation of the spoilage of raw chicken breast fillets using Fourier transform infrared spectroscopy in tandem with chemometrics. Food and Bioprocess Technology, 7(8), 2330–2341.
Xiao, K., Gao, G. & Shou, L. (2014). An Improved Method of Detecting Pork Freshness Based on Computer Vision in On-line System. Sensors & Transducers, 169(4), 42-48.
Xiong, Z., Sun, D.-W., Pu, H., Xie, A., Han, Z. & Luo, M. (2015) . Non-destructive prediction of thiobarbituric acid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chemistry, 179, 175–181.
Xiong, Zh., Sun, D.-W., Pu, H., Gao, W.& Dai, Q. (2017). Applications of Emerging Imaging Techniques for Meat Quality and Safety Detection and Evaluation: A Review. Critical Reviews in Food Science and Nutrition, 57(4), 755-768.
Zhang, M.-X., Wang, X.-C., Liu, Y., Xu, X.-L. & Zhou, G.-H. (2012). Isolation and identification of flavour peptides from Puffer fish (Takifugu obscurus) muscle using an electronic tongue and MALDI-TOF/TOF MS/MS. Food Chemistry, 135(3), 1463–1470.
Zhou, X., Yuan, J & Liu, H. (2015). A Traffic Light Recognition Algorithm Based On Compressive Tracking,  International Journal of Hybrid Information Technology. 8(6), 323-332.
Volume 48, Issue 4
December 2017
Pages 491-503
  • Receive Date: 09 May 2017
  • Revise Date: 09 August 2017
  • Accept Date: 19 August 2017
  • First Publish Date: 22 December 2017