Intelligent Classification of Fresh Chicken Meat from Frozen-Thawed Using Olfactory Machine

Subject Areas : Microbiology

A. Taheri Garavand ¹ (Assistant Professor, Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Lorestan University, Khorramabad, Iran.)
E. Mirzaeei Ghaleh ² (Assistant Professor, Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Razi University, Kermanshah, Iran.)
F. Ayari ³ (M.Sc. of Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Razi University, Kermanshah, Iran.)

Received: 2020-02-22 Accepted : 2020-02-22 Published : 2020-03-20

Keywords: Chicken Meat, Freshness, Olfactory Machine, Sensor,

Abstract :

Introduction: Meat is one of the main sources of animal protein that is required and used for human consumption. Chicken meat due to the availability and economic reasons has high consumption in human societies and freshness is the most important qualitative feature of chicken meat. A device is required and necessary for detection of fresh-chilled chicken meat from frozen-thawed ones. Materials and Methods: In this research, a portable olfactory machine based on eight semiconductor metal oxide sensors was used for detecting the freshness of chicken meat. Ten grams of meat was uesd for each test followed by preprocessing and the data were analyzed by PCA and KNN methods. Results: Based on the results of PCA and KNN analysis, the freshness of chicken meat was detected with high accuracy by e-nose system. The loading and radar charts showed that the odor of chicken meat had the highest and lowest impact on the MQ136 and the TGS822 sensors, respectively. Conclusion: The olfactory machine system is a very effective way to identify the freshness of chicken meat, which is based on the speed, low cost and reliability of the sensors.

References:

ایاری، ف. (1396). توسعه و پیاده سازی سامانه بینی الکترونیک به منظور تشخیص روغن حیوانی گاوی از نوع تقلبی. پایان نامه کارشناسی ارشد مهندسی مکانیک بیوسیستم. دانشگاه رازی. ایران.

شیبانی تذرجی، الف. ( 1394). تشخیص کیفیت و درجه بندی گوشت شترمرغ با استفاده از تکنیک ماشین بینایی. پایان نامه کارشناسی ارشد مهندسی مکانیک بیوسیستم، دانشگاه جیرفت.

صنیعی آباده، م.، محمودی، س. و طاهرپور، م. (1393). داده کاوری کاربردی. انتشارات نیاز دانش.

Arshak, K., Moore, E., Lyons, G. M., Harris, J. & Clifford, S. (2004). A review of gas sensors employed in electronic nose applications. Sensor Review, 24, 181- 198.

Atanassova, S., Stoyanchev, T., Yorgov, D. & Nachev, V. (2018). Differentiation of fresh and frozen-thawed poultry breast meat by near infrared spectroscopy. Bulgarian Journal of Agricultural Science, 24, 162-168.  

Ayari, F., Mirzaee- Ghaleh, E., Rabbani, H. & Heidarbeigi, K. (2018a). Using an E-nose machine for detection the adulteration of margarine in cow ghee. Journal of Food Process Engineering, e12806.

Ayari, F., Mirzaee- Ghaleh, E., Rabbani, H. & Heidarbeigi, K. (2018b). Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat). Inernational Journal of Food Properties, 21 (1), 1670- 1679.

Balasubramanian, S., Panigrahi, S., Logue, C., Marchello, M., Doetkott, C., Gu, H., Sherwood, J. & Nolan, L. (2009). Spoilage identification of beef using an electronic nose system. Transactions of the American Society of Agricultural Engineering, 47(5), 1625–1633.

Beygzadeh, S. & Chizaree, A. (2007). Study of marketing channel and factors affecting marketing margin of potato. Journal of Agricultural Economics and Development, 15 (57), 81-103.

Bothe, D. D. H. & Arnold, J. W. (2002).

Electronic nose analysis of volatile compounds

from poultry meat samples, fresh and after refrigerated storage. Journal of the Science of Food and Agriculture, 82, 315–322.

Changying, L., Heinemannb, P. & Richard, S. (2007). Neural network and Bayesian networkfusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection. Sensors and Actuators B: Chemical, 125, 301–310.

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.

Connell, M., Valdora, G., Peltzer, G. & Negri, R. M. (2001). A practical approach for fish freshness determinations using a portable electronic nose. Sensors and Actuators B: Chemical, 80, 149–154.

Darzi, M., Olfat Bakhsh, A., Gorgin, S., Oveisi, F., Hashemi, A. & Alavi, N. (2016). Classification of unbalanced data in the primary diagnosis of breast diseases with adebast methods, probable neural network and K to the nearest neighbor. Iranian Journal of Breast Diseases, 9 (2), 1-12.

Edita R., Darius G., Vinauskienė R., Eisinaitė V., Balčiūnas G., Dobilienė J. & Tamkutė L. (2018). Rapid evaluation of fresh chicken meat quality by electronic nose. Czech Journal of Food Science, 36, 420-426.

El Barbri, N. E., Mirhisse. J., Ionescu, R., El Bari, N., Correig. X., Bouchikhi, B. & Llobet, E. (2009(. An electronic nose system based on a micro-machined gas sensor array to assess the freshness of sardines. Sensors and Actuators B: Chemical, 141(2), 538-543.

Ellerbroek, L.I., Lichtenberg, G. & Weise, E. (1995). Differentiation between fresh and thawed meat by an enzyme profile test. Meat Science, 40, 203- 209.

Fraizer, V. & Vesthof, D. (2010). Food microbiology (5th ed). Translated by Mortazavi, A. University of ferdowsi press. Mashhad. PP, 29-40.

Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Siadat, M., Lozano, J., Ahmadi, H., Razavi, S. H. & Dicko, A. (2011). Aging fingerprint characterization of beer using electronic nose. Sensors and Actuators B: Chemical, 159, 51-59.

 

Gram, L., Ravn, L., Rasch, M., Bruhn, J. B., Christensen, A. B. & Givskov, M. (2002). Food spoilage interactions between food spoilage bacteria. Inernational Journal of Food Microbiology, 78(1-2), 79-97.‏

Heidarbeigi, K., Mohtasebi, S. S., Foroughirad, A., Ghasemi-Varnamkhasti, M, Rafiee, S. & Rezaei, K. (2015). Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties, 18, 1391-1401.

Mahmoudi, A., Omid, M., Aghagolzadeh, A. & Borghayee, A. M. (2006). Grading of Iranian's export pistachio nuts based on artificial neural networks. International Journal of Agriculture and Biology, 8(3), 371-376.

Panigrahi, S., Logue, C. M., Marchello, M., Doetkott, C., Gu, H., Sherwood, J. & Nolan, L. (2004). Spoilage identification of beef using an electronic nose system. Transactions of the ASAE, 47 (13), 1625–1633.

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.

Sanaeefar, A., Mohtasebi, S. S., Ghasemi Varnamkhasti, M. & Ahmadi, H. (2014). Evaluation of machine olfaction system (electronic nose) based on metal oxide semiconductor (MOS) sensors in detecting aroma fingerprint changes of banana storage. Innovative Food Technologies Journal (JIFT), 1(3), 29-38.

Sanaeifar, A., Mohtasebi, S. S., Ghasemi-Varnamkhasti, M. & Ahmadi, H. (2016). Application of MOS based electronic nose for the prediction of banana quality properties. Measurement, 82, 105–114.

Shao, C., Zheng, H., Ahou, Z., Li, J., Lou, X., Hui, G. & Zhao, Z. (2018). Ridgetail white prawn (Exopalaemon carinicauda) K value predicting method by using electronic nose combined with non-linear data analysis model. Food Analytical Methods, 11 (11), 3121-3129.

Škorpilová, T., Šimoniová, A., Rohlík, B. & Pipek, P. (2013). The use of aconitase for the detection of frozen/thawed poultry meat. Maso International Journal of Food Science and Technology, 1, 73-77.

Sokolova, M. & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. International Processing & Management, 45)4(, 427–437.

Timsorn, K., Wongchoosuk, C., Wattuya, P., Promdaen, S. & Sittichat, S. (2014). Discrimination of chicken freshness using electronic nose combined with PCA and ANN. 11th International conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON). Nakhon Ratchasima, Thailand.

Tohidi, M., Ghasemi- Varnamkhasti, M., Ghafari Niya, V., Mohtasebi, S. S. & Bonyadiyan, M. (2017). Construction and development of an e- nose machine in combination with pattern recognition methods for detecting formalin cheating in raw milk. Iranian Journal of Biosystem Engineering, 47 (4), 761- 770.

Varidi, M. J., Varidi, M, Vajdi, M. & Sharifpour, A. (2018). Design, development and application of electronic nose instrument to rapidly detect spoilage of air, vacuum and modified atmosphere packaged camel minced meat. Iranian Food Science & Technology Institute - Iran Food Science and Technology Society, 15 (74), 213-225.

Winquist, F., Hornsten, E.G., Sundgren, H. & Lundstrom, I. (1993). Performance of an electronic nose for quality estimation of ground meat. Measurement Science and Technology, 4, 1493–1500.

Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang.   Q.,   Motoda, H., Mclachlan, G., Ng. A., Liu, B., Yu, P., Zhou, Z., Steinbach, M., Hand, D. & Steinberg, D.  (2008). Top 10 algorithms in data mining.  Journal of Knowl Information System, 14, 1–37.

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.