Evaluation of chicken meat freshness using olfaction machine and artificial neural networks

Document Type : Research Article

Authors

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

2 Mechanical Engineering of Biosystems Department,Lorestan University, Korramabad, Iran

Abstract

Today, human’s specific attention to food quality has led to the development of fast, easy and non-destructive methods to assess the quality of foodstuffs. Meat is one of the most important foods and its freshness is considered as the most important qualitative feature. Therefore, checking its quality for consumption has great value worthwhile. The main objective of the present study is to investigate the possibility of using electronic nose and artificial neural network methods for detecting the freshness of chicken meat during storage in a refrigerator at 4 ºC. In the used neural network system, the input layer consists of 10 neurons based on the number of sensors and the output layer includes 3 neurons related to classes of different freshness classes of chicken meat. Different classifier networks were designed and after investigation of different network structures; the best structure of the network was obtained with a hidden layer and 6 neurons in that layer. Finally, the optimal network with a general structure of 10-6-3 was created to detect the freshness of chicken meat during different days of storage. The used statistical indices to assess the classifier to evaluate the freshness of chicken meat including accuracy, precision, sensitivity, specificity and area under the curve factors. The values of these indices for classification using selected characteristics are 95.77, 94.7, 92.18, 95.95, and 94.1 respectively. Therefore, given that the main objective of the present study was to develop and implementation of an intelligent diagnosis system of chicken meat freshness using an electronic nose system. The acceptable obtained results of the present study indicate that the proposed applied system based on the electronic nose system and artificial neural networks methods as a smart and reliable method can online classification of chicken meat as fast, easy, economical, non-destructive and with appropriate accuracy.

Graphical Abstract

Evaluation of chicken meat freshness using olfaction machine and artificial neural networks

Highlights

  • Utilizing olfactory machine techniques and artificial neural networks for the identification of chicken meat freshness.
  • The most effective network configuration for accurate classification and detection of freshness was found to be 3-6-10.
  • Offering a convenient and trustworthy method that enables swift, effortless, cost-effective, non-invasive, and accurate online classification of chicken meat freshness.

Keywords

Main Subjects

References

[1] Ellis, D. I., & Goodacre, R. (2001). Rapid and quantitative detection of the microbial spoilage of muscle foods: current status and future trends. Trends in Food Science & Technology, 12(11), 414-424. https://doi.org/10.1016/S0924-2244(02)00019-5
 [2] Taheri-Garavand, A., Fatahi, S., Omid, M., & Makino, Y. (2019). Meat quality evaluation based on computer vision technique: A review. Meat science, 156, 183-195. https://doi.org/10.1016/j.meatsci.2019.06.002
[3] Salinas, Y., Ros-Lis, J. V., Vivancos, J. L., Martinez-Manez, R., Marcos, M. D., Aucejo, S., ... & Lorente, I. (2012). Monitoring of chicken meat freshness by means of a colorimetric sensor array. Analyst, 137(16), 3635-3643. https://doi.org/10.1039/C2AN35211G
[4] Shi, H., Zhang, M., & Adhikari, B. (2018). Advances of electronic nose and its application in fresh foods: A review. Critical Reviews in Food Science and Nutrition, 58(16), 2700-2710. https://doi.org/10.1080/10408398.2017.1327419
[5] Chen, Q., Hui, Z., Zhao, J., & Ouyang, Q. (2014). Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost–OLDA classification algorithm. LWT-Food Science and Technology, 57(2), 502-507. https://doi.org/10.1016/j.lwt.2014.02.031
[6] Binson, V. A., George, M. M., Sibichan, M. A., Raj, M., & Prasad, K. (2023, January). Freshness Evaluation of Beef using MOS Based E-Nose. In 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) (pp. 792-797). IEEE. https://doi.org/10.1109/IDCIoT56793.2023.10053399
[7] Munekata, P. E., Finardi, S., de Souza, C. K., Meinert, C., Pateiro, M., Hoffmann, T. G., ... & Lorenzo, J. M. (2023). Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review. Sensors, 23(2), 672. https://doi.org/10.3390/s23020672
 [8] Mirzaee-Ghaleh, E., Taheri-Garavand, A., Ayari, F., & Lozano, J. (2020). Identification of fresh-chilled and frozen-thawed chicken meat and estimation of their shelf life using an E-nose machine coupled fuzzy KNN. Food Analytical Methods, 13, 678-689. https://doi.org/10.1007/s12161-019-01682-6
 [9] Balasubramanian, S., Panigrahi, S., Logue, C. M., Marchello, M., Doetkott, C., Gu, H., ... & Nolan, L. (2004). Spoilage identification of beef using an electronic nose system. Transactions of the ASAE, 47(5), 1625-1633. https://doi.org/10.13031/2013.17593
[10] Zhang, Z., Tong, J., Chen, D. H., & Lan, Y. B. (2008). Electronic nose with an air sensor matrix for detecting beef freshness. Journal of bionic Engineering, 5(1), 67-73. https://doi.org/10.1016/S1672-6529(08)60008-6
 [11] Boothe, 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(3), 315-322. https://doi.org/10.1002/jsfa.1036
[12] O’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(2), 149-154. https://doi.org/10.1016/S0925-4005(01)00904-2
 [13] 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. Iran Food Science and Technology Society. 15 (74), 213-225.  [In Persian]
[14] El Barbri, N., 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. https://doi.org/10.1016/j.snb.2009.07.034
[15] Li, X., Wang, B., Yi, C., & Gong, W. (2022). Gas sensing technology for meat quality assessment: A review. Journal of Food Process Engineering, 45(8), e14055. https://doi.org/10.1111/jfpe.14055
 [16] Ayari, F., Mirzaee‐Ghaleh, E., Rabbani, H., & Heidarbeigi, K. (2018). Using an E‐nose machine for detection the adulteration of margarine in cow ghee. Journal of Food Process Engineering, 41(6), e12806. https://doi.org/10.1111/jfpe.12806
[17] Taheri-Garavand, A., Rezaei Nejad, A., Fanourakis, D., Fatahi, S. & Ahmadi Majd, M. (2021). Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: A case study in Spathiphyllum wallisii. Acta Physiologiae Plantarum, 43(5), 78. https://doi.org/10.1007/s11738-021-03244-y
[18] Taheri-Garavand, A., Nasiri, A., Fanourakis, D., Fatahi, S., Omid, M. & Nikoloudakis, N. (2021). Automated in situ seed variety identification via deep learning: a case study in chickpea. Plants, 10(7), 1406. https://doi.org/10.3390/plants10071406
[19] Timsorn, K., Wongchoosuk, C., Wattuya, P., Promdaen, S., & Sittichat, S. (2014, May). Discrimination of chicken freshness using electronic nose combined with PCA and ANN. In 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 1-4). IEEE.
[20] Fatahi, S., Taheri Geravand, A., & Shahbazi, F. (2017). Estimate freshness of chicken meat using image processing and artificial intelligent techniques. Iranian Journal of Biosystems Engineering, 48(4), 491-503. https://doi.org/10.22059/IJBSE.2017.63814 [In Persian]
Innovative Food Technologies
Volume 10, Issue 4
August 2023
Pages 319-333

Files

History

  • Receive Date: 05 August 2023
  • Revise Date: 21 October 2023
  • Accept Date: 21 October 2023

Share

How to cite

Statistics