Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

Fish contains many nutrients beneficial to human health, which makes fish an essential component of a healthy diet. Omega-3 fatty acids, primarily found in fresh fish, can play a critical role in protecting heart and brain health. Freshness is one of the most important quality criteria of the fish to be selected for consumption. It is known that there may be pathogenic bacteria and toxins to human health in fish that are not stored in the right conditions and transferred by wrong logistics methods. One of the widely used approaches for evaluating the freshness of fish is sensory, which would be highly subjective and error-prone. Moreover, sensory analysis is widespread and one of the fastest approaches for evaluating large quantities of fish. At that point, a computer-aided diagnostic system can accelerate the evaluation of the degree of spoilage, reduce the human resources required for this task, and minimize the possibility of spoiled fish consumption. In this study, a fully automated freshness assessment mechanism based on the analysis of digital eye images of fish is proposed. Accordingly, the unsupervised clustering approach was used for feature extraction, and each image was divided into three regions according to their color distribution. The freshness was evaluated according to the intensity difference between these clusters. The results show that the proposed feature extraction approach is highly distinctive for the discrimination of spoilage and can be used to distinguish fresh fish from spoiled fish using machine learning methods without supervision.

Keywords:

Automated, Cluster, Evaluation, Fish Spoilage,

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Agustyawan, A. (2021). Fresh and not fresh fish dataset. Retrieved on January 29, 2023, from https://www.kaggle.com/datasets/arifagustyawan/fresh-and-not-fresh-fish-dataset
Azeriee, A. A., Hashim, H., Jarmin, R., & Ahmad, A. (2009). A study on freshness of fish by using fish freshness meter. In 2009 5th international colloquium on signal processing & its applications (pp. 215–219).
Dowlati, M., Mohtasebi, S. S., Omid, M., Razavi, S. H., Jamzad, M., & De La Guardia, M. (2013). Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. Journal of Food Engineering, 119(2), 277–287. https://doi.org/10.1016/j.jfoodeng.2013.05.023
Herrero, A. M. (2008). Raman spectroscopy a promising technique for quality assessment of meat and fish: A review. Food Chemistry, 107(4), 1642–1651. https://doi.org/10.1016/j.foodchem.2007.10.014
Issac, A., Dutta, M. K., & Sarkar, B. (2017). Computer vision based method for quality and freshness check for fish from segmented gills. Computers and Electronics in Agriculture, 139, 10–21. https://doi.org/10.1016/j.compag.2017.05.006
Jarmin, R., Khuan, L. Y., Hashim, H., & Rahman, N. H. A. (2012). A comparison on fish freshness determination method. In Proceedings of the 2012 International Conference on System Engineering and Technology, Bandung, Indonesia. pp. 1-6.
Lalabadi, H. M., Sadeghi, M., & Mireei, S. A. (2020). Fish freshness categorization from eyes and gills color features using multi-class artificial neural network and support vector machines. Aquacultural Engineering, 90, 102076. https://doi.org/10.1016/j.aquaeng.2020.102076
Lehane, L., & Olley, J. (2000). Histamine fish poisoning revisited. International journal of food microbiology, 58(1-2), 1-37.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations., Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability Volume 1: Statistics, University of California Press. pp. 281-297.
Muhamad, F., Hashim, H., Jarmin, R., & Ahmad, A. (2009). Fish freshness classification based on image processing and fuzzy logic. Recent Advances in Circuits, Systems, Electronics, Control and Signal Processing: Proceedings of the 8th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing (CSECS '09), Puerto de la Cruz, Tenerife, Canary Islands, Spain. pp. 109–115.
Novotny, L., Dvorska, L., Lorencova, A., Beran, V., & Pavlik, I. (2004). Fish: A potential source of bacterial pathogens for human beings. Veterinarni Medicina, 49(9), 343.
Osman, H., Suriah, A., & Law, E. (2001). Fatty acid composition and cholesterol content of selected marine fish in Malaysian waters. Food Chemistry, 73(1), 55–60. https://doi.org/10.1016/S0308-8146(00)00277-6
Pons-Sánchez-Cascado, S., Vidal-Carou, M., Nunes, M., & Veciana-Nogues, M. (2006). Sensory analysis to assess the freshness of Mediterranean anchovies (Engraulis encrasicholus) stored in ice. Food Control, 17(7), 564–569. https://doi.org/10.1016/j.foodcont.2005.02.016
Rawat, S. (2015). Food spoilage: Microorganisms and their prevention. Asian Journal of Plant Science and Research, 5(4), 47–56.
Sheng, L., & Wang, L. (2021). The microbial safety of fish and fish products: Recent advances in understanding its significance, contamination sources, and control strategies. Comprehensive Reviews in Food Science and Food Safety, 20(1), 738–786. https://doi.org/10.1111/1541-4337.12671
Shewan, J., MacIntosh, R. G., Tucker, C., & Ehrenberg, A. (1953). The development of a numerical scoring system for the sensory assessment of the spoilage of wet white fish stored in ice. Journal of the Science of Food and Agriculture, 4(6), 283–298. https://doi.org/10.1002/jsfa.2740040607
Uauy, R., & Dangour, A. D. (2006). Nutrition in brain development and aging: role of essential fatty acids. Nutrition Reviews, 64(suppl 2), S24–S33. https://doi.org/10.1301/nr.2006.may.s24-s33
Vajdi, M., Varidi, M. J., Varidi, M., & Mohebbi, M. (2019). Using electronic nose to recognize fish spoilage with an optimum classifier. Journal of Food Measurement and Characterization, 13, 1205–1217. https://doi.org/10.1007/s11694-019-00036-4
Venugopal, V. (2002). Biosensors in fish production and quality control. Biosensors and Bioelectronics, 17(3), 147–157. https://doi.org/10.1016/S0956-5663(01)00180-4
Vuori, H. (1992). Quality assurance in Finland. British Medical Journal, 304(6820), 162-164.
Weichselbaum, E., Coe, S., Buttriss, J., & Stanner, S. (2013). Fish in the diet: A review. Nutrition Bulletin, 38(2), 128–177. https://doi.org/10.1111/nbu.12021
Zhang, J., Sasaki, S., Amano, K., & Kesteloot, H. (1999). Fish consumption and mortality from all causes, ischemic heart disease, and stroke: an ecological study. Preventive Medicine, 28(5), 520–529. https://doi.org/10.1006/pmed.1998.0472