Using self-organizing map for quality classification on fish processed product

Yusraini Muharni, H.M Hartono, Maria Ulfah, Lely Herlina, Anita Cempakasari

Abstract


Assessing the quality of processed fish products stands as a critical factor in ensuring consumer satisfaction, upholding industry standards, and reducing wastage. Traditional methods for quality classification typically involve manual inspection, which is both time-consuming and subjective. In recent years, the utilization of advanced data analysis techniques, such as Self-Organizing Maps (SOMs), has emerged as a promising approach to enhance the accuracy and efficiency of quality assessment in the fish processing industry. SOMs provide a multi-dimensional map capable of representing various quality attributes of processed fish products. This study aims to classify the quality of processed fish products based on four attributes that impact their time to spoilage. The SOMs effectively segmented the dataset into two clusters, with one cluster being more prone to spoilage, while the other demonstrated a longer shelf life.


Keywords


Quality assessment; clustering; SOM; Machine Learning

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References


J. V. De Pinho, A. P. Lopes, P. De Almeida Rodrigues, R. G. Ferrari, R. A. Hauser-Davis, and C. A. Conte-Junior, “Food safety concerns on polycyclic aromatic hydrocarbon contamination in fish products from estuarine bays throughout the American continent,” Sci. Total Environ., vol. 858, p. 159930, Feb. 2023, doi: 10.1016/j.scitotenv.2022.159930.

S. A. O. Adeyeye, “Food packaging and nanotechnology: safeguarding consumer health and safety,” Nutr. Food Sci., vol. 49, no. 6, pp. 1164–1179, Nov. 2019, doi: 10.1108/NFS-01-2019-0020.

M. Sohail, D.-W. Sun, and Z. Zhu, “Recent developments in intelligent packaging for enhancing food quality and safety,” Crit. Rev. Food Sci. Nutr., vol. 58, no. 15, pp. 2650–2662, Oct. 2018, doi: 10.1080/10408398.2018.1449731.

L. Akil, H. A. Ahmad, and R. S. Reddy, “Effects of Climate Change on Salmonella Infections,” Foodborne Pathog. Dis., vol. 11, no. 12, pp. 974–980, Dec. 2014, doi: 10.1089/fpd.2014.1802.

R. Jedermann, M. Nicometo, I. Uysal, and W. Lang, “Reducing food losses by intelligent food logistics,” Philos. Trans. R. Soc. Math. Phys. Eng. Sci., vol. 372, no. 2017, p. 20130302, Jun. 2014, doi: 10.1098/rsta.2013.0302.

P. Doe and J. Olley, “Drying and Dried Fish Products,” in Seafood: Resources, Nutritional Composition, and Preservation, 1st ed., Z. E. Sikorski, Ed., CRC Press, 2020, pp. 125–145. doi: 10.1201/9781003068419-10.

G. M. Pigott and B. W. Tucker, Seafood: Effects of Technology on Nutrition, 1st ed. CRC Press, 2017. doi: 10.1201/9780203740118.

W. Min, S. Jiang, L. Liu, Y. Rui, and R. Jain, “A Survey on Food Computing,” ACM Comput. Surv., vol. 52, no. 5, pp. 1–36, Sep. 2020, doi: 10.1145/3329168.

A. Singla, L. Yuan, and T. Ebrahimi, “Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model,” in Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, Amsterdam The Netherlands: ACM, Oct. 2016, pp. 3–11. doi: 10.1145/2986035.2986039.

Q. Wu, P. Li, Z. Chen, and T. Zong, “A clustering-optimized segmentation algorithm and application on food quality detection,” Sci. Rep., vol. 13, no. 1, p. 9069, Jun. 2023, doi: 10.1038/s41598-023-36309-8.

I. Kutyauripo, M. Rushambwa, and L. Chiwazi, “Artificial intelligence applications in the agrifood sectors,” J. Agric. Food Res., vol. 11, p. 100502, Mar. 2023, doi: 10.1016/j.jafr.2023.100502.

Z. H. Khalil and S. M. Abdullaev, “Neural network for grain yield predicting based multispectral satellite imagery: comparative study,” Procedia Comput. Sci., vol. 186, pp. 269–278, 2021, doi: 10.1016/j.procs.2021.04.146.

R. Akhter and S. A. Sofi, “Precision agriculture using IoT data analytics and machine learning,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5602–5618, Sep. 2022, doi: 10.1016/j.jksuci.2021.05.013.

Víctor Martínez-Martínez, Jaime Gomez-Gil, Timothy S. Stombaugh, Michael D. Montross & Javier M. Aguiar, “Moisture Content Prediction in the Switchgrass (Panicum virgatum) Drying Process Using Artificial Neural Networks, Drying Technology, 33:14, 1708-1719,”.

Y. Lin, J. Ma, Q. Wang, and D.-W. Sun, “Applications of machine learning techniques for enhancing nondestructive food quality and safety detection,” Crit. Rev. Food Sci. Nutr., vol. 63, no. 12, pp. 1649–1669, May 2023, doi: 10.1080/10408398.2022.2131725.

Y. Muharni, Kulsum, A. Denisa, and Hartono, “The application of artificial neural network for quality prediction of industrial standard water,” IOP Conf. Ser. Earth Environ. Sci., vol. 926, no. 1, p. 012048, Nov. 2021, doi: 10.1088/1755-1315/926/1/012048.

J. J. Gonzalez De La Rosa, A. Aguera Perez, J. C. Palomares Salas, and A. Moreno-Munoz, “Amplitude-frequency classification of Power Quality transients using higher-order cumulants and Self-Organizing Maps,” in 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Taranto: IEEE, Sep. 2010, pp. 66–71. doi: 10.1109/CIMSA.2010.5611749.

R. El Chaal and M. O. Aboutafail, “Statistical Modelling by Topological Maps of Kohonen for Classification of the Physicochemical Quality of Surface Waters of the Inaouen Watershed Under Matlab,” J. Niger. Soc. Phys. Sci., pp. 223–230, May 2022, doi: 10.46481/jnsps.2022.608.

E. Alvarez‐Guerra, A. Molina, J. R. Viguri, and M. Alvarez‐Guerra, “A SOM‐based methodology for classifying air quality monitoring stations,” Environ. Prog. Sustain. Energy, vol. 30, no. 3, pp. 424–438, Oct. 2011, doi: 10.1002/ep.10474.

T. Kohonen, The self-organizing map. Springer, 2001.

G. Pölzlbauer, “Survey and Comparison of Quality Measures for Self-Organizing Maps”.

R. J. Kuo, M. Rizki, F. E. Zulvia, and A. U. Khasanah, “Integration of growing self-organizing map and bee colony optimization algorithm for part clustering,” Comput. Ind. Eng., vol. 120, pp. 251–265, Jun. 2018, doi: 10.1016/j.cie.2018.04.044.




DOI: http://dx.doi.org/10.36055/jiss.v9i2.21876

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