Food Quality Detection by Identification of Bacterial Contaminants: A Comparative Analysis of Machine Learning Predictive Models

Authors

  • Azzeddine Idhmad Laboratory of Analysis,Geometry and Applications Faculty of sciences Ibn Tofail UniversityKenitra , Morocco
  • Mohammed Kaicer Laboratory of Analysis,Geometry and Applications Faculty of sciences Ibn Tofail University Kenitra , Morocco
  • Jihane Alami Chentoufi Laboratory of research in Informatics Faculty of sciences Ibn Tofail University kenitra, morroco

Keywords:

Food,Meat, Adulteration, Bacteria, SVM, ANN, Random Forest, K-means, pathogen, Escherichia coli, Ecoli Staphylococcus and Staphylococcus Aureus

Abstract

Meat is one of the essential food items consumed by humans. Meat is vibrant is protein and has other nutrients that provide good health for human beings. Under certain circumstances, adulteration can be done in the meat. This leads to the presence of harmful pathogens to be present in it. These bacteria, namely Escherichia coli, Ecoli Staphylococcus and Staphylococcus Aureus, can lead to severe health problems when consumed with meat. Hence the detection of these harmful pathogens in meat is mandatory. This paper aims to detect the presence of these bacteria by using machine learning models. In this paper, four different classification algorithms have been implemented for bacterial identification. The algorithms used are modified support vector machine, optimized k means clustering, advanced random forest, and artificial neural network. The result obtained using the proposed algorithms have been compared, and the results have been plotted. Advanced random forest method has produced the best results and the artificial neural network has produced comparatively lower results than the other algorithms taken into account.

Downloads

Download data is not yet available.

References

Wang, Xinxin & Bouzembrak, Yamine & Lansink, AGJM & Fels‐Klerx, H.. (2021). Application of machine learning to the monitoring and prediction of food safety: A review. Comprehensive Reviews in Food Science and Food Safety. 21. 10.1111/1541-4337.12868.

Rajakumar, G., T. Ananth Kumar, T. A. Samuel, and E. Muthu Kumaran. "Iot based milk monitoring system for detection of milk adulteration." International Journal of Pure and Applied Mathematics 118, no. 9 (2018): 21-32.

Nosratabadi, Saeed, Sina Ardabili, Zoltan Lakner, Csaba Mako, and Amir Mosavi. 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS" Agriculture 11, no. 5: 408.

Hashem Ali Almashaqbeh,3Muhammad Shafiq,4A. L. Vallikannu,5K. Sakthidasan Sankaran,5Samrat Ray,6and F. Sammy7, An IoT and Machine Learning-Based Model to Monitor Perishable Food towards Improving Food Safety and Quality”, Volume 2022 | Article ID 6302331

Junming Han, Tong Li, Yun He, Quan Gao, "Using Machine Learning Approaches for Food Quality Detection", Mathematical Problems in Engineering, vol. 2022, Article ID 6852022, 9 pages, 2022..

Pugazhendiran, P., K. Suresh Kumar, T. Ananth Kumar, and S. Sundaresan. "An Advanced Revealing and Classification System for Plant Illnesses Using Unsupervised Bayesian-based SVM Classifier and Modified HOG-ROI Algorithm." In Contemporary Issues in Communication, Cloud and Big Data Analytics, pp. 259-269. Springer, Singapore, 2022.

Rajnish Kler, Ghada Elkady, Kantilal Rane, Abha Singh, Md Shamim Hossain, Dheeraj Malhotra, Samrat Ray, Komal Kumar Bhatia, "Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach", Journal of Food Quality, vol. 2022, Article ID 8521236, 9 pages, 2022.

Xin Zhang, Weiguo Tian, "Grid Supervision Path of Platform Food Safety Collaborative Governance Based on Big Data", International Transactions on Electrical Energy Systems, vol. 2022, Article ID 2605934, 14 pages, 2022..

Raymond Addo-Tham, Emmanuel Appiah-Brempong, Hasehni Vampere, Emmanuel Acquah-Gyan, Adjei Gyimah Akwasi, "Knowledge on Food Safety and Food-Handling Practices of Street Food Vendors in Ejisu-Juaben Municipality of Ghana", Advances in Public Health, vol. 2020, Article ID 4579573, 7 pages, 2020.

Abebe Bersisa, Dereje Tulu, Chaluma Negera, "Investigation of Bacteriological Quality of Meat from Abattoir and Butcher Shops in Bishoftu, Central Ethiopia", International Journal of Microbiology, vol. 2019, Article ID 6416803, 8 pages, 2019.

Richard Osafo, Gadafi Iddrisu Balali, Papa Kofi Amissah-Reynolds, Francis Gyapong, Rockson Addy, Alberta Agyapong Nyarko, Prince Wiafe, "Microbial and Parasitic Contamination of Vegetables in Developing Countries and Their Food Safety Guidelines", Journal of Food Quality, vol. 2022, Article ID 4141914, 24 pages, 2022.

J. J. Luna-Guevara, M. M. P. Arenas-Hernandez, C. Martínez de la Peña, Juan L. Silva, M. L. Luna-Guevara, "The Role of Pathogenic E. coli in Fresh Vegetables: Behavior, Contamination Factors, and Preventive Measures", International Journal of Microbiology, vol. 2019, Article ID 2894328, 10 pages, 2019.

Michael Olu-Taiwo, Prince Obeng, Akua Obeng Forson, "Bacteriological Analysis of Raw Beef Retailed in Selected Open Markets in Accra, Ghana", Journal of Food Quality, vol. 2021, Article ID 6666683, 7 pages, 2021.

Xiaohui Weng, Xiangyu Luan, Cheng Kong, Zhiyong Chang, Yinwu Li, Shujun Zhang, Salah Al-Majeed, Yingkui Xiao, "A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies", Journal of Sensors, vol. 2020, Article ID 8838535, 14 pages, 2020.

Akova F, Dundar M, Davisson VJ, Hirleman ED, Bhunia AK, Robinson JP, Rajwa B. A Machine-Learning Approach to Detecting Unknown Bacterial Serovars. Stat Anal Data Min. 2010 Oct;3(5):289-301. doi: 10.1002/sam.10085. PMID: 22162745; PMCID: PMC3230886.

Flow of the proposed system

Downloads

Published

17.02.2023

How to Cite

Idhmad, A. ., Kaicer, M. ., & Chentoufi, J. A. . (2023). Food Quality Detection by Identification of Bacterial Contaminants: A Comparative Analysis of Machine Learning Predictive Models. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 789–800. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2853

Issue

Section

Research Article