Food Quality Detection by Identification of Bacterial Contaminants: A Comparative Analysis of Machine Learning Predictive Models - Idhmad, Azzeddine; Kaicer, Mohammed; Chentoufi, Jihane Alami (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.