Predictive Microbiology and Machine Learning by Optimization Productive Process: Metanalysis
Verónica Yepes Medina*
Bacteriologist, Specialist in Biotechnology and Master’s Student in Biosciences, San Pedro, Colombia
*Corresponding Author: Verónica Yepes Medina, Bacteriologist, Specialist in Biotechnology and Master’s Student in Biosciences, San Pedro, Colombia.
December 21, 2022; Published: January 13, 2023
Unsafe food containing harmful bacteria, viruses, parasites or chemicals can cause more than 200 different illnesses, from diarrhea to cancer. Worldwide, an estimated 600 million (nearly 1 in 10 people) fall ill each year after eating contaminated food, resulting in 420.000 deaths and the loss of 33 million years of healthy life. Therefore, it is necessary to detect and respond to public health threats associated with unsafe food with enabling technologies or tools. Predictive microbiology is concerned with preventing, controlling or limiting the existence of microorganisms by mapping their potential responses to particular environmental conditions, such as temperature, pH, nutrients (protein and fat), water activity (aw) and others. And machine learning as a branch or artificial intelligence learns from these data, identifying patterns for decision making. Recent studies are based in the use of supervised machine learning models to predict the presence of a foodborne pathogenic microorganism at any stage of the production chain, the most commonly used models include Random Forest and support vector machine with rating metrics for accuracy and sensitivity >80%. The main evaluation metrics of the algorithms are: accuracy, F1 score, confusion matrix, sensitivity, specificity and area under the curve (ROC-AUC, Receiver-Operating-Characteristic). Studies have shown that Random Forest was the best model, exhibiting an accuracy of 95% and a F1 score of 98%. Here were evaluated seventeen (17) articles with library meta for of R studio version 4.2.1 and this information provides new opportunities to explore non-destructive models for rapid detection of microorganisms in the production chain.
Keywords: Predictive Microbiology; Machine Learning; Food Safety; Foodborne; Pathogens
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