Gas sensor array to classify the chicken meat with E. coli contaminant by using random forest and support vector machine

Abstract

Microbes such as Escherichia coli (E. coli) can easily contaminate raw chicken meat in clean conditions, causing decay and unpleasant scents. This study aims to characterize gas patterns by comparing fresh chicken meat and E. coli bacteria contaminated chicken meat based on shelf life using a Gas Sensor Array (GSA) system (MQ2, MQ3, MQ7, MQ8, MQ135, and MQ136) on electronic nose. The findings revealed GSA capability to detect a variety of typical gas patterns formed by the samples. This gas detection property is indicated by the appearance of the variance in the sensors output voltage pattern for each sample variation. The data for fresh and contaminated samples were classified by the random forest (RF) classifier with 99.25% and 98.42% precision, respectively. Furthermore, the support vector machine (SVM) classifier correctly identified the fresh and contaminated samples with 98.61% and 86.66% accuracy, respectively. This finding offers insight for GSA capability in classifying chicken meat contaminated with E. coli using an RF and SVM.

Keywords

• Food security
• Chicken meat
• E. coli
• Support vector machine
• Random forest
• Gas sensor array