Rapid counting of coliforms and Escherichia coli by deep learning-based classifier

Abstract

To ensure that food has been handled hygienically, manufacturers routinely examine the numbers of indicator bacteria, such as coliforms and Escherichia coli. Using the deep-learning algorithm YOLO, we developed a classifier that automatically counts the number of coliforms (red colonies) and E. coli (blue colonies) on a chromogenic agar plate. Using Citrobacter freundii IAM 12471^T and E. coli NBRC 3301, we trained our YOLO-based classifier with images of Petri dishes grown with each strain alone (10 images) and/or with a mixture of both strains (5 images). When the performance of the classifier was evaluated using 83 images, the accuracy rates for coliforms and E. coli reached 99.4% and 99.5%, respectively. We then investigated whether this classifier could detect other, non-trained coliform species (22 species) and E. coli strains (13 strains). The accuracy rates for coliforms and E. coli were 98.7% (90 Petri dishes) and 94.1% (46 Petri dishes), respectively. Furthermore, we verified the practical feasibility of the developed classifier using 38 meats (chicken, pork, and beef). The accuracy rates for coliforms and E. coli in meat isolates were 98.8% (80 Petri dishes) and 93.8% (35 Petri dishes), respectively. The time required to count coliforms/E. coli on a single plate was ~70 ms. This novel method should enable users to rapidly quantify coliforms/E. coli without relying on a human inspector's color vision, leading to improved assurance of food safety.