Detection of E.coli biofilms with hyperspectral imaging and machine learning techniques

• Published in: Korean Journal of Agricultural Science Vol. 47; no. 3; pp. 645 - 655
• Main Authors: Lee, Ahyeong, Seo, Youngwook, Lim, Jongguk, Park, Saetbyeol, Yoo, Jinyoung, Kim, Balgeum, Kim, Giyoung
• Format: Journal Article
• Language: Korean
• Published: 충남대학교 농업과학연구소 2020; 농업과학연구소
• Subjects: 농학; machine learning; hyperspectral imaging; E. coli; biofilm; E; coli
• ISSN: 2466-2402; 2466-2410
• DOI: 10.7744/kjoas.20200052

Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.