Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses

Abstract

Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting <i>Escherichia coli</i> (<i>E. coli</i>) and <i>Salmonella typhimurium</i> (<i>S. typhimurium</i>) on the surface of food processing facilities by using fluorescence hyperspectral imaging. <i>E. coli</i> and <i>S. typhimurium</i> were cultured on high-density polyethylene and stainless steel coupons, which are the main materials used in food processing facilities. We obtained fluorescence hyperspectral images for the range of 420-730 nm by emitting UV light from a 365 nm UV light source. The images were used to perform discriminant analyses (linear discriminant analysis, <i>k</i>-nearest neighbor analysis, and partial-least squares discriminant analysis) to identify and classify coupons on which bacteria could be cultured. The discriminant performances of specificity and sensitivity for <i>E. coli</i> (1-4 log CFU·cm^-2) and <i>S. typhimurium</i> (1-6 log CFU·cm^-2) were over 90% for most machine learning models used, and the highest performances were generally obtained from the <i>k</i>-nearest neighbor (<i>k</i>-NN) model. The application of the learning model to the hyperspectral image confirmed that the biofilm detection was well performed. This result indicates the possibility of rapidly inspecting biofilms using fluorescence hyperspectral images.