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 Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium) on the surface of food processing facilities by using fluorescence hyperspectral imaging. E. coli and S. typhimurium 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, k-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 E. coli (1–4 log CFU·cm−2) and S. typhimurium (1–6 log CFU·cm−2) were over 90% for most machine learning models used, and the highest performances were generally obtained from the k-nearest neighbor (k-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.

Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology

“…Biofilms are mainly composed of polysaccharides secreted by microorganisms, which can be firmly attached to the surface of processing facilities and containers (such as stainless steel, plastic). Lee et al (2021) evaluated the feasibility of fluorescent hyperspectral imaging for the rapid detection of biofilms formed by Escherichia coli and Salmonella typhimurium on high-density polyethylene and stainless steel surfaces. The k-NN algorithm can predict more than 90% of the biofilm region.…”

“…Lee et al. (2021) evaluated the feasibility of fluorescent hyperspectral imaging for the rapid detection of biofilms formed by Escherichia coli and Salmonella typhimurium on high‐density polyethylene and stainless steel surfaces. The k‐NN algorithm can predict more than 90% of the biofilm region.…”

Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods

“…This Special Issue has had a resoundingly enthusiastic response, with several submissions from academics and professional spectroscopists, resulting in a collection of 13 papers, including one exhaustive review paper [8][9][10][11][12][13][14][15][16][17][18][19][20]. The articles submitted represent the variety of the discussed field well, covering a wide range of topics related to NIR spectroscopy.…”

“…Likewise, the potential of hyperspectral imaging can be also recognized on the basis of the articles collected in this Special Issue [15][16][17]. Hyperspectral imaging (his) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity.…”

“…This technology can not only identify the physical chemistry characteristics of a substance through spectroscopic analysis, but also simultaneously obtains information about the spatial distribution of certain components through image analysis [21]. In this Special Issue, we present the possibility of rapidly inspecting and detecting Escherichia coli and Salmonella typhimurium on the surface of food processing facilities, which is a major global public health problem [22], via fluorescence hyperspectral imaging and various discriminant analysis techniques [15]. This Special Issue aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep-learning approaches for the pixel-wise classification of food products (sweet products and salmon fillets).…”