Classification between live and dead foodborne bacteria with hyperspectral microscope imagery and machine learning

Abstract

Identifying live foodborne bacteria is essential for ensuring food safety and preventing foodborne illnesses. This study investigated the use of hyperspectral microscope imaging and deep learning methods to accurately distinguish between live and dead foodborne bacteria based on their spectral and morphological features. Three deep learning models, Fusion-Net I, II, and III, were developed and evaluated for their ability to classify live and dead bacterial cells of six pathogenic strains, including Escherichia coli (EC), Listeria innocua (LI), Staphylococcus aureus (SA), Salmonella Enteritidis (SE), Salmonella Heidelberg (SH), and Salmonella Typhimurium (ST). The models utilized both morphological and spectral characteristics of the bacterial cells, with inputs of average spectra and 546 nm band images. Fusion-Net I achieved high accuracy in identifying live bacterial cells, with a classification accuracy of 100% for LI, SE, ST strains and over 92.9% for EC, SA, SH. Fusion-Net II and III models were even more robust, achieving 100% accuracy consistently in classifying dead cells in all six strains. Fusion-Net III also showed the ability to identify bacterial strains with 96.9% accuracy, making it a dual-task model with potential applications in identifying live foodborne bacteria prior to foodborne outbreaks. These findings suggest that the use of hyperspectral microscope imaging and deep learning methods could provide a new tool for quickly and accurately identifying bacterial viability, thereby improving the efficiency and reliability of food safety inspection.