Detection of Viable but Nonculturable E. coli Induced by Low-Level Antimicrobials Using AI-Enabled Hyperspectral Microscopy.

Abstract

Rapid detection of bacterial pathogens is essential for food safety and public health, yet bacteria can evade detection by entering a viable but nonculturable (VBNC) state under sublethal stress, such as antimicrobial residues. These bacteria remain active but undetectable by standard culture-based methods without extensive enrichment, necessitating advanced detection methods. This study developed an AI-enabled hyperspectral microscope imaging (HMI) framework for rapid VBNC detection under low-level antimicrobials. The objectives were to (i) induce the VBNC state in Escherichia coli K-12 by exposure to selected antimicrobial stressors, (ii) obtain HMI data capturing physiological changes in VBNC cells, and (iii) automate the classification of normal and VBNC cells using deep learning image classification. The VBNC state was induced by low-level oxidative (0.01% hydrogen peroxide) and acidic (0.001% peracetic acid) stressors for 3 days, confirmed by live-dead staining and plate counting. HMI provided spatial and spectral data, extracted into pseudo-RGB images using three characteristic spectral wavelengths. An EfficientNetV2-based convolutional neural network architecture was trained on these pseudo-RGB images, achieving 97.1% accuracy of VBNC classification (n = 200), outperforming the model trained on RGB images at 83.3%. The results highlight the potential for rapid, automated VBNC detection using AI-enabled hyperspectral microscopy, contributing to timely intervention to prevent foodborne illnesses and outbreaks.