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

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

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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 vista 5 vl5 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 weeping hemlock for sale VBNC detection using AI-enabled hyperspectral microscopy, contributing to timely intervention to prevent foodborne illnesses and outbreaks.