Timely detection and identification of pathogens are crucial for safeguarding food safety, public health, and environmental monitoring. However, conventional methods often struggle to account for subtle biological variations in microbial physiology and the nuanced differences among closely related serovars. The objectives of this work were to: i) systematically review AI applications for imaging-based pathogen detection under stress conditions; ii) develop an AI-enabled hyperspectral microscopy framework for rapid detection of stressed cells under low-level antimicrobials; iii) enhance data processing of this framework to classify Salmonella serovars. Critical gaps identified from the systematic review highlighted limited research on stressed pathogen detection and inconsistent reporting of laboratory protocols and data pipelines. An optimized laboratory protocol captured distinct hyperspectral profiles of E. coli K-12 in both normal (i.e., viable and culturable) and viable but non-culturable (VBNC) states induced by low-level antimicrobials (n = 200). An EfficientNetV2 convolutional neural network (CNN), trained on pseudo-RGB images from these hyperspectral data, achieved a 97.1% accuracy for VBNC classification, outperforming standard color images. Additionally, hyperspectral data detected subtle biological differences of five Salmonella serovars, i.e., Kentucky, Johannesburg, Infantis, Enteritidis, and 4,[5],12:i:- (n = 500). Enhanced data preprocessing techniques and multimodal fusion methods were introduced, incorporating both spectral features (via manual feature selection vs. data-driven feature extraction) and spatial features (CNN-based). Data-driven feature extraction outperformed manual selection, and multimodal fusion further improved classification accuracy to 82.40%. These findings demonstrate that integrating hyperspectral microscopy with AI-enabled data analysis enhances classification capabilities, paving the way for practical, streamlined rapid pathogen detection solutions.
Read
