Rapid Non-Invasive Detection of Pathogenic E. Coli on Spinach Leaves Using Hyperspectral Imaging and Deep Learning

Ensuring food safety necessitates rapid and accurate detection of foodborne pathogens. This study presents a novel framework integrating hyperspectral imaging (HSI) with advanced deep learning (DL) techniques to identify pathogenic Escherichia coli on spinach leaves. Fresh spinach samples were inoculated with Enterotoxigenic E. coli (ETEC), while deionized water served as the control. Hyperspectral images in the 400–1000 nm range were captured and preprocessed to normalize spectral data. Custom DL architectures were developed and applied directly to the raw hyperspectral images, exploiting the full spectral-spatial information. The models' performance was rigorously evaluated using comprehensive metrics. Results demonstrate that combining HSI with DL significantly enhances the detection of pathogenic E. coli, offering high accuracy. This innovative approach provides a rapid, non-destructive method for identifying foodborne pathogens, advancing food safety monitoring in processing and distribution chains.