What question did this study set out to answer?

The aim is to detect E. coli on food processing surfaces using advanced imaging and machine learning techniques.

January 22, 2026Applied Sciences0 citationsOpen Access

Detecting Escherichia coli on Conventional Food Processing Surfaces Using UV-C Fluorescence Imaging and Deep Learning

ZIZafar IqbalKing Saud University TBThomas F. BurksUniversity of Florida SVSnehit VaddiUniversity of Florida

Key Points

The aim is to detect E. coli on food processing surfaces using advanced imaging and machine learning techniques.
Applied four concentrations of E. coli on stainless steel and rubber surfaces.
Captured fluorescence videos and extracted droplet regions for analysis.
Employed wavelet-based denoising to enhance image quality.
Trained multiple deep learning models to classify E. coli concentration levels.
YOLO11-cls models achieved peak test accuracies of 100% on rubber and 99.60% on stainless steel.
The best balance of accuracy (98.88%) and speed (4–5 ms) was found in YOLO11-cls.
Classical machine learning models performed significantly worse, with Random Forest at 89.65% accuracy.

Abstract

Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. coli (0, 105, 107, and 108 colony forming units (CFU)/mL) and two egg solutions (white and yolk) were applied to stainless steel and white rubber to simulate realistic contamination with organic interference. For each concentration level, 256 droplets were inoculated in 16 groups, and fluorescence videos were captured. Droplet regions were extracted from the video frames, subdivided into quadrants, and augmented to generate a robust dataset, ensuring 3–4 droplets per sample. Wavelet-based denoising further improved image quality, with Haar wavelets producing the highest Peak Signal-to-Noise Ratio (PSNR) values, up to 51.0 dB on white rubber and 48.2 dB on stainless steel. Using this dataset, multiple deep learning (DL) models, including ConvNeXtBase, EfficientNetV2L, and five YOLO11-cls variants, were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize model attention to bacterial fluorescence regions. Across four dataset groupings, YOLO11-cls models achieved consistently high performance, with peak test accuracies of 100% on white rubber and 99.60% on stainless steel, even in the presence of egg substances. YOLO11s-cls provided the best balance of accuracy (up to 98.88%) and inference speed (4–5 ms) whilst having a compact size (11 MB), outperforming larger models such as EfficientNetV2L. Classical machine learning models lagged significantly behind, with Random Forest reaching 89.65% accuracy and SVM only 67.62%. Overall, the results highlight the potential of combining UV-C fluorescence imaging with deep learning for rapid and reliable detection of E. coli on stainless steel and rubber conveyor belt surfaces. Additionally, this approach could support the design of effective interventions to remove E. coli from food processing environments.

Ask AI

Helpful

Bookmark

View Full Paper