Work

Detection and visualization of Escherichia coli in pork using hyperspectral imaging-based deep learning

Year: 2025

Type: article

Abstract: Traditional detection of pathogenic microorganisms is time-consuming and costly. This study proposed a rapid, nondestructive, and environmentally friendly method for detecting Escherichia coli (E.coli) in pork using hyperspectral imaging (HSI) and deep learning. First, hyperspectral images (387.06-1003.18 nm) of fresh pork inoculated with different concentrations of E. coli were collected, and spectral reflectance was extracted from the region of interest (ROI). Six preprocessing methods and five feature wavelength selection algorithms were investigated to optimize the performance of the convolutional neural network (CNN) and support vector machine (SVM) models. The CNN-SVM based on particle swarm optimization (PSO) was proposed to develop a detection method of E. coli concentrations in the fresh pork, and PSO-CNN-SVM based on a variable iterative space shrinkage approach (VISSA) performed the best, achieving 87.50% accuracy in the testing set. Finally, the spatial distribution of E. coli in pork was visualized based on the predicted model. The results confirm HSI-based deep learning could effectively detect and visualize E. coli in fresh pork, providing a technical support for food safety.

Source: Journal of Agriculture and Food Research

DOI: 10.1016/j.jafr.2025.102107

Authors: Ying Liu, Guicai Chen, Xing Xu, Weidong Zhou, Chuanqi Xie

Institutions: Zhejiang University of Science and Technology, ZheJiang Academy of Agricultural Sciences

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDGs: Zero hunger, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,530