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Related Experiment Video

Updated: Jun 12, 2026

Tracking Microbial Contamination in Retail Environments Using Fluorescent Powder - A Retail Delicatessen Environment Example
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Published on: March 5, 2014

Rapid detection of raw meat freshness using deep learning and colorimetric/fluorescent array.

Feiran Xu1, Le Wang1, Ping Li1

  • 1School of Food and Biological Engineering, Hefei University of Technology, Hefei 230601, China.

Food Chemistry
|June 10, 2026
PubMed
Summary

This study introduces a rapid colorimetric fluorescent array (CFA) and deep learning (DL) system for detecting raw meat freshness. The advanced method ensures food safety and reduces waste with high accuracy.

Keywords:
Colorimetric/fluorescence detectionDeep learning modelsFood safetyMeat freshnessMobile applications

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Biotechnology

Background:

  • Effective monitoring of raw meat freshness is critical for public health and minimizing economic losses due to spoilage.
  • Current methods for assessing meat freshness can be time-consuming, require specialized expertise, or are destructive.
  • The presence of volatile amines, byproducts of microbial spoilage, offers a biochemical indicator of meat degradation.

Purpose of the Study:

  • To develop and validate a rapid, non-destructive, and accurate system for real-time monitoring of raw meat freshness.
  • To integrate a colorimetric fluorescent array (CFA) with advanced deep learning (DL) algorithms for enhanced detection capabilities.
  • To assess the system's performance in terms of accuracy, speed, and practicality for food safety applications.

Main Methods:

  • Fabrication of a CFA using alizarin and fluorescein isothiocyanate (FITC) loaded onto a polyvinylidene fluoride (PVDF) membrane to detect spoilage-related amines.
  • Development and training of deep learning models, including convolutional neural networks (CNNs) and Vision Transformers, using a dataset of 8170 images.
  • Evaluation of model performance using metrics such as prediction accuracy under different lighting conditions (natural and UV) and image processing time.

Main Results:

  • The CFA demonstrated a sensitive response to amines indicative of meat spoilage.
  • The CNN-based ResNet-152 model achieved high prediction accuracies: 95.82% under natural light and 97.88% under ultraviolet light.
  • The integrated system can assess meat freshness from a single image in as little as 130 milliseconds.

Conclusions:

  • The developed colorimetric fluorescent array-deep learning (CFA-DL) system provides a fast, non-destructive, and accurate method for real-time meat freshness monitoring.
  • This approach has the potential to significantly enhance food safety protocols and reduce food waste across the supply chain.
  • The system's ease of use and high accuracy make it suitable for widespread adoption without requiring specialized personnel or complex equipment.