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Smartphone-Based Deep Learning System for Detecting Ractopamine-Fed Pork Using Visual Classification Techniques.

Hong-Dar Lin1, Mao-Quan He1, Chou-Hsien Lin2

  • 1Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
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A new smartphone system visually identifies pork origin and detects ractopamine, ensuring meat authenticity. This technology empowers consumers to verify pork products, enhancing market transparency and safety.

Area of Science:

  • Food science and technology
  • Agricultural economics
  • Consumer protection

Background:

  • Ractopamine, a beta-agonist, is used to increase lean meat yield but poses health risks.
  • Taiwan permits ractopamine-containing pork imports, leading to concerns about labeling and misidentification.
  • Consumers require reliable methods to verify pork authenticity due to high demand and trade policies.

Purpose of the Study:

  • To develop a smartphone-based visual detection system for classifying meat cut, pork origin, and ractopamine presence.
  • To provide consumers with a tool for real-time meat authenticity verification in retail environments.
  • To enhance market transparency and address concerns regarding imported pork.

Main Methods:

  • A three-stage image processing approach using masking techniques (elliptical and square) to extract regions of interest (ROI).
Keywords:
MobileNetcomputer visiondeep learningractopamine-fed porkvisual inspection

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  • Utilizing the MobileNet architecture for classification tasks, including meat cut, pork origin, and ractopamine detection.
  • Conducting experiments to evaluate the system's accuracy and efficiency.

    • Main Results:

    • Achieved a 96% classification rate (CR) for meat cut identification.
    • Obtained an average CR of 79.11% and an F1 score of 90.25% for pork origin classification.
    • Reached an average CR of 80.67% and an F1 score of 80.56% for ractopamine detection.

    Conclusions:

    • The smartphone-based visual detection system effectively verifies meat authenticity and pork origin.
    • The system demonstrates high accuracy and efficiency, particularly with the MobileNet model.
    • Findings support enhanced consumer protection and market transparency in the pork industry.