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A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence

Yinyan Shi1,2, Xiaochan Wang2, Md Saidul Borhan1

  • 1Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA.

Food Science of Animal Resources
|July 22, 2021
PubMed
Summary

Artificial intelligence-driven non-destructive detection methods are crucial for ensuring meat quality and safety. These advanced techniques offer rapid, objective evaluations, meeting increasing consumer demand for high-quality meat products.

Keywords:
grading assessmentindustrial applicationkey technologymeat qualitynon-destructive detection

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Area of Science:

  • Food Science and Technology
  • Agricultural Engineering
  • Artificial Intelligence in Food Safety

Background:

  • Rising global population and meat demand necessitate stringent meat quality and safety standards.
  • Traditional meat assessment methods are often destructive, slow, and subjective.
  • Regulatory bodies are enforcing stricter guidelines on meat production.

Purpose of the Study:

  • To review key non-destructive detection technologies for meat quality assessment.
  • To compare technical characteristics and evaluation methods of various techniques.
  • To explore practical applications and future directions in AI-based meat quality analysis.

Main Methods:

  • Ultrasonic technology
  • Machine (computer) vision technology
  • Near-infrared spectroscopy
  • Hyperspectral imaging
  • Raman spectroscopy
  • Electronic nose/tongue systems

Main Results:

  • Non-destructive technologies offer objective, rapid, and accurate meat quality evaluation.
  • These AI-powered methods are increasingly applied in the meat industry for quality control.
  • The review analyzes the strengths and limitations of each surveyed technology.

Conclusions:

  • Non-destructive detection technologies are vital for meeting consumer demand for high-quality meat.
  • Integration of these systems promotes automatic, real-time inspection and quality control.
  • Future research trends point towards integrated systems for diverse quality evaluation applications.