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Vision-Based Quality Grading of Beef Steaks Using Marbling Distribution Analysis and Lean Meat Color Classification.

Hong-Dar Lin1, Rong-Lun Chung1, Chou-Hsien Lin2

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

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated vision system for grading beef steaks. It accurately analyzes fat marbling and lean meat color, overcoming frost challenges for reliable quality assessment.

Keywords:
beef steak quality gradingchi-square goodness-of-fit testcurvelet transformfat marbling distributionlean-meat color analysissupport vector machinevision-based inspection

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Published on: September 20, 2016

Area of Science:

  • Food Science and Technology
  • Computer Vision
  • Image Processing

Background:

  • Automated quality grading of beef steaks is crucial for the food industry.
  • Surface frost on frozen beef products creates specular reflections, hindering accurate segmentation of fat and lean tissues.
  • Existing methods struggle with illumination variations and complex texture analysis in beef quality assessment.

Purpose of the Study:

  • To develop a robust vision-based framework for automated inspection and quality grading of beef steaks.
  • To address the challenge of frost-induced artifacts in frozen beef image analysis.
  • To integrate fat marbling distribution and lean-meat color evaluation for comprehensive quality assessment.

Main Methods:

  • Applied homomorphic filtering to mitigate frost-induced illumination artifacts.
  • Utilized curvelet transform and square-ring filtering for multi-scale fat-lean segmentation.
  • Extracted marbling features (convex hull, skeleton) and performed chi-square tests for distribution analysis.
  • Employed Support Vector Machine (SVM) for lean-meat color classification based on RGB features.
  • Integrated marbling and color data using a weighted grading strategy.

Main Results:

  • Achieved high accuracy in fat segmentation: 92.68% detection rate, 4.97% false-positive rate, and 94.09% correct classification.
  • The SVM-based lean-meat color classifier demonstrated 96.67% accuracy.
  • The integrated grading framework reached an overall accuracy of 90.38%, showing strong agreement with human evaluations.

Conclusions:

  • The proposed vision-based framework effectively automates beef steak inspection and quality grading.
  • The integrated approach successfully overcomes challenges posed by frost artifacts, enabling precise marbling and color analysis.
  • The system demonstrates significant potential for objective and consistent beef quality assessment in the industry.