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Exploring the Longissimus Muscle: Unraveling its Correlation with Meat Quality in Bos indicus and Crossbred Bulls
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Image texture features as indicators of beef tenderness.

J Li1, J Tan, F A Martz

  • 1Department of Biological and Agricultural Engineering, University of Missouri, Columbia, MO 65211, USA.

Meat Science
|November 9, 2011
PubMed
Summary

Predicting beef tenderness is possible using image analysis of fresh beef. Image texture, color, and marbling features accurately forecast the tenderness of cooked beef steaks.

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Area of Science:

  • Food Science
  • Computer Vision
  • Agricultural Science

Background:

  • Beef tenderness is a critical quality attribute influencing consumer satisfaction.
  • Objective methods for predicting beef tenderness are needed to complement subjective sensory evaluations.
  • Current prediction methods often lack efficiency and accuracy.

Purpose of the Study:

  • To develop and validate image processing techniques for predicting cooked-beef tenderness from fresh-beef characteristics.
  • To explore the utility of color, marbling, and texture features in predicting beef tenderness.
  • To establish predictive models using statistical and neural network analyses.

Main Methods:

  • Collected fresh beef samples from cattle with varying finishing treatments.
  • Performed sensory evaluation for tenderness and image acquisition for each sample.
  • Extracted color, marbling, and texture features from beef images.
  • Utilized partial least squares and neural network models for prediction analysis.

Main Results:

  • Image texture features demonstrated significant utility as indicators of beef tenderness.
  • Predictive models incorporating color, marbling, and texture features achieved R(2)-values up to 0.70.
  • The developed models showed a strong correlation between image features and sensory tenderness scores.

Conclusions:

  • Image processing offers a viable, objective method for predicting beef tenderness.
  • Texture analysis of fresh beef images is particularly effective for tenderness prediction.
  • This approach can aid in the quality grading and selection of beef.