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Prediction of pork color attributes using computer vision system.

Xin Sun1, Jennifer Young2, Jeng Hung Liu2

  • 1Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA; Department of Engineering, Nanjing Agricultural University, Nanjing 210031, China.

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Summary

Computer vision methods accurately predict pork color. Image processing features strongly correlated with Minolta colorimeter measurements, showing potential for objective pork quality assessment.

Keywords:
Color featureImage processingPork colorRegression model

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Area of Science:

  • Food Science
  • Agricultural Engineering
  • Computer Vision

Background:

  • Objective assessment of pork quality is crucial for consumer acceptance and industry standards.
  • Traditional methods for evaluating pork color can be subjective and time-consuming.

Purpose of the Study:

  • To evaluate the effectiveness of color image processing and regression methods for predicting pork color attributes.
  • To determine the correlation between Minolta colorimeter measurements and image processing features.

Main Methods:

  • One hundred pork center cut loin samples with subjective color scores 1-5 were analyzed.
  • Eighteen image color features were extracted from RGB, HSI, and L*a*b* color spaces.
  • Linear and stepwise regression models were employed to predict pork color attributes.

Main Results:

  • Significant correlations (P<0.0001) were found between Minolta colorimeter values and image processing features for L* (0.91), a* (0.80), and b* (0.66).
  • A linear regression model achieved a higher coefficient of determination (R(2)=0.83) compared to stepwise regression (R(2)=0.70).

Conclusions:

  • Computer vision techniques demonstrate significant potential as a tool for predicting pork color attributes.
  • Image processing offers a viable, objective alternative for assessing pork quality parameters.