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<p>The impact of color on consumer perception of meat quality, market, and value continues to play an important role. This work discusses the recent progress of meat color measurement of meat color with emphasis on the technological advancements and issues related to its objective measurement using instrumental, digital, and spectroscopic approaches. Tools of colorimetry such as HunterLab and Minolta with CIE Lab* system are colorimeters with high repeatability and precision of lab results, even achieving standard deviations of L* values as low as ± 0.5. Their performance is severely restricted by the heterogeneous nature of meat surfaces, not to mention the lack of real-time applicability. The latest developments in hyperspectral imaging (HSI) and portable spectrometers have further increased spatial resolution to 0.01&#xa0;mm squared, resulting in over 90% of predictive accuracy for myoglobin oxidation and metmyoglobin formation detection. Computer vision systems (CVS), for example, have established themselves as powerful non-invasive devices, achieving up to 95 per cent classification accuracy in assessing the freshness of meat based on color attributes, making color-based texture and tenderness assessment reliable. Non-uniform illumination, surface moisture, and the need for calibration to biological variability still define these innovations' shortcomings. Smartphone applications for color analysis are more useful to end users, but their sensors and uncontrolled lighting conditions make these innovations unreliable. Implementing AI-integrated sensor fusion enables real-time monitoring of the meat's color, improving automation, quality control, traceability, and consumer confidence.</p>

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Innovations and challenges in meat color measurement technologies: a critical review

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Kasun Kumara Dissanayake,
  • Mohamed Rifky,
  • Murodjon Samadiy,
  • Darshika Attanayake,
  • Susan Gunasena,
  • Ruwan Senaarachchi,
  • Tonni Agustiono Kurniawan

摘要

The impact of color on consumer perception of meat quality, market, and value continues to play an important role. This work discusses the recent progress of meat color measurement of meat color with emphasis on the technological advancements and issues related to its objective measurement using instrumental, digital, and spectroscopic approaches. Tools of colorimetry such as HunterLab and Minolta with CIE Lab* system are colorimeters with high repeatability and precision of lab results, even achieving standard deviations of L* values as low as ± 0.5. Their performance is severely restricted by the heterogeneous nature of meat surfaces, not to mention the lack of real-time applicability. The latest developments in hyperspectral imaging (HSI) and portable spectrometers have further increased spatial resolution to 0.01 mm squared, resulting in over 90% of predictive accuracy for myoglobin oxidation and metmyoglobin formation detection. Computer vision systems (CVS), for example, have established themselves as powerful non-invasive devices, achieving up to 95 per cent classification accuracy in assessing the freshness of meat based on color attributes, making color-based texture and tenderness assessment reliable. Non-uniform illumination, surface moisture, and the need for calibration to biological variability still define these innovations' shortcomings. Smartphone applications for color analysis are more useful to end users, but their sensors and uncontrolled lighting conditions make these innovations unreliable. Implementing AI-integrated sensor fusion enables real-time monitoring of the meat's color, improving automation, quality control, traceability, and consumer confidence.