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<p>This study presents a comprehensive approach for detecting and quantifying adulteration in minced meat products using image processing and colorimetric analysis techniques under both raw and cooked conditions. Common adulterants—including soy, chicken skin, sheep lung, gizzard, and bread dough—were added to minced meat at varying concentrations (0 to 80%), and their visual changes were tracked over time using red, green, blue (RGB)-based image analysis. Calibration curves were plotted, and linear regression models were developed for each adulterant. The method demonstrated high sensitivity, with the average slopes of the calibration curves being sheep lung (1.372), chicken skin (1.219), soy (1.067), gizzard (0.704), and bread dough (0.616). Corresponding relative standard deviation (RSD) values were 21.97%, 13.22%, 7.97%, 10.56%, and 10.70%, respectively, indicating reliable accuracy, particularly for soy and gizzard samples. Importantly, even at low adulteration levels, significant deviations in RGB values were detectable, confirming the reliability of the method for early-stage adulteration. Additionally, it was observed that increasing the temperature during cooking led to a reduction in RGB values, highlighting the thermal impact on color characteristics. The results confirm that the proposed hybrid method, combining image analysis, offers a rapid, low-cost, and highly sensitive solution for routine adulteration screening in meat products.</p>

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Determination of the Kind and Level of Different Cheating in Minced Meat by Image Processing and Analysis

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • Zhenhua Cai,
  • Seyedeh Narges Mousavi

摘要

This study presents a comprehensive approach for detecting and quantifying adulteration in minced meat products using image processing and colorimetric analysis techniques under both raw and cooked conditions. Common adulterants—including soy, chicken skin, sheep lung, gizzard, and bread dough—were added to minced meat at varying concentrations (0 to 80%), and their visual changes were tracked over time using red, green, blue (RGB)-based image analysis. Calibration curves were plotted, and linear regression models were developed for each adulterant. The method demonstrated high sensitivity, with the average slopes of the calibration curves being sheep lung (1.372), chicken skin (1.219), soy (1.067), gizzard (0.704), and bread dough (0.616). Corresponding relative standard deviation (RSD) values were 21.97%, 13.22%, 7.97%, 10.56%, and 10.70%, respectively, indicating reliable accuracy, particularly for soy and gizzard samples. Importantly, even at low adulteration levels, significant deviations in RGB values were detectable, confirming the reliability of the method for early-stage adulteration. Additionally, it was observed that increasing the temperature during cooking led to a reduction in RGB values, highlighting the thermal impact on color characteristics. The results confirm that the proposed hybrid method, combining image analysis, offers a rapid, low-cost, and highly sensitive solution for routine adulteration screening in meat products.