Research Article
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Soğuk Depolama Boyunca Kırmızı Et Renginin Analizinde Kolorimetrik ve Bilgisayarlı Görüş Sistemlerinin Karşılaştırmalı Değerlendirilmesi

Year 2026, Volume: 15 Issue: 1, 1 - 10, 27.03.2026
https://doi.org/10.31196/huvfd.1789286
https://izlik.org/JA33CB63NL

Abstract

Bu çalışmada, soğukta depolama sürecinde dana bonfilesinde meydana gelen renk değişimlerinin izlenmesinde geleneksel bir kolorimetre cihazı ile bilgisayarla görme sistemi (CVS) karşılaştırılmıştır. İki ila üç yaşlı erkek Simmental sığırlarından elde edilen bonfile numuneleri, eşit dilimlere ayrılarak 4 ± 2 °C’de dört gün süreyle buzdolabında muhafaza edilmiştir. Renk ölçümleri her 24 saatte bir her iki yöntemle yapılmış, aynı zamanda aerobik bakteri sayımı ve pH değerleri de kaydedilmiştir. Dört günlük depolama boyunca her iki yöntem de L*, a*, b*, Kroma, Hue ve kırmızılık indeksi değerlerinde önemli değişiklikler belirlemiş; mikrobiyolojik ve pH analizleri ise bozulma sürecini doğrulamıştır. CVS sürekli olarak daha yüksek L*, b*, Kroma ve Hue değerleri ölçerken, kolorimetre daha düşük b* değerleri nedeniyle daha yüksek kırmızılık indeksi değerleri vermiştir. Yöntem ve gün arasındaki anlamlı etkileşim, bu farklılıkların depolama süresince devam ettiğini göstermiştir. Bulgular, yüzeye odaklı ölçüm prensibinden dolayı CVS’nin hem anlık değerlendirmeler hem de sürekli izleme için güvenilir, pratik ve tahribatsız bir yöntem sunduğunu ortaya koymaktadır.

References

  • Altun, S. K., Aydemir, M. E., Takım, K., Yilmaz, M. A., Yalcin, H. (2024). Inhibition of Nε-(carboxymethyl) lysine and Nε-(carboxyethyl) lysine formation in air-fried beef tenderloins marinated with concentrated cranberry juice. Food Bioscience, 60, 104336. https://doi.org/10.1016/j.fbio.2024.104336
  • Aydemir, M. E., Altun, S. K., Takım, K., Yilmaz, M. A., Yalcin, H. (2024). nhibitory effect of homemade hawthorn vinegar-based marinade on Nε-(carboxymethyl)lysine and Nε-(carboxyethyl) lysine formation in beef tenderloins. Meat Science, 214, 109535. https://doi.org/10.1016/j.meatsci.2024.109535
  • Barbut, S. (2001). Effect of illumination source on the appearance of fresh meat cuts. Meat Science, 59(2), 187–191. https://doi.org/10.1016/S0309-1740(01)00069-9
  • Chmiel, M., Słowiński, M., Dasiewicz, K., Florowski, T. (2012). Application of a computer vision system to classify beef as normal or dark, firm, and dry. Journal of Animal Science, 90(11), 4126–4130. https://doi.org/10.2527/jas.2011-5022
  • Girolami, A., Napolitano, F., Faraone, D., Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93(1), 111–118. https://doi.org/10.1016/j. meatsci.2012.08.010
  • Gonzalez, S. V., Zhai, C., Hernandez-Sintharakao, M. J., Geornaras, I., Nair, M. N. (2024). Evaluation of beef retail shelf-life following extended storage at low temperature. Meat and Muscle Biology, 8(1), 17649. https://doi.org/10.22175/mmb.17649
  • Güngören, A., Akkemik, Y., Tufekci, E. F., Zengin, G., Emre, G., Gungoren, G., & Baloğlu, M. C. (2025). Applying chitosan-based films enriched with Borago officinalis extract for active and green packaging of fresh rainbow trout fillets. Foods, 14(4), 639. https://doi.org/10.3390/foods14040639
  • Güngören, A., Güngören, G. (2025). Impact of correlated color temperature on red meat color assessment via image processing systems. Journal of Food Science, 90(11), e70660. https://doi.org/10.1111/1750-3841.70660
  • Hosseinpour, S., Ilkhchi, A. H., Aghbashlo, M. (2019). An intelligent machine vision-based smartphone app for beef quality evaluation. Journal of Food Engineering, 248, 9–22. https://doi. org/10.1016/j.jfoodeng.2018.12.009
  • King, D. A., Hunt, M. C., Barbut, S., Claus, J. R., Cornforth, D. P., Joseph, P., & Weber, M. (2023). American Meat Science Association guidelines for meat color measurement. Meatand Muscle Biology, 6(4), 12473. https://doi.org/10.22175/ mmb.12473
  • Lemos Junior, W. J. F., Marques Costa, L., Alberto Guerra, C., Sales de Oliveira, V., Gava Barreto, A., Alves de Oliveira, & Guerra, A. F. (2024). Microbial landscape of cooked meat products: Evaluating quality and safety in vacuum-packaged sausages using culturedependent and culture-independent methods over 1 year in a sustainable food chain. Frontiers in Microbiology, 15. https://doi.org/10.3389/fmicb.2024.1457819
  • Liu, Z., Shaposhnikov, M., Zhuang, S., Tu, T., Wang, H., Wang, L. (2023). Growth and survival of common spoilage and pathogenic bacteria in ground beef and plant-based meat analogues. Food Research International, 164, 112408. https://doi.org/10.1016/j. foodres.2022.112408
  • Ramanathan, R., Mancini, R. A., Konda, M. R., Bailey, K., More, S., Mafi, G. G. (2022). Evaluating the failure to bloom in dark-cutting and lactate-enhanced beef longissimus steaks. Meat Science,184, 108684. https://doi.org/10.1016/j.meatsci.2021.108684
  • Ramanathan, R., Suman, S. P., Faustman, C. (2020). Biomolecular interactions governing fresh meat color in post-mortem skeletal muscle: A review. Journal of Agricultural and Food Chemistry, 68(46), 12779–12787. https://doi.org/10.1021/acs. jafc.9b08098
  • Rani, Z. T., Mhlongo, L. C., Hugo, A. (2023). Microbial profiles of meat at different stages of the distribution chain from the abattoir to retail outlets. International Journal of Environmental Research and Public Health, 20(3), 1986. https://doi. org/10.3390/ijerph20031986
  • Ruedt, C., Gibis, M., Weiss, J. (2023). Meat color and iridescence: Origin, analysis, and approaches to modulation. Comprehensive Reviews in Food Science and Food Safety, 22(4), 3366–3394. https://doi.org/10.1111/1541-4337.13191
  • Sáenz, C., Hernández, B., Beriain, M. J., Lizaso, G. (2005). Meat color in retail displays with fluorescent illumination. Color Research & Application, 30(4), 304–311. https://doi. org/10.1002/col.20123
  • Suman, S. P., Joseph, P. (2013). Myoglobin chemistry and meat color. Annual Review of Food Science and Technology, 4, 79–99. https://doi.org/10.1146/annurev-food-030212-182623
  • Taheri-Garavand, A., Fatahi, S., Omid, M., Makino, Y. (2019). Meat quality evaluation based on computer vision technique: A review. Meat Science, 156, 183–195. https://doi.org/10.1016/j. meatsci.2019.06.002
  • Tomasevic, I., Djekic, I., Font-i-Furnols, M., Terjung, N., Lorenzo, J. M. (2021). Recent advances in meat color research. Current Opinion in Food Science, 41, 81–87. https://doi.org/10.1016/j.cofs.2021.02.012
  • Tomasevic, I., Tomovic, V., Milovanovic, B., Lorenzo, J., Đorđević, V., Karabasil, N., & Djekic, I. (2019). Comparison of a computer vision system vs. traditional colorimeter for color evaluation of meat products with various physical properties. Meat Science, 148, 5–12. https://doi.org/10.1016/j.meatsci.2018.09.015
  • Zhang, L., Zhang, M., Mujumdar, A. S. (2023). Technological innovations or advancement in detecting frozen and thawed meat quality: A review. Critical Reviews in Food Science and Nutrition, 63(11), 1483–1499. https://doi.org/10.1080/104083 98.2021.1964434

Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage

Year 2026, Volume: 15 Issue: 1, 1 - 10, 27.03.2026
https://doi.org/10.31196/huvfd.1789286
https://izlik.org/JA33CB63NL

Abstract

This study compared a traditional colorimeter device and a computer vision system (CVS) for monitoring color changes in beef tenderloin during refrigerated storage. Beef samples were obtained from 2-3-year-old male Simmental cattle, portioned into uniform slices, and stored at 4±2 °C for four days. Color was measured at 24-hour intervals by both methods, while aerobic plate counts (APC) and pH were also recorded. Over the 4-day storage period, both methods detected significant changes in Lab*, Chroma, Hue, and the redness index (RI), with microbiological and pH analyses confirming spoilage progression. CVS consistently yielded higher L*, b*, Chroma, and Hue values, whereas the colorimeter produced higher RI values due to lower b* measurements. A significant method and day interaction indicated that these differences persisted under dynamic storage conditions. The results suggest that due to its surface-focused measurement principle, CVS offers a reliable, practical, and non-destructive approach for both pointin- time assessments and continuous monitoring of red meat color.

Ethical Statement

This study did not involve the use of any live animals or experimental animal materials. The meat used was food-grade and obtained from a local slaughterhouse. Therefore, this study is not subject to HADYEK permission in accordance with Article 8 (k) of the Regulation on Working Procedures and Principles of Animal Experiments Ethics Committees (official gazette no: 28914).

References

  • Altun, S. K., Aydemir, M. E., Takım, K., Yilmaz, M. A., Yalcin, H. (2024). Inhibition of Nε-(carboxymethyl) lysine and Nε-(carboxyethyl) lysine formation in air-fried beef tenderloins marinated with concentrated cranberry juice. Food Bioscience, 60, 104336. https://doi.org/10.1016/j.fbio.2024.104336
  • Aydemir, M. E., Altun, S. K., Takım, K., Yilmaz, M. A., Yalcin, H. (2024). nhibitory effect of homemade hawthorn vinegar-based marinade on Nε-(carboxymethyl)lysine and Nε-(carboxyethyl) lysine formation in beef tenderloins. Meat Science, 214, 109535. https://doi.org/10.1016/j.meatsci.2024.109535
  • Barbut, S. (2001). Effect of illumination source on the appearance of fresh meat cuts. Meat Science, 59(2), 187–191. https://doi.org/10.1016/S0309-1740(01)00069-9
  • Chmiel, M., Słowiński, M., Dasiewicz, K., Florowski, T. (2012). Application of a computer vision system to classify beef as normal or dark, firm, and dry. Journal of Animal Science, 90(11), 4126–4130. https://doi.org/10.2527/jas.2011-5022
  • Girolami, A., Napolitano, F., Faraone, D., Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93(1), 111–118. https://doi.org/10.1016/j. meatsci.2012.08.010
  • Gonzalez, S. V., Zhai, C., Hernandez-Sintharakao, M. J., Geornaras, I., Nair, M. N. (2024). Evaluation of beef retail shelf-life following extended storage at low temperature. Meat and Muscle Biology, 8(1), 17649. https://doi.org/10.22175/mmb.17649
  • Güngören, A., Akkemik, Y., Tufekci, E. F., Zengin, G., Emre, G., Gungoren, G., & Baloğlu, M. C. (2025). Applying chitosan-based films enriched with Borago officinalis extract for active and green packaging of fresh rainbow trout fillets. Foods, 14(4), 639. https://doi.org/10.3390/foods14040639
  • Güngören, A., Güngören, G. (2025). Impact of correlated color temperature on red meat color assessment via image processing systems. Journal of Food Science, 90(11), e70660. https://doi.org/10.1111/1750-3841.70660
  • Hosseinpour, S., Ilkhchi, A. H., Aghbashlo, M. (2019). An intelligent machine vision-based smartphone app for beef quality evaluation. Journal of Food Engineering, 248, 9–22. https://doi. org/10.1016/j.jfoodeng.2018.12.009
  • King, D. A., Hunt, M. C., Barbut, S., Claus, J. R., Cornforth, D. P., Joseph, P., & Weber, M. (2023). American Meat Science Association guidelines for meat color measurement. Meatand Muscle Biology, 6(4), 12473. https://doi.org/10.22175/ mmb.12473
  • Lemos Junior, W. J. F., Marques Costa, L., Alberto Guerra, C., Sales de Oliveira, V., Gava Barreto, A., Alves de Oliveira, & Guerra, A. F. (2024). Microbial landscape of cooked meat products: Evaluating quality and safety in vacuum-packaged sausages using culturedependent and culture-independent methods over 1 year in a sustainable food chain. Frontiers in Microbiology, 15. https://doi.org/10.3389/fmicb.2024.1457819
  • Liu, Z., Shaposhnikov, M., Zhuang, S., Tu, T., Wang, H., Wang, L. (2023). Growth and survival of common spoilage and pathogenic bacteria in ground beef and plant-based meat analogues. Food Research International, 164, 112408. https://doi.org/10.1016/j. foodres.2022.112408
  • Ramanathan, R., Mancini, R. A., Konda, M. R., Bailey, K., More, S., Mafi, G. G. (2022). Evaluating the failure to bloom in dark-cutting and lactate-enhanced beef longissimus steaks. Meat Science,184, 108684. https://doi.org/10.1016/j.meatsci.2021.108684
  • Ramanathan, R., Suman, S. P., Faustman, C. (2020). Biomolecular interactions governing fresh meat color in post-mortem skeletal muscle: A review. Journal of Agricultural and Food Chemistry, 68(46), 12779–12787. https://doi.org/10.1021/acs. jafc.9b08098
  • Rani, Z. T., Mhlongo, L. C., Hugo, A. (2023). Microbial profiles of meat at different stages of the distribution chain from the abattoir to retail outlets. International Journal of Environmental Research and Public Health, 20(3), 1986. https://doi. org/10.3390/ijerph20031986
  • Ruedt, C., Gibis, M., Weiss, J. (2023). Meat color and iridescence: Origin, analysis, and approaches to modulation. Comprehensive Reviews in Food Science and Food Safety, 22(4), 3366–3394. https://doi.org/10.1111/1541-4337.13191
  • Sáenz, C., Hernández, B., Beriain, M. J., Lizaso, G. (2005). Meat color in retail displays with fluorescent illumination. Color Research & Application, 30(4), 304–311. https://doi. org/10.1002/col.20123
  • Suman, S. P., Joseph, P. (2013). Myoglobin chemistry and meat color. Annual Review of Food Science and Technology, 4, 79–99. https://doi.org/10.1146/annurev-food-030212-182623
  • Taheri-Garavand, A., Fatahi, S., Omid, M., Makino, Y. (2019). Meat quality evaluation based on computer vision technique: A review. Meat Science, 156, 183–195. https://doi.org/10.1016/j. meatsci.2019.06.002
  • Tomasevic, I., Djekic, I., Font-i-Furnols, M., Terjung, N., Lorenzo, J. M. (2021). Recent advances in meat color research. Current Opinion in Food Science, 41, 81–87. https://doi.org/10.1016/j.cofs.2021.02.012
  • Tomasevic, I., Tomovic, V., Milovanovic, B., Lorenzo, J., Đorđević, V., Karabasil, N., & Djekic, I. (2019). Comparison of a computer vision system vs. traditional colorimeter for color evaluation of meat products with various physical properties. Meat Science, 148, 5–12. https://doi.org/10.1016/j.meatsci.2018.09.015
  • Zhang, L., Zhang, M., Mujumdar, A. S. (2023). Technological innovations or advancement in detecting frozen and thawed meat quality: A review. Critical Reviews in Food Science and Nutrition, 63(11), 1483–1499. https://doi.org/10.1080/104083 98.2021.1964434
There are 22 citations in total.

Details

Primary Language English
Subjects Veterinary Food Hygiene and Technology, Animal Science, Genetics and Biostatistics
Journal Section Research Article
Authors

Alper Güngören 0000-0001-7818-1372

Gülşah Güngören 0000-0002-0360-7735

Submission Date September 23, 2025
Acceptance Date January 6, 2026
Publication Date March 27, 2026
DOI https://doi.org/10.31196/huvfd.1789286
IZ https://izlik.org/JA33CB63NL
Published in Issue Year 2026 Volume: 15 Issue: 1

Cite

APA Güngören, A., & Güngören, G. (2026). Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage. Harran University Journal of the Faculty of Veterinary Medicine, 15(1), 1-10. https://doi.org/10.31196/huvfd.1789286
AMA 1.Güngören A, Güngören G. Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage. Harran Univ Vet Fak Derg. 2026;15(1):1-10. doi:10.31196/huvfd.1789286
Chicago Güngören, Alper, and Gülşah Güngören. 2026. “Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage”. Harran University Journal of the Faculty of Veterinary Medicine 15 (1): 1-10. https://doi.org/10.31196/huvfd.1789286.
EndNote Güngören A, Güngören G (March 1, 2026) Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage. Harran University Journal of the Faculty of Veterinary Medicine 15 1 1–10.
IEEE [1]A. Güngören and G. Güngören, “Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage”, Harran Univ Vet Fak Derg, vol. 15, no. 1, pp. 1–10, Mar. 2026, doi: 10.31196/huvfd.1789286.
ISNAD Güngören, Alper - Güngören, Gülşah. “Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage”. Harran University Journal of the Faculty of Veterinary Medicine 15/1 (March 1, 2026): 1-10. https://doi.org/10.31196/huvfd.1789286.
JAMA 1.Güngören A, Güngören G. Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage. Harran Univ Vet Fak Derg. 2026;15:1–10.
MLA Güngören, Alper, and Gülşah Güngören. “Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage”. Harran University Journal of the Faculty of Veterinary Medicine, vol. 15, no. 1, Mar. 2026, pp. 1-10, doi:10.31196/huvfd.1789286.
Vancouver 1.Alper Güngören, Gülşah Güngören. Comparative Evaluation of Colorimetric and Computer Vision Systems for Red Meat Color Analysis During Refrigerated Storage. Harran Univ Vet Fak Derg. 2026 Mar. 1;15(1):1-10. doi:10.31196/huvfd.1789286