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Use of infrared thermography in determining meat quality

Year 2020, Volume: 4 Issue: 2, 54 - 58, 30.11.2020

Abstract

In recent years, meat quality has started to take an important place in food products. Situations such as stress, pain, and infection before slaughtering reduce the quality of the meat. There are many different techniques used in determining meat quality. There are invasive and non-invasive techniques used in determining meat quality. Because of advances in technology, the popularity of non-invasive techniques has increased. Thermography can be considered as the most recent of these invasive techniques. Thermographic measurement can be made from different parts of the body such as skin and eye. Due to the minimal artifact formation, measurement of eye temperature is used more frequently than other areas of the body. This review is aimed to give information about the non-invasive detection of meat quality by infrared thermography.

References

  • Cuthbertson H., Tarr G., Loudon K., Lomax S., White P., McGreevy P., Polkinghorne R. & González L. A. 2020. Using infrared thermography on farm of origin to predict meat quality and physiological response in cattle (Bos Taurus) exposed to transport and marketing. Meat Science, 108173.
  • Devine C. E., Lowe T. E., Wells R. W., Edwards N. J., Edwards J. H., Starbuck T. J. & Speck P. A. 2006. Pre-slaughter stress arising from on-farm handling and its interactions with electrical stimulation on tenderness of lambs. Meat Science, 73(2), 304-312.
  • George W. D., Godfrey R. W., Ketring R. C., Vinson M. C. & Willard S. T. 2014. Relationship among eye and muzzle temperatures measured using digital infrared thermal imaging and vaginal and rectal temperatures in hair sheep and cattle. Journal of Animal Science, 92(11), 4949-4955.
  • Giannetto C., Arfuso F., Giudice E., Gianesella M., Fazio F., Panzera M. & Piccione G. 2020. Infrared methodologies for the assessment of skin temperature daily rhythm in two domestic mammalian species. Journal of Thermal Biology, 102677.
  • Gonzalez-Rivas P. A., Sullivan M., Cottrell J. J., Leury B. J., Gaughan J. B. & Dunshea F. R. 2018. Effect of feeding slowly fermentable grains on productive variables and amelioration of heat stress in lactating dairy cows in a sub-tropical summer. Tropical Animal Health and Production, 50(8), 1763-1769.
  • Gregory N. G. 2008. Animal welfare at markets and during transport and slaughter. Meat Science, 80(1), 2-11.
  • Horcada A., Juárez M., Valera M. & Bartolomé E. 2020. Using infrared ocular thermography as a tool to predict meat quality from lean cattle breeds prior to slaughter: Exploratory trial. Spanish Journal of Agricultural Research, 17(4), 06-01.
  • Hughes J. M., Kearney G. & Warner R. D. 2014. Improving beef meat colour scores at carcass grading. Animal Production Science, 54(4), 422-429.
  • Jorquera-Chavez M., Fuentes S., Dunshea F. R., Jongman E. C. & Warner R. D. 2019. Computer vision and remote sensing to assess physiological responses of cattle to pre-slaughter stress, and its impact on beef quality: A review. Meat Science, 156, 11-22.
  • Mohr E., Langbein J. & Nürnberg G. 2002. Heart rate variability: A noninvasive approach to measure stress in calves and cows. Physiology & Behavior, 75(1), 251–259.
  • Möstl E. & Palme R. 2002. Hormones as indicators of stress. Domestic Animal Endocrinology, 23(1), 67–74.
  • Mpakama T., Chulayo A. Y. & Muchenje V. 2014. Bruising in slaughter cattle and its relationship with creatine kinase levels and beef quality as affected by animal related factors. Asian-Australasian Journal of Animal Sciences, 27, 717–725.
  • Nurhayati O. D., Adi K. & Pujiyanto S. 2016. Detection of the beef quality: Using mobile-based K-mean clustering method, 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, 2016, pp. 253-259.
  • Piccione G., Fazio F., Giudice E., & Refinetti R. 2009. Body size and the daily rhythm of body temperature in dogs. Journal of Thermal Biology, 34, 171-175, 2009.
  • Pighin D. G., Brown W., Ferguson D., Fisher A. & Warner R. 2014. Relationship between changes in core body temperature in lambs and post-slaughter muscle glycogen content and dark-cutting. Animal Production Science, 54(4), 459–463.
  • Pizzuti T. & Mirabelli G. 2016. Future Technology in Tracing Animals on the Food Chain. In Advances in Food Traceability Techniques and Technologies. Woodhead Publishing, pp. 165-190.
  • Prendiville D. J., Lowe J., Earley B., Spahr C. & Kettlewell P. 2003. Radiotelemetry systems for measuring body temperature in cattle. Farm and Food, 13(1), 15-18.
  • Rocha L. M. 2016. Validation of stress indicators for the assessment of animal welfare and prediction of pork meat quality variation at commercial level. Doctoral thesis Quebec, Canada: Laval University.
  • Rocha L. M., Devillers N., Maldague X., Kabemba F. Z., Fleuret J., Guay F. & Faucitano L. 2019. Validation of Anatomical Sites for the Measurement of Infrared Body Surface Temperature Variation in Response to Handling and Transport. Animals, 9(7), 425.
  • Scholz A. M., Bünger L., Kongsro J., Baulain U. & Mitchell A. D. 2015. Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review. Animal, 9(7), 1250-1264.
  • Stewart M., Webster J.R., Schaefer A.L., Cook N. J., Scott S. L. 2005. Infrared thermography as a non-invasive tool to study animal welfare. Animal Welfare, 2005, 14, 319–325.
  • Tattersall G. J. 2016. Infrared thermography: A non-invasive window into thermal physiology. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 202, 78-98.
  • Terrien J., Perret M. & Aujard F. 2011. Behavioral thermoregulation in mammals: a review. Frontiers in Bioscience, 16, 1428–1444.
  • Tvarijonaviciute A., Barranco T., Rubio M., Carrillo J. M., Martinez-Subiela S., Tecles F., Carrillo J. D. & Cerón J. J. 2017. Measurement of Creatine kinase and aspartate aminotransferase in saliva of dogs: A pilot study. BMC Veterinary Research, 13, 168.
  • Warner R., Ferguson D., Cottrell J., & Knee B. 2007. Acute stress induced by the preslaughter use of electric prodders causes tougher beef meat. Animal Production Science, 47(7), 782–788.
  • Weschenfelder A. V., Saucier L., Maldague X., Rocha L. M., Schaefer A. L. & Faucitano L. 2013. Use of infrared ocular thermography to assess physiological conditions of pigs prior to slaughter and predict pork quality variation. Meat Science, 95(3), 616-620.
  • Xiong Z., Sun D. W., Pu H., Gao W. & Dai Q. 2017. Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review. Critical Reviews in Food Science and Nutrition, 57(4), 755-768.
Year 2020, Volume: 4 Issue: 2, 54 - 58, 30.11.2020

Abstract

References

  • Cuthbertson H., Tarr G., Loudon K., Lomax S., White P., McGreevy P., Polkinghorne R. & González L. A. 2020. Using infrared thermography on farm of origin to predict meat quality and physiological response in cattle (Bos Taurus) exposed to transport and marketing. Meat Science, 108173.
  • Devine C. E., Lowe T. E., Wells R. W., Edwards N. J., Edwards J. H., Starbuck T. J. & Speck P. A. 2006. Pre-slaughter stress arising from on-farm handling and its interactions with electrical stimulation on tenderness of lambs. Meat Science, 73(2), 304-312.
  • George W. D., Godfrey R. W., Ketring R. C., Vinson M. C. & Willard S. T. 2014. Relationship among eye and muzzle temperatures measured using digital infrared thermal imaging and vaginal and rectal temperatures in hair sheep and cattle. Journal of Animal Science, 92(11), 4949-4955.
  • Giannetto C., Arfuso F., Giudice E., Gianesella M., Fazio F., Panzera M. & Piccione G. 2020. Infrared methodologies for the assessment of skin temperature daily rhythm in two domestic mammalian species. Journal of Thermal Biology, 102677.
  • Gonzalez-Rivas P. A., Sullivan M., Cottrell J. J., Leury B. J., Gaughan J. B. & Dunshea F. R. 2018. Effect of feeding slowly fermentable grains on productive variables and amelioration of heat stress in lactating dairy cows in a sub-tropical summer. Tropical Animal Health and Production, 50(8), 1763-1769.
  • Gregory N. G. 2008. Animal welfare at markets and during transport and slaughter. Meat Science, 80(1), 2-11.
  • Horcada A., Juárez M., Valera M. & Bartolomé E. 2020. Using infrared ocular thermography as a tool to predict meat quality from lean cattle breeds prior to slaughter: Exploratory trial. Spanish Journal of Agricultural Research, 17(4), 06-01.
  • Hughes J. M., Kearney G. & Warner R. D. 2014. Improving beef meat colour scores at carcass grading. Animal Production Science, 54(4), 422-429.
  • Jorquera-Chavez M., Fuentes S., Dunshea F. R., Jongman E. C. & Warner R. D. 2019. Computer vision and remote sensing to assess physiological responses of cattle to pre-slaughter stress, and its impact on beef quality: A review. Meat Science, 156, 11-22.
  • Mohr E., Langbein J. & Nürnberg G. 2002. Heart rate variability: A noninvasive approach to measure stress in calves and cows. Physiology & Behavior, 75(1), 251–259.
  • Möstl E. & Palme R. 2002. Hormones as indicators of stress. Domestic Animal Endocrinology, 23(1), 67–74.
  • Mpakama T., Chulayo A. Y. & Muchenje V. 2014. Bruising in slaughter cattle and its relationship with creatine kinase levels and beef quality as affected by animal related factors. Asian-Australasian Journal of Animal Sciences, 27, 717–725.
  • Nurhayati O. D., Adi K. & Pujiyanto S. 2016. Detection of the beef quality: Using mobile-based K-mean clustering method, 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, 2016, pp. 253-259.
  • Piccione G., Fazio F., Giudice E., & Refinetti R. 2009. Body size and the daily rhythm of body temperature in dogs. Journal of Thermal Biology, 34, 171-175, 2009.
  • Pighin D. G., Brown W., Ferguson D., Fisher A. & Warner R. 2014. Relationship between changes in core body temperature in lambs and post-slaughter muscle glycogen content and dark-cutting. Animal Production Science, 54(4), 459–463.
  • Pizzuti T. & Mirabelli G. 2016. Future Technology in Tracing Animals on the Food Chain. In Advances in Food Traceability Techniques and Technologies. Woodhead Publishing, pp. 165-190.
  • Prendiville D. J., Lowe J., Earley B., Spahr C. & Kettlewell P. 2003. Radiotelemetry systems for measuring body temperature in cattle. Farm and Food, 13(1), 15-18.
  • Rocha L. M. 2016. Validation of stress indicators for the assessment of animal welfare and prediction of pork meat quality variation at commercial level. Doctoral thesis Quebec, Canada: Laval University.
  • Rocha L. M., Devillers N., Maldague X., Kabemba F. Z., Fleuret J., Guay F. & Faucitano L. 2019. Validation of Anatomical Sites for the Measurement of Infrared Body Surface Temperature Variation in Response to Handling and Transport. Animals, 9(7), 425.
  • Scholz A. M., Bünger L., Kongsro J., Baulain U. & Mitchell A. D. 2015. Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review. Animal, 9(7), 1250-1264.
  • Stewart M., Webster J.R., Schaefer A.L., Cook N. J., Scott S. L. 2005. Infrared thermography as a non-invasive tool to study animal welfare. Animal Welfare, 2005, 14, 319–325.
  • Tattersall G. J. 2016. Infrared thermography: A non-invasive window into thermal physiology. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 202, 78-98.
  • Terrien J., Perret M. & Aujard F. 2011. Behavioral thermoregulation in mammals: a review. Frontiers in Bioscience, 16, 1428–1444.
  • Tvarijonaviciute A., Barranco T., Rubio M., Carrillo J. M., Martinez-Subiela S., Tecles F., Carrillo J. D. & Cerón J. J. 2017. Measurement of Creatine kinase and aspartate aminotransferase in saliva of dogs: A pilot study. BMC Veterinary Research, 13, 168.
  • Warner R., Ferguson D., Cottrell J., & Knee B. 2007. Acute stress induced by the preslaughter use of electric prodders causes tougher beef meat. Animal Production Science, 47(7), 782–788.
  • Weschenfelder A. V., Saucier L., Maldague X., Rocha L. M., Schaefer A. L. & Faucitano L. 2013. Use of infrared ocular thermography to assess physiological conditions of pigs prior to slaughter and predict pork quality variation. Meat Science, 95(3), 616-620.
  • Xiong Z., Sun D. W., Pu H., Gao W. & Dai Q. 2017. Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review. Critical Reviews in Food Science and Nutrition, 57(4), 755-768.
There are 27 citations in total.

Details

Primary Language English
Subjects Food Engineering
Journal Section Article
Authors

Berna Yanmaz

Publication Date November 30, 2020
Published in Issue Year 2020 Volume: 4 Issue: 2

Cite

APA Yanmaz, B. (2020). Use of infrared thermography in determining meat quality. Eurasian Journal of Food Science and Technology, 4(2), 54-58.
AMA Yanmaz B. Use of infrared thermography in determining meat quality. EJFST. November 2020;4(2):54-58.
Chicago Yanmaz, Berna. “Use of Infrared Thermography in Determining Meat Quality”. Eurasian Journal of Food Science and Technology 4, no. 2 (November 2020): 54-58.
EndNote Yanmaz B (November 1, 2020) Use of infrared thermography in determining meat quality. Eurasian Journal of Food Science and Technology 4 2 54–58.
IEEE B. Yanmaz, “Use of infrared thermography in determining meat quality”, EJFST, vol. 4, no. 2, pp. 54–58, 2020.
ISNAD Yanmaz, Berna. “Use of Infrared Thermography in Determining Meat Quality”. Eurasian Journal of Food Science and Technology 4/2 (November 2020), 54-58.
JAMA Yanmaz B. Use of infrared thermography in determining meat quality. EJFST. 2020;4:54–58.
MLA Yanmaz, Berna. “Use of Infrared Thermography in Determining Meat Quality”. Eurasian Journal of Food Science and Technology, vol. 4, no. 2, 2020, pp. 54-58.
Vancouver Yanmaz B. Use of infrared thermography in determining meat quality. EJFST. 2020;4(2):54-8.

Eurasian Journal of Food Science and Technology (EJFST)   e-ISSN: 2667-4890   Web: https://dergipark.org.tr/en/pub/ejfst   e-mail: foodsciencejournal@gmail.com