Research Article
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Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy

Year 2024, Accepted Papers, 1 - 9

Batuhan Tarcan Özlem Küplülü

https://doi.org/10.33988/auvfd.1200920

Abstract

The purpose of this study is to determine the percentage of chicken meat in beef and chicken mixes, which is the most common type of beef adultery. In this context, both ground beef and beef sausages were prepared in mixtures containing chicken meat, increasing from 0.0% to 100.0% with 5.0% steps, and analyzed with a near infrared spectroscopy device. Optimum analysis conditions were determined as a result of the examination of a wide range of generated regression models. The success of the best regression model created for ground beef mixtures is as follows: RMSEC: 2.35, RMSEV: 3.36, R2C: 0.99, R2V: 0.98; and for the beef sausages is: RMSEC: 2.56, RMSEV: 3.66, R2C: 0.99, R2V: 0.98. As a result, chicken meat ratios in beef mixtures were detected with a margin of error of 2.05% and chicken meat ratios in beef sausages were detected with a margin of error of 2.12%.

Keywords

NIRS Beef Chicken Adultery Rapid analysis

References

  • 2013 horse meat scandal. (2021). In Wikipedia. https://en.wikipedia.org/wiki/2013_horse_meat_scandal
  • Adapa, P., Karunakaran, C., Tabil, L., & Schoenau, G. (2009). Potential Applications of Infrared and Raman Spectromicroscopy for Agricultural Biomass. 25.
  • Alomar, D., Gallo, C., Castañeda, M., & Fuchslocher, R. (2003). Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Science, 63(4), 441–450. https://doi.org/10.1016/S0309-1740(02)00101-8
  • ANON. (2019). TÜRK GIDA KODEKSİ ET, HAZIRLANMIŞ ET KARIŞIMLARI VE ET ÜRÜNLERİ TEBLİĞİ. https://www.resmigazete.gov.tr/eskiler/2019/01/20190129-4.htm
  • AOAC 2007.04, Official Methods of Analysis of AOAC INTERNATIONAL (2019) 21st Ed., AOAC INTERNATIONAL, Gaithersburg, MD, USA, Official Method 2007.04.
  • Barbin, D. F., Badaró, A. T., Honorato, D. C. B., Ida, E. Y., & Shimokomaki, M. (2020). Identification of turkey meat and processed products using near infrared spectroscopy. Food Control, 107, 106816. https://doi.org/10.1016/j.foodcont.2019.106816
  • Barlocco, N., Vadell, A., Ballesteros, F., Galietta, G., & Cozzolino, D. (2006). Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy. Animal Science, 82(1), 111–116. https://doi.org/10.1079/ASC20055
  • Bilge, G., Velioglu, H. M., Sezer, B., Eseller, K. E., & Boyaci, I. H. (2016). Identification of meat species by using laser-induced breakdown spectroscopy. Meat Science, 119, 118–122. https://doi.org/10.1016/j.meatsci.2016.04.035
  • Bohrer, B. M. (2017). Review: Nutrient density and nutritional value of meat products and non-meat foods high in protein. Trends in Food Science & Technology, 65, 103–112. https://doi.org/10.1016/j.tifs.2017.04.016
  • Boyacı, İ. H., Temiz, H. T., Uysal, R. S., Velioğlu, H. M., Yadegari, R. J., & Rishkan, M. M. (2014). A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chemistry, 148, 37–41. https://doi.org/10.1016/j.foodchem.2013.10.006
  • Cozzolino, D., & Murray, I. (2004). Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT - Food Science and Technology, 37(4), 447–452. https://doi.org/10.1016/j.lwt.2003.10.013
  • DA7250-SD_Brochure_EN.pdf. (n.d.). Retrieved June 8, 2022, from https://www.s4science.at/wordpress/wp-content/uploads/2020/06/DA7250-SD_Brochure_EN.pdf Deniz, E., Güneş Altuntaş, E., Ayhan, B., İğci, N., Özel Demiralp, D., & Candoğan, K. (2018). Differentiation of beef mixtures adulterated with chicken or turkey meat using FTIR spectroscopy. Journal of Food Processing and Preservation, 42(10), e13767. https://doi.org/10.1111/jfpp.13767
  • Ding, H. B., & Xu, R. J. (2000). Near-Infrared Spectroscopic Technique for Detection of Beef Hamburger Adulteration. Journal of Agricultural and Food Chemistry, 48(6), 2193–2198. https://doi.org/10.1021/jf9907182
  • Ertugay, M. F., & Başlar, M. (2011). GIDALARIN KALİTE ÖZELLİKLERİNİN BELİRLENMESİNDE YAKIN KIZILÖTESİ (NIR) SPEKTROSKOPİSİ. The Journal of Food, 36, 49–54.
  • Geniş, H. E. (2018). Hızlı Gıda Analizlerine Yönelik Yakın Kızılötesi Spektroskopisi (NIR) Sistemi Geliştirilmesi [Doktora Tezi, Hacettepe Üniversitesi]. http://www.openaccess.hacettepe.edu.tr:8080/xmlui/bitstream/handle/11655/5758/10197818.pdf?sequence=1&isAllowed=y
  • Hart, J. R., Norris, K. H., & Golumbic, C. (1962). Determination of the Moisture Content of Seeds by Near-Infrared Spectrophotometry of Their Methanol Extracts. Cereal Chemistry, 39, 94–99.
  • ISO 12099:2017. (n.d.). ISO 12099:2017. ISO. Retrieved June 8, 2022, from https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/06/73/67352.html
  • Jr, J. W., & Weyer, L. (2012). Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy, Second Edition. CRC Press.
  • Kumar, Y., & Chandrakant Karne, S. (2017). Spectral analysis: A rapid tool for species detection in meat products. Trends in Food Science & Technology, 62, 59–67. https://doi.org/10.1016/j.tifs.2017.02.008
  • Liu, D., Guo, W., Li, Q., & Xie, D. (2019). Relationship of the bulk optical properties in 950–1650 nm wavelength range with internal quality and microstructure of kiwifruit during maturation. Biosystems Engineering, 184, 45–54. https://doi.org/10.1016/j.biosystemseng.2019.05.005
  • López-Maestresalas, A., Insausti, K., Jarén, C., Pérez-Roncal, C., Urrutia, O., Beriain, M. J., & Arazuri, S. (2019). Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control, 98, 465–473. https://doi.org/10.1016/j.foodcont.2018.12.003
  • Mamani-Linares, L. W., Gallo, C., & Alomar, D. (2012). Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Science, 90(2), 378–385. https://doi.org/10.1016/j.meatsci.2011.08.002
  • Martín, I., García, T., Fajardo, V., Rojas, M., Pegels, N., Hernández, P. E., & Martín, I. G. and R. (2009). SYBR-Green real-time PCR approach for the detection and quantification of pig DNA in feedstuffs. Meat Science, 82(2), 252–259. https://doi.org/10.1016/j.meatsci.2009.01.023
  • Nacak, B. (2020). Bitkisel Yağ Kombinasyonları ve Farklı Antioksidanlar Kullanılarak Üretilen Jel ve Çoklu Emülsiyonların Sosis Formülasyonunda Sığır Et Yağı İkamesi Olarak Kullanımının Oksidasyon Mekanizmaları Üzerine Etkisi. https://acikerisim.ege.edu.tr/xmlui/bitstream/handle/11454/69013/berkernacak2020.pdf?sequence=1&isAllowed=y
  • Nolasco-Perez, I. M., Rocco, L. A. C. M., Cruz-Tirado, J. P., Pollonio, M. A. R., Barbon, S., Barbon, A. P. A. C., & Barbin, D. F. (2019). Comparison of rapid techniques for classification of ground meat. Biosystems Engineering, 183, 151–159. https://doi.org/10.1016/j.biosystemseng.2019.04.013
  • Norris, K. H. (1996). History of NIR. Journal of Near Infrared Spectroscopy, 4(1), 31–37. https://doi.org/10.1255/jnirs.941
  • Perten DA 7250 Meat and Meat products.pdf. (n.d.).
  • Pico, Y. (2012). Chemical Analysis of Food: Techniques and Applications. Elsevier. https://doi.org/10.1016/C2010-0-64808-5
  • Rady, A., & Adedeji, A. (2018). Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. Meat Science, 136, 59–67. https://doi.org/10.1016/j.meatsci.2017.10.014
  • Restaino, E., Fassio, A., & Cozzolino, D. (2011). Discrimination of meat patés according to the animal species by means of near infrared spectroscopy and chemometrics Discriminación de muestras de paté de carne según tipo de especie mediante el uso de la espectroscopia en el infrarrojo cercano y la quimiometria. CyTA - Journal of Food, 9(3), 210–213. https://doi.org/10.1080/19476337.2010.512396
  • Schmutzler, M., Beganovic, A., Böhler, G., & Huck, C. W. (2015). Methods for detection of pork adulteration in veal product based on FT-NIR spectroscopy for laboratory, industrial and on-site analysis. Food Control, 57, 258–267. https://doi.org/10.1016/j.foodcont.2015.04.019
  • Sun, D.-W. (2009). Infrared Spectroscopy for Food Quality Analysis and Control. Academic Press.
  • Workman, Jr., Jerry, & Weyer, L. (2007). Practical Guide to Interpretive Near-Infrared Spectroscopy (0 ed.). CRC Press. https://doi.org/10.1201/9781420018318

Year 2024, Accepted Papers, 1 - 9

Batuhan Tarcan Özlem Küplülü

https://doi.org/10.33988/auvfd.1200920

Abstract

References

  • 2013 horse meat scandal. (2021). In Wikipedia. https://en.wikipedia.org/wiki/2013_horse_meat_scandal
  • Adapa, P., Karunakaran, C., Tabil, L., & Schoenau, G. (2009). Potential Applications of Infrared and Raman Spectromicroscopy for Agricultural Biomass. 25.
  • Alomar, D., Gallo, C., Castañeda, M., & Fuchslocher, R. (2003). Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Science, 63(4), 441–450. https://doi.org/10.1016/S0309-1740(02)00101-8
  • ANON. (2019). TÜRK GIDA KODEKSİ ET, HAZIRLANMIŞ ET KARIŞIMLARI VE ET ÜRÜNLERİ TEBLİĞİ. https://www.resmigazete.gov.tr/eskiler/2019/01/20190129-4.htm
  • AOAC 2007.04, Official Methods of Analysis of AOAC INTERNATIONAL (2019) 21st Ed., AOAC INTERNATIONAL, Gaithersburg, MD, USA, Official Method 2007.04.
  • Barbin, D. F., Badaró, A. T., Honorato, D. C. B., Ida, E. Y., & Shimokomaki, M. (2020). Identification of turkey meat and processed products using near infrared spectroscopy. Food Control, 107, 106816. https://doi.org/10.1016/j.foodcont.2019.106816
  • Barlocco, N., Vadell, A., Ballesteros, F., Galietta, G., & Cozzolino, D. (2006). Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy. Animal Science, 82(1), 111–116. https://doi.org/10.1079/ASC20055
  • Bilge, G., Velioglu, H. M., Sezer, B., Eseller, K. E., & Boyaci, I. H. (2016). Identification of meat species by using laser-induced breakdown spectroscopy. Meat Science, 119, 118–122. https://doi.org/10.1016/j.meatsci.2016.04.035
  • Bohrer, B. M. (2017). Review: Nutrient density and nutritional value of meat products and non-meat foods high in protein. Trends in Food Science & Technology, 65, 103–112. https://doi.org/10.1016/j.tifs.2017.04.016
  • Boyacı, İ. H., Temiz, H. T., Uysal, R. S., Velioğlu, H. M., Yadegari, R. J., & Rishkan, M. M. (2014). A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chemistry, 148, 37–41. https://doi.org/10.1016/j.foodchem.2013.10.006
  • Cozzolino, D., & Murray, I. (2004). Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT - Food Science and Technology, 37(4), 447–452. https://doi.org/10.1016/j.lwt.2003.10.013
  • DA7250-SD_Brochure_EN.pdf. (n.d.). Retrieved June 8, 2022, from https://www.s4science.at/wordpress/wp-content/uploads/2020/06/DA7250-SD_Brochure_EN.pdf Deniz, E., Güneş Altuntaş, E., Ayhan, B., İğci, N., Özel Demiralp, D., & Candoğan, K. (2018). Differentiation of beef mixtures adulterated with chicken or turkey meat using FTIR spectroscopy. Journal of Food Processing and Preservation, 42(10), e13767. https://doi.org/10.1111/jfpp.13767
  • Ding, H. B., & Xu, R. J. (2000). Near-Infrared Spectroscopic Technique for Detection of Beef Hamburger Adulteration. Journal of Agricultural and Food Chemistry, 48(6), 2193–2198. https://doi.org/10.1021/jf9907182
  • Ertugay, M. F., & Başlar, M. (2011). GIDALARIN KALİTE ÖZELLİKLERİNİN BELİRLENMESİNDE YAKIN KIZILÖTESİ (NIR) SPEKTROSKOPİSİ. The Journal of Food, 36, 49–54.
  • Geniş, H. E. (2018). Hızlı Gıda Analizlerine Yönelik Yakın Kızılötesi Spektroskopisi (NIR) Sistemi Geliştirilmesi [Doktora Tezi, Hacettepe Üniversitesi]. http://www.openaccess.hacettepe.edu.tr:8080/xmlui/bitstream/handle/11655/5758/10197818.pdf?sequence=1&isAllowed=y
  • Hart, J. R., Norris, K. H., & Golumbic, C. (1962). Determination of the Moisture Content of Seeds by Near-Infrared Spectrophotometry of Their Methanol Extracts. Cereal Chemistry, 39, 94–99.
  • ISO 12099:2017. (n.d.). ISO 12099:2017. ISO. Retrieved June 8, 2022, from https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/06/73/67352.html
  • Jr, J. W., & Weyer, L. (2012). Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy, Second Edition. CRC Press.
  • Kumar, Y., & Chandrakant Karne, S. (2017). Spectral analysis: A rapid tool for species detection in meat products. Trends in Food Science & Technology, 62, 59–67. https://doi.org/10.1016/j.tifs.2017.02.008
  • Liu, D., Guo, W., Li, Q., & Xie, D. (2019). Relationship of the bulk optical properties in 950–1650 nm wavelength range with internal quality and microstructure of kiwifruit during maturation. Biosystems Engineering, 184, 45–54. https://doi.org/10.1016/j.biosystemseng.2019.05.005
  • López-Maestresalas, A., Insausti, K., Jarén, C., Pérez-Roncal, C., Urrutia, O., Beriain, M. J., & Arazuri, S. (2019). Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control, 98, 465–473. https://doi.org/10.1016/j.foodcont.2018.12.003
  • Mamani-Linares, L. W., Gallo, C., & Alomar, D. (2012). Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Science, 90(2), 378–385. https://doi.org/10.1016/j.meatsci.2011.08.002
  • Martín, I., García, T., Fajardo, V., Rojas, M., Pegels, N., Hernández, P. E., & Martín, I. G. and R. (2009). SYBR-Green real-time PCR approach for the detection and quantification of pig DNA in feedstuffs. Meat Science, 82(2), 252–259. https://doi.org/10.1016/j.meatsci.2009.01.023
  • Nacak, B. (2020). Bitkisel Yağ Kombinasyonları ve Farklı Antioksidanlar Kullanılarak Üretilen Jel ve Çoklu Emülsiyonların Sosis Formülasyonunda Sığır Et Yağı İkamesi Olarak Kullanımının Oksidasyon Mekanizmaları Üzerine Etkisi. https://acikerisim.ege.edu.tr/xmlui/bitstream/handle/11454/69013/berkernacak2020.pdf?sequence=1&isAllowed=y
  • Nolasco-Perez, I. M., Rocco, L. A. C. M., Cruz-Tirado, J. P., Pollonio, M. A. R., Barbon, S., Barbon, A. P. A. C., & Barbin, D. F. (2019). Comparison of rapid techniques for classification of ground meat. Biosystems Engineering, 183, 151–159. https://doi.org/10.1016/j.biosystemseng.2019.04.013
  • Norris, K. H. (1996). History of NIR. Journal of Near Infrared Spectroscopy, 4(1), 31–37. https://doi.org/10.1255/jnirs.941
  • Perten DA 7250 Meat and Meat products.pdf. (n.d.).
  • Pico, Y. (2012). Chemical Analysis of Food: Techniques and Applications. Elsevier. https://doi.org/10.1016/C2010-0-64808-5
  • Rady, A., & Adedeji, A. (2018). Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. Meat Science, 136, 59–67. https://doi.org/10.1016/j.meatsci.2017.10.014
  • Restaino, E., Fassio, A., & Cozzolino, D. (2011). Discrimination of meat patés according to the animal species by means of near infrared spectroscopy and chemometrics Discriminación de muestras de paté de carne según tipo de especie mediante el uso de la espectroscopia en el infrarrojo cercano y la quimiometria. CyTA - Journal of Food, 9(3), 210–213. https://doi.org/10.1080/19476337.2010.512396
  • Schmutzler, M., Beganovic, A., Böhler, G., & Huck, C. W. (2015). Methods for detection of pork adulteration in veal product based on FT-NIR spectroscopy for laboratory, industrial and on-site analysis. Food Control, 57, 258–267. https://doi.org/10.1016/j.foodcont.2015.04.019
  • Sun, D.-W. (2009). Infrared Spectroscopy for Food Quality Analysis and Control. Academic Press.
  • Workman, Jr., Jerry, & Weyer, L. (2007). Practical Guide to Interpretive Near-Infrared Spectroscopy (0 ed.). CRC Press. https://doi.org/10.1201/9781420018318
There are 33 citations in total.

Details

Primary Language English
Subjects Veterinary Surgery
Journal Section Research Article
Authors

Batuhan Tarcan 0000-0002-9724-7450

Özlem Küplülü 0000-0002-1559-2390

Early Pub Date October 27, 2023
Publication Date
Published in Issue Year 2024Accepted Papers

Cite

APA Tarcan, B., & Küplülü, Ö. (2023). Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy. Ankara Üniversitesi Veteriner Fakültesi Dergisi1-9. https://doi.org/10.33988/auvfd.1200920
AMA Tarcan B, Küplülü Ö. Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy. Ankara Univ Vet Fak Derg. Published online October 1, 2023:1-9. doi:10.33988/auvfd.1200920
Chicago Tarcan, Batuhan, and Özlem Küplülü. “Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy”. Ankara Üniversitesi Veteriner Fakültesi Dergisi, October (October 2023), 1-9. https://doi.org/10.33988/auvfd.1200920.
EndNote Tarcan B, Küplülü Ö (October 1, 2023) Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy. Ankara Üniversitesi Veteriner Fakültesi Dergisi 1–9.
IEEE B. Tarcan and Ö. Küplülü, “Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy”, Ankara Univ Vet Fak Derg, pp. 1–9, October 2023, doi: 10.33988/auvfd.1200920.
ISNAD Tarcan, Batuhan - Küplülü, Özlem. “Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy”. Ankara Üniversitesi Veteriner Fakültesi Dergisi. October 2023. 1-9. https://doi.org/10.33988/auvfd.1200920.
JAMA Tarcan B, Küplülü Ö. Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy. Ankara Univ Vet Fak Derg. 2023;:1–9.
MLA Tarcan, Batuhan and Özlem Küplülü. “Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy”. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 2023, pp. 1-9, doi:10.33988/auvfd.1200920.
Vancouver Tarcan B, Küplülü Ö. Rapid Determination of Chicken Meat Ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) Spectroscopy. Ankara Univ Vet Fak Derg. 2023:1-9.