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Rapid determination of chicken meat ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) spectroscopy

Year 2024, Volume: 71 Issue: 3, 311 - 319

Batuhan Tarcan Özlem Küplülü

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

Abstract

This study aims to determine the percentage of chicken meat in beef and chicken mixtures, which is the most common form of beef adulteration. Ground beef and beef sausages were prepared with mixtures containing chicken meat, ranging from 0.0% to 100.0% with 5.0% increments, and analyzed using a near-infrared spectroscopy device. Optimal analysis conditions were determined through the examination of a wide range of regression models. The best regression model for ground beef mixtures yielded the following results: RMSEC (Root Mean Square Error of Calibration): 2.35, RMSEV (Root Mean Square Error of Validation): 3.36, R2C (R-Value Calibration): 0.99, R2V (R-Value Validation): 0.98. The results for beef sausages were as follows: RMSEC: 2.56, RMSEV: 3.66, R2C: 0.99, R2V: 0.98. As a result, the chicken meat content in beef mixtures was detected with a margin of error of 2.05%, while the chicken meat content in beef sausages was detected with a margin of error of 2.12%.

Keywords

Adultery Chicken Food analysis Infrared radiation Meat products

Thanks

This study was derived from the Ph.D. thesis of the first author.

References

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  • Alomar D, Gallo C, Castañeda M, et al (2003): Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Sci, 63, 441–450.
  • AOAC (2019): 21st Ed., AOAC International, Gaithersburg, MD, USA, Official Method 2007.04.
  • Barbin DF, Badaró AT, Honorato DCB, et al (2020): Identification of turkey meat and processed products using near infrared spectroscopy. Food Control, 107, 106816.
  • Barlocco N, Vadell A, Ballesteros F, et al (2006): Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy. Anim Sci, 82, 111–116.
  • Bilge G, Velioglu HM, Sezer B, et al (2016): Identification of meat species by using laser-induced breakdown spectroscopy. Meat Sci, 119, 118–122.
  • Bohrer BM (2017): Review: Nutrient density and nutritional value of meat products and non-meat foods high in protein. Trends Food Sci Technol, 65, 103-112.
  • Boyacı İH, Temiz HT, Uysal RS, et al (2014): A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chem, 148, 37–41.
  • Cozzolino D, Murray I (2004): Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT, 37, 447–452.
  • DA7250-SD (2022): Available at: https://www.s4science.at/wordpress/wp-content/uploads/ 2020/06/DA7250-SD_Brochure_EN.pdf. (Accessed June 8, 2022).
  • Deniz E, Güneş Altuntaş E, Ayhan B, et al (2018): Differentiation of beef mixtures adulterated with chicken or turkey meat using FTIR spectroscopy. J Food Process Preserv, 42, e13767.
  • Ding HB, Xu RJ (1999): Differentiation of beef and kangaroo meat by visible/near-infrared reflectance spectroscopy. J Food Sci, 64, 814-817.
  • Ding HB, Xu RJ (2000): Near-infrared spectroscopic technique for detection of beef hamburger adulteration. J Agri Food Chem, 48, 2193–2198.
  • Ertugay MF, Başlar M (2011): Gıdaların kalite özelliklerinin belirlenmesinde yakın kızılötesi (NIR) spektroskopisi. Gıda, 36, 49–54.
  • Geniş HE (2018): Hızlı Gıda Analizlerine Yönelik Yakın Kızılötesi Spektroskopisi (NIR) Sistemi Geliştirilmesi. PhD Thesis, Graduate Institute of Natural and Applied Sciences Hacettepe University.
  • Hart JR, Norris KH, Golumbic C (1962): Determination of the moisture content of seeds by near-infrared spectrophotometry of their methanol extracts. Cereal Chem, 39, 94–99.
  • ISO (2017): ISO 12099:2017. Available at https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/06/73/67352.html, (Accessed June 8, 2022).
  • Jr JW, 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 Food Sci Techno, 62, 59-67.
  • Liu D, Guo W, Li Q, et al (2019): Relationship of the bulk optical properties in 950–1650 nm wavelength range with internal quality and microstructure of kiwifruit during maturation. Biosyst Eng, 184, 45–54.
  • López-Maestresalas A, Insausti K, Jarén C, et al (2019): Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control, 98, 465–473.
  • Mamani-Linares LW, Gallo C, Alomar D (2012): Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Sci, 90, 378–385.
  • Martín I, García T, Fajardo V, et al (2009): SYBR-Green real-time PCR approach for the detection and quantification of pig DNA in feedstuffs. Meat Sci, 82, 252–259.
  • 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. Master Thesis Graduate Institute of Natural and Applied Sciences, Ege University.
  • Nolasco-Perez IM, Rocco LACM, et al (2019): Comparison of rapid techniques for classification of ground meat. Biosystems Engineering, 183, 151–159.
  • Norris KH (1996): History of NIR. J Near Infrared Spectrosc, 4, 31-37.
  • Perten DA (2022): Meat and Meat products.pdf. Available at: https://www.perten.com/Global/Application%20notes/ DA%2072xx/Perten%20DA%207250%20Meat%20and%20Meat%20products.pdf. (Accessed: June 8, 2022).
  • Pico Y (2012): Chemical Analysis of Food: Techniques and Applications. Elsevier.
  • Rady A, Adedeji A (2018): Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. Meat Sci, 136, 59–67.
  • Resmi Gazete (2019): Türk Gıda Kodeksi Et, Hazırlanmış Et Karışımları ve Et Ürünleri Tebliği. Available at : https://www.resmigazete.gov.tr/eskiler/2019/01/20190129-4.htm. (Accessed June 8, 2022).
  • 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, J Food, 9, 210–213.
  • Schmutzler M, Beganovic A, Böhler G, et al (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.
  • Sun DW (2009): Infrared Spectroscopy for Food Quality Analysis and Control. Academic Press.
  • Wikipedia (2022): 2013 horse meat scandal (2021). Available at: Wikipedia. https://en.wikipedia.org/wiki/ 2013_horse_meat_scandal. (Accessed June 8, 2022).
  • Workman Jr Jerry, Weyer L (2007): Practical Guide to Interpretive Near-Infrared Spectroscopy (0 ed.). CRC Press.

Year 2024, Volume: 71 Issue: 3, 311 - 319

Batuhan Tarcan Özlem Küplülü

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

Abstract

References

  • Adapa P, Karunakaran C, Table L, et al (2009): Potential applications of infrared and raman spectromicroscopy for agricultural biomass. CIGR J, 11.
  • Alomar D, Gallo C, Castañeda M, et al (2003): Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Sci, 63, 441–450.
  • AOAC (2019): 21st Ed., AOAC International, Gaithersburg, MD, USA, Official Method 2007.04.
  • Barbin DF, Badaró AT, Honorato DCB, et al (2020): Identification of turkey meat and processed products using near infrared spectroscopy. Food Control, 107, 106816.
  • Barlocco N, Vadell A, Ballesteros F, et al (2006): Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy. Anim Sci, 82, 111–116.
  • Bilge G, Velioglu HM, Sezer B, et al (2016): Identification of meat species by using laser-induced breakdown spectroscopy. Meat Sci, 119, 118–122.
  • Bohrer BM (2017): Review: Nutrient density and nutritional value of meat products and non-meat foods high in protein. Trends Food Sci Technol, 65, 103-112.
  • Boyacı İH, Temiz HT, Uysal RS, et al (2014): A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chem, 148, 37–41.
  • Cozzolino D, Murray I (2004): Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT, 37, 447–452.
  • DA7250-SD (2022): Available at: https://www.s4science.at/wordpress/wp-content/uploads/ 2020/06/DA7250-SD_Brochure_EN.pdf. (Accessed June 8, 2022).
  • Deniz E, Güneş Altuntaş E, Ayhan B, et al (2018): Differentiation of beef mixtures adulterated with chicken or turkey meat using FTIR spectroscopy. J Food Process Preserv, 42, e13767.
  • Ding HB, Xu RJ (1999): Differentiation of beef and kangaroo meat by visible/near-infrared reflectance spectroscopy. J Food Sci, 64, 814-817.
  • Ding HB, Xu RJ (2000): Near-infrared spectroscopic technique for detection of beef hamburger adulteration. J Agri Food Chem, 48, 2193–2198.
  • Ertugay MF, Başlar M (2011): Gıdaların kalite özelliklerinin belirlenmesinde yakın kızılötesi (NIR) spektroskopisi. Gıda, 36, 49–54.
  • Geniş HE (2018): Hızlı Gıda Analizlerine Yönelik Yakın Kızılötesi Spektroskopisi (NIR) Sistemi Geliştirilmesi. PhD Thesis, Graduate Institute of Natural and Applied Sciences Hacettepe University.
  • Hart JR, Norris KH, Golumbic C (1962): Determination of the moisture content of seeds by near-infrared spectrophotometry of their methanol extracts. Cereal Chem, 39, 94–99.
  • ISO (2017): ISO 12099:2017. Available at https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/06/73/67352.html, (Accessed June 8, 2022).
  • Jr JW, 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 Food Sci Techno, 62, 59-67.
  • Liu D, Guo W, Li Q, et al (2019): Relationship of the bulk optical properties in 950–1650 nm wavelength range with internal quality and microstructure of kiwifruit during maturation. Biosyst Eng, 184, 45–54.
  • López-Maestresalas A, Insausti K, Jarén C, et al (2019): Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control, 98, 465–473.
  • Mamani-Linares LW, Gallo C, Alomar D (2012): Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Sci, 90, 378–385.
  • Martín I, García T, Fajardo V, et al (2009): SYBR-Green real-time PCR approach for the detection and quantification of pig DNA in feedstuffs. Meat Sci, 82, 252–259.
  • 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. Master Thesis Graduate Institute of Natural and Applied Sciences, Ege University.
  • Nolasco-Perez IM, Rocco LACM, et al (2019): Comparison of rapid techniques for classification of ground meat. Biosystems Engineering, 183, 151–159.
  • Norris KH (1996): History of NIR. J Near Infrared Spectrosc, 4, 31-37.
  • Perten DA (2022): Meat and Meat products.pdf. Available at: https://www.perten.com/Global/Application%20notes/ DA%2072xx/Perten%20DA%207250%20Meat%20and%20Meat%20products.pdf. (Accessed: June 8, 2022).
  • Pico Y (2012): Chemical Analysis of Food: Techniques and Applications. Elsevier.
  • Rady A, Adedeji A (2018): Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. Meat Sci, 136, 59–67.
  • Resmi Gazete (2019): Türk Gıda Kodeksi Et, Hazırlanmış Et Karışımları ve Et Ürünleri Tebliği. Available at : https://www.resmigazete.gov.tr/eskiler/2019/01/20190129-4.htm. (Accessed June 8, 2022).
  • 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, J Food, 9, 210–213.
  • Schmutzler M, Beganovic A, Böhler G, et al (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.
  • Sun DW (2009): Infrared Spectroscopy for Food Quality Analysis and Control. Academic Press.
  • Wikipedia (2022): 2013 horse meat scandal (2021). Available at: Wikipedia. https://en.wikipedia.org/wiki/ 2013_horse_meat_scandal. (Accessed June 8, 2022).
  • Workman Jr Jerry, Weyer L (2007): Practical Guide to Interpretive Near-Infrared Spectroscopy (0 ed.). CRC Press.
There are 35 citations in total.

Details

Primary Language English
Subjects Veterinary Food Hygiene and Technology
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 2024Volume: 71 Issue: 3

Cite

APA Tarcan, B., & Küplülü, Ö. (n.d.). Rapid determination of chicken meat ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) spectroscopy. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 71(3), 311-319. 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. 71(3):311-319. 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 71, no. 3 n.d.: 311-19. https://doi.org/10.33988/auvfd.1200920.
EndNote Tarcan B, Küplülü Ö Rapid determination of chicken meat ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) spectroscopy. Ankara Üniversitesi Veteriner Fakültesi Dergisi 71 3 311–319.
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, vol. 71, no. 3, pp. 311–319, 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 71/3 (n.d.), 311-319. 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.;71:311–319.
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, vol. 71, no. 3, pp. 311-9, 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. 71(3):311-9.
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