Advertisement

A method to detect water-injected pork based on bioelectrical impedance technique

  • Yue Leng
  • Yonghai SunEmail author
  • Xiaodan Wang
  • Jumin Hou
  • Xue Bai
  • Minghui Wang
Original Paper
  • 12 Downloads

Abstract

The objective of this study was to detect water-injected pork using electrical impedance spectroscopy (EIS) and an artificial intelligence model in a rapid, accurate, and minimally destructive method. Pork loins were injected with water (0%, 3.5%, 7%, 10.5%, 14% and 17.5%, respectively) and physicochemical measurements including cooking loss, pressing loss, color, and textural properties were evaluated after injection. Results indicated that injection of water negatively affected physicochemical characteristics of meat samples (P < 0.05). Water-injected pork had increased cooking loss and pressing loss. There were also significant differences found in color and textural properties. In addition, prediction models for the correlation between impedance magnitude and water injection rate of meat samples were established using support vector regression (SVR) and partial least square regression (PLSR). The results showed that both models performed well at different water injection rate. The model based on SVR obtained optimal performance. From the analysis of meat samples, the accuracy of SVR, which yielded 85.3%, was superior to that of PLSR with accuracy of 84.0%. EIS could be a potential technique for detecting water-injected porcine meat produced in the pork industry.

Keywords

Pork loins Water-injected Electrical impedance spectroscopy SVR PLSR 

Notes

Acknowledgements

This contribution funded by National Nature Science Foundation of China (Grant No. 31271861). We wish to express our sincere thanks for their availability and help.

References

  1. 1.
    M. Mathlouthi, Water content, water activity, water structure and the stability of foodstuffs. Food Control 12(7), 409–417 (2001)CrossRefGoogle Scholar
  2. 2.
    N. Prieto, R. Roehe, P. Lavin, G. Batten, S. Andres, Application of near infrared reflectance spectroscopy to predict meat and meat products quality: a review. Meat Sci. 83(2), 175–186 (2009)CrossRefGoogle Scholar
  3. 3.
    R.G. Kauffman, G. Eikelenboom, P.G. van der Wal, B. Engel, M. Zaar, A comparison of methods to estimate water-holding capacity in post-rigor porcine muscle. Meat Sci. 18(4), 307–322 (1986)CrossRefGoogle Scholar
  4. 4.
    B.L. Booren, M.E. Castell-Perez, R.K. Miller, Effect of meat enhancement solutions with hydroxypropyl methylcellulose and konjac flour on texture and quality attributes of pale, soft, and exudative pork. J. Texture Stud. 48(5), 403–414 (2017)CrossRefGoogle Scholar
  5. 5.
    S. Gai, Z. Zhang, Y. Zou, D. Song, F. Wei, D. Liu (2017) Analysis of water relaxation characteristics of water-injected pork by low-field nuclear magnetic resonance. J. Food Saf. Qual. 06, 1980–1986Google Scholar
  6. 6.
    Z. Liu, M. Li, F. Gan, W. Zhang, R. Wang (2017) Ultrasonic nondestructive testing device for water-injected meat and extraction of feature threshold. Food Mach. 04, 70–74Google Scholar
  7. 7.
    Z. Zhang, S. Gai, Y. Zou, F. Wei, Z. Yang, Y. Han, D. Liu (2018) Effects of different water-injected ratios on eating quality of pork. Sci. Technol. Food Ind. 03, 1–5+11Google Scholar
  8. 8.
    D.M. Hao, Y.N. Zhou, Y. Wang, S. Zhang, Y.M. Yang, L. Lin, G. Li, X.L. Wang, Recognition of water-injected meat based on visible/near-infrared spectrum and sparse representation. Spectrosc. Spectral Anal. 35(1), 93–98 (2015)Google Scholar
  9. 9.
    Z. Li, N. Ren, Y. Ma, L. Yingying, W. Guo (2017) Determination of illegal drugs for water-retaining in fresh meat by UPLC-MS/MS. Food Sci. 07, 1–10Google Scholar
  10. 10.
    J. Liu, Y. Cao, Q. Wang, W. Pan, F. Ma, C. Liu, W. Chen, J. Yang, L. Zheng, Rapid and non-destructive identification of water-injected beef samples using multispectral imaging analysis. Food Chem. 190, 938–943 (2016)CrossRefGoogle Scholar
  11. 11.
    J.-L. Damez, S. Clerjon, S. Abouelkaram, J. Lepetit, Electrical impedance probing of the muscle food anisotropy for meat ageing control. Food Control 19(10), 931–939 (2008)CrossRefGoogle Scholar
  12. 12.
    H.C. Lukaski, P.E. Johnson, W.W. Bolonchuk, G.I. Lykken, Assessment of fat-free mass using bioelectrical impedance measurements of the human body. Am. J. Clin. Nutr. 41(4), 810–817 (1985)CrossRefGoogle Scholar
  13. 13.
    K.R. Segal, S. Burastero, A. Chun, P. Coronel, R.N. Pierson Jr., J. Wang, Estimation of extracellular and total body water by multiple-frequency bioelectrical-impedance measurement. Am. J. Clin. Nutr. 54(1), 26–29 (1991)CrossRefGoogle Scholar
  14. 14.
    C.E. Byrne, D.J. Troy, D.J. Buckely, Postmortem changes in muscle electrical properties of bovine M-longissimus dorsi and their relationship to meat quality attributes and pH fall. Meat Sci. 54(1), 23–34 (2000)CrossRefGoogle Scholar
  15. 15.
    D.A. Aikens, Electrochemical methods, fundamentals and applications. J. Chem. Educ. 60(1), A25 (1983)CrossRefGoogle Scholar
  16. 16.
    A. Chowdhury, P. Singh, T.K. Bera, D. Ghoshal, B. Chakraborty, Electrical impedance spectroscopic study of mandarin orange during ripening. J. Food Meas. Charact. 11(4), 1654–1664 (2017)CrossRefGoogle Scholar
  17. 17.
    M. Guermazi, O. Kanoun, N. Derbel, Investigation of long time beef and veal meat behavior by bioimpedance spectroscopy for meat monitoring. IEEE Sensors J. 14(10), 3624–3630 (2014)CrossRefGoogle Scholar
  18. 18.
    H.B. Nguyen, L.T. Nguyen, Rapid and non-invasive evaluation of pork meat quality during storage via impedance measurement. Int. J. Food Sci. Technol. 50(8), 1718–1725 (2015)CrossRefGoogle Scholar
  19. 19.
    X. Bai, J.M. Hou, L. Wang, M.H. Wang, X. Wang, C.H. Wu, L.B. Yu, J. Yang, Y. Leng, Y.H. Sun, Electrical impedance analysis of pork tissues during storage. J. Food Meas. Charact. 12(1), 164–172 (2018)CrossRefGoogle Scholar
  20. 20.
    F.C. Schmidt, A. Fuentes, R. Masot, M. Alcaniz, J.B. Laurindo, J.M. Barat, Assessing heat treatment of chicken breast cuts by impedance spectroscopy. Food Sci. Technol. Int. 23(2), 110–118 (2017)CrossRefGoogle Scholar
  21. 21.
    R. Wei, P. Wang, M. Han, T. Chen, X. Xu, G. Zhou, Effect of freezing on electrical properties and quality of thawed chicken breast meat. Asian-Australas J. Anim. Sci. 30(4), 569–575 (2017)CrossRefGoogle Scholar
  22. 22.
    J. Lepetit, P. Sale, R. Favier, R. Dalle, Electrical impedance and tenderisation in bovine meat. Meat Sci. 60(1), 51–62 (2002)CrossRefGoogle Scholar
  23. 23.
    K.O. Honikel, Reference methods for the assessment of physical characteristics of meat. Meat Sci. 49(4), 447–457 (1998)CrossRefGoogle Scholar
  24. 24.
    M.M. Farouk, K.J. Wieliczko, I. Merts, Ultra-fast freezing and low storage temperatures are not necessary to maintain the functional properties of manufacturing beef. Meat Sci. 66(1), 171–179 (2004)CrossRefGoogle Scholar
  25. 25.
    S.T. Joo, R.G. Kauffman, B.C. Kim, G.B. Park, The relationship of sarcoplasmic and myofibrillar protein solubility to colour and water-holding capacity in porcine longissimus muscle. Meat Sci. 52(3), 291–297 (1999)CrossRefGoogle Scholar
  26. 26.
    X. Wang, Y. Sun, A. Liu, X. Wang, J. Gao, X. Fan, J. Shang, Y. Wang, Modeling structural and compositional changes of beef during human chewing process. LWT-Food Sci. Technol. 60(2), 1219–1225 (2015)CrossRefGoogle Scholar
  27. 27.
    T.H. Chen, Y.P. Zhu, M.Y. Han, P. Wang, R. Wei, X.L. Xu, G.H. Zhou, Classification of chicken muscle with different freeze-thaw cycles using impedance and physicochemical properties. J. Food Eng. 196, 94–100 (2017)CrossRefGoogle Scholar
  28. 28.
    R.G. Brereton, G.R. Lloyd, Support vector machines for classification and regression. Analyst 135(2), 230–267 (2010)CrossRefGoogle Scholar
  29. 29.
    B.-H. Mevik, R. Wehrens, The pls package: principal component and partial least squares regression in R. J. Stat. Softw. 18(2), 1–23 (2007)CrossRefGoogle Scholar
  30. 30.
    C.A. Cerruto-Noya, D.L. VanOverbeke, C.A.M. DeWitt, Evaluation of 0.1% ammonium hydroxide to replace sodium tripolyphosphate in fresh meat injection brines. J. Food Sci. 74(7), C519–C525 (2009)CrossRefGoogle Scholar
  31. 31.
    P.R. Sheard, G.R. Nute, R.I. Richardson, A. Perry, A.A. Taylor, Injection of water and polyphosphate into pork to improve juiciness and tenderness after cooking. Meat Sci. 51(4), 371–376 (1999)CrossRefGoogle Scholar
  32. 32.
    Y.H. Kim, E. Huff-Lonergan, J.G. Sebranek, S.M. Lonergan, Effects of lactate/phosphate injection enhancement on oxidation stability and protein degradation in early postmortem beef cuts packaged in high oxygen modified atmosphere. Meat Sci. 86(3), 852–858 (2010)CrossRefGoogle Scholar
  33. 33.
    G.M. Suliman, E.O.S. Hussein, A.N. Al-Owaimer, Improving mature camel-meat quality characteristics with calcium chloride injection. J. Camel Pract. Res. 20(1), 53–57 (2013)Google Scholar
  34. 34.
    B.E. Greene, B.E. Greene, Lipid oxidation and pigment changes in raw beef. J. Food Sci. 34(2), 110–113 (1969)CrossRefGoogle Scholar
  35. 35.
    S.T. Joo, R.G. Kauffman, B.C. Kim, C.J. Kim, The relationship between color and water-holding capacity in postrigor porcine longissimus muscle. J. Muscle Foods 6(3), 211–226 (1995)CrossRefGoogle Scholar
  36. 36.
    M. Petracci, L. Laghi, S. Rimini, P. Rocculi, F. Capozzi, C. Cavani, Chicken breast meat marinated with increasing levels of sodium bicarbonate. J. Poult.Sci. 51(2), 206–212 (2014)Google Scholar
  37. 37.
    R. Masot, M. Alcaniz, A. Fuentes, F.C. Schmidt, J.M. Barat, L. Gil, D. Baigts, R. Martinez-Manez, J. Soto, Design of a low-cost non-destructive system for punctual measurements of salt levels in food products using impedance spectroscopy. Sensors Actuators A 158(2), 217–223 (2010)CrossRefGoogle Scholar
  38. 38.
    P.Y. Guo, J.J. Xu, P. Xu, X.D. Dong, Y.F. Liu, S.X. Xing, M. Sun, in Identifying 1method of meat containing excessive moisture based on hyperspectral and SVM multi-information fusion, ed by S.A. Hamouda, M. Mirzaei, Z. Yu. International Seminar on Applied Physics, Optoelectronics and Photonics (E D P Sciences Cedex A, 2016)Google Scholar
  39. 39.
    Z.J. Xiong, D.W. Sun, H.B. Pu, Z.W. Zhu, M. Luo, Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats. Lwt-Food Sci. Technol. 60(2), 649–655 (2015)CrossRefGoogle Scholar
  40. 40.
    Y.X. Fan, Y.T. Liao, F. Cheng, Prediction of minced pork quality attributes using visible and near infrared reflectance spectroscopy. Spectrosc. Spect. Anal. 31(10), 2734–2737 (2011)Google Scholar
  41. 41.
    Q.S. Chen, Z.M. Guo, J.W. Zhao, Q. Ouyang, Comparisons of different regressions tools in measurement of antioxidant activity in green tea using near infrared spectroscopy. J. Pharm. Biomed. Anal. 60, 92–97 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yue Leng
    • 1
  • Yonghai Sun
    • 1
    Email author
  • Xiaodan Wang
    • 1
  • Jumin Hou
    • 2
  • Xue Bai
    • 1
  • Minghui Wang
    • 1
  1. 1.College of Food Science and EngineeringJilin UniversityChangchunChina
  2. 2.College of Food Science and EngineeringChangchun UniversityChangchunChina

Personalised recommendations