Advertisement

Journal of Food Measurement and Characterization

, Volume 11, Issue 4, pp 1550–1558 | Cite as

Non-destructive measurement of water and fat contents, water dynamics during drying and adulteration detection of intact small yellow croaker by low field NMR

  • Xiu Zang
  • Zhuyi Lin
  • Tan Zhang
  • Huihui Wang
  • Shuang Cong
  • Yukun Song
  • Yao Li
  • Shasha Cheng
  • Mingqian TanEmail author
Original Paper

Abstract

Non-destructive and fast measurement and characterization of fish is highly desired during various processing treatment. In this study, water dynamics during drying process and adulteration with carrageen were detected using low field nuclear magnetic resonance (LF-NMR) technique in small yellow croaker. Prediction models of water and fat contents were established based on LF-NMR Carr–Purcell–Meiboom–Gill (CPMG) data combined with principal component regression (PCR) or partial least squares regression (PLSR). The Rcv 2 of water and fat content by PLSR model was 0.9877 and 0.9054, and the root mean square error (RMSE) of cross-validation was 9.2360 and 3.3730%, respectively. Water dynamics during hot-air drying process showed that the amount of immobile water significantly decreased, and good correlation was found between the moisture ratio and peak area by Two-term model. In addition, the adulterated small yellow croaker with carrageen or distilled water could be clearly distinguished by principal component analysis (PCA) in a fast and non-destructive manner. All the results demonstrated that the LF-NMR may have great potential in fast and non-destructive analysis of small yellow croakers during various processing treatment.

Keywords

Low filed NMR Chemometrics Small yellow croaker Water Fat 

Notes

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2016YFD0400404) and the National Nature Science Foundation of China (31501561).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.

References

  1. 1.
    Z. Li, X. Shan, X. Jin, F. Dai, Fish. Res. 110, 67–74 (2011)CrossRefGoogle Scholar
  2. 2.
    K. Khodabux, M. Lomelette, S. Jhaumeerlaulloo, P. Ramasami, P. Rondeau, Food Chem. 102, 669–675 (2007)CrossRefGoogle Scholar
  3. 3.
    O.J. Torrissen, R. Nortvedt, S. Tuene, Chemometr. Intell. Lab. Syst. 42, 199–207 (1988)Google Scholar
  4. 4.
    K. Kappel, U. Schröder, Food Control 59, 478–486 (2016)CrossRefGoogle Scholar
  5. 5.
    S.M. Jepsen, H.T. Pedersen, S.B. Engelsen, J. Sci. Food Agric. 79, 1793–1802 (1999)CrossRefGoogle Scholar
  6. 6.
    L. Zhang, M.J. Mccarthy, Postharvest Biol. Technol. 67, 96–101 (2012)CrossRefGoogle Scholar
  7. 7.
    C. Li, D. Liu, G. Zhou, X. Xu, J. Qi, P. Shi, T. Xia, Meat Sci. 92, 79–83 (2012)CrossRefGoogle Scholar
  8. 8.
    P.M. Santos, C.C. Corrêa, L.A. Forato, R.R. Tullio, G.M. Cruz, L.A. Colnago, Food Control 38, 204–208 (2014)CrossRefGoogle Scholar
  9. 9.
    J. Sánchez-Valencia, I. Sánchez-Alonso, I. Martinez, M. Careche, Food Bioprocess Tech. 8, 2137–2145 (2015)CrossRefGoogle Scholar
  10. 10.
    E. Veliyulin, C. Van Der Zwaag, W. Burk, U. Erikson, J. Sci. Food. Agr. 85, 1299–1304 (2005)CrossRefGoogle Scholar
  11. 11.
    C.M. Andersen, A. Rinnan, LWT-Food Sci. Technol. 35, 687–696 (2002)CrossRefGoogle Scholar
  12. 12.
    F.M.V. Pereira, A.D.S. Carvalho, L.F. Cabeça, L.A. Colnago, Microchem. J. 108, 14–17 (2013)CrossRefGoogle Scholar
  13. 13.
    S. Nakano, J. Kousaka, K. Fujii, K. Yorozuya, M. Yoshida, Y. Mouri, M. Akizuki, R. Tetsuka, T. Ando, T. Fukutomi, Breast Cancer Res. Treat. 134, 1179–1188 (2012)CrossRefGoogle Scholar
  14. 14.
    S. Geng, H. Wang, X. Wang, X. Ma, S. Xiao, J. Wang, M. Tan, Anal. Methods 7, 2413–2419 (2015)CrossRefGoogle Scholar
  15. 15.
    X. Zheng, Y. Jin, Y. Chi, M. Ni, Energy Fuels 27, 5787–5792 (2013)CrossRefGoogle Scholar
  16. 16.
    B.K. Arvoh, N.O. Skeie, M. Halstensen, Sep. Purif. Technol. 107, 204–210 (2013)CrossRefGoogle Scholar
  17. 17.
    S. Arazuri, J. Ignacio Arana, N. Arias, L.M. Arregui, J. Gonzalez-Torralba, C. Jaren, J. Food Eng. 111, 115–121 (2012)CrossRefGoogle Scholar
  18. 18.
    C. Collell, P. Gou, J. Arnau, J. Comaposada, Food Chem. 129, 601–607 (2011)CrossRefGoogle Scholar
  19. 19.
    G. Adiletta, G. Iannone, P. Russo, G. Patimo, S.D. Pasquale, M.D. Matteo, Int. J. Food Sci. Technol. 49, 2602–2609 (2014)CrossRefGoogle Scholar
  20. 20.
    M. Zhang, Dry. Technol. 30, 1377–1386 (2012)CrossRefGoogle Scholar
  21. 21.
    S. Wold, M. Sjöström, L. Eriksson, Chemometr. Intell. Lab. Syst. 58, 109–130 (2001)CrossRefGoogle Scholar
  22. 22.
    G. Adiletta, P. Russo, W. Senadeera, M.D. Matteo, J. Food Eng. 172, 9–18 (2016)CrossRefGoogle Scholar
  23. 23.
    H.T. Pedersen, L. Munck, S.B. Engelsen, J. Am. Oil Chem. Soc. 77, 1069–1077 (2000)CrossRefGoogle Scholar
  24. 24.
    L. Cheng, C. Bulmer, A. Margaritis, Curr. Drug Deliv. 12, 351–357 (2015)CrossRefGoogle Scholar
  25. 25.
    R.V. Kulkarni, V.V. Nagathan, P.R. Biradar, A.A. Naikawadi, Int. J. Biol. Macromol. 57, 238–244 (2013)CrossRefGoogle Scholar
  26. 26.
    D. Wu, H. Shi, Y. He, X. Yu, Y. Bao, J. Food Eng. 119, 680–686 (2013)CrossRefGoogle Scholar
  27. 27.
    J. Liu, X. Zhan, J. Wan, Y. Wang, C. Wang, Carbohyd. Polym. 121, 27–36 (2015)CrossRefGoogle Scholar
  28. 28.
    Q. Zhang, A.S.M. Saleh, Q. Shen, Food Bioprocess Technol. 6, 2562–2570 (2013)CrossRefGoogle Scholar
  29. 29.
    K.D.T.D.M. Milanez, M.J.C. Pontes, Anal. Methods 7, 145–146 (2015)Google Scholar
  30. 30.
    R.D.O.R. Ribeiro, E.T. Mársico, C.D.S. Carneiro, M.L.G. Monteiro, C.A.C. Júnior, S. Mano, E.F.O.D. Jesus, LWT-Food Sci. Technol. 55, 90–95 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.National Engineering Research Center of SeafoodSchool of Food Science and TechnologyDalianChina
  2. 2.Engineering Research Center of Seafood of Ministry of Education of ChinaDalianChina
  3. 3.School of Biological EngineeringDalian Polytechnic UniversityDalianChina

Personalised recommendations