Journal of Food Measurement and Characterization

, Volume 13, Issue 4, pp 2663–2671 | Cite as

Evaluation of beef flavor attribute based on sensor array in tandem with support vector machines

  • Hongmei Wang
  • Xiao Dan WangEmail author
  • Dengyong LiuEmail author
  • Ying Wang
  • Xing Li
  • Jinjiao Duan
Original Paper


Beef is an important red meat with abundant nutrition, and flavor is one of the most significant factors influencing beef quality. In this study, sensor array in tandem with support vector machine (SVM) was used to predict beef flavor attribute. Sensor array consisting of 12 ion-selective electrodes and 1 reference electrode was used to collect ion signal from beef samples. Besides, data acquisition card (DAQ card) and electrochemical workstation were applied to convert the ion signal to voltage signal, respectively. For the data analysis, SVM technique was used to build forecasting models for evaluating M. longissimus dorsi (LDs) flavor by combined voltage signals from DAQ card. Besides, data from electrochemical workstation were analyzed with SVM as well, which was applied to verify accuracy of data from DAQ card. The SVM with radial basis kernel function showed a better result with accuracy of 90% using data from DAQ card, and the accuracy of electrochemical workstation reached 90%. Therefore, it was possible to confirm that the integration of sensor array and SVM analysis provides an effective way for evaluating the flavor attribute of LDs. What’s more, the results of this research also indicate that DAQ card can replace the electrochemical workstation when converting the ion signal to voltage signal with a better performance.


Beef Flavor Sensor array Support vector machines 



This research was funded by the Science and Technology Development Program of Jilin Province, China (20160101274JC), and National Key R&D Program of China (2016YFD0401505).


  1. 1.
    J. Mcafee, M. Mcsorley, J. Cuskelly, W. Moss, W. Wallace, P. Bonham, M. Fearon, Red meat consumption: an overview of the risks and benefits. Meat Sci. 84(1), 1–13 (2010)CrossRefGoogle Scholar
  2. 2.
    Z. Xiong, D. Sun, X. Zeng, A. Xie, Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: a review. J. Food Eng. 132, 1–13 (2014)CrossRefGoogle Scholar
  3. 3.
    R. Zhang, Y. Ying, X. Rao, J. Li, Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. J. Sci. Food Agric. 92, 2397–2408 (2012)CrossRefGoogle Scholar
  4. 4.
    J. Wu, Y. Peng, Y. Li, W. Wang, J. Chen, S. Dhakal, Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique. J. Food Eng. 109(2), 267–273 (2012)CrossRefGoogle Scholar
  5. 5.
    K. Xu, J. Wang, Z. Wei, F. Deng, Y. Wang, S. Cheng, An optimization of the MOS electronic nose sensor array for the detection of Chinese pecan quality. J. Food Eng. 203, 25–31 (2017)CrossRefGoogle Scholar
  6. 6.
    R. Wadhwani, L.K. Murdia, D.P. Cornforth, Effect of muscle type and cooking temperature on liver-like off-flavor of five beef chuck muscles. Int. J. Food Sci. Technol. 45, 1277–1283 (2010)CrossRefGoogle Scholar
  7. 7.
    J.D. Wood, Effects of breed, diet and muscle on fat deposition and eating quality in pigs. Meat Sci. 67(4), 651–667 (2004)CrossRefGoogle Scholar
  8. 8.
    A. Buczkowska, E. Witkowska, Ł. Górski, A. Zamojska, W. Szewczyk, W. Wroblewski, P. Ciosek, The monitoring of methane fermentation in sequencing batch bioreactor with flow-through array of miniaturized solid state electrodes. Talanta 81, 1387–1392 (2010)CrossRefGoogle Scholar
  9. 9.
    L.B. Vosshall, R.F. Stocker, Molecular architecture of smell and taste in Drosophila. Annu. Rev. Neurosci. 30, 505–533 (2007)CrossRefGoogle Scholar
  10. 10.
    R. Banerjee, B. Tudu, R. Bandyopadhyay, A review on combined odor and taste sensor systems. J. Food Eng. 190, 10–21 (2016)CrossRefGoogle Scholar
  11. 11.
    M. Valle, Electronic tongues employing electrochemical sensors. Electro-analysis 22(14), 1539–1555 (2010)Google Scholar
  12. 12.
    S. Alegret, Integrated Analytical Systems (Elsevier, Amsterdam, 2003), pp. 13–16Google Scholar
  13. 13.
    N. Demir, A.C.O. Ferraz, S.A. Sargent, M.O. Balaban, Classification of impacted blueberries during storage using an electronic nose. J. Sci. Food Agric. 91, 1722–1727 (2011)CrossRefGoogle Scholar
  14. 14.
    C.M. Bishop, Pattern Recognition and Machine Learning, 1st edn. (Springer, New York, 2006)Google Scholar
  15. 15.
    A. Argyri, M. Jarvis, D. Wedge, Y. Xu, Z. Panagou, R. Goodacre, E. Nychas, A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage. Food Control 29(2), 461–470 (2013)CrossRefGoogle Scholar
  16. 16.
    J. Liu, Y. Sun, G. Xie, Classification and identification of corn juice beverage based on the array of taste sensor array. Trans. Chin. Soc. Agric. Eng. 28, 265–271 (2012)Google Scholar
  17. 17.
    S. Papadopoulou, Z. Panagou, R. Mohareb, E. Nychas, Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis. Food Res. Int. 50(1), 241–249 (2013)CrossRefGoogle Scholar
  18. 18.
    K. Brudzewski, S. Osowski, T. Markiewicz, Classification of milk by means of an electronic nose and SVM neural network. Sens. Actuators 98, 291–298 (2004)CrossRefGoogle Scholar
  19. 19.
    M. Soltani, M. Omid, Detection of poultry egg freshness by dielectric spectroscopy and machine learning techniques. LWT Food. Sci. Technol. 62(2), 1034–1042 (2015)CrossRefGoogle Scholar
  20. 20.
    Y. Han, X. Wang, Y. Cai, Z. Li, L. Zhao, H. Wang, L. Zhu, Sensor-array-based evaluation and grading of beef taste quality. Meat Sci. 129, 38–42 (2017)CrossRefGoogle Scholar
  21. 21.
    C. Liu, S.X. Yang, L. Deng, A comparative study for least angle regression on NIR spectra analysis to determine internal qualities of navel oranges. Expert Syst. Appl. 42(22), 8497–8503 (2015)CrossRefGoogle Scholar
  22. 22.
    X. Wang, Y. Sun, A. Liu, X. Wang, J. Gao, X. Fan, Y. Wang, Modeling structural and compositional changes of beef during human chewing process. LWT Food Sci. Technol. 60(2), 1219–1225 (2015)CrossRefGoogle Scholar
  23. 23.
    G. Eikelenboom, H. Barnier, H. Hoving-Bolink, M. Smulders, J. Culioli, Effect of pelvic suspension and cooking temperature on the tenderness of electrically stimulated and aged beef assessed with shear and compression tests. Meat Sci. 49(1), 89–99 (1998)CrossRefGoogle Scholar
  24. 24.
    X. Wang, Y. Sun, Y. Wang, T. Hu, M. Chen, Artificial tactile sense technique for predicting beef tenderness based on FS pressure sensor. J. Bionic Eng. 6(2), 196–201 (2009)CrossRefGoogle Scholar
  25. 25.
    AMSA, Research Guidelines for Cookery, Sensory Evaluation and Instrumental Tenderness Measurements of Fresh Meat (American Meat Science Association in cooperation with National Live Stock and Meat Board, Chicago, 2015), p. 8Google Scholar
  26. 26.
    E. Bona, I. Marquetti, J. Varaschim, G. Yasuo, F. Makimori, C. Arca, R. Jesus, Support vector machines in tandem with infrared spectroscopy for geographical classification of green arabica coffee. LWT Food. Sci. Technol. 76, 330–336 (2017)CrossRefGoogle Scholar
  27. 27.
    A.R. Di Rosa, F. Leone, F. Cheli, V. Chiofalo, Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment—a review. J. Food Eng. 210, 62–75 (2017)CrossRefGoogle Scholar
  28. 28.
    L. Xu, X. Wang, Y. Huang, Y. Wang, L. Zhu, R. Wu, A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses. Food Res. Int. 111, 650–660 (2019)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.College of Food Science and EngineeringJilin UniversityChangchunChina
  2. 2.College of Food Science and TechnologyBohai UniversityJinzhouChina
  3. 3.College of Food Science and EngineeringQingdao Agricultural UniversityQingdaoChina

Personalised recommendations