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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
  • 9 Downloads

Abstract

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.

Keywords

Beef Flavor Sensor array Support vector machines 

Notes

Acknowledgements

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).

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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

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