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Cluster Computing

, Volume 22, Supplement 4, pp 8401–8406 | Cite as

Predicting the content of camelina protein using FT-IR spectroscopy coupled with SVM model

  • Jun Liu
  • Mengting WuEmail author
  • Mingqing Wang
  • Yuntao Zou
  • Zhenglin TanEmail author
  • Donghai Wang
  • Xiuzhi Susan Sun
Article
  • 75 Downloads

Abstract

133 camelina samples were used to build the Fourier transform infrared (FT-IR) prediction model. Several methods have been used for the establishment of the predicting model, but support vector machine was rarely used in FT-IR area to build the prediction model. The aim of this study was to develop a new model for predicting protein with higher accuracy. In the spectra region 690–1700 cm\(^{-1}\), the SVM method was better than that of PLS and PCR. In the development of SVM, the \(\hbox {R}_{\mathrm{RMSEC}}^{2}\) and \(\hbox {R}_{\mathrm{RMSEP}}^{2}\) of the model were 0.83963 and 0.96578 respectively, and the RPD was 5.5016. The RPD was greater than that of PLS and PCR. The FT-IR was effective in predicting the content of camelina protein and SVM was a better method to build prediction model.

Keywords

FT-IR Camelina protein SVM 

Notes

Acknowledgements

This work was supported by the Hubei Provincial Department Education Science Technology Research Program—Outstanding Youth Talent Project (HPSFY#Q20111504), the ninth Graduate Innovation Fund of Wuhan Institute of Technology and the Foundation of Hubei Provincial Key Laboratory of Intelligent Robot (HBIR 201608).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jun Liu
    • 1
    • 6
  • Mengting Wu
    • 1
    • 6
    Email author
  • Mingqing Wang
    • 2
  • Yuntao Zou
    • 3
  • Zhenglin Tan
    • 4
    Email author
  • Donghai Wang
    • 5
  • Xiuzhi Susan Sun
    • 5
  1. 1.Hubei Key Laboratory of Intelligent RobotWuhan Institute of TechnologyWuhanChina
  2. 2.Wuhan inCarCloud Technologies Pte. Ltd.WuhanChina
  3. 3.Wuhan Winphone Technology Co., Ltd.WuhanChina
  4. 4.Department of Cuisine and NutritionHubei University of EconomicsWuhanChina
  5. 5.Department of Biological and Agricultural EngineeringKansas State UniversityManhattanUSA
  6. 6.School of Computer Science and EngineeringWuhan Institute of TechnologyWuhanChina

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