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


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.


FT-IR Camelina protein SVM 



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).


  1. 1.
    Kagale, S., Chushin, K., Nixon, J., et al.: The emerging biofuel crop Camelina sativa retains a highly undifferentiated hexaploid genome structure. Nat. Commun. 5(4), 3706 (2011)Google Scholar
  2. 2.
    Zubr, J.: Oil-seed crop: Camelina sativa. Ind. Crops Prod. 6(2), 113–119 (1997)CrossRefGoogle Scholar
  3. 3.
    Li, Y., Sun, X.S.: Camelina oil derivatives and adhesion properties. Ind. Crops Prod. 73, 73–80 (2015)CrossRefGoogle Scholar
  4. 4.
    Ryhanen, E.L., Perttila, S., Tupasela, T., et al.: Effect of Camelina sativa expeller cake on performance and meat quality of broilers. J. Sci. Food Agric. 87(8), 1489–1494 (2010)CrossRefGoogle Scholar
  5. 5.
    Rokka, T., Alen, K., Valaja, J., et al.: The effect of a Camelina sativa enriched diet on the composition and sensory quality of hen eggs. Food Res. Int. 35(2–3), 253–256 (2002)CrossRefGoogle Scholar
  6. 6.
    Li, N., Qi, G., Sun, X.S., et al.: Adhesion properties of camelina protein fractions isolated with different methods. Ind. Crops Prod. 69, 263–272 (2015)CrossRefGoogle Scholar
  7. 7.
    Zhang, K., Tan, Z., Chen, C., Sun, X.S., et al.: Rapid prediction of camlina seed oil content using near-infrared spectroscopy. Energy Fuels 31(5), 5629–5634 (2017)CrossRefGoogle Scholar
  8. 8.
    Xu, F., Yu, J., Tesso, T., et al.: Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: a mini-review. Appl. Energy 104(2), 801–809 (2013)CrossRefGoogle Scholar
  9. 9.
    Benesch, M.G., Lewis, R.N., Mannock, D.A., et al.: A DSC and FTIR spectroscopic study of the effects of the epimeric cholestan-3-ols and cholestan-3-one on the thermotropic phase behavior and organization of dipalmitoylphosphatidylcholine bilayer membranes: comparison with their 5-cholesten analogs. Chem. Phys. Lipids 188, 10–26 (2015)CrossRefGoogle Scholar
  10. 10.
    Wu, Z., Zhao, Y., Zhang, J., et al.: Quality assessment of gentiana rigescens from different geographical origins using FT-IR spectroscopy combined with HPLC. Molecules 22(7), 1238 (2017)CrossRefGoogle Scholar
  11. 11.
    Porras, M.A., Cubitto, M.A., Villar, M.A.: A new way of quantifying the production of poly(hydroxyalkanoate)s using FTIR. J. Chem. Technol. Biotechnol. 91(5), 1240–1249 (2016)CrossRefGoogle Scholar
  12. 12.
    Wu, Z., Xu, E., Long, J., et al.: Use of attenuated total reflectance mid-infrared spectroscopy for rapid prediction of amino acids in Chinese rice wine. J. Food Sci. 80(8), C1670 (2015)CrossRefGoogle Scholar
  13. 13.
    Seung Yeob, S., Young Koung, L., In-Jung, K.: Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis. Food Chem. 190, 1027–1032 (2016)CrossRefGoogle Scholar
  14. 14.
    Kumar, M., Raghava, G.P.: Prediction of nuclear proteins using SVM and HMM models. BMC Bioinf. 10(1), 22–22 (2009)CrossRefGoogle Scholar
  15. 15.
    Liu Jun, Wu, Mengting, Tan Zhenglin, et al.: Overview of data analysis methods in near-infrared spectroscopy nondestructive testing. J. Wuhan Inst. Technol 39(05), 496–502 (2017)Google Scholar
  16. 16.
    Cherkassky, V., Mulier, F.: Statistical learning theory. Encycl. Sci. Learn. 41(4), 3185–3185 (1998)zbMATHGoogle Scholar
  17. 17.
    Shao, W., Li, Y., Diao, S., et al.: Rapid classification of Chinese quince (Chaenomeles speciosa, Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration. Anal. Bioanal. Chem. 409(1), 115–120 (2017)CrossRefGoogle Scholar
  18. 18.
    Ulrichs, T., Drotleff, A.M., Ternes, W.: Determination of heat-induced changes in the protein secondary structure of reconstituted livetins (water-soluble proteins from hen’s egg yolk) by FTIR. Food Chem. 172, 909 (2015)CrossRefGoogle Scholar
  19. 19.
    Kyomugasho, C., Christiaens, S., Shpigelman, A., et al.: FT-IR spectroscopy, a reliable method for routine analysis of the degree of methylesterification of pectin in different fruit- and vegetable-based matrices. Food Chem. 176, 82–90 (2015)CrossRefGoogle Scholar

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

Personalised recommendations