Journal of Applied Spectroscopy

, Volume 84, Issue 4, pp 704–709 | Cite as

Estimating the Acquisition Price of Enshi Yulu Young Tea Shoots Using Near-Infrared Spectroscopy by the Back Propagation Artificial Neural Network Model in Conjunction with Backward Interval Partial Least Squares Algorithm

Article

Near infrared spectroscopy and the back propagation artificial neural network model in conjunction with backward interval partial least squares algorithm were used to estimate the purchasing price of Enshi yulu young tea shoots. The near-infrared spectra regions most relevant to the tea shoots price model (5700.5–5935.8, 7613.6–7848.9, 8091.8–8327.1, 8331–8566.2, 9287.5–9522.5, and 9526.6–9761.9 cm–1) were selected using backward interval partial least squares algorithm. The first five principal components that explained 99.96% of the variability in those selected spectral data were then used to calibrate the back propagation artificial neural tea shoots purchasing price model. The performance of this model (coefficient of determination for prediction 0.9724; root-mean-square error of prediction 4.727) was superior to those of the back propagation artificial neural model (coefficient of determination for prediction 0.8653, root-mean-square error of prediction 5.125) and the backward interval partial least squares model (coefficient of determination for prediction 0.5932, root-mean-square error of prediction 25.125). The acquisition price model with the combined backward interval partial least squares–back propagation artificial neural network algorithms can evaluate the price of Enshi yulu tea shoots accurately, quickly and objectively.

Keywords

Enshi yulu young tea shoots acquisition price near-infrared spectroscopy backward interval partial least squares back propagation–artificial neural network 

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References

  1. 1.
    T. Bahorun, A. Luximon-Ramma, T. Gunness, D. Sookar, S. Bhoyroo, R. Jugessur, D. Reebye, K. Googoolye, A. Crozier, and O. Aruoma, Toxicology, 278, 68–74 (2010).CrossRefGoogle Scholar
  2. 2.
    X. F. Zhou, Z. L. Yang, S. A. Haughey, P. Galvin-King, L. J. Han, and C. T. Elliott, Food Chem., 189, 13–18 (2015).CrossRefGoogle Scholar
  3. 3.
    X. M. Liu, J. S. Liu, Spectrosc. Lett., 47, 729–739 (2014).CrossRefGoogle Scholar
  4. 4.
    C. F. Wu, Z. G. Wu, R. A. Hashmonay, S. Y. Chang, Y. S. Wu, C. P. Chao, M. J. Chase, and R. H. Kagann, Atm. Environ., 82, 335–342 (2014).CrossRefGoogle Scholar
  5. 5.
    M. A. Tavanaie, N. Esmaeilian, and M. R. M. Mojtahedi, Dyes Pigments, 114, 267–272 (2015).CrossRefGoogle Scholar
  6. 6.
    M. Blanco and A. Peguero, TrAC Trend. Anal. Chem., 29, 1127–1136 (2010).CrossRefGoogle Scholar
  7. 7.
    M. J. Lee, D. Y. Seo, H. E. Lee, W. S. Kim, M. Y. Jeong, and G. J. Choi, Int. J. Pharm., 403, 66–72 (2011).CrossRefGoogle Scholar
  8. 8.
    M. S. Lee, Y. S. Hwang, J. W. Lee, and M. G. Choung, Food Chem., 158, 351–357 (2014).CrossRefGoogle Scholar
  9. 9.
    Y. Huang, G. R. Du, Y. J. Ma, and J. Zhou, Optik, 126, 2030–2034 (2015).ADSCrossRefGoogle Scholar
  10. 10.
    W. He, J. Zhou, H. Cheng, L. Y. Wang, K. Wei, W. F. Wang, and X. H. Li, Spectrochim. Acta, A: Mol. Biomol. Spectrosc., 86, 399–404 (2012).ADSCrossRefGoogle Scholar
  11. 11.
    J. Y. Shi, X. B. Zou, J. W. Zhao, and H. P. Mao, J. Infrared Millim. Waves, 30, 458–452 (2011).CrossRefGoogle Scholar
  12. 12.
    Q. S. Chen, D. L. Zhang, W. X. Pan, H. H. Li, K. Urmila, and J. W. Zhao, Trend. Food Sci. Technol., 43, 63–82 (2015).CrossRefGoogle Scholar
  13. 13.
    D. Ren, F. F. Qu, K. Lv, Z. Zhang, H. L. Xu, and X. Y. Wang, Neurocomputing, 162, 101–111 (2015).Google Scholar
  14. 14.
    Z. Z. Zhang, S. P. Wang, X. C. Wan, and S. H. Yan, Spectrosc. Eur., 23, 17–21 (2011).Google Scholar
  15. 15.
    J. Y. Shi, X. B. Zou, H. Mel, K. L. Wang, X. Wang, and H. Chen, Spectrochim. Acta, A: Mol. Biomol. Spectrosc., 94, 271–276 (2012).ADSCrossRefGoogle Scholar
  16. 16.
    D. Wu, Y. P. He, C. Nie, F. Cao, and Y. D. Bao, Anal. Chim. Acta, 659, 229–237 (2010).CrossRefGoogle Scholar
  17. 17.
    A. E. Ghaziri and E. M. Qannari, Chemometr. Intel. Lab. Syst., 148, 95–105 (2015).CrossRefGoogle Scholar
  18. 18.
    Y. L. Yan, B. Chen, and D. Z. Zhu, Near Infrared Spectroscopy Principles, Technologies and Applications, China Light Industry Press, Beijing (2013), pp. 112–114.Google Scholar
  19. 19.
    Y. D. Liu, X. D. Sun, and A. G. Ouyang, LWT-Food Sci. Technol., 43, 602–607 (2010).CrossRefGoogle Scholar
  20. 20.
    J. Wu, W. Luo, X. K. Wang, Q. Cheng, C. G. Sun, and H. Li, J. Pharm. Biomed. Anal., 80, 186–191 (2013).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute of Fruit and TeaHubei Academy of Agricultural ScienceWuhanChina
  2. 2.Enshi Agricultural BureauEnshiChina

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