Dong Ren, Chang Zhang, Shun Ren, Zhong Zhang, Ji-hua Wang, and An-xiang Lu


  1. [1] Q-B. Liu and Z-F. Yang, The situation of Chinese tea market and the brand exploration, Newsletter of Sericulture and Tea, 6, 2013, 22–23.
  2. [2] P. Williams and K. Norris, Near-infrared technology in the agricultural and food industries, 2nded. (St., Paul, MN, USA: The American of Cereal Chemists, Inc., 2001).
  3. [3] H. Ping, J-H. Wang, and G-X. Ren, Prediction of the total starch and amylose content in barley using near-infrared reflectance spectroscopy, Intelligent Automation & Soft Computing, 19(3), 2013, 231–237.
  4. [4] H-F. Yuan, W-Z. Lu, Near infrared spectrometry technology is rapidly entering into petrochemical industry, Petroleum Processing and Petrochemicals, 9, 1998, 49–52.
  5. [5] S-Z. Lei and H-G. Yao, Applications of near infrared spectrum technique for non-destructive measurement of fruit quality, Chinese Journal of Spectroscopy Laboratory, 4, 2009, 775–779.
  6. [6] H-H. Li and W. Li, Development and application of near infrared spectroscopic in tobacco fields, Journal of Anhui Agricultural Sciences, 29, 2014, 10318–10321.
  7. [7] Z. Jian and C. Hao, Study on identification and traceability of tea material cultivar by combined analysis of multi-partial least squares models based on near infrared spectroscopy, Spectroscopy and Spectral Analysis, 10, 2010, 2650–2653.
  8. [8] Q-S. Chen and J-W. Zhao, Identification of authenticity of tea with near infrared spectroscopy based on support vector machine, Acta Optica Sinica, 6, 2006, 933–937.
  9. [9] J-W. Zhao, Q-S. Chen, H-D. Zhang, and M-H. Liu, Study on the identification of tea using near infrared reflectance spectroscopy, Spectroscopy and Spectral Analysis, 9, 2006, 1601–1604.
  10. [10] U. Mall, C. Wöhler, A. Grumpe, R. Bugiolacchi, and M. Bhatt, Characterization of lunar soils through spectral features extraction in the NIR. Advances in Space Research, 54(10), 2014, 2029–2040.
  11. [11] L. Norgaard, A. Saudland, J. Wagner, J.P. Nielsen, L. Munck, and S.B. Engelsen, Interval Partial least squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy, Applied Spectroscopy, 54(3), 2000, 413–419.
  12. [12] X.-B. Zhou, J.-W. Zhao, and Y.-X. Huang, Using forward and backward interval partial least squares to establish the near infrared spectroscopy model of apple sugar content, Chinese Mechanical Engineering Society Annual Conference and China Academy of Engineering Mechanical and Vehicle Engineering Radical Annual Meeting Proceedings, Jiangsu, 2006.
  13. [13] F. Qu and D. Ren, The characteristic spectral selection method based on forward and backward interval partial least squares, Spectroscopy and Spectral Analysis, 36(2), 2016, 593–598.
  14. [14] D. Ren, F. Qu, and K. Lv, A gradient descent boosting spectrum modeling method based on back interval partial least squares, Neurocomputing, 171, 2016, 1038–1046.
  15. [15] J-H. Jiang, R.J. Berry, and H.W. Siesler Wavelength interval selection in multicomponent spectral analysis by moving window partial least squares regression with applications to mid-infrared spectroscopic data, Analytical Chemistry, 74(14), 2002, 3555.
  16. [16] H. Li, Y. Liang, and D. Cao, Model-population analysis and its applications in chemical and biological modeling, Trends in Analytical Chemistry, 38(9), 2012, 154–162.
  17. [17] F. Qu, D. Ren, and J. Wang, An ensemble successive project algorithm for liquor detection using near infrared sensor, Sensors, 16(1), 2015, 294–300.
  18. [18] H-D. Li, Y-Z. Liang, and Q-S. Xu, Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration, Analytica Chimica Acta, 648(1), 2009,77–84.
  19. [19] N-Y. Deng and Y-J. Tian, The new method of data mining– support vector machine (Beijing: Science Press, 2004, 164–219).
  20. [20] Z-Q. Bian and X-G. Zhang, Pattern recognition (Beijing: Tsinghua University Press, 2002, 284–304).
  21. [21] Y-F. Li, J-Q. Yuan, and Y-F. Xue, The selection of neural network training samples in the fermentation process, Control and Instruments in Chemical Industry, 6, 2004, 21–24.
  22. [22] G. Nokas and E. Dermatas, Continuous speech recognition in noise using a spectrum-entropy beam-former, International Journal of Research in Computer Applications & Robotics, 22(2), 2007, 103–111.

Important Links:

Go Back