User menu

Back Propagation-Artificial Neural Network Model for Prediction of the Quality of Tea Shoots through Selection of Relevant Near Infrared Spectral Data via Synergy Interval Partial Least Squares

Bibliographic reference Wang, Shengpeng ; Zhang, Zhengzhu ; Ning, Jingming ; Ren, Guangxin ; Yan, Shouhe ; et. al. Back Propagation-Artificial Neural Network Model for Prediction of the Quality of Tea Shoots through Selection of Relevant Near Infrared Spectral Data via Synergy Interval Partial Least Squares. In: Analytical Letters, Vol. 46, no. 1, p. 184-195 (2012)
Permanent URL
  1. Bahorun Theeshan, Luximon-Ramma Amitabye, Gunness Teeluck K., Sookar Dharmendra, Bhoyroo Satar, Jugessur Rabindranath, Reebye Deshmukh, Googoolye Kreshna, Crozier Alan, Aruoma Okezie I., Black tea reduces uric acid and C-reactive protein levels in humans susceptible to cardiovascular diseases, 10.1016/j.tox.2009.11.024
  2. Blanco Marcelo, Peguero Anna, Analysis of pharmaceuticals by NIR spectroscopy without a reference method, 10.1016/j.trac.2010.07.007
  3. Chen Q. S., J. Pharm. Biomed. Anal., 48, 1312 (2008)
  4. Chen Quansheng, Zhao Jiewen, Huang Xingyi, Zhang Haidong, Liu Muhua, Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy, 10.1016/j.microc.2006.01.023
  5. Chen Q. S., Czech J. Food Sci., 26, 360 (2008)
  6. Chen Quansheng, Zhao Jiewen, Liu Muhua, Cai Jianrong, Liu Jianhua, Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms, 10.1016/j.jpba.2007.10.031
  7. Chen Quansheng, Zhao Jiewen, Vittayapadung Saritporn, Identification of the green tea grade level using electronic tongue and pattern recognition, 10.1016/j.foodres.2008.03.005
  8. Cleve E, Bach E, Schollmeyer E, Using chemometric methods and NIR spectrophotometry in the textile industry, 10.1016/s0003-2670(00)00888-6
  9. Durand A., Devos O., Ruckebusch C., Huvenne J.P., Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton–viscose textiles, 10.1016/j.aca.2007.03.024
  10. He Yong, Li Xiaoli, Deng Xunfei, Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model, 10.1016/j.jfoodeng.2006.04.042
  11. Lee Min-Jeong, Seo Da-Young, Lee Hea-Eun, Wang In-Chun, Kim Woo-Sik, Jeong Myung-Yung, Choi Guang J., In line NIR quantification of film thickness on pharmaceutical pellets during a fluid bed coating process, 10.1016/j.ijpharm.2010.10.022
  12. Li Xiaoli, He Yong, Fang Hui, Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy, 10.1016/j.jfoodeng.2006.10.033
  13. Lin Jen-Kun, Liang Yu-Chih, Lin-Shiau Shoei-Yn, Cancer chemoprevention by tea polyphenols through mitotic signal transduction blockade, 10.1016/s0006-2952(99)00112-4
  14. Liu Fei, Ye Xujun, He Yong, Wang Li, Application of visible/near infrared spectroscopy and chemometric calibrations for variety discrimination of instant milk teas, 10.1016/j.jfoodeng.2009.01.004
  15. Liu Yande, Sun Xudong, Ouyang Aiguo, Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN, 10.1016/j.lwt.2009.10.008
  16. Müller Aline Lima Hermes, Picoloto Rochele Sogari, Mello Paola de Azevedo, Ferrão Marco Flores, dos Santos Maria de Fátima Pereira, Guimarães Regina Célia Lourenço, Müller Edson Irineu, Flores Erico Marlon Moraes, Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods, 10.1016/j.saa.2011.12.001
  17. Norgaard , L. 2004 . iToolbox Manual, paper available on
  18. Nørgaard L., Saudland A., Wagner J., Nielsen J. P., Munck L., Engelsen S. B., Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy, 10.1366/0003702001949500
  19. Rao C. R., Sankhya A, 26, 329 (1964)
  20. Balabin Roman M., Safieva Ravilya Z., Gasoline classification by source and type based on near infrared (NIR) spectroscopy data, 10.1016/j.fuel.2007.07.018
  21. Shi Ji-yong, Zou Xiao-bo, Zhao Jie-wen, Mel Holmes, Wang Kai-liang, Wang Xue, Chen Hong, Determination of total flavonoids content in fresh Ginkgo biloba leaf with different colors using near infrared spectroscopy, 10.1016/j.saa.2012.03.078
  22. Shi Yue, Zhao Xiu-tao, Zhang Yu-ming, Ren Nan-qi, Back propagation neural network (BPNN) prediction model and control strategies of methanogen phase reactor treating traditional Chinese medicine wastewater (TCMW), 10.1016/j.jbiotec.2009.08.014
  23. Wan , X. C. 2003 .Tea biochemistry (), 3rd ed. , eds. D. X. Zeng and W. X. Xiao . Beijing : China , Agriculture Press , pp. 8 – 58 .
  24. Wang S. P., J. Tea Sci., 31, 66 (2011)
  25. Wang Xiaofei, Bao Yanfei, Liu Guili, Li Gang, Lin Ling, Study on the Best Analysis Spectral Section of NIR to Detect Alcohol Concentration Based on SiPLS, 10.1016/j.proeng.2012.01.302
  26. Wu Di, He Yong, Nie Pengcheng, Cao Fang, Bao Yidan, Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice, 10.1016/j.aca.2009.11.045
  27. Yan Shou, Review: Evaluation of the composition and sensory properties of tea using near infrared spectroscopy and principal component analysis, 10.1255/jnirs.562
  28. Yin Q., J. Infrared Millimeter Waves, 23, 427 (2004)
  29. Zhang Z. Z., Spectrosc. Eur., 23, 17 (2011)
  30. Zhao C., Spectrosc. Spectral Anal., 24, 50 (2004)