Anal. Chim. Acta, 2017, Apr; 963:119-128. doi: 10.1016/j.aca.2017.01.032

Turn-off” fluorescent sensor for highly sensitive and specific simultaneous recognition of 29 famous green teas based on quantum dots combined with chemometrics


Li Liua, Yao Fana, Haiyan Fua*,b, Feng Chenb, Chuang Nia, Jinxing Wanga, Qiaobo Yina, Qingling Mua, Tianming Yanga, Yuanbin Shec* 

a The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China

b Department of Food, Nutrition and Packaging Sciences, Clemson University, Clemson, SC 29634, USA

c State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, PR China. 

Correspondence should be addressed to Haiyan Fu and Yuanbin She. E-mail:;



This paper is the first report on a highly sensitive and specific “Turn-off” fluorescent detection mode based on NAC-capped CdTe QDs coupled with chemometric methods for simultaneous recognition of species, geographic origins and quality grades in 29 famous green teas. It is also the first attempt to develop “Turn-off” fluorescent data sensor analysis into tackling a large-class-number classification (LCNC) problem. The NAC-capped CdTe QDs were demonstrated to be excellent probes for sensing differences in fluorescent response of different green tea. Most importantly, the authors further demonstrated the established “Turn-off” fluorescent sensor mode has several significant advantages and appealing properties including high fluorescence efficiency, classification accuracy, sensitivity and excellent specificity over the conventional fluorescent method for LCNC of different green tea. Such an approach presented in this work will hold great potential to broaden more LCNC practical applications in food, herb, pharmaceutical quality control and biological recognition to achieve higher detector response and enhance the specificity.



Figure 1: “Turn-off” fluorescent sensor based on NAC-capped CdTe QDs for highly sensitive and specific simultaneous recognition of 29 famous green with aid of chemometrics


Figure 2: Some authors of the paper (From right: Li Liu, Haiyan Fu, Jinxing Wang, Qingling Mu )



[1] J. Jankun, S.H. Selman, R. Swiercz, E. Skrzypczak-Jankun, Why drinking green tea could prevent cancer, Nature 387 (1997) 561.

[2] A. Stoddart, Green tea therapy, Nat. Mater. 13 (2014) 998.

[3] H.Y. Fu, Q.B. Yin, L. Xu, M. Goodarzi, T.M. Yang, G.F. Li, F. Qiao, Y.B. She. Challenges of large-class-number classification (LCNC): a novel ensemble strategy (ES) and its application to discriminating the geographical origins of 25 green teas. Chemom. Intell. Lab. 157 (2016) 43-49.

[4] Y. Fan, L. Liu, D.L. Sun, H.Y. Lan, H.Y. Fu, T.M. Yang, Y.B. She, C. Ni, “Turn-off” fluorescent data array sensor based on double quantum dots coupled with chemometrics for highly sensitive and selective detection of multicomponent pesticides, Anal. Chim. Acta. 916 (2016) 84-91.

[5] W. Wang, X. Ji, A. Kapur, C. Zhang, H. Mattoussi, A Multifunctional Polymer Combining the Imidazole and Zwitterion Motifs as a Biocompatible Compact Coating for Quantum Dots, J. Am. Chem. Soc. 137 (2015) 14158-14172.