Liu Li, Fan Yao, Fu Haiyan, Chen Feng, Ni Chuang, Wang Jinxing, Yin Qiaobo, Mu Qingling, Yang Tianming, She Yuanbin
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.
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; Department of Food, Nutrition and Packaging Sciences, Clemson University, Clemson, SC 29634, USA.
Anal Chim Acta. 2017 Apr 22;963:119-128. doi: 10.1016/j.aca.2017.01.032. Epub 2017 Feb 8.
Fluorescent "turn-off" sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence "turn-off" model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the "turn-off" fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established "turn-off" fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the "turn-off" model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity.
基于水溶性量子点（QDs）的荧光“猝灭”传感器因其独特性质，如高荧光量子产率、化学稳定性和低毒性，而受到越来越多的关注。在这项工作中，建立了一种基于荧光“猝灭”模型的新方法，以水溶性碲化镉量子点作为荧光探针来区分29种不同的著名绿茶。根据绿茶荧光峰的位置和强度，量子点的荧光会发生不同程度的猝灭。随后，借助经典的偏最小二乘判别分析（PLSDA），所有绿茶都能被高灵敏度、高特异性地区分，训练集的识别率达到100%，预测集的识别率达到98.3%。特别是，基于二阶导数（2nd der）且具有最小复杂度（LVs = 3）的“猝灭”荧光PLSDA模型是最有效的建模方法。最重要的是，我们进一步证明，与传统荧光方法相比，所建立的“猝灭”荧光传感器模式在绿茶大类数分类（LCNC）方面具有几个显著优势和吸引人的特性。据我们所知，这项工作是首次基于量子点“猝灭”模型结合化学计量学对如此多种著名绿茶进行快速有效识别的报道，这也意味着在具有弱荧光甚至无荧光的复杂LCNC分类系统中还有其他潜在应用，以实现更高的检测响应和特异性。
