College of Chemistry, Nanchang University, Nanchang 330031, China.
College of Chemistry, Nanchang University, Nanchang 330031, China; State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China.
Talanta. 2018 Aug 1;185:491-498. doi: 10.1016/j.talanta.2018.04.019. Epub 2018 Apr 9.
Most of the conventional multidimensional differential sensors currently need at least two-step fabrication, namely synthesis of probe(s) and identification of multiple analytes by mixing of analytes with probe(s), and were conducted using multiple sensing elements or several devices. In the study, we chose five different nucleobases (adenine, cytosine, guanine, thymine, and uracil) as model analytes, and found that under hydrothermal conditions, sodium citrate could react directly with various nucleobases to yield different nitrogen-doped carbon nanodots (CDs). The CDs synthesized from different nucleobases exhibited different fluorescent properties, leading to their respective characteristic fluorescence spectra. Hence, we combined the fluorescence spectra of the CDs with advanced chemometrics like principle component analysis (PCA), hierarchical cluster analysis (HCA), K-nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA), to present a conceptually novel "synthesis-identification integration" strategy to construct a multidimensional differential sensor for nucleobase discrimination. Single-wavelength excitation fluorescence spectral data, single-wavelength emission fluorescence spectral data, and fluorescence Excitation-Emission Matrices (EEMs) of the CDs were respectively used as input data of the differential sensor. The results showed that the discrimination ability of the multidimensional differential sensor with EEM data set as input data was superior to those with single-wavelength excitation/emission fluorescence data set, suggesting that increasing the number of the data input could improve the discrimination power. Two supervised pattern recognition methods, namely KNN and SIMCA, correctly identified the five nucleobases with a classification accuracy of 100%. The proposed "synthesis-identification integration" strategy together with a multidimensional array of experimental data holds great promise in the construction of differential sensors.
大多数传统的多维差分传感器目前至少需要两步制造，即探针的合成和通过与探针混合来识别多种分析物，并且使用多个传感元件或多个设备进行。在这项研究中，我们选择了五种不同的碱基（腺嘌呤、胞嘧啶、鸟嘌呤、胸腺嘧啶和尿嘧啶）作为模型分析物，并发现，在水热条件下，柠檬酸钠可以与各种碱基直接反应，生成不同的氮掺杂碳纳米点（CDs）。由不同碱基合成的 CDs 表现出不同的荧光性质，导致它们各自具有特征荧光光谱。因此，我们将 CDs 的荧光光谱与先进的化学计量学方法（如主成分分析（PCA）、层次聚类分析（HCA）、K-最近邻（KNN）和软独立建模分类相似性（SIMCA））相结合，提出了一种概念新颖的“合成-识别集成”策略，用于构建用于碱基识别的多维差分传感器。将 CD 的单波长激发荧光光谱数据、单波长发射荧光光谱数据和荧光激发-发射矩阵（EEM）分别用作差分传感器的输入数据。结果表明，以 EEM 数据集作为输入数据的多维差分传感器的区分能力优于以单波长激发/发射荧光数据集作为输入数据的多维差分传感器，这表明增加输入数据的数量可以提高区分能力。两种有监督的模式识别方法，即 KNN 和 SIMCA，以 100%的分类准确率正确识别了这五种碱基。所提出的“合成-识别集成”策略与多维实验数据阵列在差分传感器的构建中具有广阔的应用前景。
