期刊
TALANTA
卷 185, 期 -, 页码 491-498出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.talanta.2018.04.019
关键词
Synthesis-identification integration; Differential sensor; Carbon nanodots; Multivariate chemometrics; Nucleobases; Excitation-Emission Matrices
资金
- National Natural Science Foundation of China [NSFC - 21305061]
- Natural Science Foundation of Jiangxi Province [20171BAB203018, 20151BAB203021]
- Jiangxi Provincial Department of Education [GJJ160006, GJJ160204]
- State Key Laboratory of Electroanalytical Chemistry [SKLEAC-201802]
- State Key Laboratory of Chemo/Biosensing and Chemometrics of Hunan University [SKLCBC - 2013010]
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 hydro thermal 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.
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