A single recognition unit-based virtual sensor Array : Applying 3D fluorescence spectroscopy to inner filter effect-based sensing
Copyright © 2023 Elsevier B.V. All rights reserved..
A convenient, fast, low-cost detection and discrimination method is demanded for environmental monitoring but still it remains more technological challenges. Herein, we demonstrate that the inner filter effect (IFE), in combination with three-dimensional fluorescence spectroscopy, can offer a virtual sensor array (VSA) as apropersolution. And with the aid of pattern recognition techniques, it is feasible to recognize compounds with structural similarities economically and effectively. In this study, with the help of visual clustering plots of principal component analysis (PCA), a prediction model based on hierarchical strategy was made using support vector machine (SVM) method for the qualitative profiling of aromatic pollutants. The VSA was constructed by a single metal-organic framework (MOF) recognition unit (MOF-74 (Zn)) with the excitation wavelength as external regulatory factors. Pattern characteristics of four aromatics with very similar structures (phenylamine, chlorobenzene, nitrobenzene, and phenol), both single analyte and binary mixtures, were acquired. The primary constituents of multi-dimensional spectral signals were subsequently extracted and fed into a vector machine to construct a prediction model through 10-fold cross-validation optimization, resulting in a classification accuracy of 100% for single analytes and 96% for mixtures. Quantitative research has shown that, except for chlorobenzene, all three other analytes can be predicted in concentration within an acceptable error range, and the mixture can be predicted proportionally. Moreover, the VSA can be used to distinguish these pollutants in tap and river water also. We propose for the first time a new tack for the construction of VSA in a general manner, namely using three-dimensional full range fluorescence scanning for IFE based sensing to get multiple times of information resulting from different weak interaction between analyte and sensor for decision-making.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 2023 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:305 |
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| Enthalten in: |
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy - 305(2023) vom: 15. Jan., Seite 123470 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xue, Xiangfen [VerfasserIn] |
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| Links: |
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| Themen: |
Inner filter effect |
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| Anmerkungen: |
Date Revised 07.11.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.saa.2023.123470 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a A convenient, fast, low-cost detection and discrimination method is demanded for environmental monitoring but still it remains more technological challenges. Herein, we demonstrate that the inner filter effect (IFE), in combination with three-dimensional fluorescence spectroscopy, can offer a virtual sensor array (VSA) as apropersolution. And with the aid of pattern recognition techniques, it is feasible to recognize compounds with structural similarities economically and effectively. In this study, with the help of visual clustering plots of principal component analysis (PCA), a prediction model based on hierarchical strategy was made using support vector machine (SVM) method for the qualitative profiling of aromatic pollutants. The VSA was constructed by a single metal-organic framework (MOF) recognition unit (MOF-74 (Zn)) with the excitation wavelength as external regulatory factors. Pattern characteristics of four aromatics with very similar structures (phenylamine, chlorobenzene, nitrobenzene, and phenol), both single analyte and binary mixtures, were acquired. The primary constituents of multi-dimensional spectral signals were subsequently extracted and fed into a vector machine to construct a prediction model through 10-fold cross-validation optimization, resulting in a classification accuracy of 100% for single analytes and 96% for mixtures. Quantitative research has shown that, except for chlorobenzene, all three other analytes can be predicted in concentration within an acceptable error range, and the mixture can be predicted proportionally. Moreover, the VSA can be used to distinguish these pollutants in tap and river water also. We propose for the first time a new tack for the construction of VSA in a general manner, namely using three-dimensional full range fluorescence scanning for IFE based sensing to get multiple times of information resulting from different weak interaction between analyte and sensor for decision-making | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Inner filter effect | |
| 650 | 4 | |a Least-squares method | |
| 650 | 4 | |a Principal component analysis | |
| 650 | 4 | |a Support vector machine | |
| 650 | 4 | |a Three-dimensional fluorescence spectroscopy | |
| 650 | 4 | |a Virtual sensor array | |
| 700 | 1 | |a Wei, Mingjie |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Xinyu |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Qinghua |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Changkun |e verfasserin |4 aut | |
| 700 | 1 | |a Niu, Xiangheng |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Xiulian |e verfasserin |4 aut | |
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