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Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines.

Authors
  • Xu, Zijun1
  • Wang, Zhaokun1
  • Liu, Mingyang1
  • Yan, Binwei1
  • Ren, Xueqin2
  • Gao, Zideng3
  • 1 College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, PR China. , (China)
  • 2 College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, PR China; Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, China Agricultural University, Beijing 100193, PR China.. Electronic address: [email protected] , (China)
  • 3 College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, PR China. Electronic address: [email protected] , (China)
Type
Published Article
Journal
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Publication Date
Feb 11, 2020
Volume
232
Pages
118147–118147
Identifiers
DOI: 10.1016/j.saa.2020.118147
PMID: 32092680
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The detection and differentiation of tetracyclines (TCs) has received increasing attention due to the severe threat they pose to human health and the ecological balance. A dual-channel fluorescence sensor array based on two carbon quantum dots (CDs) was fabricated to distinguish between four TCs, including tetracycline (TC), oxytetracycline (OTC), doxycycline (DOX), and metacycline (MTC). A distinct fluorescence variation pattern (I/I0) was produced when CDs interacted with the four TCs. This pattern was analyzed by LDA and SVM. This was the first time that SVM was used for data processing of fluorescence sensor arrays. LDA and SVM showed that the array has the capacity for parallel and accurate determination of TCs at concentrations between 1.0 μM and 150 μM. In addition, the interference experiment using metal ions and antibiotics as possible coexisting interference substances proves that the sensor array has excellent selectivity and anti-interference ability. The array was also used for the accurate detection and identification of TCs in binary mixtures, and furthermore, the four TCs were successfully identified in river water and milk samples. Besides, the sensor array successfully identified the four TCs in 72 unknown samples with a 100% accuracy. The results proved that SVM can achieve the same accurate classification and prediction as LDA, and considering its additional advantages, it can be used as an optional supplementary method for data processing, thereby expanding the data processing field. Copyright © 2020. Published by Elsevier B.V.

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