Machine learning-assisted ratiometric fluorescence sensor array for recognition of multiple quinolones antibiotics

Developing analytical methods for simultaneous detection of multiple antibiotic residues is crucial for environmental protection and human health. In this study, a dual lanthanide fluorescence probe (GDP-Eu-Tb) based on nucleotides has been designed. The addition of quinolone antibiotics (QNs) quenc...

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Vydané v:Food chemistry Ročník 478; s. 143722
Hlavní autori: Li, Mengyuan, Jia, Lei, Zhao, Xiaolei, Zhang, Lina, Zhao, Dan, Xu, Jun, Zhao, Tongqian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 30.06.2025
Predmet:
Anti-Bacterial Agents - analysis
Anti-Bacterial Agents - chemistry
antibiotic residues
decision support systems
Decision tree algorithm
environmental protection
Fluorescence
fluorescent dyes
Fluorescent Dyes - chemistry
food chemistry
human health
lanthanides
levofloxacin
Machine Learning
moxifloxacin
nucleotides
principal component analysis
Quinolones - analysis
Quinolones - chemistry
Quinolones antibiotics
Ratiometric fluorescence sensor array
Spectrometry, Fluorescence - instrumentation
Spectrometry, Fluorescence - methods
The principal component algorithm
Decision tree algorithm
Quinolones antibiotics
Ratiometric fluorescence sensor array
The principal component algorithm
ISSN:0308-8146, 1873-7072, 1873-7072
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Shrnutí:Developing analytical methods for simultaneous detection of multiple antibiotic residues is crucial for environmental protection and human health. In this study, a dual lanthanide fluorescence probe (GDP-Eu-Tb) based on nucleotides has been designed. The addition of quinolone antibiotics (QNs) quench the Eu3+ fluorescence signal through the inner filter effect (IFE) and exhibit characteristic peaks, enabling ratio fluorescence detection of levofloxacin (LVLX), gatifloxacin (GTLX), and moxifloxacin (MXLX). A ratiometric fluorescence sensor array is constructed using a single sensor element (GDP-Eu-Tb), combined with principal component analysis (PCA) and decision tree (DT) algorithms to model the relationship between fluorescence intensity ratios (I450/I616, I460/I616, I463/I616, I468/I616) and QNs. The performance of the DT model is evaluated using accuracy, precision, recall, and F1 score, with stability and generalizability confirmed by stratified ten-fold cross-validation. This approach demonstrates high sensitivity, selectivity and applicability and provides an effective solution for antibiotic residue detection. [Display omitted] •A Ratiometric GDP-Eu-Tb fluorescent sensor array was fabricated.•Significant color changes for levofloxacin, gatifloxacin, and moxifloxacin sensing.•The combination of machine learning and the ratiometric fluorescent sensor array.•Accurate prediction and differentiation of mixed antibiotic samples.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0308-8146
1873-7072
1873-7072
DOI:10.1016/j.foodchem.2025.143722