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Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of
Zijun Xu1, Zhaokun Wang1, Mingyang Liu1
1College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, PR China.
Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|February 25, 2020
Summary
A novel dual-channel fluorescence sensor array using carbon quantum dots can detect and differentiate four tetracyclines (TCs). This method accurately identifies TCs in complex samples like river water and milk.
Area of Science:
- Analytical Chemistry
- Environmental Science
- Biotechnology
Background:
- Tetracyclines (TCs) pose significant risks to human health and ecosystems.
- Accurate detection and differentiation of TCs are crucial for monitoring and safety.
Purpose of the Study:
- To develop a dual-channel fluorescence sensor array for distinguishing four specific TCs.
- To evaluate the array's performance using Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM).
Main Methods:
- Fabrication of a dual-channel fluorescence sensor array using two types of carbon quantum dots (CDs).
- Analysis of distinct fluorescence variation patterns generated upon interaction with tetracycline (TC), oxytetracycline (OTC), doxycycline (DOX), and metacycline (MTC).
- Application of LDA and SVM for pattern recognition and data processing.
Main Results:
- The sensor array successfully differentiated between the four TCs based on unique fluorescence patterns.
- Accurate determination of TCs was achieved within a concentration range of 1.0–150 μM.
- The array demonstrated excellent selectivity and anti-interference capabilities against metal ions and other antibiotics.
- Successful identification of TCs in binary mixtures, river water, milk samples, and 72 unknown samples with 100% accuracy.
Conclusions:
- The developed fluorescence sensor array provides a reliable method for the parallel and accurate detection of TCs.
- Support Vector Machines (SVM) proved effective for data processing, offering an alternative to LDA with additional advantages.
- This approach expands the data processing capabilities for fluorescence sensor arrays in environmental and biological sample analysis.
