Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines
| Autor: | Zijun Xu, Zideng Gao, Mingyang Liu, Zhaokun Wang, Binwei Yan, Xueqin Ren |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Channel (digital image)
Food Contamination 02 engineering and technology 010402 general chemistry Interference (wave propagation) 01 natural sciences Fluorescence Analytical Chemistry Machine Learning Rivers Sensor array Limit of Detection Quantum Dots medicine Animals Instrumentation Spectroscopy Fluorescent Dyes Chemistry 021001 nanoscience & nanotechnology Linear discriminant analysis Carbon Atomic and Molecular Physics and Optics Anti-Bacterial Agents 0104 chemical sciences Support vector machine Milk Spectrometry Fluorescence Tetracyclines Carbon quantum dots Metacycline 0210 nano-technology Biological system Water Pollutants Chemical medicine.drug |
| Zdroj: | Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 232:118147 |
| ISSN: | 1386-1425 |
| DOI: | 10.1016/j.saa.2020.118147 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect