Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines
Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines
复制标题
机器学习辅助双通道碳量子点荧光传感器阵列检测四环素
DOI：
10.1016/j.saa.2020.118147
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Gao Zideng
中科院分区：
文献类型：
--
作者：
Xu Zijun;Wang Zhaokun;Liu Mingyang;Yan Binwei;Ren Xueqin;Gao Zideng
四环素类药物（TCs）对人类健康和生态平衡构成严重威胁，其检测和鉴别日益受到关注。制造了基于两个碳量子点（CD）的双通道荧光传感器阵列，以区分四种TC，包括四环素（TC）、土霉素（OTC）、多西环素（DOX）和美环素（MTC）。当 CD 与四种 TC 相互作用时，会产生明显的荧光变化模式 (I/I0)。通过 LDA 和 SVM 分析了该模式。这是SVM首次用于荧光传感器阵列的数据处理。 LDA 和 SVM 表明该阵列能够并行、准确地测定浓度在 1.0 μM 至 150 μM 之间的 TC。此外，以金属离子和抗生素作为可能共存干扰物质的干扰实验证明，该传感器阵列具有优异的选择性和抗干扰能力。该阵列还用于二元混合物中TC的准确检测和鉴定，并且在河水和牛奶样品中成功鉴定了四种TC。此外，传感器阵列成功识别了72个未知样品中的4个TC，准确率100%。结果证明SVM可以实现与LDA同样准确的分类和预测，并且考虑到其附加优势，可以作为数据处理的可选补充方法，从而扩展数据处理领域。
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.