Lei Jincan, Zhao Shixian, Huang Jing, Tao Ke, Dang Qi, Peng Junxi, Zhao Yun, Zhang Lili
Chongqing Engineering and Technology Research Center of Intelligent Rehabilitation and Eldercare, Chongqing City Management College, 401331, Chongqing, China.
Chongqing Shanwaishan Blood Purification Technology Co., LTD, 401120, Chongqing, China.
J Fluoresc. 2025 Jan 8. doi: 10.1007/s10895-024-04120-x.
The presence of excessive residues of pesticides poses a great threat to ecology and human health. Herein, a novel, low-cost, simple and precise quantification sensing platform was established for differentiating and monitoring four common pesticides in China. Particularly, the array-based ratio fluorescent sensor array detector (ARF-SAD) based on cross-reaction characteristics of porphyrins and other porphyrin derivative was successfully constructed and integrated into the platform. Via acquiring the fluorescent data before and after the reaction of the ARF-SAD with pesticides, a novel, unique, and recognizable pattern of fluorescence changes was developed and utilized for the rapid characterization of pesticides. In addition, after raw data processed through the intervention of machine learning algorithms (hierarchical cluster analysis, principal component analysis, fitting of a polynomial), the selected pesticides and their mixture can be accurately distinguished via the constructed fluorescence fingerprint map by the platform in terms of category. By use of ratio fluorescence strategy, the platform and fluorescent sensor array can provide good sensitivity and selectivity for the monitoring of selected pesticides with LODs less than 10 ppb. Furthermore, the reproducibility, stability and practicability analysis of real sample have been thoroughly validated simultaneously. The findings indicated that the standard recovery rates of the six categories of blended pesticides in Jialing River water samples ranged from 86.13% to 114.84%, with the lowest relative standard deviation (RSD) reaching a remarkable level of only 3.04%. All representations consistently demonstrate that the detector serves as a prompt and viable sensing platform for discriminating and quantitatively analyzing pesticides, thereby showcasing its potential in the fields of pesticide differentiation and detection.
农药残留超标对生态环境和人类健康构成了巨大威胁。在此，建立了一种新型、低成本、简单且精确的定量传感平台，用于区分和监测中国四种常见农药。特别地，基于卟啉及其衍生物的交叉反应特性，成功构建了基于阵列的比率荧光传感器阵列检测器（ARF-SAD）并将其集成到该平台中。通过获取ARF-SAD与农药反应前后的荧光数据，开发了一种新颖、独特且可识别的荧光变化模式，并将其用于农药的快速表征。此外，在通过机器学习算法（层次聚类分析、主成分分析、多项式拟合）干预处理原始数据后，该平台可以通过构建的荧光指纹图谱准确地区分所选农药及其混合物的类别。通过采用比率荧光策略，该平台和荧光传感器阵列对所选农药的监测具有良好的灵敏度和选择性，检测限低于10 ppb。此外，同时对实际样品的重现性、稳定性和实用性进行了全面验证。结果表明，嘉陵江水体中六类混合农药的标准回收率在86.13%至114.84%之间，最低相对标准偏差（RSD）仅达到3.04%这一显著水平。所有这些都一致表明，该检测器是一种用于区分和定量分析农药的快速且可行的传感平台，从而展示了其在农药鉴别和检测领域的潜力。
