A Novel Array-Based Fluorescent Sensing Approach for the Identification and Quantification of Pesticides with High Sensitivity Based on Distinguishable Cross-response Algorithm

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.