Emerging Organic\nPollutant Detection, Classification,\nand Quantification Using a LED Spectrofluorimeter and Decision Trees

This work presents\na spectrofluorimetric system for the\nrapid detection,\nclassification, and quantification of emerging organic pollutants\n(EOPs) in water samples without reagents or preparation. The system\nuses a low-cost, low-power device with LED technology and optical\nfilters for analyzing samples in the 270–700 nm excitation\nrange. Five decision tree models were developed using approximately\n500 samples of treated urban wastewater and distilled water, each\ndoped with one of 18 EOPs at concentrations from 0.001 to 50 mg/L,\nas well as undoped samples. Samples were doped with a specific EOP\nper sample, avoiding combinations, thus allowing an accurate assessment\nof the individual characteristics of each substance. One model detects\nEOP presence (<i>R</i><sup>2</sup>: 93.37%–100%),\ntwo models classify EOPs into groups A and B (100% accuracy), and\nanother model identifies the applicable classification model (<i>R</i><sup>2</sup>: 98.93%–100%). A final model estimates\nEOP concentration (<i>R</i><sup>2</sup>: 70.00%–84.21%).\nThese results demonstrate the potential of the system for real-time\nmonitoring, facilitating compliance with water protection regulations\nand safeguarding the environment, water quality, and public health.