An IoT Low-Cost Optical Sensor to Detect the Presence of Diluted Drugs in Water

In coastal areas, saltwater and freshwater converge, creating high biodiversity and significant pollution issues from industrial waste, microplastics, fertilizers, and medications. These substances disrupt ecosystems and species' life cycles that regulate key physiological processes, even affecting humans. Chromatography is the most commonly used technique for detecting drugs in water, but it is also expensive and requires specialized personnel. This study used optical sensors around a tube to detect drugs (Dacortin, Diazepam, and Prednisone) and baking flour in water by measuring light absorption at different wavelengths (visible, infrared, and ultraviolet). Data changes were processed using an Arduino Uno microprocessor, configured as an IoT device. The concentrations were 0, 10, 15, and 30 mg/100 mL. The results display several models using MATLAB. K-nearest neighbor showed the best performance with 100% accuracy and the shortest training time (0.71 seconds). Ultraviolet sensors provided the most variable and impactful data, while red and green sensors showed similar results, and blue had higher dispersion. Infrared sensors were less sensitive, requiring future adjustment for improved analysis. Regression models showed high performance, the best being Rational Quadratic Gaussian Process Regression, achieving a Root Mean Squared Error of 0.2 mg/100 mL in training and 0.01 mg/100 mL in testing. Some training errors were attributed to outliers caused by electronic or sample issues, highlighting the need for outlier filtering. Test dataset errors were minimal and consistent with training results, validating the model's accuracy.