A Portable UV-LED/RGB Sensor for Real-Time Bacteriological Water Quality Monitoring Using ML-Based MPN Estimation

Bacteriological water quality monitoring is of utmost importance for safeguarding public health against waterborne diseases. Traditional methods such as Membrane Filtration (MF), Multiple Tube Fermentation (MTF), and enzyme-based assays are effective in detecting fecal contamination indicators, but their time-consuming nature and reliance on specialized equipment and personnel pose significant limitations. This paper introduces a novel, portable, and cost-effective UV-LED/RGB water quality sensor that overcomes these challenges. The system is composed of a microfluidic device for sample-preparation-free analysis, RGB sensors for data acquisition, UV-LEDs for excitation, and a portable incubation system. Commercially available defined substrate technology, Most Probable Number (MPN) analysis, and artificial intelligence are combined for the real-time monitoring of bacteria colony-forming units (CFU) in a water sample. By eliminating the need for sample preparation, specialized equipment, and laboratory space, the system provides an efficient and affordable solution for water quality monitoring in remote and resource-limited areas. The main significance lies in the combination of miniaturization, automation, and machine learning (ML) based data analysis. Multilayer perceptron neural networks (MLPNN) and support vector machine (SVM) are used to rapidly (30 minutes) predict RGB signals from water samples in wells. By predicting the number of positive wells, the system can predict the MPN of CFU in a water sample, allowing for the rapid estimation of bacterial concentration in a low-cost and portable manner.