A continuous, in-situ, near-time fluorescence sensor coupled with a machine learning model for detection of fecal contamination risk in drinking water: Design, characterization and field validation.

We designed and validated a sensitive, continuous, in-situ, remotely reporting tryptophan-like fluorescence sensor and coupled it with a machine learning model to predict high-risk fecal contamination in water (>10 colony forming units (CFU)/100mL E. coli). We characterized the sensor's response to multiple fluorescence interferents with benchtop analysis.