Signal-Analytics Modeling of Fluorescence Time-to-Detection for E. coli in Treated Wastewater: A Joint Censoring and...

Area of Science:

• Environmental microbiology
• Biostatistics
• Analytical chemistry

Background:

• Coliform bacteria, including pathogenic Escherichia coli (E. coli), are key indicators of fecal contamination and pose significant human health risks.
• Early detection and quantification of E. coli are crucial in environmental and clinical settings due to its potential to cause severe diseases.

Purpose of the Study:

• To propose and validate a novel probabilistic model for describing time-to-detection (TTD) values of E. coli.
• To provide a statistical framework for interpreting fluorescence-signal increases from the Optical Bacterial Growth Sensor-Fluorescence Detector.
• To support routine operational monitoring of E. coli in treated wastewater.

Main Methods:

• Development of a new probabilistic model for TTD data.
• Fitting the model to minute-resolved fluorescence-signal TTD values from 47 independent treated-wastewater assays using maximum likelihood estimation.
• Comparison of the proposed model with classical distributions used for TTD data.
• Derivation of key mathematical properties and Monte Carlo simulations for validation.

Main Results:

• The proposed probabilistic model provides a more accurate description of E. coli TTD behavior in treated wastewater compared to classical models.
• The model aids in specifying the lower operational boundary of the assay counting window.
• Most TTD values were observed within a 7-10 hour operational window, with low probability beyond 12 hours.

Conclusions:

• The developed probabilistic model enhances the statistical toolkit for analyzing sensor-based TTD data for E. coli.
• The model offers practical guidance for routine monitoring and detection of E. coli in treated wastewater.
• This framework improves the reliability and interpretation of bacterial growth sensor data.