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Updated: Mar 2, 2026

Amplification of Escherichia coli in a Continuous-Flow-PCR Microfluidic Chip and Its Detection with a Capillary Electrophoresis System
Published on: November 21, 2023
On-line detection of Escherichia coli intrusion in a pilot-scale drinking water distribution system
Jenni Ikonen1, Tarja Pitkänen1, Pascal Kosse2
1Water and Health Unit, Department of Health Security, National Institute for Health and Welfare, P.O. Box 95, FI-70701, Kuopio, Finland.
Abstract:
Improvements in microbial drinking water quality monitoring are needed for the better control of drinking water distribution systems and for public health protection. Conventional water quality monitoring programmes are not always able to detect a microbial contamination of drinking water. In the drinking water production chain, in addition to the vulnerability of source waters, the distribution networks are prone to contamination. In this study, a pilot-scale drinking-water distribution network with an on-line monitoring system was utilized for detecting bacterial intrusion. During the experimental Escherichia coli intrusions, the contaminant was measured by applying a set of on-line sensors for electric conductivity (EC), pH, temperature (T), turbidity, UV-absorbance at 254 nm (UVAS SC) and with a device for particle counting. Monitored parameters were compared with the measured E. coli counts using the integral calculations of the detected peaks. EC measurement gave the strongest signal compared with the measured baseline during the E. coli intrusion. Integral calculations showed that the peaks in the EC, pH, T, turbidity and UVAS SC data were detected corresponding to the time predicted. However, the pH and temperature peaks detected were barely above the measured baseline and could easily be mixed with the background noise. The results indicate that on-line monitoring can be utilized for the rapid detection of microbial contaminants in the drinking water distribution system although the peak interpretation has to be performed carefully to avoid being mixed up with normal variations in the measurement data.
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