Predicting heterotrophic plate count exceedance in tap water: A binary classification model supervised by culture-independent data

Culture-independent data can be utilized to identify heterotrophic plate count (HPC) exceedances in drinking water. Although HPC represents less than 1% of the bacterial community and exhibits time lags of several days, HPC data are widely used to assess the microbiological quality of drinking water and are incorporated into drinking water standards. The present study confirmed the nonlinear relationships between HPC, intact cell count (ICC), and adenosine triphosphate (ATP) in tap water samples (stagnant and flushed). By using a combination of ICC, ATP, and free chlorine data as inputs, we show that HPC exceedance can be classified using a 2layer feed-forward artificial neural network (ANN). Despite the nonlinearity of HPC, the best binary classification model showed accuracies of 95%, sensitivity of 91%, and specificity of 96%. ICC and chlorine concentrations were the most important features for classifiers. The main limitations, such as sample size and class imbalance, were also discussed. The present model provides the ability to convert data from emerging measurement techniques into established and well-understood measures, overcoming culture dependence and offering near realtime data to help ensure the biostability and safety of drinking water.

Automated summary

Culture-independent data can be used to identify HPC exceedances in drinking water. The study developed an ANN model using ICC, ATP, and chlorine data, achieving high accuracy in classifying HPC exceedances. The model overcomes culture dependence and provides near real-time data for ensuring the safety of drinking water.