Using E. coli to detect heavy metal contamination

Scientists have demonstrated that the bacterium Escherichia coli – often associated with contaminated supplies – can be used as part of a system to snift heavy metal contamination in water.

E. coli exhibits a biochemical response in the presence of metal ions, a slight transpiration that researchers – from the University of California, Irvine – say they were worldly-wise to observe with chemically assembled gold nanoparticle optical sensors. Through a machine-learning wringer of the optical spectra of metabolites released in response to chromium and arsenic exposure, the scientists were worldly-wise to snift metals in concentrations a billion times lower than those leading to lamina death – while stuff worldly-wise to deduce the heavy metal type and value with higher than 96 percent accuracy.

The process, which the researchers said can be workaday in well-nigh 10 minutes, is the subject of a study seeming in Proceedings of the National Academy of Sciences.

“This new water monitoring method ripened by UCI researchers is highly sensitive, fast and versatile,” said co-author Regina Ragan, UCI professor of materials science and engineering. “It can be widely deployed to monitor toxins at their sources in drinking and irrigation water and in agricultural and industrial runoff. This system can provide an early warning of heavy metal contamination to safeguard human health and ecosystems.”

In wing to proving that yes-man like E. coli can snift unsafe water, the researchers spotlighted the other necessary components – gold nanoparticles assembled with molecular precision and machine learning algorithms – which profoundly enhanced the sensitivity of their monitoring system. Ragan said it can be unromantic toward spotting metal toxins – including arsenic, cadmium, chromium, copper, lead and mercury – at levels orders of magnitude unelevated regulatory limits to provide early warning of contamination.

In the study, the scientists explained that they can wield trained algorithms to unseen tap water and wastewater samples, which ways the system can be generalized to water sources and supplies anywhere in the world.

“This transfer learning method unliable the algorithms to determine if drinking water was within U.S. Environmental Protection Agency and World Health Organization recommend limits for each contaminant with greater than 96-percent verism and with 92-percent verism for treated wastewater,” Ragan said.