People often associate Escherichia coli with contaminated food, but E. coli has long been a workhorse in biotechnology. Scientists at the University of California, Irvine (UC Irvine) have demonstrated that the bacterium has additional value as part of a system to detect heavy metal contamination in water.
E. coli exhibit a biochemical response in the presence of metal ions. This is a slight change that researchers could observe with chemically assembled gold nanoparticle optical sensors. The scientists could detect metals in concentrations a billion times lower than those leading to cell death through a machine-learning analysis.
Their analysis was of the optical spectra of metabolites released in response to chromium and arsenic exposure. It allowed the researchers to deduce the heavy metal type and amount with higher than 96 per cent accuracy.
The process takes about ten minutes and was published in Proceedings of the National Academy of Sciences.
“This new water monitoring method developed by UCI researchers is highly sensitive, fast and versatile,” said co-author Regina Ragan. “It can be broadly deployed to monitor toxins at their sources in drinking and irrigation water. It can also be used for agricultural and industrial runoff. This system can warn early about heavy metal contamination to safeguard human health and ecosystems.”
How e coli could find other things
The researchers also spotlighted the other necessary components. That included gold nanoparticles assembled with molecular precision and machine learning algorithms. These components greatly enhanced the sensitivity of their monitoring system.
Ragan said it could be applied toward spotting metal toxins, including arsenic, cadmium, chromium, copper, lead and mercury. They were detected at orders of magnitude below regulatory limits to provide early warning of contamination.
In the study, the scientists explained that they could apply trained algorithms to unseen tap water and wastewater samples, which means the system can be generalised to water sources and supplies anywhere in the world.
“This transfer learning method allowed 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 per cent accuracy and 92 per cent accuracy for treated wastewater,” Ragan said.
“Access to safe water is necessary for the health of people and the planet,” she added. “New technology that can be mass manufactured at low-cost is needed to monitor the introduction of an array of contaminants in the water supply as a critical part of the solution for water security in the face of pollution and climate change.”
Joining Ragan on this project, which the National Science Foundation funded, were graduate students Hong Wei, Yixin Huang, and Yen-Hsiang Huang. Professors Sunny Jiang and Allon Hochbaum supported the research.
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