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A machine learning-enhanced biosensor for mercury detection based on an hydrophobin chimera.

Anna Pennacchio¹, Fabio Giampaolo², Francesco Piccialli²

¹Department of Chemical Sciences, University of Naples Federico II, Italy.

Biosensors & Bioelectronics

|October 16, 2021

Summary

A new portable biosensor detects mercury (II) in seawater using a fluorescent chimera. This method offers a cost-effective and rapid solution for monitoring marine pollution.

Keywords:

Amyloid autofluorescence Artificial intelligence Fluorescence quenching Heavy metals Histidine-rich peptide Sea water

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Area of Science:

Environmental Science
Biotechnology
Analytical Chemistry

Background:

Marine pollution from heavy metals, especially mercury, poses significant risks to human health.
Conventional mercury detection methods are often costly, time-consuming, and require specialized equipment.
There is a critical need for accessible and efficient tools for in situ water quality monitoring.

Purpose of the Study:

To develop a novel, portable, and cost-effective biosensor for detecting mercury (II) in marine environments.
To design a specific recognition element for mercury (II) that enables sensitive and selective detection.
To integrate machine learning for simplified data interpretation and in situ analysis.

Main Methods:

A hydrophobin-based chimera was engineered to bind mercury (II), causing a decrease in fluorescence.
A fluorescence-based biosensor was constructed using the engineered chimera as the recognition element.
Machine learning algorithms were employed to predict mercury concentrations without traditional readers.

Main Results:

The biosensor achieved sensitive detection of mercury (II) in the nanomolar (nM) range.
Detection limits were as low as 0.4 nM in tap water and 0.3 nM in seawater.
The system demonstrated high specificity for Hg²⁺, even in the presence of other metal ions.
Machine learning integration enabled mercury concentration prediction, facilitating in situ monitoring.

Conclusions:

A novel, portable, and sensitive fluorescence-based biosensor for mercury (II) detection in seawater has been successfully developed.
The biosensor, coupled with machine learning, offers a user-friendly and efficient platform for in situ environmental monitoring.
This technology presents a significant advancement in addressing the challenge of mercury pollution in marine ecosystems.