A novel bioluminescent bacteria-based method coupled with dynamic time warping for detecting and differentiating copper and mercury in water.

The present study offers a new method for analyzing the bioluminescence kinetics generated by the toxicity-triggered bioluminescent bacterial mutant strain TV1061 to detect and differentiate heavy metals in water samples. Specifically, as a proof-of-concept, copper and mercury were used, in single and binary mixtures, to induce the grpE heatshock promoter generating a measurable signal. The research employed the Dynamic Time Warping (DTW) algorithm to analyze the overall bioluminescence signal generated by the bacteria, rather than focusing on specific signal components, such as the response ratio. The method demonstrated high accuracy in classifying the samples (94 % accuracy). This technique is a first stepping stone in creating a database that will enable to accuractly identify mixtures of toxicants and predict their concentration in the sample. This approach is reliable, cost-effective, and rapid for monitoring and assessing toxic contaminants in water, including deciphering distinct recognition patterns, such as for copper and mercury (our chosen case specimens), including in binary mixtures, highlighting its potential for the precise identification and quantification of heavy metals in complex mixtures. Our findings support the possibility of developing a larger dataset of multiple references for multiple heavy metals and other toxicants, that will improve the overall detection capabilities of our system, all this thanks to the combined use of data mining and machine learning techniques.