Heavy metals represent a serious issue regarding both environmental and health status. Their monitoring is necessary and it is necessary the development of decentralized approaches that are able to enforce the risk assessment. Electrochemical sensors and biosensors, with the various architectures, represent a solid reality often involved for this type of analytical determination. Although these approaches offer easy-to-use and portable tools, some limitations are often highlighted in presence of multi-targets and/or real matrices. However, chemometrics- and artificial intelligence-based tools, both for designing and for data analyzing, display the capability in producing novel functionality towards the management of complex matrices which often contain more information than those that are visualized with sensor detection. Design of experiment, exploratory, predictive and regression analysis can push the world of electrochemical (bio)sensors beyond the state of the art, because is still too large the number of analytical chemists that do not deal with multivariate thinking. In this paper, the use of multivariate methods applied to electrochemical sensing of heavy metals is showed, and each approach is described in terms of efficacy and outputs.
Keywords: Artificial intelligence; Design of experiment; Electroanalysis; Heavy metals; Multivariate analysis; Sensors.
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