Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection
AbstractHerein, we report the application of a chemometric tool for the optimisation of electrochemical biosensor performances. The experimental design was performed based on the responses of an amperometric biosensor developed for metal ions detection using the flow injection analysis. The electrode preparation and the working conditions were selected as experimental parameters, and thus, were modelled by a response surface methodology (RSM). In particular, enzyme concentration, flow rates, and number of cycles were reported as continuous factors, while the sensitivities of the biosensor (S, µA·mM−1) towards metals, such as Bi3+ and Al3+ were collected as responses and optimised by a central composite design (CCD). Bi3+ and Al3+ inhibition on the Pt/PPD/GOx biosensor response is for the first time reported. The optimal enzyme concentration, scan cycles and flow rate were found to be 50 U·mL−1, 30 and, 0.3 mL·min−1, respectively. Descriptive/predictive performances are discussed: the sensitivities of the optimised biosensor agreed with the experimental design prediction. The responses under the optimised conditions were also tested towards Ni2+ and Ag+ ions. The multivariate approach used in this work allowed us to obtain a wide working range for the biosensor, coupled with a high reproducibility of the response (RSD = 0.72%). View Full-Text
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De Benedetto, G.E.; Di Masi, S.; Pennetta, A.; Malitesta, C. Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection. Biosensors 2019, 9, 26.
De Benedetto GE, Di Masi S, Pennetta A, Malitesta C. Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection. Biosensors. 2019; 9(1):26.Chicago/Turabian Style
De Benedetto, Giuseppe E.; Di Masi, Sabrina; Pennetta, Antonio; Malitesta, Cosimino. 2019. "Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection." Biosensors 9, no. 1: 26.
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