Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection
Herein, 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 prepar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/9/1/26 |
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| author | Giuseppe Egidio De Benedetto Sabrina Di Masi Antonio Pennetta Cosimino Malitesta |
| author_facet | Giuseppe Egidio De Benedetto Sabrina Di Masi Antonio Pennetta Cosimino Malitesta |
| author_sort | Giuseppe Egidio De Benedetto |
| collection | DOAJ |
| description | Herein, 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<sup>−1</sup>) towards metals, such as Bi<sup>3+</sup> and Al<sup>3+</sup> were collected as responses and optimised by a central composite design (CCD). Bi<sup>3+</sup> and Al<sup>3+</sup> 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<sup>−1</sup>, 30 and, 0.3 mL·min<sup>−1</sup>, 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 Ni<sup>2+</sup> and Ag<sup>+</sup> 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%). |
| first_indexed | 2024-12-10T22:49:40Z |
| format | Article |
| id | doaj.art-78fd068b6c1c4897ad226db6fc3bb103 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-12-10T22:49:40Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-78fd068b6c1c4897ad226db6fc3bb1032022-12-22T01:30:29ZengMDPI AGBiosensors2079-63742019-02-01912610.3390/bios9010026bios9010026Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals DetectionGiuseppe Egidio De Benedetto0Sabrina Di Masi1Antonio Pennetta2Cosimino Malitesta3Dipartimento di Beni Culturali, Università del Salento, Via D. Birago 64, 73100 Lecce, ItalyDipartimento di Scienze e Tecnologie Biologiche ed Ambientali, Via per Monteroni 1, 73100 Lecce, ItalyDipartimento di Beni Culturali, Università del Salento, Via D. Birago 64, 73100 Lecce, ItalyDipartimento di Scienze e Tecnologie Biologiche ed Ambientali, Via per Monteroni 1, 73100 Lecce, ItalyHerein, 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<sup>−1</sup>) towards metals, such as Bi<sup>3+</sup> and Al<sup>3+</sup> were collected as responses and optimised by a central composite design (CCD). Bi<sup>3+</sup> and Al<sup>3+</sup> 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<sup>−1</sup>, 30 and, 0.3 mL·min<sup>−1</sup>, 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 Ni<sup>2+</sup> and Ag<sup>+</sup> 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%).https://www.mdpi.com/2079-6374/9/1/26biosensorsenzyme inhibitionmetal ionscentral composite designresponse surface methodology |
| spellingShingle | Giuseppe Egidio De Benedetto Sabrina Di Masi Antonio Pennetta Cosimino Malitesta Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection Biosensors biosensors enzyme inhibition metal ions central composite design response surface methodology |
| title | Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection |
| title_full | Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection |
| title_fullStr | Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection |
| title_full_unstemmed | Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection |
| title_short | Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection |
| title_sort | response surface methodology for the optimisation of electrochemical biosensors for heavy metals detection |
| topic | biosensors enzyme inhibition metal ions central composite design response surface methodology |
| url | https://www.mdpi.com/2079-6374/9/1/26 |
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