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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468913/ https://www.ncbi.nlm.nih.gov/pubmed/30781820 http://dx.doi.org/10.3390/bios9010026 |
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author | De Benedetto, Giuseppe Egidio Di Masi, Sabrina Pennetta, Antonio Malitesta, Cosimino |
author_facet | De Benedetto, Giuseppe Egidio Di Masi, Sabrina Pennetta, Antonio Malitesta, Cosimino |
author_sort | De Benedetto, Giuseppe Egidio |
collection | PubMed |
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(−1)) towards metals, such as Bi(3+) and Al(3+) were collected as responses and optimised by a central composite design (CCD). Bi(3+) and Al(3+) 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 Ni(2+) 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%). |
format | Online Article Text |
id | pubmed-6468913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64689132019-04-23 Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection De Benedetto, Giuseppe Egidio Di Masi, Sabrina Pennetta, Antonio Malitesta, Cosimino Biosensors (Basel) Article 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(−1)) towards metals, such as Bi(3+) and Al(3+) were collected as responses and optimised by a central composite design (CCD). Bi(3+) and Al(3+) 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 Ni(2+) 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%). MDPI 2019-02-13 /pmc/articles/PMC6468913/ /pubmed/30781820 http://dx.doi.org/10.3390/bios9010026 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article De Benedetto, Giuseppe Egidio Di Masi, Sabrina Pennetta, Antonio Malitesta, Cosimino Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468913/ https://www.ncbi.nlm.nih.gov/pubmed/30781820 http://dx.doi.org/10.3390/bios9010026 |
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