Soft Computing Techniques in Modelling the Influence of pH and Temperature on Dopamine Biosensor

Rangelova, Vania; Tsankova, Diana; Dimcheva, Nina

doi:10.5772/7029

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…Reaction rates increase exponentially with temperature, up to 50 °C beyond which irreversible thermal denaturation of the protein occurs for most enzymes [ 71 ]. pH dependence varies by enzyme, matrix composition and temperature [ 72 ]. At the optimum pH and temperature, biosensor responses are reproducible and greatest sensitivity is achieved.…”

Section: Amperometric Enzyme Biosensorsmentioning

confidence: 99%

“…Adsorption represents the easiest way of depositing enzymes on the electrode surface [ 72 ]. Enzymes are deposited by dipping the electrodes in an enzyme solution for a set time period [ 97 ] or by adsorption and dip-evaporation, sometimes followed by exposure to glutaraldehyde [ 98 ] or other agents as glyoxal or hexamethylenediamine [ 89 ], in order to crosslink the enzymes.…”

Section: Enzyme Biosensor Analytical Performance Over Timementioning

confidence: 99%

See 1 more Smart Citation

Enzyme Biosensors for Biomedical Applications: Strategies for Safeguarding Analytical Performances in Biological Fluids

Rocchitta

Spanu

Babudieri

et al. 2016

Sensors

407

264

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Enzyme-based chemical biosensors are based on biological recognition. In order to operate, the enzymes must be available to catalyze a specific biochemical reaction and be stable under the normal operating conditions of the biosensor. Design of biosensors is based on knowledge about the target analyte, as well as the complexity of the matrix in which the analyte has to be quantified. This article reviews the problems resulting from the interaction of enzyme-based amperometric biosensors with complex biological matrices containing the target analyte(s). One of the most challenging disadvantages of amperometric enzyme-based biosensor detection is signal reduction from fouling agents and interference from chemicals present in the sample matrix. This article, therefore, investigates the principles of functioning of enzymatic biosensors, their analytical performance over time and the strategies used to optimize their performance. Moreover, the composition of biological fluids as a function of their interaction with biosensing will be presented.

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Section: Amperometric Enzyme Biosensorsmentioning

confidence: 99%

Section: Enzyme Biosensor Analytical Performance Over Timementioning

confidence: 99%

Enzyme Biosensors for Biomedical Applications: Strategies for Safeguarding Analytical Performances in Biological Fluids

Rocchitta

Spanu

Babudieri

et al. 2016

Sensors

407

264

View full text Add to dashboard Cite

show abstract

“…To nd the optimum pH and T values i.e. 32 Hence, further experiments should be carried out at ambient temperature and pH 6.5. 31 The best response values were obtained applying the Cubic Model.…”

Section: Optimization Of the Experimental Conditions For Da Determinamentioning

confidence: 99%

PVA-AWP/tyrosinase functionalized screen-printed electrodes for dopamine determination

et al. 2016

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The development of highly sensitive amperometric biosensors for dopamine determination using commercially available SPEs in combination with the PVA-AWP photopolymer as the enzyme immobilization matrix is described in this work. This research focuses on (i) the optimization of the conditions for SPE biofunctionalization by tyrosinase entrapment in PVA-AWP and of the working conditions for dopamine determination by mathematical model application, and (ii) the evaluation of the analytical performances of the PVA-AWP/tyrosinase functionalized SPEs. Dopamine determination under optimum conditions for electrode biofunctionalization (PVA-AWP 3%, 60 min UV light exposure) andunder optimum working conditions (pH 6.5, 25 C) was performed in the dynamic concentration range of 0.9-500 mmol L À1 , 0.2-400 mmol L À1 , and 0.03-150 mmol L À1 , using respectively C SPE, MWCNT-GNP/C SPE, and graphene-GNP/C SPE. The LOD was found to be 300 nmol L À1 , 60 nmol L À1 , and as low as 10 nmol L À1 , correspondingly. The excellent analytical performances of the graphene-GNP C SPE and MWCNT-GNP C SPE were associated with their large active area (19.72 mm 2 and 15.3 mm 2 ) and enhanced electrocatalytic properties (k o ¼ 8.1 Â 10 À3 cm s À1 and 4.4 Â 10 À3 cm s À1 ) compared with the bare C SPE (12.47 mm 2 and k o ¼ 3.0 Â 10 À3 cm s À1 ). The developed biosensors were stable, reproducible, and more sensitive than most of the known biosensors for dopamine determination. They were successfully applied for dopamine determination in injections. Taking into consideration the fact that the selected enzyme immobilization approach using a water-soluble photopolymer opens the possibility of SPE biofunctionalization by photolithography, the developed biosensors are promising for fast, simple, sensitive, selective, and cost effective analysis of dopamine.

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“…Recent advances contemplate the use of machine learning (ML) algorithms, such as Artificial Neural Networks or Support Vector Machines. The use of these tools in AEB modeling is an emerging topic in the specialized scientific literature [21,2]. In this sense, model construction by ML techniques becomes a reasonable strategy, given that its black-box point of view liberates the modeler to fully and clearly express the mathematical laws underlying the physical phenomenon -in our case, the amperometric response of a biosensor.…”

Section: Goab Mathematical Modelingmentioning

confidence: 99%

Modeling a second-generation glucose oxidase biosensor with statistical machine learning methods

Rentería-Gutiérrez¹,

Belanche-Muñoz²,

González-Navarro³

et al. 2014

Biosensors: Recent Advances and Mathematical Challenges

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The biosensor is an analytical compact device that embodies a biological detection element and a chemical transducer that converts the biological signal into an electrical output that is proportional to the compound that is desired to assess. The biosensors are high-technology monitoring tools that offer electrochemical measures in a continuous, fast and highly sensitive fashion. Their potential use in real-life applications span from medical and health tasks to environmental monitoring problems. This chapter focuses particularly on Glucose Oxidase Amperometric Biosensors (GOAB) modeling, in order to understand their dynamics and behavior by automated computerized algorithms. In this contribution, we step aside from traditional modeling techniques-mathematical equations and simulation models-by using statistical learning methods. We model the continuous amperometric response of a GOAB by means of several classic and statistical learning methods. Specifically, kernel-based regression techniques are used, such as the support vector machines, one of the best machine learning technique nowadays. We report promising experimental results on a GOAB data set, which is modeled by a non-linear regression method allowing a very low prediction error using a rather simple model of the biosensor output.

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Soft Computing Techniques in Modelling the Influence of pH and Temperature on Dopamine Biosensor

Cited by 8 publications

References 33 publications

Enzyme Biosensors for Biomedical Applications: Strategies for Safeguarding Analytical Performances in Biological Fluids

Enzyme Biosensors for Biomedical Applications: Strategies for Safeguarding Analytical Performances in Biological Fluids

PVA-AWP/tyrosinase functionalized screen-printed electrodes for dopamine determination

Modeling a second-generation glucose oxidase biosensor with statistical machine learning methods

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