Search

This paper deals with an analysis of the electrochemical biosensors and their response to multi-component mixtures. The main task is to build a mathematical model for estimation the concentration of each mixture component from the biosensor response data. Two different types of biosensors: amperometric and potenciometric are analysed. Due to high dimensionality of biosensor output data the principal component analysis is applied. Additional multivariate analysis of variance is used to analyze the response sensitivity of each biosensor type. Finally a concentration estimation model based on ensemble of neural networks is presented.

Biosensor response, in case of multi-substrate mixture, has nonlinear dependence on substrate concentrations. This work investigates the possibility to approximate this dependency with artificial neural network. Also the influence of external diffusion layer to results of multi-substrate determination was investigated. The numerically modelled biosensor response was used as experimental data. The principal components analysis was used to reduce the dimension of biosensor response. Prefered method gives acceptable acuratnes on multi-substrate determination and it can be improved by relatively large external diffusion layer.

This paper presents a two-dimensional-in-space mathematical model of amperometric biosensors with perforated membrane. The model is based on the diffusion equations containing a non-linear term related to the Michaelis–Menten kinetics of the enzymatic reaction. The digital simulation was carried out using the finite difference technique. Using computer simulation of the biosensors action, the influence of the geometry of the perforated membrane on the biosensor response was investigated.

In this paper total principal component regression is used for biosensors response to mixtures of compounds classification. The results are compared with the results obtained using artificial neural networks.

This paper presents a two-dimensional-in-space mathematical model of a sensor system based on an array of enzyme microreactors immobilised on a single electrode. The model is based on the diffussion equations containing a non-linear term related to the Michaelis–Menten kinetics of the enzymatic reaction. Using computer simulation the influence of the geometry of the enzyme microreactors on the biosensor response was investigated. The digital simulation was carried out using the finite difference technique.

This paper presents a system which is used for the classification of biosensor signals. The proposed system is applied to the the synthesized and experimental data. The developed system showed good prediction perfomance.

A mathematical model of amperometric biosensors is presented. The model is based on the diffusion equations containing a non-linear term related to the Michaelis-Menten kinetics of the enzyme reaction. The model takes into concideration surface roughness of the enzyme layer. Using digital simulation, the influence of the geometry of the roughness on the biosensor response was investigated. The digital simulation was carried out using the finite difference technique.