Biosensor response to multi-component mixtures statistical analysis and forecasting
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: amperome...
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| Format: | Article |
| Language: | English |
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Vilnius University Press
2023-09-01
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| Series: | Lietuvos Matematikos Rinkinys |
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| Online Access: | https://www.zurnalai.vu.lt/LMR/article/view/30739 |
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| _version_ | 1823857939523305472 |
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| author | Romas Baronas Sigitas Būda Feliksas Ivanauskas Pranas Vaitkus |
| author_facet | Romas Baronas Sigitas Būda Feliksas Ivanauskas Pranas Vaitkus |
| author_sort | Romas Baronas |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-d9d01e722e6744d8aa15358f092350eb |
| institution | Kabale University |
| issn | 0132-2818 2335-898X |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj-art-d9d01e722e6744d8aa15358f092350eb2025-02-11T18:12:33ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2023-09-0146spec.10.15388/LMR.2006.30739Biosensor response to multi-component mixtures statistical analysis and forecastingRomas Baronas0Sigitas Būda1Feliksas Ivanauskas2Pranas Vaitkus3Vilnius UniversityVilnius UniversityInstitute of Mathematics and InformaticsVilnius University 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. https://www.zurnalai.vu.lt/LMR/article/view/30739biosensormodellingneural networks |
| spellingShingle | Romas Baronas Sigitas Būda Feliksas Ivanauskas Pranas Vaitkus Biosensor response to multi-component mixtures statistical analysis and forecasting Lietuvos Matematikos Rinkinys biosensor modelling neural networks |
| title | Biosensor response to multi-component mixtures statistical analysis and forecasting |
| title_full | Biosensor response to multi-component mixtures statistical analysis and forecasting |
| title_fullStr | Biosensor response to multi-component mixtures statistical analysis and forecasting |
| title_full_unstemmed | Biosensor response to multi-component mixtures statistical analysis and forecasting |
| title_short | Biosensor response to multi-component mixtures statistical analysis and forecasting |
| title_sort | biosensor response to multi component mixtures statistical analysis and forecasting |
| topic | biosensor modelling neural networks |
| url | https://www.zurnalai.vu.lt/LMR/article/view/30739 |
| work_keys_str_mv | AT romasbaronas biosensorresponsetomulticomponentmixturesstatisticalanalysisandforecasting AT sigitasbuda biosensorresponsetomulticomponentmixturesstatisticalanalysisandforecasting AT feliksasivanauskas biosensorresponsetomulticomponentmixturesstatisticalanalysisandforecasting AT pranasvaitkus biosensorresponsetomulticomponentmixturesstatisticalanalysisandforecasting |