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Plant Tissue Modelling Using Power-Law Filters
Impedance spectroscopy has became an essential non-invasive tool for quality assessment measurements of the biochemical and biophysical changes in plant tissues. The electrical behaviour of biological tissues can be captured by fitting its bio-impedance data to a suitable circuit model. This paper i...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370976/ https://www.ncbi.nlm.nih.gov/pubmed/35957213 http://dx.doi.org/10.3390/s22155659 |
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author | Gadallah, Samar I. Ghoneim, Mohamed S. Elwakil, Ahmed S. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. |
author_facet | Gadallah, Samar I. Ghoneim, Mohamed S. Elwakil, Ahmed S. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. |
author_sort | Gadallah, Samar I. |
collection | PubMed |
description | Impedance spectroscopy has became an essential non-invasive tool for quality assessment measurements of the biochemical and biophysical changes in plant tissues. The electrical behaviour of biological tissues can be captured by fitting its bio-impedance data to a suitable circuit model. This paper investigates the use of power-law filters in circuit modelling of bio-impedance. The proposed models are fitted to experimental data obtained from eight different fruit types using a meta-heuristic optimization method (the Water Cycle Algorithm (WCA)). Impedance measurements are obtained using a Biologic SP150 electrochemical station, and the percentage error between the actual impedance and the fitted models’ impedance are reported. It is found that a circuit model consisting of a combination of two second-order power-law low-pass filters shows the least fitting error. |
format | Online Article Text |
id | pubmed-9370976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93709762022-08-12 Plant Tissue Modelling Using Power-Law Filters Gadallah, Samar I. Ghoneim, Mohamed S. Elwakil, Ahmed S. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. Sensors (Basel) Article Impedance spectroscopy has became an essential non-invasive tool for quality assessment measurements of the biochemical and biophysical changes in plant tissues. The electrical behaviour of biological tissues can be captured by fitting its bio-impedance data to a suitable circuit model. This paper investigates the use of power-law filters in circuit modelling of bio-impedance. The proposed models are fitted to experimental data obtained from eight different fruit types using a meta-heuristic optimization method (the Water Cycle Algorithm (WCA)). Impedance measurements are obtained using a Biologic SP150 electrochemical station, and the percentage error between the actual impedance and the fitted models’ impedance are reported. It is found that a circuit model consisting of a combination of two second-order power-law low-pass filters shows the least fitting error. MDPI 2022-07-28 /pmc/articles/PMC9370976/ /pubmed/35957213 http://dx.doi.org/10.3390/s22155659 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gadallah, Samar I. Ghoneim, Mohamed S. Elwakil, Ahmed S. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. Plant Tissue Modelling Using Power-Law Filters |
title | Plant Tissue Modelling Using Power-Law Filters |
title_full | Plant Tissue Modelling Using Power-Law Filters |
title_fullStr | Plant Tissue Modelling Using Power-Law Filters |
title_full_unstemmed | Plant Tissue Modelling Using Power-Law Filters |
title_short | Plant Tissue Modelling Using Power-Law Filters |
title_sort | plant tissue modelling using power-law filters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370976/ https://www.ncbi.nlm.nih.gov/pubmed/35957213 http://dx.doi.org/10.3390/s22155659 |
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