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Mathematical Model Coupled to Neural Networks Calculates the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic Matrix
[Image: see text] Unknown extraction recovery from solid matrix samples leads to meaningless chemical analysis results. It cannot always be determined, and it depends on the complexity of the matrix and properties of the extracted substances. This paper combines a mathematical model with the machine...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190882/ https://www.ncbi.nlm.nih.gov/pubmed/34124484 http://dx.doi.org/10.1021/acsomega.1c01737 |
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author | Drevinskas, Tomas Maruška, Audrius Bimbiraitė-Survilienė, Kristina Du̅da, Gediminas Stankevičius, Mantas Tiso, Nicola Mickienė, Ru̅ta Pedišius, Vilmantas Levišauskas, Donatas Kaškonienė, Vilma Ragažinskienė, Ona Grigiškis, Saulius Donati, Enrica Zacchini, Massimo |
author_facet | Drevinskas, Tomas Maruška, Audrius Bimbiraitė-Survilienė, Kristina Du̅da, Gediminas Stankevičius, Mantas Tiso, Nicola Mickienė, Ru̅ta Pedišius, Vilmantas Levišauskas, Donatas Kaškonienė, Vilma Ragažinskienė, Ona Grigiškis, Saulius Donati, Enrica Zacchini, Massimo |
author_sort | Drevinskas, Tomas |
collection | PubMed |
description | [Image: see text] Unknown extraction recovery from solid matrix samples leads to meaningless chemical analysis results. It cannot always be determined, and it depends on the complexity of the matrix and properties of the extracted substances. This paper combines a mathematical model with the machine learning method—neural networks that predict liquid extraction recovery from solid matrices. The prediction of the three-stage extraction recovery of polycyclic aromatic hydrocarbons from a wooden railway sleeper matrix is demonstrated. Calculation of the extraction recovery requires the extract’s volume to be measured and the polycyclic aromatic hydrocarbons’ concentration to be determined for each stage. These data are used to calculate the input values for a neural network model. Lowest mean-squared error (0.014) and smallest retraining relative standard deviation (20.7%) were achieved with the neural network setup 6:5:5:4:1 (six inputs, three hidden layers with five, five, and four neurons in a layer, and one output). To train such a neural network, it took less than 8000 steps—less than a second––using an average-performance laptop. The relative standard deviation of the extraction recovery predictions ranged between 1.13 and 5.15%. The three-stage recovery of the extracted dry sample showed 104% of three different polycyclic aromatic hydrocarbons. The extracted wet sample recovery was 71, 98, and 55% for phenanthrene, anthracene, and pyrene, respectively. This method is applicable in the environmental, food processing, pharmaceutical, biochemical, biotechnology, and space research areas where extraction should be performed autonomously without human interference. |
format | Online Article Text |
id | pubmed-8190882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-81908822021-06-11 Mathematical Model Coupled to Neural Networks Calculates the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic Matrix Drevinskas, Tomas Maruška, Audrius Bimbiraitė-Survilienė, Kristina Du̅da, Gediminas Stankevičius, Mantas Tiso, Nicola Mickienė, Ru̅ta Pedišius, Vilmantas Levišauskas, Donatas Kaškonienė, Vilma Ragažinskienė, Ona Grigiškis, Saulius Donati, Enrica Zacchini, Massimo ACS Omega [Image: see text] Unknown extraction recovery from solid matrix samples leads to meaningless chemical analysis results. It cannot always be determined, and it depends on the complexity of the matrix and properties of the extracted substances. This paper combines a mathematical model with the machine learning method—neural networks that predict liquid extraction recovery from solid matrices. The prediction of the three-stage extraction recovery of polycyclic aromatic hydrocarbons from a wooden railway sleeper matrix is demonstrated. Calculation of the extraction recovery requires the extract’s volume to be measured and the polycyclic aromatic hydrocarbons’ concentration to be determined for each stage. These data are used to calculate the input values for a neural network model. Lowest mean-squared error (0.014) and smallest retraining relative standard deviation (20.7%) were achieved with the neural network setup 6:5:5:4:1 (six inputs, three hidden layers with five, five, and four neurons in a layer, and one output). To train such a neural network, it took less than 8000 steps—less than a second––using an average-performance laptop. The relative standard deviation of the extraction recovery predictions ranged between 1.13 and 5.15%. The three-stage recovery of the extracted dry sample showed 104% of three different polycyclic aromatic hydrocarbons. The extracted wet sample recovery was 71, 98, and 55% for phenanthrene, anthracene, and pyrene, respectively. This method is applicable in the environmental, food processing, pharmaceutical, biochemical, biotechnology, and space research areas where extraction should be performed autonomously without human interference. American Chemical Society 2021-05-21 /pmc/articles/PMC8190882/ /pubmed/34124484 http://dx.doi.org/10.1021/acsomega.1c01737 Text en © 2021 The Authors. Published by American Chemical Society Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Drevinskas, Tomas Maruška, Audrius Bimbiraitė-Survilienė, Kristina Du̅da, Gediminas Stankevičius, Mantas Tiso, Nicola Mickienė, Ru̅ta Pedišius, Vilmantas Levišauskas, Donatas Kaškonienė, Vilma Ragažinskienė, Ona Grigiškis, Saulius Donati, Enrica Zacchini, Massimo Mathematical Model Coupled to Neural Networks Calculates the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic Matrix |
title | Mathematical Model Coupled to Neural Networks Calculates
the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic
Matrix |
title_full | Mathematical Model Coupled to Neural Networks Calculates
the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic
Matrix |
title_fullStr | Mathematical Model Coupled to Neural Networks Calculates
the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic
Matrix |
title_full_unstemmed | Mathematical Model Coupled to Neural Networks Calculates
the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic
Matrix |
title_short | Mathematical Model Coupled to Neural Networks Calculates
the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic
Matrix |
title_sort | mathematical model coupled to neural networks calculates
the extraction recovery of polycyclic aromatic hydrocarbons in problematic
matrix |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190882/ https://www.ncbi.nlm.nih.gov/pubmed/34124484 http://dx.doi.org/10.1021/acsomega.1c01737 |
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