DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Scientia Generalis |
Texto Completo: | https://scientiageneralis.com.br/index.php/SG/article/view/161 |
Resumo: | The aim of this study was to develop a chemometric model based on the application of the GA-LDA computational algorithm in the analysis of mid-infrared data to detect dengue virus from a set of spectra of clinical samples. In this approach, the GA selects the most important variables in the set of spectra, while the LDA. works on the discrimination between classes, based on these selected variables. The study investigated the advantages of this approach compared to standard techniques, since there is a need for a technique that allows for a quick response with low cost and good specificity. The data set used in this study was cordially made available by the Research Group on Biological Chemistry and Chemometrics at UFRN. The model was built using the “MATLAB” software, where computational analysis and calculations of quality measures (figures of merit) were performed. It was found that the technique achieved a good prediction, with 100% sensitivity and specificity, of the correct class of all samples used in the test set. Furthermore, it has been shown not to need the use of reagents or kits for the analysis, in addition to providing faster results compared to the standard techniques used. From these results, it was possible to verify the importance of applying a computational tool in the field of virology, since a great potential for this application was observed. However, further studies are needed. |
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DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODELDETECCIÓN DEL VIRUS DEL DENGUE MEDIANTE ESPECTROSCOPÍA INFRARROJA MEDIA EN CONJUNCIÓN CON ALGORITMO GENÉTICO Y ANÁLISIS DISCRIMINANTE LINEAL: un modelo quimiométricoDETECÇÃO DO VÍRUS DA DENGUE POR ESPECTROSCOPIA NO INFRAVERMELHO MÉDIO EM CONJUNTO COM ALGORITMO GENÉTICO E ANÁLISE DISCRIMINANTE LINEARDengue detectionComputational algorithmsPattern RecognitionGA-LDADetecção da DengueAlgoritmos computacionaisReconhecimento de padrõesGA-LDAThe aim of this study was to develop a chemometric model based on the application of the GA-LDA computational algorithm in the analysis of mid-infrared data to detect dengue virus from a set of spectra of clinical samples. In this approach, the GA selects the most important variables in the set of spectra, while the LDA. works on the discrimination between classes, based on these selected variables. The study investigated the advantages of this approach compared to standard techniques, since there is a need for a technique that allows for a quick response with low cost and good specificity. The data set used in this study was cordially made available by the Research Group on Biological Chemistry and Chemometrics at UFRN. The model was built using the “MATLAB” software, where computational analysis and calculations of quality measures (figures of merit) were performed. It was found that the technique achieved a good prediction, with 100% sensitivity and specificity, of the correct class of all samples used in the test set. Furthermore, it has been shown not to need the use of reagents or kits for the analysis, in addition to providing faster results compared to the standard techniques used. From these results, it was possible to verify the importance of applying a computational tool in the field of virology, since a great potential for this application was observed. However, further studies are needed.El objetivo de este estudio fue desarrollar un modelo quimiométrico basado en la aplicación del algoritmo computacional GA-LDA en el análisis de datos de infrarrojo medio para detectar el virus del dengue a partir de un conjunto de espectros de muestras clínicas. En este enfoque, el GA selecciona las variables más importantes en el conjunto de espectros, mientras que el LDA trabaja en la discriminación entre clases, en función de estas variables seleccionadas. El estudio investigó las ventajas de este enfoque en comparación con las técnicas estándar, ya que existe la necesidad de una técnica que permita una respuesta rápida con bajo costo y buena especificidad. El conjunto de datos utilizado en este estudio fue puesto a disposición cordialmente por el Grupo de Investigación en Química Biológica y Quimiometría de la UFRN. El modelo se construyó utilizando el software “MATLAB”, donde se realizaron análisis computacionales y cálculos de medidas de calidad (cifras de mérito). Se encontró que la técnica logró una buena predicción, con 100% de sensibilidad y especificidad, de la clase correcta de todas las muestras utilizadas en el conjunto de prueba. Además, se ha demostrado que no necesita el uso de reactivos o kits para el análisis, además de proporcionar resultados más rápidos en comparación con las técnicas estándar utilizadas. A partir de estos resultados se pudo constatar la importancia de aplicar una herramienta computacional en el campo de la virología, ya que se observó un gran potencial para esta aplicación. Sin embargo, se necesitan más estudios.O objetivo deste estudo foi desenvolver um modelo quimiométrico baseado na aplicação do algoritmo computacional GA-LDA na análise de dados de infravermelho médio para detectar o vírus da dengue a partir de um conjunto de espectros de amostras clínicas. Nessa abordagem, o GA seleciona as variáveis mais importantes no conjunto de espectros, enquanto o LDA trabalha na discriminação entre as classes, baseando-se nestas variáveis selecionadas. O estudo investigou as vantagens desta abordagem frente às técnicas padrão, uma vez que que existe a necessidade da existência de uma técnica que permita unir uma resposta rápida com custo baixo e boa especificidade. O conjunto de dados utilizados neste estudo foi disponibilizado cordialmente pelo Grupo de Pesquisa em Química Biológica e Quimiometria da UFRN. O modelo foi construído com o uso do software “MATLAB”, onde foi feita a análise computacional e cálculos de medidas de qualidade (figuras de mérito). Constatou-se que a técnica conseguiu uma boa previsão, com 100% de sensibilidade e especificidade, da classe correta de todas as amostras utilizadas no conjunto teste. Além disso, demonstrou-se não precisar do uso de reagentes nem de kits para a análise, além de fornecer resultados mais rápidos frente às técnicas padrão utilizadas. A partir destes resultados foi possível verificar a importância da aplicação de uma ferramenta computacional no campo da virologia, uma vez que foi observado um grande potencial para esta aplicação. Contudo, são necessários estudos mais aprofundados.Scientia GeneralisScientia GeneralisScientia Generalis2021-07-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://scientiageneralis.com.br/index.php/SG/article/view/161Scientia Generalis; v. 2 n. 1 (2021); 143-151Scientia Generalis; Vol. 2 No. 1 (2021); 143-151Scientia Generalis; Vol. 2 Núm. 1 (2021); 143-1512675-2999reponame:Scientia Generalisinstname:Publicação independenteinstacron:INDEPporhttps://scientiageneralis.com.br/index.php/SG/article/view/161/124Copyright (c) 2021 Gigliane Joice Santos da Silva , Marcos Claudino Batista dos Santos Filho , Paulo Venicius Messias dos Santos, Ricardo Fernandes dos Santos, Marfran Claudino Domingos dos Santoshttps://creativecommons.org/licenses/by-sa/4.0info:eu-repo/semantics/openAccessSilva , Gigliane Joice Santos da Filho , Marcos Claudino Batista dos Santos Santos, Paulo Venicius Messias dos Santos, Ricardo Fernandes dos Santos, Marfran Claudino Domingos dos 2023-08-01T03:32:04Zoai:ojs2.scientiageneralis.com.br:article/161Revistahttps://scientiageneralis.com.br/index.php/SGPRIhttps://scientiageneralis.com.br/index.php/SG/oaieditor@scientiageneralis.com.br2675-29992675-2999opendoar:2023-08-01T03:32:04Scientia Generalis - Publicação independentefalse |
dc.title.none.fl_str_mv |
DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL DETECCIÓN DEL VIRUS DEL DENGUE MEDIANTE ESPECTROSCOPÍA INFRARROJA MEDIA EN CONJUNCIÓN CON ALGORITMO GENÉTICO Y ANÁLISIS DISCRIMINANTE LINEAL: un modelo quimiométrico DETECÇÃO DO VÍRUS DA DENGUE POR ESPECTROSCOPIA NO INFRAVERMELHO MÉDIO EM CONJUNTO COM ALGORITMO GENÉTICO E ANÁLISE DISCRIMINANTE LINEAR |
title |
DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL |
spellingShingle |
DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL Silva , Gigliane Joice Santos da Dengue detection Computational algorithms Pattern Recognition GA-LDA Detecção da Dengue Algoritmos computacionais Reconhecimento de padrões GA-LDA |
title_short |
DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL |
title_full |
DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL |
title_fullStr |
DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL |
title_full_unstemmed |
DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL |
title_sort |
DENGUE VIRUS DETECTION BY MIDDLE INFRARED SPECTROSCOPY IN CONJUNCTION WITH GENETIC ALGORITHM AND LINEAR DISCRIMINANT ANALYSIS: A CHEMOMETRIC MODEL |
author |
Silva , Gigliane Joice Santos da |
author_facet |
Silva , Gigliane Joice Santos da Filho , Marcos Claudino Batista dos Santos Santos, Paulo Venicius Messias dos Santos, Ricardo Fernandes dos Santos, Marfran Claudino Domingos dos |
author_role |
author |
author2 |
Filho , Marcos Claudino Batista dos Santos Santos, Paulo Venicius Messias dos Santos, Ricardo Fernandes dos Santos, Marfran Claudino Domingos dos |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Silva , Gigliane Joice Santos da Filho , Marcos Claudino Batista dos Santos Santos, Paulo Venicius Messias dos Santos, Ricardo Fernandes dos Santos, Marfran Claudino Domingos dos |
dc.subject.por.fl_str_mv |
Dengue detection Computational algorithms Pattern Recognition GA-LDA Detecção da Dengue Algoritmos computacionais Reconhecimento de padrões GA-LDA |
topic |
Dengue detection Computational algorithms Pattern Recognition GA-LDA Detecção da Dengue Algoritmos computacionais Reconhecimento de padrões GA-LDA |
description |
The aim of this study was to develop a chemometric model based on the application of the GA-LDA computational algorithm in the analysis of mid-infrared data to detect dengue virus from a set of spectra of clinical samples. In this approach, the GA selects the most important variables in the set of spectra, while the LDA. works on the discrimination between classes, based on these selected variables. The study investigated the advantages of this approach compared to standard techniques, since there is a need for a technique that allows for a quick response with low cost and good specificity. The data set used in this study was cordially made available by the Research Group on Biological Chemistry and Chemometrics at UFRN. The model was built using the “MATLAB” software, where computational analysis and calculations of quality measures (figures of merit) were performed. It was found that the technique achieved a good prediction, with 100% sensitivity and specificity, of the correct class of all samples used in the test set. Furthermore, it has been shown not to need the use of reagents or kits for the analysis, in addition to providing faster results compared to the standard techniques used. From these results, it was possible to verify the importance of applying a computational tool in the field of virology, since a great potential for this application was observed. However, further studies are needed. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-02 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://scientiageneralis.com.br/index.php/SG/article/view/161 |
url |
https://scientiageneralis.com.br/index.php/SG/article/view/161 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://scientiageneralis.com.br/index.php/SG/article/view/161/124 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by-sa/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-sa/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Scientia Generalis Scientia Generalis Scientia Generalis |
publisher.none.fl_str_mv |
Scientia Generalis Scientia Generalis Scientia Generalis |
dc.source.none.fl_str_mv |
Scientia Generalis; v. 2 n. 1 (2021); 143-151 Scientia Generalis; Vol. 2 No. 1 (2021); 143-151 Scientia Generalis; Vol. 2 Núm. 1 (2021); 143-151 2675-2999 reponame:Scientia Generalis instname:Publicação independente instacron:INDEP |
instname_str |
Publicação independente |
instacron_str |
INDEP |
institution |
INDEP |
reponame_str |
Scientia Generalis |
collection |
Scientia Generalis |
repository.name.fl_str_mv |
Scientia Generalis - Publicação independente |
repository.mail.fl_str_mv |
editor@scientiageneralis.com.br |
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