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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
28/09/2021 |
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Data da última atualização: |
10/06/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CHRISTINELLI, W. A.; SHIMIZU, F. M.; FACURE, M. H. M.; CERRI, R.; OLIVEIRA JUNIOR, O. N.; CORREA, D. S.; MATTOSO, L. H. C. |
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Afiliação: |
DANIEL SOUZA CORREA, CNPDIA; LUIZ HENRIQUE CAPPARELLI MATTOSO, CNPDIA. |
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Título: |
Two-dimensional MoS2-based impedimetric electronic tongue for the discrimination of endocrine disrupting chemicals using machine learning. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Sensors and Actuators: B. Chemical, v. 336, 129696, 2021. |
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Páginas: |
1 - 11 |
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ISSN: |
0925-4005 |
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DOI: |
https://doi.org/10.1016/j.snb.2021.129696 |
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Idioma: |
Inglês |
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Conteúdo: |
In this paper, we report on machine learning to analyze the capacitance spectra obtained with an electronic tongue (e-tongue) and discriminate three endocrine-disrupting chemicals (EDC): bisphenol A, estrone, and 17- β-estradiol, and their mixtures. The e-tongue comprised seven sensing units made with interdigitated gold electrodes coated with layer-by-layer films of poly(o-methoxy aniline), poly(3-thiophene acetic acid), and molybdenum disulfide (MoS2). The Multilayer Perceptron (MLP), Random Forest, and Extreme Gradient Boosting (XGBoost) models were applied for multi-target regression to predict the concentration of individual contaminants and their mixtures. These machine learning models were evaluated according to the root mean square error (RMSE) values. The best performance was achieved with XGBoost for which RMSE ranged from 0.19 to 3.37 for individual contaminants, from 0.12 to 0.25 for the mixtures, and from 0.34 to 3.46 for the entire dataset. The high performance was only possible with a multi-target regression strategy, including a feature selection procedure. In the latter, the data were plotted with the parallel coordinate technique, and the silhouette coefficient was calculated, which is a quantitative measure of the ability to distinguish similar samples in a dataset. The usefulness of the machine learning methods is demonstrated by noting that the data from mixtures of EDCs could not be distinguished using multidimensional projections. Also significant is that this combination of machine learning and information visualization methodology is entirely generic; it may be applied to analyze data from etongues and other sensing and biosensing devices in prediction tasks as demanding as in the discrimination of mixtures of EDCs at concentrations below nmol L− 1 . MenosIn this paper, we report on machine learning to analyze the capacitance spectra obtained with an electronic tongue (e-tongue) and discriminate three endocrine-disrupting chemicals (EDC): bisphenol A, estrone, and 17- β-estradiol, and their mixtures. The e-tongue comprised seven sensing units made with interdigitated gold electrodes coated with layer-by-layer films of poly(o-methoxy aniline), poly(3-thiophene acetic acid), and molybdenum disulfide (MoS2). The Multilayer Perceptron (MLP), Random Forest, and Extreme Gradient Boosting (XGBoost) models were applied for multi-target regression to predict the concentration of individual contaminants and their mixtures. These machine learning models were evaluated according to the root mean square error (RMSE) values. The best performance was achieved with XGBoost for which RMSE ranged from 0.19 to 3.37 for individual contaminants, from 0.12 to 0.25 for the mixtures, and from 0.34 to 3.46 for the entire dataset. The high performance was only possible with a multi-target regression strategy, including a feature selection procedure. In the latter, the data were plotted with the parallel coordinate technique, and the silhouette coefficient was calculated, which is a quantitative measure of the ability to distinguish similar samples in a dataset. The usefulness of the machine learning methods is demonstrated by noting that the data from mixtures of EDCs could not be distinguished using multidimensional projections. Also significant is t... Mostrar Tudo |
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Palavras-Chave: |
Information visualization; Machine learning; XGBoost. |
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Categoria do assunto: |
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Marc: |
LEADER 02660naa a2200265 a 4500
001 2134828
005 2022-06-10
008 2021 bl uuuu u00u1 u #d
022 $a0925-4005
024 7 $ahttps://doi.org/10.1016/j.snb.2021.129696$2DOI
100 1 $aCHRISTINELLI, W. A.
245 $aTwo-dimensional MoS2-based impedimetric electronic tongue for the discrimination of endocrine disrupting chemicals using machine learning.$h[electronic resource]
260 $c2021
300 $a1 - 11
520 $aIn this paper, we report on machine learning to analyze the capacitance spectra obtained with an electronic tongue (e-tongue) and discriminate three endocrine-disrupting chemicals (EDC): bisphenol A, estrone, and 17- β-estradiol, and their mixtures. The e-tongue comprised seven sensing units made with interdigitated gold electrodes coated with layer-by-layer films of poly(o-methoxy aniline), poly(3-thiophene acetic acid), and molybdenum disulfide (MoS2). The Multilayer Perceptron (MLP), Random Forest, and Extreme Gradient Boosting (XGBoost) models were applied for multi-target regression to predict the concentration of individual contaminants and their mixtures. These machine learning models were evaluated according to the root mean square error (RMSE) values. The best performance was achieved with XGBoost for which RMSE ranged from 0.19 to 3.37 for individual contaminants, from 0.12 to 0.25 for the mixtures, and from 0.34 to 3.46 for the entire dataset. The high performance was only possible with a multi-target regression strategy, including a feature selection procedure. In the latter, the data were plotted with the parallel coordinate technique, and the silhouette coefficient was calculated, which is a quantitative measure of the ability to distinguish similar samples in a dataset. The usefulness of the machine learning methods is demonstrated by noting that the data from mixtures of EDCs could not be distinguished using multidimensional projections. Also significant is that this combination of machine learning and information visualization methodology is entirely generic; it may be applied to analyze data from etongues and other sensing and biosensing devices in prediction tasks as demanding as in the discrimination of mixtures of EDCs at concentrations below nmol L− 1 .
653 $aInformation visualization
653 $aMachine learning
653 $aXGBoost
700 1 $aSHIMIZU, F. M.
700 1 $aFACURE, M. H. M.
700 1 $aCERRI, R.
700 1 $aOLIVEIRA JUNIOR, O. N.
700 1 $aCORREA, D. S.
700 1 $aMATTOSO, L. H. C.
773 $tSensors and Actuators: B. Chemical$gv. 336, 129696, 2021.
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Embrapa Instrumentação (CNPDIA) |
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| Registros recuperados : 57 | |
| 41. |  | MERCANTE, L. A.; FACURE, M. H. M.; LOCILENTO, D.; MIGLIORINI, F. L.; SANFELICE, R. C.; MATTOSO, L. H. C.; CORREA, D. S. Composite membranes based on polymeric nanofibers/graphene oxide with dye sorption capability. In: BRAZIL MRS MEETING - SBPMAT, 15, 2016, Rio de Janeiro. Proceedings... Rio de Janeiro: SBPMat, 2016. p. 388.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Instrumentação. |
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| 42. | | PAULA, K. T.; SANTOS, S. N. C.; FACURE, M. H. M.; ARAUJO, F. L.; ANDRADE, M. B.; CORREA, D. S.; MENDONÇA, C. R. Fabrication of interdigitated electrodes of graphene oxide/silica by femtosecond laser-induced forward transfer for sensing applications. Journal Applied Physics, v. 133, 053103, 2023. 1 - 11| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 43. | | PAULA, K. T.; GAÁL, G.; ALMEIDA, G. F. B.; ANDRADE, M. B.; FACURE, M. H. M.; CORREA, D. S.; RIUL JUNIOR, A.; RODRIGUES, V.; MENDONÇA, C. R. Femtosecond laser micromachining of polylactic acid/graphene composites for designing interdigitated microelectrodes for sensor applications. Optics and Laser Technology, v. 101, p. 74-79, 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 44. | | CARDOSO, R. M.; PEREIRA, T. S.; FACURE, M. H. M.; SANTOS, D. M.; MERCANTE, L. A.; MATTOSO, L. H. C.; CORREA, D. S. Current progress in plant pathogen detection enabled by nanomaterials-based (bio)sensors. Sensors and Actuators Reports, v. 4, 100068, 2022. 18 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 45. | | FUKUSHIMA, K. L.; SCAGION, V. P.; FACURE, M. H. M.; PINHEIRO, A. C. M.; CORREA, D. S.; NUMES, C. A.; OLIVEIRA, J. E. Development of an electronic tongue based on a nanocomposite for discriminating flavor enhancers and commercial salts. IEEE Sensors Journal, v. 21, n. 2, 2021. 1250-1256| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 46. | | GUEDES, M. D. V.; MARQUES, M. S.; BERLITZ, S. J.; FACURE, M. H. M.; CORREA, D. S.; STEFFENS, C.; CONTRI, R. V.; KÜLKAMP-GUERRREIRO, I. C. Lamivudine and Zidovudine-Loaded Nanostructures: Green Chemistry Preparation for Pediatric Oral Administration. Nanomaterials, v. 13, 770, 2023. 1 - 18| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 3 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 47. | | PAULA, K. T.; SANTOS, M. V.; FACURE, M. H. M.; ANDRADE, M. B.; ARAÚJO, F. L.; CORREA, D. S.; RIBEIRO, S. J. L.; MENDONÇA, K. R. Laser patterning and induced reduction of graphene oxide functionalized silk fibroin. Optical Materials, n. 99, a. 109540, 2020. 1 - 6| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 48. | | MERCANTE, L. A.; SCHNEIDER, R.; FACURE, M. H. M.; ANDRE. R. S.; TEODORO, K.; MARTINS, D.; MIGLIORINI, F.; CORREA, D. S. ELETROFIAÇÃO: PARÂMETRO E CONFIGURAÇÕES DE SISTEMA, TIPOS DE FIBRAS E MÉTODOS DE MODIFICAÇÃO. In: MERCANTE, L. A.; CORRÊA, D. S. (org.). Eletrofiação e nanofibras: fundamentos e aplicações. Ponta Grossa, PR: Atena Editora, 2023. cap. 2. 13-51| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Instrumentação. |
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| 50. | | ALVARENGA, A. D.; FACURE, M. H. M.; MONTES-SÁNCHEZ, I.; OLIVEIRA, G. O. S.; LANZA, M. R. V.; MERCANTE, L. A.; CORREA, D. S. Multifunctional and sustainable soot-modified nanofibrous membrane for adsorption, sensing and hydrogen peroxide electrogeneration. Journal of Cleaner Production, n. 422, 138697, 2023.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 51. | | ANDRÉ, R. S.; SANTOS, D. M.; MERCANTE, L. A.; FACURE, M. H. M.; CAMPANA-FILHO, S. P.; MATTOSO, L. H. C.; CORREA, D. S. Nanochitin-based composite films as a disposable ethanol sensor. Journal of Environmental Chemical Engineering, v. 8, 104163, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 53. | | SOUSA, L. P. O.; FUKUSHIMA, K. L.; SCAGION, V. P.; FACURE, M. H. M.; CORREA, D. S.; OLIVEIRA, J. E.; FERREIRA, D. D. A principal curves-based method for electronic tongue data analysis. IEEE Sensors Journal, v. 21, n. 4, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 55. | | ANDRE, R. S.; FACURE, M. H. M.; SCHNEIDER, R.; MIGLIORINI, F. L.; SANTOS, D. M. dos; MERCANTE, L. A.; CORREA, D. S. Sensing Materials: Nanofibers Produced by Electrospinning and Solution Blow Spinning. In: Narayan, R. (Ed.), Encyclopedia of Sensors and Biosensors, v. 2, 2023. 521-541| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Instrumentação. |
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| 56. | | CHRISTINELLI, W. A.; SHIMIZU, F. M.; FACURE, M. H. M.; CERRI, R.; OLIVEIRA JUNIOR, O. N.; CORREA, D. S.; MATTOSO, L. H. C. Two-dimensional MoS2-based impedimetric electronic tongue for the discrimination of endocrine disrupting chemicals using machine learning. Sensors and Actuators: B. Chemical, v. 336, 129696, 2021. 1 - 11| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 57. | | PAVINATTO, A.; MERCANTE, L. A.; FACURE, M. H. M.; PENA, R. B.; SANFELICE, R. C.; MATTOSO, L. H. C.; CORREA, D. S. Ultrasensitive biosensor based on polyvinylpyrrolidone/chitosan/reduced graphene oxide electrospun nanofibers for 17alfa - Ethinylestradiol electrochemical detection. Applied Surface Science, v. 458, p. 431-437, 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| Registros recuperados : 57 | |
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