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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
06/06/2023 |
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Data da última atualização: |
06/06/2023 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
LAMEIRA, O. A.; CORDEIRO, I. M. C. C.; OLIVEIRA, E. C. P. de; RODRIGUES, S. de M.; CAMPELO, M. F.; NEVES, R. L. P.; FERREIRA, T. A. A.; SILVA, A. C. B. da; GUEDES, A. S. |
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Afiliação: |
OSMAR ALVES LAMEIRA, CPATU; IRACEMA MARIA CASTRO COIMBRA CORDEIRO, UNIVERSIDADE FEDERAL RURAL DA AMAZÔNIA; ELAINE CRISTINA PACHECO DE OLIVEIRA, UNIVERSIDADE FEDERAL DO OESTE DO PARÁ; SIMONE DE MIRANDA RODRIGUES, CPATU; MEICIANE FERREIRA CAMPELO, UNIVERSIDADE FEDERAL RURAL DA AMAZÔNIA; RAPHAEL LOBATO PRADO NEVES, UNIVERSIDADE FEDERAL RURAL DA AMAZÔNIA; TASSIA ALANA ALVES FERREIRA, UNIVERSIDADE FEDERAL DO PARÁ; ANA CAROLINE BATISTA DA SILVA, UNIVERSIDADE FEDERAL RURAL DA AMAZÔNIA; ALEX SANTOS GUEDES, UNIVERSIDADE FEDERAL DO PARÁ. |
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Título: |
Phytochemistry of Medicinal Plants of the Amazon. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: METHODOLOGY focused on the area of interdisciplinarity. São José dos Pinhais: Seven Editora, 2022. |
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Páginas: |
Cap. 60, p. 54-65. |
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DOI: |
https://doi.org/10.56238/methofocusinterv1-060 |
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Idioma: |
Inglês |
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Conteúdo: |
The results of the phytochemical research involving medicinal plants of the Amazon traditionally used by the population are described in this chapter as Pilocarpus microphyllus, Copaifera sp, Carapa guianensis, and Bauhinia sp, as well as qualitative and quantitative analysis of the phenolic content of Connarus perrottetti, Cecropia obtusa, Cecropia palmata, and Mansoa alliacea. A method was developed and validated to characterize the presence of phenolic compounds in extracts of medicinal plants from the Amazon for different years of sampling. A complete evaluation of the different possible causes of ion suppression in the analysis of extracts of medicinal plants by UHPLC-MS / MS using electrospray ionization is shown. The mineral content of dry matter composition of Cecropia palmata and Cecropia obtusa at different seasons evaluated the nutritional properties of the tea produced by infusion of the leaves of these two species of Cecropia. In the phytochemical analysis carried out in some species, the presence of sugar, protein, amino acid reducers, foamy saponin, and tannins was revealed. All of these results are part of this chapter. |
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Thesagro: |
Carapa Guianensis; Pilocarpus Microphyllus; Planta Medicinal. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1154283/1/Phytochemistry-of-Medicinal-Plants.pdf
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Marc: |
LEADER 02039naa a2200277 a 4500
001 2154283
005 2023-06-06
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.56238/methofocusinterv1-060$2DOI
100 1 $aLAMEIRA, O. A.
245 $aPhytochemistry of Medicinal Plants of the Amazon.$h[electronic resource]
260 $c2022
300 $aCap. 60, p. 54-65.
520 $aThe results of the phytochemical research involving medicinal plants of the Amazon traditionally used by the population are described in this chapter as Pilocarpus microphyllus, Copaifera sp, Carapa guianensis, and Bauhinia sp, as well as qualitative and quantitative analysis of the phenolic content of Connarus perrottetti, Cecropia obtusa, Cecropia palmata, and Mansoa alliacea. A method was developed and validated to characterize the presence of phenolic compounds in extracts of medicinal plants from the Amazon for different years of sampling. A complete evaluation of the different possible causes of ion suppression in the analysis of extracts of medicinal plants by UHPLC-MS / MS using electrospray ionization is shown. The mineral content of dry matter composition of Cecropia palmata and Cecropia obtusa at different seasons evaluated the nutritional properties of the tea produced by infusion of the leaves of these two species of Cecropia. In the phytochemical analysis carried out in some species, the presence of sugar, protein, amino acid reducers, foamy saponin, and tannins was revealed. All of these results are part of this chapter.
650 $aCarapa Guianensis
650 $aPilocarpus Microphyllus
650 $aPlanta Medicinal
700 1 $aCORDEIRO, I. M. C. C.
700 1 $aOLIVEIRA, E. C. P. de
700 1 $aRODRIGUES, S. de M.
700 1 $aCAMPELO, M. F.
700 1 $aNEVES, R. L. P.
700 1 $aFERREIRA, T. A. A.
700 1 $aSILVA, A. C. B. da
700 1 $aGUEDES, A. S.
773 $tIn: METHODOLOGY focused on the area of interdisciplinarity. São José dos Pinhais: Seven Editora, 2022.
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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
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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Circulação/Nível: |
A - 1 |
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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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