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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
27/08/2024 |
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Data da última atualização: |
27/08/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BAQUETA, M. R.; MARINI, F.; TEIXEIRA, A. L.; GOULART, B. H. F.; PILAU, E. J.; VALDERRAMA, P.; PALLONE, J. A. L. |
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Afiliação: |
MICHEL ROCHA BAQUETA, UNIVERSIDADE ESTADUAL DE CAMPINAS; FEDERICO MARINI, UNIVERSITY OF ROME; ALEXSANDRO LARA TEIXEIRA, CNPCA; BRUNO HENRIQUE FERMINO GOULART, UNIVERSIDADE ESTADUAL DE MARINGÁ; EDUARDO JORGE PILAU, UNIVERSIDADE ESTADUAL DE MARINGÁ; PATRÍCIA VALDERRAMA, UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ; JULIANA AZEVEDO LIMA PALLONE, UNIVERSIDADE ESTADUAL DE CAMPINAS. |
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Título: |
Spectroscopic and sensory characterization of Brazilian Coffea canephora terroir and botanical varieties produced in the Amazon and Espírito Santo implementing multi-block approaches. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Journal of Food Composition and Analysis, v. 133, 106442, 2024. |
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Páginas: |
12 p. |
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DOI: |
https://doi.org/10.1016/j.jfca.2024.106442 |
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Idioma: |
Inglês |
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Conteúdo: |
Specialty Brazilian Canephora coffees are produced in the Amazon by indigenous and non-indigenous people and in Espírito Santo. Their distinctive quality, origin, and varietal were verified by integrated analytical techniques to understand better their chemical and sensory aspects and protect their origin and traceability. In this context, chemometric multi-block approaches represent a holistic way to integrate the multi-source data and then extract their complementary information. The samples were analyzed by near-infrared (NIR) spectroscopy on portable and benchtop instruments, ultraviolet-visible (UV-Vis) spectroscopy, mass spectrometry, and sensory analysis. Each piece of relevant information was interpreted before being integrated through exploratory data analysis and predictive modelling by multi-block methods. Although subtle, ComDim analysis showed a tendency to separate the indigenous and non-indigenous, and Espírito Santo coffees. Pre-processing ensembles with ROSA calibration (PROSAC) discriminated the three coffees with mean correct classification rate of 91.1 % in the test, using benchtop NIR with 1st derivative, mass spectrometry water spectra with Pareto scaling, and autoscaled sensory data. Sequential and orthogonalized partial least square-linear discriminant analysis (SO-PLS-LDA) performed better than PROSAC, showing 94.2 % of recognition in the test, using benchtop NIR with standard normal variate, mass spectrometry organic spectra with Pareto scaling, and portable NIR with 2nd derivative. Integrating complementary information from different blocks also improves classification accuracy compared to analyzing individual matrices. MenosSpecialty Brazilian Canephora coffees are produced in the Amazon by indigenous and non-indigenous people and in Espírito Santo. Their distinctive quality, origin, and varietal were verified by integrated analytical techniques to understand better their chemical and sensory aspects and protect their origin and traceability. In this context, chemometric multi-block approaches represent a holistic way to integrate the multi-source data and then extract their complementary information. The samples were analyzed by near-infrared (NIR) spectroscopy on portable and benchtop instruments, ultraviolet-visible (UV-Vis) spectroscopy, mass spectrometry, and sensory analysis. Each piece of relevant information was interpreted before being integrated through exploratory data analysis and predictive modelling by multi-block methods. Although subtle, ComDim analysis showed a tendency to separate the indigenous and non-indigenous, and Espírito Santo coffees. Pre-processing ensembles with ROSA calibration (PROSAC) discriminated the three coffees with mean correct classification rate of 91.1 % in the test, using benchtop NIR with 1st derivative, mass spectrometry water spectra with Pareto scaling, and autoscaled sensory data. Sequential and orthogonalized partial least square-linear discriminant analysis (SO-PLS-LDA) performed better than PROSAC, showing 94.2 % of recognition in the test, using benchtop NIR with standard normal variate, mass spectrometry organic spectra with Pareto scaling, and... Mostrar Tudo |
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Thesagro: |
Coffea Canephora. |
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Thesaurus Nal: |
Chemometrics. |
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Categoria do assunto: |
-- |
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Marc: |
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024 7 $ahttps://doi.org/10.1016/j.jfca.2024.106442$2DOI
100 1 $aBAQUETA, M. R.
245 $aSpectroscopic and sensory characterization of Brazilian Coffea canephora terroir and botanical varieties produced in the Amazon and Espírito Santo implementing multi-block approaches.$h[electronic resource]
260 $c2024
300 $a12 p.
520 $aSpecialty Brazilian Canephora coffees are produced in the Amazon by indigenous and non-indigenous people and in Espírito Santo. Their distinctive quality, origin, and varietal were verified by integrated analytical techniques to understand better their chemical and sensory aspects and protect their origin and traceability. In this context, chemometric multi-block approaches represent a holistic way to integrate the multi-source data and then extract their complementary information. The samples were analyzed by near-infrared (NIR) spectroscopy on portable and benchtop instruments, ultraviolet-visible (UV-Vis) spectroscopy, mass spectrometry, and sensory analysis. Each piece of relevant information was interpreted before being integrated through exploratory data analysis and predictive modelling by multi-block methods. Although subtle, ComDim analysis showed a tendency to separate the indigenous and non-indigenous, and Espírito Santo coffees. Pre-processing ensembles with ROSA calibration (PROSAC) discriminated the three coffees with mean correct classification rate of 91.1 % in the test, using benchtop NIR with 1st derivative, mass spectrometry water spectra with Pareto scaling, and autoscaled sensory data. Sequential and orthogonalized partial least square-linear discriminant analysis (SO-PLS-LDA) performed better than PROSAC, showing 94.2 % of recognition in the test, using benchtop NIR with standard normal variate, mass spectrometry organic spectra with Pareto scaling, and portable NIR with 2nd derivative. Integrating complementary information from different blocks also improves classification accuracy compared to analyzing individual matrices.
650 $aChemometrics
650 $aCoffea Canephora
700 1 $aMARINI, F.
700 1 $aTEIXEIRA, A. L.
700 1 $aGOULART, B. H. F.
700 1 $aPILAU, E. J.
700 1 $aVALDERRAMA, P.
700 1 $aPALLONE, J. A. L.
773 $tJournal of Food Composition and Analysis$gv. 133, 106442, 2024.
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Registro original: |
Embrapa Café (CNPCa) |
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