Data/hora: 07/06/2024 10:43:26

Biblioteca(s): 	Embrapa Rondônia.	
Data corrente: 	24/04/2024	
Data da última atualização: 	30/04/2024	
Autoria: 	BAQUETA, M. R.; VALDERRAMA, P.; MANDRONE, M.; POLI, F.; COQUEIRO, A.; COSTA-SANTOS, A. C.; REBELLATO. A. A.; LUZ, G. M.; ROCHA, R. B.; PALLONE, J. A. L.; MARINI, F.	
Afiliação: 	MICHEL ROCHA BAQUETA, UNIVERSIDADE DE CAMPINAS; PATRÍCIA VALDERRAMA UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ, UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ; MANUELA MANDRONE, UNIVERSITY OF BOLOGNA; FERRUCCIO POLI UNIVERSITY OF BOLOGNA, UNIVERSITY OF BOLOGNA; ALINE COQUEIRO, UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ; AUGUSTO CESAR COSTA-SANTOS, UNIVERSIDADE ESTADUAL DE CAMPINAS; ANA PAULA REBELLATO, UNIVERSIDADE ESTADUAL DE CAMPINAS; GISELE MARCONDES LUZ, UNIVERSIDADE ESTADUAL DE CAMPINAS; RODRIGO BARROS ROCHA, CPAF-RO; JULIANA AZEVEDO LIMA PALLONE, UNIVERSIDADE ESTADUAL DE CAMPINAS; FEDERICO MARINI, UNIVERSITY OF ROME.	
Título: 	H NMR, FAAS, portable NIR, benchtop NIR, and ATR-FTIR-MIR spectroscopies for characterizing and discriminating new Brazilian Canephora coffees in a multi-block analysis perspective.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	Chemometrics and Intelligent Laboratory Systems, v. 240, 2023.	
Idioma: 	Inglês	
Conteúdo: 	Different analytical techniques, mixing single and multi-block chemometric analyses in supervised and unsu- pervised approaches, and the selection of variables in the coffee discrimination domain have been reported. Molecular and atomic spectroscopic techniques (1H NMR, portable NIR, benchtop NIR, ATR-FTIR-MIR, and FAAS) were used to characterize and discriminate Brazilian Canephora coffees of specific producers, including two with geographical indication, and also to differentiate them from the Arabica. The sample set comprised 100 Canephora samples of different geographical origins in Brazil (Conilon from Espírito Santo, Amazonian Robusta from indigenous and non-indigenous producers of Rondonia, and Conilon from Bahia) and Arabica coffee (25 samples). ComDim exploratory multi-block analysis was first used to evaluate the contributions of all the different data blocks that characterized the samples and determine the calibration and validation sets. Multi- block discrimination by the SO-PLS-LDA was then used to validate the feasibility of discrimination, and to identify the relevant analytical techniques. The discrimination based on all the blocks presented 100% correct discrimination on training, cross-validation, and test sets, and suggested that only a single block (benchtop NIR) is needed to achieve a perfect discrimination. PLS-DA was then applied to the data from portable NIR to evaluate whether comparable performances could be achieved; all test samples were correctly discriminated with the exception of two Robusta Amazonico from non-indigenous producers and two Conilon from Espírito Santo, indicating that very accurate results can be obtained also using a portable instrument. Finally, the use of CovSel- DA allowed the discrimination to be optimized on the most cost-effective analytical technique (portable NIR). CovSel-LDA models selected the best variables for benchtop and portable NIR, impacting positively portable NIR performance and suggesting that portable NIR could bring comparable or at least only slightly worse perfor- mance than benchtop NIR for discrimination.	
Palavras-Chave: 	Coffee; Conilon; Data fusio; Fine Amazonian Robusta coffee; Multi-block.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1163879/1/2-Baqueta-Chemolab-2023-AINFO.pdf	
Registro original: 	Embrapa Rondônia (CPAF-RO)	
Exemplares: 	CPAF-RO - (19075-1) - UPC - AP - DD -  -  -  - DIS