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Título: | Brazilian Canephora coffee evaluation using NIR spectroscopy and discriminant chemometric techniques. |
Autor: | BAQUETA, M. R.![]() ![]() ALVES, E. A. ![]() ![]() VALDERRAMA, P. ![]() ![]() PALLONE, J. A. L. ![]() ![]() |
Afiliación: | MICHEL ROCHA BAQUETA, UNICAMP; ENRIQUE ANASTACIO ALVES, CPAF-RO; PATRÍCIA VALDERRAMA, UTFPR; JULIANA AZEVEDO LIMA PALLONE, UNICAMP. |
Año: | 2023 |
Referencia: | Journal of Food Composition and Analysis, v. 116, 2023. |
Descripción: | High-quality Brazilian Canephora coffees are rising to the level of specialty coffees in the face of a new industry perception. In this framework, spectra from 527 coffees were analyzed in the near-infrared (NIR) region. Prin- cipal component analysis distinguished Brazilian Canephora producing states, botanical varieties, low and high- quality Canephora, Canephora and Arabica, and Canephora with geographical indication (GI) from those without GI. Also, Canephora coffee cultivars from Western Brazilian Amazon were distinguished. Three multi-class PLS- DA (traditional, hard, and soft versions) were compared to discriminate 5 classes: Robusta Amaz?onico from traditional (1) and indigenous (2) producers of Rond?onia, Conilon from Espírito Santo (3), Conilon from Bahia (4), and specialty Arabica (5). Binary PLS-DA discriminated GI Canephora and non-GI Canephora with 100% sensitivity and specificity. Carbohydrates, chlorogenic acids, lipids, caffeine, and proteins were dominant ab- sorption bands in coffee classifications. The proposed method is objective, simple, fast, and could be used in the routine analysis of coffee to verify claims of identity, variety, and origin. |
Palabras clave: | Amazonian robusta Conilon Geographical origin Multivariate classification NIR spectroscopy PLS-DA |
Tipo de Material: | Artigo de periódico |
Acceso: | openAccess |
Aparece en las colecciones: | Artigo em periódico indexado (CPAF-RO)![]() ![]() |
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Fichero | Descripción | Tamaño | Formato | |
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2023-01-Brazilian-Canephora-coffee-evaluation-using-NIR-spectroscopy-and-discriminant-chemometric-techniques-J.-Food-Composition-and-Analysis-116.pdf | 4.79 MB | Adobe PDF | ![]() Visualizar/Abrir |