Brazilian Canephora Coffee Evaluation Using Nir Spectroscopy and Discriminant Chemometric Techniques

32 Pages Posted: 17 Sep 2022

See all articles by Michel Baqueta

Michel Baqueta

Sapienza University of Rome - Department of Chemistry

Enrique Alves

Brazilian Agricultural Research Corporation - Embrapa

Patricia Valderrama

Federal Technological University of Paraná

Juliana Azevedo Lima Pallone

Universidade Estadual de Campinas (UNICAMP)

Abstract

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. Principal component analysis distinguished the main Canephora-producing states of Brazil, its botanical varieties, low-quality Canephora from specialty Canephora, specialty Canephora from specialty Arabica, and Canephora with geographical indication (GI) from those without this indication. Also, Canephora coffee cultivars from Western Brazilian Amazon were distinguished in the exploratory analysis. Three multi-class PLS-DA (traditional, hard, and soft versions) were compared for the discrimination of the 5 main classes under study: Robustas Amazônicos from traditional (1) and indigenous (2) producers of Rondônia, Conilon from Espírito Santo (3), Conilon from Bahia (4), and specialty Arabica coffees (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 absorption bands in coffee classifications. The proposed method is objective, simple, fast, and could be easily and reproducibly used in the routine analysis of coffee to verify claims of its identity, variety, and origin.

Keywords: Conilon, Geographical origin, Multivariate classification, NIR spectroscopy, PLS-DA, Robusta

Suggested Citation

Baqueta, Michel and Alves, Enrique and Valderrama, Patricia and Azevedo Lima Pallone, Juliana, Brazilian Canephora Coffee Evaluation Using Nir Spectroscopy and Discriminant Chemometric Techniques. Available at SSRN: https://ssrn.com/abstract=4222082 or http://dx.doi.org/10.2139/ssrn.4222082

Michel Baqueta

Sapienza University of Rome - Department of Chemistry ( email )

Enrique Alves

Brazilian Agricultural Research Corporation - Embrapa ( email )

Patricia Valderrama

Federal Technological University of Paraná ( email )

Juliana Azevedo Lima Pallone (Contact Author)

Universidade Estadual de Campinas (UNICAMP) ( email )

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