The terroir of Brazilian Coffea canephora: Characterization of the chemical composition.

FTIR spectroscopy and multivariate analysis were used in the chemical study of the terroirs of Coffea canephora. Conilon coffees from Espírito Santo and Amazon robusta from Matas of Rondônia, were separated by PCA, with lipids and caffeine being the markers responsible for the separation. Coffees from Bahia, Minas Gerais, and São Paulo did not exhibit separation, indicating that the botanical variety had a greater effect on the terroir than geographic origin. Thus, the genetic factor was investigated considering the conilon and robusta botanical varieties. This last group was composed of hybrid robusta and apoatã. The DD-SIMCA favored the identification of the genetic predominance of the samples. PLS-DA had a high classification performance regarding the conilon, hybrid robusta, and apoatã genetic nature. Lipids, caffeine, chlorogenic acids, quinic acid, trigonelline, proteins, amino acids, and carbohydrates were identified as chemical markers that discriminated the genetic groups.

Discrimination of Robusta Amazônico coffee farmed by indigenous and non-indigenous people in Amazon: comparing benchtop and portable NIR using ComDim and duplex.

Robusta Amazônico is the name given to the Amazonian coffee that has been becoming popular and has recently been registered as a geographical indication in Brazil. It is produced by indigenous and non-indigenous coffee producers in regions that are geographically very close to one another. There is a need to authenticate whether coffee is truly produced by indigenous people and near-infrared (NIR) spectroscopy is an excellent technique for this. To meet the substantial trend towards NIR spectroscopy miniaturization, this work compared benchtop and portable NIR instruments to discriminate Robusta Amazônico samples using partial least squares discriminant analysis (PLS-DA). To ensure the results to be fairly comparable and, at the same time, to guarantee representative selection of both training and test set for the discriminant analysis, a sample selection strategy based on coupling ComDim multi-block analysis and the duplex algorithm was applied. Different pre-processing techniques were tested to create multiple matrices to be used in ComDim, as well as to build the discriminant models. The best PLS-DA model for benchtop NIR provided an accuracy of 96% for the test samples, while for the portable NIR the correct classification rate was 92%. It was demonstrated that portable NIR provides similar results to benchtop NIR for coffee origin classification by performing an unbiased sample selection strategy.