Discrimination of Brazilian green canephora coffee beans by ultraviolet–visible spectroscopy as a non-target analysis: A tool for recognizing geographical indications

Abstract

Specialty green coffee beans have a higher commercial value and some of them have recently been classified in Brazil based on the indication of provenance and denomination of origin. In this context, the classification of the type of coffee bean is still a challenge, using traditional analytical techniques. Thus, alternative analytical techniques, such as ultraviolet–visible spectroscopy (UV–Vis), as non-target analysis can be applied as a quick and reliable method of coffee classification using data science, such as chemometric tools. In the present study, UV–Vis were evaluated as a new strategy for discrimination of green beans of Brazilian specialty canephora coffees with recognized geographical indications (Robusta Amazônico and Conilon from state of Espírito Santo), for the first time. Spectra obtained from the aqueous extract of 222 samples. The Principal Component Analysis (PCA) was performed and subsequently Partial Least Squares with Discriminant Analysis (PLS-DA) model developed. The PCA indicated tendency to group the samples in their respective classes, pointing to the similarities in the spectra of samples of the same origin. The PLS-DA model obtained showed figures of merit values starting at 89.3% in the test set. The VIP scores showed that the variables associated with chlorogenic acids, caffeine and chlorophyll are the most important for differentiating the studied coffees. The results obtained showed that UV–Vis fingerprint − non-targeted analysis associated with PLS-DA is appropriate for the discrimination of green beans of Brazilian specialty coffee from different origins, in a simple way, using common equipment in several laboratories.