Chemical data as markers of the geographical origins of sugarcane spirits

• A geographical pattern recognition of sugarcane spirits was evaluate.
• The correlation between analytical data and samples were achieved using chemometrics.
• PCA defines five clusters according the states where the samples were produced.
• Chemical similarities between SP and MG samples and between RJ and PB were observed.
• KNN correctly predicted the geographic origin of the 86% cachaças.

In an attempt to classify sugarcane spirits according to their geographic region of origin, chemical data for 24 analytes were evaluated in 50 cachaças produced using a similar procedure in selected regions of Brazil: São Paulo – SP (15), Minas Gerais – MG (11), Rio de Janeiro – RJ (11), Paraiba –PB (9), and Ceará – CE (4). Multivariate analysis was applied to the analytical results, and the predictive abilities of different classification methods were evaluated. Principal component analysis identified five groups, and chemical similarities were observed between MG and SP samples and between RJ and PB samples. CE samples presented a distinct chemical profile. Among the samples, partial linear square discriminant analysis (PLS-DA) classified 50.2% of the samples correctly, K-nearest neighbor (KNN) 86%, and soft independent modeling of class analogy (SIMCA) 56.2%. Therefore, in this proof of concept demonstration, the proposed approach based on chemical data satisfactorily predicted the cachaças’ geographic origins.