Green chemistry method based on PARAFAC EEM data modeling for Benzo[a]pyrene quantitation in distilled spirit

Abstract

Benzo[a]pyrene (BaP) is often used as a marker of polycyclic aromatic hydrocarbons (PAHs) in beverages. This marker is often quantified by chromatographic methods, which require sample preparations involving the use of reagents, solvents, extraction, pre-concentration, and/or cleanup steps. In this study, a new method for quantification of BaP in cachaças (liquors) that does not use any sample preparation was developed. Interferents in cachaças were overcome using excitation-emission matrices data modeling with parallel factor analysis (PARAFAC). The recoveries ranged from 93.20 to 101.13%, and the relative error of prediction and limit of detection were, respectively, estimated at 2.66% and 2.88 ng mL-1. The proposed method is inexpensive and less time consuming than other approaches described in the literature, uses no reagents, solvents or extraction, has no pre-concentration or cleanup steps, contributing to green analytical chemistry.