Delineating unique and discriminant chemical traits in Brazilian and Italian extra-virgin olive oils by quantitative 2D-fingerprinting and pattern recognition algorithms

Abstract

Brazil is the second-largest importer, after the United States, of extra-virgin olive oils with an internal production that just reaches 0.25 % of the volume imported and consumed. Information about diagnostic chemical signatures characterizing Brazilian oils is lacking, especially as related to geographical markers and key-aroma compounds distribution. This study fills this gap; by comprehensive two-dimensional gas chromatography (GC×GC) combined with headspace solid-phase microextraction (HS-SPME) and parallel detection (MS/FID), diagnostic quali-quantitative signatures of volatiles from Brazilian oils are obtained. Comprehensively mapping of untargeted and targeted (UT) features through fingerprinting algorithms provides effective classification models, benchmarking Brazilian oils (n = 28) vs. a large selection of Italian high-quality extra-virgin olive oils (n = 111). Identitation of Brazilian oils is achieved by combining six different feature selection strategies, the resulting pattern of predictors includes: 1-methoxy-2-propanol, ethyl acetate, (E)-4,8-dimethyl-1,3,7-nonatriene, propanoic acid, limonene, (E)-2-hexenal, hexanoic acid, pentanal, butanoic acid, hexanal, α-pinene, (E)-β-ocimene, and propanal. Compositional differences also are evidenced in composite-class images resembling 2D patterns characteristic of the two production areas. Quantitative fingerprinting, resembling an artificial intelligence smelling machine, characterizes the unique aroma with 1-penten-3-one and (Z)− 3-hexenal, dominating with pungent and green notes, followed by (E)− 2-hexenal (bitter-almond), (E,E)− 2,4-heptadienal (fatty), eucalyptol (herbal), benzaldehyde (almond), hexanal (green), and (E)− 2-hexen-1-ol (green grass).