An Electronic Nose as a Non-Destructive Analytical Tool to Identify the Geographical Origin of Portuguese Olive Oils from Two Adjacent Regions

Abstract

The geographical traceability of extra virgin olive oils (EVOO) is of paramount importance for oil chain actors and consumers. Oils produced in two adjacent Portuguese regions, Côa (36 oils) and Douro (31 oils), were evaluated and fulfilled the European legal thresholds for EVOO categorization. Compared to the Douro region, oils from Côa had higher total phenol contents (505 versus 279 mg GAE/kg) and greater oxidative stabilities (17.5 versus 10.6 h). The majority of Côa oils were fruity-green, bitter, and pungent oils. Conversely, Douro oils exhibited a more intense fruity-ripe and sweet sensation. Accordingly, different volatiles were detected, belonging to eight chemical families, from which aldehydes were the most abundant. Additionally, all oils were evaluated using a lab-made electronic nose, with metal oxide semiconductor sensors. The electrical fingerprints, together with principal component analysis, enabled the unsupervised recognition of the oils' geographical origin, and their successful supervised linear discrimination (sensitivity of 98.5% and specificity of 98.4%; internal validation). The E-nose also quantified the contents of the two main volatile chemical classes (alcohols and aldehydes) and of the total volatiles content, for the studied olive oils split by geographical origin, using multivariate linear regression models (0.981 ≤ R² ≤ 0.998 and 0.40 ≤ RMSE ≤ 2.79 mg/kg oil; internal validation). The E-nose-MOS was shown to be a fast, green, non-invasive and cost-effective tool for authenticating the geographical origin of the studied olive oils and to estimate the contents of the most abundant chemical classes of volatiles.

Keywords: EVOO quality; feature extraction parameters; metal oxide semiconductor sensors; multivariate qualitative-quantitative analysis; oxidative stability; resistance electrical signals; sensory analysis.

Figure 1

Côa Valley and Douro Valley sampling regions. Map projected in ETRS89/PT-TM06.

Figure 2

Relative abundance (%) of the chemical classes of the identified VOCs for the olive oils from Côa or Douro geographical origins.

Figure 3

Unsupervised pattern recognition of olive oils by geographical origin (Côa Valley: ●, and, Douro Valley: ■) based on the principal component analysis (3D plot of the three first PCs) based on: (A) physicochemical quality data (FA, PV, K₂₃₂ and K₂₆₈), oxidative stability (OS), and antioxidant reducing capacity (TPC and DPPH); (B) contents of the volatiles belonging to eight chemical families (alcohols, aldehydes, alkanes, alkenes, esters, ethers, ketones, and terpenes); (C) intensities of the perceived olfactory sensations (fruity greenly or ripely, apple, banana, cabbage, dry fruits, tomato, dry herbs, fresh herbs, and tomato leaves); and (D) intensities of the perceived gustatory sensations (fruity greenly or ripely, bitter, sweet, pungent, apple, banana, cabbage, dry fruits, plum, tomato, dry herbs, fresh herbs, olive leaves, and tomato leaves).

Figure 4

Unsupervised pattern recognition of olive oils by geographical origin (Côa Valley: ●, and, Douro Valley: ■) based on the principal component analysis (3D plot of the first three PCs) based on the mean resistance signals acquired by the nine E-nose-MOS sensors (S1_MEAN to S9_MEAN).

Figure 5

Contents (in mg/kg of oil) of volatiles predicted by the MLRMs established based on the selected feature extracted variables from the electrical resistance signal curves acquired by the nine-MOS sensors comprised on the lab-made E-nose, versus the experimental data determined by the HS-SPME-GC-MS technique.