Volatile-Olfactory Profiles of cv. Arbequina Olive Oils Extracted without/with Olive Leaves Addition and Their Discrimination Using an Electronic Nose

Oils from cv. Arbequina were industrially extracted together with olive leaves of cv. Arbequina or Santulhana (1%, w/w), and their olfactory and volatile profiles were compared to those extracted without leaves addition (control). The leaves incorporation resulted in green fruity oils with fresh herbs and cabbage olfactory notes, while control oils showed a ripe fruity sensation with banana, apple, and dry hay grass notes. In all oils, total volatile contents varied from 57.5 to 65.5 mg/kg (internal standard equivalents), being aldehydes followed by esters, hydrocarbons, and alcohols the most abundant classes. No differences in the number of volatiles were observed. The incorporation of cv. Arbequina or Santulhana leaves significantly reduced the total content of alcohols and esters (minus 37–56% and 10–13%, respectively). Contrary, cv. Arbequina leaves did not influence the total content of aldehydes or hydrocarbons, while cv. Santulhana leaves promoted a significant increase (plus 49 and 10%, respectively). Thus, a leaf-cultivar dependency was observed, tentatively attributed to enzymatic differences related to the lipoxygenase pathway. Olfactory or volatile profiles allowed the successful unsupervised differentiation of the three types of studied cv. Arbequina oils. Finally, a lab-made electronic nose was applied to allow the nondestructive discrimination of cv. Arbequina oils extracted with or without the incorporation of olive leaves (100% and 99 ± 5% of correct classifications for leave-one-out and repeated K-fold cross-validation variants), being a practical tool for ensuring the label correctness if future commercialization is envisaged. Moreover, this finding also strengthened that olive oils extracted with or without olive leaves incorporation possessed quite different olfactory patterns, which also depended on the cultivar of the olive leaves.

Discrimination of Camellia seed oils extracted by supercritical CO2 using electronic tongue technology

Analytical method which combines electronic tongue technique and chemometrics analysis is developed to discriminate oil types and predict oil quality. All the studied Camellia oil samples from pressing, n-hexane extraction and supercritical CO₂ extraction (SCCE), were successfully identified by principal component analysis (PCA) and hierarchical cluster analysis (HCA). Furthermore, multi factor linear regression model (MLRM) was established to predict oil quality, which are indicated by acid value (AV) and peroxide value (POV). The practical potential of e-tongue for the discrimination and assessment of Camellia oils has shown promising application in the characterization of Camellia oils in the oil quality evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-021-00973-1.

Seeking for sensory differentiated olive oils? The urge to preserve old autochthonous olive cultivars

Mediterranean olive heritage richness is poorly characterized. Olive oils from minor cultivars of Northeast-Portugal (Lentisca, Madural, Rebolã, Redondal, Verdeal and Verdeal Transmontana) from centenarian trees were chemical and sensory characterized, aiming to identify autochthonous cultivars capable of producing differentiated olive oils. All oils, produced during two campaigns, were classified as extra virgin. Cv. Redondal showed the highest oxidative stability (OS), total phenols, vitamin E and C_18:1/C_18:2. Contrary, cv. Madural presented the lowest OS and C_18:1/C_18:2 ratios, supporting the importance of fatty acids on OS, while cv. Verdeal had the lowest total phenols and vitamin E contents. Sensory notes of tomato, apple, dry fruits, fresh herbs, tomato leaves and cabbage were predominant on the oils of most cultivars, whilst some attributes were more specific, such as banana and kiwi (Madural), cherry and apricot (cvs. Lentisca and Madural). The chemical and sensory diversity enabled the statistical discrimination of all cultivars and harvesting years.

Application of an electronic tongue as a single-run tool for olive oils' physicochemical and sensory simultaneous assessment

Olive oil is highly appreciated due to its nutritional and organoleptic characteristics. However, a huge compositional variation is observed between olive oils, requiring the use of diverse analytical techniques for its classification including titration, spectrophotometry and chromatography, as well as sensory analysis. Chemical analysis is usually time-consuming, expensive and require skilled technicians, while the sensorial ones are dependent upon individual subjective evaluations, even if performed by trained panellists. This work evaluated and demonstrated the feasibility of using a potentiometric electronic tongue, comprising non-specific lipid polymeric and cross-sensitive sensor membranes, coupled with chemometric tools based on different sub-sets of sensors (from 11 to 14 sensors), to predict key quality parameters of olive oils based on single-run assays. The multivariate linear models established for 23 centenarian olive trees from different cultivars allowed predicting peroxide value, oxidative stability, total phenols and tocopherols contents, CIELAB scale parameters (L*, a* and b* values), as well as 11 gustatory-retronasal positive attributes (green, sweet, bitter, pungent, tomato and tomato leaves, apple, banana, cabbage, fresh herbs and dry fruits) with satisfactory accuracy (0.90 ± 0.07 ≤ R² ≤ 0.98 ± 0.02 for the repeated K-fold-CV procedure, which ensured that 25% of the data was used for internal-validation purposes). The electronic tongue device had an accuracy statistically similar to that achieved with standard analytical techniques, pointing out the versatility of the device for the fast and simultaneous chemical and sensory analysis of olive oil.