Extra virgin olive oil under simulated consumption conditions: Evaluation of quality, health, and flavour properties

Markers of sensory dynamics in phenols-rich virgin olive oils under optimal storage conditions

Early changes in sensory quality of phenols-rich virgin olive oil (VOO) and their relationship with the chemical changes are less studied in the literature. Therefore, the objective of this study was to propose a predictive model of dynamics of sensory changes based on specific chemical markers. The evolution of the sensory quality of phenol-rich VOOs from Tuscan cultivars stored under optimal storage conditions (i.e., absence of light, no O2 exposure, low temperature) was investigated using a multi-step methodological approach combining sensory (official sensory analysis (so-called Panel Test), Descriptive Analysis and Temporal Dominance of Sensation) and chemical measurements. The sensory map from descriptive data was related to the phenolic and volatile profiles, measured using HPLC-DAD and HS-SPME-GC-MS, respectively. A predictive model of the sensory changes over storage based on chemical compounds was developed. Results showed that very early changes involving phenolic and volatile compounds profiles occur in VOOs stored under optimal storage conditions, which turn in changes in sensory properties evaluated by the official panel test, the descriptive analysis and the temporal dominance of sensation. Furthermore, a chemical marker of sensory dynamics of oils during storage was identified as the ratio between two groups of secoiridoids. The proposed model, supported by the mentioned chemical marker, has the potential of improving the control of sensory changes in phenols-rich virgin olive oils during storage in optimal conditions.

HS-SPME-GC-MS and Chemometrics for the Quality Control and Clustering of Monovarietal Extra Virgin Olive Oil: A 3-Year Study on Terpenes and Pentene Dimers of Italian Cultivars

Terpenes and pentene dimers are less studied volatile organic compounds (VOCs) but are associated with specific features of extra virgin olive oils (EVOOs). This study aimed to analyze mono- and sesquiterpenes and pentene dimers of Italian monovarietal EVOOs over 3 years (14 cultivars, 225 samples). A head space-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) method recently validated was used for terpene and pentene dimer quantitation. The quantitative data collected were used for both the characterization and clustering of the cultivars. Sesquiterpenes were the molecules that most characterized the different cultivars, ranging from 3.908 to 38.215 mg/kg; different groups of cultivars were characterized by different groups of sesquiterpenes. Pentene dimers (1.336 and 3.860 mg/kg) and monoterpenes (0.430 and 1.794 mg/kg) showed much lower contents and variability among cultivars. The application of Kruskal-Wallis test-PCA-LDA-HCA to the experimental data allowed defining 4 clusters of cultivars and building a predictive model to classify the samples (94.3% correct classification). The model was further tested on 33 EVOOs, correctly classifying 91% of them.

Characterization and Comparison of Extra Virgin Olive Oils of Turkish Olive Cultivars

Extra virgin olive oils (EVOOs) obtained from five Turkish olive cultivars widely produced in the Aegean and Marmara regions were investigated based on their total antioxidant capacity (TAC), total phenolic content (TPC), pigment contents, fatty acid (FA) profiles, phenolic compounds (PC), volatile compounds (VC), and sensory properties. The results showed that all properties of EVOO samples were significantly affected by the olive cultivar used. The pigment contents in Ayvalık (9.90 mg·kg^-1) and Uslu (9.00 mg·kg^-1) oils were higher than the others (p < 0.05). The greatest values for oleic acid (74.13%) and TPC (350.6 mg·kg^-1) were observed in Gemlik and Domat oils, respectively (p < 0.05). Edincik oil showed the maximum hydroxytyrosol content (48.022 mg·kg^-1) and TAC value (515.36 mg TE·kg^-1) (p < 0.05). The Edincik, Domat, and Uslu oils were significantly not different for the total content of C6 compounds derived by lipoxygenase, which are the main volatiles responsible for the typical aroma of EVOOs (p > 0.05). Domat oil also exhibited the highest scores for bitterness and pungency perceptions (p < 0.05). The fruitiness scores of the oil samples (except for Ayvalık oil) were close to each other, even if they were statistically different (p < 0.05). Principal component analysis (PCA) indicated that the Ayvalık oil was separated from the others due to its poor-quality characteristics. As a result, it can be stated that Domat olive oil has better quality than the others.

Redundancy Analysis to Reduce the High-Dimensional Near-Infrared Spectral Information to Improve the Authentication of Olive Oil

The high price of marketing of extra virgin olive oil (EVOO) requires the introduction of cost-effective and sustainable procedures that facilitate its authentication, avoiding fraud in the sector. Contrary to classical techniques (such as chromatography), near-infrared (NIR) spectroscopy does not need derivatization of the sample with proper integration of separated peaks and is more reliable, rapid, and cost-effective. In this work, principal component analysis (PCA) and then redundancy analysis (RDA)—which can be seen as a constrained version of PCA—are used to summarize the high-dimensional NIR spectral information. Then PCA and RDA factors are contemplated as explanatory variables in models to authenticate oils from qualitative or quantitative analysis, in particular, in the prediction of the percentage of EVOO in blended oils or in the classification of EVOO or other vegetable oils (sunflower, hazelnut, corn, or linseed oil) by the use of some machine learning algorithms. As a conclusion, the results highlight the potential of RDA factors in prediction and classification because they appreciably improve the results obtained from PCA factors in calibration and validation.

Compositional Changes in the Extra Virgin Olive Oil Used as a Medium for Cheese Preservation

The influence of semi-hard (C1), hard (C2), and soft whey cheese (C3) immersed in extra virgin olive oil (EVOO) on its oxidative and hydrolytic parameters, fatty acids, and phenolic composition during two months of simultaneous storage was investigated. Accelerated hydrolytic and oxidative degradation was noted in EVOO stored with the immersed cheese compared to control oil. Oxidation indicator (K232), myristic (C 14:0), and trans-oleic fatty acid (C18:1t) exceeded the prescribed limit for the EVOO category in oils stored with immersed C1 and C2, which indicated that standard analytical parameters are ineffective as tools to examine the declared quality and authenticity of such topping oils. The noted changes in fatty acid profile were primarily prescribed to the migration of fats. C1 and C2 influenced a comparable reduction in EVOO total identified phenolic content (−92.1% and −93.5%, respectively), despite having a different content of total proteins and moisture, whereas C3 influenced a slightly lower reduction (−85.0%). Besides the protein profile, other cheese compounds (e.g., moisture, carbohydrates) have been shown to have a considerable role in the development of the EVOO phenolic profile. Finally, compositional changes in EVOO used as a medium for cheese preservation are under significant influence of the cheese’s chemical composition.