Extra Virgin Olive Oil Varieties Research Articles

Bioactive compounds in Spanish extra virgin olive oils: Migration and stability according to the culinary technique used

Extra virgin olive oil (EVOO) is a basic food of the Mediterranean diet and an important source of bioactive compounds, especially phenolic substances. The culinary techniques to which the oil is subjected before consumption cause the migration of these compounds, hence the importance of studying their stability before and after culinary treatment. We determined the behaviour of the phenols present in EVOO and its total antioxidant capacity before and after the use of various culinary techniques such as deep frying, boiling (in a water/oil mixture (W/O) and sauteing, observing that the study parameters varied according to the variety of oil and the culinary technique used. Significant statistical differences were observed between the different varieties of EVOO according to the culinary technique used. But this was not the case with respect to polyphenol content, for which no statistically significant differences were observed among the different varieties of EVOO according to the culinary techniques employed (p > 0.05), except with the Arbequina variety (p < 0.05). With respect to the individual polyphenols – tyrosol, p-vainillin, vanillic acid, gallic acid, trans-caffeic acid, ferulic acid and luteolin – our analysis shows that although there were differences in content between raw EVOO and EVOO treated with each of the culinary techniques, these differences were not statistically significant (p > 0.05). There were significant losses of oleocanthal with the W/O boiling technique, but content increases were observed following sauteing and deep frying with respect to raw EVOO. Total antioxidant capacity presented a similar pattern in all samples, with increases after sauteing and decreases after W/O boiling and deep frying. ABTS was the most suitable technique for determining antioxidant capacity in EVOO. In short, the behaviour of the bioactive compounds in EVOO depends on the temperature and the cooking medium used.

Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties

To discriminate samples from three varieties of Tunisian extra virgin olive oils, weighted and non-weighted multiblock partial least squares – discriminant analysis (MB-PLS1-DA) models were compared to PLS1-DA models using data obtained by gas chromatography (GC), or global composition through mid-infrared spectra (MIR). Models performances were determined using percentages of sensitivity, specificity and total correct classification. The choice of threshold level for the interpretation of PLS1-DA results was considered. PLS1-DA models using GC data gave better results than those using MIR data. Even with the most conservative threshold, PLS1-DA on GC data allowed very good predictions for Chemlali variety (99% correct classification), but had more difficulty to discriminate Chetoui and Oueslati samples (95% and 84% correct classification respectively). Non-weighted MB-PLS1-DA models benefiting from the synergy between the two sources of data were more discriminative than simple PLS1-DA, yielding better prediction for Chetoui and Oueslati varieties (98% and 90% correct classification respectively).

Effect of phenolic extracts from different extra-virgin olive oil varieties on osteoblast-like cells.

The reported incidence of osteoporosis is lower in countries in which the Mediterranean diet predominates, and this apparent relationship may be mediated by the phenolic compounds present in olive oil. The objective of this study was to determine the effect of phenolic extracts from different varieties of extra-virgin olive oil (Picual, Arbequina, Picudo, and Hojiblanca) on the differentiation, antigenic expression, and phagocytic capacity of osteoblast-like MG-63 cells. At 24 h of treatment a significant increase in phosphatase alkaline activity and significant reductions in CD54, CD80, and HLA-DR expression and in phagocytic activity were observed in comparison to untreated controls. The in vitro study performed has demonstrated that phenolic compounds from different extra virgin olive oil varieties can modulate different parameters related to osteoblast differentiation and function.

Chemical profiles and Sensory analysis of four varieties of olive oil cultivated in Morocco

To ensure olive oil quality, a combination of physico-chemical and sensory methods is necessary. Utility and importance of these methods in the international norm for olive oil are presente . The aim of the present study is to evaluate the chemical characterization of main compounds and Sensory analysis of four extra virgin olive oil varieties grown in Morocco (Moroccan Picholine, Picual, Arebiquine and Koroneiki). Quality parameters monitored over the study were acidity, peroxide value, UV absorption (E232 and E270), tocopherol, fatty acid composition, total polyphenol and Rancimat test. Results show that the Koroneiki variety presents the highest concentrations of C18:1. This variety is also rich in phenolic and tocopherol compounds. In contrast, Arebquina oil shows the lowest contents of C18:1, phenolic and tocopherol compounds. This explains the low stability values for this cultivar. From the sensory results the Picholine, Picual and Koroneiki variety was characterized as fruity, but Arbequina variety did not meet the criteria of extra virgin oil.

Anti-inflammatory and antioxidant activities of different extra virgin olive oil varieties extracts

Olive oil is a distinct food nutrient source because of its several biological effects, including anti-allergy and anti-inflammatory activities, which can be very important factor for cosmetic’s production. Indeed, olive oil active molecules do not cross the hepatic barrier and not metabolized. The aim of this work is to evaluate and compare the anti-inflammatory potential of extracts of different extra virgin olive oil (EVOO) varieties from different harvest years (2012 to 2014). We assessed the anti-inflammatory effect of SPE-Diol extract in LPS/IFNγ stimulated J774.A1 macrophages by measuring the production of several pro-inflammatory mediators such as nitric oxide (NO), prostaglandin (PGE2), tumor necrosis factor alpha (TNFα), interleukin 6 (IL-6), and expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). The results showed that the anti-inflammatory effect differs between the EVOO varieties and were concentration-dependent. Among the tested varieties, Picholine had the highest anti-inflammatory activity, with a significant scavenging activity against the free radical. Nevertheless, the anti-inflammatory effect of each variety stayed in the same trend regardless of the harvest year. Moreover, the results showed that total polyphenols content stayed approximately the same for each variety whatever the harvest year, and that activity is correlated with total polyphenol content and radical scavenging activity.

Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils.

It was previously demonstrated that Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares (PLS1) were successfully used to assess whether an olive oil was extra virgin, and if adulterated, with which type of vegetable oil and by how much using previously developed PLS1 calibration models. This last prediction required an initial set of four PLS1 calibration models that were based on gravimetrically prepared mixtures of a specific variety of extra virgin olive oil (EVOO) spiked with adulterants. The current study was undertaken after obtaining a range of EVOO varieties grown in different countries. It was found that all the different types of EVOO varieties investigated belonged to four distinct groups, and each required the development of additional sets of specific PLS1 calibration models to ensure that they can be used to predict low concentrations of vegetable oils high in linoleic, oleic, or palmitic acid, and/or refined olive oil. These four distinct sets of PLS1 calibration models were required to cover the range of EVOO varieties with a linoleic acid content from 1.3 to 15.5% of total fatty acids. An FT-NIR library was established with 66 EVOO products obtained from California and Europe. The quality and/or purity of EVOO were assessed by determining the FT-NIR Index, a measure of the volatile content of EVOO. The use of these PLS1 calibration models made it possible to predict the authenticity of EVOO and the identity and quantity of potential adulterant oils in minutes.

Determination of apolar and minor polar compounds and other chemical parameters for the discrimination of six different varieties of Tunisian extra-virgin olive oil cultivated in their traditional growing area

A study on the characterization of Tunisian extra-virgin olive oil varieties produced in their place of origin has been carried out. Due to the influence of the genotype and environmental, agronomic and technological factors on the chemical composition of olive oil and its quality, all the olives studied were collected on the same season, and the oil was obtained under the same processing technique. Several analyses were performed to characterize the different olive oils: free acidity, peroxide value, fatty acid composition, radical scavenging activity, Rancimat assay, pigments content and phenolic compounds by HPLC–MS. In order to evaluate all the results obtained (36 parameters for each variety), different statistical analyses were used to discriminate the extra-virgin olive oil varieties: one-way analysis of variance was performed to check significant differences among cultivars (p ≤ 0.05); PCA was applied to the data showing that variables such as oleic, linoleic, quinic and vanillic acids, apigenin, luteolin, taxifolin, oleuropein aglycon, pinoresinol acetate, elenolic acid and oxidative stability allowed discriminating among the different varieties of extra-virgin olive oil studied. Besides, LDA model was able to classify the samples depending on their geographical origin in Tunisia (North, Centre and South).

Prediction of Extra Virgin Olive Oil Varieties through Their Phenolic Profile. Potential Cytotoxic Activity against Human Breast Cancer Cells

The aim of this work was to develop a rapid resolution liquid chromatography coupled to electrospray ionization time-of-flight mass spectrometry (RRLC-ESI-TOF-MS) method followed by tetrazolium salt (MTT)-based cell viability assays for qualitative and quantitative classification of extra virgin olive oil (EVOO) varieties by phenolic and other polar compound contents as well as for rapid characterization of putative cytotoxic activities against human cancer cells. Five different Spanish EVOO varieties were analyzed, and RRLC-ESI-TOF-MS method was applied for qualitative and quantitative identification of most important phenolic compounds. We finally employed MTT-based cell viability protocol to assess the effects of crude EVOO phenolic extracts (PEs) on the metabolic status of cultured SKBR3 human breast cancer cells. MTT-based cell viability assays revealed a wide range of breast cancer cytotoxic potencies among individual crude PE obtained from EVOO monovarietals. Remarkably, breast cancer cell sensitivity to crude EVOO-PEs was up to 12 times higher in secoiridoids enriched-PE than in secoiridoids-low/null EVOO-PE.

Characterization and quantification of phenolic compounds of extra-virgin olive oils with anticancer properties by a rapid and resolutive LC-ESI-TOF MS method

The characterization and quantification of extra-virgin olive oil (EVOO) phenolic compounds by a rapid resolution liquid chromatography (RRLC) method coupled to diode-array and time of flight mass spectrometry (TOF) detection systems was developed. The RRLC method transferred from a conventional HPLC one achieved better performance with shorter analysis times. The phenolic compounds were separated with a C18 column (150 mm × 4.6 mm, 1.8 μm) using water with 0.5% acetic acid and acetonitrile as mobile phases. Good peak resolution was obtained and 19 different phenols were identified in less than 20 min providing a new level of information about the samples in shorter time. The applicability of this analytical approach was confirmed by the successful analysis of three different EVOO varieties (Picual, Hojiblanca, and Arbequina) obtained from different trademarks. Besides identification of the most important phenolic compounds and their quantification in three different ways (RRLC-UV, RRLC-MS and a new approach using the total polyphenol content obtained with Folin Ciocalteau, the relative areas and the response factors), we also described the occurrence of correlations between the phenolic composition of EVOO-derived crude phenolic extracts and their anti-proliferative abilities toward human breast cancer-derived cell lines. When compared with lignans-rich EVOO varieties, secoiridoids-rich EVOO had a significantly strong ability to alter cell viability in four different types of human breast carcinoma cells.

Quality of extra virgin olive oil affected by several packaging variables

The aim of this study was to determine the evolution of the quality index of three extra-virgin olive oil varieties (EVOO), stored according to different packaging variables for one year. The selected quality parameters were: acidity index, peroxide value (PV), K270 coefficient, fatty acid (FA) profile, carotenoids and chlorophylls. This study was carried out by monthly analyzing several bottled EVOO varieties -Picual, Hojiblanca and Arbequina- obtained during the present harvesting season. In addition, other Picual EVOO stored in a mill deposit, as well as bottled Picual EVOO from the previous harvesting season were analyzed monthly. The oils were packaged in dark and transparent glass bottles. The results showed that the acidity and K270 parameters increased slightly in all cases, while PV value changes were significant in EVOOs stored in transparent glass bottles. FA profiles were slightly modified throughout the storage period, although oleic acid slightly increased at the end of the analytical period, especially in the EVOO stored in deposits. Regarding pigment, chlorophylls losses were more noticeable than those related to carotenoid. According to the present results, the best packaging conditions for EVOO were deposits, followed by dark glass bottles. In addition, this study demonstrated that EVOO collected from the previous harvesting season and stored under nitrogen atmosphere could be packaged in glass bottles without appreciable quality changes, as compared with EVOO packaged in the same bottles and obtained during the current harvesting season.

Use of different artificial neural networks to resolve binary blends of monocultivar Italian olive oils

The problem of authenticating extra virgin olive oil varieties is particularly important from the standpoint of quality control. After having shown in our previous works the possibility of discriminating oils from a single variety using chemometrics, in this study a combination of two different neural networks architectures was employed for the resolution of simulated binary blends of oils from different cultivars. In particular, a Kohonen self-organizing map was used to select the samples to include in the training, test and validation sets, needed to operate the successive calibration stage, which has been carried out by means of several multilayer feed-forward neural networks. The optimal model resulted in a validation Q 2 in the range 0.91–0.96 (10 data sets), corresponding to an average prediction error of about 5–7.5%, which appeared significantly better than in the case of random or Kennard–Stone selection.

Supervised pattern recognition to authenticate Italian extra virgin olive oil varieties

This work is the logical consecution of our previous investigation on the classification of “monocultivar” olive oils, in which 153 samples from the five cultivars Carboncella, Frantoio, Leccino, Moraiolo and Pendolino [harvested from 1997 to 1999, in the same geographical area (Sabina, Lazio)] were discriminated according to their variety, using Linear Discriminant Analysis (LDA) and Back-propagation Artificial Neural Network (BP-ANN). This study has been now extended to include 50 new samples from three (Frantoio, Leccino, Moraiolo) of the previously examined cultivars and 373 samples from other nine olive varieties (Minuta, Moraiolo, Nocellara del Belice, Nociara, Ortice, Ortolana, Ottobratica, Peranzana, Racioppella and Sinopolese). These new samples were harvested from 1996 to 2000 in six Italian regions (Calabria, Campania, Lazio, Molise, Puglia and Sicilia). Kennard-Stone algorithm was used to partition the samples into the training and test sets and the value of Fisher F-ratio was computed to identify the most discriminating indices in order to reduce the number of input variables. A first study, restricted to the original five cultivars only, showed that 12 variables are necessary in the best LDA model, which was able to correctly recognize 92.7% of the training samples and to correctly predict 90.6% of the test set. On the other hand, the first seven variables only were necessary to obtain a null prediction error over the test and validation set samples using BP-ANN. In a successive stage, ANNs have been used to extend the study to all the 14 cultivars (576 samples). In this case, the first 16 variables according to the value of Fisher F-ratio were included in the best classification model. This model was able to correctly recognize all the samples in the training set (RMS<0.00001) and to correctly predict all the samples in the test (RMS error=0.0008) and validation (RMS error=0.001) sets.

Chemical authentication of extra virgin olive oil varieties by supervised chemometric procedures.

This work has focused on discriminating extra virgin olive oils from Sabina (Lazio, Italy) by olive fruit variety (cultivar). A set of oils from five of the most widespread cultivars (Carboncella, Frantoio, Leccino, Moraiolo, and Pendolino) in this geographical area was analyzed for chemical composition using only the Official Analytical Methods, recognized for the quality control and commercial classification of this product. The obtained data set was converted into a computer-compatible format, and principal component analysis (PCA) and a method based on the Fisher F ratio were used to reduce the number of variables without a significant loss of chemical information. Then, to differentiate these samples, two supervised chemometric procedures were applied to process the experimental data: linear discriminant analysis (LDA) and artificial neural network (ANN) using the back-propagation algorithm. It was found that both of these techniques were able to generalize and correctly predict all of the samples in the test set. However, these results were obtained using 10 variables for LDA and 6 (the major fatty acid percentages, determined by a single gas chromatogram) for ANN, which, in this case, appears to provide a better prediction ability and a simpler chemical analysis. Finally, it is pointed out that, to achieve the correct authentication of all samples, the selected training set must be representative of the whole data set.

Sensory Authentication of European Extra-Virgin Olive Oil Varieties by Mathematical Procedures

Multidimensional scaling and a fuzzy filter qualifier were used together for authenticating and characterising four olive oil varieties—Picual and Arbequina (Spain), Coratina (Italy) and Koroneiki (Greece)—representative of the most important producer countries. Twenty four samples were evaluated by five panels of different nationalities—British, Greek, Italian and Spanish—these being representatives of traditional and potential consumers of virgin olive oil. Each sample was initially characterised by 74 sensory attributes. The paper analyses the most outstanding attributes and their evaluation intensities that characterise the varieties and gives the sensory profiling of each variety. Green attributes characterise Koroneiki variety, Arbequina variety is characterised by sweet–fruity sensory attributes, the bitter–pungent sensory attributes can identify Coratina while a strong fruity odour explains the sensory characteristics of Picual variety.