Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS-DA models trained on low-field and high-field 1H NMR data.

INTRODUCTION: Olive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price. OBJECTIVE: There is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high-field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low-field benchtop NMR presents an affordable alternative. METHODS: We compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop proton (1H) NMR and high-field 400 MHz 1H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils. RESULTS: We demonstrate that PLS-DA models trained on low-field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high-field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths.

In vitro study of potential prebiotic properties of monovarietal extra virgin olive oils.

Olive oil, essential ingredient of the Mediterranean diet, is attracting a growing interest due to increasing evidence on its beneficial effects on human health. This study investigated whether extra virgin olive oil (EVOO) possess prebiotic properties. Twenty different monovarietal EVOO samples from 5 Marche region cultivars (Italy) were studied. The prebiotic activity of EVOOs was assessed monitoring the selective stimulation of gut bacterial species and the short chain fatty acids (SCFAs) production, using an in vitro fermentation system. All EVOOs selectively stimulated Lactobacillus spp., with a stronger activity than that observed in the inulin fermentation (positive control). Also, the bifidobacteria population increased; this bifidogenic stimulation was of EVOOs from Raggia cultivar. SCFAs appeared significantly higher after 24 h in all EVOO fermentations than in the control. Acetic and propionic acids production was particularly stimulated. Overall, most of the investigated EVOOs had a potential prebiotic activity, similar or stronger than inulin.

Effect of Heating Temperature of High-Quality Arbequina, Picual, Manzanilla and Cornicabra Olive Oils on Changes in Nutritional Indices of Lipid, Tocopherol Content and Triacylglycerol Polymerization Process.

The aim of the study was to determine the stability and heat resistance of extra premium olive oil. The study material consisted of six extra virgin olive oils (EVOO) obtained from Spain. Four samples were single-strain olive oils: Arbequina, Picual, Manzanilla, and Cornicabra. Two samples were a coupage of Arbequina and Picual varieties: Armonia (70% Arbequina and 30% Picual) and Sensation (70% Picual and 30% Arbequina). Olive oil samples were heated at 170 °C and 200 °C in a pan (thin layer model). In all samples, changes in indexes of lipid nutritional quality (PUFA/SFA, index of atherogenicity, index of thrombogenicity, and hypocholesterolemic/hypercholesterolemic ratio), changes in tocopherol, total polar compounds content, and triacylglycerol polymers were determined. Heating olive oil in a thin layer led to its degradation and depended on the temperature and the type of olive oil. Increasing the temperature from 170 to 200 °C resulted in significantly higher degradation of olive oil. At 200 °C, deterioration of lipid nutritional indices, total tocopherol degradation, and formation of triacylglycerol polymers were observed. A twofold increase in the polar fraction was also observed compared to samples heated at 170 °C. The most stable olive oils were Cornicabra and Picual.

Evaluation of matrix effect in one-dimensional and comprehensive two-dimensional liquid chromatography for the determination of the phenolic fraction in extra virgin olive oils.

Olea europaea, meaning "European olive," is a small tree belonging to the family Oleaceae, occurring in the Mediterranean Basin. Olive oil is an essential component of a balanced diet because of its nutritional value. Among micronutrients, phenolic compounds did show important beneficial effects for human health. The majority of the research studies on the phenol content are carried out by liquid chromatography combined to photodiode array and/or mass spectrometry detection; however, because of matrix complexity, one-dimensional liquid chromatography cannot be sometimes sufficient to obtain rewarding separations, requiring more advanced analytical techniques. In this work, comprehensive two-dimensional liquid chromatography, incorporating RP-Amide and C18 stationary phases, in the first and second dimension, respectively, both under reversed phase conditions, was investigated for the determination of the phenolic fraction in extra virgin olive oil samples. As far as detection is concerned, triple quadrupole mass spectrometry was employed under multi reaction monitoring mode offering superior selectivity and sensitivity. The reduction of matrix effects, when using comprehensive two-dimensional liquid chromatography with respect to conventional one-dimensional liquid chromatography, was assessed by comparing the slopes of calibration curves built from standard solutions and spiked olive oil samples.

Comparative Analysis of Volatiles of 15 Brands of Extra-Virgin Olive Oils Using Solid-Phase Micro-Extraction and Solvent-Assisted Flavor Evaporation.

Aroma profiles, key aroma compound quantification, and cluster analysis of 15 brands of extra-virgin olive oils (EVOOs) from three countries (Spain, Italy, and Greece) were investigated in the current study. Aroma compounds were isolated from the oil by using solvent-assisted flavor evaporation (SAFE) and solid-phase micro-extraction (SPME) and analyzed by gas chromatography-olfactometry mass spectrometry (GC-MS/O). A total of 89 compounds were screened by SPME/SAFE-GC-MS/O with chromatographic columns in 15 brands of samples. Eighty and 54 compounds were respectively identified by SPME- and SAFE-GC-MS/O. Of those, 44 compounds were detected by both methods. Undecanol, (Z)-4-decenal, (E)-2-dodecenal, and 2-nonanone extracted by SAFE were not found in EVOOs before. Eight classes of aroma compounds were identified, including 17 alcohols, 22 aldehydes, 9 ketones, 4 acids, 14 esters, 5 aromatics, 12 alkene, and 6 others. Eleven compounds were identified as the key aroma compounds in alternative brands of EVOOs by SAFE-aroma extract dilution analysis (AEDA). Hexanal, (E)-2-hexenal, (E)-3-hexenol, acetic acid, and (E)-2-heptenal were the common key aroma compounds by AEDA and odor activity values (OAVs). From the cluster analysis of the heatmap, the aroma compounds of all the Spain EVOOs were similar, and there were some differences from the samples of Italy and Greece. It suggested that both the amount and concentration of aroma compounds determine the similarity of aroma in EVOOs.

Study on Extra Virgin Olive Oil: Quality Evaluation by Anti-Radical Activity, Color Analysis, and Polyphenolic HPLC-DAD Analysis.

This study aimed to evaluate the quality of oils available on the Italian market and purchased directly from the mill or in the supermarket and labelled as extra virgin olive oils (EVOOs). As one of the most relevant foods of the Mediterranean diet and recognized as a functional food if regularly consumed, the quality of EVOO needs to be continuously monitored. Different analytical protocols were applied. The spectrophotometric parameters used to classify the extra virgin olive oils-a CIEL*a*b*color analysis and the quali-quantitative analysis of bioactive molecules by HPLC-DAD detection and the anti-radical activity, by the DPPH method, were evaluated and compared among the samples. This study confirmed a very high variation in terms of quality, both in oils purchased directly from mills throughout Italy, but also in oils labeled as "100% of Italian origin". Due to the high variability reconfirmed in the monitored samples, it is necessary to carry out a capillary control, not limited only to the parameters indexed by law. A useful complementary method could be represented by reflectance colorimetric analysis.

Concentration of Potentially Bioactive Compounds in Italian Extra Virgin Olive Oils from Various Sources by Using LC-MS and Multivariate Data Analysis.

High quality extra virgin olive oils represent an optimal source of nutraceuticals. The European Union (EU) is the world's leading olive oil producer, with the Mediterranean region as the main contributor. This makes the EU the greatest exporter and consumer of olive oil in the world. However, small olive oil producers also contribute to olive oil production. Beneficial effects on human health of extra virgin olive oil are well known, and these can be correlated to the presence of vitamin E and phenols. Together with the origin of the olives, extraction technology can influence the chemical composition of extra virgin olive oil. The aim of this study was to investigate the concentration of potentially bioactive compounds in Italian extra virgin olive oils from various sources. For this purpose, vitamin E and phenolic fractions were characterized using high-performance liquid chromatography (HPLC) coupled with fluorescence, photodiode array and mass spectrometry detection in fifty samples of oil pressed at industrial plants and sixty-six samples of oil produced in low-scale mills. Multivariate statistical data analysis was used to determine the applicability of selected phenolic compounds as potential quality indicators of extra virgin olive oils.

The First Identification of the Uniqueness and Authentication of Maltese Extra Virgin Olive Oil Using 3D-Fluorescence Spectroscopy Coupled with Multi-Way Data Analysis.

The potential application of multivariate three-way data analysis techniques, namely parallel factor analysis (PARAFAC) and discriminant multi-way partial least squares regression (DN-PLSR), on three-dimensional excitation emission matrix (3D-EEM) fluorescent data were used to identify the uniqueness and authenticity of Maltese extra virgin olive oil (EVOO). A non-negativity constrained PARAFAC model revealed that a four-component model provided the most appropriate solution. Examination of the extracted components in mode 2 and 3 showed that these belonged to different fluorophores present in extra virgin olive oil. Application of linear discriminate analysis (LDA) and binary logistic regression analysis on the concentration of the four extracted fluorophores, showed that it is possible to discriminate Maltese EVOOs from non-Maltese EVOOs. The application of DN-PLSR provided superior means for discrimination of Maltese EVOOs. Further inspection of the extracted latent variables and their variable importance plots (VIPs) provided strong proof of the existence of four types of fluorophores present in EVOOs and their potential application for the discrimination of Maltese EVOOs.

Fatty Acids and Phenolic Profiles of Extravirgin Olive Oils from Selected Italian Cultivars Introduced in Southwestern Province of Pakistan.

Mediterranean olive trees have been cultivated in Pakistan for decades to promote olive cultivation and use of olive oil. The qualitative characteristics of seven mono and one multi-varietal extra virgin olive oils (EVOOs) extracted from Italian cultivars grown in different areas of Balochistan a southwestern province of Pakistan were evaluated. Present study aims to assess the impact of bioclimatological change on biochemical profile of exotic cultivars. The dominating fatty acids found in analyzed EVOOs were oleic (65-72%), linoleic (10.61-18.33%) and palmitic acids (12-16%). The tocopherols α, (ß+γ) and δ contents showed a great diversity which ranged from (60-408) mg/kg while, total phenol concentration ranged from (200-370) mg/kg. The analyses of phenolic compounds revealed the presence of phenolic acids, phenolic alcohols, secoiridoids, flavonoids, oleuropein and verbascosides. One-way ANOVA revealed significant differences (p < 0.05) regarding studied parameters. Principal component analysis (PCA) was applied to identify the main components and to classify samples into groups in terms of fatty acids and phenolic profiles. The first group (Frantoio, Moraiolo, Pendolino, Multi-varietal mixture) characterized by high amount of oleic acid and MUFAs/PUFAs ratio. The second group (Maurino and Leccino) correlates with SFAs and third (Ottobrattica, Coratina) with PUFAs. Based on the PCA of phenolic profile the studied cultivars were divided into two main groups. Morialo, Pendolino and Maurino correlated with (phenolic acids, hydroxytyrosol, flavonoids and secoiridoids). Frantoio, Ottobrattica, Coratina, multi-varietal and Leccino were correlated with oleuropein, tyrosol and ligstroside aglycon. The obtained data was compared with those obtained from same cultivars in their original and/or different growing area. Marked differences were observed in the composition of oleic, linoleic, palmitic acids, secoiridoids and total phenolic contents. These differences could be due to change in geographical location and climatical condition of Balochistan. The cultivar Moraiolo has shown best adaptation and preserved its biochemical composition among all studied cultivars.

Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils.

Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction-gas chromatography-mass spectrometry corresponding to 4-methyl-2-pentanol, (E)-2-hexenal, 1-tridecene, hexyl acetate, (Z)-3-hexenyl acetate, (E)-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control.

Fast determination of extra-virgin olive oil acidity by voltammetry and Partial Least Squares regression.

In this paper, an approach for the detection of extra-virgin olive oil (EVOO) free-acidity, based on combination of voltammetric profiles (Voltammetry) and Partial Least Squares (PLS) multivariate regression, is described. Voltammetric measurements are performed with a 12.5 µm radius platinum microdisk, directly in the oil samples mixed with 0.5 M of the room temperature ionic liquid (RTIL) tri-hexyl(tetradecyl)phosphonium bis(trifluoromethylsulfonyl)imide, which acted as a supporting electrolyte, and allows achieving a suitable conductivity in the matrices. Multivariate regression is performed directly on full voltammetric responses recorded over a properly chosen negative potential range and scan rate, where essentially all free fatty acids, characterizing EVOOs, can be reproducibly reduced. PLS regression models are built by employing Italian EVOO samples training sets at different acidity levels (over the range 0.2% w/w - 1.5% w/w; (% w/w) represents mass percentage) and optimized by choosing the optimal complexity, in terms of number of latent variables (LVs). The free-acidity prediction is made through a multivariate model, constructed by using standards of known acidity (determined by the official volumetric titration method) and validated on an external sample set. To show the validity of the proposed method, the PLS/Voltammetry predictions of the free-acidity of a series of commercially available Italian EVOOs, ranging from 0.2 to 0.41 %w/w, are obtained and the values compared with those determined by the official titration approach. Differences found between the two methods are within 5% RSD.

Determination of phenolic acids in extra virgin olive oil using supercritical fluid chromatography coupled with single quadrupole mass spectrometry.

Phenolic acids represent one third of the total phenolic compounds in extra virgin olive oil (EVOO) and contribute greatly to the biological and sensory properties of EVOO. In the present research, an analytical method based on supercritical fluid chromatography coupled with single quadrupole mass spectrometry (SFC-MS) was developed for the analysis of phenolic acids in EVOOs. The chromatographic and MS conditions were optimized in terms of selectivity, peak shape and sensitivity. Satisfactory separation of 12 phenolic acids was achieved within 20 min on a Platisil CN column (250 mm × 4.6 mm, 5 µm) using 7% (v/v) water and 0.5% (v/v) formic acid in MeOH as mobile phase additives at the backpressure of 140 bar and temperature of 60 °C. The developed method was validated by evaluating the linearity, sensitivity, repeatability, intermediate precision and accuracy. The limits of detection (LODs) and limits of quantification (LOQs) of the SFC-MS method for phenolic acids ranged from 0.03 to 5.00 µg/mL and 0.08 to 15.0 µg/mL, respectively. The relative standard deviations (RSDs) for intraday and interday precision were lower than 9.91% and the recoveries ranged from 80.2 to 117% and 80.9 to 118% for low-level and high-level concentrations of standards spiked in olive oil, respectively. Compared to an HPLC method, the SFC method was about three times faster and provided better selectivity. Finally, the SFC-MS method was successfully employed for the quantitative analysis of phenolic acids in EVOOs. Differences in individual and total phenolic acid contents were observed between six varieties.

Application of an advanced and wide scope non-target screening workflow with LC-ESI-QTOF-MS and chemometrics for the classification of the Greek olive oil varieties.

An optimized and validated LC-ESI-QTOF-MS method with an integrated non-target screening workflow was applied in the investigation of the metabolomic profile of 51 Greek monovarietal extra virgin olive oils (EVOOs) from the varieties: Manaki, Ladoelia, Koroneiki, Amfissis, Chalkidikis and Kolovi. Data processing was carried out with the R language and XCMS package. A local database consisting of 1608 compounds naturally occurring in different organs of Olea Europa L. was compiled in order to accelerate the identification workflow. The preliminary examination of the distribution of EVOOs toward their cultivars was achieved by Principal Component Analysis (PCA). Ant Colony Optimization-Random Forest (ACO-RF) was developed to prioritize over 250 features and to establish a classification tree. Apigenin, vanillic acid, luteolin 7-methyl ether and oleocanthal were suggested as the markers responsible for the classification of Greek EVOOs' cultivars.

Assessing the varietal origin of extra-virgin olive oil using liquid chromatography fingerprints of phenolic compound, data fusion and chemometrics.

High Performance Liquid Chromatography (HPLC) with diode array (DAD) and fluorescence (FLD) detection was used to acquire the fingerprints of the phenolic fraction of monovarietal extra-virgin olive oils (extra-VOOs) collected over three consecutive crop seasons (2011/2012-2013/2014). The chromatographic fingerprints of 140 extra-VOO samples processed from olive fruits of seven olive varieties, were recorded and statistically treated for varietal authentication purposes. First, DAD and FLD chromatographic-fingerprint datasets were separately processed and, subsequently, were joined using "Low-level" and "Mid-Level" data fusion methods. After the preliminary examination by principal component analysis (PCA), three supervised pattern recognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogies (SIMCA) and K-Nearest Neighbors (k-NN) were applied to the four chromatographic-fingerprinting matrices. The classification models built were very sensitive and selective, showing considerably good recognition and prediction abilities. The combination "chromatographic dataset+chemometric technique" allowing the most accurate classification for each monovarietal extra-VOO was highlighted.

Comparing two metabolic profiling approaches (liquid chromatography and gas chromatography coupled to mass spectrometry) for extra-virgin olive oil phenolic compounds analysis: A botanical classification perspective.

Over the last decades, the phenolic compounds from virgin olive oil (VOO) have become the subject of intensive research because of their biological activities and their influence on some of the most relevant attributes of this interesting matrix. Developing metabolic profiling approaches to determine them in monovarietal virgin olive oils could help to gain a deeper insight into olive oil phenolic compounds composition as well as to promote their use for botanical origin tracing purposes. To this end, two approaches were comparatively investigated (LC-ESI-TOF MS and GC-APCI-TOF MS) to evaluate their capacity to properly classify 25 olive oil samples belonging to five different varieties (Arbequina, Cornicabra, Hojiblanca, Frantoio and Picual), using the entire chromatographic phenolic profiles combined to chemometrics (principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA)). The application of PCA to LC-MS and GC-MS data showed the natural clustering of the samples, seeing that 2 varieties were dominating the models (Arbequina and Frantoio), suppressing any possible discrimination among the other cultivars. Afterwards, PLS-DA was used to build four different efficient predictive models for varietal classification of the samples under study. The varietal markers pointed out by each platform were compared. In general, with the exception of one GC-MS model, all exhibited proper quality parameters. The models constructed by using the LC-MS data demonstrated superior classification ability.