Classification Of Olive Oils Research Articles

Electrochemical Multi-sensors Device Coupled with Heuristic or Meta-heuristic Selection Algorithms for Single-cultivar Olive Oil Classification

Potentiometric electrochemical multi-sensors’ performance highly depends on the capability of selecting the best set of sensors. Indeed, signals are usually collinear resulting in over-fitted multivariate models with low predictive applicability. In this work, a comparative study was made to evaluate the predictive performance of classification models coupled with heuristic or meta- heuristic variable selection algorithms. In this study, eleven single-cultivar extra virgin olive oils, from two crop years, were used. The results demonstrated that linear discriminant analysis with simulated annealing algorithm allowed selecting the best subset of sensors enabling 100% of correct cross-validation classifications, considering samples split by crop year.

Proteins in Olive Fruit and Oil

This paper is a comprehensive review grouping the information on the extraction, characterization, and quantitation of olive and olive oil proteins and providing a practical guide about these proteins. Most characterized olive proteins are located in the fruit, mainly in the seed, where different oleosins and storage proteins have been found. Unlike the seed, the olive pulp contains a lower protein content having been described a polypeptide of 4.6 kDa and a thaumain-like protein. Other important proteins studied in olive fruits have been enzymes which could play important roles in olives characteristics. Part of these proteins is transferred from the fruit to the oil during the manufacturing process of olive oil. In fact, the same polypeptide of 4.6 kDa found in the pulp has been described in the olive oil and, additionally, the presence of other proteins and enzymes have also been described. Protein profiles have recently been proposed as an interesting strategy for the varietal classification of olive fruits and oils. Nevertheless, there is still a lot of knowledge without being explored requiring new studies focused on the determination and characterization of these proteins.

Assess the impact of subjective norms of consumers' behaviour in the Greek olive oil market

The objective of the research was to highlight the role that socio-economic and spatial attributes of consumers – households exert on their choices regarding not only the supply modes but also the price they are willing to pay for different categories of olive oil. Regarding WTP for different olive oil labels, consumers are willing to pay premiums only for olive oils being processed by either private companies or cooperatives, with the latter to gain 34% of them in case they would decide to change the olive oil they usually purchase. The most important consumer profile is the young educated consumer one.

Classification of Western Greek virgin olive oils according to geographical origin based on chromatographic, spectroscopic, conventional and chemometric analyses

Forty seven monovarietal olive oils were collected from four Western Greek islands during the 2007–2008 harvesting period. Samples were subjected to physicochemical parameter analysis including determination of acidity, peroxide value, K232, K270, ΔK indices, chlorophyll and carotenoid content, trace elements, fatty acids and δ18O and δ13C isotope ratios in an effort to classify olive oil samples according to geographical origin. Chemometric analysis including MANOVA and Linear Discriminant Analysis (LDA) showed that when all above (14 different physicochemical parameters) were combined, a correct classification rate of 94.5% and 82.6% was achieved using the original and cross validation method respectively.

Discrimination of edible olive oils by means of synchronous fluorescence spectroscopy with multivariate data analysis

The potential of fluorescence spectroscopy for the classification of olive oils was investigated. Synchronous fluorescence spectra were collected in the region of 240-700 nm with the wavelength intervals of 10, 30, 60 and 80 nm. Successive projection algorithm (SPA) was applied for the determination of representative wavelengths while the linear discriminant analysis (LDA) method was used to classify olive oils. The classification error of the method was low (0,9-6,4%) for measurements collected at all wavelength intervals. The best classification accuracy was obtained for synchronous fluorescence intensities acquired at 10 selected wavelengths with the wavelength interval equal to 10 nm.

Chemical composition of virgin olive oils according to the ripening in olives

The aim of this study was the classification of olive oil according to the stage of ripeness of the olives used for its manufacture. A discriminant analysis was applied taking as dependent variables the fatty acids, triglycerides and sterols profile. The evolution of parameters of quality of the oils during ripening of the fruit has also been studied in three successive crops. The results of the classification shows that 88.5% of selected original grouped cases are correctly classified (85.7% green, 80% spotted and 78.9% ripe) and furthermore the validation classified the 69.6% of the unselected original cases. The most discriminating variables have been: avenasterol, linolenic acid, β-sitosterol and gadoleico. Fig. 1 shows graphically the values of the canonical discriminant functions and the centroids of the intervals of three or the maturity index.

Terminología, normalización y comunicación

The existing language of oliviculture and elaiotechnics makes evident the significant development of this specialised field over the past decades. The progressive industrialisation and professionalization of this sector has progressively given rise to a new terminology that is updating the old jargon and is undergoing important normalisation processes. The present work focuses on the normalisation of the designations and definitions of olive oil categories. We also consider the usefulness of such actions and evaluate the differences between the currently marketed types of olive oil in order to reduce the difficulties in their analysis, classification, control and marketing. This study finally brings forward a denominative and definitional proposal for olive oil categories based on the theoretical assumptions of the project Olivaterm. Our proposal is directed at making information clear to the consumer as well as at facilitating the retrieval of specialised knowledge.

Relevance of minor components stability in commercial olive oil quality during the market period

AbstractThe aim of this research was to study the relevance of the stability of minor components in commercial Spanish extra virgin and olive oil (OO) categories, particularly with respect to the individual phenolic compounds, tocopherols (vitamin E) and volatile compounds, during the market period (12 months). The study was carried out classifying several commercial olive oils (n = 39) according to their content in phenolic compounds into three different groups: extra virgin olive oils with high (>290 mg/kg; HP‐VOO) and low (<210 mg/kg; LP‐VOO) phenolic level and OO, due to the impact of biophenols on oxidative stability, sensory properties and nutritional aspects. After a market period of 1 year, the α‐tocopherol content fell significantly (25–30% of its initial content) in the commercial oils. While the decrease in total phenolics and o‐diphenols was not statistically significant, a relevant increase (33% on average) in simple phenolics (hytroxytyrosol and tyrosol) was observed in all the studied samples, suggesting its use as freshness or ageing marker. Throughout the market period, a low oxidation rate and therefore a high stability of the products was observed. Moreover, the results found in the study suggest that a higher market price OO is apparently related to better quality and process control, giving a superior final product in terms of its content of minor components related to organoleptic properties.Practical applications: The quality of virgin olive oil – mainly its sensory characteristics and nutritional value – is directly related to its content in minor components, in particular phenolics and volatile compounds, and therefore to their stability during the market period. Nevertheless, this information is not available to consumers. As a consequence, it is very important for both producers and consumers to clarify and establish the real relevance of the stability of minor components in commercial olive oil categories during their shelf‐life: how long they will keep their stability, organoleptic and nutritional characteristics. This information would highlight the individual quality properties of a specific oil product, beyond the sometimes misleading commercial category.

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

Extra virgin olive oil samples were collected from different geographical areas of Central Dalmatia region in Croatia including locations both on the coast and islands. This set included 41 oils of cul-tivar Oblica, 1 per cultivars Coratina and Leccino, and 5 of mixed cultivars Lastovka and Oblica. Attenuated total reflectance (ATR) spectra of non-treated samples were recorded and principal component analysis (PCA) was carried out on a set of obtained spectra, as well as their first and second derivatives. The quality of PCA models was assessed using leave-one-out cross-validation and optimal number of principal components was determined. In the case of ATR spectra, the first principal component accounted for 42.92 % of the total variance among the samples and the optimal number of components was 6, whereas in the case of second derivatives the first principal component accounted for 95.76 % of the total variance and the optimal number of components was 3. Classification of olive oils on the basis of geographical origin was proposed and underlying spectral differences among the spectra were determined by investigating principal component loadings. These differences arise as a result of variations in fatty acid composition. It was found out that ATR in combination with PCA could easily distinguish between samples collected from the coastal area and those from the islands. Classification results were further confirmed by using spherical principal components procedure, projection pursuit and robust PCA, as well as hierarchical cluster analysis.

The Greek olive oil market and the factors affecting it

The objective of the research was to highlight the role that socio-economic and spatial attributes of consumers – households exert on their choices regarding not only the supply modes but also the price they are willing to pay for different categories of olive oil, especially the packaged one. Three aspects are successively examined as regards alternative characteristics of olive oil that are proposed: (a) the label, (b) the quality certification protocol and finally (c) the respect of environmental criteria in the production process. It verifies that significant market shares belong to informal networks, which are analyzed to self-consumption, and purchase from either relatives or friends who produce it. The most influential factors purchasing olive oil, amongst others being examined, are the age, the educational level, and the placement of residents in olive oil producing zones. Regarding the wtp for different olive oil labels, consumers are willing to pay premiums only for olive oils being processed by either private companies or cooperatives, with the latter to gain 34% of them in the case they would decide to change the olive oil they usually purchase. The most important consumer profiles are that young educated consumers turn to buy olive oil from sm, do not want to pay less for sm own label olive oil, and they perceive olive oil being packaged by cooperatives as a distinct product they want to use, and pay a premium for it. On the contrary, elderly people are willing to pay less for own label olive oil.

Classification of Spanish extra virgin olive oils by data fusion of visible spectroscopic fingerprints and chemical descriptors

The potential of visible fingerprints and physical–chemical parameters in combination with multivariate data analysis was examined to classify extra virgin olive oils (EVOOs) from different Spanish regions according to their geographical origin. Firstly, spectral and quality parameters matrices were processed separately and subsequently were joined to evaluate the effect of synergy on the information obtained from the different methodologies. Linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) were performed as classification methods. The results revealed a perfect discrimination between the defined categories after performing PLS-DA on the Fused matrix, reaching 100% of correct classifications and showed a clear improvement in the overall prediction rates (92.5%), so that the effect of synergy was confirmed. These accurate results emphasise the feasibility of the proposed strategy and encourage the development of similar approaches based on visible spectroscopy in olive oil quality and traceability determination.

Phenolic characterization and geographical classification of commercial Arbequina extra-virgin olive oils produced in southern Catalonia

The aim of this work was to characterize Arbequina extra-virgin olive oils (EVOOs) from different locations in southern Catalonia (Spain) in terms of their phenolic profile, to show the classification of oil samples with respect to geographical area. The phenolic compounds present in 32 olive-oil samples were analyzed by a rapid and effective HPLC–ESI-TOF/MS method, and 18 phenolic compounds belonging to different phenolic types were identified. The results showed no qualitative differences in the phenolic fractions among EVOO from different geographical region. However, quantitative differences were observed in a wide number of phenolic compounds. In all olive-oil samples studied, secoiridoids were the most abundant, followed by lignans, phenolic alcohols, and flavonoids, respectively. Multivariate data were analysed by canonical discriminant analyses. Seventeen variables were used without a variable reduction step. Phenolic content of extra-virgin olive oils was found to depend highly on geographical area.

Towards new analyses of aroma and volatiles to understand sensory perception of olive oil

AbstractThe rich flavor of virgin olive oil (VOO) is one of the reasons why it stands out as a gourmet product. The classification of olive oils into quality categories basically relies on the sensory assessment, together with some chemical indices that do not provide information about aroma or taste. Some analytical techniques attempt to analyze the volatile compounds of olive oil, which are actually responsible for the aroma, and give objective chemical information that can explain the sensory perception. They are based on gas chromatography (GC) and gas sensor, such as metal oxide semiconductor (MOS) sensors. In both cases, the chemical data should be interpreted from a sensory perspective, considering the impact of each compound on aroma and trying to get as much information as possible with new data processing. In the case of MOS sensors, the coupling of a GC column allows gaining more information on aroma. Despite the extensive knowledge on the volatile compounds of olive oil, these techniques still fail in reproducing some results of the panellists. The physiological processes implied in the olfaction needs to be explored with new approaches to understand the sensory perception. These new approaches will support the alternatives to sensory assessment.

Determination of Oil and Moisture Content in Intact Olive Fruits by Portable near Infrared Spectroscopy

Introduction W orld olive oil production and consumption has increased steadily in recent years mainly due to scientific demonstration of the beneficial properties of olive oil for the protection of human health as well as increased appreciation of its sensory and cooking properties. Currently, Spain is the main olive oil producing country in the world, accounting for almost half of the total world olive oil production (around 3 million tonnes). In olive growing for oil production, the evaluation of oil content and moisture in olive fruits are key parameters. Oil content, as a percentage of fresh weight, determines the final oil yield of the orchard and is generally used for payment to growers. Fruit moisture influences the extraction process, particularly in some cultivars which exhibit a tendency to produce emulsions and have low oil extractability. Oil and moisture contents change during the ripening period and are affected by both the cultivar and environmental conditions. In the northern hemisphere, accumulation of oil in the fruit starts in August and ends in December and is normally used to determine the time for harvest. However, conventional laboratory techniques for the determination of these parameters are based on destructive measurements, are time-consuming and require considerable sample manipulation, which can be challenging for large sample sets. As an alternative, near infrared (NIR) spectroscopy offers many advantages, such as the rapid and simultaneous nondestructive analysis of many traits with low analytical cost per sample and without the use of chemical reagents. Recently, several works have demonstrated the analytical possibilities offered by NIR spectroscopy at a number of different steps during the olive oil extraction process. Application of NIR analysis to fruits, pastes and oils have been reported for quantitative analysis of some components or discrimination of samples according to cultivar, origin, classification and authentication of olive oils. Vibrational spectroscopic techniques have been suggested to monitor oil accumulation in olive fruits by providing a rapid tool to classify olives depending on their maturation stage on the basis of spectroscopic criteria which are more objective than the visual inspection of colour changes traditionally employed. In recent years, the development of low cost miniaturised spectro photometers has provided a real possibility of portable measurement devices, offering the possibility of direct analysis of intact fruits in the field which would allow new applications to be developed. In this article, the feasibility of using portable NIR technology for the determination of olive oil and moisture contents has been evaluated. To do that, a wide range of samples was used to test the robustness of the calibration models—this included fruit samples from different plant materials collected on-tree in the field and under laboratory conditions, in different years and harvesting periods.

A new concept for olive oil classification based on the oleocanthal and oleacein content through H-NMR quantitation

Extra virgin olive oil contains multiple minor bioactive components, especially phenolic compounds. Among them, (-)- decarboxymethyl ligstroside aglycone, also known as oleocanthal, has been shown to possess strong anti-inflammatory and neuroprotective properties, while (-)- decarboxymethyl oleuropein aglycone, also known as oleacein is considered as the most powerful antioxidant of olive oil. We developed a method for direct measurement of the oleocanthal and oleacein levels by quantitative 1H-NMR in CDCl3 at 600 or 800MHz in order to identify possible differences between extra virgin olive oils. The method was applied on >200 monovarietal commercial olive oil samples from Greece and California. The main findings were: 1. There are olive varieties that independently from geographic origin and harvest time produce olive oil that contains both compounds in low levels 2. There are olive cultivars that are able to produce olive oil rich in these substances, depending on the harvest and milling conditions. 3. The oleacein to oleocanthal ratio seems to depend on the olive cultivar, whereas it is independent from the olive extraction procedure. We propose a new index to classify extra virgin olive oils as a combination of D1=oleocanthal + oleacein and D2=oleacein/oleocanthal. The significant differences between the samples regarding the D1 (0–451mg/L) and D2 (0–1.3) could be used to discriminate the olive oils according to their possible health effects, organoleptic properties and varietal/geographic origin.

Logit modeling for classification of monocultivar olive oils from southwest Spain: A preliminary study

AbstractA characterization study of the main olive oil cultivars of southwest Spain (Picual, Arbequina, and Verdial) has been performed in order to establish logistic regression models. Several quality characteristics (free acidity, peroxide value, K232, K270, oxidative stability index) and chemical data (fatty acids, sterols, erythrodiol–uvaol composition) were measured. Logit regressions were used to evaluate the correlation of the parameters and to create models that allow saving costs on identifying oils as Arbequina, Picual, or Verdial type. Multiple logit regression models were developed: one for Arbequina, three models for Picual, and two models for Verdial cultivar, allowing in this way to minimize the cost for classifying oil samples.Practical application: The olive oil marketing is increasingly focused on the chemical differentiation and characterization of the product because the chemical composition of these virgin oils is responsible for their valuable sensory and nutritional properties. Here we present a characterization study (quality characteristics and chemical data) from the main olive oil cultivars of southwest Spain, Picual, Arbequina, and Verdial, as a first step for the traceability of these three types of monocultivar virgin olive oils. The results may be used as a training to create models for other olive oil cultivars.

Effect of Olive Leaves Addition during the Extraction Process of Overmature Fruits on Olive Oil Quality

The harvest period is one of the most important factors influencing olive oil quality. This period is extended for several months and the late-extracted olive oils are characterized by quality loss and reduced resistance to oxidation. The aim of this work was to verify the effect of olive leaves addition during the oil extraction process in the olive oils quality and composition. In two consecutive years (2009 and 2010), different olive leaves amounts (1%, 2.5%, 5% and 10% w/w) were added during the extraction process of cv. Cobrancosa olive fruits, collected in the late season. Standard quality parameters, oxidative stability, fatty acids profile, tocopherols, chlorophylls, and carotenoids contents were evaluated. Olive leaves addition induces a slight increase in acidity, peroxide value, K232, and K270 without compromising olive oils classification, but the resistance to oxidation was significantly improved. Vitamin E increased nearly 30% with 10% of leaves added mainly due to the considerable increase in α-tocopherol. A similar effect was observed in the contents of chlorophylls (chlorophyll a and pheophytin a) and carotenoids (lutein and β-carotene), that attributed a more intense greener pigmentation and enhanced nutritional attributes. Significant correlations were observed for several parameters with the amounts of leaves added. Moreover, leaves addition modified the characteristics and composition of the olive oils in a way that was possible to discriminate and to classify each group.

Multivariate Data Analysis of Fatty Acid Content in the Classification of Olive Oils Developed Through Controlled Crossbreeding

AbstractThe fatty acid (FA) composition of 540 Tunisian virgin olive oil hybrids (VOO) were classified by principal component analysis (PCA). Pearson correlation between FA variables revealed an inverse association between C18:1 and C18:2; C18:1 and C16:0, while C16:0 and C16:1 were positively correlated. PCA yielded five significant PCs, which together account for 79.95% of the total variance; with PC1 contributing 36.84% of the total. Eigenvalue analysis revealed that PC1 was mainly attributed to C18:1, monounsaturated fatty acids (MUFA) and the ratios oleic/linoleic (O/L) and monounsaturated fatty acids/polyunsaturated fatty acids (MUFA/PUFA); PC2, by C16:0, saturated fatty acids (SFA) and the palmitic/linoleic ratio (P/L); PC3 by C18:2 and C22:0, PC4 by C18:0 and PC5, by C17:1. Then, PCA analysis indicated that in addition to C16:0, C18:0, C18:1, C17:1, and C22:0, MUFA, SFA and the ratios O/L, P/L and MUFA/PUFA were determined to be the main factors responsible for the olive oil hybrids discrimination.

Improving the training and data processing of an electronic olfactory system for the classification of virgin olive oil into quality categories

A large amount of virgin olive oil samples and different feature selections have been employed in order to improve the classification capacity of an electronic olfactory system, based on thin film metal oxide semiconductor, to be applied into the virgin olive oil industrial analysis, according to the current European Union regulation. The basic ability of the employed equipment for aroma discrimination was tested by using pure aromatic compounds some of them usually present in the virgin olive oil. For these samples, the employed system shows well different responses and identification patterns. After that, more than three hundred virgin olive oil samples from different origins were analysed. The fingerprints of such samples anticipate good discrimination between higher and lower quality samples. Multivariate analysis (principal component analysis and discriminate factorial analysis) was used in order to obtain the best pattern recognition algorithms and the better classification of unknown samples. The real improvement was obtained when an alternative feature selection named Five Contiguous Points was used. This feature selection was revealed to increase the separation between the different quality categories due to a better use of the information contained in the sensor response curves. In summary, the best results for virgin olive oil identification were obtained by the Five Contiguous Points–discriminate factorial analysis combination, with the increase of the number of samples demonstrating that the classification ability is clearly improved. Finally, this study also provides an overview on future application.

Multi-capillary column-ion mobility spectrometry: a potential screening system to differentiate virgin olive oils

The potential of a headspace device coupled to multi-capillary column-ion mobility spectrometry has been studied as a screening system to differentiate virgin olive oils ("lampante," "virgin," and "extra virgin" olive oil). The last two types are virgin olive oil samples of very similar characteristics, which were very difficult to distinguish with the existing analytical method. The procedure involves the direct introduction of the virgin olive oil sample into a vial, headspace generation, and automatic injection of the volatiles into a gas chromatograph-ion mobility spectrometer. The data obtained after the analysis by duplicate of 98 samples of three different categories of virgin olive oils, were preprocessed and submitted to a detailed chemometric treatment to classify the virgin olive oil samples according to their sensory quality. The same virgin olive oil samples were also analyzed by an expert's panel to establish their category and use these data as reference values to check the potential of this new screening system. This comparison confirms the potential of the results presented here. The model was able to classify 97% of virgin olive oil samples in their corresponding group. Finally, the chemometric method was validated obtaining a percentage of prediction of 87%. These results provide promising perspectives for the use of ion mobility spectrometry to differentiate virgin olive oil samples according to their quality instead of using the classical analytical procedure.