Classification Of Extra Virgin Olive Oils Research Articles

A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine

Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classified EVOO, refined-pomace olive oil (R-POO), rapeseed oil (RO), soybean oil (SO), peanut oil (PO), sunflower oil (SFO), flaxseed oil (FO), corn oil (CO), extra virgin olive oil adulterated with rapeseed oil (EVOO-RO) and extra virgin olive oil adulterated with corn oil (EVOO-CO). Support vector machine (SVM) classification of EVOO, other edible oils, and EVOO adulteration identification achieved 100% accuracy for the training set sample and 94.44% accuracy for the test set sample. As a result, this SVM model could identify effectively the adulteration EVOO with the limit of 1% RO and 1% CO. Therefore, the excellent classification and predictive power of this model indicated pigments could be used as potential markers for identifying EVOO adulteration.

Quality assessment and geographical origin classification of extra-virgin olive oils imported into China

Extra-virgin olive oil (EVOO) is an important imported commercial product in China. This study comprehensively evaluated the quality of EVOOs imported from different countries into China based on chemical parameters and sensory attributes. Multivariate statistics were used to authenticate their geographical origins. 71.8% of the oils failed to meet the current official standards at inland and abroad established for commercial EVOO category. Pyropheophytin, defect attributes, peroxide value, and K270 were above the limits in 32%, 24%, 23%, and 5% of the failed samples respectively, while free acidity, K232 and delta K were above the limits in 4%. Fatty acids and delta ECN42 were beyond the limits in 2% of the failed samples. Linear discriminant analysis (LDA) and orthogonal partial least squares discriminant analysis (OPLS-DA) based on fatty acids and triacyglycerols succeeded in identifying the origin of olive oils. 3.5% of the total might be mislabelled the geographical origin. The main quality problems of the analyzed oils include oxidation, counterfeiting with refined oils and origin fraud. Considering the results and the fact that most EVOOs in China are imported from other countries, chemometrics combined with critical quality attributes should be an ideal way of EVOO quality control, protecting consumers from frauds.

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.

Does water addition during the industrial milling phase affect the chemical-sensory quality of olive oils? The case of cv. Arbequina oils

At olive oil industrial extraction, water addition is a practice overcoming the formation of thick pastes. The effect of water addition (0 to 6.2%, kgadded water/kgolives), during the industrial milling of cv. Arbequina olives, on the oils’ chemical-sensory quality, was evaluated. Despite the extra virgin olive oil classification, compared with the water incorporation (1.2–6.2%), extraction without water addition resulted into oils that showed less primary oxidation (lower peroxide values and K232), greater total phenolic content (+12–22%) and higher oxidative stability (+22–31%). No water addition increased the oils secoiridoids content (+5–13%), mainly oleacein (+27–79%). Oils extracted without water addition had a more intense ripe fruity sensation (≥ +11%) but lower fruit intensities (at least −4%). Thus, the quality and stability of the cv. Arbequina oils can be favoured if extracted without adding water during the olives industrial milling.

Fatty acid ethyl esters in virgin olive oils: a correlation study with the volatile profile

Fatty acid ethyl esters are the newest mandatory quality parameters introduced for extra virgin olive oil classification linked to fermentative processes occurring in low-quality olives. Although it is clear the strong linkage of ethyl esters with sensory fermentative defects there is a lack of knowledge about their relationship with specific volatile compounds. Hence, a study was carried out to evaluate the correlation among extra virgin olive oil quality indices and its respective volatile profile with fatty acid ethyl esters. Thirty-five virgin olive oils were collected from local oily industry and analysed for the fatty acid ethyl esters content, the basic quality parameters, the total phenolic content and the volatile profile. The obtained results showed that the fatty acid ethyl esters were negatively correlated to total phenolic compounds (p<0.05) and trans-2-hexenal content (p<0.001) while positively correlated (p<0.001) to ethyl acetate and ethyl acetate/trans-2-hexenal and ethyl acetate/hexanal ratios. Interestingly, ethanol was not correlated at all with the ethyl esters content in oils.

FT-IR extra virgin olive oil classification based on ethyl ester content

According to the Regulation (EU) 2016/2095, extra virgin olive oils (EVOO) must contain a maximum of 35 mg kg−1 of fatty acid ethyl esters (FAEE). The official method for FAEE quantification is time-consuming and requires the use of a large amount of toxic solvents. Thus, the aim of this work was the application of FT-IR spectroscopy for the development of classification models (i.e. Linear Discriminant Analysis, LDA; Soft Independent Modelling of Class Analogy, SIMCA) able to discriminate EVOO from non-EVOO based on FAEE content. To the aim, 113 EVOO and 46 non-EVOO samples were analysed. Since the Principal Component Analysis revealed that the whole FT-IR spectral range (both raw or pre-treated) was not promising in EVOO and non-EVOO distinction, a variable selection strategy was applied (i.e. SELECT algorithm). All the classification models were validated both by cross validation and with three different external test sets. The best and more robust LDA model was obtained with the raw FT-IR selected variables, reaching 96–100% of correct classification in prediction. SIMCA models resulted less reliable. In particular, the low specificity values (40–67%) revealed that there is a high probability of assignment of non-EVOO to the EVOO class. In conclusion, FT-IR spectroscopy coupled with a discriminant classification approach is a useful tool for a rapid and fast discrimination of EVOO and non-EVOO based on FAEE content. Since the variable selection strategy was effective, the development of simplified and cheap instruments can boost the FT-IR spectroscopy application also in small enterprises, giving the opportunity to acquire many important information about olive oils.

FATTY ACIDS METHYL AND ETHYL ESTERS BEHAVIOUR DURING OLIVES PROCESSING BY MEANS OF TECHNOLOGICAL COADJUVANTS

Recently, the quantification of fatty acids alkyl esters (FAAE) has become mandatory for the extra virgin olive oil classification. However, the behaviour of such metabolites, during olives processing, is not yet well understood. Thus, the present paper aims to point out the influence of the use of calcium carbonate on the fatty acids alkyl esters content of the oils. The results showed that the content of FAAE was significantly influenced by the technological coadjuvant. The use of calcium carbonate led to a general reduction of FAAE compared to the untreated samples. Methyl esters of fatty acids were more susceptible to the use of processing aid than the ethyl esters.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactory–gustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Characterization of Turkish Virgin Olive Oils Produced from Early Harvest Olives

AbstractOlives were collected from various districts of Turkey (North and South Aegean sub‐region, Bursa‐Akhisar, South East Anatolia region) harvested over seven (2001–2007) seasons. The aim of this study was to characterize the chemical profiles of the oils derived from single variety Turkish olives including Ayvalik, Memecik, Gemlik, Erkence, Nizip Yaglik and Uslu. The olive oils were extracted by super press and three phase centrifugation from early harvest olives. Chosen quality indices included free fatty acid content (FFA), peroxide value (PV) and spectrophotometric characteristics in the ultraviolet (UV) region. According to the FFA results, 46% (11 out of 24 samples) were classified as extra virgin olive oils; whereas using the results of PV and UV, over 83% (over 19 of the 24 samples) had the extra virgin olive oil classification. Other measured parameters included oil stability (oxidative stability, chlorophyll pigment, pheophytin‐α), cis–trans fatty acid composition and color index. Oxidative stability among oils differed whereas the cis–trans fatty acid values were within the national and international averages. Through the application of two multivariate statistical methods, Principal component and hierarchical analyses, early harvest virgin olive oil samples were classified according to the geographical locations categorized in terms of fatty acid profiles. Such statistical clustering gave rise to defined groups. These data provide evidence of the variation in virgin olive oil quality, especially early harvest and cis–trans isomers of fatty acid profiles from the diverse agronomic conditions in the olive growing regions of Turkey.