Virgin olive oil volatile fingerprint and chemometrics: Towards an instrumental screening tool to grade the sensory quality

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.

Analysis of Volatile Hydrocarbons (Pentene Dimers and Terpenes) in Extra Virgin Olive Oil: Optimization by Response Surface Methodology and Validation of HS-SPME-GC-MS Method

A head space-solid phase microextraction-gas chromatography-mass spectrometery (HS-SPME-GC-MS) method for the simultaneous analysis of pentene dimers from lipoxygenase (LOX) pathway, monoterpenes, and sesquiterpenes in extra virgin olive oil (EVOO) was proposed. A Doehlert design was performed; the conditions of the HS-SPME preconcentration step (extraction temperature, extraction time, sample amount, and desorption time) were optimized by response surface methodology, allowing defining the method operable design region. A quantitative method was set up using the multiple internal standard normalization approach: four internal standards were used, and the most suitable one was selected for area normalization of each external standard. The quantitative method was successfully validated and applied to a series of monocultivar EVOOs. This is the first paper in which a quantitative method using commercial standards has been proposed for the analysis of an important class of molecules of EVOO such as pentene dimers. The optimized method is suitable for routine analysis aimed at characterizing high quality EVOOs.

Comparing the potential of IR-spectroscopic techniques to gas chromatography coupled to ion mobility spectrometry for classifying virgin olive oil categories

Virgin olive oil (OO) can be classified into three different categories: extra virgin, virgin and lampante. The official method for this classification, based on physicochemical analysis and sensory tasting, is considered useful and effective, although it is a costly and time-consuming process. The aim of this study was to assess the potential of some analytical techniques for classifying and predicting different OO categories to support official methods and to provide olive oil companies with a rapid tool to assess product quality. Thus, mid and near infrared spectroscopies (MIR and NIR) have been compared by using different instruments and with head-space gas chromatography coupled to an ion mobility spectrometer (HS-GC-IMS). High classification success rates in validation models were obtained using IR spectrometers (>70% and > 80% in average for ternary and binary classifications, respectively), although HS-GC-IMS showed greater classification potential (>85% and > 90%).

Identifying markers volatiles in Brazilian virgin oil by multiple headspace solid-phase microextraction, and chemometrics tools

A protocol was optimized to determine the volatile profile from monovarietal virgin olive oil (VOO) by multiple headspace solid-phase microextraction (MHS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) analysis. For this, a Plackett-Burman (PB) and central composite rotational designs (CCRD) were used to define the best condition of extraction. Moreover, fatty acids profile and principal component analysis (PCA) was used to identify markers among the cultivars. The amount of 0.1 g of sample was enough to express the volatile composition of the olive oils by MHS-SPME. Volatile compounds [nonanal, (Z)-3-Hexen-1-ol, (Z)-3-Hexenyl Acetate, Hexyl Acetate, 3-Methylbutyl Acetate, (E)-2-Hexen-1-ol, (E)-2-Hexenyl Acetate] and fatty acids [C17:1, C18, C18:1, C18:2] were those reported such as the markers in the varieties of olive oils. The PCA analysis allowed the classification of the most representative volatiles and fatty acids for each cultivar. Through two principal components was possible to obtain 81.9% of explanation of the variance of the compounds. The compounds were quantified using a validated method. The MHS-SPME combined with multivariate analysis showed a promising tool to identify markers and for the discrimination of olive oil varieties.

One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts

A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.

Aromatic, Sensory, and Fatty Acid Profiles of Arbequina Extra Virgin Olive Oils Produced Using Different Malaxation Conditions

The demand for high-quality extra virgin olive oil (EVOO) is growing due to its unique characteristics. The aroma and flavor of EVOO depend on its content of volatile organic compounds (VOCs), whose formation is affected by the olive variety and maturity index, and the oil production process. In this study, the sensory quality and VOC and fatty acid (FA) profiles were determined in Arbequina olive oils produced by applying different malaxation parameters (20, 25, and 30 °C, and 30 and 45 min). All the olive oils were classified as EVOO by a sensory panel, regardless of the production conditions. However, cold extraction at 20 °C resulted in more positive sensory attributes (complexity). The FA concentration increased significantly with the malaxation temperature, although the percentage profile remained unaltered. Finally, an OPLS-DA model was generated to identify the discriminating variables that separated the samples according to the malaxation temperature. In conclusion, the tested range of malaxation parameters appeared not to degrade the distinctive attributes/organoleptic profile of olive oil and could be applied to obtain an EVOO of high sensory quality, especially at 20 °C.

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents.

Exploring multiple-cumulative trapping solid-phase microextraction coupled to gas chromatography-mass spectrometry for quality and authenticity assessment of olive oil

This study explores the potential of an innovative multi-cumulative trapping headspace solid-phase microextraction approach coupled with untargeted data analysis to enhance the information provided by aroma profiling of virgin olive oil. Sixty-nine samples of different olive oil commercial categories (extra-virgin, virgin and lampante oil) and different geographical origins were analysed using this novel workflow. The results from each sample were aligned and compared using for the first time a tile-based approach to enable the mining of all of the raw data within the chemometrics platform without any pre-processing methods. The data matrix obtained allowed the extraction of multiple-level information from the volatile profile of the samples. Not only was it possible to classify the samples within the commercial category that they belonged to, but the same data also provided interesting information regarding the geographical origin of the extra-virgin olive oil.

Analyzing the Organoleptic Quality of Commercial Extra Virgin Olive Oils: IOC Recognized Panel Tests vs. Electronic Nose

Virgin olive oil (VOO) classification into quality categories determines its labeling and market price. This procedure involves performing a series of chemical–physical analyses and, ultimately, a sensory analysis through the panel test. This work explores the analysis of VOOs quality with an electronic olfactory system (EOS) and examines its abilities using the panel test as a reference. To do this, six commercial olive oils labelled as extra virgin were analyzed with an EOS and classified by three panels recognized by the International Olive Council. The organoleptic analysis of the oils by the panels indicated that most of the oils in the study were in fact not extra virgin. Besides this, the classifications showed inconsistencies between panels, needing statistical treatment to be used as a reference for the EOS training. The analysis of the same oils by the EOS and their subsequent statistical analysis by PCA revealed a good correlation between the first principal component and the olive oil quality from the panels using average scores. It also showed a more consistent classification than the panels. Overall, the EOS proved to be a cheaper, faster, and highly reliable method as a complement to the panel test for the olive oil classification.

Geographical origin discrimination of "Ntopia" olive oil cultivar from Ionian islands using volatile compounds analysis and computational statistics

The aim of the present study was to characterize the aroma profile of olive oil of the "Ntopia" (local) cultivar from the Ionian islands (Zakynthos, Kefalonia, Leukada, and Kerkyra) (Greece), and investigate whether specific volatile compounds could be considered as indicators of olive oil geographical origin, using computational statistics. In this context, 137 olive oil samples were subjected to headspace solid phase microextraction coupled to gas chromatography/mass spectrometry using the internal standard method. Computational statistics on the semi-quantitative data of olive oil samples, as rapid machine learning algorithms, showed that specific volatile compounds could be used as indicators of geographical origin of olive oil of the "Ntopia" cultivar, among the four main Ionian islands. Volatile compounds such as ethanol, pentanal, 2,4-dimethylheptane, 3,7-dimethyl-1,3,6-octatriene (E), 2,5-dimethylnonane, 1-hexanol, 6-methyl-5-hepten-2-one, octanal, dl-Limonene, acetic acid hexyl ester and dodecane could aid to the geographical origin discrimination of "Ntopia" olive oil cultivar when two (Zakynthos and Kefalonia) or four (Zakynthos, Kefalonia, Leukada and Kerkyra) Ionian islands are subjected to statistical analysis. The discrimination rate using the cross-validation method was 100% and 85.7%, respectively. These results were further evaluated using training and holdout partitions, during which a comparable classification rate was obtained.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00217-021-03863-2.

Chromatographic Fingerprinting Enables Effective Discrimination and Identitation of High-Quality Italian Extra-Virgin Olive Oils

The challenging process of high-quality food authentication takes advantage of highly informative chromatographic fingerprinting and its identitation potential. In this study, the unique chemical traits of the complex volatile fraction of extra-virgin olive oils from Italian production are captured by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and explored by pattern recognition algorithms. The consistent realignment of untargeted and targeted features of over 73 samples, including oils obtained by different olive cultivars (n = 24), harvest years (n = 3), and processing technologies, provides a solid foundation for sample identification and discrimination based on production region (n = 6). Through a dedicated multivariate statistics workflow, identitation is achieved by two-level partial least-square (PLS) regression, which highlights region diagnostic patterns accounting between 58 and 82 of untargeted and targeted compounds, while sample classification is performed by sequential application of soft independent modeling for class analogy (SIMCA) models, one for each production region. Samples are correctly classified in five of the six single-class models, and quality parameters [i.e., sensitivity, specificity, precision, efficiency, and area under the receiver operating characteristic curve (AUC)] are equal to 1.00.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Delineating the extra-virgin olive oil aroma blueprint by multiple headspace solid phase microextraction and differential-flow modulated comprehensive two-dimensional gas chromatography

Comprehensive two-dimensional gas chromatography with parallel mass spectrometry and flame ionization detection (GC × GC-MS/FID) enables effective chromatographic fingerprinting of complex samples by comprehensively mapping untargeted and targeted components. Moreover, the complementary characteristics of MS and FID open the possibility of performing multi-target quantitative profiling with great accuracy. If this synergy is applied to the complex volatile fraction of food, sample preparation is crucial and requires appropriate methodologies capable of providing true quantitative results. In this study, untargeted/targeted (UT) fingerprinting of extra-virgin olive oil volatile fractions is combined with accurate quantitative profiling by multiple headspace solid phase microextraction (MHS-SPME). External calibration on fifteen pre-selected analytes and FID predicted relative response factors (RRFs) enable the accurate quantification of forty-two analytes in total, including key-aroma compounds, potent odorants, and olive oil geographical markers. Results confirm good performances of comprehensive UT fingerprinting in developing classification models for geographical origin discrimination, while quantification by MHS-SPME provides accurate results and guarantees data referability and results transferability over years. Moreover, by this approach the extent of internal standardization procedure inaccuracy, largely adopted in food volatiles profiling, is measured. Internal standardization yielded an average relative error of 208 % for the fifteen calibrated compounds, with an overestimation of + 538% for (E)-2-hexenal, the most abundant yet informative volatile of olive oil, and a -89% and -80% for (E)-2-octenal and (E)-2-nonenal respectively, analytes with a lower HS distribution constant. Compared to existing methods based on 1D-GC, the current procedure offers better separation power and chromatographic resolution that greatly improve method specificity and selectivity and results in lower LODs and LOQs, high calibration performances (i.e., R² and residual distribution), and wider linear range of responses. As an artificial intelligence smelling machine, the MHS-SPME-GC × GC-MS/FID method is here adopted to delineate extra-virgin olive oil aroma blueprints; an objective tool with great flexibility and reliability that can improve the quality and information power of each analytical run.

Volatile Profile of Two-Phase Olive Pomace (Alperujo) by HS-SPME-GC-MS as a Key to Defining Volatile Markers of Sensory Defects Caused by Biological Phenomena in Virgin Olive Oil

An olive pomace from the two-phase decanter stored in different conditions was used as a model to simulate the detrimental biological phenomena occurring during olive oil processing and storage. A group of EVOO and defective oils were also analyzed. The volatile fraction was studied with HS-SPME-GC-MS; 127 volatiles were identified (55 of which tentatively identified) and evaluated over time. Seven volatiles were tentatively identified for the first time in olive oil; the role of C6 alcohols in detrimental biological phenomena was highlighted. Suitable volatile markers for defects of microbiological origin were defined, particularly the fusty/muddy sediment. They were then applied to olive oils with different quality categories; one of the markers was able to discriminate among EVOOs and all the defective samples, including the borderline ones. The marker was constituted by the sum of concentrations of 10 esters, 4 alcohols, 1 ketone, and 1 α-hydroxy-ketone but no carboxylic acids.

Chromatographic Fingerprinting Strategy to Delineate Chemical Patterns Correlated to Coffee Odor and Taste Attributes

Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor-taste integration.

Virgin Olive Oil Volatile Compounds: Composition, Sensory Characteristics, Analytical Approaches, Quality Control, and Authentication

Volatile organic compounds strongly contribute to both the positive and negative sensory attributes of virgin olive oil, and more and more studies have been published in recent years focusing on several aspects regarding these molecules. This Review is aimed at giving an overview on the state of the art about the virgin olive oil volatile compounds. Particular emphasis was given to the composition of the volatile fraction, the analytical issues and approaches for analysis, the sensory characteristics and interaction with phenolic compounds, and the approaches for supporting the Panel Test in virgin olive oil classification and in authentication of the botanical and geographic origin based on volatile compounds. A pair of detailed tables with a total of approximately 700 volatiles identified or tentatively identified to date and tables dealing with analytical procedures, sensory characteristics of volatiles, and specific chemometric approaches for quality assessment are also provided.

Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics

The commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reducing the assessors’ workload and improving their performance. The present work aims to in-house validate a screening strategy consisting of two sequential binary partial least squares-discriminant analysis (PLS-DA) models that was suggested to be successful in a proof-of-concept study. This approach is based on the volatile fraction fingerprint obtained by HS-SPME–GC–MS from more than 300 virgin olive oils from two crop seasons graded by six different sensory panels into extra virgin, virgin or lampante categories. Uncertainty ranges were set for the binary classification models according to sensitivity and specificity by means of receiver operating characteristics (ROC) curves, aiming to identify boundary samples. Thereby, performing the screening approach, only the virgin olive oils classified as uncertain (23.3%) would be assessed by a sensory panel, while the rest would be directly classified into a given commercial category (78.9% of correct classification). The sensory panel’s workload would be reduced to less than one-third of the samples. A highly reliable classification of samples would be achieved (84.0%) by combining the proposed screening tool with the reference method (panel test) for the assessment of uncertain samples.

Determination of volatile compounds responsible for sensory characteristics from Brazilian extra virgin olive oil using HS-SPME/GC-MS direct method

Producing of extra virgin olive oils (EVOOs) containing pleasant sensory notes depends on fruits quality and production process and is strongly associated with their classification that is based on aroma and sensory taste. Consolidated as an efficient method, the direct headspace solid phase microextraction technique (HS-SPME) was utilized to characterize the volatile organic compounds (VOCs) profile, which contributes to the aroma of olive oils from southwestern (Serra da Mantiqueira region) and southern (Campanha Gaúcha region) Brazil. In this work, the relationship between the VOCs and sensory characteristics has been established; 19 EVOO samples (12 from Campanha Gaúcha and 7 from Serra da Mantiqueira) were studied. Indeed, the main volatile compounds were analyzed and grouped by their classification as well stood up with the trained sensorial panel perceptions. Relevant correlation between artichoke notes and ripe EVOO and between herbaceous notes and green EVOO was found. Additional correlations were observed for C5 and C6 VOCs with green and fruit/floral notes. The results denote the high quality among the samples and imply that besides the genetic factor, ripe or green classification influenced the volatile composition. PRACTICAL APPLICATION: As the Brazilian olive oil production is increasing, knowing about different sensory characteristics and its correlation with the volatile compounds of extra virgin olive oil represents a good tool to improve the quality. Moreover, the application of direct SPME method was possible evidence in the differentiation of ripe and green olive oils, beyond the production region and in consonance with its sensory notes and characteristics.

A Non-Targeted High-Resolution Mass Spectrometry Study for Extra Virgin Olive Oil Adulteration with Soft Refined Oils: Preliminary Findings from Two Different Laboratories

This work presents a non-targeted high-resolution mass spectrometry inter-laboratory study for the detection of new chemical markers responsible of soft refined oils addition to extra virgin olive oils. Refined oils (soft deodorized and soft deacidified) were prepared on a laboratory scale starting from low-quality olive oils and analyzed together with a set of pure extra virgin olive oil (EVOO) samples and with mixtures of adulterated and pure EVOO at different percentages. The same analytical workflow was applied in two different laboratories equipped with two types of instrumentation (Q-Orbitrap and Q-TOF); a group of discriminant molecules was selected, and a tentative identification of compounds was also proposed. In summary, 12 molecules were identified as markers of this specific adulteration, and seven of them were selected as discriminative in both the laboratories, with a similar trend throughout the samples (i.e., propylene glycol 1 stearate). The results obtained in the two laboratories are comparable, concretely demonstrating the inter-laboratory repeatability of non-targeted studies. As a confirmation, the same markers were detected also in "in-house" mixtures and in suspect commercial deodorized mixtures, reinforcing the robustness of the results obtained and proving that, thanks to these molecules, mixtures containing at least 40% of adulterated oils can be detected.

Headspace Gas Chromatography Coupled to Mass Spectrometry and Ion Mobility Spectrometry: Classification of Virgin Olive Oils as a Study Case

Due to its multiple advantages, ion mobility spectrometry (IMS) is being considered as a complementary technique to mass spectrometry (MS). The goal of this work is to investigate and compare the capacity of IMS and MS in the classification of olive oil according to its quality. For this purpose, two analytical methods based on headspace gas chromatography (HS-GC) coupled with MS or with IMS have been optimized and characterized for the determination of volatile organic compounds from olive oil samples. Both detectors were compared in terms of sensitivity and selectivity, demonstrating that complementary data were obtained and both detectors have proven to be complementary. MS and IMS showed similar selectivity (10 out of 38 compounds were detected by HS-GC-IMS, whereas twelve compounds were detected by HS-GC-MS). However, IMS presented slightly better sensitivity (Limits of quantification (LOQ) ranged between 0.08 and 0.8 µg g⁻¹ for HS-GC-IMS, and between 0.2 and 2.1 µg g⁻¹ for HS-GC-MS). Finally, the potential of both detectors coupled with HS-GC for classification of olive oil samples depending on its quality was investigated. In this case, similar results were obtained when using both HS-GC-MS and HS-GC-IMS equipment (85.71 % of samples of the external validation set were classified correctly (validation rate)) and, although both techniques were shown to be complementary, data fusion did not improve validation results (80.95% validation rate).

Flash Gas Chromatography in Tandem with Chemometrics: A Rapid Screening Tool for Quality Grades of Virgin Olive Oils

This research aims to develop a classification model based on untargeted elaboration of volatile fraction fingerprints of virgin olive oils (n = 331) analyzed by flash gas chromatography to predict the commercial category of samples (extra virgin olive oil, EVOO; virgin olive oil, VOO; lampante olive oil, LOO). The raw data related to volatile profiles were considered as independent variables, while the quality grades provided by sensory assessment were defined as a reference parameter. This data matrix was elaborated using the linear technique partial least squares-discriminant analysis (PLS-DA), applying, in sequence, two sequential classification models with two categories (EVOO vs. no-EVOO followed by VOO vs. LOO and LOO vs. no-LOO followed by VOO vs. EVOO). The results from this large set of samples provide satisfactory percentages of correctly classified samples, ranging from 72% to 85%, in external validation. This confirms the reliability of this approach in rapid screening of quality grades and that it represents a valid solution for supporting sensory panels, increasing the efficiency of the controls, and also applicable to the industrial sector.

Comparison of the Novel Thin Film-Solid Phase Microextraction and Sorptive Extraction Methods for Picual and Hojiblanca Olive Oil Volatile Fraction Analysis in Headspace

For first time, the new device named thin film solid phase microextraction (TF-SPME) has been used to determine the volatile profile of the Picual and Hojiblanca varieties of extra virgin olive oils. To this end, different traditional sampling methods such as headspace sorptive extraction (HSSE) with polydimethylsiloxane (PDMS) and polyethyleneglycol-modified silicone (EG/Silicone) Twisters^® have been compared with the TF-SPME devices coated with different extraction polymeric phases. PARADISe software was used as a non-targeting method to process all data. The best results were obtained by HSSE-PDMS and 2TF-SPME. Moreover, the 2TF-SPME extraction method achieved the most adequate results of linearity for most compounds, according to F-values, while the intermediate precision results were similar for both 2TF-SPME and HSSE-PDMS sampling methods. Different sensitivity was observed between both sampling methods depending on the volatile compound, without being clearly influenced by the polarity of them. Although both sampling methods enabled the main active aroma of olive oil to be determined and for them to be differentiated according to olive variety, the 2TF-SPME method appears to be the most suitable for this goal.

Virgin olive oil metabolomics: A review

Metabolomics involvement in the study of foods is steadily growing. Such a rise is a consequence of the increasing demand in the food sector to address challenges regarding the issues of food safety, quality, and authenticity in a more comprehensive way. Virgin olive oil (VOO) is a key product of the Mediterranean diet, with a globalized consumer interest as it may be associated with various nutritional and health benefits. Despite the strict legislation to protect this high added-value agricultural commodity and offer guarantees to consumers and honest producers, there are still analytical issues needing to be further addressed. Thus, this review aims to present the efforts made using targeted and untargeted metabolomics approaches, namely nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry-based techniques (mainly LC/GC-MS) combined with multivariate statistical analysis. Case-studies focusing on geographical/varietal classification and detection of adulteration are discussed with regards to the identification of possible markers. The advantages and limitations of each of the aforementioned techniques applied to VOO analysis are also highlighted.

An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test

Sensory evaluation, carried out by panel tests, is essential for quality classification of virgin olive oils (VOOs), but is time consuming and costly when many samples need to be assessed; sensory evaluation could be assisted by the application of screening methods. Rapid instrumental methods based on the analysis of volatile molecules might be considered interesting to assist the panel test through fast pre-classification of samples with a known level of probability, thus increasing the efficiency of quality control. With this objective, a headspace gas chromatography-ion mobility spectrometer (HS-GC-IMS) was used to analyze 198 commercial VOOs (extra virgin, virgin and lampante) by a semi-targeted approach. Different partial least squares-discriminant analysis (PLS-DA) chemometric models were then built by data matrices composed of 15 volatile compounds, which were previously selected as markers: a first approach was proposed to classify samples according to their quality grade and a second based on the presence of sensory defects. The performance (intra-day and inter-day repeatability, linearity) of the method was evaluated. The average percentages of correctly classified samples obtained from the two models were satisfactory, namely 77% (prediction of the quality grades) and 64% (prediction of the presence of three defects) in external validation, thus demonstrating that this easy-to-use screening instrumental approach is promising to support the work carried out by panel tests.

Tracking Sensory Characteristics of Virgin Olive Oils During Storage: Interpretation of Their Changes from a Multiparametric Perspective

Virgin olive oil is inevitably subject to an oxidation process during storage that can affect its stability and quality due to off-flavors that develop before the oil surpasses its ‘best before’ date. Many parameters are involved in the oxidation process at moderate conditions. Therefore, a multiparametric study is necessary to establish a link between physico-chemical changes and sensory quality degradation in a real storage experiment. In this context, a storage experiment of 27 months was performed for four monovarietal virgin olive oils, bottled in transparent 500-mL PET bottles and subjected to conditions close to a supermarket scenario. Volatile composition, quality parameters and phenolic compounds were determined monthly. Simultaneously, an accredited sensory panel assessed their sensory characteristics. The stability of the fresh samples was also studied with the oxidative stability index (OSI) and mesh cell-FTIR. (E)-2-hexenal, (Z)-3-hexen-1-ol and (E)-2-hexen-1-ol were identified as markers of the fruity attribute. Hexanal and nonanal were also identified as compounds that were associated with the rise of median of defect during storage. Some disagreements were observed between the sensory assessment and the OSI analyzed by Rancimat. However, the increase of concentration of rancid markers agreed with the increase of aldehyde band measured with mesh cell-FTIR.

Alignment and Proficiency of Virgin Olive Oil Sensory Panels: The OLEUM Approach

A set of 334 commercial virgin olive oil (VOO) samples were evaluated by six sensory panels during the H2020 OLEUM project. Sensory data were elaborated with two main objectives: (i) to classify and characterize samples in order to use them for possible correlations with physical–chemical data and (ii) to monitor and improve the performance of panels. After revision of the IOC guidelines in 2018, this work represents the first published attempt to verify some of the recommended quality control tools to increase harmonization among panels. Specifically, a new “decision tree” scheme was developed, and some IOC quality control procedures were applied. The adoption of these tools allowed for reliable classification of 289 of 334 VOOs; for the remaining 45, misalignments between panels of first (on the category, 21 cases) or second type (on the main perceived defect, 24 cases) occurred. In these cases, a “formative reassessment” was necessary. At the end, 329 of 334 VOOs (98.5%) were classified, thus confirming the effectiveness of this approach to achieve a better proficiency. The panels showed good performance, but the need to adopt new reference materials that are stable and reproducible to improve the panel’s skills and agreement also emerged.