Olive oil quality classification and measurement of its organoleptic attributes by untargeted GC-MS and multivariate statistical-based approach

Prospective exploration of hazelnut's unsaponifiable fraction for geographical and varietal authentication: A comparative study of advanced fingerprinting and untargeted profiling techniques

This study compares two data processing techniques (fingerprinting and untargeted profiling) to authenticate hazelnut cultivar and provenance based on its unsaponifiable fraction by GC-MS. PLS-DA classification models were developed on a selected sample set (n = 176). As test cases, cultivar models were developed for "Tonda di Giffoni" vs other cultivars, whereas provenance models were developed for three origins (Chile, Italy or Spain). Both fingerprinting and untargeted profiling successfully classified hazelnuts by cultivar or provenance, revealing the potential of the unsaponifiable fraction. External validation provided over 90 % correct classification, with fingerprinting slightly outperforming. Analysing PLS-DA models' regression coefficients and tentatively identifying compounds corresponding to highly relevant variables showed consistent agreement in key discriminant compounds across both approaches. However, fingerprinting in selected ion mode extracted slightly more information from chromatographic data, including minor discriminant species. Conversely, untargeted profiling acquired in full scan mode, provided pure spectra, facilitating chemical interpretability.

Tensor methods in data analysis of chromatography/mass spectroscopy-based plant metabolomics

Plant metabolomics is an important research area in plant science. Chemometrics is a useful tool for plant metabolomic data analysis and processing. Among them, high-order chemometrics represented by tensor modeling provides a new and promising technical method for the analysis of complex multi-way plant metabolomics data. This paper systematically reviews different tensor methods widely applied to the analysis of complex plant metabolomic data. The advantages and disadvantages as well as the latest methodological advances of tensor models are reviewed and summarized. At the same time, application of different tensor methods in solving plant science problems are also reviewed and discussed. The reviewed applications of tensor methods in plant metabolomics cover a wide range of important plant science topics including plant gene mutation and phenotype, plant disease and resistance, plant pharmacology and nutrition analysis, and plant products ingredient characterization and quality evaluation. It is evident from the review that tensor methods significantly promote the automated and intelligent process of plant metabolomics analysis and profoundly affect the paradigm of plant science research. To the best of our knowledge, this is the first review to systematically summarize the tensor analysis methods in plant metabolomic data analysis.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

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.

Discrimination of Olive Oil and Extra-Virgin Olive Oil from Other Vegetable Oils by Targeted and Untargeted HRMS Profiling of Phenolic and Triterpenic Compounds Combined with Chemometrics

Extra-virgin olive oil (EVOO) and virgin olive oil (VOO) are valuable natural products of great economic interest for their producing countries, and therefore, it is necessary to establish methods capable of proving the authenticity of these oils on the market. This work presents a methodology for the discrimination of olive oil and extra-virgin olive oil from other vegetable oils based on targeted and untargeted high-resolution mass spectrometry (HRMS) profiling of phenolic and triterpenic compounds coupled with multivariate statistical analysis of the data. Some phenolic compounds (cinnamic acid, coumaric acids, apigenin, pinocembrin, hydroxytyrosol and maslinic acid), secoiridoids (elenolic acid, ligstroside and oleocanthal) and lignans (pinoresinol and hydroxy and acetoxy derivatives) could be olive oil biomarkers, whereby these compounds are quantified in higher amounts in EVOO compared to other vegetable oils. The principal component analysis (PCA) performed based on the targeted compounds from the oil samples confirmed that cinnamic acid, coumaric acids, apigenin, pinocembrin, hydroxytyrosol and maslinic acid could be considered as tracers for olive oils authentication. The heat map profiles based on the untargeted HRMS data indicate a clear discrimination of the olive oils from the other vegetable oils. The proposed methodology could be extended to the authentication and classification of EVOOs depending on the variety, geographical origin, or adulteration practices.

Influence of the Washing Process and the Time of Fruit Harvesting throughout the Day on Quality and Chemosensory Profile of Organic Extra Virgin Olive Oils

In recent years, there has been a growing demand for organic extra virgin olive oils (OEVOOs) as quality products with greater added value. The aim of the present work was to determine whether the washing process and time of harvesting (morning or afternoon) plays an important role in the quality of Picual OEVOOs by studying quality parameters (degree of acidity, peroxide value, K232, K270, oxidative stability), and volatile and sensory profiles. Olive fruits were harvested at three maturity stages and within each phase at two times of the day (morning and afternoon). Then, samples were divided into two blocks (washed and unwashed). Volatile profile was obtained by headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS). Regarding the physicochemical quality parameters, sensory and volatile profiles, there were no differences between the oils produced under the two treatments applied (washed/non-washed). However, the time of harvesting (morning or afternoon) did influence the volatile and sensory profile, with higher values in the oils obtained from fruits harvested in the morning, being statistically significant for the families of aldehydes, hydrocarbures and lactones. Besides, the olives harvested during the mornings gave rise to oils with higher values in the green and apple fruit attributes.

Volatile Olfactory Profiles of Umbrian Extra Virgin Olive Oils and Their Discrimination through MOX Chemical Sensors

Extra virgin olive oil (EVOO) is the best vegetable oil worldwide but, at the same time, is one of the product victims of fraud in the agri-food sector, and the differences about quality within the extra-virgin olive oil category are often missed. Several scientific techniques were applied in order to guarantee the authenticity and quality of this EVOO. In the present study, the volatile compounds (VOCs) by gas chromatography-mass spectrometry with solid-phase micro-extraction detection (GC-MS SPME), organoleptic analysis by the official Slow Food panel and the detection by a Small Sensor System (S3) were applied. Ten EVOOs from Umbria, a central Italian region, were selected from the 2021 Slow Food Italian extra virgin olive oil official guide, which includes hundreds of high-quality olive oils. The results demonstrated the possibility to discriminate the ten EVOOs, even if they belong to the same Italian region, by all three techniques. The result of GC-MS SPME detection was comparable at the discrimination level to the organoleptic test with few exceptions, while the S3 was able to better separate some EVOOs, which were not discriminated perfectly by the other two methods. The correlation analysis performed among and between the three methodologies allowed us to identify 388 strong associations with a p value less than 0.05. This study has highlighted how much the mix of VOCs was different even among few and localized EVOOs. The correlation with the sensor detection, which is faster and chipper compared to the other two techniques, elucidated the similarities and discrepancies between the applied methods.

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.

Effect of freezing, fast-freezing by liquid nitrogen or refrigeration to preserve premium extra virgin olive oil during storage

During storage, premium extra virgin olive oils (PEVOO), which are oils of exceptional sensory quality, may lose the organoleptic characteristics that define them. This study assessed the effect of applying modified atmospheres and low temperatures (refrigeration and freezing) on the quality of 4 PEVOO for 24 months. Also, the effect of two freezing methods was studied (in the freezer at − 20 °C and in a bath of liquid nitrogen), along with the impact of freezing on the quality of the oils after thawing and storing at room temperature. Official quality parameters, organoleptic assessment, phenolic compounds, volatile compounds and oxidative stability index were measured periodically. While no significant effect of headspace composition was found, the oils stored at − 20 °C maintained their initial quality better than the oils stored at room temperature. Physicochemical quality parameters remained unchanged throughout the 24 months at − 20 °C. Polar phenolic and volatile compounds associated with green and fruity aromas were better preserved at − 20 °C, which translated into a minimum change in the sensory profile of the oils. While no significant difference was observed regarding oxidative parameters, freezing at − 20 °C maintained the initial volatile and sensory profile of the oils better than freezing with liquid nitrogen. Lastly, quality of thawed oils showed no significant differences compared to control oils during storage at room temperature. In conclusion, storage at − 20 °C maintains the quality of PEVOO, especially their sensory profile, and does not compromise their quality after thawing.

Rapid Identification of Adulteration in Extra Virgin Olive Oil via Dynamic Headspace Sampling and High-Pressure Photoionization Time-of-Flight Mass Spectrometry

High-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) combined with dynamic headspace sampling was developed for rapid identification of adulteration in extra virgin olive oil (EVOO). The volatile organic compound (VOC) fingerprints of EVOO, refined rapeseed oil (r-RO), peanut oil (PO), corn oil (CO), fragrant rapeseed oil (f-RO), and sunflower oil (SO) were obtained in just 1.5 min, which enabled satisfactory classification of different edible oils. 1,4-Bis(methylene)cyclohexane and dimethyl disulfide were unique VOCs in r-RO and f-RO, respectively, while 2,5-dimethylpyrazine and 2-methylpyrazine were distinctive VOCs in PO. Percentages as low as 3% r-RO, 1% PO, and 1% f-RO in r-RO-EVOO, PO-EVOO, and f-RO-EVOO mixtures, respectively, were successfully identified based on the characteristic VOCs. Linear regression equations of these VOCs were established and utilized for predicting the adulteration proportions. The good agreements between the actual adulteration proportions and the predicted ones demonstrated that HPPI-TOFMS was reliable for the quantification of EVOO adulteration.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Multivariate Optimization Procedure for Dynamic Headspace Extractions Coupled to GC(×GC)

Volatile organic compounds (VOCs) are ubiquitous chemicals of great interest in the study of aromas and flavours of foods. Many recent studies present optimized headspace (HS) and dynamic headspace (DHS) methods for specific sample types; however, the literature does not present ­(to the best of our knowledge) a generalized procedure for the thorough optimization of a DHS extraction. This article presents an approach using design of experiments (DoE) for the optimization of DHS extraction parameters. The approach is demonstrated for two different food sample types with diverse populations of VOCs: active sourdough colony as an example with a high moisture content, and sourdough bread as an example with a lower moisture content. Optimized methods are assessed for VOC extraction reproducibility and exhaustiveness; guidelines for DHS optimization are presented.

Novel sampling strategy for alive animal volatolome extraction combined with GC-MS based untargeted metabolomics: Identifying mouse pup pheromones

In this study, we identify 11 mouse pup volatiles putatively involved in maternal care induction in adult females. For this purpose, we have adapted the dynamic headspace methodology to extract the volatolome of whole alive animals. Untargeted metabolomic methodology was used to compare the volatolome of neonatal (4-6 days) with elder pups until the age of weaning (21-23 days old). Pup volatolome was analyzed by gas chromatography (GC) coupled to single quadrupole mass spectrometry (MS) using automated thermal desorption for sample introduction. After data processing and multivariate statistical analysis, comparison with NIST spectral library allowed identifying compounds secreted preferentially by neonatal pups: di(propylen glycol) methyl ether, 4-nonenal, di(ethylene glycol) monobutyl ether, 2-phenoxyethanol, isomethyl ionone, tridecanal, 1,3-diethylbenzene, 1,2,4,5-tetramethylbenzene, 2-ethyl-p-xylene and tri(propylene glycol) methyl ether. Palmitic acid was enriched in the volatolome of fourth week youngsters compared to neonatal pups. The results demonstrated the great potential of the new sampling procedure combined with GC-MS based untargeted volatolomics to identify volatile pheromones in mammals.

Characteristic fingerprints and volatile flavor compound variations in Liuyang Douchi during fermentation via HS-GC-IMS and HS-SPME-GC-MS

The present study investigates volatile organic compound (VOC) compositional changes in Liuyang Douchi during fermentation via a HS-GC-IMS and HS-SPME-GC-MS combination approach. A total of 115 VOCs were identified from Douchi, most of which were accumulated during pile fermentation. Notably, most alcohols and acids decreased with fermentation, while esters, ketones, pyrazines, and phenols accumulated during pile fermentation. Depending on the VOCs identified by GC-IMS/MS, the different fermentation stages of Douchi could be facilely distinguished. Of these, 49 VOCs were regarded as the marker VOCs of Douchi in different fermentation stage: hexanol, hexanal, and propanoic acid was the marker VOCs of the black beans before fermentation and contributing beany and grassy odors; 1-octen-3-ol and 3-octanone supplying a mushroom aroma to the Douchi fermented for 3-9 days; and esters and pyrazine, especially ethyl acetate and 2,6-dimethylpyrazine, contributing the cocoa, fruity, and nutty aromas of matured Douchi.

Comprehensive Strategy for Sample Preparation for the Analysis of Food Contaminants and Residues by GC-MS/MS: A Review of Recent Research Trends

Food safety and quality have been gaining increasing attention in recent years. Gas chromatography coupled to tandem mass spectrometry (GC-MS/MS), a highly sensitive technique, is gradually being preferred to GC-MS in food safety laboratories since it provides a greater degree of separation on contaminants. In the analysis of food contaminants, sample preparation steps are crucial. The extraction of multiple target analytes simultaneously has become a new trend. Thus, multi-residue analytical methods, such as QuEChERs and adsorption extraction, are fast, simple, cheap, effective, robust, and safe. The number of microorganic contaminants has been increasing worldwide in recent years and are considered contaminants of emerging concern. High separation in MS/MS might be, in certain cases, favored to sample preparation selectivity. The ideal sample extraction procedure and purification method should take into account the contaminants of interest. Moreover, these methods should cooperate with high-resolution MS, and other sensitive full scan MSs that can produce a more comprehensive detection of contaminants in foods. In this review, we discuss the most recent trends in preparation methods for highly effective detection and analysis of food contaminants, which can be considered tools in the control of food quality and safety.

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.

Multivariate optimization of dual-sorbent dynamic headspace extraction of volatiles in wine analysis

The main critical point of newly developed miniaturized sample preparation techniques is a limited extraction capacity. Dynamic headspace extraction offers increased volume of sorbent which is commonly used in environmental analysis. Application of two sorbents (Carbopack B/Carbopack X and Tenax® TA) at different extraction temperatures allows enhancing a range of volatile organic compounds available for analysis. Such approach was applied in our research for quantification of volatile organic compounds in botrytized wines with gas chromatography. The central composite design was included to analysis simultaneous effects of incubation time, incubation temperature, purge volume and purge flow. In attempt to properly assess results, the data evaluation involved Pareto charts, surface response methodology and principal component analysis. Multivariate experimental design revealed statistical significance of purge volume and quadratic terms of incubation time and temperature, for response of volatiles. The quantification method with 0.2-2.0 µg/L LOD and 0.5-5.0 µg/L LOQ values, was developed under simultaneously optimized experimental conditions such as a 54 °C incubation temperature, a 20.18 min incubation time, a 344.3 mL purge volume and a 16.0 mL/min purge flow. The increased levels of linalool oxide, ethyl phenylacetate, γ-hexalactone and α-terpineol were observed in the samples, that correlated with botrytized wine technology.

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.

HPLC-HRMS Global Metabolomics Approach for the Diagnosis of "Olive Quick Decline Syndrome" Markers in Olive Trees Leaves

Olive quick decline syndrome (OQDS) is a multifactorial disease affecting olive plants. The onset of this economically devastating disease has been associated with a Gram-negative plant pathogen called Xylella fastidiosa (Xf). Liquid chromatography separation coupled to high-resolution mass spectrometry detection is one the most widely applied technologies in metabolomics, as it provides a blend of rapid, sensitive, and selective qualitative and quantitative analyses with the ability to identify metabolites. The purpose of this work is the development of a global metabolomics mass spectrometry assay able to identify OQDS molecular markers that could discriminate between healthy (HP) and infected (OP) olive tree leaves. Results obtained via multivariate analysis through an HPLC-ESI HRMS platform (LTQ-Orbitrap from Thermo Scientific) show a clear separation between HP and OP samples. Among the differentially expressed metabolites, 18 different organic compounds highly expressed in the OP group were annotated; results obtained by this metabolomic approach could be used as a fast and reliable method for the biochemical characterization of OQDS and to develop targeted MS approaches for OQDS detection by foliage analysis.

Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine

The establishment and development of a set of methods of oil accurate recognition in a different environment are of great significance to the effective management of oil spill pollution. In this work, the concentration-emission matrix (CEM) is formed by introducing the concentration dimension. The principal component analysis (PCA) is applied to extract the spectral feature. The classification methods, such as Probabilistic Neural Networks (PNNs) and Genic Algorithm optimization Support Vector Machine (SVM) parameters (GA-SVM), are used for oil identification and the recognition accuracies of the two classification methods are compared. The results show that the GA-SVM combined with PCA has the highest recognition accuracy for different oils. The proposed approach has great potential in rapid and accurate oil source identification.

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.

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).

Evaluation of a Simplified Method for GC/MS Qualitative Analysis of Polycyclic Aromatic Hydrocarbons, Polychlorinated Biphenyls, and Organic Pesticides Using PARADISe Computer Program

For complex matrices such as environmental samples, there is usually a problem with not fully resolved peaks during GC/MS analysis. The PARADISe computer program (based on the PARFAC2 model) allows the identification of peaks using the deconvoluted mass spectra and the NIST MS library. The number of repetitions required by this software (at least five) is a real limitation for the determination of semi-volatile compounds, like polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and organic pesticides in environmental samples. In this work, the method to overcome this condition was proposed and evaluated. The sets of the five files required by PARADISe were prepared by mathematically modifying the original GC/MS chromatograms obtained for the standard mixture (C = 2 µg/mL of 40 compounds) and real sample extracts (soil samples with different total organic carbon content and one cardboard extract) spiked with standards. Total average match factor for all the substances identified in a standard mixture was 874 (near 900—“excellent match”), and for all the substances in the real samples, it was 786 (near 800—“good match”). The results from PARADISe were comparable to those obtained with other programs: AMDIS (NIST) and MassHunter (Agilent), tested also in this work. PARADISe software can be effectively used for chromatogram deconvolution and substance identification.

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.

Headspace Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry Quantification of the Volatile Profile of More than 1200 Virgin Olive Oils for Supporting the Panel Test in Their Classification: Comparison of Different Chemometric Approaches

A reliable and robust tool for supporting the panel test in virgin olive oil classification is still required. We propose four chemometric approaches based on t test, principal component analysis (PCA) and linear discriminant analysis (LDA), applied for combining sensorial data, and chemical measurements. The former was from the panel test, and the latter was from headspace solid-phase microextraction-gas chromatography-mass spectrometry quantitation of 73 volatile organic compounds (VOCs) of 1223 typical commercial virgin olive oils, with most of them recognized as difficult to classify with accuracy by the panel test. The approaches were developed and validated, and the best results, with 83.5% correct classification, were using the PCA-LDA approach. Among the other methods, developed for proposing simplified procedures based on a smaller number of VOCs, the best method gave 80.1% correct classification only using 10 VOCs. All of the approaches suggested that octane, heptanal, pent-1-en-3-ol, Z-3-hexenal, nonanal, and 4-ethylphenol should be considered as a basis of volatiles for classification of olive oil samples.