Quality classification of Spanish olive oils by untargeted gas chromatography coupled to hybrid quadrupole-time of flight mass spectrometry with atmospheric pressure chemical ionization and metabolomics-based statistical approach

PaMYB11 promotes suberin deposition in Norway spruce embryogenic tissue during cryopreservation: A novel resistance mechanism against osmosis

The osmotic resistance mechanism has been extensively studied in whole plants or plant tissues. However, little is known about it in embryogenic tissue (ET) which is widely used in plant-based biotechnological systems. Suberin, a cell wall aliphatic and aromatic heteropolymer, plays a critical role in plant cells against osmosis stress. The suberin regulatory biosynthesis has rarely been studied in gymnosperms. Here, PaMYB11, a subgroup 11 R2R3-MYB transcription factor, plays a key role in the osmotic resistance of Norway spruce (Picea abies) ETs during cryoprotectant pretreatment. Thus, RNA-seq, histological, and analytical chemical analyses are performed on the stable transformations of PaMYB11-OE and PaMYB11-SRDX in Norway spruce ETs. DAP-seq, Y1H, and LUC are further combined to explore the PaMYB11 targets. Activation of PaMYB11 is necessary and sufficient for suberin lamellae deposition on Norway spruce embryogenic cell walls, which plays a decisive role in ET survival under osmotic stress. Transcriptome analysis shows that PaMYB11 enhances suberin lamellae monomer synthesis by promoting very long-chain fatty acid (VLCFA) synthesis. PaPOP, PaADH1, and PaTET8L, the first two (PaADH1 and PaPOP, included) involved in VLCFA synthesis, are proved to be the direct targets of PaMYB11. Our study identified a novel osmotic response directed by PaMYB11 in Norway spruce ET, which provides a new understanding of the resistance mechanism against osmosis in gymnosperms.

Identification and quantification of flavor compounds in smoked tuna fish based on GC-Orbitrap volatolomics approach

Cold smoking enhances the appeal of fish products, offering consumers a smooth texture and a delicate smoky flavor. This study aims to explore variations in the volatile profile from different exposure times during cold smoking processing (light, moderate, and full-cure) in tune samples. An innovative untargeted analytical approach, headspace solid-phase microextraction combined with gas chromatography and a hybrid quadrupole-orbitrap mass analyzer, was employed to identify 86 volatiles associated with the cold smoking process. Most of these compounds, including phenols, furan derivates, aldehydes, cyclic ketones, and different aromatic species, were found to contribute to the smoke odor. The development of a QuEChERS-based extraction and clean-up method facilitated the quantification of 25 relevant smoky markers across all smoking degrees, revealing significant concentration differences after 15 h of smoking. This research sheds light on the dynamics of cold smoking impact and its on the flavor profile and safety quality of processed fish products.

A comprehensive HRMS methodology using LC-(ESI)-/GC-(APCI)-QTOF MS complementary platforms for wide-scope target screening of >750 pesticides in olive oil

This study presents the development and validation of a comprehensive high-resolution mass spectrometry (HRMS) methodology for the detection of 771 pesticides in olive oil, using liquid chromatography with electrospray ionization, operating in positive and negative mode, and gas chromatography with atmospheric-pressure chemical ionization in positive mode, both coupled to quadrupole-time-of-flight mass spectrometry (LC-(ESI)-/GC-(APCI)-QTOF MS). Special reference is made to the post-acquisition evaluation step, in which all LC/GC-HRMS analytical evidence (i.e. mass accuracy, retention time, isotopic pattern, MS/MS fragmentation) is taken into account in order to successfully identify the compounds. The sample preparation of the method involves a QuEChERS-based protocol, common for both techniques, differentiated only on the reconstitution step, making the method highly applicable in routine analysis. A smart evaluation of method's performance was carried out, with 65 representative analytes comprising the validation set. The method was validated in terms of linearity, accuracy, matrix effect and precision, while the limits of detection and quantification of the method were estimated. Finally, twenty Greek olive oil samples were analysed in both analytical platforms and the findings included the pesticides lambda-cyhalothrin, chlorpyrifos, phosphamidon, pirimiphos-methyl and esprocarb at low ng g-1 level.

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%).

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.

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.

Analysis of Grape Volatiles Using Atmospheric Pressure Ionization Gas Chromatography Mass Spectrometry-Based Metabolomics

Analysis of volatile compounds in fruits and plants can be a challenging task as they present in a large amount with structural diversity and high aroma threshold, the information on molecular ion can be very useful for compound identification. Electron ionization gas-chromatography-mass spectrometry (EI-GC-MS) which is widely used for the analysis of plant volatiles has a certain limitation providing the limited capability to characterize novel metabolites in a complex biological matrix due to hard fragmentation level. Atmospheric pressure ionization using APGC source in combination with high-resolution time-of-flight mass spectrometry (TOF-MS) provides an excellent combination of GC with high-resolution mass spectrometry. The APGC-MS approach provides several advantages over the conventional EI and CI based GC-MS techniques in metabolomics studies due to highly reduced fragmentation, which preserves molecular ion, and accurate mass measurement by HRMS allows to deduce the elemental composition of the volatile compounds. Moreover, the use of MS^E mode provides spectral similarity to EI in high-energy mode which can be used for the further confirmation of metabolite identity. We describe an APGC-MS-based untargeted metabolomics approach with a case study of grape volatile compounds and the development of a spectral library for metabolite identification.

Influence of the fatty acid profile on the volatile components of virgin olive oil subjected to thermal stress

BACKGROUND

Virgin olive oil (VOO) is greatly appreciated for its organoleptic features, which can be ascribed mainly to the presence of very chemically diverse volatile components. It is well known that the VOO volatile fraction depends strongly on different aspects, which encompass genetic, agronomic, processing, and post-processing factors. In this research, we developed a method for the qualitative and semiquantitative determination of volatile components in VOOs subjected to thermal stress by headspace extraction online coupled to gas chromatography-mass spectrometry (HS-GC-MS).

RESULTS

The method was applied to 100 extra-virgin olive oil (EVOO) samples, which led to the tentative identification of 52 volatile components, including 12 alcohols, 17 aldehydes, three ketones, one ether, two furans, two carboxylic acids, and 15 hydrocarbons. The method was used to study the cultivar effect and the main biochemical pathways involved in the synthesis of volatile compounds, with special emphasis on those formed by degradation of unsaturated fatty acids (FAs). Principal component analysis (PCA), explaining 76.7% of the total variability, showed that the volatile profile of EVOOs subjected to thermal stress allowed discriminating samples from different cultivars.

CONCLUSION

Volatiles detected in EVOOs subjected to thermal stress with the highest contribution to discrimination between the selected cultivars were correlated with the concentration of the three main FAs in VOO, namely oleic, linoleic, and linolenic acids. The FA profile seems to be especially relevant to explain the concentration of certain volatile compounds with direct incidence on the organoleptic properties. © 2021 Society of Chemical Industry.

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.

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.

Development of Chemometric Models Based on a LC-qToF-MS Approach to Verify the Geographic Origin of Virgin Olive Oil

In the presented study a non-targeted approach using high-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (HPLC-ESI-qToF-MS) combined with chemometric techniques was used to build a statistical model to verify the geographic origin of virgin olive oils. The sample preparation by means of liquid/liquid extraction of polar compounds was optimized regarding the number of multiple extractions, application of ultrasonic treatment and temperature during concentration of the analytes. The presented workflow for data processing aimed to identify the most predictive features and was applied to a set of 95 olive oils from Spain, Italy, Portugal and Greece. Different strategies for data reduction and multivariate analysis were compared. Stepwise variable selection showed for both applied multivariate models—linear discriminant analysis (LDA) and logit regression (LR)—to be the most suitable variable selection strategy. The 10-fold cross validation of the LDA showed a classification rate of 83.1% for the test set. For the LR models the prediction accuracy of the test set was even higher with values of 90.4% (Portugal), 86.2% (Italy), 93.8% (Greece) and 88.3% (Spain). Moreover, the reduction of features allows an easier following up strategy for identification of the unknowns and defining marker substances.

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.

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).

Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies - A review

Extra Virgin Olive Oil (EVOO), the emblematic food of the Mediterranean diet, is recognized for its nutritional value and beneficial health effects. The main authenticity issues associated with EVOO's quality involve the organoleptic properties (EVOO or defective), mislabeling of production type (organic or conventional), variety and geographical origin, and adulteration. Currently, there is an emerging need to characterize EVOOs and evaluate their genuineness. This can be achieved through the development of analytical methodologies applying advanced "omics" technologies and the investigation of EVOOs chemical fingerprints. The objective of this review is to demonstrate the analytical performance of High Resolution Mass Spectrometry (HRMS) in the field of food authenticity assessment, allowing the determination of a wide range of food constituents with exceptional identification capabilities. HRMS-based workflows used for the investigation of critical olive oil authenticity issues are presented and discussed, combined with advanced data processing, comprehensive data mining and chemometric tools. The use of unsupervised classification tools, such as Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), as well as supervised classification techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Orthogonal Projection to Latent Structure-Discriminant Analysis (OPLS-DA), Counter Propagation Artificial Neural Networks (CP-ANNs), Self-Organizing Maps (SOMs) and Random Forest (RF) is summarized. The combination of HRMS methodologies with chemometrics improves the quality and reliability of the conclusions from experimental data (profile or fingerprints), provides valuable information suggesting potential authenticity markers and is widely applied in food authenticity studies.

Volatile organic compounds gas sensor based on quartz crystal microbalance for fruit freshness detection: A review

In this review article, the state of the art of gas sensors based on quartz crystal microbalance (QCM) for fruit freshness detection is overviewed from the aspects of development history, working principle, selection and modification of sensitive materials, and volatile organic compounds detection of fruits. According to the characteristics of respiratory intensity at the stage of fruit ripening, fruits can be divided into respiration climacteric fruits and non-climacteric fruits. In recent years, research has mainly focused on respiration climacteric fruits, such as bananas and mangoes, etc., while related studies on non-climacteric fruits have been rarely reported, except for citrus fruits. The preparation methods and structure design of sensitive materials based on physical/chemical adsorption mechanisms are further discussed according to the odor components that affect the freshness of fruits, namely alkenes, esters, aldehydes and alcohols.

Recent advances in the application of metabolomics for food safety control and food quality analyses

As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.

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.

Age-dependent characterization of volatile organic compounds and age discrimination in Chinese rice wine using an untargeted GC/MS-based metabolomic approach

An untargeted gas chromatography/mass spectrometry (GC/MS)-based metabolomics by XCMS-Online software combined with partial least squares regression (PLSR) was applied to characterize volatile organic compounds (VOCs) during Chinese rice wine aging and discriminate ages for the first time. Finally, seven different ages between 0 and 15 years were well discriminated by PLSR. Total 104 feature groups were isolated from all optimized candidate peaks, and 94 VOCs (including unknowns) were preliminarily identified as aging markers. Therein, alcohols, sulfides, phenols and their derivatives, small esters and acids exhibited significantly better discrimination of short-aged rice wines. Correspondingly, furans, aromatics, aldehydes, ketones, most esters and acids, discriminated the long-aged samples better. Meanwhile, the potential origins of certain VOCs were also proposed for further research. Overall, this untargeted GC/MS-based metabolomics coupled with PLSR was a feasible tool for a rapidly and globally age-dependent characterization of volatile metabolomic signals in Chinese rice wine and thus for age discrimination.

Chemical Fingerprinting of Olive Oils by Electrospray Ionization-Differential Mobility Analysis-Mass Spectrometry: A New Alternative to Food Authenticity Testing

Recently, the olive oil industry has been the subject of harsh criticism for false labeling and even adulterating olive oils. This situation in which both the industry and the population are affected leads to an urgent need to increase controls to avoid fraudulent activities around this precious product. The aim of this work is to propose a new analytical platform by coupling electrospray ionization (ESI), differential mobility analysis (DMA), and mass spectrometry (MS) for the analysis of olive oils based on the information obtained from the chemical fingerprint (nontargeted analyses). Regarding the sample preparation, two approaches were proposed: (i) sample dilution and (ii) liquid-liquid extraction (LLE). To demonstrate the feasibility of the method, 30 olive oil samples in 3 different categories were analyzed, using 21 of them to elaborate the classification model and the remaining 9 to test it (blind samples). To develop the prediction model, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used. The overall success rate of the classification to differentiate among extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO) was 89% for the LLE samples and 67% for the diluted samples. However, combining both methods, the ability to differentiate EVOO from lower-quality oils (VOO and LOO) and the edible oils (EVOO and VOO) from nonedible oil (LOO) was 100%. The results show that ESI-DMA-MS can become an effective tool for the olive oil sector.

Deep Learning Techniques to Improve the Performance of Olive Oil Classification

The olive oil assessment involves the use of a standardized sensory analysis according to the “panel test” method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014–2015 and 2015–2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.

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.

Determination of volatile compounds and their sensory impact in a biopolymer based on polylactic acid (PLA) and polyester

Polylactic acid (PLA) is one of the most commonly used biopolymers for manufacturing food packaging; its control is very important to ensure consumers' health. In this work, a blend of PLA and polyester was studied and its volatile composition in the polymer and in the migration to food simulants was determined by gas chromatography-mass spectrometry (GC-MS) and atmospheric pressure gas chromatography-quadrupole-time of flight mass spectrometry (APGC-QTOF). The results showed that both techniques provided complementary information, to give complete information on the biopolymer's composition. Some compounds such as lactide or cyclopentanone were detected only by GC-MS while others, such as the cyclic dimer [AA-BD]₂ (AA:adipic acid, BD:butanediol), were detected only by APGC-MS. In migration, lactide, AA-BD and [AA-BD]₂ were identified in ethanol 95%. A GC-olfactometry study was also carried out. Some compounds showed sensory impact on the polymer odor but not in migration.

Use of whole electrophoretic profile and chemometric tools for the differentiation of three olive oil qualities

Olive oil is a liquid fat obtained from olives (the fruit of Olea europea). It is one of the most important ingredients of the Mediterranean diet, due to its health benefits. Depending on its quality, olive oil can be classified as extra virgin (EVOO), virgin (VOO) and lampante (LOO). Currently, an official method defines the quality parameters of the different categories of olive oil using different analytical techniques and a sensory analysis through a Panel Test. However, the evaluation of olive oil quality by tasting panels has some drawbacks, such as the subjectivity of the analysis and the lack of panels accredited outside Spain. For this reason, fast, simple and reliable analytical methods, which can differentiate the categories of olive oil are needed. In this work, the potential of a method using capillary electrophoresis (CE) with ultraviolet (UV) detection as an additional method to the ones already included in the official method has been investigated. The separations were performed using a 45 mM sodium tetraborate buffer (pH 9), and the analytes were measured at 200 nm. For chemometric model construction, the whole electrophoretic profile was processed. It required a correction of migration time shift, which was solved using two internal standards (naphthol and benzoic acid), and a correction of the drift baseline. The results obtained after applying the method to 130 olive oil samples are very promising, achieving success rates above 91%. Finally, the use of all information found in the electropherogram was compared with that based on the selection and integration of only some peaks.

Metabolic profiling reveals the heterogeneity of vascular endothelial function phenotypes in individuals at extreme cardiovascular risk

Maladapted vascular endothelial metabolism in the context of endothelial function differing in phenotype remains unknown, which limits our understanding of the heterogeneous pathogenesis of atherosclerotic cardiovascular disease (CVD). This study aimed to profile serum metabolic alterations of different vascular endothelial function phenotypes in asymptomatic adults at extreme cardiovascular risk. In addition to 12 CVD patients, 103 individuals free of CVD were categorized as having normal endothelial function (NEF) (n = 30), cardiovascular risk-promoting endothelial function (PEF) (n = 18), cardiovascular risk-resistant endothelial function (REF) (n = 25), and vulnerable endothelial function (VEF) (n = 30). Serum metabolic profiles were detected using gas chromatography time-of-flight/mass spectrometry and multivariate statistics. Compared to the NEF group, a total of 17, 17, 22, and 13 differential metabolites were identified in the PEF, REF, VEF, and CVD groups, respectively. Of the altered metabolic pathways, multiple pathways were consistent between the PEF and CVD groups, including pyrimidine metabolism, starch and sucrose metabolism, aminoacyl-tRNA biosynthesis, arginine and proline metabolism, and d-glutamine and d-glutamate metabolism. Notably, a relative increase in low-calorie sugar in galactose metabolism was exclusively found in the REF group, and a relative increase in the ratio of acetyl-CoA to CoA was suggested in the VEF group based on elevated butanoate metabolism and reduced pantothenate and CoA biosynthesis. Our findings clearly indicate distinct metabolic patterns across groups with heterogeneous vascular endothelial function in the context of extreme cardiovascular risk, and improve our understanding of the pathogenic heterogeneity of early CVD in asymptomatic populations.

This metabolomics analysis has revealed the maladapted vascular endothelial metabolism across individuals with heterogeneous vascular endothelial function in the context of extreme cardiovascular risk.

Comparison of gas chromatography/quadrupole time-of-flight and quadrupole Orbitrap mass spectrometry in anti-doping analysis: I. Detection of anabolic-androgenic steroids

RATIONALE

The World Anti-Doping Agency (WADA) encourages drug-testing laboratories to develop screening methods that can detect as many doping substances as possible in urine. The use of full-scan high-resolution acquisition (FS/HR) with gas chromatography/mass spectrometry (GC/MS) for the detection of known and unknown trimethylsilyl (TMS) derivatives of anabolic-androgenic steroids (AAS) provides anti-doping testing bodies with a new analytical tool.

METHODS

The AAS were extracted from urine samples by generic liquid-liquid extraction, after enzymatic hydrolysis, and TMS derivatization. The extracted urine was analyzed by GC/Q-TOF and GC/Q-Orbitrap to compare the performance of the two instrument types for the detection of 46 AAS in human urine. The quantitation of endogenous anabolic steroids and the ability of the two analytical platforms to comply with the requirements for testing as part of the WADA Athlete Biological Passport (ABP) were also assessed.

RESULTS

The data presented show that the analytical performance for both instruments complies with the WADA specifications. The limits of detection (LODs) for both instruments are well below the WADA 50% Minimum Required Performance Levels. The mass errors in the current study for the GC/Q-Orbitrap platform are lower than those obtained for the GC/Q-TOF instrument.

CONCLUSIONS

The data presented herein proved that both molecular profiling platforms can be used for antidoping screening. The mass accuracies are excellent in both instruments; however, the GC/Q-Orbitrap performs better as it provides higher resolution than the GC/Q-TOF platform.

Review of recent developments in GC-MS approaches to metabolomics-based research

BACKGROUND

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available.

AIM OF REVIEW

This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part II: applications

BACKGROUND

The organoleptic quality of virgin olive oil depends on positive and negative sensory attributes. These attributes are related to volatile organic compounds and phenolic compounds that represent the aroma and taste (flavour) of the virgin olive oil. The flavour is the characteristic that can be measured by a taster panel. However, as for any analytical measuring device, the tasters, individually, and the panel, as a whole, should be harmonized and validated and proper olive oil standards are needed.

RESULTS

In the present study, multivariate approaches are put into practice in addition to the rules to build a multivariate control chart from chromatographic volatile fingerprinting and chemometrics. Fingerprinting techniques provide analytical information without identify and quantify the analytes. This methodology is used to monitor the stability of sensory reference materials.

CONCLUSION

The similarity indices have been calculated to build multivariate control chart with two olive oils certified reference materials that have been used as examples to monitor their stabilities. This methodology with chromatographic data could be applied in parallel with the 'panel test' sensory method to reduce the work of sensory analysis. © 2018 Society of Chemical Industry.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part I: framework and fundamentals

BACKGROUND

Virgin olive oil is the only food product for which sensory analysis is regulated to classify it in different quality categories. To harmonize the results of the sensorial method, the use of standards or reference materials is crucial. The stability of sensory reference materials is required to enable their suitable control, aiming to confirm that their specific target values are maintained on an ongoing basis. Currently, such stability is monitored by means of sensory analysis and the sensory panels are in the paradoxical situation of controlling the standards that are devoted to controlling the panels.

RESULTS

In the present study, several approaches based on similarity analysis are exploited. For each approach, the specific methodology to build a proper multivariate control chart to monitor the stability of the sensory properties is explained and discussed.

CONCLUSION

The normalized Euclidean and Mahalanobis distances, the so-called nearness and hardiness indices respectively, have been defined as new similarity indices to range the values from 0 to 1. Also, the squared mean from Hotelling's T² -statistic and Q² -statistic has been proposed as another similarity index. © 2018 Society of Chemical Industry.

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

The capabilities of dynamic headspace entrainment followed by thermal desorption in combination with gas chromatography (GC) coupled to single quadrupole mass spectrometry (MS) have been tested for the determination of volatile components of olive oil. This technique has shown a great potential for olive oil quality classification by using an untargeted approach. The data processing strategy consisted of three different steps: component detection from GC-MS data using novel data treatment software PARADISe, a multivariate analysis using EZ-Info, and the creation of the statistical models. The great number of compounds determined enabled not only the development of a quality classification method as a complementary tool to the official established method "PANEL TEST" but also a correlation between these compounds and different types of defect. Classification method was finally validated using blind samples. An accuracy of 85% in oil classification was obtained, with 100% of extra virgin samples correctly classified.