A novel headspace solid-phase microextraction arrow method employing comprehensive two-dimensional gas chromatography-mass spectrometry combined with chemometric tools for the investigation of wine aging.

BACKGROUND: Omics is used as an analytical tool to investigate wine authenticity issues. Aging authentication ensures that the wine has undergone the necessary maturation and developed its desired organoleptic characteristics. Considering that aged wines constitute valuable commodities, the development of advanced omics techniques that guarantee aging authenticity and prevent fraud is essential. RESULTS: Α solid phase microextraction Arrow method combined with comprehensive two-dimensional gas chromatography-mass spectrometry was developed to identify volatiles in red wines and investigate how aging affects their volatile fingerprint. The method was optimized by examining the critical parameters that affect the solid phase microextraction Arrow extraction (stirring rate, extraction time) process. Under optimized conditions, extraction took place within 45 min under stirring at 1000 rpm. In all, 24 monovarietal red wine samples belonging to the Xinomavro variety from Naoussa (Imathia regional unit of Macedonia, Greece) produced during four different vintage years (1998, 2005, 2008 and 2015) were analyzed. Overall, 237 volatile compounds were tentatively identified and were treated with chemometric tools. Four major groups, one for each vintage year were revealed using the Hierarchical Clustering Analysis. The first two Principal Components of Principal Component Analysis explained 86.1% of the total variance, showing appropriate grouping of the wine samples produced in the same crop year. A two-way orthogonal partial least square - discriminant analysis model was developed and successfully classified all the samples to the proper class according to the vintage age, establishing 17 volatile markers as the most important features responsible for the classification, with an explained total variance of 88.5%. The developed prediction model was validated and the analyzed samples were classified with 100% accuracy according to the vintage age, based on their volatile fingerprint. SIGNIFICANCE: The developed methodology in combination with chemometric techniques allows to trace back and confirm the vintage year, and is proposed as a novel authenticity tool which opens completely new and hitherto unexplored possibilities for wine authenticity testing and confirmation.

Investigation of Rosa species by an optimized LC-QTOF-MS/MS method using targeted and non-targeted screening strategies combined with multivariate chemometrics.

INTRODUCTION: Plants of the Rosa genus are renowned for their pronounced and pleasant aroma and colors. OBJECTIVE: The aim of this work was to develop a novel liquid chromatographic triple quadrupole time-of-flight tandem mass spectrometric (LC-QTOF-MS/MS) method for the investigation of the bioactive fingerprint of petals of different genotypes belonging to Rosa damascena and Rosa centifolia species. METHODOLOGY: Central composite design (CCD) of response surface methodology (RSM) was used for the optimization of the LC-QTOF-MS/MS method. The method was validated and target, suspect, and non-target screening workflows were applied. Statistical analysis and chemometric tools were utilized to explore the metabolic fingerprint of the Rosa species. RESULTS: RSM revealed that the optimal extraction parameters involved mixing 11 mg of sample with 1 mL of MeOH:H2O (70:30, v/v). Target analysis confirmed the presence of 11 analytes, all of which demonstrated low limits of quantification (LOQs; as low as 0.048 ng mg-1) and sufficient recoveries (RE: 85%-107%). In total, 28 compounds were tentatively identified through suspect analysis. Non-target analysis enabled the generation of robust OPLS-DA and HCA models that classified the samples according to their species with 100% accuracy. CONCLUSIONS: A novel LC-QTOF-MS/MS method was developed and applied in the analysis of 47 R. centifolia and R. damascena flowers belonging to different genotypes.

Design and development of second-generation fabric phase sorptive extraction membranes: Proof-of-concept for the extraction of organophosphorus pesticides from apple juice prior to GC-MS analysis.

In this work, different sol-gel sorbent-coated second-generation fabric phase sorptive extraction (FPSE) membranes were synthesized using titania-based sol-gel precursors. The proposed membranes were tested for their efficiency to extract eleven selected organophosphorus pesticides (OPPs) from apple juice samples. Among the examined materials, sol-gel C18 coated titania-based FPSE membranes showed the highest extraction efficiency. These membranes were used for the optimization and validation of an FPSE method prior to analysis by gas chromatography-mass spectrometry. The detection limits for OPPs ranged between 0.03 and 0.08 ng mL-1. Moreover, the relative standard deviation was < 8.2% and 8.4% for intra-day and inter-day studies, respectively. The relative recoveries were 91-110% (intra-day study) and 90-106% (inter-day study) for all the target analytes, demonstrating good overall method accuracy. Moreover, the novel membranes were reusable at least 5 times. The titania-based membranes were compared to the conventional silica-based membranes and their utilization resulted in higher extraction recoveries.

Headspace Solid-Phase Microextraction Followed by Gas Chromatography-Mass Spectrometry as a Powerful Analytical Tool for the Discrimination of Truffle Species According to Their Volatiles.

This study provides the first assessment of the volatile metabolome map of Tuber Aestivum and Tuber Borchii originating from Greece using headspace solid-phase micro-extraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS). For the extraction of the volatile fraction, the SPME protocol was optimized after examining the effects of sample mass, extraction temperature, and extraction time using the one-variable at-a-time approach (OVAT). The optimum parameters involved the extraction of 100 mg of homogenized truffle for 45 min at 50°C. Overall, 19 truffle samples were analyzed, and the acquired data were normalized and further processed with chemometrics. Agglomerative hierarchical clustering (HCA) was used to identify the groups of the two species. Partial least squares-discriminant analysis (PLS-DA) was employed to develop a chemometric model that could discriminate the truffles according to the species and reveal characteristic volatile markers for Tuber Aestivum and Tuber Borchii grown in Greece.

Characterization of the Phenolic Fingerprint of Kolovi Extra Virgin Olive Oils from Lesvos with Regard to Altitude and Farming System Analyzed by UHPLC-QTOF-MS.

Extra virgin olive oil (EVOO) is recognized for its nutritional virtues and the beneficial health effects deriving from its hydrophilic fraction (phenolic acids, phenolic alcohols, flavonoids, and secoiridoids). The phenolic compounds of EVOOs possess multiple biological properties such as antioxidant, antimicrobial, anticarcinogenic, and anti-inflammatory properties, among others. Considering that EVOOs produced in Greece are recognized as high-quality products due to their rich phenolic content, it is imperative to characterize Greek monovarietal EVOOs and ensure that their uniqueness is closely linked to their botanical and territorial origin. In this work, an ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method combined with target and suspect screening was used to characterize monovarietal EVOOs of the Kolovi variety from Lesvos, and thereby establish their phenolic fingerprint. Overall, 25 phenols were determined, and the total quantification and semi-quantification results ranged between 251 and 1230 mg/kg, highlighting the high phenolic content of the Kolovi variety from the island of Lesvos in the North Aegean.

A green molecular imprinted solid-phase extraction protocol for bisphenol A monitoring with HPLC-UV to guarantee the quality and safety of walnuts under different storage conditions.

Monitoring the residual toxicant concentrations in foods is the key step for minimizing potential hazards. The huge interest about food contamination and exposure to endocrine disruptors such as bisphenol A has emerged the development of sensitive analytical methodologies to guarantee the safety and quality of foods. In this work, a green molecularly imprinted solid-phase extraction protocol coupled with high-performance liquid chromatography with UV detection was optimized following the principles of green analytical chemistry. An imprinted sol-gel silica-based hybrid inorganic-organic polymeric sorbent was used to monitor the leaching of bisphenol A from different packaging materials (glass vessels, cans, and polypropylene containers) in walnuts stored within a period of 6 months at 25 and 4°C. Extraction parameters including loading time (5-20 min), solvent type (acetonitrile, ethanol, methanol, acetone, acetonitrile:methanol, 50:50, v/v), and elution flow rate (0.2-1 mL/min) were optimized with one-factor-at-a-time method. The selected extraction optimum parameters incorporated elution with acetonitrile at 0.2 mL/min flow rate, for 10 min sample holding time. The imprinting factor was equal to 4.55 ± 0.26 (n = 3). The optimized method presented high recovery (94.3 ± 4.2%, n = 3), good linearity (>0.999), intra-assay repeatability (90.2-95.6%, n = 3), and interassay precision (86.7-93.1%, n = 3).

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.

Application of an advanced and wide scope non-target screening workflow with LC-ESI-QTOF-MS and chemometrics for the classification of the Greek olive oil varieties.

An optimized and validated LC-ESI-QTOF-MS method with an integrated non-target screening workflow was applied in the investigation of the metabolomic profile of 51 Greek monovarietal extra virgin olive oils (EVOOs) from the varieties: Manaki, Ladoelia, Koroneiki, Amfissis, Chalkidikis and Kolovi. Data processing was carried out with the R language and XCMS package. A local database consisting of 1608 compounds naturally occurring in different organs of Olea Europa L. was compiled in order to accelerate the identification workflow. The preliminary examination of the distribution of EVOOs toward their cultivars was achieved by Principal Component Analysis (PCA). Ant Colony Optimization-Random Forest (ACO-RF) was developed to prioritize over 250 features and to establish a classification tree. Apigenin, vanillic acid, luteolin 7-methyl ether and oleocanthal were suggested as the markers responsible for the classification of Greek EVOOs' cultivars.

Investigating the organic and conventional production type of olive oil with target and suspect screening by LC-QTOF-MS, a novel semi-quantification method using chemical similarity and advanced chemometrics.

The discrimination of organic and conventional production has been a critical topic of public discussion and constitutes a scientific issue. It remains a challenge to establish a correlation between the agronomical practices and their effects on the composition of olive oils, especially the phenolic composition, since it defines their organoleptic and nutritional value. Thus, a liquid chromatography-electrospray ionization-quadrupole time of flight tandem mass spectrometric method was developed, using target and suspect screening workflows, coupled with advanced chemometrics for the identification of phenolic compounds and the discrimination between organic and conventional extra virgin olive oils. The method was optimized by one-factor design and response surface methodology to derive the optimal conditions of extraction (methanol/water (80:20, v/v), pure methanol, or acetonitrile) and to select the most appropriate internal standard (caffeic acid or syringaldehyde). The results revealed that extraction with methanol/water (80:20, v/v) was the optimum solvent system and syringaldehyde 1.30 mg L-1 was the appropriate internal standard. The proposed method demonstrated low limits of detection in the range of 0.002 (luteolin) to 0.028 (tyrosol) mg kg-1. Then, it was successfully applied in 52 olive oils of Kolovi variety. In total, 13 target and 24 suspect phenolic compounds were identified. Target compounds were quantified with commercially available standards. A novel semi-quantitation strategy, based on chemical similarity, was introduced for the semi-quantification of the identified suspects. Finally, ant colony optimization-random forest model selected luteolin as the only marker responsible for the discrimination, during a 2-year study. Graphical abstract Investigation of the organic and conventional production type of olive oil by LC-QTOF-MS.

Olive oil authenticity studies by target and nontarget LC-QTOF-MS combined with advanced chemometric techniques.

Food analysis is continuously requiring the development of more robust, efficient, and cost-effective food authentication analytical methods to guarantee the safety, quality, and traceability of food commodities with respect to legislation and consumer demands. Hence, a novel reversed-phase ultra high performance liquid chromatography-electrospray ionization quadrupole time of flight tandem mass spectrometry analytical method was developed that uses target, suspect, and nontarget screening strategies coupled with advanced chemometric tools for the investigation of the authenticity of extra virgin olive oil. The proposed method was successfully applied in real olive oil samples for the identification of markers responsible for the sensory profile. The proposed target analytical method includes the determination of 14 phenolic compounds and demonstrated low limits of detection ranging from 0.015 µg mL-1 (apigenin) to 0.039 µg mL-1 (vanillin) and adequate recoveries (96-107 %). A suspect list of 60 relevant compounds was compiled, and suspect screening was then applied to all the samples. Semiquantitation of the suspect compounds was performed with the calibration curves of target compounds having similar structures. Then, a nontarget screening workflow was applied with the aim to identify additional compounds so as to differentiate extra virgin olive oils from defective olive oils. Robust classification-based models were built with the use of supervised discrimination techniques, partial least squares-discriminant analysis and counterpropagation artificial neural networks, for the classification of olive oils into extra virgin olive oils or defective olive oils. Variable importance in projection scores were calculated to select the most significant features that affect the discrimination. Overall, 51 compounds were identified and suggested as markers, among which 14, 26, and 11 compounds were identified by target, suspect, and nontarget screening respectively. Retrospective analysis was also performed and identified 19 free fatty acids. Graphical Abstract Development of a novel RP-LC-ESI-QTOFMS analytical method employing target, suspect and non-target screening strategies coupled to advanced chemometric tools for the investigation of markers responsible for the sensory profile of extra virgin olive oil and guarantee authenticity.