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

Evaluation of extra virgin olive oil compounds using computational methods: in vitro, ADMET, DFT, molecular docking and human gene network analysis study

This study investigates the phenolic compounds (PC), volatile compounds (VC), and fatty acids (FA) of extra virgin olive oil (EVOO) derived from the Turkish olive variety "Sarı Ulak", along with ADMET, DFT, molecular docking, and gene network analyses of significant molecules identified within the EVOO. Chromatographic methods (GC-FID, HPLC) were employed to characterize FA, PC, and VC profiles, while quality parameters, antioxidant activities (TAC, ABTS, DPPH) were assessed via spectrophotometry. The analysis revealed a complex composition of 40 volatile compounds, with estragole, 7-hydroxyheptene-1, and 3-methoxycinnamaldehyde as the primary components. Hydroxytyrosol, tyrosol, oleuropein, apigenin, ferulic acid, and vanillic acid emerged as main phenolic constituents, with hydroxytyrosol and apigenin exhibiting high bioavailability. Molecular docking highlighted oleuropein and pinoresinol as compounds with strong binding affinities, though only hydroxytyrosol, apigenin, and pinoresinol fully met Lipinski and other drug-likeness criteria. DFT analysis showed that oleuropein and pinoresinol have notable dipole moments, reflecting polar and asymmetrical structures. KEGG enrichment analysis further linked key molecules like oleuropein and apigenin with pathways related to lipid metabolism and atherosclerosis, underscoring their potential bioactivity and relevance in health-related applications.

Comparison of Untargeted and Markers Analysis of Volatile Organic Compounds with SIFT-MS and SPME-GC-MS to Assess Tea Traceability

Tea is one of the most consumed beverages in the world and presents a great aromatic diversity depending on the origin of the production and the transformation process. Volatile organic compounds (VOCs) greatly contribute to the sensory perception of tea and are excellent markers for traceability and quality. In this work, we analyzed the volatile organic compounds (VOCs) emitted by twenty-six perfectly traced samples of tea with two analytical techniques and two data treatment strategies. First, we performed headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) as the most widely used reference method for sanitary and quality controls of food. Next, we analyzed the samples with selected-ion flow-tube mass spectrometry (SIFT-MS), an emerging method for direct analysis of food products and aroma. We compared the performances of both techniques to trace the origin and the transformation processes. We selected the forty-eight most relevant markers with HS-SPME-GC-MS and evaluated their concentrations with a flame ionization detector (FID) on the same instrument. This set of markers permitted separation of the origins of samples but did not allow the samples to be differentiated based on the color. The same set of markers was measured with SIFT-MS instrument without success for either origin separation or color differentiation. Finally, a post-processing treatment of raw data signals with an untargeted approach was applied to the GC-MS and SIFT-MS dataset. This strategy allowed a good discrimination of origin and color with both instruments. Advantages and drawbacks of volatile profiles with both instruments were discussed for the traceability and quality assessment of food.

PFAS emissions from functional textiles using micro-chamber and thermal desorption coupled to two-dimensional gas chromatography-time of flight mass spectrometry (TD-GC×GC-TOF MS)

Per- and polyfluoroalkyl substances (PFAS), in the polymeric form, have been used extensively in functional textiles, including firefighter's turnout gear (e.g., jackets and pants), where PFAS are applied to confer oil and water resistance. However, growing concerns over the persistence, potential toxicity, and environmental impact of PFAS have prompted a thorough assessment of potential exposure pathways. Here, we report the emission of PFAS from three firefighter turnout gear jackets at 38 °C. Volatile emissions from the three layers (outer layer, moisture barrier, and thermal liner) were collected onto sorbent tubes via dynamic headspace sampling using a micro-scale chamber device kept at 38 °C for one hour. The emission was characterized using thermal desorption (TD) coupled to two-dimensional gas chromatography - time-of-flight mass spectrometry (GC×GC-TOF MS). The enhanced separation capacity of GC×GC was essential due to the high number of compounds present in each sample, especially for the fabrics from used turnout gear jackets. Based on the filtering expressions, including two-dimensional retention time (1tr and 2tr) and PFAS diagnostic fragment ions (m/z 69, 95, and 131), fluorotelomer alcohols (FTOHs) and fluorotelomer acrylates were identified using standards and spectral matching with the NIST database. After conducting a non-targeted tile-based workflow, jackets (both used and unused) and layers were compared, resulting in the identification of the top 15 discriminating features from over 400 chromatographic peaks. Finally, preliminary FTOH emission experiments showed some usage and layers trends that are aligned with those reported using solvent extraction. Highest levels of FTOHs were found in the moisture barrier, followed by the outer layer and the thermal liner. Older jackets emitted higher levels of 8:2 FTOH and 10:2 FTOH than a newer jacket. In contrast, a newer jacket used for one year had emissions containing higher levels of 6:2 FTOH. Investigating routes of exposure and identifying new PFAS targets are critical steps in evaluating the environmental and health impacts of these persistent chemicals.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

Vacuum-assisted and multi-cumulative trapping in headspace solid-phase microextraction combined with comprehensive multidimensional chromatography-mass spectrometry for profiling virgin olive oil aroma

In the present work vacuum (Vac) and multiple cumulative trapping (MCT) headspace solid phase microextraction (HS-SPME) were evaluated as alternative or combined techniques for the volatile profiling. A higher extraction performance for semi-volatiles was shown by all three techniques. Synergic combination of Vac and MCT showed up to 5-times extraction power for less volatile compounds. The hyphenation of said techniques with comprehensive two-dimensional gas chromatography (GC × GC) enabled a comprehensive analysis of the volatilome. Firstly, 18 targeted quality markers, previously defined by means of classical HS-SPME, were explored for their ability to classify commercial categories. The applicability of such markers proved to be limited with the alternative sampling techniques. An untargeted approach enables the selection of specific features for each technique showing a better classification capacity of the commercial categories. No misclassifications were observed, except for one extra virgin olive oil classified as virgin olive oil in 3 × 10 min Vac-MCT-HS-SPME.

Solid-phase microextraction coupled to comprehensive multidimensional gas chromatography for food analysis

Solid-phase microextraction and comprehensive multidimensional gas chromatography represent two milestone innovations that occurred in the field of separation science in the 1990s. They have a common root in their introduction and have found a perfect coupling in their evolution and applications. This review will focus on food analysis, where the paradigm has changed significantly over time, moving from a targeted analysis, focusing on a limited number of analytes at the time, to a more holistic approach for assessing quality in a larger sense. Indeed, not only some major markers or contaminants are considered, but a large variety of compounds and their possible interaction, giving rise to the field of foodomics. In order to obtain such detailed information and to answer more sophisticated questions related to food quality and authenticity, the use of SPME-GC × GC-MS has become essential for the comprehensive analysis of volatile and semi-volatile analytes. This article provides a critical review of the various applications of SPME-GC × GC in food analysis, emphasizing the crucial role this coupling plays in this field. Additionally, this review dwells on the importance of appropriate data treatment to fully harness the results obtained to draw accurate and meaningful conclusions.

Tile-Based Random Forest Analysis for Analyte Discovery in Balanced and Unbalanced GC × GC-TOFMS Data Sets

In this study, we introduce a new nontargeted tile-based supervised analysis method that combines the four-grid tiling scheme previously established for the Fisher ratio (F-ratio) analysis (FRA) with the estimation of tile hit importance using the machine learning (ML) algorithm Random Forest (RF). This approach is termed tile-based RF analysis. As opposed to the standard tile-based F-ratio analysis, the RF approach can be extended to the analysis of unbalanced data sets, i.e., different numbers of samples per class. Tile-based RF computes out-of-bag (oob) tile hit importance estimates for every summed chromatographic signal within each tile on a per-mass channel basis (m/z). These estimates are then used to rank tile hits in a descending order of importance. In the present investigation, the RF approach was applied for a two-class comparison of stool samples collected from omnivore (O) subjects and stored using two different storage conditions: liquid (Liq) and lyophilized (Lyo). Two final hit lists were generated using balanced (8 vs Eight comparison) and unbalanced (8 vs Nine comparison) data sets and compared to the hit list generated by the standard F-ratio analysis. Similar class-distinguishing analytes (p < 0.01) were discovered by both methods. However, while the FRA discovered a more comprehensive hit list (65 hits), the RF approach strictly discovered hits (31 hits for the balanced data set comparison and 29 hits for the unbalanced data set comparison) with concentration ratios, [OLiq]/[OLyo], greater than 2 (or less than 0.5). This difference is attributed to the more stringent feature selection process used by the RF algorithm. Moreover, our findings suggest that the RF approach is a promising method for identifying class-distinguishing analytes in settings characterized by both high between-class variance and high within-class variance, making it an advantageous method in the study of complex biological matrices.

Exploring different high-capacity tools and extraction modes to characterize the aroma of brewed coffee

In the present work, the potential benefit of using multi-cumulative trapping headspace extraction was explored by comparing the results using solid-phase microextraction (SPME) coated with divinylbenzene/carboxen/polydimethylsiloxane and a probe-like tool coated with polydimethylsiloxane. The efficiency of a single 30-min extraction, already explored in previous work, was compared with that of multiple shorter extractions. We evaluated three different conditions, i.e., three repeated extractions for 10 min each from different sample vials (for both the probe-like tool and SPME) or from the same vial (for SPME) containing brewed coffee. The entire study was performed using comprehensive two-dimensional gas chromatography coupled with mass spectrometry. The two-dimensional plots were aligned and integrated using a tile-sum approach before any statistical analysis. A detailed comparison of all the tested conditions was performed on a set of 25 targeted compounds. Although a single 30-min extraction using the probe-like tool provided a significantly higher compound intensity than SPME single extraction, the use of multiple shorter extractions with SPME showed similar results. However, multiple extractions with the probe-like tool showed a greater increase in the number of extracted compounds. Furthermore, an untargeted cross-sample comparison was performed to evaluate the ability of the two tested tools and the different extraction procedures in differentiating between espresso-brewed coffee samples obtained from capsules made of different packaging materials (i.e., compostable capsules, aluminum capsules, aluminum multilayer pack). The highest explained variance was obtained using the probe-like tool and multiple extractions (91.6% compared to 83.9% of the single extraction); nevertheless, SPME multiple extractions showed similar results with 88.3% of variance explained.

Comprehensive analysis of floral scent and fatty acids in nectar of Silene nutans through modern analytical gas chromatography techniques

The aim of this work was to show the potential of multidimensional gas chromatography combined with mass spectrometry and suitable chemometrics means based on untargeted and profiling data analysis to strengthen the information provided by floral scent and nectar fatty acids of four genetically differentiated lineages (E1, W1, W2, and W3) of the nocturnal moth-pollinated herb Silene nutans. Volatile organic compounds emitted by flowers were trapped for a total of 42 samples by in-vivo sampling dynamic head space for analysing floral scent by untargeted approach, while 37 samples of nectar were collected for analysing fatty acids through profiling analysis. The resulting data from floral scent analysis were aligned and compared using a tile-based methodology followed by data mining to access high-level information. Based on floral scent and nectar fatty acid results, it was possible to distinguish E1 from the W lineages, and W3 from W1 and W2. This work puts the bases for a larger study aiming to clarify the existence of prezygotic barriers involved in speciation among lineages of S. nutans, and thus the possible implication of different flower scents and nectar compositions in this phenomenon.

Characterization and Comparison of Extra Virgin Olive Oils of Turkish Olive Cultivars

Extra virgin olive oils (EVOOs) obtained from five Turkish olive cultivars widely produced in the Aegean and Marmara regions were investigated based on their total antioxidant capacity (TAC), total phenolic content (TPC), pigment contents, fatty acid (FA) profiles, phenolic compounds (PC), volatile compounds (VC), and sensory properties. The results showed that all properties of EVOO samples were significantly affected by the olive cultivar used. The pigment contents in Ayvalık (9.90 mg·kg-1) and Uslu (9.00 mg·kg-1) oils were higher than the others (p < 0.05). The greatest values for oleic acid (74.13%) and TPC (350.6 mg·kg-1) were observed in Gemlik and Domat oils, respectively (p < 0.05). Edincik oil showed the maximum hydroxytyrosol content (48.022 mg·kg-1) and TAC value (515.36 mg TE·kg-1) (p < 0.05). The Edincik, Domat, and Uslu oils were significantly not different for the total content of C6 compounds derived by lipoxygenase, which are the main volatiles responsible for the typical aroma of EVOOs (p > 0.05). Domat oil also exhibited the highest scores for bitterness and pungency perceptions (p < 0.05). The fruitiness scores of the oil samples (except for Ayvalık oil) were close to each other, even if they were statistically different (p < 0.05). Principal component analysis (PCA) indicated that the Ayvalık oil was separated from the others due to its poor-quality characteristics. As a result, it can be stated that Domat olive oil has better quality than the others.

Solid-Phase Microextraction-Gas Chromatography Analytical Strategies for Pesticide Analysis

Due to their extensive use and the globalized commerce of agricultural goods, pesticides have become a global concern. Despite the undoubtful advantages of their use in agricultural practices, their misuse is a threat to the environment and human health. Their analysis in environmental samples and in food products continues to gain interest in the analytical chemistry community as they are challenging matrices, and legal concentration limits are particularly low (in the order of ppb). In particular, the use of solid-phase microextraction (SPME) has gained special attention in this field thanks to its potential to minimize the matrix effect, while enriching its concentration, allowing very low limits of detection, and without the need of a large amount of solvents or lengthy procedures. Moreover, its combination with gas chromatography (GC) can be easily automated, making it a very interesting approach for routine analysis. In this review, advances and analytical strategies for the use of SPME coupled with GC are discussed and compared for the analysis of pesticides in food and environmental samples, hopefully encouraging its further development and routine application in this field.

Multidimensional Gas Chromatography: Benefits and Considerations for Current and Prospective Users

Two-dimensional gas chromatography (GC×GC) offers improved separation power for complex samples containing hundreds to thousands of analytes. However, several considerations must be made to determine whether multidimensional gas chromatography (MDGC) is the logical instrument choice to answer a particular scientific question, including, but not limited to, whether the analysis is targeted or non-targeted, the number of analytes of interest, and the presence of interferences that are coeluted, as well as any potential regulatory or industrial constraints. Currently, MDGC remains daunting for many users because of data complexity and the limited tools commercially available, which are critical for improving the accessibility of MDGC. Herein, we discuss considerations that may assist analysts, laboratory managers, regulatory agents, instrument and software vendors, and those interested in understanding the applicability of 2D-GC for the scientific question being investigated.