Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies

A polyaniline-enhanced quartz crystal microbalance sensor for room-temperature camphor detection

A method to detect camphor gas is considered indispensable in the pharmaceutical industry. Unfortunately, the available sensors to detect the presence of camphor in the air are very limited and still on a laboratory scale, such as using chromatography-mass spectroscopy (GC-MS). The research's main focus is to obtain a portable sensing system with excellent sensitivity and selectivity. This study explored polyaniline (PANi) concentrations cast over PVAc nanofiber as a matrix to detect camphor gas using a quartz crystal microbalance (QCM) system to measure camphor exposure. Scanning electron microscopy (SEM) and Fourier-transform infrared (FTIR) were used to analyze the morphology and chemical composition of the fabricated active layer (i.e., nanofiber with PANi thin film). Increasing the PANi concentration provides more PANi on the sensor surface, thus amassing the active groups to interact with camphor gas molecules. It shows that a sensor with a 0.08% PANi thin film (Nano-PANi8) has a sensitivity of 2.594 Hz ppm-1, much greater than the sensor without PANi, which is only 0.305 Hz ppm-1. In addition, the sensor also has good repeatability and rapid response and recovery time of 47 s and 133 s, respectively. Compared to other gaseous compounds, the sensor also has excellent selectivity for camphor and robust long-term stability over three weeks of testing. The produced QCM sensor employing PANi thin film can give a camphor sensor superior performance, including excellent sensitivity, selectivity, and long-term stability. Furthermore, the use of QCM as a base sensor also makes the fabricated sensor portable.

Comprehensive two-dimensional gas chromatography as a tool for targeted and non-targeted analysis of contaminants of emerging concern in wastewater

Wastewater is a major reservoir for chemical contaminants, both anthropogenic and biogenic. Recent chemical and toxicological analysis reveals the abundance and impact of these compounds, often termed contaminants of emerging concern (CECs). Concurrently, incomplete removal of these compounds in wastewater treatment plants sets a precedent for detailed characterisation and monitoring of such substances. Although liquid chromatography (LC) is frequently used for analysis of CECs in wastewater, gas chromatography (GC) maintains its significance for non-polar to mid-polar analytes. GC offers advantages such as increased separation efficiency, fewer matrix effects, and greater availability and reliability of reference mass spectra compared to LC. Comprehensive two-dimensional gas chromatography (GC × GC) delivers unmatched peak capacity and separational capabilities, critical in the resolution of diverse compound groups present within wastewater. When coupled with high resolution mass spectrometry, it provides a powerful identification tool with spectral databases and both 1st and 2nd dimensional retention indices, and has allowed for the separation, reliable annotation and characterisation of diverse CECs within wastewater in recent years. Herein, on the basis of recent studies from the last fifteen years, we outline cutting-edge methodologies and strategies for wastewater analysis using GC × GC. This includes sample preparation, derivatization of polar analytes, instrumental setup, and data analysis, ultimately providing the reader a framework for future non-targeted analysis of wastewater and other complex environmental matrices.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

Translating 2D-Chromatographic Fingerprinting to Quantitative Volatilomics: Unrevealing Compositional Changes in Maize Silage Volatilome for Robust Marker Discovery

This study examines the complex volatilome of maize silage, both with and without commercial heterolactic strain inoculation, conserved for 100 days, using quantitative volatilomics. Chemical classes linked to microbial metabolism were analyzed across a concentration range from 10 μg g-1 to 1 ng g-1. A reference method using comprehensive two-dimensional gas chromatography (GC × GC) and time-of-flight mass spectrometry (TOF MS) with loop-type thermal modulation (TM) was translated to a differential-flow modulation (FM) platform with parallel MS and flame ionization detector (FID) detection. With translation, the original method's analyte elution order and resolution are preserved. The new method allowed for accurate quantification using multiple headspace solid-phase microextraction (MHS-SPME) and FID-predicted relative response factors (RRFs). Both methods showed comparable discriminatory power with FM GC × GC-MS/FID achieving satisfactory quantification accuracy without external calibration. Analysis of 98 volatiles provided insights into silage fermentation, supporting marker discovery and correlations with silage quality and stability.

Artificial intelligence decision making tools in food metabolomics: Data fusion unravels synergies within the hazelnut (Corylus avellana L.) metabolome and improves quality prediction

This study investigates the metabolome of high-quality hazelnuts (Corylus avellana L.) by applying untargeted and targeted metabolome profiling techniques to predict industrial quality. Utilizing comprehensive two-dimensional gas chromatography and liquid chromatography coupled with high-resolution mass spectrometry, the research characterizes the non-volatile (primary and specialized metabolites) and volatile metabolomes. Data fusion techniques, including low-level (LLDF) and mid-level (MLDF), are applied to enhance classification performance. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) reveal that geographical origin and postharvest practices significantly impact the specialized metabolome, while storage conditions and duration influence the volatilome. The study demonstrates that MLDF approaches, particularly supervised MLDF, outperform single-fraction analyses in predictive accuracy. Key findings include the identification of metabolites patterns causally correlated to hazelnut's quality attributes, of them aldehydes, alcohols, terpenes, and phenolic compounds as most informative. The integration of multiple analytical platforms and data fusion methods shows promise in refining quality assessments and optimizing storage and processing conditions for the food industry.

Phytochemical Characterization of Two New Olive Oil Genotypes Growing in Southern Tunisia

This research can be considered as the first complete survey for the valorization of new olive genotypes cultivated in the South-East of Tunisia as well as their oils. The study aimed to characterize the phytochemical composition of virgin olive oil produced from two olive cultivars, namely Nourgou and Gousalani. The pomological characterization of fruits, the quality criteria and the phytochemical profile were quantified. Additionally, antioxidant activity was evaluated using Ferric reducing antioxidant power (FRAP) and Oxygen radical absorbance capacity (ORAC) tests to also obtain a bioactive characterization of these monovarietal olive oils. The obtained results revealed that the analyzed olive oils samples can be classified into Extra Virgin category (EVOO) according to the regulated physicochemical characteristics. Our findings showed a significant variability in the chemical parameters of the analyzed EVOO likely associated with the genetic potential, mainly for chlorophylls contents (1.37-1.64 mg/kg), in carotenoids pigments (3.97-10.86 mg/kg), in α-tocopherol (175.59-186.87 mg/kg), in sterols (1036.4-1931.4 mg/kg) in oleic acid (65.33-68.73%), in palmitic acid (C16:0) (13.32-17.48%), in linoleic acid (C18:2) (11.06-13.47%). Additionally, the HPLC-MS/MS analysis showed that the two EVOOs analyzed contained appreciable amounts of total polyphenols, ranging from 348.03 up to 516.16 mg/kg, in Nourgou and Gousalani oils, respectively. Regarding the individual phenolic compounds, the EVOO samples were mainly characterized by phenolic alcohols, phenolic acids, secoiridoids, verbascoside, flavonoids and phenolic aldehydes. The prevalent simple phenolics detected were secoiridoids with the dominance of the oleuropein aglycone in Gousalani oil. In addition, findings from in vitro antioxidant assays (FRAP and ORAC) revealed that the two studied oils possessed a powerful antiradical activity and a good reducing power capacity. In conclusion, these new EVOOs exhibited a superior quality compared to other Tunisian varieties, considering their antiradical activity and reducing power capacity.

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.

Characterization of volatiles in flowers from four Rosa chinensis cultivars by HS-SPME-GC × GC-QTOFMS

Rosa chinensis cultivars with volatile aromas are important resources in the perfume industry. The four rose cultivars introduced to Guizhou province are rich in volatile substances. In this study, volatiles from four Rosa chinensis cultivars were extracted using headspace-solid phase microextraction (HS-SPME), and analyzed with two-dimensional gas chromatography quadrupole time of flight mass spectrometry (GC × GC-QTOFMS). A total of 122 volatiles were identified; the main compounds in these samples were benzyl alcohol, phenylethyl alcohol, citronellol, beta-myrcene and limonene. A total of 68, 78, 71, and 56 volatile compounds were identified in Rosa 'Blue River' (RBR), Rosa 'Crimson Glory' (RCG), Rosa 'Pink Panther' (RPP), and Rosa 'Funkuhr' (RF) samples, respectively. The total volatile contents were in the following order: RBR > RCG > RPP > RF. Four cultivars exhibited similar volatility profiles, with alcohols, alkanes, and esters as the major chemical groups, followed by aldehydes, aromatic hydrocarbons, ketones, benzene, and other compounds. Alcohols and aldehydes were quantitatively the two most abundant chemical groups that included the highest number and highest content of compounds. Different cultivars have different aromas, and RCG had high contents of phenyl acetate, rose oxide, trans-rose oxide, phenylethyl alcohol and 1,3,5-trimethoxybenzene, characterized by floral and rose descriptors. RBR contained a high content of phenylethyl alcohol, and RF contained a high content of 3,5-dimethoxytoluene. Hierarchical cluster analysis (HCA) of all volatiles showed that the three cultivars (RCG, RPP, and RF) had similar volatile characteristics and were significantly different from RBR. Differential metabolites among cultivars were screened based on the OPLS-DA model, and there were six main enriched pathways of differential metabolites: biosynthesis of secondary metabolites, monoterpenoid biosynthesis, metabolic pathways, limonene and pinene degradation, sesquiterpenoid and triterpenoid biosynthesis, and alpha-linolenic acid metabolism. The biosynthesis of secondary metabolites is the most differential metabolic pathway.

Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.

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.

Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome

Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables "computers and systems to derive useful information from digital images" giving access to higher-level information and "take actions or make recommendations based on that information". Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability.

Analytical Methods for the Characterization of Vegetable Oils

The determination of the authenticity of extra virgin olive oils (EVOOs) has become more interesting in recent years. Italy is the first country in Europe in terms of number of Protected Designation of Origin (PDO) oils, which connects consumers to a feeling of tradition and thus to higher quality standards. This work focused on the consideration of the inorganic content as a possible marker of EVOOs. Ten vegetable oils (VOs), eight Italian EVOOs and seven not Italian EVOOs were analyzed. After pretreatment by acid mineralization, Al, Ba, Ca, Cu, Fe, K, Li, Mg, Mn, Na, P, Sb, Se and Zn were determined by ICP-OES. The electrochemical properties of a selected group of EVOOs and other vegetal oils of different botanical origin were investigated by voltammetry. Carbon paste electrodes (CPEs) were prepared. The features observed in the voltammograms reflect the reactions of electroactive compounds, which are present in the oils. A chemometric treatment of the results was performed to assess the possibility to distinguish (i) the region of provenience considering the inorganic profile; and (ii) the plant species from which each oil was obtained on the basis of the current profile registered during voltammetric analysis. Inorganic composition seems to be a useful marker for the assessment of the geographical origin of an EVOO. The EVOO-CPEs voltammetry seems to have a good ability to distinguish the plant species of origin. This method could be useful to monitor the conservation status of the oils, as the redox profile is linked to the oxidative degradation state.

Effect of Inoculation with Lentilactobacillus buchneri and Lacticaseibacillus paracasei on the Maize Silage Volatilome: The Advantages of Advanced 2D-Chromatographic Fingerprinting Approaches

In this study, the complex volatilome of maize silage samples conserved for 229 d, inoculated with Lentilactobacillus buchneri (Lbuc) and Lacticaseibacillus paracasei (Lpar), is explored by means of advanced fingerprinting methodologies based on comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry. The combined untargeted and targeted (UT) fingerprinting strategy covers 452 features, 269 of which were putatively identified and assigned within their characteristic classes. The high amounts of short-chain free fatty acids and alcohols were produced by fermentation and led to a large number of esters. The impact of Lbuc fermentation was not clearly distinguishable from the control samples; however, Lpar had a strong and distinctive signature that was dominated by propionic acid and 1-propanol characteristic volatiles. The approach provides a better understanding of silage stabilization mechanisms against the degradative action of yeasts and molds during the exposure of silage to air.

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

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

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

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

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.

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

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

A method for gaining a deeper insight into the aroma profile of olive oil

Volatile compounds in food play a crucial role in affecting food quality and consumer preference, but the volatile compounds in olive oil are not fully understood due to the matrix effect of oil. The oiling-out assisted liquid-liquid extraction (OA-LLE), which we previously reported, is an effective method for isolating volatile compounds from edible oils with a strong matrix effect. However, when we apply OA-LLE to extra virgin olive oil (EVOO), the aromatic extracts contain non-volatile compounds such as pigments because of solvent-based extraction. Solvent-assisted flavor evaporation (SAFE) can remove such non-volatiles from extracts, but SAFE is affected by a matrix effect during distillation, resulting in a decrease in performance. By combining the advantages of OA-LLE and SAFE, we propose an effective approach, OA-LLE followed by SAFE (OA-LLE + SAFE), for extracting aroma compounds from EVOO. The "two assists" should help to better understand the native aroma profile of EVOO.

Exploring the Extra-Virgin Olive Oil Volatilome by Adding Extra Dimensions to Comprehensive Two-Dimensional Gas Chromatography and Time-of-Flight Mass Spectrometry Featuring Tandem Ionization: Validation of Ripening Markers in Headspace Linearity Conditions

BACKGROUND

Comprehensive two-dimensional gas chromatography (GC×GC) combined with time-of-flight (TOF) MS is the most informative analytical approach for chemical characterization of the complex food volatilome. Key analytical features include separation power and resolution enhancement, improved sensitivity, and structured separation patterns from chemically correlated analytes.

OBJECTIVE

In this study, we explore the complex extra-virgin olive oil volatilome by combining headspace (HS) solid-phase microextraction (SPME), applied under HS linearity conditions to GC×GC-TOF MS and featuring hard and soft ionization in tandem.

METHOD

Multiple analytical dimensions are combined in a single run and evaluated in terms of chemical dimensionality, method absolute and relative sensitivity, identification reliability provided by spectral signatures acquired at 70 and 12 eV, and dynamic and linear range of response provided by soft ionization.

RESULTS

Method effectiveness is validated on a sample set of oils from Picual olives at different ripening stages. Ripening markers [3,4-diethyl-1,5-hexadiene (RS/SR), 3,4-diethyl-1,5-hexadiene (meso), (5Z)-3-ethyl-1,5-octadiene, (5E)-3-ethyl-1,5-octadiene, (E, Z)-3,7-decadiene and (E, E)-3,7-decadiene, (Z)-2-hexenal, (Z)-3-hexenal and (Z)-3-hexenal, (E)-2-pentenal, (Z)-2-pentenal, 1-pentanol, 1-penten-3-ol, 3-pentanone, and 1-penten-3-one] and quality indexes [(Z)-3-hexenal/nonanal, (Z)-3-hexenal/octane, (E)-2-pentenal/nonanal, and (E)-2-pentenal/octane] are confirmed for their validity in HS linearity conditions.

CONCLUSIONS

For the complex olive oil volatilome, the proposed approach offers concrete advantages for the validation of the informative role of existing analytes while suggesting new potential markers to be studied in larger sample sets.

HIGHLIGHTS

The accurate fingerprinting of volatiles by HS-SPME operating in HS linearity conditions followed by GC×GC-TOF MS featuring tandem ionization gives the opportunity to improve the quality of analytical data and reliability of results.

A comprehensive review of integrative pharmacology-based investigation: A paradigm shift in traditional Chinese medicine

Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics (PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes in vivo. Then, the main research contents of TCMIP are introduced as follows: chemical and ADME/PK profiles of TCM formulas; confirming the three forms of active substances and the three action modes; establishing the qualitative PK-PD correlation; and building the quantitative PK-PD correlations, etc. After that, we summarize the existing data resources, computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods. Finally, we further discuss the applications of TCMIP for the improvement of TCM quality control, clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs, especially TCM-related combination drug discovery.

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

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

Chemical fingerprinting strategies based on comprehensive two-dimensional gas chromatography combined with gas chromatography-olfactometry to capture the unique signature of Piemonte peppermint essential oil (Mentha x piperita var Italo-Mitcham)

Accurate, reliable, and informative mapping of untargeted and targeted components across many samples is here performed by combining off-line GC-Olfactometry (GC-O) and comprehensive two-dimensional gas chromatography (GC×GC) coupled to time-of-flight mass spectrometry with variable ionization energy (TOF MS featuring Tandem Ionization™). In particular, untargeted and targeted (UT) features patterns are processed by chromatographic fingerprinting, giving differential priority to potent odorants' retention-times regions. Distinguishing peppermint essential oil (EO) from Piedmont (Italy - Mentha × piperita L. var. Italo-Mitcham - Menta di Pancalieri EO), with its unique sensory fingerprint (i.e., freshness and long-lasting sweetness), from high-quality peppermint EOs produced in other areas poses a great challenge. Chromatographic UT fingerprinting provided a great chemical dimensionality by mapping more than 350 peak-regions at 70 eV and 135 at 12 eV. From them, 95 components were identified and responses compared to available literature. Then, potent odorants, detected by GC-O using the aroma extraction dilution analysis (AEDA), were tracked over the chromatographic space and tentatively identified. With the highest flavor dilution (FD), 1,8-cineole (eucalyptus, fresh, camphoraceous); menthone (minty, herbaceous); and menthofuran (minty, musty, petroleum-like) were highlighted. Responsible for creamy and coumarinic notes were the diasteroisomers of (3,6)-dimethyl-4,5,6,7-tetrahydrobenzo[b]-furan-2(3H)-one (i.e., menthofurolactones), detected in higher relative abundance in Pancalieri EOs. By prioritizing the investigation of volatiles on higher LogFD retention regions, including 131 untargeted/targeted features, Pancalieri EOs were separately clustered from United States samples. Besides pre-targeted analytes, additional untargeted features were post-processed for identification within marker chemicals. Myrtenyl methyl ether, ethyl 3-methyl butanoate, propyl-2-methylbutanoate, and (E)-2-hexenal were putatively identified. Of the "unknown - knowns" with diagnostic roles, all metadata were collected including low energy spectra at 12 eV, which were found to be highly complementary to 70 eV spectra.

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.

Exploring extra dimensions to capture saliva metabolite fingerprints from metabolically healthy and unhealthy obese patients by comprehensive two-dimensional gas chromatography featuring Tandem Ionization mass spectrometry

This study examines the information potential of comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) and variable ionization energy (i.e., Tandem Ionization™) to study changes in saliva metabolic signatures from a small group of obese individuals. The study presents a proof of concept for an effective exploitation of the complementary nature of tandem ionization data. Samples are taken from two sub-populations of severely obese (BMI > 40 kg/m²) patients, named metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). Untargeted fingerprinting, based on pattern recognition by template matching, is applied on single data streams and on fused data, obtained by combining raw signals from the two ionization energies (12 and 70 eV). Results indicate that at lower energy (i.e., 12 eV), the total signal intensity is one order of magnitude lower compared to the reference signal at 70 eV, but the ranges of variations for 2D peak responses is larger, extending the dynamic range. Fused data combine benefits from 70 eV and 12 eV resulting in more comprehensive coverage by sample fingerprints. Multivariate statistics, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) show quite good patient clustering, with total explained variance by the first two principal components (PCs) that increases from 54% at 70 eV to 59% at 12 eV and up to 71% for fused data. With PLS-DA, discriminant components are highlighted and putatively identified by comparing retention data and 70 eV spectral signatures. Within the most informative analytes, lactose is present in higher relative amount in saliva from MHO patients, whereas N-acetyl-D-glucosamine, urea, glucuronic acid γ-lactone, 2-deoxyribose, N-acetylneuraminic acid methyl ester, and 5-aminovaleric acid are more abundant in MUO patients. Visual feature fingerprinting is combined with pattern recognition algorithms to highlight metabolite variations between composite per-class images obtained by combining raw data from individuals belonging to different classes, i.e., MUO vs. MHO.

Graphical abstract

More Data, Please: Machine Learning to Advance the Multidisciplinary Science of Human Sociochemistry

Communication constitutes the core of human life. A large portion of our everyday social interactions is non-verbal. Of the sensory modalities we use for non-verbal communication, olfaction (i.e., the sense of smell) is often considered the most enigmatic medium. Outside of our awareness, smells provide information about our identity, emotions, gender, mate compatibility, illness, and potentially more. Yet, body odors are astonishingly complex, with their composition being influenced by various factors. Is there a chemical basis of olfactory communication? Can we identify molecules predictive of psychological states and traits? We propose that answering these questions requires integrating two disciplines: psychology and chemistry. This new field, coined sociochemistry, faces new challenges emerging from the sheer amount of factors causing variability in chemical composition of body odorants on the one hand (e.g., diet, hygiene, skin bacteria, hormones, genes), and variability in psychological states and traits on the other (e.g., genes, culture, hormones, internal state, context). In past research, the reality of these high-dimensional data has been reduced in an attempt to isolate unidimensional factors in small, homogenous samples under tightly controlled settings. Here, we propose big data approaches to establish novel links between chemical and psychological data on a large scale from heterogeneous samples in ecologically valid settings. This approach would increase our grip on the way chemical signals non-verbally and subconsciously affect our social lives across contexts.

An effective chromatographic fingerprinting workflow based on comprehensive two-dimensional gas chromatography - Mass spectrometry to establish volatiles patterns discriminative of spoiled hazelnuts (Corylus avellana L.)

The volatile fraction of hazelnuts encrypts information about: cultivar/geographical origin, post-harvest treatments, oxidative stability and sensory quality. However, sensory features could be buried under other dominant chemical signatures posing challenges to an effective classification based on pleasant/unpleasant notes. Here a novel workflow that combines Untargeted and Targeted (UT) fingerprinting on comprehensive two-dimensional gas-chromatographic patterns is developed to discriminate spoiled hazelnuts from those of acceptable quality. By flash-profiling, six hazelnut classes are defined: Mould, Mould-rancid-solvent, Rancid, Rancid-stale, Rancid-solvent, and Uncoded KO. Chromatographic fingerprinting on composite 2D chromatograms from samples belonging to the same class (i.e., composite class-images) enabled effective selection of chemical markers: (a) octanoic acid that guides the sensory classification being positively correlated to mould; (b) ƴ-nonalactone, ƴ-hexalactone, acetone, and 1-nonanol that are decisive to classify OK and rancid samples; (c) heptanoic and hexanoic acids and ƴ-octalactone present in high relative abundance in rancid-solvent and rancid-stale samples.

A non-targeted data processing workflow for volatile organic compound data acquired using comprehensive two-dimensional gas chromatography with dual channel detection

There has been an influx of technology for comprehensive two-dimensional gas chromatography analyses in recent years, calling for development of guided workflows and rigorous reporting of processes. This research focuses on the processing method for data collected on a dual channel detection system using flame ionization detection (FID) and quadrupole mass spectrometry (qMS) for the analysis of volatile organic compounds (VOCs). The samples analyzed were kava (Piper methysticum), which has a rich VOC profile that benefits substantially from a multidimensional approach due to enhanced peak capacity. The procedure which was customized here was the data processing workflow from a manual single-sample analysis to an integrated batch workflow that can be applied across studies.•Parameter choice for baseline correction and peak detection were defined when handling batch data.•Elution regions were defined using qMS data to automate compound identification.•Stencils were transformed onto FID data and sequenced for quantitative information.This dataset can be used as a training tool, as all details, methods and results for the workflow have been provided for users to compare with. The focus on data workflow reproducibility in the field of multidimensional chromatography will assist in adoption by users in new application areas.

Climate and Processing Effects on Tea (Camellia sinensis L. Kuntze) Metabolome: Accurate Profiling and Fingerprinting by Comprehensive Two-Dimensional Gas Chromatography/Time-of-Flight Mass Spectrometry

This study applied an untargeted–targeted (UT) fingerprinting approach, based on comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF MS), to assess the effects of rainfall and temperature (both seasonal and elevational) on the tea metabolome. By this strategy, the same compound found in multiple samples need only to be identified once, since chromatograms and mass spectral features are aligned in the data analysis process. Primary and specialized metabolites of leaves from two Chinese provinces, Yunnan (pu′erh) and Fujian (oolong), and a farm in South Carolina (USA, black tea) were studied. UT fingerprinting provided insight into plant metabolism activation/inhibition, taste and trigeminal sensations, and antioxidant properties, not easily attained by other analytical approaches. For example, pu′erh and oolong contained higher relative amounts of amino acids, organic acids, and sugars. Conversely, black tea contained less of all targeted compounds except fructose and glucose, which were more similar to oolong tea. Findings revealed compounds statistically different between spring (pre-monsoon) and summer (monsoon) in pu′erh and oolong teas as well as compounds that exhibited the greatest variability due to seasonal and elevational differences. The UT fingerprinting approach offered unique insights into how differences in growing conditions and commercial processing affect the nutritional benefits and sensory characteristics of tea beverages.

Enhancement of volatile profiling using multiple-cumulative trapping solid-phase microextraction. Consideration on sample volume

In the present work, the performance of the multiple-cumulative trapping headspace solid-phase microextraction technique used in the headspace linearity range and saturated headspace was investigated and compared, with the ultimate goal of maximizing the fingerprinting information extractable using a cross-sample comparison algorithm for olive oil quality assessment. It was highlighted as the use of 0.1 g of olive oil provides comparable or even better profiling than 1.5 g at a little expense of sensitivity. However, the use of multiple-cumulative-solid-phase microextraction, along with the correct sample volume, improved not only the overall sensitivity but significantly burst the level of information for cross-sample studies.

Highly Informative Fingerprinting of Extra-Virgin Olive Oil Volatiles: The Role of High Concentration-Capacity Sampling in Combination with Comprehensive Two-Dimensional Gas Chromatography

The study explores the complex volatile fraction of extra-virgin olive oil by combining high concentration-capacity headspace approaches with comprehensive two-dimensional gas chromatography, which is coupled with time of flight mass spectrometry. The static headspace techniques in this study are: (a) Solid-phase microextraction, with multi-polymer coating (SPME- Divinylbenzene/Carboxen/Polydimethylsiloxane), which is taken as the reference technique; (b) headspace sorptive extraction (HSSE) with either a single-material coating (polydimethylsiloxane—PDMS) or a dual-phase coating that combines PDMS/Carbopack and PDMS/EG (ethyleneglycol); (c) monolithic material sorptive extraction (MMSE), using octa-decyl silica combined with graphite carbon (ODS/CB); and dynamic headspace (d) with either PDMS foam, operating in partition mode, or Tenax TA™, operating in adsorption mode. The coverage of both targeted and untargeted 2D-peak-region features, which corresponds to detectable analytes, was examined, while concentration factors (CF) for a selection of informative analytes, including key-odorants and off-odors, and homolog-series relative ratios were calculated and the information capacity was discussed. The results highlighted the differences in concentration capacities, which were mainly caused by polymer-accumulation characteristics (sorptive/adsorptive materials) and its amount. The relative concentration capacity for homologues and potent odorants was also discussed, while headspace linearity and the relative distribution of analytes, as a function of different sampling amounts, was examined. This last point is of particular interest in quantitative studies where accurate data is needed to derive consistent conclusions.