Beyond Volatile Phenols: An Untargeted Metabolomic Approach to Revealing Additional Markers of Smoke Taint in Grapevines (Vitis vinifera L.) cv. Merlot.

When bushfires occur near wine regions, vineyards are frequently exposed to environmental smoke, which can negatively affect grapes and wine. For evaluating the severity of smoke exposure, volatile phenols and their glycosides are commonly used as biomarkers of smoke exposure. While critical to refining smoke taint diagnostics, few studies have comprehensively assessed the compositional impact of smoke exposure of grapes. In this study, Merlot grapevines were exposed to smoke post-véraison, with grapes being sampled both pre-smoke exposure and repeatedly post-smoke exposure, for analysis by liquid chromatography-high-resolution mass spectrometry. Volatile phenol glycosides were detected in control and smoke-affected grapes at ≤22 µg/kg and up to 160 µg/kg, respectively. The metabolite profiles of control and smoke-affected grapes were then compared using an untargeted metabolomics approach and compounds differentiating the sample types tentatively identified. The results demonstrate the presence of novel phenolic glycoconjugates as putative metabolites from environmental smoke together with stress-related grapevine metabolites and highlight the need to further characterize the consequences of grapevine smoke exposure with respect to the regulation of abiotic stress and plant defense mechanisms.

Apple Pomace Compositional Data Highlighting the Proportional Contribution of Polymeric Procyanidins.

Recent years have seen an increase in research focusing on the amelioration of apple pomace waste for use in the food and nutraceutical industries. Much of this work has concentrated on the characterisation of the polyphenol composition of apple pomace materials to determine their role in conferring nutritional and health benefits. Although apples contain substantial quantities of polymeric procyanidins (condensed tannins), this class of compounds has received limited attention in apple research. This study quantified the polymeric procyanidins in apple pomace extracts using a rapid, methyl-cellulose precipitation (MCP) approach for the first time. In addition, a non-targeted metabolomics approach was applied to determine the most abundant phenolic classes present. Polymeric procyanidins were found to be the most abundant type of polyphenol in apple pomace extracts and were generally oligomeric in nature. Multivariate statistical analysis revealed that the ferric-reducing antioxidant power (FRAP) was most strongly correlated with the polymeric procyanidin concentration. Noting that polymeric procyanidins may not cross the cell layer to exert antioxidant activity in vivo, their presence in apple pomace extracts may therefore overestimate the FRAP. This work highlights the importance of polymeric procyanidins in the phenolic diversity of apple pomaces, and it is proposed that in future studies, rapid MCP assays may be used for their quantification.

MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization.

Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.

Preliminary Study of Australian Pinot Noir Wines by Colour and Volatile Analyses, and the Pivot© Profile Method Using Wine Professionals.

The aim of this preliminary study was to identify potential colour components, volatile and sensory attributes that could discriminate Pinot noir wines from five Australian winegrowing regions (Adelaide Hills, Yarra Valley, Mornington Peninsula, Northern and Southern Tasmania). The sensory analysis consisted of the Pivot© Profile method that was performed by wine professionals. A headspace solid-phase microextraction-gas chromatography-mass spectrometry method was used to quantify multiple volatile compounds, while the Modified Somers method was used for colour characterisation. Analysis of data suggested ethyl decanoate, ethyl 2-methylpropanoate, ethyl 2-methylbutanoate, in addition to decanoic acid as important contributors to the discrimination between regions. Similarly, wine hue, chemical age indices, total anthocyanin, and (%) non-bleachable pigment also discriminated wines between regions. The sensory analysis showed that wines from Mornington Peninsula were associated with the 'red fruits' aroma, 'acidic', and 'astringency' palate descriptors, while those from Adelaide Hills were associated with the 'brown' colour attribute. This study indicates regionality is a strong driver of aroma typicity of wine.

The metaRbolomics Toolbox in Bioconductor and beyond.

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES)-An Approach To Use Artificial Intelligence in Determining Food Odor Codes.

Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory system, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-based expert system (SEBES) that is able to reliably predict the key aroma compounds of a given food in a limited number of runs without using the human olfactory system. First, a successful method for the quantitation of nearly 100 (out of the 226 known KFOs) components was developed in combination with a software allowing the direct use of the identification and quantitation data for the calculation of odor activity values (OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples, the quantitative results obtained by the new SEBES method were compared to data obtained by applying an aroma extract dilution analysis and stable isotope dilution assays required in the classical Sensomics approach. A good agreement of the results was found with differences below 20% for most of the compounds considered. By implementing the GC × GC data analysis software with the in-house odor threshold database, odor activity values (ratio of concentration to odor threshold) were directly displayed in the software pane. The OAVs calculated by the software were in very good agreement with data manually calculated on the basis of the data obtained by SIDA. Thus, it was successfully shown that it is possible to characterize key food odorants with one single analytical platform and without using the human olfactory system, that is, by "artificial intelligence smelling".

Differentiation of Rums Produced from Sugar Cane Juice (Rhum Agricole) from Rums Manufactured from Sugar Cane Molasses by a Metabolomics Approach.

A large set of volatiles (a metabolome) was isolated by SAFE distillation from 25 high priced rums prepared from sugar cane juice (SCJ) and 26 high priced rums manufactured from sugar cane molasses (SCM). The volatile fractions were first analyzed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF-MS), and the "comprehensive template matching fingerprinting" was used to extract the entire features present in the respective set of volatile compounds. After raw data pretreatment, chemometrics was used to locate marker compounds. Following, a sparse-partial-least-squares discriminant analysis ( sPLS-DA) and a partial-least-squares discriminant analysis (PLS-DA) were applied to a training data set for creating a model. The model was validated using leave-one-out cross validation and tested over an independent data set to evaluate its predictive power. The characteristic fingerprint resulted in a 100% correct classification of sugar cane juice rums, thus achieving the first aim of locating markers for these higher quality rums. Then, past-processing identification within the discriminant features was done to characterize 12 significant marker compounds as 1-decanol, γ-dodecalactone, ethyl 3-methylbutanoate, ethyl nonanoate, 3-furancarboxaldehyde, 1-hexanol, ß-ionone, 2- and 3-methylbutanol, methyl decanoate, 3-octanol, and 2-undecanone. Quantitation of eight selected markers by stable isotope dilution assays confirmed higher concentrations in SCJ compared to SCM and served as the final proof to differentiate both types of spirits.

Studies on the volatile fraction composition of three native Amazonian-Brazilian fruits: Murici (Byrsonima crassifolia L., Malpighiaceae), bacuri (Platonia insignis M., Clusiaceae), and sapodilla (Manilkara sapota L., Sapotaceae).

The volatile fraction of murici, bacuri and sapodilla are here studied because of their increasing interest for consumers, abundance of production in Brazil, and the general demand for new flavors and aromas. Their volatile profiles were studied by two High Concentration Capacity Headspace techniques (HCC-HS), Headspace Solid Phase Microextraction (HS-SPME) and Headspace Sorptive Extraction (HSSE), in combination with GC-MS. Murici volatile fraction mainly contains esters (38%), carboxylic acids (19%), aldehydes (11%), alcohols (14%), others (13%) and sulfur compounds; bacuri is characterized by terpenes (41%), non-terpenic alcohols (24%), esters (15%), aldehydes (6%), and others (12%); sapodilla consists of esters (33%), alcohols (27%), terpenes (18%) and others (21%). The GC-MS component co-elution was overcome by GC×GC-qMS. The adoption of modern analysis technologies afforded to achieve a better knowledge of the volatile fraction composition of these fruit pulps by increasing substantially the number of compounds identified.

Parallel dual secondary column-dual detection: a further way of enhancing the informative potential of two-dimensional comprehensive gas chromatography.

Comprehensive two-dimensional gas chromatography (GC×GC) coupled with Mass Spectrometry (MS) is one of today's most powerful analytical platforms for detailed analysis of medium-to-high complexity samples. The column set usually consists of a long, conventional-inner-diameter first dimension ((1)D) (typically 15-30m long, 0.32-0.25mm dc), and a short, narrow-bore second dimension ((2)D) column (typically 0.5-2m, 0.1mm dc) where separation is run in a few seconds. However, when thermal modulation is used, since the columns of a set are coupled in series, a flow mismatch occurs between the two dimensions, making it impossible to operate simultaneously at optimized flow conditions. Further, short narrow-bore capillaries can easily be overloaded, because of their lower loadability, limiting the effectiveness of (2)D separation. In this study, improved gas linear velocities in both chromatographic dimensions were achieved by coupling the (1)D column with two parallel (2)D columns, having identical inner diameter, stationary phase chemistry, and film thickness. In turn, these were connected to two detectors: a fast quadrupole Mass Spectrometer (MS) and a Flame Ionization Detector (FID). Different configurations were tested and performances compared to a conventional set-up; experimental results on two model mixtures (n-alkanes and fourteen medium-to-high polarity volatiles of interest in the flavor and fragrance field) and on the essential oil of Artemisia umbelliformis Lam., show the system provides consistent results, in terms of analyte identification (reliability of spectra and MS matching) and quantitation, also affording an internal cross-validation of quantitation accuracy.

High concentration capacity sample preparation techniques to improve the informative potential of two-dimensional comprehensive gas chromatography-mass spectrometry: application to sensomics.

This study reports and critically discusses the results of a systematic investigation on the effectiveness of different and complementary sampling approaches, based on either sorption and adsorption, treated as a further dimension of a two-dimensional comprehensive gas chromatography-mass spectrometry analytical platform for sensomics. The focus is on the potentials of a group of high concentration capacity (HCC) sample preparation (Solid Phase Microextraction, SPME, Stir Bar Sorptive Extraction, SBSE and Headspace Sorptive Extraction, HSSE) and Dynamic Headspace (D-HS) techniques investigated to provide information useful for fingerprinting and profiling studies of food aroma. Volatiles and semi-volatiles contributing to define whole and nonfat dry milk aroma have been successfully characterized thanks to the combination of effective and selective sampling by HCC and D-HS techniques, high separation and detection power of GC×GC-MS and suitable data elaboration (i.e., Comprehensive Template Matching Fingerprinting - CTMF). Out of the sample preparation techniques investigated, HSSE and SBSE have shown to be really effective for sensomics studies because of their high concentration factors, providing highly representative profiles as well as analyte recovery suitable for GC-Olfactometry even with high odor threshold (OT) markers or potent odorants in sub-trace amounts.

Quantitative fingerprinting by headspace--two-dimensional comprehensive gas chromatography-mass spectrometry of solid matrices: some challenging aspects of the exhaustive assessment of food volatiles.

The study proposes an investigation strategy that simultaneously provides detailed profiling and quantitative fingerprinting of food volatiles, through a "comprehensive" analytical platform that includes sample preparation by Headspace Solid Phase Microextraction (HS-SPME), separation by two-dimensional comprehensive gas chromatography coupled with mass spectrometry detection (GC×GC-MS) and data processing using advanced fingerprinting approaches. Experiments were carried out on roasted hazelnuts and on Gianduja pastes (sugar, vegetable oil, hazelnuts, cocoa, nonfat dried milk, vanilla flavorings) and demonstrated that the information potential of each analysis can better be exploited if suitable quantitation methods are applied. Quantitation approaches through Multiple Headspace Extraction and Standard Addition were compared in terms of performance parameters (linearity, precision, accuracy, Limit of Detection and Limit of Quantitation) under headspace linearity conditions. The results on 19 key analytes, potent odorants, and technological markers, and more than 300 fingerprint components, were used for further processing to obtain information concerning the effect of the matrix on volatile release, and to produce an informative chemical blueprint for use in sensomics and flavoromics. The importance of quantitation approaches in headspace analysis of solid matrices of complex composition, and the advantages of MHE, are also critically discussed.

Volatile profiling of high quality hazelnuts (Corylus avellana L.): chemical indices of roasting.

The study proposes an investigation strategy to identify sensitive, robust and reliable chemical markers of hazelnut roasting. A fully-automated and validated analytical method, based on Headspace Solid Phase Microextraction (HS-SPME) coupled with Gas Chromatography-Mass Spectrometric detection (GC-MS), for effective off-line monitoring of changes in the volatile profile of high-quality hazelnuts was developed. Samples from two different harvests were submitted to roasting, following different time/temperature protocols and different technologies, enabling chemical changes to be correlated with technological processing and sensory quality. Chemical indices, expressed as analyte response ratio, were defined and their trend observed across roasting profiles. Reliability and robustness of chemical indices were also evaluated, in view of their application to on-line monitoring with Mass Spectrometry-based electronic nose technology (MS-nose). Experiments, simulating on-line chemical characterisation of the volatile fraction, were performed through a fully-automated system. The results confirmed: (a) the effectiveness of single process indicators of roasting selected by the separative method (5-methylfurfural, 1(H)-pyrrole, furfuryl alcohol, 1(H)-pyrrole-2-carboxaldehyde, 1-hydroxy-2-propanone, dihydro-2(3H)-furanone, 5-methyl-(E)-2-hepten-4-one, acetic acid, pyridine, furfural, pyrazine, and several alkyl-pyrazines); and, (b) the reliability of proposed chemical indices: 5-methylfurfural/2,5-dimethylpyrazine, 5-methylfurfural/2-methylpyrazine, 2,5-dimethylpyrazine/2,3-dimethylpyrazine; these maintained a consistent trend versus harvest and sampling/analysis technology.

Performance evaluation of non-targeted peak-based cross-sample analysis for comprehensive two-dimensional gas chromatography-mass spectrometry data and application to processed hazelnut profiling.

The continuous interest in non-targeted profiling induced the development of tools for automated cross-sample analysis. Such tools were found to be selective or not comprehensive thus delivering a biased view on the qualitative/quantitative peak distribution across 2D sample chromatograms. Therefore, the performance of non-targeted approaches needs to be critically evaluated. This study focused on the development of a validation procedure for non-targeted, peak-based, GC×GC-MS data profiling. The procedure introduced performance parameters such as specificity, precision, accuracy, and uncertainty for a profiling method known as Comprehensive Template Matching. The performance was assessed by applying a three-week validation protocol based on CITAC/EURACHEM guidelines. Optimized ¹D and ²D retention times search windows, MS match factor threshold, detection threshold, and template threshold were evolved from two training sets by a semi-automated learning process. The effectiveness of proposed settings to consistently match 2D peak patterns was established by evaluating the rate of mismatched peaks and was expressed in terms of results accuracy. The study utilized 23 different 2D peak patterns providing the chemical fingerprints of raw and roasted hazelnuts (Corylus avellana L.) from different geographical origins, of diverse varieties and different roasting degrees. The validation results show that non-targeted peak-based profiling can be reliable with error rates lower than 10% independent of the degree of analytical variance. The optimized Comprehensive Template Matching procedure was employed to study hazelnut roasting profiles and in particular to find marker compounds strongly dependent on the thermal treatment, and to establish the correlation of potential marker compounds to geographical origin and variety/cultivar and finally to reveal the characteristic release of aroma active compounds.