Development and validation of a GC × GC-ToFMS method for the quantification of pesticides in environmental waters.

Water is a fundamental resource for living things, which is why its control is necessary. The widespread use of pesticides for agricultural and non-agricultural purposes has resulted in the presence of their residues in surface water and groundwater resources. Their presence in water is regulated through different directives, such as the Groundwater Directive, the Drinking Water Directive, and the Water Framework Directive, modified later several times, setting a maximum concentration of 0.1 µg.L-1 for individual pesticides and their degradation products, and 0.5 µg.L-1 for total pesticide residues present in a sample. There are different kinds of pesticides (e.g., organophosphorus and organochlorine pesticides, triazines, chloroacetamides, triazoles, (thio)carbamates) that have diverse chemical structures. Their determination and monitoring in a single analytical procedure are possible through multiresidue methods. In this study, 53 pesticides belonging to different chemical classes and their metabolites were selected based on their local occurrence and investigated in surface water and groundwater from agricultural areas susceptible to pesticide contamination. The methodology consisted of a classical solid-phase extraction (SPE) for the purification and enrichment of the pesticides, with a subsequent analysis in multidimensional gas chromatography coupled to mass spectrometry (GC×GC-MS). The quantification method was validated according to the Eurachem Guide in terms of linearity, precision, accuracy, limit of detection, and limit of quantification. After validation, the method was applied to 34 real-world water samples, and the results were compared with those obtained by a GC-QMS routine method.

Current Developments of Analytical Methodologies for Aflatoxins' Determination in Food during the Last Decade (2013-2022), with a Particular Focus on Nuts and Nut Products.

This review aims to provide a clear overview of the most important analytical development in aflatoxins analysis during the last decade (2013-2022) with a particular focus on nuts and nuts-related products. Aflatoxins (AFs), a group of mycotoxins produced mainly by certain strains of the genus Aspergillus fungi, are known to impose a serious threat to human health. Indeed, AFs are considered carcinogenic to humans, group 1, by the International Agency for Research on Cancer (IARC). Since these toxins can be found in different food commodities, food control organizations worldwide impose maximum levels of AFs for commodities affected by this threat. Thus, they represent a cumbersome issue in terms of quality control, analytical result reliability, and economical losses. It is, therefore, mandatory for food industries to perform analysis on potentially contaminated commodities before the trade. A full perspective of the whole analytical workflow, considering each crucial step during AFs investigation, namely sampling, sample preparation, separation, and detection, will be presented to the reader, focusing on the main challenges related to the topic. A discussion will be primarily held regarding sample preparation methodologies such as partitioning, solid phase extraction (SPE), and immunoaffinity (IA) related methods. This will be followed by an overview of the leading analytical techniques for the detection of aflatoxins, in particular liquid chromatography (LC) coupled to a fluorescence detector (FLD) and/or mass spectrometry (MS). Moreover, the focus on the analytical procedure will not be specific only to traditional methodologies, such as LC, but also to new direct approaches based on imaging and the ability to detect AFs, reducing the need for sample preparation and separative techniques.

The contribution of high-resolution GC separations in plastic recycling research.

One convenient strategy to reduce environmental impact and pollution involves the reuse and revalorization of waste produced by modern society. Nowadays, global plastic production has reached 367 million tons per year and because of their durable nature, their recycling is fundamental for the achievement of the circular economy objective. In closing the loop of plastics, advanced recycling, i.e., the breakdown of plastics into their building blocks and their transformation into valuable secondary raw materials, is a promising management option for post-consumer plastic waste. The most valuable product from advanced recycling is a fluid hydrocarbon stream (or pyrolysis oil) which represents the feedstock for further refinement and processing into new plastics. In this context, gas chromatography is currently playing an important role since it is being used to study the pyrolysis oils, as well as any organic contaminants, and it can be considered a high-resolution separation technique, able to provide the molecular composition of such complex samples. This information significantly helps to tailor the pyrolysis process to produce high-quality feedstocks. In addition, the detection of contaminants (i.e., heteroatom-containing compounds) is crucial to avoid catalytic deterioration and to implement and design further purification processes. The current review highlights the importance of molecular characterization of waste stream products, and particularly the pyrolysis oils obtained from waste plastics. An overview of relevant applications published recently will be provided, and the potential of comprehensive two-dimensional gas chromatography, which represents the natural evolution of gas chromatography into a higher-resolution technique, will be underlined.

Top-Down Approach to Retention Time Prediction in Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry.

In this contribution, we describe a novel modeling approach to predicting retention times (tr) in comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-ToF-MS) with a particular emphasis on the second-dimension (2D) retention time predictions (2tr). This approach is referred to as a "top-down" approach in that it breaks down the complete GC × GC separation into two independent one-dimensional gas chromatography separations (1D-GC). In this regard, both dimensions, that is, first dimension (1D) and second dimension (2D) are treated separately, and the cryogenic modulator is simply considered as a second consecutive injection device. Separate 1D-GC tr predictions are performed on both dimensions using the same flow rate as the one deployed in the conventional GC × GC system. The separate tr predictions are then combined to account for the two-dimensional separation. This model was applied to 24 analytes from 2 standard mixtures (Grob Test Mix and Fragrance Materials Test Mix) and assessed across 9 GC × GC chromatographic conditions. The experimental and predicted chromatographic retention space occupations were assessed by using the convex hull approach defined by the Delaunay triangulation. The predicted percentage of space occupation corresponded favorably with the experimental values. Furthermore, the top-down approach enabled an accurate prediction of the 2tr of all investigated analytes, providing an average 2tr modeling error of 0.26 ± 0.01 s.

Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GC×GC-TOFMS, Chemometrics, and Machine Learning.

Coffee, one of the most popular beverages in the world, attracts consumers by its rich aroma and the stimulating effect of caffeine. Increasing consumers prefer decaffeinated coffee to regular coffee due to health concerns. There are some main decaffeination methods commonly used by commercial coffee producers for decades. However, a certain amount of the aroma precursors can be removed together with caffeine, which could cause a thin taste of decaffeinated coffee. To understand the difference between regular and decaffeinated coffee from the volatile composition point of view, headspace solid-phase microextraction two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) was employed to examine the headspace volatiles of eight pairs of regular and decaffeinated coffees in this study. Using the key aroma-related volatiles, decaffeinated coffee was significantly separated from regular coffee by principal component analysis (PCA). Using feature-selection tools (univariate analysis: t-test and multivariate analysis: partial least squares-discriminant analysis (PLS-DA)), a group of pyrazines was observed to be significantly different between regular coffee and decaffeinated coffee. Pyrazines were more enriched in the regular coffee, which was due to the reduction of sucrose during the decaffeination process. The reduction of pyrazines led to a lack of nutty, roasted, chocolate, earthy, and musty aroma in the decaffeinated coffee. For the non-targeted analysis, the random forest (RF) classification algorithm was used to select the most important features that could enable a distinct classification between the two coffee types. In total, 20 discriminatory features were identified. The results suggested that pyrazine-derived compounds were a strong marker for the regular coffee group whereas furan-derived compounds were a strong marker for the decaffeinated coffee samples.

Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning.

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography-quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.

Exploiting targeted and untargeted approaches for the analysis of bacterial metabolites under altered growth conditions.

In the host, pathogenic microorganisms have developed stress responses to cope with constantly changing environments. Stress responses are directly related to changes in several metabolomic pathways, which could hamper microorganisms' unequivocal identification. We evaluated the effect of various in vitro stress conditions (acidic, basic, oxidative, ethanolic, and saline conditions) on the metabolism of Staphylococcus aureus, Bacillus cereus, and Pseudomonas aeruginosa, which are common lung pathogens. The metabolite profiles of the bacteria were analyzed using liquid chromatography coupled to triple quadrupole and quadrupole time-of-flight mass spectrometry. The advantages of targeted and untargeted analysis combined with univariate and multivariate statistical analysis (principal component analysis, hierarchical cluster analysis, partial least square discriminant analysis, random forest) were combined to unequivocally identify bacterial species. In normal in vitro conditions, the targeted methodology, based on the analysis of primary metabolites, enabled the rapid and efficient discrimination of the three bacteria. In changing in vitro conditions and specifically in presence of the various stressors, the untargeted methodology proved to be more valuable for the global and accurate differentiation of the three bacteria, also considering the type of stress environment within each species. In addition, species-specific metabolites (i.e., fatty acids, polysaccharides, peptides, and nucleotide bases derivatives) were putatively identified. Good intra-day repeatability and inter-day repeatability (< 10% RSD and < 15% RSD, respectively) were obtained for the targeted and the untargeted methods. This untargeted approach highlights its importance in unusual (and less known) bacterial growth environments, being a powerful tool for infectious disease diagnosis, where the accurate classification of microorganisms is sought.

Modeling approaches for temperature-programmed gas chromatographic retention times under vacuum outlet conditions.

This contribution evaluates the performance of two predictive approaches in calculating temperature-programmed gas chromatographic retention times under vacuum outlet conditions. In the first approach, the predictions are performed according to a thermodynamic-based model, while in the second approach the predictions are conducted by using the temperature-programmed retention time equation. These modeling approaches were evaluated on 47 test compounds belonging to different chemical classes, under different experimental conditions, namely, two modes of gas flow regulation (i.e., constant inlet pressure and constant flow rate), and different temperature programs (i.e., 7 °C/min, 5 °C/min, and 3 °C/min). Both modeling approaches gave satisfactory results and were able to accurately predict the elution profiles of the studied test compounds. The thermodynamic-based model provided more satisfying results under constant flow rate mode, with average modeling errors of 0.43%, 0.33%, and 0.15% across all the studied temperature programs. Nevertheless, under constant inlet pressure mode, lower modeling errors were achieved when using the temperature-programmed retention time equation, with average modeling errors of 0.18%, 0.18%, and 0.31% across the used temperature programs.

Breath can discriminate tuberculosis from other lower respiratory illness in children.

Pediatric tuberculosis (TB) remains a global health crisis. Despite progress, pediatric patients remain difficult to diagnose, with approximately half of all childhood TB patients lacking bacterial confirmation. In this pilot study (n = 31), we identify a 4-compound breathprint and subsequent machine learning model that accurately classifies children with confirmed TB (n = 10) from children with another lower respiratory tract infection (LRTI) (n = 10) with a sensitivity of 80% and specificity of 100% observed across cross validation folds. Importantly, we demonstrate that the breathprint identified an additional nine of eleven patients who had unconfirmed clinical TB and whose symptoms improved while treated for TB. While more work is necessary to validate the utility of using patient breath to diagnose pediatric TB, it shows promise as a triage instrument or paired as part of an aggregate diagnostic scheme.

The role of sample preparation in multidimensional gas chromatographic separations for non-targeted analysis with the focus on recent biomedical, food, and plant applications.

In this review, we consider and discuss the affinity and complementarity between a generic sample preparation technique and the comprehensive two-dimensional gas chromatography process. From the initial technical development focus (e.g., on the GC×GC and solid-phase microextraction techniques), the trend is inevitably shifting toward more applied challenges, and therefore, the preparation of the sample should be carefully considered in any GC×GC separation for an overreaching research. We highlight recent biomedical, food, and plant applications (2016-July 2020), and specifically those in which the combination of tailored sample preparation methods and GC×GC-MS has proven to be beneficial in the challenging aspects of non-targeted analysis. Specifically on the sample preparation, we report on gas-phase, solid-phase, and liquid-phase extractions, and derivatization procedures that have been used to extract and prepare volatile and semi-volatile metabolites for the successive GC×GC analysis. Moreover, we also present a milestone section reporting the early works that pioneered the combination of sample preparation techniques with GC×GC for non-targeted analysis.

Impact of the adsorbent material on volatile metabolites during in vitro and in vivo bio-sampling.

The increased attraction of biological volatile compounds has opened the route to a wide variety of sampling techniques, amongst which trap tubes packed with adsorbent materials are commonly used. Many types of adsorbent materials are available and the choice of the adsorbent can impact the obtained results in untargeted analysis. Therefore, a proper combination of the adsorbent material and the sample is necessary to increase the robustness and reproducibility of biological studies. In this study, the sampling performance of thermal desorption tubes with six common adsorbent material combinations, i.e., Tenax® TA, Tenax® TA/Carbopack™ B, Tenax® TA/Sulficarb, Tenax® TA/Carbograph™ 5TD, Tenax® TA/Carbograph™ 1TD/Carboxen® 1003, and Carboxen® 1016/Carbograph™ 5TD, was evaluated in two different setups: in vitro and in vivo sampling. The in vitro setup consisted of the headspace dynamic extraction of spiked serum, and a mixture of 19 standards was evaluated in terms of response and reproducibility. The in vivo setup consisted into two parts: the first one was based the evaluation of the standard mixture, which was flash-vaporised into Tedlar® bags containing exhaled breath; the second part was based on the longitudinal monitoring of breath metabolites originating from a beverage intake (i.e., brewed coffee), over a 90 min time period. The tubes were all desorbed and analysed in a comprehensive two-dimensional gas chromatography system coupled to a high-resolution time-of-flight mass spectrometer (GC × GC-HR ToF MS). In both sampling setups, the widest analytes coverage and the overall best extraction yield on the selected compounds were obtained using Tenax® TA, followed by Tenax® TA/Carbopack™ B. Tenax® TA provided the highest sampling reproducibility with 12 %RSD, 10 %RSD and <5 %RSD of the response during the experiments using the in vitro setup, the in vivo setup, and during the longitudinal tracking, respectively.

30th Anniversary of comprehensive two-dimensional gas chromatography: Latest advances.

In this review, we report on the latest (2020-Early 2021) instrumental advances and applications of comprehensive two-dimensional gas chromatography (GC×GC), including its hyphenation with novel upstream or downstream processes (sample preparation approaches or detection technologies). We also discuss software and analysis workflow developments necessary to elaborate the dense chemical information obtained. Thirty years after its inception, the use of GC×GC, as the main analytical tool or as a complementary platform, is undoubtedly shifting toward more applied challenges in a vast breadth of applications. Therefore, we consider the major fields (energy, fuel, foodstuff, plant, biological, and environmental) in which GC×GC has been successfully used, discussing some of the recent innovative research works.

In-Depth Cannabis Multiclass Metabolite Profiling Using Sorptive Extraction and Multidimensional Gas Chromatography with Low- and High-Resolution Mass Spectrometry.

The present research reports on the development of a methodology to unravel the complex phytochemistry of cannabis. Specifically, cannabis inflorescences were considered and stir bar sorptive extraction (SBSE) was used for the preconcentration of the metabolites. Analytes were thermally desorbed into a comprehensive two-dimensional (2D) gas chromatography (GC × GC) system coupled with low- and high-resolution mass spectrometry (MS). Particular attention was devoted to the optimization of the extraction conditions, to extend the analytes' coverage, and the chromatographic separation, to obtain a robust data set for further untargeted analysis. Monoterpenes, sesquiterpenes, hydrocarbons, cannabinoids, other terpenoids, and fatty acids were considered to optimize the extraction conditions. The response of selected ions for each chemical class, delimited in specific 2D chromatographic regions, enabled an accurate and fast evaluation of the extraction variables (i.e., time, temperature, solvent, salt addition), which were then selected to have a wide analyte selection and good reproducibility. Under optimized SBSE conditions, eight different cannabis inflorescences and a quality control sample were analyzed and processed following an untargeted and unsupervised approach. Principal component analysis on all detected metabolites revealed chemical differences among the sample types which could be associated with the plant subspecies. With the same SBSE-GC × GC-MS methodology, a quantitative targeted analysis was performed on three common cannabinoids, namely, Δ9-tetrahydrocannabinol, cannabidiol, and cannabinol. The method was validated, giving correlation factors over 0.98 and <20% reproducibility (relative standard deviation). The high-resolution MS acquisition allowed for high-confidence identification and post-targeted analysis, confirming the presence of two pesticides, a plasticizer, and a cannabidiol degradation product in some of the samples.

Evaluation of different adsorbent materials for the untargeted and targeted bacterial VOC analysis using GC×GC-MS.

The analysis of bacterial volatile organic compounds has gained attraction as a non-invasive way to identify disease-causing organisms, given that bacteria have unique metabolisms and volatile metabolic byproducts. In the present research, different adsorbent materials (Carbopack Y, X, B, Carboxen 1000 and Tenax TA), packed singularly or in combination, were compared in terms of sampling performance (sensitivity, repeatability and selectivity) for the extraction of standards and bacterial volatile metabolites in vitro (from Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). After extraction, bacterial volatile organic compounds were desorbed and analyzed in a comprehensive two-dimensional gas chromatography system coupled to a time-of-flight mass spectrometer (GC × GC-ToF MS). The results show that Tenax has the greater ability to extract the standard mix as well as volatile organic compounds with better repeatability (4-26 RSD%), higher sensitivity (on average ∼24 fold) compared to Carbopack Y, X and Carboxen 1000 tube, which followed in terms of performance. In addition, Tenax confirmed the best sensitivity and discriminatory power with no misclassification in the untargeted and unsupervised analysis for the differentiation of the bacterial species.

Study of the Lipid Profile of ATCC and Clinical Strains of Staphylococcus aureus in Relation to Their Antibiotic Resistance.

A number of reports have indicated a relationship between bacterial resistance to antibiotics and their lipid composition. In the present study, we characterized the lipid profiles of American Type Culture Collection (ATCC) and clinical strains of Staphylococcus aureus and its correlation with antibiotic resistance and hydrophobicity. The following strains were used: S. aureus ATCC 6538P, S. aureus ATCC 43300 (MRSA), seven clinical strains from the pharynges, two strains from duodenal ulcers, four strains from hip prostheses, and one strain from the conjunctiva. Lipid-related differentiation was observed across the S. aureus strains: the higher abundance of anteiso-pentadecanoic acid (anteiso-C15:0) and anteiso-heptadecanoic acid (anteiso-C17:0), followed by iso-pentadecanoic acid (iso-C15:0), suggested that these were common lipids. Iso-tridecanoic acid (iso-C13:0) and anteiso-tridecanoic acid (anteiso-C13:0), iso-hexadecanoic acid (iso-C16:0) and anteiso-hexadecanoic acid (anteiso-C16:0), and all forms of octadecanoic acid (C18:0) were usually detected in low abundance. Strains isolated from pharynges showed the highest ratio of branched/straight chains. A distinction in two clusters based on the amount and type of bacterial lipids identified was obtained, which correlated to the antibiotic resistance, the strains origin, and the cell-surface hydrophobicity. We report a potential correlation between the lipid profile of S. aureus strains, site of infection, antibiotic resistance, and cell-surface hydrophobicity. These results, which still need further insights, could be a first step to identifying antibiotic resistance in response to environmental adaptation.

Breath metabolome of mice infected with Pseudomonas aeruginosa.

INTRODUCTION: The measurement of specific volatile organic compounds in breath has been proposed as a potential diagnostic for a variety of diseases. The most well-studied bacterial lung infection in the breath field is that caused by Pseudomonas aeruginosa. OBJECTIVES: To determine a discriminatory core of molecules in the "breath-print" of mice during a lung infection with four strains of P. aeruginosa (PAO1, PA14, PAK, PA7). Furthermore, we attempted to extrapolate a strain-specific "breath-print" signature to investigate the possibility of recapitulating the genetic phylogenetic groups (Stewart et al. Pathog Dis 71(1), 20-25, 2014. https://doi.org/10.1111/2049-632X.12107 ). METHODS: Breath was collected into a Tedlar bag and shortly after drawn into a thermal desorption tube. The latter was then analyzed into a comprehensive multidimensional gas chromatography coupled with a time-of-flight mass spectrometer. Random forest algorithm was used for selecting the most discriminatory features and creating a prediction model. RESULTS: Three hundred and one molecules were significantly different between animals infected with P. aeruginosa, and those given a sham infection (PBS) or inoculated with UV-killed P. aeruginosa. Of those, nine metabolites could be used to discriminate between the three groups with an accuracy of 81%. Hierarchical clustering showed that the signature from breath was due to a specific response to live bacteria instead of a generic infection response. Furthermore, we identified ten additional volatile metabolites that could differentiate mice infected with different strains of P. aeruginosa. A phylogram generated from the ten metabolites showed that PAO1 and PA7 were the most distinct group, while PAK and PA14 were interspersed between the former two groups. CONCLUSIONS: To the best of our knowledge, this is the first study to report on a 'core' murine breath print, as well as, strain level differences between the compounds in breath. We provide identifications (by running commercially available analytical standards) to five breath compounds that are predictive of P. aeruginosa infection.

Printable QR code paper microfluidic colorimetric assay for screening volatile biomarkers.

We present a QR code paper microfluidic colorimetric assay that can exploit the hardware and software on mobile devices, and circumvent sample preparation by directly targeting volatile biomarkers. Our platform is a printable microarray of well-defined reaction regions, which outputs an instant diagnosis by directing the user to a URL containing their test result, while simultaneously storing epidemiological data for remote access and bioinformatics. To assist in the rapid identification of Escherichia coli in bloodstream infections, we employed an existing colorimetric reagent (p-dimethylaminocinnamaldehyde) and adapted its use to detect volatile indole, a biomarker produced by E. coli. Our assay was able to quantitatively detect indole in the headspace of E. coli culture after 12 h of growth (27.0 ±â€¯3.1 ppm), assisting in species-level identification hours earlier than existing methods. Results were confirmed with headspace solid-phase microextraction (HS-SPME) two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-ToFMS), which estimated indole concentration in E. coli culture to average 32.3 ±â€¯5.2 ppm after 12 h of growth. This QR paper microfluidic platform represents a novel development in both telemedicine and diagnostics using volatile biomarkers. We envision that our QR code platform can be extended to other colorimetric assays for real-time diagnostics in low-resource environments.

Exhaled human breath analysis in active pulmonary tuberculosis diagnostics by comprehensive gas chromatography-mass spectrometry and chemometric techniques.

Tuberculosis (TB) is the deadliest infectious disease, and yet accurate diagnostics for the disease are unavailable for many subpopulations. In this study, we investigate the possibility of using human breath for the diagnosis of active TB among TB suspect patients, considering also several risk factors for TB for smokers and those with human immunodeficiency virus (HIV). The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, non-invasive, and readily available diagnostic service that could positively change TB detection. A total of 50 individuals from a clinic in South Africa were included in this pilot study. Human breath has been investigated in the setting of active TB using the thermal desorption-comprehensive two-dimensional gas chromatography-time of flight mass spectrometry methodology and chemometric techniques. From the entire spectrum of volatile metabolites in breath, three machine learning algorithms (support vector machines, partial least squares discriminant analysis, and random forest) to select discriminatory volatile molecules that could potentially be useful for active TB diagnosis were employed. Random forest showed the best overall performance, with sensitivities of 0.82 and 1.00 and specificities of 0.92 and 0.60 in the training and test data respectively. Unsupervised analysis of the compounds implicated by these algorithms suggests that they provide important information to cluster active TB from other patients. These results suggest that developing a non-invasive diagnostic for active TB using patient breath is a potentially rich avenue of research, including among patients with HIV comorbidities.

Investigation of mycobacteria fatty acid profile using different ionization energies in GC-MS.

Gas chromatography (GC) coupled with electron ionization (EI) mass spectrometry (MS) is a well-established technique for the analysis of volatile and semi-volatile compounds. The main advantage is the highly repeatable fragmentation of the compounds into the ion source, generating intense and diagnostic fragmentation when the ionization is performed at 70 eV; this is considered the standard ionization condition and has been used for creating many established databases, which are of great support in the analyte identification process. However, such an intense fragmentation often causes the loss of the molecular ion or more diagnostic ions, which can be detrimental for the identification of homologous series or isomers, as for instance fatty acids. To obtain this information chemical or soft ionization can be used, but dedicated ion sources and conditions are required. In this work, we explored different ionization voltages in GC-EI-MS to preserve the intensity of the molecular ion using a conventional quadrupole MS. Twenty, 30, 50, and 70 eV were tested using a mixture of fatty acid methyl esters standards. Intensity and repeatability of the most informative ions were compared. Twenty and 70 eV were then used to analyze the fatty acid composition of six different strains of mycobacteria. Two approaches were used for elaborating the data: (1) a single average spectrum of the entire chromatogram was derived, which can be considered (in terms of concept) as a direct EI-MS analysis; (2) the actual chromatographic separation of the compounds was considered after automatic alignment. The results obtained are discussed herein. Graphical abstract ᅟ.

The volatile molecular profiles of seven Streptococcus pneumoniae serotypes.

In this study, the volatile molecule profile of Streptococcus pneumoniae serotypes was evaluated using solid phase microextraction (SPME) and two dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). Here, seven serotypes (6B, 14, 15, 18C, 19F, 9V, and 23F) were analyzed in an isogenic background. We identified 13 core molecules associated with all seven serotypes, and seven molecules that were differentially produced between serotypes. Serotype 14 was found to have the most distinct volatile profile, and could be discriminated from the other six serotypes in aggregate with an area under the curve (AUC) of 89%. This study suggests that molecules from S. pneumoniae culture headspace show potential for rapid serotype identification.