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Introduction: For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results. Objectives: Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies. Methods: Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data. Results: Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes. Conclusion: TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community. [ABSTRACT FROM AUTHOR]

The metabolic profiles of human feces are influenced by various genetic and environmental factors, which makes feces an attractive biosample for numerous applications, including the early detection of gut diseases. However, feces is complex, heterogeneous, and dynamic with a significant live bacterial biomass. With such challenges, stool metabolomics has been understudied compared to other biospecimens, and there is a current lack of consensus on methods to collect, prepare, and analyze feces. One of the critical steps required to accelerate the field is having a metabolomics stool reference material available. Fecal samples are generally presented in two major forms: fecal water and lyophilized feces. In this study, two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) was used as an analytical platform to characterize pooled human feces, provided by the National Institute of Standards and Technology (NIST) as Research-Grade Test Materials. The collected fecal samples were derived from eight healthy individuals with two different diets: vegans and omnivores, matched by age, sex, and body mass index (BMI), and stored as fecal water and lyophilized feces. Various data analysis strategies were presented to determine the differences in the fecal metabolomic profiles. The results indicate that the sample storage condition has a major influence on the metabolic profiles of feces such that the impact from storage surpasses the metabolic differences from the diet types. The findings of the current study would contribute towards the development of a stool reference material. [ABSTRACT FROM AUTHOR]

The essential oil (EO) from the leaves of Zanthoxylum caribaeum (syn. Chiloperone) (Rutaceae) was studied previously for its acaricidal, antimicrobial, antioxidant, and insecticidal properties. In prior studies, the most abundant compound class found in leaf oils from Brazil, Costa Rica, and Paraguay was terpenoids. Herein, essential oil from the leaves of Zanthoxylum caribaeum (prickly yellow, bois chandelle blanc (FWI), peñas Blancas (Costa Rica), and tembetary hu (Paraguay)) growing in Guadeloupe was analyzed with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS), and thirty molecules were identified. A comparison with previously published leaf EO compositions of the same species growing in Brazil, Costa Rica, and Paraguay revealed a number of molecules in common such as β-myrcene, limonene, β-caryophyllene, α-humulene, and spathulenol. Some molecules identified in Zanthoxylum caribaeum from Guadeloupe showed some antimetabolic effects on enzymes; the in-depth study of this plant and its essential oil with regard to metabolic diseases merits further exploration. [ABSTRACT FROM AUTHOR]

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one‐ and two‐dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over‐redundancy in two‐dimensional gas chromatography‐mass spectrometry data by limiting the data to a few unique/pseudo‐unique ions, sub‐peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two‐dimensional gas chromatography‐mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two‐dimensional gas chromatography‐mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo‐unique ions, one sub‐peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy. [ABSTRACT FROM AUTHOR]

Purpose – Fibre content can influence the intensity of odour that develops within clothing fabrics. However, little is known about how effective laundering is at removing malodours in clothing which differ by fibre type. The purpose of this paper is to investigate whether a selected cotton fabric differed in odour intensity following multiple wear and wash cycles compared to a polyester fabric. Design/methodology/approach – Eight (male and female) participants wore bisymmetrical cotton/polyester t-shirts during 20 exercise sessions over a ten-week trial period. Odour was evaluated via a sensory panel, bacterial populations were counted and selected odorous volatile organic compounds were measured with comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry detection. Analysis occurred both before and after the final (20th) wash cycle. Findings – Findings showed that laundering was effective in reducing overall odour intensity (p⩽0.001) and bacterial populations (p⩽0.001) in both cotton and polyester fabrics. Odour was most intense on polyester fabrics following wear, not just before, but also after washing (p⩽0.001); although, no differences in bacterial counts were found between fibre types (p>0.05). Chemical analysis found C4-C8 chained carboxylic acids on both types of unwashed fabrics, although they were more prevalent on polyester. Originality/value – The findings suggest that the build-up of odour in polyester fabrics may be cumulative as important odorants such as the carboxylic acids were not as effectively removed from polyester compared to cotton. [ABSTRACT FROM AUTHOR]

Abstract: Metabolomics has been defined as the quantitative measurement of all low molecular weight metabolites (sugars, amino acids, organic acids, fatty acids and others) in an organism''s cells at a specified time under specific environmental/biological conditions. Currently, there is considerable interest in developing a single method of derivatization and separation that satisfies the needs for metabolite analysis while recognizing the many chemical classes that constitute the metabolome. Chemical derivatization considerably increases the sensitivity and specificity of gas chromatography–mass spectrometry for compounds that are polar and have derivatizable groups. Microwave-assisted derivatization (MAD) of a set of standards spanning a wide range of metabolites of interest demonstrates the potential of MAD for metabolic profiling. A final protocol of 150W power for 90s was selected as the derivatization condition, based upon the study of each chemical class. A study of the generation of partially derivatized components established the conditions where this could potentially be a problem; the use of greater volumes of reagent ensured this would not arise. All compounds analyzed by comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry in a standard mixture showed good area ratio reproducibility against a naphthalene internal standard (RSD<10% in all but one case). Concentrations tested ranged from 1μg/mL to 1000μg/mL, and the calibration curves for the standard mixtures were satisfactory with regression coefficients generally better than 0.998. The application to gas chromatography–quadrupole mass spectrometry and comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry for a typical reference standard of relevance to metabolomics is demonstrated. [Copyright &y& Elsevier]

Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent. A number of solutions to handling GC×GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection. The proposed model captures over 99.9% of variance for a synthetic data set, presenting an extreme example of peak drift and co-elution across two modes of separation. [Display omitted] • Two PARAFAC2 models coupled to model drift along two modes. • Informative components deconvolved from co-elutions in GC × GC-TOFMS data. • Proposed model is flexible and handles extreme drift. [ABSTRACT FROM AUTHOR]

Dunaliella tertiolecta is a marine microalgae that has been studied extensively as a potential carbon-neutral biofuel source (Tang et al., 2011). Microalgae oil contains high quantities of energy-rich fatty acids and lipids, but is not yet commercially viable as an alternative fuel. Carefully optimised growth conditions, and more recently, algal-bacterial co-cultures have been explored as a way of improving the yield of D. tertiolecta microalgae oils. The relationship between the host microalgae and bacterial co-cultures is currently poorly understood. Here, a complete workflow is proposed to analyse the global metabolomic profile of co-cultured D. tertiolectra and Phaeobacter italicus R11 , which will enable researchers to explore the chemical nature of this relationship in more detail. To the best of the authors' knowledge this study is one of the first of its kind, in which a pipeline for an entirely untargeted analysis of the algal metabolome is proposed using a practical sample preparation, introduction, and data analysis routine. [Display omitted] • A method for direct microalgae sample introduction is proposed. • Simple large volume injection for dilute samples. • Methodology maintains system cleanliness and reproducible performance. • Advanced chemometric tools can extract useful and discriminating metabolomic features even from very noisy complex datasets. [ABSTRACT FROM AUTHOR]