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Abstract: A method was developed to accurately predict both the primary and secondary retention times for a series of alkanes, ketones and alcohols in a flow-modulated GC×GC system. This was accomplished through the use of a three-parameter thermodynamic model where ΔH, ΔS, and ΔC p for an analyte''s interaction with the stationary phases in both dimensions are known. Coupling this thermodynamic model with a time summation calculation it was possible to accurately predict both 1 t r and 2 t r for all analytes. The model was able to predict retention times regardless of the temperature ramp used, with an average error of only 0.64% for 1 t r and an average error of only 2.22% for 2 t r . The model shows promise for the accurate prediction of retention times in GC×GC for a wide range of compounds and is able to utilize data collected from 1D experiments. [Copyright &y& Elsevier]

Comprehensive two-dimensional gas chromatography (GC × GC) provides enhanced separation power over its one-dimensional counterpart – gas chromatography (GC). This enhancement is achieved by the inclusion of a secondary column, the choice of which is a major determinant on the quality of the ultimate separation. When developing and optimizing a new GC × GC method, the choices of stationary phase chemistries, geometries and configurations (which phase serves in which dimension) are of fundamental importance, and must often be addressed even before the manipulation of instrumental conditions. These choices are often made using educated guesses, literature searches, or trial and error. Thermodynamic models of GC separations; however, provide a fast and easy means of acquiring information for guiding these choices. By using characteristic thermodynamic parameters (characteristic temperatures, T char , and characteristic thermal constants, θ char), we demonstrate the generation of maps that can inform the choices of column chemistries, phase ratios and configurations for GC × GC separations. Image 1 • Novel application of distribution-centric 3-parameter thermodynamic model of GC. • Rapidly evaluate column combinations for GC × GC before buying columns. • Extends easily to GC × GC × GC separations. [ABSTRACT FROM AUTHOR]

Biomass burning is a global source of climate- and health-affecting emissions. The impacts of biomass burning emissions (BBE) are tied to their complex and variable chemical makeup. For instance, the nitrogen content of BBE influences their capacity to absorb light, and therefore affect the Earth's radiative budget. Factors such as temperature, biomass type, or air flow rate during the combustion all modify the composition of BBE, making accurate characterization challenging. Herein, for the first time, principal component analysis (PCA) was applied to emissions gathered during laboratory-based combustion of wood and cow dung biomass in a tube furnace. A thermal desorption two dimensional time-of-flight gas chromatography mass spectrometry (TD-GC × GC-ToF-MS) setup was employed to separate and identify chemical species. By combining these techniques with a feature selection algorithm, we determined that low temperature and air flow rate lead to greater feature separation on PCA scores plots. Of the 729 variables used to construct the plots, 61 were identified as significant. These species - including sugars such as d -Allose and melezitose, as well as tracers such as levoglucosan and guaiacol - significantly differentiated emissions from wood versus cow dung biomass, especially at lower temperatures. In particular, combustion of either fuel at 0.2 slpm and 500 °C, lead to 20 times the variability in levoglucosan peak area over more efficient furnace parameters. Chemical species evolved only from dung burning contained on average 0.595 nitrogen atoms versus 0.515 for wood, indicating that a higher nitrogen content of the base fuel may not necessarily translate into emission of unique nitrogen containing species, potentially causing the underestimation of dung burning impacts. Overall, TD-GC × GC-ToF-MS coupled to PCA reliably separated emissions from wood and dung biomass while simultaneously identifying significant chemical features, displaying the suitability of this combination of techniques towards characterizing complex BBE matrices in the future. GCxGC, PCA, Air quality, Particulate Matter, Biomass Burning, Brown Carbon, Molecular Tracer, Solid Fuel, Levoglucosan. [Display omitted] • GC × GC-PCA can separate and identify chemical markers in biomass burning emissions. • Temperature and air flow rate modify biomass emission composition most significantly. • Nitrogen compounds are emitted from dung burning in larger quantities than from wood. • Nitrogen compounds emitted from dung burning are akin to those emitted from wood. [ABSTRACT FROM AUTHOR]

Detection and identification of ignitable liquids (ILs) in arson debris is a critical part of arson investigations. The challenge of this task is due to the complex and unpredictable chemical nature of arson debris, which also contains pyrolysis products from the fire. ILs, most commonly gasoline, are complex chemical mixtures containing hundreds of compounds that will be consumed or otherwise weathered by the fire to varying extents depending on factors such as temperature, air flow, the surface on which IL was placed, etc. While methods such as ASTM E-1618 are effective, data interpretation can be a costly bottleneck in the analytical process for some laboratories. In this study, we address this issue through the application of chemometric tools. Prior to the application of chemometric tools such as PLS-DA and SIMCA, issues of chromatographic alignment and variable selection need to be addressed. Here we use an alignment strategy based on a ladder consisting of perdeuterated n-alkanes. Variable selection and model optimization was automated using a hybrid backward elimination (BE) and forward selection (FS) approach guided by the cluster resolution (CR) metric. In this work, we demonstrate the automated construction, optimization, and application of chemometric tools to casework arson data. The resulting PLS-DA and SIMCA classification models, trained with 165 training set samples, have provided classification of 55 validation set samples based on gasoline content with 100% specificity and sensitivity. [ABSTRACT FROM AUTHOR]

[Display omitted] • Traditional kefir has more yeast associated metabolites than a reconstituted kefir. • Altering microbial composition of the kefir consortium changes metabolite profile. • Lactobacilli drive organic acid production in kefir consortium fermentations. Kefir is fermented traditionally with kefir grains, but commercial kefir production often relies on fermentation with planktonic cultures. Kefir has been associated with many health benefits, however, the utilization of kefir grains to facilitate large industrial production of kefir is challenging and makes to difficult to ensure consistent product quality and consistency. Notably, the microbial composition of kefir fermentations has been shown to impact kefir associated health benefits. This study aimed to compare volatile compounds, organic acids, and sugar composition of kefir produced through a traditional grain fermentation and through a reconstituted kefir consortium fermentation. Additionally, the impact of two key microbial communities on metabolite production in kefir was assessed using two modified versions of the consortium, with either yeasts or lactobacilli removed. We hypothesized that the complete kefir consortium would closely resemble traditional kefir, while the consortia without yeasts or lactobacilli would differ significantly from both traditional kefir and the complete consortium fermentation. Kefir fermentations were examined after 12 and 18 h using two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) to identify volatile compounds and high performance liquid chromatography (HPLC) to identify organic acid and sugar composition. The traditional kefir differed significantly from the kefir consortium fermentation with the traditional kefir having 15–20 log 2 (fold change) higher levels of esters and the consortium fermented kefir having between 1 and 3 log 2 (fold change) higher organic acids including lactate and acetate. The use of a version of kefir consortium that lacked lactobacilli resulted in between 2 and 20 log 2 (fold change) lower levels of organic acids, ethanol, and butanoic acid ethyl ester, while the absence of yeast from the consortium resulted in minimal change. In summary, the kefir consortium fermentation is significantly different from traditional grain fermented kefir with respect to the profile of metabolites present, and seems to be driven by lactobacilli, as evidenced by the significant decrease in multiple metabolites when the lactobacilli were removed from the fermentation and minimal differences observed upon the removal of yeast. [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]

Enantiomeric fractions (EFs) are used extensively in environmental pollutant research because of the insights on biochemical weathering available from quantifying enantiomeric composition. While this analysis is powerful, it can also be subject to significant error, depending on how chromatographic peaks are integrated. Two methods of integration, the common valley drop method (VDM) and the deconvolution method (DM) were compared using both instrumental and simulated chromatograms to assess their performance when integrating pairs of enantiomers. The effect of peak parameters such as true EF, peak resolution, signal-to-noise ratio, and asymmetry were also investigated. The VDM biased EFs by up to +6% to −4% (relative to the 0–1 EF scale) for symmetric peaks, and as low as −20% for asymmetric peaks. For both instrumental and simulated data, biases tended to increase with decreasing resolution and more extreme (nonracemic) EFs. In contrast, the DM produced biases that were less than 1% in most cases, including at very low resolutions. Estimates from previously published studies based on EF, such as biotransformation rate and source apportionment, could be dramatically affected by small errors in EF. Our results suggest that a deconvolution-based integration method is preferable for the handling of enantiomer compositions. Caution is also advised when comparing published studies on chiral environmental pollutants as most do not specify how chromatographic data is processed. [Copyright &y& Elsevier]

• Clubroot, Plasmodiophora brassicae affects the host searching behavior of a generalist herbivore via plant-mediated cues. • Clubroot suppresses growth of susceptible canola and impacts oviposition and offspring fitness of a generalist herbivore. • Infected susceptible canola may negatively affect generalist herbivores through an increase in salicylic acid. Infection of plants by pathogens can result in the upregulation of induced defenses; plants may be more or less susceptible to attack by insect herbivores following infection. We investigated the interaction between canola, Brassica napus L., plants infected with clubroot, Plasmodiophora brassicae Woronin, and a generalist herbivore the bertha armyworm (BAW) Mamestra configurata Walker using two canola cultivars that varied in susceptibility to clubroot disease. Volatile organic compounds released from experimental plants differed with infection and female adult BAW could discriminate between canola plants inoculated with P. brassicae and disease-free plants. Adult female moths preferentially laid eggs on disease-free plants of the susceptible cultivar to P. brassicae. Inoculation of resistant canola with P. brassicae , however, did not influence oviposition by female BAW. The fitness of BAW larvae was reduced when they were reared on susceptible canola inoculated with P. brassicae. Salicylic acid and its conjugates in susceptible canola plants were induced following P. brassicae inoculation as compared to disease-free susceptible plants. We conclude that suppression of BAW oviposition and offspring fitness may result in part from a change in the volatile profile of the plant as a result of inoculation and the induction of defenses in inoculated susceptible canola. [ABSTRACT FROM AUTHOR]