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Aroma profiles of blueberries contain a heterogeneous mixture of volatile organic compounds (VOCs), and headspace sampling is the most efficient approach to VOC extraction from berries. Typically, headspace solid-phase microextraction (HS-SPME) has been applied to blueberry VOCs. Though HS-SPME has been the most common choice for headspace sampling, the fiber chemistry introduces selectivity to the extraction: preferential sorption may skew aroma profiles. Dynamic headspace (DHS) is an attractive substitute for HS-SPME, given its improved sensitivity and lessened bias imparted due to extraction phase selectivity. DHS extractions tend to yield a larger number of compounds and require even less sample than HS-SPME. These two extraction techniques were applied to homogenized blueberries and extracts were subjected to analysis by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS). Major chemical classes were identified with filtering scripts for GC×GC-TOFMS data. The HS-SPME and DHS methods were assessed for reproducibility and chemical diversity (i.e., recovered chemical classes).

Secondary metabolites are bioactive compounds produced by living organisms that can be indicative of symbiotic relationships in nature. Prior studies have demonstrated putative symbiotic interactions between decapod crabs and epibiotic bacteria in their environment. This study presents a sample preparation protocol for metabolomics analysis of lyophilized crab gill samples from four brachyuran crab species inhabiting different regions of Guadeloupe, Lesser Antilles. Sample extracts were prepared using a two-step methoximation-trimethylsilylation derivatization protocol and analyzed by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS). Three main secondary metabolites identified in the crab gills and discussed herein include cycloserine, trigonelline, and amphetamine. Cycloserine is a bacterial-produced metabolite with antibiotic properties, and along with the antimicrobial activity of trigonelline, these compounds may influence the microbial community of the crabs and/or protect them from bacterial infection in the frame of the symbiotic relationships. Amphetamine is a psychostimulant that has been previously detected in crab muscles, and has been found in crab gills in this study. Cycloserine was present in the gills of Ucides cordatus and Percnon gibbesi, trigonelline was present in all crabs except Ucides cordatus, and amphetamine was detected in all crab gill samples. Further research into these secondary metabolites is facilitated with the sample preparation protocol and can elucidate the various symbiotic interactions of crabs and their microbiome; potentially for other crustaceans as well.

Dispersive liquid-liquid microextraction (DLLME) was compared to liquid-liquid extraction (LLE) in terms of the methods’ abilities to analyze samples and ultimately to provide information to an analyst. A simple method of relative quantification by compound class approximation was introduced and its validity tested, while the merits of comprehensive gas chromatography – time-of-flight mass spectrometry (GC×GC-TOFMS) were exhibited on multiple sets of locally obtained environmental water samples. 20 classes of compounds were effectively identified by the DLLME-GC×GC-TOFMS protocol proposed. The limits of detection (LOD) of the protocol ranged between 17 and 377 times lower than the conventional protocol designed for targeted total extractable hydrocarbon (TEH) and total petroleum hydrocarbon (TPH) analysis, with LODs of selected compounds between 0.08 and 1.0 µg/L. Relative quantification shows promising results and was used for quantification of non-targeted analytes within real samples. This quantification method will be used to inform future work to be implemented in industrial environmental analysis.

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC–MS) data present several challenges for analysis largely because chemical factors drift along the chromatographic modes across different chromatographic runs, and there is frequently a lack of reliable molecular ion measurements with which to align data across multiple samples. Tensor decomposition techniques such as Parallel Factor Analysis (PARAFAC2/PARAFAC2×N) allow analysts to deconvolve closely eluting signals for quantitative and qualitative purposes. These techniques make relatively few assumptions about chromatographic peak shapes or the relative abundance of noise and allow for highly accurate representations of the underlying chemical phenomena using well-characterized and scrutinized principles of chemometrics. However, expert intervention and supervision is required to select appropriate Regions of Interest (ROI) and numbers of chemical components present in each ROI. We previously reported an automated ROI selection algorithm for GC–MS data in Giebelhaus et al. where we observed the ratio of the first and second eigenvalues within a moving window across the entire chromatogram. Here, we present an extension of this work to automatically detect ROIs in GC×GC–MS chromatograms, while making no assumptions about peak shape. First, we calculate the probabilities of each acquisition being in a ROI, then apply connected components segmentation to discretize the regions of interest. For sparse chromatograms we found the algorithm detected spurious peaks. To address this, we implemented an iterative ROI selection process where we autoscaled the moving window to the standard deviation of the noise from the previous iteration. Using three user-defined parameters, we generated informative ROIs on a wide range of GC×GC-TOFMS chromatograms.