Region of Interest Selection for GC×GC-MS Data using a Pseudo Fisher Ratio Moving Window with Connected Components Segmentation

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

Limits of Detection and Quantification in Comprehensive Multidimensional Separations. 1. A Theoretical Look

Comprehensive multidimensional separations (e.g., GC×GC, LC×LC, etc.) are increasingly popular tools for the analysis of complex samples, due to their many advantages, such as vastly increased peak capacity, and improvements in sensitivity. The most well-established of these techniques, GC×GC, has revolutionized analytical separations in fields as diverse as petroleum, environmental research, food and flavors, and metabolic profiling. Using multidimensional approaches, analytes can be quantified at levels substantially lower than those possible by one-dimensional techniques. However, it has also been shown that the modulation process introduces a new source of error to the measurement. In this work, we present the results of a study into the limits of quantification and detection (LOQ and LOD) in comprehensive multidimensional separations using GC×GC and the more popular “two-step” integration algorithm as an example. Simulation of chromatographic data permits precise control of relevant parameters of peak geometry and modulation phase. Results are expressed in terms of the dimensionless parameter of signal-to-noise ratio of the base peak (<i>S</i>/<i>N</i>_BP) making them transportable to any result where quantification is performed using a two-step algorithm. Based on these results, the LOD is found to depend upon the modulation ratio used for the experiment and vary between a <i>S</i>/<i>N</i>_BP of 10–17, while the LOQ depends on both the modulation ratio and the phase of the modulation for the peak and ranges from a <i>S</i>/<i>N</i>_BP of 10 to 50, depending on the circumstances

PARAFAC2×\timesN: Coupled Decomposition of Multi-modal Data with Drift in N Modes

Reliable analysis of comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC$\times$GC-TOFMS) data is considered to be a major bottleneck for its widespread application. For multiple samples, GC$\times$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 and sample dimensions is for all practical purposes nonexistent. A number of solutions to handling GC$\times$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

Discriminating Extra Virgin Olive Oils from Common Edible Oils: Comparable Performance of PLS-DA Models Trained on Low-Field and High-Field 1H NMR Data

Olive oil, the oil derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or mislabel cheaper oils to increase profitability. These other edible oils can have chemical profiles similar to extra virgin olive oil but can cause allergies in sensitive individuals. Given these consequences, there is a need for methods to rapidly authenticate olive oils. Nuclear magnetic resonance (NMR) has been used for this purpose, as it requires minimal sample preparation and is non-destructive. By utilizing NMR spectra of the samples and machine learning models trained on known olive oil and edible oils, oil samples can be classified and authenticated. While high-field NMRs are commonly used due to their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate, for routine screening purposes. Low-field benchtop NMR presents an affordable alternative. Here, we compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop 1H NMR and high-field 400 MHz 1H NMR spectra. We demonstrated that PLS-DA models trained on low-field spectra perform comparably to those trained on high-field spectra

Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 co-cultures using thermal desorption - comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (TD-GC×GC-TOFMS)

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.Agency for Science, Technology and Research (A*STAR)The authors wish to acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the support given to The Metabolomics Innovation Centre (TMIC) through grants from Genome Alberta, Genome Canada, and The Canada Foundation for Innovation. This work was also supported by A*STAR SFS IAF-PP grant (A20H7a0152) awarded to Rebecca Case

The Metabolomic Profile of the Essential Oil from Zanthoxylum caribaeum (syn. chiloperone) Growing in Guadeloupe FWI using GC &times; GC-TOFMS

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), pe&ntilde;as 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 &times; 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 &beta;-myrcene, limonene, &beta;-caryophyllene, &alpha;-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