Enabling cuboid-based fisher ratio analysis using total-transfer comprehensive three-dimensional gas chromatography with time-of-flight mass spectrometry.

Comprehensive three-dimensional (3D) gas chromatography with time-of-flight mass spectrometry (GC3-TOFMS) is a promising instrumental platform for the separation of volatiles and semi-volatiles due to its increased peak capacity and selectivity relative to comprehensive two-dimensional gas chromatography with TOFMS (GC×GC-TOFMS). Given the recent advances in GC3-TOFMS instrumentation, new data analysis methods are now required to analyze its complex data structure efficiently and effectively. This report highlights the development of a cuboid-based Fisher ratio (F-ratio) analysis for supervised, non-targeted studies. This approach builds upon the previously reported tile-based F-ratio software for GC×GC-TOFMS data. Cuboid-based F-ratio analysis is enabled by constructing 3D cuboids within the GC3-TOFMS chromatogram and calculating F-ratios for every cuboid on a per-mass channel basis. This methodology is evaluated using a GC3-TOFMS data set of jet fuel spiked with both non-native and native components. The neat and spiked jet fuels were collected on a total-transfer (100 % duty cycle) GC3-TOFMS instrument, employing thermal modulation between the first (1D) and second dimension (2D) columns and dynamic pressure gradient modulation between the 2D and third dimension (3D) columns. In total, cuboid-based F-ratio analysis discovered 32 spiked analytes in the top 50 hits at concentration ratios as low as 1.1. In contrast, tile-based F-ratio analysis of the corresponding GC×GC-TOFMS data only discovered 28 of the spiked analytes total, with only 25 of them in the top 50 hits. Along with discovering more analytes, cuboid-based F-ratio analysis of GC3-TOFMS data resulted in fewer false positives. The increased discoverability is due to the added peak capacity and selectivity provided by the 3D column with GC3-TOFMS resulting in improved chromatographic resolution.

Tailoring Amphiphilic Copolymers for Improved Aqueous Foam Stability.

Amphiphilic copolymers of various-molecular-weight (MW) poly(ethylene glycol) (PEG) were synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization. The first PEG series, poly(ethylene glycol)monomethacrylate (PEGMA, average Mn 200 and 400 MW), contained an -OH terminal group, and the second series, poly(ethylene glycol) monomethyl ether monomethacrylate (PEGMMA, average Mn 200, 400, and 1000 MW), possessed an -OCH3 terminal group. A total of five PEG-functionalized copolymers contained the same hydrophobic monomer, butyl acrylate (BA), and were successfully reproduced via a one-pot synthesis. The resulting PEG-functionalized copolymers provide a systematic trend of properties including surface tension, critical micelle concentration (CMC), cloud point (CP), and foam lifetime based on the average MW of the PEG monomer and final polymer properties. In general, the PEGMA series produced more stable foams with PEGMA200 demonstrating the least change in foam height with time over a 10 min period. The important exception is that at elevated temperatures, the PEGMMA1000 copolymer had longer foam lifetimes. The self-assembling copolymers were characterized by gel permeation chromatography (GPC), 1H nuclear magnetic resonance (NMR), attenuated total reflection Fourier transform infrared (FTIR-ATR), CMC, surface tension, dynamic light scattering (DLS), as a foam using a dynamic foam analyzer (DFA), and foam lifetime at ambient and elevated temperatures. The copolymers described highlight the importance of the PEG monomer MW and terminal end group for surface interaction and final polymer properties for foam stabilization.

Tile-based variance rank initiated-unsupervised sample indexing for comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry.

Tile-based variance rank initiated-unsupervised sample indexing (VRI-USI) analysis is introduced for comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). VRI-USI analysis addresses the challenge that irrelevant variables can often obscure true chemical variation when using other unsupervised chemometric tools. Implementation of VRI-USI analysis with GC×GC-TOFMS data incorporates the tile-based Fisher ratio (F-ratio) analysis software platform that mitigates the effects of retention shifting in both separation dimensions with an unsupervised variance metric (instead of the F-ratio metric) as the initial step of ranking the hitlist. Next, implementation of k-means clustering, k, per hit using the silhouette metric, Smax, is used to reveal to what extent recurring indexed sample clusters are uncovered. Finally, based upon a probability-based evaluation of how the individual samples cluster throughout the hitlist an unsupervised class membership is revealed. For a JP8 jet fuel dataset spiked with a sulfur-containing analyte mix at 30-ppm, 15-ppm, and neat, clustering by spike level at k = 3 was the most commonly re-occurring set of index assignments, occurring for 11 out of 14 spiked analytes. Upon application of these k-means index assignments to the entire hitlist, all 14 spiked hits had one way ANOVA p-values < 0.05, validating the presumption of classes. Next, application of VRI-USI to a 3-ppm spiked and neat JP8 jet fuel comparison exhibited similar performance to F-ratio analysis for analyte discovery. In the last study, for a dataset of J1800A, JP4, and JP8 jet fuel, each spiked with the sulfur-containing analyte mix at 30-ppm and neat, 453 out of 520 hits in the hitlist exhibited index assignments indicative of fuel type clustering, with the remaining 67 hits having contradictory assignments. Scrutinization of these 67 hits revealed nine hits with "split combinations" in index assignments, whereby the spiked and neat samples for a given fuel were in separate clusters. Eight of these hits were identified as spiked sulfur analytes. Interestingly, these hits also had large Smax indicative of a true sub-cluster. Thus, tile-based VRI-USI analysis appears to be a promising tool for unsupervised multi-class classification studies using GC×GC-TOFMS data.

Untargeted profiling and differentiation of geographical variants of wine samples using headspace solid-phase microextraction flow-modulated comprehensive two-dimensional gas chromatography with the support of tile-based Fisher ratio analysis.

The volatile fraction of food, also called the food volatilome, is increasingly used to develop new fingerprinting approaches. The characterization of the food volatilome is important to achieve desired flavor profiles in food production processes, or to differentiate different products, with winemaking being one popular area of interest. In the present research, headspace solid-phase microextraction (HS SPME) coupled to flow-modulated comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (FM GC×GC-TOFMS) was used to characterize geographical-based differences in the volatilome of five white "Grillo" wines (of Sicilian origin), comprising the five sample classes. All wines were produced with the same vinification method in 2019. To minimize the influence of minor bottle-to-bottle differences, three bottles of the same wine were randomly selected, and three samples were collected per bottle, resulting in nine sample replicates per wine. Particular emphasis was devoted to the operational conditions of a novel low duty cycle flow modulator. A fast FM GC×GC-TOFMS method with a modulation period of 700 ms and a re-injection period of 80 ms was developed. Following, the instrumental software was exploited to identify class-distinguishing analytes in the dataset via tile-based Fisher ratio analysis (i.e., ChromaTOF Tile). A tile size of 10 modulations (7 s) on the first dimension and 45 spectra (300 ms) on the second dimension was used to encompass average peak widths and to account for minor retention time shifting. Off-line software was used to apply an ANOVA test. A p-value of 0.01 was applied in order to select the most important class-distinguishing analytes, which were input to principal component analysis (PCA). The PCA scores plot showed distinct clustering of the wines according to geographical origin, although the loadings revealed that only a few analytes were necessary to differentiate the wines. However, a comprehensive flavor profile assessment underscored the importance of all the information output by the ChromaTOF Tile software.

Class comparison enabled mass spectrum purification for comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry.

Tile-based Fisher ratio (F-ratio) analysis is emerging as a versatile data analysis tool for supervised discovery-based experimentation using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). None the less, analyte identification can often be marred by poor 2D resolution and low analyte abundance relative to overlapping compounds. Linear algebra-based chemometric methods, in particular multivariate curve resolution alternating least squares (MCR-ALS), parallel factor analysis (PARAFAC) and PARAFAC2, are often applied in an effort to address this situation. However, these chemometric methods can fail to produce an accurate spectrum when the analyte is at low 2D resolution and/or in low relative abundance. To address this challenge, we introduce class comparison enabled mass spectrum purification (CCE-MSP), a method that utilizes the underlying requirement for signal consistency of the background interference compounds between the two classes in the F-ratio analysis to purify the mass spectrum of the analyte hits. CCE-MSP is validated using a dataset obtained for a neat JP-8 jet fuel spiked with 14 sulfur containing compounds at two levels (15 ppm and 30 ppm), using the p-value and lack-of-fit (LOF) for each analyte hit as consistency metrics. A purified mass spectrum was produced for each spiked analyte hit and their mass spectrum match value (MV) was compared to the MV obtained by MCR-ALS, PARAFAC, and PARAFAC2. The resulting MV for CCE-MSP were found to be as good or better than these chemometric methods, eg., for 2-butyl-5-ethylthiophene with an analyte-to-interference relative signal abundance of 1:87 and a 2D resolution of 0.2, CCE-MSP produced a MV of 831, compared to 476 for MCR-ALS, 403 for PARAFAC, and 336 for PARAFAC2. CCE-MSP is also extended to obtain the purified spectrum for more than one analyte, eg., two analyte hits in overlapping hit locations. The spectra produced by CCE-MSP can also be utilized as estimates to facilitate quantitative signal decomposition using MCR-ALS.

Development of variance rank initiated-unsupervised sample indexing for gas chromatography-mass spectrometry analysis.

Traditional non-targeted chemometric workflows for gas chromatography-mass spectrometry (GC-MS) data rely on using supervised methods, which requires a priori knowledge of sample class membership. Herein, we propose a simple, unsupervised chemometric workflow known as variance rank initiated-unsupervised sample indexing (VRI-USI). VRI-USI discovers analyte peaks exhibiting high relative variance across all samples, followed by k-means clustering on the individual peaks. Based upon how the samples cluster for a given peak, a sample index assignment is provided. Using a probabilistic argument, if the same sample index assignment appears for several discovered peaks, then this outcome strongly suggests that the samples are properly classified by that particular sample index assignment. Thus, relevant chemical differences between the samples have been discovered in an unsupervised fashion. The VRI-USI workflow is demonstrated on three, increasingly difficult datasets: simulations, yeast metabolomics, and human cancer metabolomics. For simulated GC-MS datasets, VRI-USI discovered 85-90% of analytes modeled to vary between sample classes. Nineteen out of 53 peaks in the peak table developed for the yeast metabolome dataset had the same sample index assignments, indicating that those indices are most likely due to class-distinguishing chemical differences. A t-test revealed that 22 out of 53 peaks were statistically significant (p < 0.05) when using those sample index assignments. Likewise, for the human cancer metabolomics study, VRI-USI discovered 25 analytes that were statistically different (p < 0.05) using the sample index assignments determined to highlight meaningful sample-based differences. For all datasets, the sample index assignments that were deduced from VRI-USI were the correct class-based difference when using prior knowledge. VRI-USI holds promise as an exploratory data analysis workflow for studies in which analysts do not readily have a priori class information or want to uncover the underlying nature of their dataset.

Baseline correction method for dynamic pressure gradient modulated comprehensive two-dimensional gas chromatography with flame ionization detection.

A baseline correction method is developed for comprehensive two-dimensional (2D) chromatography (GC × GC) with flame-ionization detection (FID) using dynamic pressure gradient modulation (DPGM). The DPGM-GC × GC-FID utilized porous layer open tubular (PLOT) columns in both dimensions to focus on light hydrocarbon separations. Since DPGM is nominally a stop-flow modulation technique, a rhythmic baseline disturbance is observed in the FID signal that cycles with the modulation period (PM). This baseline disturbance needs to be corrected to optimize trace analysis. The baseline correction method has three steps: collection of a background "blank" chromatogram and multiplying it by an optimized normalization factor, subtraction of the normalization-optimized background chromatogram from a sample chromatogram, and application of Savitzky-Golay smoothing. An alkane standard solution, containing pentane, hexane and heptane was used for method development, producing linear calibration curves (r2 > 0.991) over a broad concentration range (7.8 ppm - 4000 ppm). Further, the limit-of-detection (LOD) and limit-of-quantification (LOQ) were determined for pentane (LOD = 2.5 ppm, LOQ = 8.2 ppm), hexane (LOD = 0.9 ppm, LOQ = 3.0 ppm), and heptane (LOD = 1.9 ppm, LOQ = 6.4 ppm). A natural gas sample separation illustrated method applicability, whereby the DPGM produced a signal enhancement (SE) of 30 for isopentane, where SE is defined as the height of the tallest 2D peak in the modulated chromatogram for the analyte divided by the height of the unmodulated 1D peak. The 30-fold SE resulted in about a 10-fold improvement in the signal-to-noise ratio (S/N) for isopentane. Additional versatility of the baseline correction method for more complicated samples was demonstrated for an unleaded gasoline sample, which enabled the detection (and visual appearance) of trace components.

Investigation of the limit of discovery using tile-based Fisher ratio analysis with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is followed by tile-based Fisher ratio (F-ratio) analysis to investigate the "limit of discovery" for low concentration levels of sulfur-containing compounds in JP8 jet fuel. A mixture of 14 sulfur-containing compounds was spiked at 30 ppm, 15 ppm, 3 ppm and 1.5 ppm into the neat fuel prior to GC×GC-TOFMS analysis with a "reversed" column format (aka polar first dimension (1D) and non-polar second dimension (2D) column). Prior standard implementation of tile-based F-ratio analysis utilized an average F-ratio requiring a minimum of 3 mass channels (m/z) with the highest F-ratios. Herein, we explore the notion that use of the top F-ratio m/z for hitlist ranking is superior to the standard implementation for analytes near their limit-of-quantitation (LOQ), defined as an analyte concentration that produces a signal equal to ten times the standard deviation of the baseline noise (10σn). Hitlist ranking comparisons revealed that using only the top F-ratio m/z resulted in impressive improvements in discoverability for the low concentration comparisons. Specifically, for the 3 ppm versus neat hitlist, 1,4-oxathiane (LOQ = 2.5 ppm) improved from hit 114 via standard F-ratio analysis, to hit 25. For the 1.5 ppm versus neat hitlist, 2-propylthiophene (LOQ = 0.64 ppm) improved from hit 59 to 17, benzo[b]thiophene (LOQ = 1.1 ppm) from hit 98 to 28, and 2,5-dimethylthiophene (LOQ = 1.3 ppm) from hit 262 to 39. Additional hitlist ranking comparisons revealed the importance of proper tile size selection, as analyte discoverability deteriorated upon using either an inappropriately too small or too large of a tile.

Total-transfer comprehensive three-dimensional gas chromatography with time-of-flight mass spectrometry.

Although comprehensive two-dimensional (2D) gas chromatography (GC × GC) is a powerful technique for complex samples, component overlap remains likely. An intriguing route to address this challenge is to utilize the additional peak capacity and chemical selectivity provided by comprehensive three-dimensional (3D) gas chromatography (GC3), especially with time-of-flight mass spectrometry detection (GC3-TOFMS). However, the GC3-TOFMS instrumentation reported to date has employed one or both modulators with a duty cycle < 100%, making the potential gain in detection sensitivity over GC × GC modest, or perhaps even worse. Herein, we describe instrumentation for GC3-TOFMS in which both modulators provide total-transfer (100% duty cycle). Specifically, the instrument is based on the facile modification of a commercial thermally modulated comprehensive GC × GC-TOFMS platform for modulation from the 1D column to the 2D column, with recently described dynamic pressure gradient modulation (DPGM) as the second modulator from the 2D column to the 3D column, which is a total-transfer flow modulation technique. Area measurements of 1D peaks are compared to the sum of 3D peak areas to validate the assumption that total-transfer from 1D to 3D is accomplished. Additionally, peak heights were amplified by as high as a factor of 177 (x̅ = 130, s = 47) via comparison of 1D peak heights to the maximum 3D peak heights. Column selection is explored, with emphasis on the resulting peak width-at-base on each dimension and usage of 3D space as evaluation metrics. Using a nonpolar × polar × ionic liquid column combination, an effective peak capacity which considers modulation-induced broadening as high as 32,300 for select analytes was achieved (x̅ = 19,900, s = 10,700). The analytical benefits of employing three selective phases, mass spectrometry detection, and total-transfer modulation are explored with separations of a metabolomics-type sample, i.e., derivatized porcine serum, and a jet fuel spiked with various sulfur-containing compounds.

Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review.

Gas chromatography (GC) is undoubtedly the analytical technique of choice for compositional analysis of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC × GC) has evolved, fuel analysis has often been highlighted in scientific reports, since the complexity of fuel analysis allows for illustration of the impressive peak capacity gains afforded by GC × GC. Indeed, several research groups in recent years have applied GC × GC and chemometric data analysis to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC × GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC × GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC × GC analyses of fuels for compliance and forensic applications, to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC × GC technology (column stationary phases and modulators) and several chemometric methods is provided. More specifically, chemometric methods will be broken down into (1) signal preprocessing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed with particular emphasis on the demonstrated success of the employed chemometric methods. This review will hopefully make 1D-GC and GC × GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.

Impact of data bin size on the classification of diesel fuels using comprehensive two-dimensional gas chromatography with principal component analysis.

Principal component analysis (PCA) is a widely applied chemometric tool for classifying samples using comprehensive two-dimensional (2D) gas chromatography (GC × GC) separation data. Classification via PCA can be improved by 2D binning of the data. A "standard operating procedure (SOP) bin size" is often applied to improve the S/N and to mitigate potential retention time misalignment issues. The SOP bin size is generally selected to be slightly larger than the typical 2D peak dimensions. In this study we examine to what extent a single SOP bin size is optimal for all of the class comparisons that can be made in a single PCA scores plot. For this purpose, a GC × GC-FID dataset comprised of 5 different diesel fuels (i.e., 5 sample classes), each run with 4 replicates using a reverse column configuration (polar 1D column and non-polar 2D column) was utilized. The dataset was collected within about one day, which minimized retention time misalignment in order to allow the study to focus on S/N enhancement concurrent with maintaining the chemical selectivity provided by the GC × GC separations. A total of 110 bin sizes were evaluated. Degree-of-class separation (DCS) was utilized as a quantitative metric to assess the impact of binning in improving separation in the scores plot. The DCS was calculated pair-wise between nearest neighbor sample classes for each of the 5 sample classes in the scores plot (5 sample class pairs). Results indicated the SOP bin size did not provide the highest DCS for any of the 5 fuel pairs. Each fuel pair is found to have its own optimal bin size, suggesting the binning finds the balance between S/N optimization concurrent with leveraging the chemical selectivity information differences in the samples as manifested in their GC × GC separation "patterns". Robustness of the findings in this study were supported by leaving out one fuel at a time and re-running the PCA models.

Ultrafast separations via pulse flow valve modulation to enable high peak capacity multidimensional gas chromatography.

Ultrafast modulation with a modulation period PM ≥ 50ms via a pulse flow valve is demonstrated for comprehensive two-dimensional gas chromatography (GC×GC) and comprehensive three-dimensional (3D) gas chromatography (GC3). Significant increases in peak capacity and peak capacity production are achieved for GC×GC and GC3 relative to previous studies due to using pulse flow valve modulation. Due to the nature of the "partial" modulation process, the separation dimension following pulse flow valve modulation is not a traditional chromatogram, rather requires data processing to convert the data to expose the encoded chromatographic information, producing "apparent" chromatographic peaks. In the GC×GC mode, a 115-component test mixture was evaluated using a PM of 500ms, creating an apparent 2D peak width-at-base 2W with an average of 25ms, producing a 2nc of 20. Based on the average 1W of 1.0s for the 6min first dimension 1D separation, an ideal peak capacity nc,2D of 7200 is achieved (1,200/min peak production). For a high-speed GC×GC separation (30s run), a PM of 75ms produced apparent 2W of 8ms, ideal for the third dimension of a GC3 instrument. Using the knowledge gained from this high-speed GC×GC experiment, the pulse flow valve was implemented as the second modulator in GC3. Three samples were evaluated in the GC3 mode: a simple mixture containing 18 compounds (to illustrate basic concepts), the 115-component test mixture (to determine peak capacity figures-of-merit), and a diesel spiked with 8 polar compounds (to illustrate chemical selectivity benefits of GC3). For the 115-component test mixture with a 1PM of 1.2s and a 2PM of 60ms, average 1W of 3.2s, 2W of 130ms, and apparent 3W of 13ms were produced, resulting in a 1nc of 210, 2nc of 9.2, and 3nc of 5, respectively. Hence, an ideal peak capacity, nc,3D of ∼10,000 for GC3 was achieved for the 11min 1D separation window of the 115-component test mixture.