Optimization of Parameters for ROI Data Compression for Nontargeted Analyses Using LC-HRMS.

Nontargeted analyses of low-concentration analytes in the information-rich data collected by liquid chromatography with high-resolution mass spectrometry detection can be challenging to accomplish in an efficient and comprehensive manner. The aim of this study is to demonstrate a workflow involving targeted parameter optimization for entire chromatograms using region of interest (ROI) data compression uncoupled from a subsequent tile-based Fisher ratio (F-ratio) analysis, a supervised discovery-based method, for the discovery of low-concentration analytes. Soil samples spiked with 18 pesticides at nominal concentrations ranging from 0.1 to 50 ppb for a total of six sample classes served as challenging samples to demonstrate the overall workflow. Optimization of two parameters proved to be the most critical for ROI data compression: the signal threshold parameter and the admissible mass deviation parameter. The parameter optimization method workflow we introduce is based upon spiking known analytes into a representative sample and determining the number of detectable spikes and the Δppm for various combinations of the signal threshold and admissible mass deviation, where Δppm is the absolute value of the difference between the theoretical m/z and the ROI m/z. Once optimal parameters are determined providing the lowest average Δppm and the greatest number of detectable analytes, the optimized parameters can be utilized for the intended analysis. Herein, tile-based F-ratio analysis was performed on the ROI compressed data of all spiked soil samples first by applying ROI parameters recommended in the literature, referred to herein as the initial ROI parameters, and finally by the combination of the two optimized parameters. Using the initial ROI parameters, three pesticides were discovered, whereas all 18 spiked pesticides were discovered by optimizing both ROI parameters.

Tile-Based Random Forest Analysis for Analyte Discovery in Balanced and Unbalanced GC × GC-TOFMS Data Sets.

In this study, we introduce a new nontargeted tile-based supervised analysis method that combines the four-grid tiling scheme previously established for the Fisher ratio (F-ratio) analysis (FRA) with the estimation of tile hit importance using the machine learning (ML) algorithm Random Forest (RF). This approach is termed tile-based RF analysis. As opposed to the standard tile-based F-ratio analysis, the RF approach can be extended to the analysis of unbalanced data sets, i.e., different numbers of samples per class. Tile-based RF computes out-of-bag (oob) tile hit importance estimates for every summed chromatographic signal within each tile on a per-mass channel basis (m/z). These estimates are then used to rank tile hits in a descending order of importance. In the present investigation, the RF approach was applied for a two-class comparison of stool samples collected from omnivore (O) subjects and stored using two different storage conditions: liquid (Liq) and lyophilized (Lyo). Two final hit lists were generated using balanced (8 vs Eight comparison) and unbalanced (8 vs Nine comparison) data sets and compared to the hit list generated by the standard F-ratio analysis. Similar class-distinguishing analytes (p < 0.01) were discovered by both methods. However, while the FRA discovered a more comprehensive hit list (65 hits), the RF approach strictly discovered hits (31 hits for the balanced data set comparison and 29 hits for the unbalanced data set comparison) with concentration ratios, [OLiq]/[OLyo], greater than 2 (or less than 0.5). This difference is attributed to the more stringent feature selection process used by the RF algorithm. Moreover, our findings suggest that the RF approach is a promising method for identifying class-distinguishing analytes in settings characterized by both high between-class variance and high within-class variance, making it an advantageous method in the study of complex biological matrices.

Using solid-phase extraction to facilitate a focused tile-based Fisher ratio analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry data: comparative analysis of aerospace fuel composition.

Tile-based Fisher ratio (F-ratio) analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) data is a powerful, supervised discovery methodology for pinpointing sample class-distinguishing analytes between two or more sample classes. Herein, we extend this analytical methodology to focus upon specific chemical groups in kerosene-based aerospace fuel using solid-phase extraction (SPE). Treating samples with SPE removes specific compounds depending on the SPE stationary phase (i.e., silica), creating an altered "pass" sample, identical to the original "neat" sample except for the extracted compounds. Application of F-ratio analysis to the neat samples against the pass samples provides global discovery with a numerically sorted hit list of all analytes affected by the SPE procedure. Sections of GC × GC-TOFMS data from the top analyte hits are reconstructed to form a "stitch" chromatogram to visualize the sample class-distinguishing compounds, revealing excellent agreement with the extract chromatogram. Additionally, utilizing the four-grid tiling scheme developed for tile-based F-ratio analysis, we demonstrate a tile-based pairwise analysis method, referred to as 1v1 analysis, to discover analytes that differ in concentration between two fuel chromatograms. Application of 1v1 analysis is highly efficient since replicates do not necessarily need to be run on the GC × GC-TOFMS instrument, which is beneficial for sample-limited applications. The 1v1 analyses discovered most of the same features as F-ratio analysis, ranging from 69 to 81% of the features discovered by F-ratio analysis while requiring one-sixth the data. Lastly, the overall methodology is applied to three candidate rocket fuels to better understand the compound class-distinguishing differences. The separate hit lists produced for high-concentration bulk hydrocarbon differences and low-concentration level polar compound differences provided valuable insight into these candidate rocket fuels.

Profiling Olefins in Gasoline by Bromination Using GC×GC-TOFMS Followed by Discovery-Based Comparative Analysis.

An analytical workflow for the analysis of olefins in gasoline that combines selective bromination and comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) with discovery-based analysis is reported. First, a standard mix containing n-alkanes, 1-alkenes, and aromatic species was brominated and quantified using % reacted as a metric for each compound class, defined as the difference in the total peak area between the brominated and original samples normalized to the original sample. The average % reacted (1 s.d.) values were -1.45% (2.8%) for the alkanes, 99.5% (0.4%) for the alkenes, and 6.7% (11.6%) for the aromatics, demonstrating excellent selectivity toward the alkenes with only minor aromatic bromination. The bromination chemistry was then applied to gasoline, followed by GC×GC-TOFMS analysis of the original and brominated gasoline. This GC×GC-TOFMS data set was then submitted to the supervised discovery tool tile-based F-ratio analysis (FRA), which reduced the large data set to only the chromatographic regions which distinguish between the original and brominated gasoline samples. FRA discovered 314 hits, 56 of which were determined statistically significant using combinatorial null distribution analysis (CNDA), a permutation-based significance test. Since the brominated olefins elute in an uncrowded region of the 2D chromatogram and have no signal in the original sample, their discoverability was greatly increased relative to the original olefins. By combining the information gained from brominated olefin standards and the structured patterns of the GC×GC separations, the top hits were identified as the dibromoalkane products of mono-olefins, with five C5 mono-olefins identified on a species level. The analytical workflow has broad implications for using selective reaction chemistries to facilitate supervised discovery by targeting desired compound classes.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification.

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

Determination of the Signal-To-Noise Ratio Enhancement in Comprehensive Three-Dimensional Gas Chromatography.

We investigate the extent to which comprehensive three-dimensional gas chromatography (GC3) provides a signal enhancement (SE) and a signal-to-noise ratio enhancement (S/NRel) relative to one-dimensional (1D)-GC. Specifically, the SE is defined as the ratio of the tallest 3D peak height from the GC3 separation to the 1D peak height from the unmodulated 1D-GC separation. A model is proposed which allows the analyst to predict the theoretically attainable SE (SET) based upon the peak width and sampling density inputs. The model is validated via comparison of the SET to the experimentally measured SE (SEM) obtained using total-transfer GC3 (100% duty cycle for both modulators) with time-of-flight mass spectrometry detection. Two experimental conditions were studied using the same GC3 column set, differing principally in the modulation period from the 1D to 2D columns: 4 s versus 8 s. Under the first set of conditions, the average SEM was 97 (±22), in excellent agreement with the SET of 97 (±18). The second set of conditions improved the average SEM to 181 (±27), also in agreement with the average SET of 176 (±26). The average S/NRel following correction for the mass spectrum acquisition frequency was 38.8 (±11.2) and 59.0 (±27.2) for the two sets of conditions. The enhancement in S/N is largely attributed to moving the signal to a higher frequency domain where the impact of "low frequency" noise is less detrimental. The findings here provide strong evidence that GC3 separations can provide enhanced detectability relative to 1D-GC and comprehensive two-dimensional gas chromatography (GC×GC) separations.

Development of an Enhanced Total Ion Current Chromatogram Algorithm to Improve Untargeted Peak Detection.

Accurate analyte peak detection from the background noise is a fundamental step in data analysis. Often, this is initially performed on the total ion current chromatogram (TIC), which is the summed signal from all mass spectral channels. Despite the detection of many of the most abundant peaks within a chromatogram, a large fraction of peaks remains undetected in the standard TIC due to their signal being below the limit of detection. To find peaks obscured by background noise, an untargeted peak detection method termed the "enhanced TIC algorithm" was developed for comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). The reported algorithm utilizes the entire mass spectral dimension to find regions of analytical signal above a threshold while zeroing the background noise. The resulting chromatographic data is summed together to create the enhanced TIC. The utility of the enhanced TIC algorithm is demonstrated using serial dilutions from a 10 parts-per-thousand (ppth) test mixture. For the chromatograms collected at 1 and 10 parts-per-million (ppm), the enhanced TIC algorithm recovered 62% and 93%, respectively, of the original peaks observed in the 10 ppth mixture, while the standard TIC recovered only 0% and 45%, respectively. The improvement in signal enhancement was also shown on a separation of a yeast cell metabolite extract, where the enhanced TIC found 33-64% more peaks than the standard TIC. Chromatographic simulations with increasing levels of background noise were also conducted to compare the enhanced and standard TICs in the context of statistical overlap theory (SOT). Simulated chromatograms with lower signal-to-noise were more accurately modeled by the SOT after enhanced TIC processing compared to those processed by the standard TIC. The enhanced TIC method demonstrates an immense benefit in peak discovery to improve data analysis efforts.

Control-Normalized Fisher Ratio Analysis of Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry Data for Enhanced Biomarker Discovery in a Metabolomic Study of Orthopedic Knee-Ligament Injury.

An innovative form of Fisher ratio (F-ratio) analysis (FRA) is developed for use with comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GC × GC-TOFMS) data and applied to the investigation of the changes in the metabolome in human plasma for patients with injury to their anterior cruciate ligament (ACL). Specifically, FRA provides a supervised discovery of metabolites that express a statistically significant variance in a two-sample class comparison: patients and healthy controls. The standard F-ratio utilizes the between-class variance relative to the pooled within-class variance. Because standard FRA is adversely impacted by metabolites expressed with a large within-class variance in the patient class, "control-normalized FRA" has been developed to provide complementary information, by normalizing the between-class variance to the variance of the control class only. Thirty plasma samples from patients who recently suffered from an ACL injury, along with matched controls, were subjected to GC × GC-TOFMS analysis. Following both standard and control-normalized FRA, the concentration ratio for the top 30 "hits" in each comparison was obtained and then t-tested for statistical significance. Twenty four out of 30 metabolites plus the therapeutic agent, naproxen (24/30), passed the t-test for the control-normalized FRA, which included 8/24 unique to control-normalized FRA and 16/24 in common with the standard FRA. Likewise, standard FRA provided 21/30 metabolites passing the t-test, with 5/21 undiscovered by control-normalized FRA. The complementary information obtained by both F-ratio analyses demonstrates the general utility of the new approach for a variety of applications.

Development of Ultrafast Separations Using Negative Pulse Partial Modulation To Enable New Directions in Gas Chromatography.

Partial modulation via a pulse flow valve operated in the negative pulse mode is developed for high-speed one-dimensional gas chromatography (1D-GC), comprehensive two-dimensional (2D) gas chromatography (GC × GC), and comprehensive three-dimensional gas chromatography (GC3). The pulse flow valve readily provides very short modulation periods, PM, demonstrated herein at 100, 200, and 300 ms, and holds significant promise to increase the scope and applicability of GC instrumentation. The negative pulse mode creates an extremely narrow, local analyte concentration pulse. The reproducibility of the negative pulse mode is validated in a 1D-GC mode, where a pseudosteady-state analyte stream is modulated, and 8 analytes are baseline resolved (resolution, Rs ≥ 1.5) in a 200 ms window, providing a peak capacity, nc, of 14 at unit resolution ( Rs = 1.0). Additionally, the pulse width, pw, of the pulse flow valve "injection" relationship to peak width-at-base, wb, resolution between peaks and detection sensitivity are studied. To demonstrate the applicability to GC × GC, a high-speed separation of a 20-component test mixture of similar, volatile analytes is shown. Analytes were separated on the second-dimension column, 2D, with 2 wb ranging from 7 to 12 ms, providing an exceptional 2D peak capacity, 2 nc, of ∼12 using a modulation period ( PM) of 100 ms. Next, a 12 min separation of a diesel sample using a PM of 300 ms is presented. The 1 wb is ∼4 s, resulting in a 1 nc of ∼180, and 2 wb is ∼18 ms, resulting in a 2 nc of ∼17, thus achieving a nc,2D of ∼3000 in this rapid GC × GC diesel separation. Finally, GC3 with time-of-flight mass spectrometry (TOFMS) detection using a PM of 100 ms applied between the 2D and 3D columns is reported. Narrow third dimension, 3D, peaks with 3 wb of ∼15 ms were obtained, resulting in a GC3 peak capacity, nc,3D, of ∼35 000 in a 45 min separation.

Comprehensive Three-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry.

Development of comprehensive, three-dimensional (3D) gas chromatography with time-of-flight mass spectrometric detection (GC3/TOFMS) is described. This instrument provides four dimensions (4D) of chemical selectivity and includes significant improvements to total selectivity (mass spectrometric and chromatographic), peak identification, and operational temperature range relative to previous models of the GC3 reported. The new instrumental design and data output are evaluated and illustrated via two samples, a 115-component test mixture and a diesel fuel spiked with several compounds, for the purpose of illustrating the chemical selectivity benefits of this instrumental platform. Useful approaches to visualize the 4D data are presented. The GC3/TOFMS instrument experimentally achieved total peak capacity, nc,3D, ranging from 5000 to 9600 (x̅ = 7000, s = 1700) for 10 representative analytes for 50 min separations with component dimensional peak capacities averaging 406, 3.6, and 4.9 for 1D, 2D, and 3D, respectively. Particularly, GC3/TOFMS achieved a combined 2D × 3D peak capacity ranging from 10 to 26 (x̅ = 17.6, s = 5.0), which is similar to what is achieved by 2D alone in a GC × GC operating at equivalent modulation period conditions. The analytical benefits of employing three varied chemical selectivities in the 3D separation coupled with TOFMS are illustrated through the separation and detection of 1,6-dichlorohexane and cyclohexyl isothiocyanate as part of the diesel fuel analysis.

Using Receiver Operating Characteristic Curves To Optimize Discovery-Based Software with Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry.

We report a quantitative approach to optimize implementation of discovery-based software for comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). The software performs a tile-based Fisher ratio (F-ratio) analysis and facilitates a supervised nontargeted analysis based upon the experimental design to aid in the discovery of analytes with statistically different variances between sample classes. The quantitative approach for software optimization uses receiver operating characteristic (ROC) curves. The area under the curve (AUC) for each ROC curve serves as a quantitative metric to optimize two key algorithm parameters: the signal-to-noise ratio (S/N) threshold of the data prior to calculating F-ratios at each m/z mass channel and the number of these F-ratios per m/z used to calculate the average F-ratio of a tile. A total of 25 combinations of S/N threshold by number of m/z were studied. Fifty analytes were spiked into a diesel fuel at two concentration levels to produce two sample classes that should in principle produce 50 positive instances in the ROC curves. The "sweet spot" for F-ratio analysis was determined to be a S/N threshold of 10 coupled with a maximum of the 10 most chemically selective m/z (requiring a minimum of 3 m/z), corresponding to an ∼21% improvement in the discrimination of true positives relative to prior studies. This equates to an additional 9 true positives being discovered at a false positive probability of 0.2 and 5 additional true positives being found overall. Furthermore, optimization of these software parameters did not depend upon a priori determination of the statistically correct number of positive instances in the sample classes. The AUC metric appears to be suitable for the evaluation of all data analysis methods that utilize the proper experimental design.

Determining the Probability of Achieving a Successful Quantitative Analysis for Gas Chromatography-Mass Spectrometry.

A new approach is presented to determine the probability of achieving a successful quantitative analysis for gas chromatography coupled with mass spectrometry (GC-MS). The proposed theory is based upon a probabilistic description of peak overlap in GC-MS separations to determine the probability of obtaining a successful quantitative analysis, which has its lower limit of chromatographic resolution Rs at some minimum chemometric resolution, Rs*; that is to say, successful quantitative analysis can be achieved when Rs ≥ Rs*. The value of Rs* must be experimentally determined and is dependent on the chemometric method to be applied. The approach presented makes use of the assumption that analyte peaks are independent and randomly distributed across the separation space or are at least locally random, namely, that each analyte represents an independent Bernoulli random variable, which is then used to predict the binomial probability of successful quantitative analysis. The theoretical framework is based on the chromatographic-saturation factor and chemometric-enhanced peak capacity. For a given separation, the probability of quantitative success can be improved via two pathways, a chromatographic-efficiency pathway that reduces the saturation of the sample and a chemometric pathway that reduces Rs* and improves the chemometric-enhanced peak capacity. This theory is demonstrated through a simulation-based study to approximate the resolution limit, Rs*, of multivariate curve resolution-alternating least-squares (MCR-ALS). For this study, Rs* was determined to be ∼0.3, and depending on the analytical expectations for the quantitative bias and the obtained mass-spectral match value, a lower value of Rs* ∼ 0.2 may be achievable.

Tile-based Fisher ratio analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) data using a null distribution approach.

Comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) is a versatile instrumental platform capable of collecting highly informative, yet highly complex, chemical data for a variety of samples. Fisher-ratio (F-ratio) analysis applied to the supervised comparison of sample classes algorithmically reduces complex GC × GC-TOFMS data sets to find class distinguishing chemical features. F-ratio analysis, using a tile-based algorithm, significantly reduces the adverse effects of chromatographic misalignment and spurious covariance of the detected signal, enhancing the discovery of true positives while simultaneously reducing the likelihood of detecting false positives. Herein, we report a study using tile-based F-ratio analysis whereby four non-native analytes were spiked into diesel fuel at several concentrations ranging from 0 to 100 ppm. Spike level comparisons were performed in two regimes: comparing the spiked samples to the nonspiked fuel matrix and to each other at relative concentration factors of two. Redundant hits were algorithmically removed by refocusing the tiled results onto the original high resolution pixel level data. To objectively limit the tile-based F-ratio results to only features which are statistically likely to be true positives, we developed a combinatorial technique using null class comparisons, called null distribution analysis, by which we determined a statistically defensible F-ratio cutoff for the analysis of the hit list. After applying null distribution analysis, spiked analytes were reliably discovered at ∼1 to ∼10 ppm (∼5 to ∼50 pg using a 200:1 split), depending upon the degree of mass spectral selectivity and 2D chromatographic resolution, with minimal occurrence of false positives. To place the relevance of this work among other methods in this field, results are compared to those for pixel and peak table-based approaches.

Serial plasma metabolites following hypoxic-ischemic encephalopathy in a nonhuman primate model.

Biomarkers that indicate the severity of hypoxic-ischemic brain injury and response to treatment and that predict neurodevelopmental outcomes are urgently needed to improve the care of affected neonates. We hypothesize that sequentially obtained plasma metabolomes will provide indicators of brain injury and repair, allowing for the prediction of neurodevelopmental outcomes. A total of 33 Macaca nemestrina underwent 0, 15 or 18 min of in utero umbilical cord occlusion (UCO) to induce hypoxic-ischemic encephalopathy and were then delivered by hysterotomy, resuscitated and stabilized. Serial blood samples were obtained at baseline (cord blood) and at 0.1, 24, 48, and 72 h of age. Treatment groups included nonasphyxiated controls (n = 7), untreated UCO (n = 11), UCO + hypothermia (HT; n = 6), and UCO + HT + erythropoietin (n = 9). Metabolites were extracted and analyzed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and quantified by PARAFAC (parallel factor analysis). Using nontargeted discovery-based methods, we identified 63 metabolites as potential biomarkers. The changes in metabolite concentrations were characterized and compared between treatment groups. Further comparison determined that 8 metabolites (arachidonic acid, butanoic acid, citric acid, fumaric acid, lactate, malate, propanoic acid, and succinic acid) correlated with early and/or long-term neurodevelopmental outcomes. The combined outcomes of death or cerebral palsy correlated with citric acid, fumaric acid, lactate, and propanoic acid. This change in circulating metabolome after UCO may reflect cellular metabolism and biochemical changes in response to the severity of brain injury and have potential to predict neurodevelopmental outcomes.

Modeling RP-1 fuel advanced distillation data using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and partial least squares analysis.

Recent efforts in predicting rocket propulsion (RP-1) fuel performance through modeling put greater emphasis on obtaining detailed and accurate fuel properties, as well as elucidating the relationships between fuel compositions and their properties. Herein, we study multidimensional chromatographic data obtained by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC-TOFMS) to analyze RP-1 fuels. For GC × GC separations, RTX-Wax (polar stationary phase) and RTX-1 (non-polar stationary phase) columns were implemented for the primary and secondary dimensions, respectively, to separate the chemical compound classes (alkanes, cycloalkanes, aromatics, etc.), providing a significant level of chemical compositional information. The GC × GC-TOFMS data were analyzed using partial least squares regression (PLS) chemometric analysis to model and predict advanced distillation curve (ADC) data for ten RP-1 fuels that were previously analyzed using the ADC method. The PLS modeling provides insight into the chemical species that impact the ADC data. The PLS modeling correlates compositional information found in the GC × GC-TOFMS chromatograms of each RP-1 fuel, and their respective ADC, and allows prediction of the ADC for each RP-1 fuel with good precision and accuracy. The root-mean-square error of calibration (RMSEC) ranged from 0.1 to 0.5 °C, and was typically below ∼0.2 °C, for the PLS calibration of the ADC modeling with GC × GC-TOFMS data, indicating a good fit of the model to the calibration data. Likewise, the predictive power of the overall method via PLS modeling was assessed using leave-one-out cross-validation (LOOCV) yielding root-mean-square error of cross-validation (RMSECV) ranging from 1.4 to 2.6 °C, and was typically below ∼2.0 °C, at each % distilled measurement point during the ADC analysis.

Enhancing gas chromatography-time of flight mass spectrometry data analysis using two-dimensional mass channel cluster plots.

A novel data reduction and representation method for gas chromatography time-of-flight mass spectrometry (GC-TOFMS) is presented that significantly facilitates separation visualization and analyte peak deconvolution. The method utilizes the rapid mass spectral data collection rate (100 scans/s or greater) of current generation TOFMS detectors. Chromatographic peak maxima (serving as the retention time, tR) above a user specified signal threshold are located, and the chromatographic peak width, W, are determined on a per mass channel (m/z) basis for each analyte peak. The peak W (per m/z) is then plotted against its respective tR (with 10 ms precision) in a two-dimensional (2D) format, producing a cluster of points (i.e., one point per peak W versus tR in the 2D plot). Analysis of GC-TOFMS data by this method produces what is referred to as a two-dimensional mass channel cluster plot (2D m/z cluster plot). We observed that adjacent eluting (even coeluting) peaks in a temperature programmed separation can have their peak W vary as much as ∼10-15%. Hence, the peak W provides useful chemical selectivity when viewed in the 2D m/z cluster plot format. Pairs of overlapped analyte peaks with one-dimensional GC resolution as low as Rs ≈ 0.03 can be visually identified as fully resolved in a 2D m/z cluster plot and readily deconvoluted using chemometrics (i.e., demonstrated using classical least-squares analysis). Using the 2D m/z cluster plot method, the effective peak capacity of one-dimensional GC separations is magnified nearly 40-fold in one-dimensional GC, and potentially ∼100-fold in the context of comparing it to a two-dimensional separation. The method was studied using a 73 component test mixture separated on a 30 m × 250 µm i.d. RTX-5 column with a LECO Pegasus III TOFMS.

Cardiac-specific deletion of acetyl CoA carboxylase 2 prevents metabolic remodeling during pressure-overload hypertrophy.

RATIONALE: Decreased fatty acid oxidation (FAO) with increased reliance on glucose are hallmarks of metabolic remodeling that occurs in pathological cardiac hypertrophy and is associated with decreased myocardial energetics and impaired cardiac function. To date, it has not been tested whether prevention of the metabolic switch that occurs during the development of cardiac hypertrophy has unequivocal benefits on cardiac function and energetics. OBJECTIVE: Because malonyl CoA production via acetyl CoA carboxylase 2 (ACC2) inhibits the entry of long chain fatty acids into the mitochondria, we hypothesized that mice with a cardiac-specific deletion of ACC2 (ACC2H-/-) would maintain cardiac FAO and improve function and energetics during the development of pressure-overload hypertrophy. METHODS AND RESULTS: ACC2 deletion led to a significant reduction in cardiac malonyl CoA levels. In isolated perfused heart experiments, left ventricular function and oxygen consumption were similar in ACC2H-/- mice despite an ≈60% increase in FAO compared with controls (CON). After 8 weeks of pressure overload via transverse aortic constriction (TAC), ACC2H-/- mice exhibited a substrate utilization profile similar to sham animals, whereas CON-TAC hearts had decreased FAO with increased glycolysis and anaplerosis. Myocardial energetics, assessed by 31P nuclear magnetic resonance spectroscopy, and cardiac function were maintained in ACC2H-/- after 8 weeks of TAC. Furthermore, ACC2H-/--TAC demonstrated an attenuation of cardiac hypertrophy with a significant reduction in fibrosis relative to CON-TAC. CONCLUSIONS: These data suggest that reversion to the fetal metabolic profile in chronic pathological hypertrophy is associated with impaired myocardial function and energetics and maintenance of the inherent cardiac metabolic profile and mitochondrial oxidative capacity is a viable therapeutic strategy.

Enhancing gas chromatography-mass spectrometry resolution and pure analyte discovery using intra-chromatogram elution profile matching.

Chemometric decomposition methods like multivariate curve resolution-alternating least squares (MCR-ALS) are often employed in gas chromatography-mass spectrometry (GC-MS) to improve analyte identification and quantitation. However, these methods can perform poorly for analytes with a low chromatographic resolution (Rs) and a high degree of spectral contamination from noise and background interferences. Thus, we propose a novel computational algorithm, termed mzCompare, to improve analyte identification and quantitation when coupled to MCR-ALS. The mzCompare method utilizes an underlying requirement that the retention time and peak shape between mass channels (m/z) of the same analyte should be similar. By discovering the selective m/z for a given analyte in a chromatogram, a pure elution profile can be generated and used as an equality constraint in MCR-ALS. The performance of the mzCompare methodology is demonstrated with both experimental and simulated chromatograms. Experimentally, unresolved analytes with a Rs as low as 0.05 could be confidently identified with mzCompare assisted MCR-ALS. Furthermore, application of the mzCompare algorithm to a complex aerospace fuel resulted in the discovery of 335 analytes, a 44 % increase compared to conventional peak detection methods. GC-MS simulations of target-interferent analyte pairs demonstrated that the performance of MCR-ALS deteriorated below a Rs of ∼0.25. However, mzCompare assisted MCR-ALS showed excellent identification and acceptable quantitative accuracy at a Rs of ∼0.02. These results show that the mzCompare algorithm can help analysts overcome modeling ambiguities resulting from the chemometric multiplex disadvantage.

Simultaneous discovery of compounds dominated by either molding kinetics or geographical region of origin for moisture damaged cacao beans using orthogonally applied tile-based fisher ratio analysis of GC×GC-TOFMS data.

Herein, two "orthogonal" characteristics of moisture damaged cacao beans (temporally dependent molding kinetics versus the time-independent geographical region of origin) are simultaneously analyzed in a comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) dataset using tile-based Fisher ratio (F-ratio) analysis. Cacao beans from six geographical regions were analyzed once a day for six days following the initiation of moisture damage to trigger the molding process. Thus, there are two "extremes" to the experimental sample class design: six time points for the molding kinetics versus the six geographical regions of origin, resulting in a 6 × 6 element signal array referred to as a composite chemical fingerprint (CCF) for each analyte. Usually, this study would involve initial generation of two separate hit lists using F-ratio analysis, one hit list from inputting the data with the six time point classes, then another hit list from inputting the dataset from the perspective of geographic region of origin. However, analysis of two separate hit lists with the intent to distill them down to one hit list is extremely time-consuming and fraught with shortcomings due to the challenges associated with attempting to match analytes across two hit lists. To address this challenge, tile-based F-ratio analysis is "orthogonally applied" to each analyte CCF to simultaneously determine two F-ratios at the chromatographic 2D location (F-ratiokinetic and F-ratioregion) for each hit, by ranking a single hit list using the higher of the two F-ratios resulting in the discovery of 591 analytes. Further, using a pseudo-null distribution approach, at the 99.9% threshold over 400 analytes were deemed suitable for PCA classification. Using a more stringent 99.999% threshold, over 100 analytes were explored more deeply using PARAFAC to provide a purified mass spectrum.