Robert E. Synovec

Dissolution behavior and surface tension effects of organic compounds in nucleating cloud droplets

Solubilities and surface tensions were measured for difunctional organic acids in various concentrations of (NH4)2SO4 and NH4HSO4 aqueous solutions. Model results using these data indicate that the organic compounds affect cloud droplet growth by two mechanisms: by gradual dissolution in the growing droplet which affects the shape of the Kohler growth curve, and by lowering of surface tension which decreases the critical supersaturation.

Data Analysis Methods

Data analysis methods are essential to transform chromatographic data into useful information. There is an ongoing evolution of data analysis methods as increasingly challenging applications need to be addressed, instrumentation advances occur, separation speeds increase, data dimensionality increases, and growing volumes of data are collected. As the traditional data analysis methods have been covered in prior chapters, this chapter focuses primarily on advanced data analysis methods known as chemometrics. Chemometrics are mathematical methods that glean useful information from chemical data, especially large volumes of complex data that are not amenable to traditional or manual analysis. Chemometrics fill several analytical roles. First, the ability to identify and quantify analytes at extremely low chromatographic resolution can be provided. Second, pattern recognition and classification are often implemented, where a chromatogram provides a chemical fingerprint. Third, groups of compounds may be quantified in concert using chemometric methods. Finally, another area of broad interest is retention prediction for separation optimization.

High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel profiles obtained using gas chromatography (GC). Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current chemometric techniques to correctly model information that shifts from variable to variable within a dataset. The alignment algorithm developed is shown to increase the efficacy of pattern recognition methods applied to diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retention time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical. Two sets of diesel fuel gas chromatograms were studied using the novel alignment algorithm followed by principal component analysis (PCA). In the first study, retention times for corresponding chromatographic peaks in 60 chromatograms varied by as much as 300 ms between chromatograms before alignment. In the second study of 42 chromatograms, the retention time shifting exhibited was on the order of 10 s between corresponding chromatographic peaks, and required a coarse retention time correction prior to alignment with the algorithm. In both cases, an increase in retention time precision afforded by the algorithm was clearly visible in plots of overlaid chromatograms before and then after applying the retention time alignment algorithm. Using the alignment algorithm, the standard deviation for corresponding peak retention times following alignment was 17 ms throughout a given chromatogram, corresponding to a relative standard deviation of 0.003% at an average retention time of 8 min. This level of retention time precision is a 5-fold improvement over the retention time precision initially provided by a state-of-the-art GC instrument equipped with electronic pressure control and was critical to the performance of the chemometric analysis. This increase in retention time precision does not come at the expense of chemical selectivity, since the PCA results suggest that essentially all of the chemical selectivity is preserved. Cluster resolution between dissimilar groups of diesel fuel chromatograms in a two-dimensional scores space generated with PCA is shown to substantially increase after alignment. The alignment method is robust against missing or extra peaks relative to a target chromatogram used in the alignment, and operates at high speed, requiring roughly 1 s of computation time per GC chromatogram.

Cyclic changes in metabolic state during the life of a yeast cell

Budding yeast undergo robust oscillations in oxygen consumption during continuous growth in a nutrient-limited environment. Using liquid chromatography-mass spectrometry and comprehensive 2D gas chromatography-mass spectrometry-based metabolite profiling methods, we have determined that the intracellular concentrations of many metabolites change periodically as a function of these metabolic cycles. These results reveal the logic of cellular metabolism during different phases of the life of a yeast cell. They may further indicate that oscillation in the abundance of key metabolites might help control the temporal regulation of cellular processes and the establishment of a cycle. Such oscillations in metabolic state might occur during the course of other biological cycles.

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.

Pattern recognition of jet fuels: comprehensive GC×GC with ANOVA-based feature selection and principal component analysis

Abstract Two-dimensional comprehensive gas chromatography (GC×GC) is applied to a pattern recognition problem involving classification of jet fuel mixtures. Analysis of variance (ANOVA)-based feature selection is initially used to identify and select chromatographic features relevant to a given classification in two studies. Then, principal component analysis (PCA) was used for pattern recognition classification. In the first study, a 1% volumetric composition change in mixtures of JP-5 and JP-7 jet fuel is readily distinguished. In this first study, the effective combination of GC×GC, ANOVA-based feature selection and PCA is developed and evaluated as a chemical analysis tool. The second study involved the analysis of three samples each of three different jet fuel types, JP-5, JP-8, and JP-TS, as well as blends incorporating two or three jet fuels. Each of the nine jet fuel samples originated from various geographic locations within the United States. These samples were analyzed in order to determine if a classification based on fuel type is possible in the presence of sample variability (due to geographic origin) with GC×GC/pattern recognition analysis. Chromatographic features that are adept at classification of jet fuel type and are not sensitive to geographic origin of the sample were generated for the sample set consisting of the original fuel types as well as mixtures of the three different, original jet fuels. The combination of GC×GC with ANOVA-based feature selection was found to be a useful tool to enhance the chemical selectivity, and thus the classification power of the analytical procedure, when coupled with PCA.

Review of chemometric analysis techniques for comprehensive two dimensional separations data

Comprehensive two-dimensional (2D) separations, such as comprehensive 2D gas chromatography (GC×GC), liquid chromatography (LC×LC), and related instrumental techniques, provide very large and complex data sets. It is often up to the software to assist the analyst in transforming these complex data sets into useful information, and that is precisely where the field of chemometric data analysis plays a pivotal role. Chemometric tools for comprehensive 2D separations are continually being developed and applied as researchers make significant advances in novel state-of-the-art algorithms and software, and as the commercial sector continues to provide user friendly chemometric software. In this review, we build upon previous reviews of this topic, by focusing primarily on advances that have been reported in the past five years. Most of the reports focus on instrumental platforms using GC×GC with either flame ionization detection (FID) or time-of-flight mass spectrometry (TOFMS) detection, or LC×LC with diode array absorbance detection (DAD). The review covers the following general topics: data preprocessing techniques, target analyte techniques, comprehensive nontarget analysis techniques, and software for chemometrics in multidimensional separations.

Comprehensive analysis of yeast metabolite GC×GC–TOFMS data: combining discovery-mode and deconvolution chemometric software

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Students t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

A principal component analysis based method to discover chemical differences in comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) separations of metabolites in plant samples

Comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GCxGC-TOFMS) provides high resolution separations of complex samples with a mass spectrum at every point in the separation space. The large volumes of multidimensional data obtained by GCxGC-TOFMS analysis are analyzed using a principal component analysis (PCA) method described herein to quickly and objectively discover differences between complex samples. In this work, we submitted 54 chromatograms to PCA to automatically compare the metabolite profiles of three different species of plants, namely basil (Ocimum basilicum), peppermint (Mentha piperita), and sweet herb stevia (Stevia rebaudiana), where there were 18 chromatograms for each type of plant. The 54 scores of the m/z 73 data set clustered in three groups according to the three types of plants. Principal component 1 (PC 1) separated the stevia cluster from the basil and peppermint clusters, capturing 61.84% of the total variance. Principal component 2 (PC 2) separated the basil cluster from the peppermint cluster, capturing 16.78% of the total variance. The PCA method revealed that relative abundances of amino acids, carboxylic acids, and carbohydrates were responsible for differentiating the three plants. A brief list of the 16 most significant metabolites is reported. After PCA, the 54 scores of the m/z 217 data set clustered in three groups according to the three types of plants, as well, yielding highly loaded variables corresponding with chemical differences between plants that were complementary to the m/z 73 information. The PCA data mining method is applicable to all of the monitored selective mass channels, utilizing all of the collected data, to discover unknown differences in complex sample profiles.

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection: analysis of amino acid and organic acid trimethylsilyl derivatives, with application to the analysis of metabolites in rye grass samples.

First, standard mixtures of trimethylsilyl (TMS) derivatives of amino acid and organic acid are analyzed by comprehensive two-dimensional (2D) gas chromatography (GC) coupled to time-of-flight mass spectrometry (GC x GC/TOFMS) in order to illustrate important issues regarding application of the technique. Specifically of interest is the extent to which the peak capacity of the 2D separation space has been utilized and the procedure by which the derivative standards are identified in the 2D separations using the mass spectral information. The resulting 2D separation is found to make extensive use of the GC x GC separation space provided by the complementary stationary phases employed. Second, in order to demonstrate GC x GC/TOFMS on two real sample types, trimethylsilyl metabolite derivatives were analyzed from extracts of common lawn grass samples (i.e., perennial rye grass), as a means to provide insight into both the pre and post harvest physiology. Various chemical components in the two rye grass extract samples were found to either emerge or disappear in relation to the trauma response. For example, a significant difference in the peak for the TMS derivative of malic acid was found. The successful analysis of various components was readily facilitated by the 2D separation, while a 1D separation would have produced too much peak overlap, thus impeding the analysis. The importance of using a GC x GC separation approach for the analysis of complex samples, such as metabolite extracts, is therefore demonstrated. The real-time analysis capability of GC x GC/TOFMS for multidimensional metabolite analysis makes this technique well suited to the high-throughput analysis of metabolomic samples, especially compared to slower, stopped-flow type separation approaches.