Analytical Determination of the Severity of Potato Taste Defect in Roasted East African Arabica Coffee.

The quality of East African coffee beans has been significantly reduced by a flavor defect known as potato taste defect (PTD) due to the presence of 2-isopropyl-3-methoxypyrazine (IPMP) and 2-isobutyl-3-methoxypyrazine (IBMP). Therefore, the aims of this study were to determine the correlation between these methoxypyrazines and the severity of odor attributed to PTD and discover additional analytes that may be correlated with PTD using Fisher ratio analysis, a supervised discovery-based data analysis method. Specialty ground roasted coffees from East Africa were classified as clean (i.e., no off-odor), mild, medium, or strong PTD. For the samples examined, IPMP was found to discriminate between non-defective and defective samples, while IBMP did not do so. Samples affected by PTD exhibited a wide range of IPMP concentration (1.6-529.9 ng/g). Except for one sample, the IPMP concentration in defective samples was greater than the average IPMP concentration in the non-defective samples (2.0 ng/g). Also, an analysis of variance found that IPMP concentrations were significantly different based on the severity of odor attributed to PTD (p < 0.05). Fisher ratio analysis discovered 21 additional analytes whose concentrations were statistically different based on the severity of PTD odor (p < 0.05). Generally, analytes that were positively correlated with odor severity generally had unpleasant sensory descriptions, while analytes typically associated with desirable aromas were found to be negatively correlated with odor severity. These findings not only show that IPMP concentration can differentiate the severity of PTD but also that changes in the volatile analyte profile of coffee beans induced by PTD can contribute to odor severity.

Statistical inference of mass channel purity from Fisher ratio analysis using comprehensive two-dimensional gas chromatography with time of flight mass spectrometry data.

Tile-based Fisher ratio (F-ratio) analysis has recently been developed and validated for discovery-based studies of highly complex data collected using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). In previous studies, interpretation and utilization of F-ratio hit lists has relied upon manual decomposition and quantification performed by chemometric methods such as parallel factor analysis (PARAFAC), or via manual translation of the F-ratio hit list information to peak table quantitative information provided by the instrument software (ChromaTOF). Both of these quantification approaches are bottlenecks in the overall workflow. In order to address this issue, a more automatable approach to provide accurate relative quantification for F-ratio analyses was investigated, based upon the mass spectral selectivity provided via the F-ratio spectral output. Diesel fuel spiked with 15 analytes at four concentration levels (80, 40, 20, and 10 ppm) produced three sets of two class comparisons that were submitted to tile-based F-ratio analysis to obtain three hit lists, with an F-ratio spectrum for each hit. A novel algorithm which calculates the signal ratio (S-ratio) between two classes (eg., 80 ppm versus 40 ppm) was applied to all mass channels (m/z) in the F-ratio spectrum for each hit. A lack of fit (LOF) metric was utilized as a measure of peak purity and combined with F-ratio and p-values to study the relationship of each of these metrics with m/z purity. Application of a LOF threshold coupled with a p-value threshold yielded a subset of the most pure m/z for each of the 15 spiked analytes, evident by the low deviations (< 5%) in S-ratio relative to the true concentration ratio. A key outcome of this study was to demonstrate the isolation of pure m/z without the need for higher level signal decomposition algorithms.

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.

Methods of discovery-based and targeted metabolite analysis by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection.

The investigation of naturally volatile and derivatized metabolites in biological tissues by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) can provide highly complex and information-rich data for comprehensive metabolomics analysis. The addition of the second separation dimension with GC × GC provides additional chemical selectivity, and the fast scanning time of TOFMS offers benefits in chemical selectivity and overall peak capacity compared to traditional one-dimensional (1D) GC. Furthermore, methods of derivatization to facilitate volatility and thermal stability, the most prominent being the silylation of organic compounds, have extended the use of GC as an important metabolomics tool. The highly information-rich data from GC × GC-TOFMS benefits from sophisticated comprehensive targeted and nontargeted algorithmic software methods. Herein, we detail a robust derivatization and instrumental method for metabolomics analysis and provide a brief overview of possible methods for data analysis.

Correlation of rocket propulsion fuel properties with chemical composition using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry followed by partial least squares regression analysis.

There is an increased need to more fully assess and control the composition of kerosene-based rocket propulsion fuels such as RP-1. In particular, it is critical to make better quantitative connections among the following three attributes: fuel performance (thermal stability, sooting propensity, engine specific impulse, etc.), fuel properties (such as flash point, density, kinematic viscosity, net heat of combustion, and hydrogen content), and the chemical composition of a given fuel, i.e., amounts of specific chemical compounds and compound classes present in a fuel as a result of feedstock blending and/or processing. Recent efforts in predicting fuel chemical and physical behavior through modeling put greater emphasis on attaining detailed and accurate fuel properties and fuel composition information. Often, one-dimensional gas chromatography (GC) combined with mass spectrometry (MS) is employed to provide chemical composition information. Building on approaches that used GC-MS, but to glean substantially more chemical information from these complex fuels, we recently studied the use of comprehensive two dimensional (2D) gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOFMS) using a "reversed column" format: RTX-wax column for the first dimension, and a RTX-1 column for the second dimension. In this report, by applying chemometric data analysis, specifically partial least-squares (PLS) regression analysis, we are able to readily model (and correlate) the chemical compositional information provided by use of GC×GC-TOFMS to RP-1 fuel property information such as density, kinematic viscosity, net heat of combustion, and so on. Furthermore, we readily identified compounds that contribute significantly to measured differences in fuel properties based on results from the PLS models. We anticipate this new chemical analysis strategy will have broad implications for the development of high fidelity composition-property models, leading to an improved approach to fuel formulation and specification for advanced engine cycles.

Tile-based Fisher-ratio software for improved feature selection analysis of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry data.

Comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) is a highly capable instrumental platform that produces complex and information-rich multi-dimensional chemical data. The data can be initially overwhelming, especially when many samples (of various sample classes) are analyzed with multiple injections for each sample. Thus, the data must be analyzed in such a way as to extract the most meaningful information. The pixel-based and peak table-based Fisher ratio algorithmic approaches have been used successfully in the past to reduce the multi-dimensional data down to those chemical compounds that are changing between the sample classes relative to those that are not changing (i.e., chemical feature selection). We report on the initial development of a computationally fast novel tile-based Fisher-ratio software that addresses the challenges due to 2D retention time misalignment without explicitly aligning the data, which is often a shortcoming for both pixel-based and peak table-based algorithmic approaches. Concurrently, the tile-based Fisher-ratio algorithm significantly improves the sensitivity contrast of true positives against a background of potential false positives and noise. In this study, eight compounds, plus one internal standard, were spiked into diesel at various concentrations. The tile-based F-ratio algorithmic approach was able to "discover" all spiked analytes, within the complex diesel sample matrix with thousands of potential false positives, in each possible concentration comparison, even at the lowest absolute spiked analyte concentration ratio of 1.06, the ratio between the concentrations in the spiked diesel sample to the native concentration in diesel.

Sample preparation methodology for mouse heart metabolomics using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry.

The investigation of naturally volatile and derivatized metabolites in mammalian tissues by comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) can provide the data for a comprehensive analysis of the pathophysiology of disease processes. When relative quantification is needed for hypothesis testing, the preparation of sample tissue must provide clear evidence that a quantitative relationship exists between the final detected signal and the amount of metabolite in the tissue. Herein, we report the optimization of a metabolite extraction method for mouse heart tissue for GC × GC-TOFMS analysis. A recursive extraction experiment was initially performed to measure the extraction efficiency of representative target metabolites (sugars, tricarboxylic acid cycle metabolites, amino acids, lipid and signaling molecules) in the aqueous fraction of a three-phase extraction system involving tissue, methanol:water, and chloroform. Some metabolites suffered from incomplete extraction with a single extraction of ≈ 40 mg in 600 µl organic and 400 µl aqueous phases, possibly caused by saturation effects. Subsequent experiments, calibrating resulting metabolite signal to the mass of heart tissue extracted, demonstrated that doubling the solvent volumes and a lower tissue mass was needed to provide accurate relative quantification of the derivatized mouse heart metabolome. We demonstrate quantitative extraction of metabolites from ≈ 20 mg of heart tissue using 1200 µl organic phase (chloroform) and 800 µl aqueous phase (methanol:water in equal parts by volume).

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.

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.

Simultaneous quantification of apolipoprotein A-I and apolipoprotein B by liquid-chromatography-multiple- reaction-monitoring mass spectrometry.

BACKGROUND: If liquid-chromatography-multiple-reaction-monitoring mass spectrometry (LC-MRM/MS) could be used in the large-scale preclinical verification of putative biomarkers, it would obviate the need for the development of expensive immunoassays. In addition, the translation of novel biomarkers to clinical use would be accelerated if the assays used in preclinical studies were the same as those used in the clinical laboratory. To validate this approach, we developed a multiplexed assay for the quantification of 2 clinically well-known biomarkers in human plasma, apolipoprotein A-I and apolipoprotein B (apoA-I and apoB). METHODS: We used PeptideAtlas to identify candidate peptides. Human samples were denatured with urea or trifluoroethanol, reduced and alkylated, and digested with trypsin. We compared reversed-phase chromatographic separation of peptides with normal flow and microflow, and we normalized endogenous peptide peak areas to internal standard peptides. We evaluated different methods of calibration and compared the final method with a nephelometric immunoassay. RESULTS: We developed a final method using trifluoroethanol denaturation, 21-h digestion, normal flow chromatography-electrospray ionization, and calibration with a single normal human plasma sample. For samples injected in duplicate, the method had intraassay CVs <6% and interassay CVs <12% for both proteins, and compared well with immunoassay (n = 47; Deming regression, LC-MRM/MS = 1.17 × immunoassay - 36.6; S(x|y) = 10.3 for apoA-I and LC-MRM/MS = 1.21 × immunoassay + 7.0; S(x|y) = 7.9 for apoB). CONCLUSIONS: Multiplexed quantification of proteins in human plasma/serum by LC-MRM/MS is possible and compares well with clinically useful immunoassays. The potential application of single-point calibration to large clinical studies could simplify efforts to reduce day-to-day digestion variability.

Isopropanol protein precipitation for the analysis of plasma free metanephrines by liquid chromatography-tandem mass spectrometry.

BACKGROUND: High-performance liquid chromatography-tandem mass spectrometric (LC-MS/MS)1 analysis of plasma free metanephrines is the most diagnostically sensitive and specific screening test for the diagnosis of pheochromocytoma. We sought to develop an in-house method for this expensive test METHODS: We used off-line isopropanol protein precipitation of plasma to remove interfering substances before LC-MS/MS analysis. We compared the extraction efficiency and limits of quantification of protein precipitation to those of previously reported solid-phase techniques. RESULTS: The new method had limits of quantification of 0.09 nmol/L and 0.17 nmol/L for metanephrine and normetanephrine, respectively. Method comparison with a previously described solid-phase extraction method revealed Deming regression slopes of 0.904 and 0.994, intercepts of 0.007 and 0.023, and SEs of the residuals (S(y/x)) of 0.071 and 0.284 for metanephrine and normetanephrine, respectively. Extraction efficiency of isopropanol protein precipitation was 66% for metanephrine and 35% for normetanephrine, results that were superior to the efficiencies of 4% and 1% for our adapted solid-phase extraction method. No ion suppression was observed at the retention times for metanephrine and normetanephrine. CONCLUSIONS: Isopropanol protein precipitation is a novel and effective off-line sample preparation method for metanephrines that offers a less expensive alternative to on-line solid-phase extraction for low-volume testing and requires a sample volume of only 200 microL. The mass spectrometric analysis time is equivalent to that of solid-phase techniques.