Computer reconstructed gas and liquid chromatograms

This paper describes a Lab Basic II program which allows the reconstruction of GLC-chromatograms on a Hewlett-Packard 2648A graphics terminal, starting from raw data gathered by the HP-3354A Laboratory Data System. The introduction of a considerable number of automatic parameter selections restricts user's interventions. The program can be run in terminal mode or be recalled as a post-analysis program when working in autocall mode.     

Combination of dynamic time warping and multivariate analysis for the comparison of comprehensive two-dimensional gas chromatograms: application to plant extracts

Comprehensive two-dimensional gas chromatography (GC x GC) is now recognized as the preferred technique for the detailed analysis and characterization of complex mixtures of volatile compounds. However, for comparison purposes, taking into account all the information contained in the chromatogram is far from trivial. In this paper, it is shown that the combination of peak alignment by dynamic time warping and multivariate analysis facilitated the comparison of complex chromatograms of tobacco extracts. The comparison is shown to be efficient enough to provide a clear discrimination among three types of tobacco. A tentative interpretation of loadings is presented in order to give access to the compounds which differ from one sample to another. Once located, mass spectrometry was used to identify markers of tobacco type.     

Classification of GC‐MS measurements of wines by combining data dimension reduction and variable selection techniques

Different classification methods (Partial Least Squares Discriminant Analysis, Extended Canonical Variates Analysis and Linear Discriminant Analysis), in combination with variable selection approaches (Forward Selection and Genetic Algorithms), were compared, evaluating their capabilities in the geographical discrimination of wine samples. Sixty‐two samples were analysed by means of dynamic headspace gas chromatography mass spectrometry (HS‐GC‐MS) and the entire chromatographic profile was considered to build the dataset. Since variable selection techniques pose a risk of overfitting when a large number of variables is used, a method for coupling data dimension reduction and variable selection was proposed. This approach compresses windows of the original data by retaining only significant components of local Principal Component Analysis models. The subsequent variable selection is then performed on these locally derived score variables. The results confirmed that the classification models achieved on the reduced data were better than those obtained on the entire chromatographic profile, with the exception of Extended Canonical Variates Analysis, which gave acceptable models in both cases. Copyright © 2008 John Wiley & Sons, Ltd.     

Application of target factor analysis of gas chromatography. Reproduction,prediction and classification

Summary Using the method of target factor analysis (TFA) described by Malinowski and Howery a computer program has been developed to study different sets of gas chromatographic retention data. Physico-chemical, topological and uniqueness parameters have been found to be basic factors to describe solute behaviour problems. Factor analytical solutions have been used to reproduce the data matrices and to make predictions based on best sets of basic factors. The mean absolute error in the reproduction step is between 1.72 retention index units (i.u.) for a relatively simple matrix consisting of retention indices of alcohols and 7.36 i.u. for a combined data matrix of alcohol, aldehyde and ketone retention indices. TFA has also been used to classify solutes based on their retention behaviour. Alkanes have been classified from cycloalkanes, alkanes from alkenes, and alcohols from aldehydes and ketones using only their retention data and a special kind of uniqueness vector.     

General equation to describe the dependence of the gas holdup time on the initial temperature and program rate in temperature-programmed gas chromatography

Summary The influence of the program rate and initial temperature on the holdup time in linear temperature-programmed gas chromatography was studied. An equation describing the dependence of the holdup time on the program rate and the initial temperature is given. Experimental verification of the equation was carried out on three capillary columns coated with stationary phases having different polarities, at three program rates and four initial temperatures. Enlarged text of the paper presented at the Eighth International Symposium on Capillary Chromatography, Riva del Garda, Italy, May 19–21, 1987.     

Resolving co-eluting chromatographic patterns by means of dissimilarity analysis in iterative target transformation factor analysis

The initialization of concentration vector for iterative target transformation factor analysis (ITTFA) and identification of pure or key variables are the important issue in MCR. In this study, dissimilarity analysis and evolving factor analysis (EFA) are combined to find the selective or key variables and subsequently obtain initial estimates of the concentration vectors for resolution of gas chromatography/mass spectrometry (GC/MS) data by ITTFA. For systems containing components with highly similar mass spectra, a new constraint setting the elements out of elution window to 0 is used to improve convergence rate and accuracy of results. Tested by standard mixture of two wax esters and real GC/MS data of gasoline 97#, dissimilarity based ITTFA could obtain accurate results (average Dot product of concentration vectors, average deviation of peak area ratio and average similarity of mass spectra are 0.9929, 0.0228 and 981.0, respectively).     

A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification

In the analysis of gene expression profiles, the number of tissue samples with genes expression levels available is usually small compared with the number of genes. This can lead either to possible overfitting or even to a complete failure in analysis of microarray data. The selection of genes that are really indicative of the tissue classification concerned is becoming one of the key steps in microarray studies. In the present paper, we have combined the modified discrete particle swarm optimization (PSO) and support vector machines (SVM) for tumor classification. The modified discrete PSO is applied to select genes, while SVM is used as the classifier or the evaluator. The proposed approach is used to the microarray data of 22 normal and 40 colon tumor tissues and showed good prediction performance. It has been demonstrated that the modified PSO is a useful tool for gene selection and mining high dimension data.     

实时生物分子相互作用分析技术用于链霉亲和素-生物素化抗体的层层组装研究

使用生物分子相互作用分析（Biomolecular interaction analysis,BIA)技术实时监测了在链霉素和素表面层层组装亲和素－生物素化抗体多层膜的过程，结果表明，通过链霉素和素与生物素之间的强亲和作用，能够在表面形成均一的多层膜，并用实时BIA技术求得了每层蛋白质的表面浓度，对于生物素化抗体，单层吸附表面浓度为1.32ng/mm^2；对于链霉亲和素，单层吸附表面浓度为2.93ng/mm^2。同时对蛋白质在表面的排列状态进行了探讨。     

Evaluation of alignment methods and data pretreatments on the determination of the most important peaks for the discrimination of coffee varieties Arabica and Robusta using gas chromatography-mass spectroscopy

Coffee samples were analyzed by GC/MS in order to determine the most important peaks for the discrimination of the varieties Arabica and Robusta. The resulting peak tables from chromatographic analysis were aligned and pretreated before being submitted to multivariate analysis. A rapid and easy-to-perform peak alignment procedure, which does not require advanced programming skills to use, was compared with the tedious manual alignment procedure. The influence of three types of data pretreatment, normalization, logarithmic and square root transformations and their combinations, on the variables selected as most important by the regression coefficients of partial least squares-discriminant analysis (PLS-DA), are shown. Test samples different from those used in the calibration and comparison with the substances already known as being responsible for Arabica and Robusta coffees discrimination were used to determine the best pretreatments for both datasets. The data pretreatment consisting of square root transformation followed by normalization (RN) was chosen as being the most appropriate. The results obtained showed that the much quicker automated aligned method could be used as a substitute for the manually aligned method, allowing all the peaks in the chromatogram to be used for multivariate analysis.     

Simultaneous determination of cypermethrin and fenvalerate residues in tomato by gas chromatography and their applications to kinetic studies after field treatment

In this study simultaneous determination of cypermethrin and fenvalerate residues in tomato fruit (Lycopesicon esculentum) grown in Khartoum, Sudan, was carried out using gas chromatography with electron capture detector (GC‐ECD). The method was linear in the ranges of 0.075–0.009 and 0.75–0.037 mg/mL for cypermethrin and fenvalerate, respectively. The limits of detection and quantification were found to be 0.003 and 0.01 mg/mL and 0.014, 0. 047 mg/mL for cypermethrin and fenvalerate, respectively. The recoveries of cypermethrin and fenvalerate spiked in tomato were 97 ± 7.5 and 99.5 ± 1.8%, respectively. The kinetic study of the degradation of both pesticides was performed and the ultimate evaluation of the kinetic data revealed a first‐order kinetics with respect to the tomato fruit; an explanation was put forward to account for the results. Copyright © 2011 John Wiley & Sons, Ltd.     

Genetic algorithms applied to the selection of factors in principal component regression

Using principal component regression (PCR) as a multivariate calibration tool, always brings up the question what subset of factors, i.e. principal components (PCs) gives the best calibration model. Normally factor selection is based on deterministic methods like top–down procedures, forward–backward-stepwise variable selection or correlated principal component regression (CPCR). In contrast to this, we applied a stochastic method, i.e. a genetic algorithm (GA) for factor selection in this paper. A new kind of fitness function was applied which combined the prediction error of the calibration and an independent validation set. The performance of eigenvalue and correlation ranking was compared. A general statistical criterion for judging the significance of differences between individual calibration models is introduced. In this context it could be shown that for the uncertainties of the standard deviations representing the prediction errors a very simple approximation formula holds which only includes the number of standards. For the current applications it is shown that the GA gives a result very close to CPCR-solutions.     

Metabonomic profiling of liver metabolites by gas chromatography–mass spectrometry and its application to characterizing hyperlipidemia

The measurement of metabolites in tissues is of great importance in metabonomic research in the biomedical sciences, providing more relevant information than is available from systemic biofluids. The liver is the most important organ/tissue for most biochemical reactions, and the metabolites in the liver are of great interest to scientists. To develop an optimized extraction method and comprehensive profiling technique for liver metabolites, organic solvents of various compositions were designed using design of experiments to extract metabolites from the liver, and the metabolites were profiled by gas chromatography/time‐of‐flight mass spectrometry (GC/TOF‐MS). The resolved peak areas were processed by principle components analysis, partial least‐squares projections to latent structures, and discriminant analysis. The results suggest the highest extraction efficiency was for methanol–water, which maximized the majority of GC/TOF‐MS responses. The optimal solvent was applied to extract metabolites in liver of hyperlipidemia hamster and the control. The GC/TOF‐MS profiles of liver metabolites showed obvious differences between hyperlipidemic hamsters and controls. A comparison of liver and serum data from the same animals identified common biomarkers and presented complementary information. Our results suggest that liver metabonomics is a valuable technique and that the combined analysis of systematic biofluids and local tissues is meaningful and complementary, recovering more comprehensive metabonomic data than either analysis alone. Copyright © 2009 John Wiley & Sons, Ltd.     

Identification of a lipid‐related peak set to enhance the interpretation of TOF‐SIMS data from model and cellular membranes

Principal component analysis (PCA) of time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) data enables differentiating structurally similar molecules according to linear combinations of multiple peaks in their spectra. However, in order to use PCA to correctly identify variations in lipid composition between samples, the discrimination achieved must be based on chemical differences that are related to the lipid species, and not sample‐associated contamination. Here, we identify the positive‐ion TOF‐SIMS peaks that are related to phosphatidylcholine lipid headgroups and tail groups by PCA of spectra acquired from lipid isotopologs. We demonstrate that restricting PCA to a contaminant‐free lipid‐related peak set reduces the variability in the spectra acquired from lipid samples that is due to contaminants, which enhanced differentiating different lipid standards, but adversely affected the contrast in PC scores images of phase‐separated lipid membranes. We also show that PCA of a restricted data set consisting of the peaks related to lipids and amino acids increases the likelihood that the discrimination of TOF‐SIMS data acquired from intact cells is based on differences in the lipids and proteins on the cell surface, and not sample‐specific contamination without compromising sample discrimination. We expect that the lipid‐related peak database established herein will facilitate interpreting the TOF‐SIMS data and PCA results from studies of both model and cellular membranes, and enhance identifying the origins of the peaks that contribute to discriminating different types of cells. Copyright © 2011 John Wiley & Sons, Ltd.     

Shifting of gas chromatographic retention times due to solvent effects — a study using sulfur chemiluminescence detection

Gas chromatrography (GC) with sulfur chemiluminescence detection (SCD) is an outstanding combination for selectively determining trace concentrations of sulfur compounds in hydrocarbon samples. GC peaks can be identified by retention times when reproducible, automated injection techniques are used. However, as described in this work, analysts should be on the look out for retention shifting due to solvent effects from sample components. Three examples of retention shifting are presented: (1) thiophene by benzene; (2) methylthiophenes by toluene; and (3) dibenzothiophene by gas oil. Depending on samples and analysis conditions, retention shifts from a few hundredths to 1.5 min have been observed. Such retention shifts are likely to cause errors in peak identifications. Therefore, when using SCD, simultaneous FID monitoring is recommended as an aid in evaluating chromatograms for possible retention shifting due to solvent effects.     

气相色谱-四极杆高分辨飞行时间质谱分析复杂芳烃体系中的苯甲醛、苯甲醇、苯乙酮与苯乙醛

建立了气相色谱-四极杆高分辨飞行时间串联质谱分析复杂芳烃体系样品中苯甲醛、苯甲醇、苯乙酮与苯乙醛的方法。该方法通过使用高分辨质谱得到目标化合物特征离子的精确质量数,结合色谱保留时间,有效排除了复杂芳烃体系样品本底中碎片离子的干扰,提高了定性与定量分析的准确性。使用目标化合物特征离子的质谱峰面积作为定量计算依据。苯甲醛、苯甲醇、苯乙酮与苯乙醛在各自的质量浓度范围内呈现良好的线性响应,回收率为87.97%~103.01%,定量限分别为0.04、0.10、0.08与0.03 mg/L,检出限分别为0.01、0.03、0.02与0.01 mg/L。对3份实际样品中的苯甲醛、苯甲醇、苯乙酮与苯乙醛进行了定量分析。本研究为气相色谱-四极杆高分辨飞行时间质谱技术应用于复杂体系中的微量杂质分析提供了一种新的思路和手段。通过精确的质量分辨和测定可以降低对色谱分离的依赖,弥补传统气相色谱-四极杆低分辨质谱技术的不足。     

Extension of the two-dimensional mass channel cluster plot method to fast separations utilizing low thermal mass gas chromatography with time-of-flight mass spectrometry

Implementation of a data reduction and visualization method for use with high-speed gas chromatography and time-of-flight mass spectrometry (GC-TOFMS) is reported. The method, called the “2D m/z cluster method” facilitates analyte detection, deconvolution, and identification, by accurately measuring peak widths and retention times using a fast TOFMS sampling frequency (500 Hz). Characteristics and requirements for high speed GC are taken into consideration: fast separations with narrow peak widths and high peak capacity, rapid data collection rate, and effective peak deconvolution. Transitioning from standard GC (10–60+ minute separations) to fast GC (1–10 min separations) required consideration of how to properly analyze the data. This report validates use of the 2D m/z cluster method with newly developed GC technology that produces ultra-fast separations (∼1 min) with narrow analyte peak widths. Low thermal mass gas chromatography (LTM-GC) operated at a heating rate of 250 °C/min coupled to a LECO Pegasus III TOFMS analyzed a 115 component test mixture in 120 s with peak widths-at-base, w_b (4σ), of 350 ms (average) to produce a separation with a high peak capacity, n_c ∼ 340 (at unit resolution R_s = 1). The 2D m/z cluster method is shown to separate overlapped analytes to a limiting R_s ∼ 0.03, so the effective peak capacity was increased nearly 30-fold to n_c ∼10,000 in the 120 s separation. The method, when coupled with LTM-GC-TOFMS, is demonstrated to provide unambiguous peak rank (i.e. the number of analytes per overlapped peak in the total ion current (TIC)), by visualizing locations of pure and chromatographically overlapped m/z. Hence, peak deconvolution and identification using MCR-ALS (multivariate curve resolution – alternating least squares) is demonstrated.     

Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n = 180), validation (n = 90) and prediction (n = 90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs, and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer.     

Stir bar sorptive extraction and comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry for ultra-trace analysis of organochlorine pesticides in river water

A method for the determination of ultra-trace amounts of organochlorine pesticides (OCPs) in river water was developed by using stir bar sorptive extraction (SBSE) followed by thermal desorption and comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (SBSE-TD-GC×GC-HRTOF-MS). SBSE conditions such as extraction time profiles, phase ratio (β: sample volume/polydimethylsiloxane (PDMS) volume), and modifier addition, were examined. Fifty milli-liter sample including 10% acetone was extracted for 3 h using stir bars with a length of 20 mm and coated with a 0.5 mm layer of PDMS (PDMS volume, 47 μL). The stir bar was thermally desorbed and subsequently analyzed by GC×GC-HRTOF-MS. The method showed good linearity over the concentration range from 50 to 1000 pg L(-1) or 2000 pg L(-1) for all analytes, and the correlation coefficients (r(2)) were greater than 0.9903 (except for β-HCH, r(2)=0.9870). The limit of detection (LOD) ranged from 10 to 44 pg L(-1). The method was successfully applied to the determination of 16 OCPs at pg L(-1) to ng L(-1) in river water. The results agree fairly well with the values obtained by a conventional liquid-liquid extraction (LLE)-GC-HRMS (selected ion monitoring: SIM) method using large sample volume (20 L). The method also allows screening of non-target compounds, e.g. pesticides and their degradation products, polyaromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and pharmaceuticals and personal care products (PPCPs) and metabolites in the same river water sample, by using full spectrum acquisition with accurate mass in GC×GC.     

Environmental application of elemental speciation analysis based on liquid or gas chromatography hyphenated to inductively coupled plasma mass spectrometry—A review

In recent years the number of environmental applications of elemental speciation analysis using inductively coupled plasma mass spectrometry (ICP-MS) as detector has increased significantly. The analytical characteristics, such as extremely low detection limits (LOD) for almost all elements, the wide linear range, the possibility for multi-elemental analysis and the possibility to apply isotope dilution mass spectrometry (IDMS) make ICP-MS an attractive tool for elemental speciation analysis. Two methodological approaches, i.e. the combination of ICP-MS with high performance liquid chromatography (HPLC) and gas chromatography (GC), dominate the field. Besides the investigation of metals and metalloids and their species (e.g. Sn, Hg, As), representing “classic” elements in environmental science, more recently other elements (e.g. P, S, Br, I) amenable to ICP-MS determination were addressed. In addition, the introduction of isotope dilution analysis and the development of isotopically labeled species-specific standards have contributed to the success of ICP-MS in the field. The aim of this review is to summarize these developments and to highlight recent trends in the environmental application of ICP-MS coupled to GC and HPLC.     

Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition

A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.