GCALIGNER 1.0: An alignment program to compute a multiple sample comparison data matrix from large eco‐chemical datasets obtained by GC

GCALIGNER 1.0 is a computer program designed to perform a preliminary data comparison matrix of chemical data obtained by GC without MS information. The alignment algorithm is based on the comparison between the retention times of each detected compound in a sample. In this paper, we test the GCALIGNER efficiency on three datasets of the chemical secretions of bumble bees. The algorithm performs the alignment with a low error rate (<3%). GCALIGNER 1.0 is a useful, simple and free program based on an algorithm that enables the alignment of table‐type data from GC.     

Automated alignment of one-dimensional chromatographic fingerprints

A general framework for the automatic alignment of one-dimensional chromatographic signals is presented in this article. The alignment of signals was achieved by explicitly modeling the warping function. Its shape was estimated using a linear combination of several B-spline functions. The coefficients of the spline functions were found in the course of an optimization procedure to maximize the Pearson's correlation coefficient between a target chromatogram and aligned chromatogram(s). The computational requirements of the method are discussed with respect to the correlation optimized warping method, frequently used for the alignment of chromatographic signals. As illustrated with two sets of one-dimensional chromatographic fingerprints, the automatic alignment approach performs well even when non-linear peak shifts need to be corrected. It can be applied in an on-the-fly manner since the alignment of signals is rapid.     

Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data

In chromatography-based metabonomic research, retention time (RT) alignment of chromatographic peaks poses a challenge for the accurate profiling of biomarkers. Although a number of RT alignment software has been reported, the performance of these software packages have not been comprehensively evaluated. This study aimed to evaluate the RT alignment accuracy of publicly available and commercial RT alignment software. Two gas chromatography/mass spectrometry (GC/MS) datasets acquired from a mixture of standard metabolites and human bladder cancer urine samples, were used to assess three publicly available software packages, MetAlign, MZmine and TagFinder, and two commercial applications comprising the Calibration feature and Statistical Compare of ChromaTOF software. The overall RT alignment accuracies in aligning standard compounds mixture were 93, 92, 74, 73 and 42% for Calibration feature, MZmine, MetAlign, Statistical Compare and TagFinder, respectively. Additionally, unique trends were observed for the individual software with regards to the different experimental conditions related to extent and direction of RT shifts. Conflicting performance was observed for human urine samples suggesting that RT misalignments still occurred despite the use of RT alignment software. While RT alignment remains an inevitable step in data preprocessing, metabonomic researchers are recommended to perform manual check on the RT alignment of important biomarkers as part of their validation process.     

Comparative study of the alignment method on experimental and simulated chromatographic data

One of the major problems in the signal comparison of chromatographic data is the variability of response caused by instrumental drifts and others instabilities. Measures of quality control and evaluation of conformity are inherently sensitive to shift. It is essential to be able to compare test samples to reference samples in an evolutionary analytical environment by offsetting the inevitable drift. Therefore, prior to any multivariate analysis, the alignment of analytical signals is a compulsory preprocessing step. During recent years, many researchers have taken a greater interest in the study of the alignment. The present paper is an updated review on the alignment algorithms, methods, and improvements used in chromatography. The study is dedicated to one‐dimensional signals. Several of the exposed methods have common theoretical bases and can differ through their optimization methods. The main issue for the operator is to choose the appropriate method according to the type of signals to be processed.     

Longest distance shifting: A simple and efficient approach for the alignment of shifted chromatographic peaks

The preprocessing of chromatograms, such as the alignment of retention time shifts, is often a crucial step in the proper data analysis chain. Here, an efficient approach to align shifted chromatographic signals, longest distance shifting, is presented and highlighted. The performance of this novel strategy was demonstrated by using both simulated chromatograms that covered the different kinds of retention time shifts and the real experimental chromatograms of Pudilan Xiaoyan Tablets obtained by high‐performance liquid chromatography with photodiode array detection. The averaged correlation coefficient for experimental chromatograms were in the range of 0.9517–0.9840 and the peak factor was 0.9989. As a comparison, all the chromatograms have also been aligned using correlation optimized warping and Interval Correlation Optimized Shifting algorithms. The obtained results indicate that the longest distance shifting algorithm is simpler, faster and more effective, and will be potentially suitable for the alignment of other types of signals.     

Chemometrics for the analysis of chromatographic data in metabolomics investigations

Metabolomics aims to better understand biological systems through the chemical analysis of an organism's metabolic profile. One common method of analysis is mass spectrometry preceded by chromatographic separations. Samples produced by metabolomics investigations can contain hundreds to thousands of compounds, which can put great strain on the instrumental analysis. In order to improve these analyses, the data analysis must not be overlooked. The ever‐evolving field of chemometrics provides many useful tools for the analysis of chromatographic data. These include methods for preprocessing data to extract a maximum amount of information from the data as well as pattern recognition in order to find the compounds that vary the most in relation to the perturbation to the biological system under study. This article aims to highlight and provide future outlooks on current chemometric methods for chromatographic‐based metabolomics investigations. Copyright © 2014 John Wiley & Sons, Ltd.     

Evaluation of different warping methods for the analysis of CE profiles of urinary nucleosides

Nowadays, numerous metabolite concentrations can readily be determined in a given biological sample by high-throughput analytical methods. However, such raw analytical data comprise noninformative components due to many disturbances normally occurring in the analyses of biological material. To eliminate those unwanted original analytical data components, advanced chemometric data preprocessing methods might be of help. Here, such methods are applied to electrophoretic nucleoside profiles in urine samples of cancer patients and healthy volunteers. In this study, three warping methods: dynamic time warping (DTW), correlation optimized warping (COW), and parametric time warping (PTW) were examined on two sets of electrophoretic data by means of quality of peaks alignment, time of preprocessing, and way of customization. The application of warping methods helped to limit shifting of peaks and enabled differentiation between whole electropherograms of healthy and cancer patients objectively by a principal component analysis (PCA). The evaluation of preprocessed data and raw data by PC analysis confirms differences between the applied warping tools and proves their suitability in metabonomic data interpretation.     

Correlation of supercritical-fluid extraction recoveries with supercritical-fluid chromatographic retention data: A fundamental study

The possibility of using supercritical-fluid chromatographic retention data for examining the effects of operational parameters, such as pressure and flow rate, on the extraction characteristics in supercritical-fluid extraction (SFE) was investigated. A model was derived for calculating the extraction efficiency in SFE from retention data and peak shapes measured in supercritical-fluid chromatography (SFC). By performing the SFC experiments at the same pressure and temperature as the SFE extractions using the SFE extraction cell as the SFC column, an accurate prediction of extraction efficiencies could be made. Finally, the effects of matrix composition and analyte concentration on extraction efficiency were studied.     

Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross‐correlation

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross‐correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross‐correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross‐correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross‐correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2 .     

Differential scanning calorimetry: a potential tool for discrimination of olive oil commercial categories

Differential scanning calorimetry thermograms of five commercial categories of olive oils (extra virgin olive oil, olive oil, refined olive oil, olive-pomace oil and refined olive-pomace oil) were performed in both cooling and heating regimes. Overlapping transitions were resolved by deconvolution analysis and all thermal properties were related to major (triacylglycerols, total fatty acids) and minor (diacylglycerols, lipid oxidation products) chemical components.All oils showed two well distinguishable exothermic events upon cooling. Crystallization enthalpies were significantly lower in olive oils due to a more ordered crystal structure, which may be related to the higher triolein content. Pomace oils exhibited a significantly higher crystallization onset temperature and a larger transition range, possibly associated to the higher amount of diacylglycerols. Heating thermograms were more complex: all oils exhibited complex exo- and endothermic transitions that could differentiate samples especially with respect to the highest temperature endotherm.These preliminary results suggest that both cooling and heating thermograms obtained by means of differential scanning calorimetry may be useful for discriminating among olive oils of different commercial categories.     

Considerations for the automated collection of thermodynamic data in gas chromatography

Thermodynamic modeling of retention times in gas chromatography depends on the accurate estimation of thermodynamic parameters. Previous research has used manual injections of samples with coinjection of a dead time marker to obtain accurate measurements of the retention factor of analytes. Ideally this process would be automated. Herein an approach is presented by which thermodynamic parameters can be estimated both autonomously and accurately. This method also allows for a consistent estimation of thermodynamic parameters regardless of factors such as data system delays and the nature of the void time marker employed. Ignoring these factors can lead to significant errors in the prediction of retention times when using thermodynamic models.     

iMatch: a retention index tool for analysis of gas chromatography-mass spectrometry data

A method was developed to employ National Institute of Standards and Technology (NIST) 2008 retention index database information for molecular retention matching via constructing a set of empirical distribution functions (DFs) of the absolute retention index deviation to its mean value. The effects of different experimental parameters on the molecules' retention indices were first assessed. The column class, the column type, and the data type have significant effects on the retention index values acquired on capillary columns. However, the normal alkane retention index (I(norm)) with the ramp condition is similar to the linear retention index (I(T)), while the I(norm) with the isothermal condition is similar to the Kováts retention index (I). As for the I(norm) with the complex condition, these data should be treated as an additional group, because the mean I(norm) value of the polar column is significantly different from the I(T). Based on this analysis, nine DFs were generated from the grouped retention index data. The DF information was further implemented into a software program called iMatch. The performance of iMatch was evaluated using experimental data of a mixture of standards and metabolite extract of rat plasma with spiked-in standards. About 19% of the molecules identified by ChromaTOF were filtered out by iMatch from the identification list of electron ionization (EI) mass spectral matching, while all of the spiked-in standards were preserved. The analysis results demonstrate that using the retention index values, via constructing a set of DFs, can improve the spectral matching-based identifications by reducing a significant portion of false-positives.     

Aligning retention time shifts in HPLC three‐dimensional spectra by icoshift approach combined with data arrangement methods and the release of a graphical user interface

High‐performance liquid chromatography coupled with photodiode array detection has been extensively applied in many fields and the peaks among the analyzed samples can be shifted due to the variations of instrumental and experimental conditions. In multivariate analysis, retention time alignment is an important pretreatment step. Hence, the shifted peaks in high‐performance liquid chromatography coupled with photodiode array detection three‐dimensional spectra should be aligned for further analysis. Being motivated by this purpose, the interval correlated shifting method combined with the proposed data arrangement methods are recommended and employed on high‐performance liquid chromatography coupled with photodiode array detection data as a demonstration. We validate the alignment performance of the proposed method through comparison the consistency of the retention time before and after alignment. The obtained results demonstrated that the proposed method is capable of successful aligning the employed data. Additionally, the interval correlated shifting method combined with the data arrangement modes is implemented in an easy‐to‐use graphical user interface environment and so can be operated easily by users not familiar with programming languages.     

Pharmacophore alignment search tool: influence of scoring systems on text-based similarity searching

The text-based similarity searching method Pharmacophore Alignment Search Tool is grounded on pairwise comparisons of potential pharmacophoric points between a query and screening compounds. The underlying scoring matrix is of critical importance for successful virtual screening and hit retrieval from large compound libraries. Here, we compare three conceptually different computational methods for systematic deduction of scoring matrices: assignment-based, alignment-based, and stochastic optimization. All three methods resulted in optimized pharmacophore scoring matrices with significantly superior retrospective performance in comparison with simplistic scoring schemes. Computer-generated similarity matrices of pharmacophoric features turned out to agree well with a manually constructed matrix. We introduce the concept of position-specific scoring to text-based similarity searching so that knowledge about specific ligand-receptor binding patterns can be included and demonstrate its benefit for hit retrieval. The approach was also used for automated pharmacophore elucidation in agonists of peroxisome proliferator activated receptor gamma, successfully identifying key interactions for receptor activation.     

Parameter selection for peak alignment in chromatographic sample profiling: objective quality indicators and use of control samples

In chromatographic profiling applications, peak alignment is often essential as most chromatographic systems exhibit small peak shifts over time. When using currently available alignment algorithms, there are several parameters that determine the outcome of the alignment process. Selecting the optimum set of parameters, however, is not straightforward, and the quality of an alignment result is at least partly determined by subjective decisions. Here, we demonstrate a new strategy to objectively determine the quality of an alignment result. This strategy makes use of a set of control samples that are analysed both spiked and non-spiked. With this set, not only the system and the method can be checked but also the quality of the peak alignment can be evaluated. The developed strategy was tested on a representative metabolomics data set using three software packages, namely Markerlynx™, MZmine and MetAlign. The results indicate that the method was able to assess and define the quality of an alignment process without any subjective interference of the analyst, making the method a valuable contribution to the data handling process of chromatography-based metabolomics data.     

Dynamic correlation for MCSCF wave functions: An effective potential method

A method is suggested which allows the inclusion of dynamic correlation into CASSCF calculations. An effective Coulomb hole potential is added to the Hamiltonian. The potential has a simple form, which allows its implementation into existing LCAO programs using Gaussian integral packages. The parameters appearing in the potential are determined by fitting to empirical valence correlation energies for first row atoms. Calculations of ionization energies and electron affinities show considerable improvement compared to the MCSCF values. Test calculations on three molecules give the following results, H₂ r _e=0.745 (0.741) Å, D _e=4.62 (4.75) eV; N₂ r _e=1.099 (1.098) Å, D _e= 10.42 (9.91) eV; O₂ r _e=1.198 (1.207) Å, D _e=4.73 (5.21) eV. Experimental values within parenthesis. On leave from: Institute of Organic Chemistry, Polish Academy of Sciences, PL-01-224 Warszawa 42, ul. Kasprzaka 44, Poland.     

Pharmacophore alignment search tool: influence of the third dimension on text-based similarity searching

Previously (Hähnke et al., J Comput Chem 2010, 31, 2810) we introduced the concept of nonlinear dimensionality reduction for canonization of two‐dimensional layouts of molecular graphs as foundation for text‐based similarity searching using our Pharmacophore Alignment Search Tool (PhAST), a ligand‐based virtual screening method. Here we apply these methods to three‐dimensional molecular conformations and investigate the impact of these additional degrees of freedom on virtual screening performance and assess differences in ranking behavior. Best‐performing variants of PhAST are compared with 16 state‐of‐the‐art screening methods with respect to significance estimates for differences in screening performance. We show that PhAST sorts new chemotypes on early ranks without sacrificing overall screening performance. We succeeded in combining PhAST with other virtual screening techniques by rank‐based data fusion, significantly improving screening capabilities. We also present a parameterization of double dynamic programming for the problem of small molecule comparison, which allows for the calculation of structural similarity between compounds based on one‐dimensional representations, opening the door to a holistic approach to molecule comparison based on textual representations. © 2011 Wiley Periodicals, Inc. J Comput Chem , 2011.     

Prediction of liquid chromatographic retention for differentiation of structural isomers

A liquid chromatography (LC) retention time prediction software, ACD/ChromGenius, was employed to calculate retention times for structural isomers, which cannot be differentiated by accurate mass measurement techniques alone. For 486 drug compounds included in an in-house database for urine drug screening by liquid chromatography/quadrupole time-of-flight mass spectrometry (LC/Q-TOFMS), a retention time knowledge base was created with the software. ACD/ChromGenius calculated retention times for compounds based on the drawn molecular structure and given chromatographic parameters. The ability of the software for compound identification was evaluated by calculating the retention order of the 118 isomers, in 50 isomer groups of 2–5 compounds each, included in the database. ACD/ChromGenius predicted the correct elution order for 68% (34) of isomer groups. Of the 16 groups for which the isomer elution order was incorrectly calculated, two were diastereomer pairs and thus difficult to distinguish using the software. Correlation between the calculated and experimental retention times in the knowledge base tested was moderate, r² = 0.8533. The mean and median absolute errors were 1.12 min, and 0.84 min, respectively, and the standard deviation was 1.04 min. The information generated by ACD/ChromGenius, together with other in silico methods employing accurate mass data, makes the identification of substances more reliable. This study demonstrates an approach for tentatively identifying compounds in a large target database without a need for primary reference standards.