Unsupervised parameter optimization for automated retention time alignment of severely shifted gas chromatographic data using the piecewise alignment algorithm

Simulated chromatographic separations were used to study the performance of piecewise retention time alignment and to demonstrate automated unsupervised (without a training set) parameter optimization. The average correlation coefficient between the target chromatogram and all remaining chromatograms in the data set was used to optimize the alignment parameters. This approach frees the user from providing class information and makes the alignment algorithm applicable to classifying completely unknown data sets. The average peak in the raw simulated data set was shifted up to two peak-widths-at-base (average relative shift=2.0) and after alignment the average relative shift was improved to 0.3. Piecewise alignment was applied to severely shifted GC separations of gasolines and reformate distillation fraction samples. The average relative shifts in the raw gasolines and reformates data were 4.7 and 1.5, respectively, but after alignment improved to 0.5 and 0.4, respectively. The effect of piecewise alignment on peak heights and peak areas is also reported. The average relative difference in peak height was -0.20%. The average absolute relative difference in area was 0.15%.     

Beam search for peak alignment of NMR signals

Recent work by Forshed et al. [Anal. Chim. Acta 487 (2003) 189] resulted in an important tool for aligning two NMR spectra. The recognition that the problem posed by Forshed et al. is separable results in fast heuristics that give results that are at least as good as the in an order of magnitude less time. A beam search algorithm is described along with experiments using two different NMR spectrometers and sets of subjects.     

Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis

A fast and objective chemometric classification method is developed and applied to the analysis of gas chromatography (GC) data from five commercial gasoline samples. The gasoline samples serve as model mixtures, whereas the focus is on the development and demonstration of the classification method. The method is based on objective retention time alignment (referred to as piecewise alignment) coupled with analysis of variance (ANOVA) feature selection prior to classification by principal component analysis (PCA) using optimal parameters. The degree-of-class-separation is used as a metric to objectively optimize the alignment and feature selection parameters using a suitable training set thereby reducing user subjectivity, as well as to indicate the success of the PCA clustering and classification. The degree-of-class-separation is calculated using Euclidean distances between the PCA scores of a subset of the replicate runs from two of the five fuel types, i.e., the training set. The unaligned training set that was directly submitted to PCA had a low degree-of-class-separation (0.4), and the PCA scores plot for the raw training set combined with the raw test set failed to correctly cluster the five sample types. After submitting the training set to piecewise alignment, the degree-of-class-separation increased (1.2), but when the same alignment parameters were applied to the training set combined with the test set, the scores plot clustering still did not yield five distinct groups. Applying feature selection to the unaligned training set increased the degree-of-class-separation (4.8), but chemical variations were still obscured by retention time variation and when the same feature selection conditions were used for the training set combined with the test set, only one of the five fuels was clustered correctly. However, piecewise alignment coupled with feature selection yielded a reasonably optimal degree-of-class-separation for the training set (9.2), and when the same alignment and ANOVA parameters were applied to the training set combined with the test set, the PCA scores plot correctly classified the gasoline fingerprints into five distinct clusters.     

Chemometric detectors for selective detection in gas chromatographic analyses

Chemometric methods are applied to data generated by chromatograph-spectrometer systems. Results are chromatograms selective for certain chemical classes. Selectivity can be increased using appropriate software. Intelligent spectral features are the key to successful classification of spectra.     

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.     

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.     

The routine use of selective gas chromatographic detectors

Summary The availability of gas chromatographic detectors selective for phosphorus, sulfur, nitrogen and halogens and the possibility of their simultaneous operation with each other or with conventional universal detectors has tremendously increased the amount of qualitative information that can be gathered from an unknown sample. Selective detectors when used simultaneously permit the calculation of percentage ratios of heteroatoms which represent additional information for qualitative identification. Combination of parallel columns with selective detectors reduces the total time needed for analysis and introduces additional information due to the different polarity of the columns.     

Methods for the exploratory analysis of two-dimensional chromatographic signals

In this article several approaches for the exploratory analysis of two-dimensional chromatographic signals (fingerprints) are presented. Their usefulness is illustrated on experimental chromatographic data obtained from high performance liquid chromatography using the photodiode-array detector (HPLC-DAD). Among the methods discussed are principal component analysis (PCA), hierarchical clustering methods and several N-way techniques such as PARAFAC, PARAFAC2 and Tucker3. In addition to the N-way methods, other approaches that allow for comparing samples represented by two-dimensional fingerprints are also presented (the Rv coefficient, the STATIS approach and ‘fuzzy’ variants of the similarity matrix). Exploratory analysis of the HPLC-DAD data with peak shifts is also discussed.     

icoshift: An effective tool for the alignment of chromatographic data

The Interval Correlation Optimised Shifting algorithm (icoshift) has recently been introduced for the alignment of nuclear magnetic resonance spectra. The method is based on an insertion/deletion model to shift intervals of spectra/chromatograms and relies on an efficient Fast Fourier Transform based computation core that allows the alignment of large data sets in a few seconds on a standard personal computer. The potential of this programme for the alignment of chromatographic data is outlined with focus on the model used for the correction function. The efficacy of the algorithm is demonstrated on a chromatographic data set with 45 chromatograms of 64,000 data points. Computation time is significantly reduced compared to the Correlation Optimised Warping (COW) algorithm, which is widely used for the alignment of chromatographic signals. Moreover, icoshift proved to perform better than COW in terms of quality of the alignment (viz. of simplicity and peak factor), but without the need for computationally expensive optimisations of the warping meta-parameters required by COW. Principal component analysis (PCA) is used to show how a significant reduction on data complexity was achieved, improving the ability to highlight chemical differences amongst the samples.     

Adaptive numerical morphological filter for identifying chromatographic signals.

A morphological algorithm has been proposed to filter away impulsive noises confounded in the chromatographic signal. Compared with the conventional median filtering method, the results showed that the proposed method has the advantages of a better filtering effect and less distortion. In particular, the morphological filter with adaptive scale gives very good results.     

Active wavelength selection for mixture identification with tunable mid-infrared detectors

This article presents a wavelength selection framework for mixture identification problems. In contrast with multivariate calibration, where the mixture constituents are known and the goal is to estimate their concentration, in mixture identification the goal is to determine which of a large number of chemicals is present. Due to the combinatorial nature of this problem, traditional wavelength selection algorithms are unsuitable because the optimal set of wavelengths is mixture dependent. To address this issue, our framework interleaves wavelength selection with the sensing process, such that each subsequent wavelength is determined on-the-fly based on previous measurements. To avoid early convergence, our approach starts with an exploratory criterion that samples the spectrum broadly, then switches to an exploitative criterion that selects increasingly more relevant wavelengths as the solution approaches the true constituents of the mixture. We compare this “active” wavelength selection algorithm against a state-of-the-art passive algorithm (successive projection algorithm), both experimentally using a tunable spectrometer and in simulation using a large spectral library of chemicals. Our results show that our active method can converge to the true solution more frequently and with fewer measurements than the passive algorithm. The active method also leads to more compact solutions with fewer false positives.     

Resolving batch chromatographic overlapping peaks of flavoring essence using stepwise key spectrum selection

Stepwise key spectrum selection (SKSS) was introduced to resolve batch overlapping peaks from gas chromatography-mass spectrometry (GC-MS) analysis of ten batch tobacco flavoring samples in different storage times. Resolution was implemented on a software platform that embedded the SKSS method. The data from GC-MS analysis of the samples were saved and prepared in ASCII files and then were inputted into the software platform for visual inspections. The data segment with overlapping peaks was precut for subsequent analysis. Spectral background in the data was removed using a linear fitting of the baseline. Four components in the overlapping peaks were automatically detected by the SKSS method. The resolution of the concentration profiles and spectra of the four components was conducted by setting only one parameter, the negative area ratio, as 0.01. The fixed size moving window evolving factor analysis and evolving factor analysis were applied to validate the resolved concentration profiles. The resolved mass spectra were validated by the searched standard through library search at the pure component regions revealed by the resolved concentration profiles. The results showed that the SKSS method could be a simple but powerful tool in resolving batch chromatographic overlapping peaks.     

Evaluation and comparison of selective gas chromatographic detectors for the analysis of pesticide residues

Summary A series of studies is described on the evaluation and comparison of some selective gas chromatographic detectors used in pesticide residue analysis. A detailed study of the optimization and response characteristics of the CsBr and RbCl three-electrode alkali flame ionization detector for N and P compounds, the Coulson electrolytic conductivity detector in the nitrogen, sulphur and pyrolytic modes of operation and the sulphur phosphorus emission detector, a type of flame photometric detector, was carried out to obtain maximum sensitivity and reliability for the analysis of pesticide residues in various biological substrates. It was observed that the alkali flame and electrolytic conductivity detector responses to nitrogen compounds were of the same order, while the electrolytic conductivity detector was more sensitive than the flame photometric detector to sulphur compounds. Also, attempts were made to correlate the responses from these different detector systems using the insecticide chlorpyrifos which contains P, S, Cl and N atoms. The use of chlorpyrifos as an evaluation standard in verifying the acceptable performance of these types of detectors is recommended.Chemistry and Biology Research Institute Contribution No 901     

Evaluation of stationary phases and gas chromatographic detectors for determination of amines in water

For the first time, a systematic overview deals with the advantages and disadvantages of several stationary phases (polar and non‐polar) and gas chromatographic detectors (flame ionization detector, nitrogen–phosphorus detector and MS) for the determination of 27 amines (aliphatic and aromatic amines and N‐nitrosamines) in water samples. To increase sensitivity (250 mL of sample was eluted with 150 μL of solvent) and matrix elimination, an automatic SPE system was employed prior to GC determination. The best results in terms of resolution and retention times were achieved using a column coated with 5% phenyl‐dimethylpolysiloxane (DB‐5). Capacity factor (k) values for the 27 amines increased with the rise in the polarity of the stationary phase, ranging from 3.0–27.7 and 2.2–14.4 for polar (polyethylene glycol) and non‐polar (DB‐5) columns, respectively. The detection limits of the method were 0.9–9 μg/L for flame ionization detector, 8–95 ng/L for nitrogen–phosphorus detector and 0.2–6.3 ng/L for MS. The precision was similar for the three detectors (RSD, 3.7–6.0%). The GC‐MS method was applied with a high degree of accuracy and precision to determine amines in real samples including tap, river, pond, well, swimming pool and wastewaters.     

Variable selection for discriminating herbal medicines with chromatographic fingerprints

When discriminating herbal medicines with pattern recognition based on chromatographic fingerprints, typically, the majority of variables/data points contain no discrimination information. In this paper, chemometric approaches concerning forward selection and key set factor analysis using principal component analysis (PCA), unweighted and weighted methods based on the inner- and outer-variances, Fisher coefficient from the between- and within-class variations were investigated to extract representative variables. The number of variables retained was determined based on the cumulative variance percent of principal components, the ratio of observations to variables and the factor indicative function (IND). In order to assess the methods for variable selection and criteria levels to determine the number of variables retained, the original and reduced datasets were compared with Procrustes analysis and a weighted measure of similarity. Moreover, the tri-variate plots of the first three PCA scores were used to visually examine the reduced datasets in low dimensional space. Herbal samples were finally discriminated by use of Bayes discrimination analysis with the reduced subsets. The case study for 79 herbal samples showed that, the methods of forward selection associating the variables with the loadings closest to 0 and key set factor analysis were preferable to determine the representative variables. Procrustes analysis and the weighted measure were not indicative to extract representative variables. High matching between the original and reduced datasets did not suggest high prediction accuracy. Visually examining the PC1-PC2-PC3 scores projection plots with the reduced subsets, not all the herb samples could be separated due to the complexity of chromatographic fingerprints.     

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.     

Configuration and optimization of semiconductor gas sensors as gas chromatographic detectors

A tin oxide, gas-sensitive semiconductor sensor was configured as a gas chromatographic detector and its performance was optimized. Two sensor housings were compared but little difference was found in performance. The flow rate and temperature of the column and the internal heater voltage of the sensor affected both the sensitivity and peak shape. The temperature of the sensor surface was the most critical parameter. Optimal conditions for the gas chromatographic detection of a mixture of alkanes (C₁–C₅) and hydrogen were identified by using the simplex technique. The detection limit for hydrogen was improved by a factor of five to 20 ppb (v/v), illustrating the value of optimization and the excellent sensitivity of the detector. It is concluded that semiconductor gas sensors offer significant advantages as gas chromatographic detectors for the determination of reducing gases.     

A liquid chromatographic method for the simultaneous determination of promethazine and three of its metabolites in plasma using electrochemical and UV detectors

A new assay method has been developed for the quantitation of promethazine (PMZ) with a sensitivity and reproducibility as good as any previously reported method. This method is also capable of quantitatively determining three metabolites of PMZ (monodemethylated, sulphoxidated, and monodemethylated sulphoxidated PMZ), which has not been previously described. The method uses high-performance liquid chromatography with amperometric and UV detection simultaneously and requires only one extraction step from serum with chloroform. The method uses trifluoperazine as the internal standard. The limit of detection level for PMZ is 1.0 ng/ml when a 0.2-mL specimen of plasma is assayed. A validation study is also conducted for evaluating the recovery, precision, linearity of response, sensitivity, and selectivity of the method.