Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance (¹H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the ¹H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices.     

Development, optimization and validation of gas chromatographic fingerprinting of Brazilian commercial gasoline for quality control

Three-step development, optimization and validation strategy for GC fingerprints of Brazilian commercial gasoline is described. A suitable chromatographic system was selected first. The following step was to improve acceptable chromatographic resolution with reduced analysis time, which is recommended for routine applications. Optimization was carried out using full three-level factorial designs. Optimal conditions were obtained for flow rate, oven ramps, injection volume and split ratio. Finally, several validation parameters were performed. Therefore, a feasible and reliable fingerprint was established to identify Brazilian commercial gasoline quality. This strategy can also be applied to develop fingerprints for quality control of other fuels.     

A simple gas chromatographic method for the analysis of oxygenates in gasoline

Chromatographia - A number of oxygenated compounds such as C1?C4 alcohols, methyl tert-butyl ether (MTBE), and tertamyl methyl ether (TAME) are increasingly being used in gasoline as octane...     

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.     

Importance of quality control in a gas chromatographic method to characterize crude oils

A quality control scheme has been developed to achieve reproducible capillary GC characterization of crude oils and petroleum condensates. The method uses an internal standard to quantify the amount of non-eluted material in the crude oil sample. Correlation between the gas chromatographic results and actual distillation was 2 %. After repeated use, however, weekly analysis of the same standard crude oil did not give the correct composition. This was a result of discrimination, which worsened with time, as a result of leaks in the septum or the graphite ferrules. More reproducible results were obtained by performing frequent analyses of quality control samples to ensure that the gas chromatographic system was operating properly. The use of quality control charts was a convenient way of ensuring correct operation and identifying the need for corrective action on the gas chromatographic system.     

High resolution capillary gas chromatographic hydrocarbon-type analysis of naphtha and gasoline

A slightly modified, commercially available high resolution capillary gas chromatograph and a PC-based data processing system running proprietary software (“PONA”) have been employed in the development of a system for petroleum type analysis which would be equivalent or superior to the FIA (fluorescent indicator adsorption) method in terms of ease of use and the speed, variety, and accuracy of the analytical data produced. The system is capable of performing identification and quantitation of most of the individual components in a complex gasoline sample consisting of more than 230 components and can report weight percentage and/or volume percentage for each component as well as types by carbon number (e.g., isoparaffins, normal paraffins, olefins, naphthenes, aromatic compounds) within a ca. 70 minute analysis cycle. Precolumn sulfonation to trap olefins and aromatic compounds has been used as an complementary technique to the basic mass spectrometric identification of components of interest. The estimation of correction factors for weight percentage (or volume percentage) calculation are also discussed. Comparisons are made between this system and others, and the results indicate that the proposed method supersedes the conventional method employing FIA.     

A quality control method for geranium oil based on vibrational spectroscopy and chemometric data analysis

Rose-scented geranium, a commercially important cultivar originating from Pelargonium graveolens L’Her. ex Ait., is a high value essential oil extensively used in flavour and fragrance formulations. The oil is variable in composition with ‘Bourbon geranium’ (from Reunion Island) regarded as the highest quality geranium oil. Quality assessment of geranium oil involves profiling seven major volatile constituents (geraniol, citronellol, geranyl formate, citronellyl formate, linalool, isomenthone and guaia-6,9-diene) using gas chromatography (GC). The aim of this study was to explore the feasibility of vibrational spectroscopy in tandem with chemometric methods as a rapid and low-cost alternative quality control method. Geranium oil samples (n = 70) were obtained from different suppliers representing cultivation sites in South Africa, Egypt, India, Reunion Island, China and Madagascar. Reference analysis was performed using gas chromatography coupled to mass spectrometry (GC–MS). The mid-infrared (MIR) and near-infrared (NIR) spectra of the oils were recorded with a total of 32 scans accumulated for each sample. Partial least squares (PLS) multivariate calibration models were developed. The calibration models obtained for both MIR and NIR data produced good correlation coefficients (R² > 0.90) between the predicted and reference values for all seven marker molecules. Generally, the error parameters (RMSEE and RMSEP) after external validation were low (<1.0%) for all compounds guaranteeing reliable predictions. The results show convincingly the potential of both MIRS and NIRS as alternative methods that can be used in quality assessment of geranium oil providing sufficiently accurate results.     

Inverse gas chromatographic and chemometric tools for determination of interactions between the components of polymeric composition

Compositions of polymers (polyethylene and polyuretane) and fillers (initial silica and silicas modified with: N-2-aminoethyl-3-aminopropyltrimethoxysilane, 3-aminopropyltriethoxy-silane, 3-merkaptopropyltrimethoxysilane, octyltriethoxysilane) were examined by inverse gas chromatography at 383 K. Small amounts of the following test solutes were injected to achieve the infinite dilution conditions: pentane, hexane, heptane, octane, nonane, dichloromethane, chloroform, carbon tetrachloride and 1,2-dichloroethane.The retention times for these test solutes were determined and Flory-Huggins parameters were calculated. Flory-Huggins parameters characterize interactions between components: - between composition and the test solute and χ^′23 - between polymer and filler in the composition.Values of both physicochemical parameters characterizing the examined materials were arranged in a matrix form: in the rows the compositions were enumerated at different temperatures whereas the columns contained the test solutes. After standardization the input matrix was subjected to principal component analysis. Three principal components explain more than 93% of the total variance in the data. Almost all test solutes carry very similar information. Therefore, it is justified to eliminate most of them from the series of test solutes.     

Simultaneous simplification of loading and core matrices in N-way PCA: application to chemometric data arrays

In the Tucker3 model of N-way principal components analysis (NPCA), a so-called core matrix describes the possible interactions between components from different modes. For an easy interpretation of solutions, it is necessary to have as few interactions as possible (in conventional PCA of data tables, such interactions can always be avoided). This goal may be realized by various approaches of core matrix transformations. At the same time, it is desirable to have simple component (or loading) matrices. Usually, the simplicity of the core conflicts to a certain degree with the simplicity of the components. The paper demonstrates how the conditional optimization of both goals can be used to find a compromise. For the purpose of illustration, the procedure is first applied to a small three-way data array from heavy metal analysis of tissues in different samples of game. Later, a data array of bigger size from a three-way interlaboratory study is considered. Received: 21 November 1997 / Revised: 11 February 1998 / Accepted: 15 February 1998     

Simultaneous simplification of loading and core matrices in N-way PCA: application to chemometric data arrays

In the Tucker3 model of N-way principal components analysis (NPCA), a so-called core matrix describes the possible interactions between components from different modes. For an easy interpretation of solutions, it is necessary to have as few interactions as possible (in conventional PCA of data tables, such interactions can always be avoided). This goal may be realized by various approaches of core matrix transformations. At the same time, it is desirable to have simple component (or loading) matrices. Usually, the simplicity of the core conflicts to a certain degree with the simplicity of the components. The paper demonstrates how the conditional optimization of both goals can be used to find a compromise. For the purpose of illustration, the procedure is first applied to a small three-way data array from heavy metal analysis of tissues in different samples of game. Later, a data array of bigger size from a three-way interlaboratory study is considered. Received: 21 November 1997 / Revised: 11 February 1998 / Accepted: 15 February 1998     

Fast data preprocessing for chromatographic fingerprints of tomato cell wall polysaccharides using chemometric methods

The variability in the chemistry of cell wall polysaccharides in pericarp tissue of red-ripe tomato fruit (Solanum lycopersicon Mill.) was characterized by chemical methods and enzymatic degradations coupled to high performance anion exchange chromatography (HPAEC) and mass spectrometry analysis. Large fruited line, Levovil (LEV) carrying introgressed chromosome fragments from a cherry tomato line Cervil (CER) on chromosomes 4 (LC4), 9 (LC9), or on chromosomes 1, 2, 4 and 9 (LCX) and containing quantitative trait loci (QTLs) for texture traits, was studied. In order to differentiate cell wall polysaccharide modifications in the tomato fruit collection by multivariate analysis, chromatograms were corrected for baseline drift and shift of the component elution time using an approach derived from image analysis and mathematical morphology. The baseline was first corrected by using a "moving window" approach while the peak-matching method developed was based upon location of peaks as local maxima within a window of a definite size. The fast chromatographic data preprocessing proposed was a prerequisite for the different chemometric treatments, such as variance and principal component analysis applied herein to the analysis. Applied to the tomato collection, the combined enzymatic degradations and HPAEC analyses revealed that the firm LCX and CER genotypes showed a higher proportion of glucuronoxylans and pectic arabinan side chains while the mealy LC9 genotype demonstrated the highest content of pectic galactan side chains. QTLs on tomato chromosomes 1, 2, 4 and 9 contain important genes controlling glucuronoxylan and pectic neutral side chains biosynthesis and/or metabolism.     

Automated optimization and construction of chemometric models based on highly variable raw chromatographic data

Direct chemometric interpretation of raw chromatographic data (as opposed to integrated peak tables) has been shown to be advantageous in many circumstances. However, this approach presents two significant challenges: data alignment and feature selection. In order to interpret the data, the time axes must be precisely aligned so that the signal from each analyte is recorded at the same coordinates in the data matrix for each and every analyzed sample. Several alignment approaches exist in the literature and they work well when the samples being aligned are reasonably similar. In cases where the background matrix for a series of samples to be modeled is highly variable, the performance of these approaches suffers. Considering the challenge of feature selection, when the raw data are used each signal at each time is viewed as an individual, independent variable; with the data rates of modern chromatographic systems, this generates hundreds of thousands of candidate variables, or tens of millions of candidate variables if multivariate detectors such as mass spectrometers are utilized. Consequently, an automated approach to identify and select appropriate variables for inclusion in a model is desirable. In this research we present an alignment approach that relies on a series of deuterated alkanes which act as retention anchors for an alignment signal, and couple this with an automated feature selection routine based on our novel cluster resolution metric for the construction of a chemometric model. The model system that we use to demonstrate these approaches is a series of simulated arson debris samples analyzed by passive headspace extraction, GC-MS, and interpreted using partial least squares discriminant analysis (PLS-DA).     

Toward automated chromatographic fingerprinting: A non-alignment approach to gas chromatography mass spectrometry data

In contrast to targeted analysis of volatile compounds, non-targeted approaches take information of known and unknown compounds into account, are inherently more comprehensive and give a more holistic representation of the sample composition. Although several non-targeted approaches have been developed, there's still a demand for automated data processing tools, especially for complex multi-way data such as chromatographic data obtained from multichannel detectors. This work was therefore aimed at developing a data processing procedure for gas chromatography mass spectrometry (GC–MS) data obtained from non-targeted analysis of volatile compounds. The developed approach uses basic matrix manipulation of segmented GC–MS chromatograms and PARAFAC multi-way modelling. The approach takes retention time shifts and peak shape deformations between samples into account and can be done with the freely available N-way toolbox for MATLAB. A demonstration of the new fingerprinting approach is presented using an artificial GC–MS data set and an experimental full-scan GC–MS data set obtained for a set of experimental wines.     

Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing

A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GCxGC-FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples.     

A gas chromatographic method for the microdetermination of iodine

Summary Iodine was quantitatively converted by reaction with acetone to a form which was easily separated by gas chromatography and which was easily detected with an electron capture detector. Linear response of the detector was obtained for solutions which contained up to 1g/ml with an uncertainty of 8%. Milligram amounts of chloride or bromide andg amounts of iodide do not interfere. Amplification of the amount of reaction product and the determination of both iodine and iodide was accomplished by the addition of KIO₃.

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Zusammenfassung Jod wird quantitativ mit Aceton umgesetzt und läßt sich dann gaschromatographisch abtrennen und mit einem Elektroneneinfangdetektor nachweisen. Dessen Anzeige ist bis zu 1g/ml linear mit einer Unsicherheit von 8%. Milligrammengen Chlorid oder Bromid bzw. Mikrogrammengen Jodid stören nicht. Die Menge Reaktionsprodukt läßt sich vervielfachen. Die Bestimmung von Jod und Jodid wird durch Zusatz von Kaliumjodat zu Ende geführt.

     

Combination of chromatographic and chemometric methods to study the interactions between DNA strands

This work describes the combination of size-exclusion chromatography and chemometric resolution methods to study the formation of complex DNA structures from individual strands. This combined procedure has been applied to two different experimental data. Firstly, the formation of an intermolecular Watson–Crick duplex structure formed by the individual unstructured strands. Secondly, the competition between the intermolecular Watson–Crick duplex and intramolecular quadruplex structures formed by two sequences found in the hTERT gene has been studied.