Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review.

Gas chromatography (GC) is undoubtedly the analytical technique of choice for compositional analysis of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC × GC) has evolved, fuel analysis has often been highlighted in scientific reports, since the complexity of fuel analysis allows for illustration of the impressive peak capacity gains afforded by GC × GC. Indeed, several research groups in recent years have applied GC × GC and chemometric data analysis to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC × GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC × GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC × GC analyses of fuels for compliance and forensic applications, to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC × GC technology (column stationary phases and modulators) and several chemometric methods is provided. More specifically, chemometric methods will be broken down into (1) signal preprocessing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed with particular emphasis on the demonstrated success of the employed chemometric methods. This review will hopefully make 1D-GC and GC × GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.

Classification of biodiesel and fuel blends using gas chromatography - differential mobility spectrometry with cluster analysis and isolation of C18:3 me by dual ion filtering.

Fatty acid alkyl esters (FAAEs) were determined at 10-100mg/L in biodiesel and blends with petrodiesel without sample pre-treatment using gas chromatography with a tandem differential mobility detector. Selectivity was provided through chromatographic separations and atmospheric pressure chemical ionization reactions in the detector with mobility characterization of gas ions. Limits of detection were ~0.5ng with an average of 2.98% RSD for peak area precision, ≤1.3% RSD for retention time precision, and ≤9.2% RSD for compensation voltage precision. Biodiesel blends were classified using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Unsupervised cluster analysis captured 52.72% of variance in a single PC while supervised analysis captured 71.64% of variance using Fisher ratio feature selection. Test set predictions showed successful clustering according to source or feedstock when regressed onto the training set model. Detection of the regulated substance methyl linolenate (C18:3 me) was achieved in 6-10s with a 1m long capillary column using dual ion filtering in the tandem differential mobility detector.

Saxitoxin and the Ochre Sea Star: Molecule of Keystone Significance and a Classic Keystone Species.

Saxitoxins (STXs) are paralytic alkaloids produced by marine dinoflagellates in response to biotic and abiotic stressors yielding harmful algal blooms. Because STX impacts coastal, near-shore communities to a greater extent than would be predicted by its relative abundance, it has been referred to as a "molecule of keystone significance" in reference to Robert Paine's Keystone Species Concept. Pisaster ochraceus, the predator upon which Paine's concept was founded, inhabits waters regularly plagued by harmful algal blooms, but the effects of STX on Pisaster have not yet been investigated. Here, we used laboratory and field experiments to examine the potential consequences of exposure to STX on sea stars' feeding, attachment to the substrate, and success in fertilization. Pisaster exhibited similar feeding behaviors when offered non-toxic prey, STX-containing prey, or a combination of the two. Although feeding behavior is unaffected, consumption of STX poses a physiological tradeoff. Sea stars in the laboratory and field had significantly lower thresholds of the force needed to detach them from their substrates after either being exposed to, or consuming, STX. High pressure (or high performance) liquid chromatography analysis indicated an accumulation of STX (and structural analogues) in sea stars' viscera, likely due to trophic transfer from toxic prey. Incidence of fertilization tended to decrease when gametes were exposed to high, yet ecologically relevant, STX concentrations of STX. These findings suggest that the molecule of keystone significance, STX, produced during harmful algal blooms extends its impacts to rocky intertidal communities by way of the keystone predator P. ochraceus.

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.

Development toward rapid and efficient screening for high performance hydrolysate lots in a recombinant monoclonal antibody manufacturing process.

Plant-derived hydrolysates are widely used in mammalian cell culture media to increase yields of recombinant proteins and monoclonal antibodies (mAbs). However, these chemically varied and undefined raw materials can have negative impact on yield and/or product quality in large-scale cell culture processes. Traditional methods that rely on fractionation of hydrolysates yielded little success in improving hydrolysate quality. We took a holistic approach to develop an efficient and reliable method to screen intact soy hydrolysate lots for commercial recombinant mAb manufacturing. Combined high-resolution (1) H nuclear magnetic resonance (NMR) spectroscopy and partial least squares (PLS) analysis led to a prediction model between product titer and NMR fingerprinting of soy hydrolysate with cross-validated correlation coefficient R(2) of 0.87 and root-mean-squared-error of cross-validation RMSECV% of 11.2%. This approach screens for high performance hydrolysate lots, therefore ensuring process consistency and product quality in the mAb manufacturing process. Furthermore, PLS analysis was successful in discerning multiple markers (DL-lactate, soy saccharides, citrate and succinate) among hydrolysate components that positively and negatively correlate with titer. Interestingly, these markers correlate to the metabolic characteristics of some strains of taxonomically diverse lactic acid bacteria (LAB). Thus our findings indicate that LAB strains may exist during hydrolysate manufacturing steps and their biochemical activities may attribute to the titer enhancement effect of soy hydrolysates.

Predicting feedstock and percent composition for blends of biodiesel with conventional diesel using chemometrics and gas chromatography-mass spectrometry.

The two main goals of the analytical method described herein were to (1) use principal component analysis (PCA), hierarchical clustering (HCA) and K-nearest neighbors (KNN) to determine the feedstock source of blends of biodiesel and conventional diesel (feedstocks were two sources of soy, two strains of jatropha, and a local feedstock) and (2) use a partial least squares (PLS) model built specifically for each feedstock to determine the percent composition of the blend. The chemometric models were built using training sets composed of total ion current chromatograms from gas chromatography-quadrupole mass spectrometry (GC-qMS) using a polar column. The models were used to semi-automatically determine feedstock and blend percent composition of independent test set samples. The PLS predictions for jatropha blends had RMSEC=0.6, RMSECV=1.2, and RMSEP=1.4. The PLS predictions for soy blends had RMSEC=0.5, RMSECV=0.8, and RMSEP=1.2. The average relative error in predicted test set sample compositions was 5% for jatropha blends and 4% for soy blends.

Predicting percent composition of blends of biodiesel and conventional diesel using gas chromatography-mass spectrometry, comprehensive two-dimensional gas chromatography-mass spectrometry, and partial least squares analysis.

The percent composition of blends of biodiesel and conventional diesel from a variety of retail sources were modeled and predicted using partial least squares (PLS) analysis applied to gas chromatography-total-ion-current mass spectrometry (GC-TIC), gas chromatography-mass spectrometry (GC-MS), comprehensive two-dimensional gas chromatography-total-ion-current mass spectrometry (GCxGC-TIC) and comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS) separations of the blends. In all four cases, the PLS predictions for a test set of chromatograms were plotted versus the actual blend percent composition. The GC-TIC plot produced a best-fit line with slope=0.773 and y-intercept=2.89, and the average percent error of prediction was 12.0%. The GC-MS plot produced a best-fit line with slope=0.864 and y-intercept=1.72, and the average percent error of prediction was improved to 6.89%. The GCxGC-TIC plot produced a best-fit line with slope=0.983 and y-intercept=0.680, and the average percent error was slightly improved to 6.16%. The GCxGC-MS plot produced a best-fit line with slope=0.980 and y-intercept=0.620, and the average percent error was 6.12%. The GCxGC models performed best presumably due to the multidimensional advantage of higher dimensional instrumentation providing more chemical selectivity. All the PLS models used 3 latent variables. The chemical components that differentiate the blend percent compositions are reported.

Analysis of commercial beverage products by size exclusion chromatography coupled with UV-vis absorbance detection and dynamic surface tension detection.

Multidimensional analysis of instant coffee and barley beverage samples using size exclusion chromatography (SEC) combined with a dynamic surface tension detector (DSTD) and a UV-vis absorbance detector (UV) is reported. A unique finding of this study was the action of the tetrabutylammonium (TBA) cation as a modifying agent (with bromide as the counter anion) that substantially increased the surface pressure signal and sensitivity of many of the proteins in the chromatographically separated samples. The tetrabutylammonium bromide (TBAB) enhancement of the surface pressure signal was further investigated by studying the response of 12 commercial standard proteins (alpha-lactalbumin, beta-lactoglobulin, human serum albumin (HSA), albumin from chicken egg white (OVA), bovine serum albumin (BSA), hemoglobin, alpha-chymotrypsinogen A, cytochrome C, myoglobin, RNase A, carbonic anhydrase, and lysozyme) in buffer performed using flow injection analysis (FIA) coupled with the DSTD with and without various concentrations of TBAB. The FIA-DSTD data show that 1mM TBAB enhances sensitivity of HSA detection, by lowering the limit of detection (LOD) from 2mg/mL to 0.1mg/mL. Similarly, the LOD for BSA was reduced from 1mg/mL to 0.2mg/mL. These FIA-DSTD experiments allowed the detection conditions to be optimized for further SEC-UV/DSTD experiments. Thus, the SEC-UV/DSTD system has been optimized and successfully applied to the selective analysis of surface-active protein fractions in a commercial instant coffee sample and in a soluble barley sample. The complementary selectivity of using the DSTD relative to an absorbance detector is also demonstrated.

Constituents with independence from growth temperature for bacteria using pyrolysis-gas chromatography/differential mobility spectrometry with analysis of variance and principal component analysis.

Four bacteria, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus warneri, and Micrococcus luteus, were grown at temperatures of 23, 30, and 37 degrees C and were characterized by pyrolysis-gas chromatography/differential mobility spectrometry (Py-GC/DMS) providing, with replicates, 120 data sets of retention time, compensation voltage, and ion intensity, each for negative and positive polarity. Principal component analysis (PCA) for 96 of these data sets exhibited clusters by temperature of culture growth and not by genus. Analysis of variance was used to isolate the constituents with dependences on growth temperature. When these were subtracted from the data sets, Fisher ratios with PCA resulted in four clusters according to genus at all temperatures for ions in each polarity. Comparable results were obtained from unsupervised PCA with 24 of the original data sets. The ions with taxonomic features were reconstructed into 3D plots of retention time, compensation voltage, and Fisher ratio and were matched, through GC-mass spectrometry (MS), with chemical standards attributed to the thermal decomposition of proteins and lipid A. Results for negative ions provided simpler data sets than from positive ions, as anticipated from selectivity of gas phase ion-molecule reactions in air at ambient pressure.

Recent advancements in comprehensive two-dimensional separations with chemometrics.

Comprehensive two-dimensional (2D) separations provide the analyst with a tremendous amount of complex data. In order to glean useful information from this complex data, advancements in commercially available software that implement chemometrics are currently available and continue to evolve. Future advancements will no doubt involve commercializing (or adapting) specialized, in-house chemometric techniques that are currently found only in the hands of technical experts and researchers in industry, government, and academia. In order to make timely advancements, future commercialization of novel chemometric techniques should involve collaborations among instrument software manufacturers, professional programmers, technical experts, and researchers. During the last decade, this field has seen a steady advancement from single analyte target analysis to comprehensive non-target analysis of entire multidimensional sample profiles (involving sample classification and/or data mining for discovery-based sample comparisons). The advancements in instrumentation and chemometric software tools have a tremendous impact in various applications: fuels, food, environmental, pharmaceuticals, metabolomics, etc. Most of the development has been for software to apply with gas chromatography-based instrumentation, such as comprehensive two-dimensional gas chromatography (GC x GC) and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC x GC-TOF-MS). More recently there have been notable advancements in liquid-phase instrumentation as well.

Analysis of bacteria by pyrolysis gas chromatography-differential mobility spectrometry and isolation of chemical components with a dependence on growth temperature.

Pyrolysis gas chromatography-differential mobility spectrometry (py-GC-DMS) analysis of E. coli, P. aeruginosa, S. warneri and M. luteus, grown at temperatures of 23, 30, and 37 degrees C, provided data sets of ion intensity, retention time, and compensation voltage for principal component analysis. Misaligned chromatographic axes were treated using piecewise alignment, the impact on the degree of class separation (DCS) of clusters was minor. The DCS, however, was improved between 21 to 527% by analysis of variance with Fisher ratios to remove chemical components independent of growth temperature. The temperature dependent components comprised 84% of all peaks in the py-GC-DMS analysis of E. coli and were attributed to the pyrolytic decomposition of proteins rather than lipids, as anticipated. Components were also isolated in other bacteria at differing amounts: 41% for M. luteus, 14% for P. aeruginosa, and 4% for S. warneri, and differing patterns suggested characteristic dependence on temperature of growth for these bacteria. These components are anticipated to have masses from 100 to 200 Da by inference from differential mobility spectra.

Comprehensive analysis of yeast metabolite GC x GC-TOFMS data: combining discovery-mode and deconvolution chemometric software.

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Student's t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

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%.

Classification of high-speed gas chromatography-mass spectrometry data by principal component analysis coupled with piecewise alignment and feature selection.

A useful methodology is introduced for the analysis of data obtained via gas chromatography with mass spectrometry (GC-MS) utilizing a complete mass spectrum at each retention time interval in which a mass spectrum was collected. Principal component analysis (PCA) with preprocessing by both piecewise retention time alignment and analysis of variance (ANOVA) feature selection is applied to all mass channels collected. The methodology involves concatenating all concurrently measured individual m/z chromatograms from m/z 20 to 120 for each GC-MS separation into a row vector. All of the sample row vectors are incorporated into a matrix where each row is a sample vector. This matrix is piecewise aligned and reduced by ANOVA feature selection. Application of the preprocessing steps (retention time alignment and feature selection) to all mass channels collected during the chromatographic separation allows considerably more selective chemical information to be incorporated in the PCA classification, and is the primary novelty of the report. This methodology is objective and requires no knowledge of the specific analytes of interest, as in selective ion monitoring (SIM), and does not restrict the mass spectral data used, as in both SIM and total ion current (TIC) methods. Significantly, the methodology allows for the classification of data with low resolution in the chromatographic dimension because of the added selectivity from the complete mass spectral dimension. This allows for the successful classification of data over significantly decreased chromatographic separation times, since high-speed separations can be employed. The methodology is demonstrated through the analysis of a set of four differing gasoline samples that serve as model complex samples. For comparison, the gasoline samples are analyzed by GC-MS over both 10-min and 10-s separation times. The successfully classified 10-min GC-MS TIC data served as the benchmark analysis to compare to the 10-s data. When only alignment and feature selection was applied to the 10-s gasoline separations using GC-MS TIC data, PCA failed. PCA was successful for 10-s gasoline separations when the methodology was applied with all the m/z information. With ANOVA feature selection, chromatographic regions with Fisher ratios greater than 1500 were retained in a new matrix and subjected to PCA yielding successful classification for the 10-s separations.

Fisher ratio method applied to third-order separation data to identify significant chemical components of metabolite extracts.

This report is about applying a Fisher ratio method to entire four dimensional (4D) data sets from third-order instrumentation data. The Fisher ratio method uses a novel indexing scheme to discover the unknown chemical differences among known classes of complex samples. This is the first report of a Fisher ratio analysis procedure applied to entire 4D data sets of third-order separation data, which, in this case, is comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry analyses of metabolite extracts using all of the collected mass channels. Current analysis methods for third-order separation data use only user-defined subsets of the 4D data set. First, in a validation study, the Fisher ratio method was demonstrated to objectively evaluate and determine the chemical differences between three controlled urine samples that differed by known spiked chemical components. It was determined that, out of more than 600 recognizable chemical components in a single sample, the six spiked components, along with only two other matrix components, differed most significantly in concentration among the control samples. In a second study, the Fisher ratio method was used in a novel application to discover the unknown chemical differences between urine metabolite samples from pregnant women and nonpregnant women. A brief list of the top 11 components that were most significantly different in concentration between the pregnant and nonpregnant samples was generated. Because the Fisher ratio calculation statistically differentiates regions of the chromatogram with large class-to-class variations from regions containing large within-class variations, the Fisher ratio method should generally be robust against biological diversity in a sample population. Indeed, application of principal component analysis in this second study failed due to biological diversity of the samples.

A principal component analysis based method to discover chemical differences in comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GCxGC-TOFMS) separations of metabolites in plant samples.

Comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GCxGC-TOFMS) provides high resolution separations of complex samples with a mass spectrum at every point in the separation space. The large volumes of multidimensional data obtained by GCxGC-TOFMS analysis are analyzed using a principal component analysis (PCA) method described herein to quickly and objectively discover differences between complex samples. In this work, we submitted 54 chromatograms to PCA to automatically compare the metabolite profiles of three different species of plants, namely basil (Ocimum basilicum), peppermint (Mentha piperita), and sweet herb stevia (Stevia rebaudiana), where there were 18 chromatograms for each type of plant. The 54 scores of the m/z 73 data set clustered in three groups according to the three types of plants. Principal component 1 (PC 1) separated the stevia cluster from the basil and peppermint clusters, capturing 61.84% of the total variance. Principal component 2 (PC 2) separated the basil cluster from the peppermint cluster, capturing 16.78% of the total variance. The PCA method revealed that relative abundances of amino acids, carboxylic acids, and carbohydrates were responsible for differentiating the three plants. A brief list of the 16 most significant metabolites is reported. After PCA, the 54 scores of the m/z 217 data set clustered in three groups according to the three types of plants, as well, yielding highly loaded variables corresponding with chemical differences between plants that were complementary to the m/z 73 information. The PCA data mining method is applicable to all of the monitored selective mass channels, utilizing all of the collected data, to discover unknown differences in complex sample profiles.

A comprehensive two-dimensional retention time alignment algorithm to enhance chemometric analysis of comprehensive two-dimensional separation data.

A comprehensive two-dimensional (2D) retention time alignment algorithm was developed using a novel indexing scheme. The algorithm is termed comprehensive because it functions to correct the entire chromatogram in both dimensions and it preserves the separation information in both dimensions. Although the algorithm is demonstrated by correcting comprehensive two-dimensional gas chromatography (GC x GC) data, the algorithm is designed to correct shifting in all forms of 2D separations, such as LC x LC, LC x CE, CE x CE, and LC x GC. This 2D alignment algorithm was applied to three different data sets composed of replicate GC x GC separations of (1) three 22-component control mixtures, (2) three gasoline samples, and (3) three diesel samples. The three data sets were collected using slightly different temperature or pressure programs to engender significant retention time shifting in the raw data and then demonstrate subsequent corrections of that shifting upon comprehensive 2D alignment of the data sets. Thirty 12-min GC x GC separations from three 22-component control mixtures were used to evaluate the 2D alignment performance (10 runs/mixture). The average standard deviation of first column retention time improved 5-fold from 0.020 min (before alignment) to 0.004 min (after alignment). Concurrently, the average standard deviation of second column retention time improved 4-fold from 3.5 ms (before alignment) to 0.8 ms (after alignment). Alignment of the 30 control mixture chromatograms took 20 min. The quantitative integrity of the GC x GC data following 2D alignment was also investigated. The mean integrated signal was determined for all components in the three 22-component mixtures for all 30 replicates. The average percent difference in the integrated signal for each component before and after alignment was 2.6%. Singular value decomposition (SVD) was applied to the 22-component control mixture data before and after alignment to show the restoration of trilinearity to the data, since trilinearity benefits chemometric analysis. By applying comprehensive 2D retention time alignment to all three data sets (control mixtures, gasoline samples, and diesel samples), classification by principal component analysis (PCA) substantially improved, resulting in 100% accurate scores clustering.

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.