Carlos G. Fraga

Enhancing the Limit of Detection for Comprehensive Two‐Dimensional Gas Chromatography (GC×GC) using Bilinear Chemometric Analysis

The chemometric method referred to as the generalized rank annihilation method (GRAM) is used to improve the precision, accuracy, and resolution of comprehensive two-dimensional gas chromatography (GC x GC) data. Because GC × GC signals follow a bilinear structure, GC x GC signals can be readily extracted from noise by chemometric techniques such as GRAM. This resulting improvement in signal-to-noise ratio (S/N) and detectability is referred to as bilinear signal enhancement. Here, GRAM uses bilinear signal enhancement on both resolved and unresolved GC x GC peaks that initially have a low S/N in the original GC × GC data. In this work, the chemometric method of GRAM is compared to two traditional peak integration methods for quantifying GC x GC analyte signals. One integration method uses a threshold to determine the signal of a peak of interest. With this integration method only those data points above the limit of detection and within a selected area are integrated to produce the total analyte signal for calibration and quantification. The other integration method evaluated did not employ a threshold, and simply summed all the data points in a selected region to obtain a total analyte signal. Substantial improvements in quantification precision, accuracy, and limit of detection are obtained by using GRAM, as compared to when either peak integration method is applied. In addition, the GRAM results are found to be more accurate than results obtained by peak integration, because GRAM more effectively corrects for the slight baseline offset remaining after the background subtraction of data. In the case of a 2.7-ppm propylbenzene synthetic sample the quantification result with GRAM is 2.6 times more precise and 4.2 times more accurate than the integration method without a threshold, and 18 times more accurate than the integration method with a threshold. The limit of detection for propylbenzene was 0.6 ppm (parts per million by mass) using GRAM, without implementing any sample preconcentration prior to injection. GRAM is also demonstrated as a means to resolve overlapped signals, while enhancing the S/N. Four alkyl benzene signals of low S/ N which were not resolved by GC x GC are mathematically resolved and quantified.

Impurity Profiling of a Chemical Weapon Precursor for Possible Forensic Signatures by Comprehensive Two-Dimensional Gas Chromatography/Mass Spectrometry and Chemometrics

In this report we present the feasibility of using analytical and chemometric methodologies to reveal and exploit the chemical impurity profiles from commercial dimethyl methylphosphonate (DMMP) samples to illustrate the type of forensic information that may be obtained from chemical-attack evidence. Using DMMP as a model compound of a toxicant that may be used in a chemical attack, we used comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GC x GC/TOF-MS) to detect and identify trace organic impurities in six samples of commercially acquired DMMP. The GC x GC/TOF-MS data was analyzed to produce impurity profiles for all six DMMP samples using 29 analyte impurities. The use of PARAFAC for the mathematical resolution of overlapped GC x GC peaks ensured clean spectra for the identification of many of the detected analytes by spectral library matching. The use of statistical pairwise comparison revealed that there were trace impurities that were quantitatively similar and different among five of the six DMMP samples. Two of the DMMP samples were revealed to have identical impurity profiles by this approach. The use of nonnegative matrix factorization indicated that there were five distinct DMMP sample types as illustrated by the clustering of the multiple DMMP analyses into five distinct clusters in the scores plots. The two indistinguishable DMMP samples were confirmed by their chemical supplier to be from the same bulk source. Sample information from the other chemical suppliers supported the idea that the other four DMMP samples were likely from different bulk sources. These results demonstrate that the matching of synthesized products from the same source is possible using impurity profiling. In addition, the identified impurities common to all six DMMP samples provide strong evidence that basic route information can be obtained from impurity profiles. Finally, impurities that may be unique to the sole bulk manufacturer of DMMP were found in some of the DMMP samples.

Comprehensive two-dimensional gas chromatography of volatile and semi-volatile components using a diaphragm valve-based instrument.

A high-temperature configuration for a diaphragm valve-based gas chromatography (GCXGC) instrument is demonstrated. GCxGC is a powerful instrumental tool often used to analyze complex mixtures. Previously, the temperature limitations of valve-based GCxGC instruments were set by the maximum operating temperature of the valve, typically 175 degrees C. Thus, valve-based GCxGC was constrained to the analysis of mainly volatile components; however, many complex mixtures contain semi-volatile components as well. A new configuration is described that extends the working temperature range of diaphragm valve-based GCxGC instruments to significantly higher temperatures, so both volatile and semi-volatile compounds can be readily separated. In the current investigation, separations at temperatures up to 250 degrees C are demonstrated. This new design features both chromatographic columns in the same oven with the valve interfacing the two columns mounted in the side of the oven wall so the valve is both partially inside as well as outside the oven. The diaphragm and the sample ports in the valve are located inside the oven while the temperature-restrictive portion of the valve (containing the O-rings) is outside the oven. Temperature measurements on the surface of the valve indicate that even after a sustained oven temperature of 240 degrees C, the portions of the valve directly involved with the sampling from the first column to the second column track the oven temperature to within 1.2% while the portions of the valve that are temperature-restrictive remain well below the maximum temperature of 175 degrees C. A 26-component mixture of alkanes, ketones, and alcohols whose boiling points range from 65 degrees C (n-hexane) to 270 degrees C (n-pentadecane) is used to test the new design. Peak shapes along the first column axis suggest that sample condensation or carry-over in the valve is not a problem. Chemometric data analysis is performed to demonstrate that the resulting data have a bilinear structure. After over 6 months of use and temperature conditions up to 265 degrees C, no deterioration of the valve or its performance has been observed.

Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery.

Preprocessing software, which converts large instrumental data sets into a manageable format for data analysis, is crucial for the discovery of chemical signatures in metabolomics, chemical forensics, and other signature-focused disciplines. Here, four freely available and published preprocessing tools known as MetAlign, MZmine, SpectConnect, and XCMS were evaluated for impurity profiling using nominal mass GC/MS data and accurate mass LC/MS data. Both data sets were previously collected from the analysis of replicate samples from multiple stocks of a nerve-agent precursor and method blanks. Parameters were optimized for each of the four tools for the untargeted detection, matching, and cataloging of chromatographic peaks from impurities present in the stock samples. The peak table generated by each preprocessing tool was analyzed to determine the number of impurity components detected in all replicate samples per stock and absent in the method blanks. A cumulative set of impurity components was then generated using all available peak tables and used as a reference to calculate the percent of component detections for each tool, in which 100% indicated the detection of every known component present in a stock. For the nominal mass GC/MS data, MetAlign had the most component detections followed by MZmine, SpectConnect, and XCMS with detection percentages of 83, 60, 47, and 41%, respectively. For the accurate mass LC/MS data, the order was MetAlign, XCMS, and MZmine with detection percentages of 80, 45, and 35%, respectively. SpectConnect did not function for the accurate mass LC/MS data. Larger detection percentages were obtained by combining the top performer with at least one of the other tools such as 96% by combining MetAlign with MZmine for the GC/MS data and 93% by combining MetAlign with XCMS for the LC/MS data. In terms of quantitative performance, the reported peak intensities from each tool had averaged absolute biases (relative to peak intensities obtained using instrument software) of 41, 4.4, 1.3 and 1.3% for SpectConnect, MetAlign, XCMS, and MZmine, respectively, for the GC/MS data. For the LC/MS data, the averaged absolute biases were 22, 4.5, and 3.1% for MetAlign, MZmine, and XCMS, respectively. In summary, MetAlign performed the best in terms of the number of component detections; however, more than one preprocessing tool should be considered to avoid missing impurities or other trace components as potential chemical signatures.

Impurity profiling to match a nerve agent to its precursor source for chemical forensics applications.

Chemical forensics is a developing field that aims to attribute a chemical (or mixture) of interest to its source by the analysis of the chemical itself or associated material constituents. Herein, for the first time, trace impurities detected by gas chromatography/mass spectrometry and originating from a chemical precursor were used to match a synthesized nerve agent to its precursor source. Specifically, six batches of sarin (GB, isopropyl methylphosphonofluoridate) and its intermediate methylphosphonic difluoride (DF) were synthesized from two commercial stocks of 97% pure methylphosphonic dichloride (DC); the GB and DF were then matched by impurity profiling to their DC stocks from a collection of five possible stocks. Source matching was objectively demonstrated through the grouping by hierarchal cluster analysis of the GB and DF synthetic batches with their respective DC precursor stocks based solely upon the impurities previously detected in five DC stocks. This was possible because each tested DC stock had a unique impurity profile that had 57% to 88% of its impurities persisting through product synthesis, decontamination, and sample preparation. This work forms a basis for the use of impurity profiling to help find and prosecute perpetrators of chemical attacks.

Signature-Discovery Approach for Sample Matching of a Nerve-Agent Precursor Using Liquid Chromatography−Mass Spectrometry, XCMS, and Chemometrics

This report demonstrates the use of bioinformatic and chemometric tools on liquid chromatography-mass spectrometry (LC-MS) data for the discovery of trace forensic signatures for sample matching of ten stocks of the nerve-agent precursor known as methylphosphonic dichloride (dichlor). XCMS, a software tool primarily used in bioinformatics, was used to comprehensively search and find candidate LC-MS peaks in a known set of dichlor samples. These candidate peaks were down selected to a group of 34 impurity peaks. Hierarchal cluster analysis and factor analysis demonstrated the potential of these 34 impurities peaks for matching samples based on their stock source. Only one pair of dichlor stocks was not differentiated from one another. An acceptable chemometric approach for sample matching was determined to be variance scaling and signal averaging of normalized duplicate impurity profiles prior to classification by K-nearest neighbors. Using this approach, a test set of seven dichlor samples were all correctly matched to their source stock. The sample preparation and LC-MS method permitted the detection of dichlor impurities quantitatively estimated to be in the parts-per-trillion (w/w). The detection of a common impurity in all dichlor stocks that were synthesized over a 14-year period and by different manufacturers was an unexpected discovery. Our described signature-discovery approach should be useful in the development of a forensic capability to assist investigations following chemical attacks.

Improved quantitative analysis of ion mobility spectrometry by chemometric multivariate calibration

Traditional peak-area calibration and the multivariate calibration methods of principal component regression (PCR) and partial least squares (PLS), including unfolded PLS (U-PLS) and multi-way PLS (N-PLS), were evaluated for the quantification of 2,4,6-trinitrotoluene (TNT) and cyclo-1,3,5-trimethylene-2,4,6-trinitramine (RDX) in Composition B explosive mixtures analyzed by temperature step desorption ion mobility spectrometry (TSD-IMS). TSD is a technique used to partially resolve mixture components before ion mobility analysis by exploiting differences in thermal desorption profiles. While TSD was used here, the results and conclusions presented are universally applicable to IMS. Although IMS is used extensively for trace explosive detection, it has not been sufficiently demonstrated in the past for the detailed analysis of specific compositions of explosive mixtures. This manuscript combines IMS with multivariate chemometric data methods to enhance the quantitative performance of IMS needed for detailed explosive analyses. This is demonstrated using data from the replicate TSD-IMS analyses of eight different Composition B samples. The true TNT and RDX concentrations were determined by analyzing the Composition B samples by high performance liquid chromatography with UV absorbance detection. Most of the Composition B samples were found to have distinct TNT and RDX concentrations. The data from each TSD-IMS analysis were a 2-D array that was reduced by averaging into a vector or mean IMS spectrum. Although the mean IMS peaks for TNT and RDX were sufficiently resolved to use peak area to generate linear calibration curves, the peak-area variability was too large to differentiate Composition B samples based on their predicted RDX and TNT concentrations. Applying PCR and PLS on the exact same IMS spectra used for the peak-area study improved quantitative accuracy and precision approximately 3-to 5-fold and 2- to 4-fold, respectively. This in turn improved the probability of correctly identifying Composition B samples based upon the estimated RDX and TNT concentrations from 11% with peak area to 44% and 89% with PLS. The success of PLS over peak area is attributed to multivariate signal averaging and the simultaneous maximization of correlation between the entire span of the IMS mean spectra and the known TNT and RDX concentrations. In this study, PLS also outperformed PCR and had similar quantitative results to U-PLS. In terms of N-PLS, its mean bias values were up to 2.8 times larger and the mean RSD values were at least 40% larger than those obtained by PLS.

ROC-curve approach for determining the detection limit of a field chemical sensor.

The detection limit of a field chemical sensor under realistic operating conditions is determined by receiver operator characteristic (ROC) curves. The chemical sensor is an ion mobility spectrometry (IMS) device used to detect a chemical marker in diesel fuel. The detection limit is the lowest concentration of the marker in diesel fuel that obtains the desired true-positive probability (TPP) and false-positive probability (FPP). A TPP of 0.90 and a FPP of 0.10 were selected as acceptable levels for the field sensor in this study. The detection limit under realistic operating conditions is found to be between 2 to 4 ppm (w/w). The upper value is the detection limit under challenging conditions. The ROC-based detection limit is very reliable because it is determined from multiple and repetitive sensor analyses under realistic circumstances. ROC curves also clearly illustrate and gauge the effects data preprocessing and sampling environments have on the sensors detection limit.

Profiling of volatile impurities in tetramethylenedisulfotetramine (TETS) for synthetic-route determination

This study investigated the feasibility of using volatile impurities from the rodenticide tetramethylenedisulfotetramine (TETS) for the discrimination of TETS produced by three synthetic routes. Each route was used to make one batch of TETS by reacting sulfamide with one of three formaldehyde analogs in the presence of either trifluroacetic acid (TFA) or hydrochloric acid. Ten impurities useful for differentiating the three TETS batches were discovered and tentatively identified by headspace solid-phase microextraction comprehensive two-dimensional gas chromatography-mass spectrometry (HS-SPME/GC×CG-MS). Of the ten identified impurities, the alkyl trifluoroacetate and alkyl chloride impurities distinguished TETS routes based on their use of either TFA or HCl as catalyst. On the other hand, four 6-carbon ketone impurities appeared to be batch specific rather than route specific and hence potentially useful for sample matching. Interestingly, 1,3,5-trioxane was not found in the TETS batch where it was used as a reactant, but instead was found in the two batches that did not have 1,3,5-trioxane as the reactant. In brief, the limited work discussed in this paper supports: (1) the feasibility of sampling and detecting volatile organic impurities from a solid chemical-threat agent, (2) the probable forensic benefit of catalysts acting as reactants in side reactions, (3) the uniqueness of a synthetic batchs impurity profile for potential sample matching, and (4) the possibility that some impurities, such as formaldehyde analogs, are not forensically helpful and may lead to an incorrect estimate about the synthetic route if not supported by sound chemical knowledge.

Anionic Forensic Signatures for Sample Matching of Potassium Cyanide Using High Performance Ion Chromatography and Chemometrics

Potassium cyanide was used as a model toxicant to determine the feasibility of using anionic impurities as a forensic signature for matching cyanide salts back to their source. In this study, portions of eight KCN stocks originating from four countries were separately dissolved in water and analyzed by high performance ion chromatography (HPIC) using an anion exchange column and conductivity detection. Sixty KCN aqueous samples were produced from the eight stocks and analyzed for 11 anionic impurities. Hierarchal cluster analysis and principal component analysis were used to demonstrate that KCN samples cluster according to source based on the concentrations of their anionic impurities. The Fisher-ratio method and degree-of-class separation (DCS) were used for feature selection on a training set of KCN samples in order to optimize sample clustering. The optimal subset of anions needed for sample classification was determined to be sulfate, oxalate, phosphate, and an unknown anion named unk5. Using K-nearest neighbors (KNN) and the optimal subset of anions, KCN test samples from different KCN stocks were correctly determined to be manufactured in the United States. In addition, KCN samples from stocks manufactured in Belgium, Germany, and the Czech Republic were all correctly matched back to their original stocks because each stock had a unique anionic impurity profile. The application of the Fisher-ratio method and DCS for feature selection improved the accuracy and confidence of sample classification by KNN.