Kevin J. Johnson

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.

Pattern recognition of jet fuels: comprehensive GC×GC with ANOVA-based feature selection and principal component analysis

Abstract Two-dimensional comprehensive gas chromatography (GC×GC) is applied to a pattern recognition problem involving classification of jet fuel mixtures. Analysis of variance (ANOVA)-based feature selection is initially used to identify and select chromatographic features relevant to a given classification in two studies. Then, principal component analysis (PCA) was used for pattern recognition classification. In the first study, a 1% volumetric composition change in mixtures of JP-5 and JP-7 jet fuel is readily distinguished. In this first study, the effective combination of GC×GC, ANOVA-based feature selection and PCA is developed and evaluated as a chemical analysis tool. The second study involved the analysis of three samples each of three different jet fuel types, JP-5, JP-8, and JP-TS, as well as blends incorporating two or three jet fuels. Each of the nine jet fuel samples originated from various geographic locations within the United States. These samples were analyzed in order to determine if a classification based on fuel type is possible in the presence of sample variability (due to geographic origin) with GC×GC/pattern recognition analysis. Chromatographic features that are adept at classification of jet fuel type and are not sensitive to geographic origin of the sample were generated for the sample set consisting of the original fuel types as well as mixtures of the three different, original jet fuels. The combination of GC×GC with ANOVA-based feature selection was found to be a useful tool to enhance the chemical selectivity, and thus the classification power of the analytical procedure, when coupled with PCA.

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.

GC × GC temperature programming requirements to produce bilinear data for chemometric analysis

A diaphragm valve-based comprehensive two-dimensional gas chromatography (GC x GC) instrument with the two columns under independent temperature control is demonstrated. A fifteen-component mixture of alkanes, alkyl aromatics, ketones, and alcohols was separated using this system in only 45 s. Independent temperature control of the two columns allows for high-speed analysis of complex samples while retaining the bilinear data structure that is necessary to apply many chemometric peak-resolving methods. An important part of high-speed GC x GC is sharp injections onto the second column. In this work, 10-ms peak widths on the second column are demonstrated. A peak capacity per time of 240 peaks/min was readily achieved. This work is aimed at providing a high-speed GC system for the quantitative and qualitative analysis of complex process streams, such as natural products.

High-Speed Gas Chromatographic Separations with Diaphragm Valve-Based Injection and Chemometric Analysis as a Gas Chromatographic''Sensor''

Abstract A high-speed gas chromatography system, the gas chromatographic sensor (GCS), is developed and evaluated. The GCS combines fast separations and chemometric analysis to produce an instrument capable of high-speed, high-throughput screening and quantitative analysis of complex chemical mixtures on a similar time scale as typical chemical sensors. The GCS was evaluated with 28 test mixtures consisting of 15 compounds from four chemical classes: alkanes, ketones, alkyl benzenes, and alcohols. The chromatograms are on the order of one second in duration, which is considerably faster than the traditional use of gas chromatography. While complete chromatographic separation of each analyte peak is not aimed for, chemical information is readily extracted through chemometric data analysis and quantification of the samples is achieved in considerably less time than conventional gas chromatography. Calibration models to predict percent volume content of either alkanes or ketones were constructed using partial least squares (PLS) regression on calibration sets consisting of the five replicate GCS runs of six different samples. The percent volume content of the alkane and ketone chemical classes were predicted on five replicate runs of the 22 remaining samples ranging from 0 to 50 or 60% depending on the class. Root mean square errors of prediction were 2–3% relative to the mean percent volume values for either alkane or ketone prediction models, depending on the samples chosen for the calibration set of that model. The alkyl benzenes and alcohols present in the calibration sets or samples were treated as variable background interference. It is anticipated that the GCS will eventually be used to rapidly sample and directly analyze industrial processes or for the high throughput analysis of batches of samples.

Humidity Affects Relative Ion Abundance in Direct Analysis in Real Time Mass Spectrometry of Hexamethylene Triperoxide Diamine

Unstable explosive hexamethylene triperoxide diamine (HMTD) is dangerous in quantity and benefits from the minimal sampling handling associated with atmospheric pressure chemical ionization for mass spectral analysis. Seasonal variation observed in HMTD mass spectra suggested a humidity dependence. Therefore, direct analysis in real time (DART) ionization mass spectra were acquired at a range of humidity values. An enclosure was designed to fit around the ion source and mass spectrometer inlet at atmospheric pressure. The enclosure was supplied with controlled amounts of humidified air from a test atmosphere generator to create programmable conditions for ambient analysis. The relative abundance and fragmentation of analyte ions were observed to change reliably with changing humidity values and, to a lesser degree, temperature. Humidity at such plasma-based ion sources should be regulated to avoid ∼90% shifts in relative ion abundance and provide stability and reproducibility of HMTD analysis.

Humidity Effects on Fragmentation in Plasma-Based Ambient Ionization Sources

AbstractPost-plasma ambient desorption/ionization (ADI) sources are fundamentally dependent on surrounding water vapor to produce protonated analyte ions. There are two reports of humidity effects on ADI spectra. However, it is unclear whether humidity will affect all ADI sources and analytes, and by what mechanism humidity affects spectra. Flowing atmospheric pressure afterglow (FAPA) ionization and direct analysis in real time (DART) mass spectra of various surface-deposited and gas-phase analytes were acquired at ambient temperature and pressure across a range of observed humidity values. A controlled humidity enclosure around the ion source and mass spectrometer inlet was used to create programmed humidity and temperatures. The relative abundance and fragmentation of molecular adduct ions for several compounds consistently varied with changing ambient humidity and also were controlled with the humidity enclosure. For several compounds, increasing humidity decreased protonated molecule and other molecular adduct ion fragmentation in both FAPA and DART spectra. For others, humidity increased fragment ion ratios. The effects of humidity on molecular adduct ion fragmentation were caused by changes in the relative abundances of different reagent protonated water clusters and, thus, a change in the average difference in proton affinity between an analyte and the population of water clusters. Control of humidity in ambient post-plasma ion sources is needed to create spectral stability and reproducibility.n Graphical Abstractᅟ