Anal. Chem. 1998, 70, 716-723
Chemometric Resolution of Mixture Components by Cleardown Rates Paul J. Rauch and Peter de B. Harrington*
Chemistry Department, Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Athens, Ohio 45701-2979 Dennis M. Davis
U.S. Army ERDEC, Aberdeen Proving Grounds, Maryland 21010-5432
Ion mobility spectrometry (IMS) has recently been gaining attention due to its low cost, light weight, durability, versatility, and portability. IMS combined with time series analysis (TSA) has proved useful for identification of individual chemical species. These methods require the vapor pressure concentration to vary independently with respect to time. When sampling vapors are obtained from liquid mixtures, the analyte vapor compositions may not vary independently. A vapor mixture may be resolved by differential cleardown rates in IMS instruments. Cleardown is the decay in analyte signal when the sample is removed from the instrument. If different mixture components exhibit different affinities in the instrument, then the concentration of the mixture components will vary independently during cleardown, and SIMPLISMA, a feature extraction method, may resolve the components. This approach is an example of reverse frontal chromatography for which the sample is introduced into the detector (the IMS) and removed chromatographically by adsorption onto the molecular sieves. In this research, mixtures of three nerve agent simulants are used to demonstrate this new data extraction method with SIMPLISMA. Ion mobility spectrometry (IMS) has been developed during the past two decades into a useful sensor for the determination of trace quantities of volatile organic compounds. Much of the present interest in IMS can be attributed to its low cost, high sensitivity, fast response, low detection limits, and portability for in situ analysis.1 The advantages of these practical features are offset by challenges in data processing for which there are few demonstrated aids. For example, data acquisition is rapid, and thousands of spectra may comprise a single monitoring experiment. Another challenge in data analysis arises through the atmospheric pressure chemical ionization (APCI) reactions. In IMS, product ions (commonly M‚H+ in positive polarity) are created from the analyte through APCI reactions. Cluster ions (e.g., M2‚H+ and M3‚H+) may form, and such changes may introduce nonlinear variations into the ion mobility spectra. Cluster ion formation is usually related to the analyte vapor-phase (1) Eiceman, G. A.; Karpas, Z. Ion Mobility Spectrometry; CRC Press: Boca Raton, FL, 1994.
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concentration, and for many cases the concentration may fluctuate during the measurement. For large data collections, peaks may be hidden or distorted by perspective when data are presented in waterfall or scrolling 3-D displays. An alternative method of data processing, simpleto-use interactive self-modeling mixture analysis (SIMPLISMA2-4), can be used to simplify these large data sets and to exploit temporal variations that occur during the measurement. IMS data differ from data obtained by other spectrometric methods in that the features of a spectrum are closed (i.e., spectral features integrate to the same area). Consequently, the background signal in IMS data (also termed reactant ion peak, or RIP) is inversely correlated to an analyte peak in a spectrum. As an analyte peak increases with concentration, the RIP decreases due to the conservation of charge and competitive charge transfer among ions. In contrast to other spectrometric methods, matrix effects in IMS can be severe, and spectral features for mixtures can be mutually dependent with respect to changes in concentration of an analyte. This characteristic trait of IMS is important with respect to data analysis and stems from the APCI reactions that occur within the IMS. The ion mobility spectrum of a single vapor may be considered a binary mixture of the reactant ions and the analyte ions. This process increases with respect to complexity with increased vapor concentrations through the formation of cluster ions. Commonly, a proton-bound dimer ion (M2‚H+) is observed during IMS experiments. The relationship between oligomeric peak areas is not stoichiometric. When a proton-bound dimer peak has the same peak area as a protonated monomer peak, the proton-bound dimer peak will represent twice the analyte concentration of the protonated monomer peak, per eqs 1 and 2. In eq 1, the formation of a monomer product ion is given for a water
H+‚(H2O)m+1 + M a M‚H+(H2O)m + H2O
(1)
reactant ion and a neutral analyte (M). Thus, all ion mobility spectra obtained from APCI reactions for either pure samples or (2) Windig, W.; Guilment, J. Anal. Chem. 1991, 63, 1425. (3) Windig, W.; Heckler, C, E.; Agblevor, F. A.; Evans, R. J. Chemom. Intell. Lab. Syst. 1992, 14, 195. (4) Windig, W.; Stephenson, D. A. Anal. Chem. 1992, 64, 4, 2735. S0003-2700(97)00625-2 CCC: $15.00
© 1998 American Chemical Society Published on Web 01/21/1998
vapor mixtures can be regarded as mixtures suited for signal processing methods. At high analyte concentrations, protonated dimer ions may form as per eq 2. Competitive charge transfer
M‚H+‚(H2O)m + M a M2‚H+(H2O)m-1 + H2O
(2)
makes IMS data analysis intricate. Equation 3 typifies the
M‚H+‚(H2O)m + N a N‚H+(H2O)m + M
(3)
suppression of an analyte signal (M) by the introduction of a species (N) with a greater proton affinity or a greater concentration. Mixed ions may also form via the process given in eq 4.
M‚H+(H2O)m + N / M‚N‚H+(H2O)m-1 + H2O
(4)
Heterogeneous complex ions are highly variable with respect to time or analyte concentrations. In other instances, they may exist but may not be discernible in low-resolution drift tubes. The distribution of ions in pure or mixed systems is governed by the proton affinities, ionization potentials, electronegativities, the concentrations of the analytes, and other experimental factors, including drift tube temperature and humidity of the reactant ion source region. One means of reducing the complexity of the response caused by charge-transfer reactions is to couple a gas chromatograph to the inlet of the IMS. A practical penalty of a hyphenated GC-IMS occurs in response time, cost, and complexity of the instrumentation. When ion mobility spectrometers are used for continuous monitoring of samples collected directly from the atmosphere, the resulting data set may be processed using time series analysis (TSA). Under such conditions, analyte concentrations may vary independently with respect to time as they are sampled by the spectrometer. In general, TSA methods attempt to decompose a matrix of data into a matrix of concentration profiles and a matrix of extracted spectra. The decomposition is accomplished by modeling the number of species that are varying independently with time. The concentration profiles are the change in concentration of each species with respect to time. The number of species is determined by the number of features that vary independently with respect to time above the measurement noise level. For IMS, ion clusters are the species that are modeled by TSA, which may be used to resolve overlapping spectral features, to correlate spectral features to sampling events, and to reduce noise in the extracted spectra. Major benefits of SIMPLISMA, which is a TSA method, are computational efficiency and simplicity toward the assumptions regarding the concentration profiles. SIMPLISMA has been successfully applied to depth profiling of polymer laminates by infrared spectrometry5 and to assessment of peak purity in liquid chromatography.6 This method has also been used for mixture analysis,7-9 pure component analysis,10 and the detection and (5) Guilment, J.; Markel, S.; Windig, W. Appl. Spectrosc. 1994, 48, 320. (6) Sanchez, F. C.; Massart, D. L. Anal. Chim. Acta 1994, 298, 331. (7) Windig, W. A Simple-to-Use Method for Interactive Self-Modeling Mixture Analysis. In Computer-Enhanced Analytical Spectroscopy; Jurs, P. C., Ed.; Plenum Press: New York, 1992; Vol. 3, Chapter 4.
characterization of cobalt species in zeolites by diffuse reflectance spectroscopy.11 Further, SIMPLISMA and eigenstructure tracking analysis have been used to study protonation equilibria of nucleotides.12 SIMPLISMA was useful for the identification and modeling of IMS cluster ions of a complex mixture.13 SIMPLISMA relies on changes in concentration over time to identify pure variables. When several analytes are sampled at constant concentration, TSA methods cannot resolve the analytes. IMS instruments afford the unique opportunity to selectively resolve mixture components by cleardown rates. Cleardown occurs when the sample is removed from the instrument. Sample vapors inside the instrument are removed by affinity of the analyte to the molecular sieves housed within the instrument. Different analytes may selectively adsorb to the molecular sieves and will decay at different rates in the instrumental response. Therefore, TSA applied to cleardown data may detect the concentration changes of the individual analytes. This work developed a modified SIMPLISMA algorithm that was able to resolve mixtures of nerve agent simulants from cleardown data. THEORY SIMPLISMA. SIMPLISMA finds pure variables in the data set and uses the pure variable intensities to estimate the concentration profiles of the analytes. A pure variable is a point in the spectrum that offers high selectivity for an analyte. In other words, a pure variable has a unique variance with respect to concentration for a single analyte in a mixture and no variance for the other mixture components. The purity of a variable is a measure of selectivity for a particular analyte. The complex ions in IMS are features that vary nonlinearly with respect to concentration changes. These features may exhibit unique pure variables, so they are referred to as components. The method is applied to a data set of spectra acquired over a period of time. However, the technique can also be applied to spectra collected from several different experiments that have been combined into a single data set. The fundamental assumption is that the analyte concentrations change independently as a function of time. SIMPLISMA will model the data set by the changes in the spectral features and characterize these variances. The n rows of the data matrix (D) are spectra which are ordered with respect to time, and the v columns are the resolution elements. Spectral scan number is reported instead of time, and each scan number designates a row of D. The objective is to decompose D into
D ) CST
(5)
for which C has n rows and r columns and S has v rows and r columns. The number of extracted components is r. The concentration profiles are obtained from the columns of C, and the extracted spectra are obtained from the columns of S. (8) Windig, W.; Markel, S. J. Mol. Struct. 1993, 292, 161. (9) Windig, W. Chemom. Intell. Lab. Syst. 1994, 23, 71. (10) Mansueto, E. S.; Wight, C. A. Appl. Spectrosc. 1992, 46, 1799. (11) Verberckmoes, A. A.; Weckhuysen, B. M.; Pelgrims, J.; Schoonheydt, R. A. J. Phys. Chem. 1995, 99, 15222. (12) Gargallo, R.; Sanchez, F. C.; Izqwuierdo-Ridorsa, A. I.; Massart, D. L. Anal. Chem. 1996, 68, 2241. (13) Harrington, P.; Reese, E.; Rauch, P.; Hu, L.; Davis, D. Appl. Spectrosc. 1997, 51, 808.
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SIMPLISMA estimates C with pure variables. A pure variable is a data channel whose intensities are correlated with concentration of a single analyte. Pure variables have two properties: they characterize relatively larger variances, and they are mutually independent. SIMPLISMA determines the first pure variable by finding the variable that maximizes the purity (pij) given by
pij )
σj w µj + R ij
(6)
for which i is the number of components that have been extracted, j is the candidate variable, µj is the mean, and σj is the standard deviation calculated from the intensities of the jth variable. The first factor in eq 6 may be recognized as an expression for standard error. This factor finds variables that have a large relative variation with respect to the time axis. The w term characterizes the linear independence of the jth variable with respect to the previously extracted i - 1 components. To remove the influence of noise, which also varies independently with respect to time or concentration, a fraction of the largest intensity in the data set (R) is added to the denominators of eqs 6 and 7. For this work, an initial value for R was one-fifth of the maximum peak intensity of the data set. For the first component, the weight value is
w1j )
σj σj + R
(7)
To calculate pure variables for the other components, the determinant of the correlation matrix about the origin is used. This step is important, because the pure variables should be mutually independent. The correlation matrix is obtained by first normalizing the columns of the data matrix, dj.
cj )
dj
x∑
(8)
n
(dij + R)2
i)1
A correlation matrix is created from the normalized columns for the jth candidate variable and the previously extracted i - 1 component by
[
c1Tc1
c1Tc2 T T wij ) c2 c1 c2 c2 l l (cj)iTc1 (cj)iTc2
‚‚‚ ‚‚‚ ‚‚‚ ‚‚‚
c1T(cj)i c2T(cj)i l (cj)iT(cj)i
]
(9)
for which wij is the weight obtained for the ith component and the jth variable. For each candidate variable j, the determinant must be recalculated with the ith row and ith column replaced with new inner product values. The determinant of the concentration profile correlation matrix will be maximized as the normalized column vectors become independent. The ith pure variable is obtained as pi by eq 6. As the number of components (r) is increased, the concentration profile correlation matrix is augmented. 718
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Each pi corresponds to a dj. The purity of the v variables is calculated for each new component, and the variable that maximizes the purity (pi) is stored for the ith component. The concentration profiles for the components are the dj and comprise the r columns of C. The spectra are extracted from the concentration profiles by
S ) DTC(CTC)-1
(10)
for which S is a matrix. Each column of S contains an extracted spectrum for each component. The independence of the pure variables stabilizes the inversion of the concentration profiles during the least-squares regression step (eq 10). The spectra are then normalized to positive unit area and are used to generate new concentration profiles by
C ) DS(STS)-1
(11)
The normalization step gives the concentration profiles units of signal intensity (i.e., millivolts). Normalizing the extracted spectra in this manner is appropriate, because the spectral intensities correspond to the ion current at the detector, and charge is usually conserved for each spectrum. Initially, a number of components are selected. For IMS data, the most useful indicator for the number of components has been the visual examination of C. The concentration profiles are examined for loss of structure that relates to sampling events. For spurious components, the concentration profile maximum will be small compared to the other component concentration profiles. In addition, examination of the extracted spectra S and the purity values for each pure variable are also good indicators of the correct number of components. Pure variables that are in regions where no IMS peaks are probable also indicate spurious components. Once the correct number of components is determined, the spectra and concentration profiles are estimated again by regression with the correct number of components. Modifications to SIMPLISMA. There was a problem separating peaks that had similar exponential decay rates when using the previously described version of SIMPLISMA. Monomer peaks of different analytes that decay exponentially are highly correlated, and points that correspond to noise were selected as pure variables. Modifications to SIMPLISMA’s variable selection method were made to compensate for the problem caused by similar exponential decay rates. The SIMPLISMA algorithm was modified as follows. The largest intensity in each column of the data matrix defines a spectrum that is composed of maximum intensities. This resulting spectrum is used to identify peaks starting with the largest. The variable at the largest peak maximum is stored as p1, and the next largest peak is sought. Peaks are detected by finding the most intense point after convolving with a five-point average function. Each peak maximum is found and stored as a pure component in descending order of peak intensities. The pure variable candidates are ranked from the highest to lowest purity. A different measure of independence is used to calculate the weights (wij). The candidate concentration profiles are projected onto an orthonormal basis. The sine of the angle of the concentration profile and basis is used as the weight. The Gram-
Figure 1. Schematic of a typical IMS instrument including gas flow paths and molecular sieve packs.
Schmidt orthonormalization process is used to form the basis from the concentration profiles of the pure variable components. The procedure has been described in detail.14 The pure variables’ intensities are regressed onto the full data set to produce the extracted spectra. The extracted spectra are normalized to unit area and regressed back onto the full data set to regenerate scaled concentration profiles. This method of pure variable selection works for IMS data by discriminating against smaller but uncorrelated spectral features in the data and is referred to as PSIMPLISMA, for peak selection SIMPLISMA. Essentially only a single modification was made to SIMPLISMA. The points that are evaluated as pure variables are constrained to peak maxima that occur during the measurement period. IMS Cleardown Process. A schematic of a typical IMS is given in Figure 1. The path the sample vapor follows is represented in Figure 1 by a dashed line, while the path of the drift gas is a dot-dashed line. Once the sample vapors diffuse across the membrane, they are in a closed system. The drift gas is pumped into the ion source region that is used to sweep neutral species out of the source region. When the ion shutter is pulsed, the ions are accelerated into the drift region and focused by the linear voltage drop across the IMS. In the drift region, the ion clusters described earlier separate by size-to-charge ratios. The separation process in IMS instruments is very similar to that of a time-of-flight mass spectrometer. The drift rings given in Figure 1 are used to maintain the linear voltage drop across the IMS. Drift gas flowing through the drift region acts as a countercurrent gas that serves to remove any neutral species from the region. Molecular sieves are housed within the closed system of the instrument to remove any remaining vapors from inside the instrument. The molecular sieves clean the drift gas of residual analytes that might interfere with the measurements. (14) Rauch, P.; Harrington, P.; Davis, D. Chemom. Intell. Lab. Syst. 1998, 39, 175-185.
The process of self-cleaning depends on the sample affinity to the molecular sieves. When different chemical species have different affinities to the molecular sieves, the rates at which they are removed from the instrument differ, thus causing a change in vapor phase concentration. As the sample is swept thought the molecular sieves and back through the IMS, the vapor-phase concentration inside of the IMS changes. Monitoring the cleardown of chemical species inside of an IMS instrument is typically used only to ensure that the instrument is clean for the next sampling experiment. To our knowledge, this research is the first time IMS cleardown information has been used to allow a feature extraction method, such as SIMPLISMA, to resolve mixtures of chemical species. EXPERIMENTAL SECTION Samples. Chemicals used for these experiments were dimethyl methylphosphonate (DMMP) (Lancaster, 97%), diethyl methylphosphonate (DEMP) (Lancaster, 98%), and diisopropyl methylphosphonate (DIMP) (Lancaster, 98%). Dry air was generated by a Whatman model 76-02 air-drying tower with two extra drying towers (4-cm i.d. × 17-cm length) connected in series to the outlet of the Whatman tower. The extra drying towers were packed with 2-3-cm layers of grade 44 indicating silica gel, 3-8 mesh size; a 1:1 mix of 13× and 5-Å molecular sieves; and Pyrex Fiberglas glass wool. The purpose of the extra towers was to scrub the air that had been dried by the Whatman tower of residual organic compounds. Instrumentation. IMS data were generated using two chemical agent monitor (CAM) type 482-301N (Grasby Ionics, Ltd., Watford, Herts, U.K.) units, each with a single modification. The reagent chemistry was based on water rather than acetone, which was attained by removing the reagent gas source from the recirculated gas system of the instrument. One CAM was connected through a National Instruments (AT-MIO-16X) data acquisition board. The analog-to-digital converter (ADC) board Analytical Chemistry, Vol. 70, No. 4, February 15, 1998
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Table 1. IMS and Data Collection Parameters
points per spectrum gating pulse delay (µs) acquisition frequency no. of spectra coadded no. of blank spectra no. of sample spectra total no. of spectra delay between spectra
DMMP and DIMP mixture
DMMP, DEMP, and DIMP mixture
1300 1000 80 kHz 10 20 20 350 scans 0-99 (0-s delay) scans 200-269 (1-min delay) scans 270-350 (2-min delay)
1300 1000 66 kHz 16 20 20 4499 0s
was used in an AMD 5X86 computer operating at 133 MHz with 32 MB of RAM. The operating system was Microsoft Windows 95. The U.S. Army IMS data engine (AIDE) was used to collect the data from the National Instruments data acquisition board. (AIDE was developed by Dennis M. Davis at the Edgewood Research, Development, and Engineering Center Chemical/ Biological Defense Command, Edgewood, MD.) The second CAM was connected through a Grasby WASP ADC. The ADC was controlled by a 486 Intel 80486 computer operating at 25 MHz with 16 MB of RAM, operating under Microsoft Windows 3.11. The ADC was accessed with the Grasby WASP software developed by the manufacturer. The computer used for data analysis was a single processor Pentium Pro 200 with 64 MB of RAM, running Windows NT 4.0. Code development was performed on the data analysis computer using Watcom 10.6 C++. Procedures. Standards for each nerve agent simulant were prepared by moistening tissues with the pure compound and placing the moist tissues into a 2-oz. wide-mouth glass bottle with a ground glass stopper (Fisher 2-920). Tissues used for preparing
the standards were laboratory cleaning tissues (Kimwipe, 5 × 8 3/ , Fisher, 06-666A). Three binary mixtures and one ternary 8 mixture were prepared in the same manner as the standards. The binary mixtures were DMMP-DEMP, DMMP-DIMP, and DEMP-DIMP. The ternary mixture was DMMP, DEMP, and DIMP. Also, a liquid binary mixture of DMMP and DIMP was prepared by placing 1 mL of each standard into a glass bottle. Eight glass bottles were used to generate eight separate experiments. The IMS instruments were stored in a dry air line and allowed to collect 20 spectra (blank spectra) before being exposed to the sample vapor from one of the glass bottles (sample spectra). The IMS was replaced into the dry air line and allowed to cleardown while data were collected. The typical data collection cycle ran for approximately 4 h. During the data collection, typically over 4000 spectra were collected. The data presented in this work are typical of the data collection performed on nerve agent simulants. A liquid binary mixture of DMMP and DIMP was collected with 20 blank spectra, followed by the collection of 20 spectra while the instrument was exposed to sample vapor. The IMS was then allowed to collect 160 spectra as the instrument cleared-down, at which time the acquisition rate was changed so that a spectrum was collected each minute. After 270 min, the delay between spectra was changed from 1 to 2 min. The second data set to be discussed was generated by first collecting 20 blanks, followed by 20 spectra while the IMS was exposed to the ternary mixture of DMMP, DEMP, and DIMP. The IMS was then allowed to clear-down while in a dry air line as data were collected. A summary of IMS and data collection parameters is given in Table 1 for both data sets. RESULTS AND DISCUSSION A 3-D surface plot of the entire data set for the liquid DMMP and DIMP mixture is given in Figure 2 and demonstrates many of the problems associated with visual interpretation of large data
Figure 2. Positive ion spectra for a liquid mixture of DMMP and DIMP (liquid mixture in glass bottle). 720
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Figure 3. Standard positive ion spectra for DMMP, DEMP, and DIMP (tissue samples from glass bottle).
Figure 4. Spectrum number 200 of a liquid mixture of DMMP and DIMP.
sets. It should be noted that the data are obscured and difficult to interpret at points due to the perspective or surrounding features in the spectra. During sample exposure, scans 21-40,
the reactant ion peak (RIP), which is the large peak at 6 ms, disappears and then slowly increases after sample exposure ends. The reappearance of the RIP indicates the removal of sample from the inlet of the instrument. While the IMS is exposed to the sample, a single dimer dominates the spectra. Once the sample is removed from the inlet of the instrument, the concentration of the analytes in the instrument decreases by adsorption of the analytes onto the molecular sieves. At this point, many other product ion peaks appear. Selective affinity toward the sieves causes the peaks to decay at different rates. The standard spectra for each compound are very similar. The peaks occur at different drift times due to the size differences of each simulant. Figure 3 gives the standard spectra for DMMP, DEMP, and DIMP overlaid onto one graph so that the similarities and differences of the spectra can be observed. The first peak, at 6 ms for all three compounds, is the RIP. Each standard consists of a monomer peak and a dimer peak that occur after the reactant ion peak at shorter and longer drift times, respectively. When two standards are mixed together, such as DMMP and DIMP, an additional peak is observed between the expected dimers (Figure 4). The new peak is halfway between the two dimer peaks, indicating a mixed dimer of DMMP and DIMP. The formation of mixed dimers increases the complexity of the APCI reactions, which in turn increases the complexity of IMS data. Figure 4 is spectrum 200 from the data set for the liquid binary mixture of DMMP and DIMP given in Figure 2. Analysis of the DMMP and DIMP mixture data (Figure 2) using the modified SIMPLISMA produces a set of extracted spectra (Figure 5) and concentration profiles (Figure 6). In Figure 5, note that all the peaks, including the RIP and mixed dimer peak, were extracted as separate components. The separation of monomers and dimers as individual components is desirable, because they yield information regarding the concentrations of the analytes. Due to charge conservation in IMS when one peak increases or decreases, peaks at other drift times must compen-
Figure 5. SIMPLISMA extracted spectra for the liquid DMMP and DIMP mixture using the peak selection method of component extraction (liquid mixture in glass bottle).
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Figure 6. SIMPLISMA extracted concentration profiles related to the extracted spectra in Figure 5 and displaying the areas where significant changes took place in the data collection procedure (liquid mixture in glass bottle).
Figure 8. Extracted spectra from a mixture of DMMP, DEMP, and DIMP using the peak selection approach to SIMPLISMA (tissue sample from glass bottle).
peaks, and changes in humidity. The ternary mixture demonstrates the power of feature extraction methods such as SIMPLISMA and the value of information available from the cleardown process of an IMS instrument. CONCLUSIONS
Figure 7. Spectrum 150 of a mixture of DMMP, DEMP, and DIMP from moist tissues contained in a glass bottle. Labels indicate the identity of peaks based on comparisons to standard spectra similar to those shown in Figure 3.
sate. An example of this peak interaction is visible as negative peaks in Figure 5. The RIP when composed of water is variable, and the negative peak indicates a shift to a later drift time when the DIMP monomer occurs. Once the peaks are identified as individual species, the drift time can be used to identify the peaks with reference spectra. The concentration profiles related to the extracted spectra in Figure 5 are given in Figure 6. Abrupt changes in the concentration profiles indicate points where changes were made during the data collection and are noted in Figure 5 and Table 1. The IMS cleardown data were used to resolve features of very similar compounds in a ternary mixture of nerve agent simulants. Spectrum 150 from the DMMP, DEMP, and DIMP mixture experiment is given in Figure 7; note that the mixture of the three simulants produces a more complex spectrum than that seen for the binary mixture (Figure 4). The complexity of the ternary mixture is caused by the presence of the third simulant (DEMP), which forms additional mixed dimers, and an RIP shoulder. Even with the additional complexity of the ternary mixture, the modified SIMPLISMA algorithm is able to isolate the individual compounds (Figure 8). In Figure 8, all 10 peaks were resolved from these intricate data that had overlapping peaks, mixed dimers, correlated 722 Analytical Chemistry, Vol. 70, No. 4, February 15, 1998
Cleardown data are typically discarded in IMS measurements; however, this process provides beneficial information. It is possible to isolate individual features in IMS data using SIMPLISMA when analytes change in vapor-phase concentration over the measurement period. IMS cleardown can be used to selectively change concentrations of analytes, even though the concentrations of analytes are constant during sampling. Similar chemical species can be resolved by SIMPLISMA of IMS cleardown data. The use of IMS cleardown data for identification of individual species in mixture data is an example of a novel chromatographic separation. Exposing the detector to the mixture of chemical species and then chromatographically removing the species from the detector is an example of frontal chromatographic method performed in a reverse mode. IMS can use reverse frontal chromatography for the separation of mixtures for cases where vapor-phase analyte concentrations are constant. This case is typically encountered when sampling the headspace of liquid and solid mixtures. The coupling of self-modeling methods to dynamic instrumental response may yield valuable information. The dynamic response that occurs during sample introduction or removal may provide characteristic information, especially if the instrument or sensor responds selectively to the analytes. The application of self-modeling methods to these data may provide information that may otherwise be overlooked from data that are often discarded. SIMPLISMA is a powerful method of feature extraction for IMS data but has a problem separating components that are correlated with concentration. Modification to the component selection of SIMPLISMA yields PSIMPLISMA, which uses peaks to isolate spectral features. Individual mixture components tend to decay at exponential rates. PSIMPLISMA resolves these correlated features by restricting the variables to peaks that occur during the measurement. Current analysis has demonstrated that the
PSIMPLISMA feature extraction method is very powerful and able to analyze complex vapor mixtures on the basis of IMS cleardown data using dynamic measurements.
for publication. The Army ERDEC-CBD is thanked for their financial support for this research through Grant No. DAM01-95C0042.
ACKNOWLEDGMENT Lijuan Hu, Eric Reese, Chunsheng Cai, Chuanhao Wan, Ronald Tucceri, Crystal Scheunemann, and Victoria North are thanked for their assistance in collecting data and preparing this paper
Received for review June 17, 1997. Accepted December 5, 1997. AC970625O
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