Semi-Automated Detection of Trace Explosives in Fingerprints on Strongly Interfering Surfaces with Raman Chemical Imaging ASHISH TRIPATHI, ERIK D. EMMONS, PHILLIP G. WILCOX, JASON A. GUICHETEAU, DARREN K. EMGE, STEVEN D. CHRISTESEN, and AUGUSTUS W. FOUNTAIN III* Science Applications International Corporation, PO Box 68, Gunpowder Branch, Aberdeen Proving Ground, Maryland 21010-5424 (A.T.); National Research Council at the Research and Technology Directorate, Edgewood Chemical Biological Center, Aberdeen Proving Ground, Maryland 21010-5424 (E.D.E.); and Research and Technology Directorate, Edgewood Chemical Biological Center, Aberdeen Proving Ground, Maryland 21010-5424 (P.G.W., J.A.G., D.K.E., S.D.C., A.W.F.)
We have previously demonstrated the use of wide-field Raman chemical imaging (RCI) to detect and identify the presence of trace explosives in contaminated fingerprints. In this current work we demonstrate the detection of trace explosives in contaminated fingerprints on strongly Raman scattering surfaces such as plastics and painted metals using an automated background subtraction routine. We demonstrate the use of partial least squares subtraction to minimize the interfering surface spectral signatures, allowing the detection and identification of explosive materials in the corrected Raman images. The resulting analyses are then visually superimposed on the corresponding bright field images to physically locate traces of explosives. Additionally, we attempt to address the question of whether a complete RCI of a fingerprint is required for trace explosive detection or whether a simple non-imaging Raman spectrum is sufficient. This investigation further demonstrates the ability to nondestructively identify explosives on fingerprints present on commonly found surfaces such that the fingerprint remains intact for further biometric analysis. Index Headings: Raman spectroscopy; Explosives; Chemical imaging; Hyperspectral imaging; Biometrics; Forensics.
INTRODUCTION Standoff and trace detection of explosives have received a significant level of attention in recent years due to the continued threat posed by terrorists in possession of explosive materials. A wide variety of analytical techniques have been considered in order to develop detect-to-warn capabilities for the presence of explosives.1–3 In addition to standoff detection for protective purposes, techniques are also being explored for the forensic examination of different scenes where explosives and potential chemical precursors may be present. As an example, detecting the fingerprints deposited by individuals who have recently handled explosives is of tremendous interest. Vibrational spectroscopy, including Raman spectroscopy, has figured prominently among these techniques.2 Raman spectroscopy is a particularly appealing technique for detecting explosives on surfaces because it is typically nondestructive and requires no sample preparation. The former trait is important in forensic applications because it leaves the sample intact for further analysis. Additionally, the spectral features associated with Raman scattered light are generally very sharp, allowing for the detection and analysis of complex mixtures. Raman chemical imaging (RCI) provides an additional level of discrimination due to the fact that many Received 15 December 2010; accepted 7 March 2011. * Author to whom correspondence should be sent. Email: augustus.w.
[email protected]. DOI: 10.1366/10-06214
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components are spatially separated in a heterogeneous sample. Previously, we demonstrated the RCI detection of explosives in contaminated fingerprints deposited on aluminum-coated microscope slides that yielded low levels of background/ surface spectral interference.3 Other groups have used point Raman microspectroscopy to examine contaminated fingerprints for the presence of explosives and other illicit substances such as drugs.4–10 However, to our knowledge there has been no previous work on Raman chemical imaging of contaminated fingerprints on highly interfering surfaces. The only works focusing on RCI in this context are our previous article and a single band image obtained by Cheng et al., both of which dealt with non-interfering surfaces.3,4 Although most studies using Raman spectroscopy for explosive identification have focused on non-interfering background surfaces, Ali et al. recently used point Raman microscopy to detect the presence of explosives on clothing11 and human fingernails.12 In these two works, confocal microscopy was used with a tightly focused laser beam that allowed for minimization of the background signal. A single band image was also obtained,11 similar to that of Cheng et al.4 In these studies, overcoming the background interference was not a significant difficulty, unlike the present work. In the studies of Ali et al., the analyte of interest had a significantly stronger signal than the background, whereas in the present case the reverse is true. In addition to Raman spectroscopy, other techniques have been used to examine explosive-contaminated fingerprints. To provide a basis for these studies, Verkouteren used scanning electron microscopy and optical microscopy to determine the particle size distributions of RDX and PETN crystals in plastic explosives that had undergone grinding processes.13 The objective was to determine the particle size distribution and use it to guide the detection of fingerprint residues using ionmobility spectrometry. Other techniques used to examine fingerprints include laser fluorescence, X-ray fluorescence, and infrared chemical imaging.14–18 A significant body of work has been performed using infrared chemical imaging to examine the chemical composition of fingerprint deposits, obtain fingerprint ridge patterns, and detect the presence of explosives. Kazarian et al. reported the use of infrared chemical imaging for the examination of fingerprint ridge patterns and trace contaminants within them.19–22 Imaging, as opposed to area-averaged measurements, significantly improved the limits of detection for heterogeneous samples, such as mixtures of powders19 and particles lifted with tape.21 After lifting fingerprints with tape, they were able to use infrared chemical imaging to map the ridge patterns left in latent fingerprints using attenuated total reflection (ATR) Fourier transform infrared (FT-IR) spectros-
0003-7028/11/6506-0611$2.00/0 Ó 2011 Society for Applied Spectroscopy
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copy.20,22 There are several other examples in the literature of infrared imaging of fingerprints by other groups.23–28 In this study, more challenging and commonly occurring surfaces were examined that have significant levels of background interference due to intrinsic fluorescence and Raman scattering, including painted metals and polymers (plastics). By combining baseline correction and partial least squares subtraction the interfering fluorescence and Raman contributions can be minimized. A variety of references in the literature have addressed the issue of subtraction of fluorescence and Raman features that interfere with the Raman features of interest.29–31 This work adds to previous studies of contaminated fingerprints in that it reports the investigation of fingerprints on surfaces that may be encountered in realistic scenarios. We describe an automated procedure for determining the native surface Raman spectrum and then minimizing the contribution of the surface spectrum to reveal Raman spectral features of the analyte present on the surface. Discrimination of analytes from background interference is an important problem in surface detection. This is an important practical advance because most surfaces encountered in forensic applications will likely have significant backgrounds that must be discriminated against.
EXPERIMENTAL Sample Preparation. The explosives studied include RDX (1,3,5-trinitro-1,3,5-triazane), HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocane), PETN (pentaerythritol tetranitrate), and ammonium nitrate (NH4NO3). Ammonium nitrate powder (99.5% purity) was obtained from Sigma-Aldrich. Additionally raw, powdered military grade RDX, HMX, PETN, and ammonium nitrate, as well as samples of military grade C4 (;90% RDX, along with polyisobutylene as a binder and other components), were obtained from the pyrotechnics team at the U.S. Army Edgewood Chemical Biological Center and used without further purification. The library reference spectra were obtained from the powdered samples. Fingers were contaminated with explosives by pressing against a small sample of powder and removing the excess by scrubbing the thumb across the surface of the contaminated finger. The contaminated finger was then pressed on a clean surface, leaving a fingerprint. Figure 1 shows an example of this process. Raman Chemical Imaging. A Falcon II wide-field Raman chemical imaging microscope (ChemImage, Pittsburgh, PA) was used for the acquisition of Raman data. Excitation of the Raman scattering was performed with a 532 nm Coherent Verdi V-2 laser (Coherent, Santa Clara, CA). This system uses the wide-field imaging method in which a laser spot completely envelopes the field of view (FOV). To spectrally resolve the Raman scattered light, a liquid crystal tunable filter (LCTF) was used for imaging onto a charge-coupled device (CCD) camera. The native spatial resolution of the Raman CCD camera is 5123512 pixels. Binning groups of 333 pixels produced an effective resolution of the Raman CCD camera of 1703170 binned pixel groups (BPG). The LCTF was sequentially tuned to allow Raman shifted scattered radiation from the 500 to 1850 cm1 range in 10 cm1 increments, with an LCTF full width at half-height (FWHH) of 8 cm1. At each wavenumber shift an image was taken with the Raman CCD camera with 15 second integrated exposures and two co-added replicates, producing a Raman
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hyperspectral cube (RHSC). The RHSC comprises 136 image frames, with each image frame representing a Raman wavenumber shift. Each binned pixel group in the RHSC provided the Raman spectrum from that region of the field-ofview to which it was exposed. It typically took 68 minutes to acquire a complete RHSC. In addition, a mercury lamp was used to excite the sample for fluorescence imaging. Excitation was at 365 nm, with the broadband fluorescence emission being detected from 400 to 720 nm. Microscope objectives with 53, 103, and 203 magnifications were used to obtain the Raman chemical images with laser spot diameters of approximately 650 lm, 325 lm, and 160 lm, respectively. The spatial resolution of the BPGs for these objectives were 3.4 lm 3 3.4 lm, 1.7 lm 3 1.7 lm, and 0.85 lm 3 0.85 lm, respectively. Laser power densities varied in the range of 35 to 500 W/cm2.
DATA PROCESSING Trace detection of a target analyte deposited on a Raman active surface is confounded by the Raman spectral features contributed by the surface itself. After the spectral contributions of the surface are removed or minimized, further processing can be performed to detect and identify the target analyte. However, prior to this analysis, a second-order baseline correction using the ChemImage, Corp., CI Xpert software version 2.4.5 was performed. Baseline removal effectively removes broad fluorescence, which is an important source of interference in realistic scenarios. Additionally, a median filter (seven times standard deviation, three-point window) based cosmic ray correction routine, from CI Xpert software version 2.4.5, was also performed. After these preprocessing steps, the spectra were normalized. After the initial preprocessing, the data analysis process used in this work is conducted in three sequential phases. The first is to automatically identify the background Raman contribution of the surface. In the second, each of the binned pixel group spectra is processed to minimize the spectral contribution of the surface using a partial least squares technique. Finally, each processed BPG spectrum is compared to the target analyte spectra to determine their level of similarity. This section details the three phases involved in the analysis. Determination of Background Spectra. For our purposes, the background spectrum is the Raman spectral contribution from the surface on which the fingerprint is present. Common surfaces can include CD-ROM discs, wooden surfaces, plastic bottles, car panels, metals, glassware, etc. Some of these surfaces are composed of strong polymeric Raman scatterers, such as polystyrene, polycarbonate, and polyethylene. The spectral contribution due to these materials is expected to dominate the Raman signatures of the sparsely distributed analytes of interest. Other surfaces, such as glass and metal surfaces, are weak Raman scatterers and should make it relatively easy to detect the explosive. However, any algorithm must be versatile enough to automatically discriminate the target spectrum in the presence of both strong and weak Raman scattering backgrounds. It is assumed that a finger contaminated with an explosive material (which could be a single-component or multicomponent explosive) will leave only a trace residue of the explosive on a surface it comes in contact with. Under this assumption the predominantly occurring spectral signature will be from the surface material. To determine the most frequently
FIG. 1. (A) Finger after pressing into sample (not used for analysis). (B) Finger in (A) after scrubbing bulk residual and used to produce fingerprint in (C). (C) Fingerprint on a surface ready for analysis.
occurring spectral signature, defined here as the background spectrum, Ibkgr, the following procedure was used. Evaluate the arithmetic average of all the binned pixel spectra, nXL XW o Savg ¼ I LW ð1Þ i¼1 j¼1 ij where Savg is the average of all binned pixel spectra, Iij is the BPG spectrum of the ith pixel along the length and jth pixel along the width of the field of view, with L and W are the numbers of pixels along the length and width, respectively. Step 1: Evaluate the coefficient of correlation, rij, between each of the binned pixel group spectra, Iij, and the average spectrum, Savg: Xwn ðI k Iij ÞðSkavg Savg Þ k¼1 ij ð2Þ rij ¼ hX i1 Xwn wn 2 2 2 k k ðI I Þ ðS S Þ ij avg k¼1 ij k¼1 avg where Iijk and Skavg are the intensity values at the kth wavenumber element of the binned pixel group spectrum Iij and the average spectrum Savg, respectively. The upper limit of the sums, wn, is the number of points in each spectrum, and Iij and Savg are the means of all the intensity values of Iij and Savg, respectively. Step 2: Construct a histogram of coefficients of correlation, rij. First determine the number of bins Nbins into which the range of rij needs to be divided, based on Scott’s criterion,
which is given as:32
max
Nbins ¼
i¼1; L; j¼1;W
ðrij Þ
min
i¼1; L; j¼1;W
ðrij Þ
h
ð3Þ
where h¼
3:5r ðLWÞ1=3
ð4Þ
and r is the standard deviation of the coefficient of correlation rij values. Step 3: From the histogram determine the value of the coefficient of correlation that has the maximum rate of occurrence. This value will be labeled rfmax. Based on the assumption that the analyte is present in trace amounts, the coefficient of correlation corresponding to maximum frequency count can be viewed as resulting from the surface background. Step 4: Average all the spectra that have rfmax coefficient of correlation and define this as the background spectrum Ibkgr. Minimization of Background Spectral Contributions. Each Raman spectrum in the Raman hyperspectral cube is the combination of the contributions from the background, as described above, and the analyte of interest. Assuming a linearly additive model, Iij ¼ Iunk þ aij Ibkgr þ g
ð5Þ
FIG. 2. (a) Bright field image of a region of a fingerprint contaminated with RDX. (b) FCI of the same region. (c) Overlay of the RCI corresponding to RDX on the bright field image. The arrows in the images point to the detected RDX particles.
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FIG. 3. (a) Algorithmically identified surface background spectrum (polystyrene). (b) Library Raman spectrum of RDX. (c) Fifteen uncorrected extracted individual binned group spectra from a region containing an RDX particle on the polystyrene surface. Only a hint of the RDX feature at 880 cm1 is observable in the unsubtracted spectra. (d) Results from background correction performed on the spectra shown in (c). Notice the likeness to the RDX library spectrum.
where the binned pixel group spectrum Iij, the observed spectrum, is the summation of the background Raman spectrum, Ibkgr, the Raman spectrum Iunk of the unknown component located on top of the background surface, and some residual noise, g. Iunk can be determined by subtracting a multiple of Ibkgr from Iij: Iunk ¼ Iij aij Ibkgr
ð6Þ
where aij is a scaling factor. Here the residual noise g has been ignored. The scaling factor can be determined by the method of partial least squares fitting. This method determines the loading of a component vector in the result vector by minimizing the squared error between the component and the result. In this case, it determines the loading of the background vector (or spectrum) in the observed binned pixel group vector (or spectrum). The following mathematical equation is used: 0 0 Iij Þ=ðIbkgr Ibkgr Þ aij ¼ ðIbkgr
ð7Þ
Here the primes indicate vector transposition. Detection and Identification of Trace Explosives. The minimization of the background spectral contribution results in the extraction of the Raman spectrum of the unknown component Iunk. This background-subtracted spectrum is compared to all the library spectra to determine a measure of similarity. The match is estimated using Pearson’s crosscorrelation technique, obtaining a value of Pearson’s r, the correlation coefficient. The definition of R is given by the
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formula " n X
# ðxi x¯ Þðyi y¯ Þ
i¼1
ðn 1Þ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ R ¼ v" # u n n X u X t ðn 1Þ ðxi x¯ Þ2 ðyi y¯ Þ2 i¼1
covðx; yÞ sx sy
i¼1
ð8Þ Here xi denotes the intensity in the ith wavenumber channel in the reference spectrum and yi denotes the intensity in the ith wavenumber channel in the spectrum of a point in the Raman map. In a procedure similar to that of our previous work, the cross-correlation coefficient runk with respect to each library spectrum is determined. In order to assign identification of Iunk to a library spectrum, two conditions must be met. First, the value of runk must exceed 0.7 (this value of runk corresponds to a spectral mapping angle of 458), and second, the value of runk should be the highest with respect to the identified component library spectrum. If both of these conditions are met, the unknown component is considered identified.3
RESULTS Two aspects pertaining to the detection of trace exogenous debris in a fingerprint will be examined: exploitation of Raman The equation for Pearson’s correlation coefficient was incorrectly printed in Ref. 3 and is being reprinted here for clarity and to avoid further confusion or error.
FIG. 4. Images of a fingerprint contaminated with PETN and RDX. (a) Bright field image. (b) FCI. (c) RCI corresponding to PETN. (d) RCI corresponding to RDX. (e) Overlay of the RCIs on the bright field image.
chemical imaging microscopy (RCIM) for detection of the trace materials on commonly encountered surfaces, and an examination of the necessity of using RCIM in detecting trace exogenous contamination. Most of these studies were performed in a blind fashion, i.e., the RCI acquisition and data analysis were performed by an experimenter who was not aware ahead of time of the identity of the contaminants in the fingerprints. In each fingerprint analysis, multiple regions of interest (ROI) were selected within the fingerprint for Raman chemical imaging. The criterion for the manual selection of the ROI was that the ROI should contain particulate debris. This selection step is the only non-automated step in the analysis. Raman Chemical Imaging Microscopy. The background correction procedure described in the previous section is tested
here with polystyrene, polycarbonate (a CD-ROM), and a painted metal car panel, which all exhibit strong Raman spectral features. Figure 2 shows an example of our background removal application for a selected region of a fingerprint contaminated with the plasticized RDX explosive C4 pressed on a polystyrene surface. The bright field image (BFI) is shown in Fig. 2a, while the fluorescence chemical image (FCI) is shown in Fig. 2b, with the resulting RCI processed image seen in Fig. 2c. The arrows in the images point to the detected RDX particle. The purpose of the fluorescence image is to visually assist in targeting potential exogenous bound material in the fingerprint. When compared to the BFI, it is noticeable that the pixels associated with the suspected RDX particle are dark as they do not fluoresce under the illumination from the UV lamp. After collection of the image, cross-correlation analysis was performed on the background-corrected Raman hyperspectral cube (RHSC) as described in our earlier work. Figure 2c shows the crosscorrelation map of the processed RHSC with respect to RDX overlaid on the bright field image. The blue-colored pixels in this image are the pixels that correlated with an RDX particle of approximately 7.5 lm diameter. The other particles present in the field of view did not correlate to any library spectrum and are possibly residual skin fragments, dust particles, and/or plasticizers used in C4. Skin fragments are known from previous experience to strongly fluoresce, although they exhibit relatively weak Raman scattering and are difficult to observe in an RCI. A spectral representation of the analysis is shown in Fig. 3. Figure 3a shows the result of the automated identification of the background spectrum. This spectrum has a cross-correlation of 0.99 with respect to the polystyrene reference spectrum and, therefore, it can be safely deduced that the surface was composed of polystyrene. Figure 3b shows the reference library spectrum of RDX. Note that the peak at 880 cm1 is the most intense feature of the RDX spectrum and corresponds to a C–N ring stretching mode.33 Figure 3c shows 15 uncorrected extracted binned pixel group (BPG) spectra from the region that was identified as containing RDX (as shown by the bluecolored pixels in Fig. 2c). Visually, the uncorrected spectra clearly bear a much stronger resemblance to polystyrene than RDX. Only a small peak is observed in the uncorrected spectra near 880 cm1, where RDX has its most intense band. This fact makes background correction necessary for the successful detection of an exogenous analyte located on the surface of a strong Raman scatterer. Figure 3d shows the corrected BPG spectra obtained for this example. The corrected BPG spectra visually match with RDX, despite being dwarfed by the spectra of the background polystyrene surface before subtraction. The cross-correlation values with respect to RDX of each of these corrected BPG spectra exceed 0.78 (the minimum value observed for the identified RDX pixels), making it possible to reveal the spectral signature of the analyte. Following the methodology described above, a fingerprint contaminated with multiple explosives was analyzed with no prior knowledge of the components. Figure 4 shows a fingerprint contaminated with RDX and PETN deposited on a polycarbonate surface (the bottom or data surface of a Verbatim Datalife Plus CD-R disc). Figure 4a shows the bright field image, while Fig. 4b shows the FCI. In this case, the particle appears to fluoresce; this may be due to additional fluorescing material of biological origin covering the RDX
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FIG. 5. (a) Calculated surface background spectrum (polycarbonate). (b) Library Raman spectrum of RDX. (c) Several background-corrected extracted individual binned group spectra from the region containing an RDX particle (from the blue area in Fig. 4e). (d) Library Raman spectrum of PETN. (c) Several backgroundcorrected extracted individual binned group spectra from the region containing a PETN particle (from the green area in Fig. 4e).
particle. The cross-correlation maps of the processed RHSC corresponding to RDX and PETN are shown in Figs. 4c and 4d. Figure 4e is the overlay of the RCIs on the bright field image. The spectral results of the analysis are displayed in Fig. 5. The calculated background spectrum (Fig. 5a) had a coefficient of correlation of 0.99 with respect to the reference spectrum of polycarbonate. Figure 5c shows several extracted background-corrected BPG spectra that correlated at a minimum level of 0.94 with the library spectrum of RDX (Fig. 5b). Figures 5d and 5e similarly show the PETN reference library spectrum and several extracted background-corrected BPG spectra that correlated with PETN (r . 0.86 for all these BPGs). All three materials, polycarbonate, RDX, and PETN, have strong bands near 875 cm1, so this example also serves as a good test of the ability of the algorithm to distinguish between different materials present in the same field of view that share strong spectral bands near the same frequency. In addition to plastics, painted metal car panels with explosive-contaminated fingerprints were examined. Figure 6 shows images of a region of interest of a PETN-contaminated fingerprint on a car panel painted white. Figure 6a is the bright field image, while Fig. 6b displays the cross-correlation map of the processed RHSC corresponding to PETN overlaid on the bright field image. The car panel exhibited moderate fluorescence, weak Raman scattering from the polymer clear coat, and relatively strong Raman scattering from TiO2 pigment in the white paint (present at ;600 cm1). Figure 6c shows the algorithmically determined surface spectrum of the
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white car panel. Again, Fig. 6d is the library spectrum of PETN, while the spectral traces in Fig. 6e are several extracted background-corrected BPG spectra that correlated well with PETN. The minimum value for the correlation coefficient for the individual BPGs in Fig. 6e is 0.87. Explosive contamination on the painted car panel was relatively easy to detect compared to that on the plastic surfaces, since only the thin layer of the clear coat material contained a significant number of interfering Raman bands. Necessity of Raman Chemical Imaging Microscopy. Raman chemical imaging microscopy (RCIM), at its current stage of development, is an expensive and immobile technique, especially when compared to the low cost and high mobility options currently available for performing Raman spectroscopy in a field or operational environment. Because of these limitations, we found it is imperative to investigate whether RCIM is needed for trace Raman based exogenous debris detection and, if so, what level of effective spatial resolution is needed for RCIM. Work done by Verkouteren shows that RDX and PETN particles smaller than 10 lm in diameter have the highest frequency of occurrence in a fingerprint.13 For this technique to be successful, it will therefore be required that the Raman spectral features be identifiable from an explosive particle of less than 10 lm diameter present on a surface. Re-examining the particular case of C4 (mostly RDX) in an explosive-contaminated region of interest as discussed in Fig. 2 may offer an answer to the following question: does one need RCIM to detect trace exogenous material present on a strongly
FIG. 6. (a) Bright field image and (b) RCI corresponding to PETN overlaid with the bright field image for an explosive-contaminated fingerprint deposited on a white painted car panel. (c) Estimated background spectrum. (d) Reference library spectrum of PETN. (e) Several extracted background-corrected binned pixel group (BPG) spectra that correlated well with PETN.
Raman scattering surface? The identified RDX particle is shown in Fig. 2c (colored blue). It is estimated to be about 7.5 lm in diameter (corresponding to an approximate mass of ;0.4 ng). A simulation of varying spatial resolutions with Raman imaging was conducted to estimate the lower bound of resolution required to achieve identification of the target analyte. In other words, we estimate the lower bound of the ratio of the particle footprint to the laser spot area at which the detection is still possible. Figure 7 shows this analysis in detail. Figure 7a shows the Raman chemical image frame at 1000 cm1 in a field of view around the RDX particle (as shown in Fig. 2). The colored concentric rings encasing the particle simulate different laser beam diameters. The background surface spectrum (polystyrene) is shown in Fig. 7b. The RHSC was acquired with high spatial resolution (it is comprised of 28 900 single BPG spectra). The average spectrum from a collection of single BPG spectra encased within the boundaries of each of the simulated laser beam diameters (as shown by colored concentric rings in Fig. 7a) are evaluated. The evaluated average spectra are shown in the Fig. 7c. The colors of these spectral traces correspond to the colors of the rings that simulate varying laser beam diameters. Clearly, a cursory glance at the figure shows a strong likeness with the polystyrene spectrum. The inset in Fig. 7c is the expanded spectral region around 880 cm1. The peak at 880 cm1 is the strongest spectral feature in the RDX spectrum (as shown in Fig. 7d). It is clearly observed that as the simulated laser beam diameter increases, the contribution of the 880 cm1 feature from RDX diminishes. After minimizing the contribution of the background (polystyrene) spectrum, the background-corrected spectra are obtained. These spectra are
shown in Fig. 7e. Only the first four spectra show a visual likeness to RDX. Plotting the cross-correlation coefficient r of each of the background-corrected spectra with respect to the RDX spectrum as a function of percent fill-factor illustrates this effect. Figure 8 shows that as the fill-factor decreases the spectral correlation with the analyte, r, decreases as well. Any fill factor less than ;0.34 produces a correlation value below our detection threshold of 0.7. Stated another way, if the laser beam diameter is more than 70% in excess of the particle footprint diameter, the detection of RDX on the polystyrene surface will be difficult. Therefore, it follows that while Raman chemical imaging is needed for trace detection as described in the current work, the required spatial resolution of Raman imaging will be determined by the minimum size of the trace particle along with the Raman cross-section of the trace analyte and the substrate material; the smaller the size of the particle, the greater the required spatial resolution of Raman chemical imaging. Also, a larger spatial resolution is needed for Raman chemical imaging if the Raman cross-section of the substrate is larger than that of the analyte. In the example discussed here, for an RDX particle with 7.5 lm diameter deposited on a polystyrene substrate, Raman chemical imaging with a spatial resolution of no more than 13 lm (70% in excess of the particle diameter) is required.
CONCLUSION We have demonstrated detection of trace amounts of exogenous explosive material on strongly Raman scattering surfaces using an automated background subtraction algorithm. The explosives RDX and PETN were detected on polystyrene,
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FIG. 7. (a) The Raman chemical image frame at 1000 cm1 with the colored concentric rings encasing the RDX particle used to simulate different laser beam diameters. (b) The background (polystyrene) surface spectrum. (c) Several unprocessed average spectra from the area encased in the colored rings, with the inset showing the spectral features around 880 cm1. (d) Library Raman spectrum of RDX. (e) Average background-corrected spectra.
polycarbonate, and painted white car panels. The procedures were also successfully used to detect HMX, ammonium and sodium nitrate, PETN, and RDX on black and silver car panels (not shown). Raman chemical imaging allows for the automated estimation of the surface background. The estimated background spectral contribution can be minimized with a simple partial least squares analysis and the processed spectral data used for identification. In the case of RDX present on a polystyrene background, a fill factor analysis reveals that low spatial resolution but targeted Raman imaging microscopy is needed for a successful detection strategy. These measurements are an important advance as they show that it is possible to detect traces of explosives on realistic surfaces that may be encountered in everyday situations. Further work is continuing here at this laboratory directed towards speeding up the imaging process and for automated targeting of regions of interest. Work towards miniaturizing and ruggedizing RCI systems by instrument manufacturers so
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FIG. 8. The relationship between r values and the fill-factor.
that they could become field portable would be of great value. Ultimately, with sufficient advances, these techniques could be used on-scene for forensic analysis. ACKNOWLEDGMENTS This research is funded under Army Technology Objective R.FP.2010.01 ‘‘Detection of Unknown Bulk Explosives’’. This effort was performed while E.D. Emmons held a National Research Council Research Associateship Award at the U.S. Army Edgewood Chemical Biological Center. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. 1. D. S. Moore, Rev. Sci. Instrum. 75, 2499 (2004). 2. L. Pacheco-London˜ o, W. Ortiz-Rivera, O. Primera-Pedrozo, and S. Herna´ndez-Rivera, Anal. Bioanal. Chem. 395, 323 (2009). 3. E. D. Emmons, A. Tripathi, J. A. Guicheteau, S. D. Christesen, and A. W. Fountain, Appl. Spectrosc. 63, 1197 (2009). 4. C. Cheng, T. E. Kirkbride, D. N. Batchelder, R. J. Lacey, and T. G. Sheldon, J. Forensic Sci. 40, 31 (1995). 5. J. S. Day, H. G. M. Edwards, S. A. Dobrowski, and A. M. Voice, Spectrochim. Acta, Part A 60A, 563 (2004). 6. H. G. M. Edwards and J. S. Day, J. Raman Spectrosc. 35, 555 (2004). 7. J. S. Day, H. G. M. Edwards, S. A. Dobrowski, and A. M. Voice, Spectrochim. Acta, Part A 60A, 1725 (2004). 8. H. G. M. Edwards and J. S. Day, Vib. Spectrosc. 41, 155 (2006). 9. I. P. Hayward, T. E. Kirkbride, D. N. Batchelder, and R. J. Lacey, J. Forensic Sci. 40, 883 (1995). 10. M. J. West and M. J. Went, Spectrochim. Acta, Part A 71, 1984 (2009). 11. E. M. A. Ali, H. G. M. Edwards, and I. J. Scowen, Talanta 78, 1201 (2009). 12. E. M. A. Ali, H. G. M. Edwards, M. D. Hargreaves, and I. J. Scowen, J. Raman Spectrosc. 40, 144 (2009).
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APPLIED SPECTROSCOPY
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