Fast and sensitive recognition of various explosive compounds using Raman spectroscopy and principal component analysis

Journal of Molecular Structure 1039 (2013) 130–136

Contents lists available at SciVerse ScienceDirect

Journal of Molecular Structure journal homepage: www.elsevier.com/locate/molstruc

Fast and sensitive recognition of various explosive compounds using Raman spectroscopy and principal component analysis Joonki Hwang a, Namhyun Choi a, Aaron Park b, Jun-Qyu Park b, Jin Hyuk Chung c, Sunghyun Baek c, Soo Gyeong Cho c, Sung-June Baek b,⇑, Jaebum Choo a,⇑ a b c

Department of Bionano Engineering, Hanyang University, Ansan 426-791, South Korea Department of Electronics Engineering, Chonnam National University, Gwangju 500-757, South Korea Agency for Defense Development, Daejeon 305-152, South Korea

h i g h l i g h t s " A rapid and sensitive explosive recognition technique has been developed. " Pattern recognition technique of PCA with a Raman detection system has been utilized. " This is well suited for the identification of explosives in the field.

a r t i c l e

i n f o

Article history: Received 19 December 2012 Received in revised form 28 January 2013 Accepted 28 January 2013 Available online 16 February 2013 Keywords: Raman spectroscopy Explosives detection Principal component analysis Feature extraction Spectral database

a b s t r a c t We report a rapid and sensitive recognition technique for explosive compounds using Raman spectroscopy and principal component analysis (PCA). Seven hundreds of Raman spectra (50 measurements per sample) for 14 selected explosives were collected, and were pretreated with noise suppression and baseline elimination methods. PCA, a well-known multivariate statistical method, was applied for the proper evaluation, feature extraction, and identification of measured spectra. Here, a broad wavenumber range (200–3500 cm1) on the collected spectra set was used for the classification of the explosive samples into separate classes. It was found that three principal components achieved 99.3% classification rates in the sample set. The results show that Raman spectroscopy in combination with PCA is well suited for the identification and differentiation of explosives in the field. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction Recently, the development of methods for the identification of explosive materials that are faster, more sensitive, easier to use, and more cost-effective has become a very important issue for homeland security and counter-terrorism applications [1,2]. Several analytical methods, including ion mobility spectroscopy [3], mass spectroscopy [4], terahertz spectroscopy [5], infrared spectroscopy [6], laser-induced breakdown spectroscopy [7], cavity ring down spectroscopy [8], electrochemical sensors [9] and immunosensors [10], have been employed. However, some of the problems associated with these analytical methods, such as poor detection limits caused by the low vapor pressures of explosives, the incapability of detecting explosives in a sealed container, the limited portability of instruments, and false alarms due to the inherent lack of selectivity, have made most of these detection methods less attrac⇑ Corresponding authors. Tel.: +82 31 400 5201; fax: +82 31 436 8188 (J. Choo). E-mail address: [email protected] (J. Choo). 0022-2860/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.molstruc.2013.01.079

tive [11,12]. In particular, no detection system can satisfactorily detect explosives at a safe distance, instead requiring the system to approach the target. Raman spectroscopy has received a growing interest due to its stand-off capacity, which allows samples to be analyzed at distance from the instrument [1,12,13]. In addition, Raman spectroscopy has the capability to detect explosives in sealed containers such as glass or plastic bottles [14,15]. However, two essential requirements need to be resolved if Raman spectroscopy is to be successfully applied to stand-off detection of unknown explosives in realistic field environments. First, technological advances in sensitivity, portability and speed of the signal analysis are essential for Raman detection system [2,16]. Second, a technique to remove external background noise and to quickly identify a target sample should be developed [17,18]. Raman spectra taken in external environments will include background interferences due to the auto-fluorescence emission from the sample as well as various external conditions [19]. Thus, in order to construct a spectral database, this background noise will need to be effectively removed from the raw

J. Hwang et al. / Journal of Molecular Structure 1039 (2013) 130–136

spectra. In addition, a spectral database for the accurate and fast identification of unknown explosives should be developed [20]. In this study, we selected 14 species of representative explosive materials, including 2-methyl-1,3,5-trinitrobenzene (TNT), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), 3-nitrooxy-2,2-bis(nitrooxymethyl) propylnitrate (PETN), ammonium nitrate (AN), 2,4,6,8,10,12hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (HNIW), 5-nitro1,2-dihydro-1,2,4-triazole-3-one (NTO), nitroguanidine (NQ), ammonium dinitramide (ADN), ammonium perchlorate (AP), 2,3dimethyl-2,3-dinitrobutane (DMDNB), N-methyl-N,2,4,6-tetranitro aniline (Tetryl), 2-methyl-1,3-dinitrobenzene (2,6-DNT) and 4-methyl-3,5-dinitroaniline (4-ADNT) [21–24], and measured their Raman spectra using three different wavelength lasers (514.5, 632.7, and 785 nm). On the basis of the measured spectral data, the excitation wavelength dependence, and fluorescence effects and their structure–property relationships were investigated. The optimal Raman spectrum for each explosive material was selected and analyzed for use in the spectral database. For the systematic construction of a database of the Raman spectra, baseline correction and noise reduction were performed on raw spectral data. Finally, principal component analysis (PCA) was carried out for a Raman spectral database of 14 species. PCA is the preferred method for feature extraction and can be used to reduce the dimension of feature set [18]. Pattern recognition of PCA was performed using a linear discriminant function of the Raman data [25,26]. Based on the PCA of 14 different explosive materials, it is possible to classify each explosive species into three dimensional domains. In the

131

present work, a Raman database for 14 different explosives was constructed, and their PCA was performed for their identification. The final goal of this work is the development of (1) the Raman spectral database capable of identifying and correctly classifying mixtures containing explosives and (2) a reliable technique that can identify unknown explosives automatically from the Raman spectral database. 2. Experimental 2.1. Materials Eleven explosive materials, TNT, HMX, RDX, PETN, AN, HNIW, NTO, NQ, ADN, AP and DMDNB were provided by the Agency for Defense Development (ADD) in South Korea. All explosive samples were in powdered form of high purity, which were in accordance with corresponding military specifications. They were used without further purification. Tetryl, 2,6-DNT, and 4-ADNT were purchased from AccuStandard Inc., New Haven, CT, USA), and were in the form of a standard solution (1000 lg/ml solution in 1:1 acetonitrile:methanol). To acquire the powdered forms of these three compounds, their solvents were evaporated at room temperature using a centrifugal evaporator (CVE-200D, Eyela, Tokyo, Japan). 2.2. Instrumentation Raman measurements were performed using two laser-induced Raman systems: a Renishaw 2000 Raman microscope system

Fig. 1. Molecular structures of the 14 selected explosive materials.

132

J. Hwang et al. / Journal of Molecular Structure 1039 (2013) 130–136

Table 1 List of the 14 selected explosive compounds and their physical properties. Compound (IUPAC name)

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n)

2-Methyl-1,3,5-trinitrobenzene Octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine 1,3,5-Trinitroperhydro-1,3,5-triazine [3-Nitrooxy-2,2-bis(nitrooxymethyl)propyl]nitrate N-methyl-N,2,4,6-tetranitroaniline Ammonium nitrate 2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane 5-Nitro-1,2-dihydro-1,2,4-triazole-3-one 1-Nitroguanidine Ammonium dinitramide Ammonium perchlorate 2,3-Dimethyl-2,3-dinitrobutane 2-Methyl-1,3-dinitrobenzene 4-Methyl-3,5-dinitroaniline

Abbreviation

TNT HMX RDX PETN Tetryl AN HNIW, CL-20 NTO NQ ADN AP DMDNB 2,6-DNT 4-ADNT

CAS-number

118-96-3 2691-41-0 121-82-4 78-11-5 479-45-8 6484-52-2 135285-90-4 932-64-9 556-88-7 140456-78-6 7790-98-9 3964-18-9 606-20-2 19406-51-0

Chemical formula

Density (g/cm3)

Explosive properties C–J pressure (GPa)

Detonation velocity (m/s)

Impact sensitivity (H50%, cm)

C7H5N3O6 C4H8N8O8 C3H6N6O6 C5H8N4O12 C7H5N5O8 H4N2O3 C6H6N12O12 C2H2N4O3 CH4N4O2 H4N4O4 ClH4NO4 C6H12N2O4 C7H6N2O4 C7H7N3O4

1.654 1.910 1.816 1.778 1.731 1.725 2.044 1.913 1.707 1.812 1.950 1.429 1.522 1.490

18.2 36.5 32.3 29.6 24.3 13.9 44.5 28.7 25.0 22.3 14.4 – 11.1 –

6735 9084 8739 8390 7567 6556 9789 8338 8105 7881 6248

160, 98 26, 26–32 24, 24–28 12, 12–16 32, 25 – 12–21 291 >320 – – – – –

5589

powder. For each powder sample, 50 Raman spectra were measured by moving the laser spot, and spectra were collected for statistical analysis. 2.3. Pretreatment of measured Raman spectra

Fig. 2. Flowchart for the construction of a Raman database for 14 different explosive materials using PCA.

(Renishaw plc, Wotton-under-Edge, UK) and a DeltaNu Inspector portable Raman system (DeltaNu LLC, Laramie, WY, USA). For the Renishaw Raman system, a Spectra Physics He–Ne laser (Research Electro-Optics, Inc., Boulder, CO, USA) operating at k = 633 nm and an Ar ion laser operating at 514.5 nm were used as excitation sources. In both cases, the laser power was kept lower than 1.0 mW to avoid laser heating. The Rayleigh line was removed from the collected Raman scattering using a holographic notch filter located in the collection path. Spectra were collected via a static scan in the region of 200–3500 cm1. The collection time was 5 s and a 50 objective lens was used to focus the laser. Raman scattering was collected using a charge-coupled device (CCD) camera at a spectral resolution of 4 cm–1. For the DeltaNu Inspector portable Raman system, a diode laser operating at 785 nm was used as an excitation source. In this case, the detectable spectral range was 200–2000 cm1 and the laser power was approximately 10 mW. Raman scattering was collected using a CCD camera at a spectral resolution of 8 cm1. Thus, two different Raman systems were used to investigate the wavelength effects of an excitation laser source for each explosive material. To collect spectra, the explosive powders were placed on a glass substrate. In the case of explosive solutions, the solvent was evaporated in air. Once the sample was dry, Raman measurements could be performed on the deposited

The measured Raman spectra usually contain two different types of noise; one is the additive noise from external conditions, and the other is a background noise from auto-fluorescence. These noises should be carefully treated because they strongly take an effect on the performance of the analysis system. A Savitzky–Golay filter was used to suppress additive noise [19] because it is effective for preserving higher moments of the peak such as line width. On the other hand, two different methods were considered for the suppression of background noise; one is a smoothed derivative and the other is a linear programming method. The smoothed derivative method includes high frequency noise removal, estimation of the background derivative, peak detection, interpolation, and background elimination [20] while the linear programming method adopt a polynomial as an approximating function [27]. According to the preliminary experimental results with the collected explosive compounds, the smoothed derivative method was adopted since it showed a better recognition performance. 2.4. Principal component analysis (PCA) and discriminant analysis After the noise reduction, PCA was performed. It is a preferred method for data reduction since it identifies orthogonal components based on uncorrelated projections. Principal components can be obtained via eigenvalue decomposition of the following scatter matrix S:

S¼

X ðdk  lÞðdk  lÞT ;

ð1Þ

k

where dk is a kth input pattern and l is the mean of dk. If we let D be a diagonal matrix of eigenvalues in descending order and E be an orthogonal matrix whose columns are the corresponding eigenvectors, the principal components Xk can be obtained as follows:

S ¼ EDET ;

ð2Þ

and

X k ¼ ET dk :

ð3Þ

Data reduction is accomplished by discarding the unimportant elements of dk. The number of retained principal components would be determined according to the classification results.

J. Hwang et al. / Journal of Molecular Structure 1039 (2013) 130–136

133

Fig. 3. Comparison of Raman spectra of (a) TNT, (b) HMX and (c) RDX using three different excitation wavelengths (green: Ar ion laser at 514.5 nm; red: He–Ne laser at 632.8 nm; purple: diode laser at 785 nm). The laser powers were 20 mW, 30 mW and 100 mW for the Ar ion, He–Ne, and diode lasers, respectively. The accumulation time was 10 s for all measurements. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3. Results and discussion

Fig. 4. Raman spectrum of TNT at an excitation wavelength of 514.5 nm. The inset is the measured Raman spectra for 50 different spots on the same sample.

The maximum a posteriori probability (MAP) was used for the discriminant analysis of the feature vectors. The MAP is a type of linear discriminant function that fits multivariate normal densities with covariance estimates stratified by a group. In the MAP classification, we select the class xi that maximizes the posterior probP ability P(xi|x). Let li, i be a mean vector and a covariance matrix, and ni be the number of patterns in xi. Assuming that the class conditional probability density is multivariate Gaussian, MAP classification rule can be described with a discriminating function gi. Given input feature x, decide xi if gi(x) P gi(x) and xj otherwise, where

g i ðxÞ ¼ ð1=2ÞxT

1 1 X X xþ li þ r i ; i

r i ¼ ð1=2ÞlTi

1 X i

li

  X 1   ;  i 

li  ð1  2Þ ln 

ni X ¼ ð1=ni Þ xk ;

X i

i

ð4Þ

k¼1 ni X

ðxk  li Þðxk  li ÞT :

¼ ð1=ni Þ

k¼1

The 700 spectra (50 spectra for each explosive sample) were divided into two groups, the training and test sets. Initially, 80% of the data were used as a training set and the remaining 20% were used as the test set. Then, 20% of the spectra were eliminated from the training set and used as new test data once classification was completed. The previous test data were then joined to the training set. In this way, all spectra were used in the test set.

The final goal of this study is the development of a software algorithm to identify unknown explosives using Raman spectroscopy and PCA analysis. Fig. 1 displays the molecular structures of the 14 explosive compounds investigated in this work. Most explosives have higher oxygen and nitrogen portion in their molecular composition than non-explosive materials since explosive should have highly energetic groups such as nitro, azide, and peroxide groups and utilize internal oxygen to produce light gases during the greatly fast reaction. The explosives tested in this work can be classified into four categories: nitroaromatics, nitramines, nitrate esters, and peroxides. Table 1 shows a list of the 14 explosive compounds and their physical properties. This includes important features to characterize explosives, such as density, C–J pressure, detonation velocity, and impact sensitivity. Detonation velocity is the rate at which the shock wave travels through a detonated explosive. The greater the detonation velocity, the greater the power or ‘shattering’ effect of an explosive. C–J pressure is the pressure in the reaction zone as an explosive detonates. Both detonation velocity and C–J pressure provide a significant indicator of the power of an explosive. Impact sensitivity is a safety measure how an explosive can be detonated by the impact. Although there are several other ways an explosive is detonated such as friction or electrostatic shock, many explosives scientists rely on the value of impact sensitivity to understand how safe an explosive is. Fig. 2 shows a schematic flow chart for the identification process. In the first place, 14 explosive materials were selected and their Raman spectra were measured 50 times per sample. Secondly, high frequency noise suppression and baseline correction were performed on the raw Raman spectra. Then all of the Raman spectra were transformed into high signal-to-noise spectra suitable for PCA. Thirdly, the features for each Raman spectrum were extracted using PCA. The elemental compositions of the explosive materials can be visualized by two- or three-dimensional cluster analysis in order to statistically reduce the dimensionality of the elemental composition data to a smaller set of theoretically meaningful component variables. Correspondingly, we chose three components from PCA for the reduced feature set. Lastly, MAP classification was carried out for the discriminant analysis of the feature vectors obtained from the Raman spectral data. Fig. 3 displays the Raman spectra of TNT, HMX and RDX obtained using the three different laser excitation wavelengths (514.5, 632.8 and 785 nm). All three wavelengths showed a similar response and were comparable in spectral detail. Strong fluorescence was not observed at any wavelength with the pure samples, with the exception of a small residual spectral background with the 632.8 nm and 785 nm excitation wavelengths. A major issue with the measurement of explosive compounds by Raman spectroscopy is the fluorescence interference caused either by the sam-

134

J. Hwang et al. / Journal of Molecular Structure 1039 (2013) 130–136

Fig. 5. Raman spectra of 14 selected explosive materials measured at an excitation wavelength of 514.5 nm.

Fig. 6. A Raman spectrum of DMDNB measured at an excitation laser power of 20 mW at 514.5 nm. Also shown is the Raman spectrum after the high frequency noise suppression and baseline elimination for PCA analysis.

ple itself or by the presence of small amounts of impurities. Fluorescence can be avoided if a longer wavelength laser is used as an excitation source. In this case, however, the intensity of the Raman signal is weaker with a longer wavelength excitation than with a shorter wavelength excitation because the Raman scattering intensity is inversely proportional to the fourth power of the laser wavelength. In addition, the efficiency of the silicon-based CCD detector decreases significantly in the longer wavelength region. Here, the benefits of scattering efficiency outweigh the disadvan-

tages of fluorescence interference by a shorter visible wavelength. For example, Fig. 4 displays the Raman spectra of TNT with excitation at 514.5 nm. In order to construct a reliable Raman database for PCA analysis, Raman spectra were collected at 50 different locations on the same sample using an automatic mapping stage. Fig. 5 shows the Raman spectra of the 14 selected explosive compounds measured at an excitation wavelength of 514.5 nm. Although the baseline was not slightly stabilized due to fluorescence interference in all of the spectra, this problem can be easily resolved using baseline eliminations. Consequently, the Raman spectra of selected explosive compounds measured at 514.5 nm were collected for the construction of the Raman spectral database. As shown in Fig. 6a, the measured Raman spectra of explosive compounds include frequency noises and unstable baselines, which degrade the accuracy and precision of the spectral analysis. Frequency noises and unstable baseline could be successfully removed using a Savitzky–Golay filter and the smoothed derivative background elimination method as shown in Fig. 6b. Thus, all the Raman spectra from 14 explosive compounds were pretreated prior to PCA by the same way. PCA is a well-known method for reducing the dimensionality of a data set while preserving most of the variance of the data. To see how well the feature vectors of 700 Raman spectra from 14 different explosives were separated, they were plotted in Fig. 7, where the position of each explosive compound is reported in the three-dimensional space for the three principal components (PC1, PC2, and PC3). This figure clearly shows that the input feature vectors are well separated using this feature space. To display the sep-

135

J. Hwang et al. / Journal of Molecular Structure 1039 (2013) 130–136

Fig. 7. Three-dimensional scattered plot for the PCA-transformed Raman spectra for 14 explosive materials. The inset is the projected two-dimensional scattered plot for selected explosive samples.

Table 2 Confusion matrix for PCA classification among the Raman spectra of 14 explosive materials.

TNT TNT HMX

HMX

Tetryl

AN

HNIW

NTO

NQ

ADN

AP

DMD NB

2,6-DNT 4-ADNT

1 1 0.98

0.02

Tetryl

0.96

AN HNIW

PETN

1

RDX PETN

RDX

0.04 0.98

0.02

0.02 0.98

NTO NQ

1 1

ADN

1

AP

1

DMD NB

1

2,6-DNT

1

4-ADNT

aration between the different explosives more clearly, the zoomedin space for 10 explosive compounds is shown in the inset of Fig. 7. Table 2 shows the confusion matrix for analyte classification using PCA and MAP. The explosive analytes are given in the rows and their response vectors for classification are given in the columns. Each row sums to 1, and perfect classification yields an identity matrix (number = 1) along the diagonal. As shown in this table, 10 out of the 14 analytes (TNT, HMX, RDX, NTO, NQ, ADN, AP, DMDMB, 2,6-DNT and 4-ADNT) were perfectly classified. On the other hand, PETN, AN and HNIW were misclassified as HMX, ADN and RDX, in 2% of cases, respectively. Tetryl was misclassified as 4-ADNT in 4% of cases. However, the average classification rate of the 14 explosive compounds was about 99.3%, and it can be concluded that the overall extent of false recognition of Raman peaks by PCA is trivial. This capability to identify and discriminate among 14 explosive compounds will be very useful in the application of a detector for specific explosives.

1

4. Conclusion We developed a fast and sensitive recognition system for 14 different explosive compounds using Raman spectroscopy and PCA. To obtain optimal spectral data of the 14 explosive compounds, their Raman spectra were measured using lasers of three different wavelengths. The best signal-to-noise Raman spectra could be achieved with laser excitation at 514.5 nm due to its high scattering efficiency. For 14 explosive compounds, 700 Raman spectra (50 measurements per sample) were collected and pretreated using noise suppression and baseline elimination methods. Features for discrimination among different explosive compounds were obtained using PCA. The MAP analysis demonstrates an excellent discrimination capability of 99.3% average classification rate. In comparison with conventional explosive detection techniques, Raman spectroscopy combined with multivariate analysis has several

136

J. Hwang et al. / Journal of Molecular Structure 1039 (2013) 130–136

advantages. It is rapid and inexpensive because it does not require complicated sample preparation. Due to its instantaneous action and stand-off capacity, it allows explosive samples to be analyzed at a distance. In addition, the combination of the pattern recognition technique of PCA with a Raman detection system can be effective to detect explosives over a short period. This expedient and accurate capability to discriminate between explosive compounds would be also useful for fast detection and identification of explosives in the field. Acknowledgments This work is supported by ADD in South Korea. This work was also partially supported by the Ministry of Knowledge Economy (MKE) and Korea Industrial Technology Foundation (KOTEF) through the Human Resource Training Project for Strategic Technology. References [1] [2] [3] [4]

J.S. Caygill, F. Davis, S.P.J. Higson, Talanta 88 (2011) 14–29. R.S. Golightly, W.E. Doering, M.J. Natan, ACS Nano 3 (2009) 2859–2869. R.G. Ewing, D.A. Atkinson, G. Eiceman, G. Ewing, Talanta 54 (2001) 515–529. Z. Takßts, I. Cotte-Rodriguez, N. Talaty, H. Chen, R.G. Cooks, Chem. Commun. (2005) 1950–1952. [5] E.M. Witko, W.D. Buchanan, T.M. Korter, J. Phys. Chem. A 115 (2011) 12410– 12418.

[6] K. Banas, A. Banas, H. Moser, M. Bahou, W. Li, P. Yang, M. Cholewa, S. Lim, Anal. Chem. 82 (2010) 3038–3044. [7] J.L. Gottfried, F.C. De Lucia Jr., C.A. Munson, A.W. Miziolek, J. Anal. At. Spectrom. 23 (2008) 205–216. [8] C. Ramos, P.J. Dagdigian, Appl. Opt. 46 (2007) 620–627. [9] Y. Engel, R. Elnathan, A. Pevzner, G. Davidi, E. Flaxer, F. Patolsky, Angew. Chem. Int. Ed. 49 (2010) 6830–6835. [10] A.A. Adams, P.T. Charles, J.R. Deschamps, A.W. Kusterbeck, Anal. Chem. 83 (2011) 8411–8419. [11] D. Moore, Rev. Sci. Instrum. 75 (2004) 2499–2512. [12] E.L. Izake, Forensic Sci. Int. 202 (2010) 1–8. [13] D.S. Moore, R.J. Scharff, Anal. Bioanal. Chem. 393 (2009) 1571–1578. [14] C. Eliasson, N. Macleod, P. Matousek, Anal. Chem. 79 (2007) 8185–8189. [15] B. Zachhuber, C. Gasser, E.H. Chrysostom, B. Lendl, Anal. Chem. 83 (2011) 9438–9442. [16] F. Yan, T. Vo-Dinh, Sen. Actuators, B 121 (2007) 61–66. [17] P. Chen, A. Shen, X. Zhou, J. Hu, Anal. Methods 3 (2011) 1257–1269. [18] H.J. Im, B.C. Song, Y.J. Park, K. Song, Appl. Radiat. Isot. 67 (2009) 1458–1462. [19] S.J. Baek, A. Park, J. Kim, A. Shen, J. Hu, Chemometr. Intell. Lab. Syst. 98 (2009) 24–30. [20] A. Savitzky, M.J.E. Golay, Anal. Chem. 36 (1964) 1627–1639. [21] Y. Salinas, R. Martínez-Máñez, M.D. Marcos, F. Sancenón, A.M. Costero, M. Parra, S. Gil, Chem. Soc. Rev. 41 (2012) 1261–1296. [22] L.E. Fried, W. Howard, P. Souers, CHEETAH 2.0 User’s Manual, Lawrence Livermore National Laboratory, Livermore, CA, UCRL-MA-117541, Rev, vol. 5, 1998. [23] C. Storm, J. Stine, J. Kramer, Sensitivity Relationships in Energetic Materials, Los Alamos National Lab., NM, USA, 1989. [24] B.M. Rice, J.J. Hare, J. Phys. Chem. A 106 (2002) 1770–1783. [25] M.D. Woodka, V.P. Schnee, M.P. Polcha, Anal. Chem. 82 (2010) 9917–9924. [26] V.H. Segtnan, Š. Šašic, T. Isaksson, Y. Ozaki, Anal. Chem. 73 (2001) 3153–3161. [27] S.J. Baek, A. Park, A. Shen, J. Hu, J. Raman Spectrosc. 42 (2011) 1987–1993.