Standoff Detection of Explosives at 1 m using Laser Induced Breakdown Spectroscopy

We report the ‘standoff detection’ of explosives at 1 m in laboratory conditions, for the first time in India, using Laser Induced Breakdown Spectroscopy combined with multivariate analysis. The spectra of a set of five secondary explosives were recorded at a distance of 1 m from the focusing as well as collection optics. The plasma characteristics viz., plasma temperature and electron density were estimated from Boltzmann statistics and Stark broadening respectively. Plasma temperature was estimated to be of the order of (10.9 ± 2.1) .103 K and electron density of (3.9 ± 0.5) .1016 cm-3. Using a ratiometric approach, C/H and H/O ratios showed a good correlation with the actual stoichiometric ratios and a partial identification success could be achieved. Finally employing principle component analysis, an excellent classification could be attained.

Laser Induced Breakdown Spectroscopy for Classification of High Energy Materials using Elemental Intensity Ratios

A simple, yet efficient, methodology is proposed to classify three high energy materials (HEMs) with diverse composition using nanosecond laser induced breakdown spectroscopic data. We have calculated O/N, N/H, and O/H elemental peaks ratios using a ratiometric method. The present work describes a novel way to construct 1D, 2D, and 3D classification model using the above mentioned ratios. Multivariate statistical methods are followed for construction of the classification models. A detailed procedure for classification of three different HEMs is presented here.Defence Science Journal, Vol. 64, No. 4, July 2014, pp.332-338, DOI:http://dx.doi.org/10.14429/dsj.64.474

Investigation of Hazardous Materials in Firecrackers using LIBS Coupled with a Chemometric Method and FTIR Spectroscopy

This article reports the detection and quantification of toxic constituents in firecrackers using LIBS coupled with PCA and FTIR. Spectral signatures of lethal elements along with other elements and electronic bands of Cyanide, AlO, BaO, and CaO are seen in their LIBS spectra which confirms the presence of inorganic and organic compound in the fireworks. The concentration of each constituent/element is determined using the CF-LIBS method and results are compared with ICP-OES results. The concentration of Al is in adequate amount except S4 (b). Li and Ba are present in all samples with maximum amount in S4 (b) and S3 respectively. Molecular stretching of SO4-, C4 H8 - , CuCl- , CO3 - , and NO3 - are observed in the FTIR spectra of the samples. The combined results of LIBS and FTIR recommends the presence of BaNO3 , LiCO3 , SrCO3 , Al-chip, and charcoal in the firecrackers. To discriminate various firecrackers, PCA of the LIBS data is performed. The results show that S3 and S4 (b) are more harmful as they contain higher concentration the compounds of Al, Ba, Li, Sr i.e BaNO3 , LiCO3 , SrCO3 , (Cu3 As2 O3 Cu(C2 H3 O2 )2 )

Non-Gated Laser Induced Breakdown Spectroscopy Provides a Powerful Segmentation Tool on Concomitant Treatment of Characteristic and Continuum Emission

We demonstrate the application of non-gated laser induced breakdown spectroscopy (LIBS) for characterization and classification of organic materials with similar chemical composition. While use of such a system introduces substantive continuum background in the spectral dataset, we show that appropriate treatment of the continuum and characteristic emission results in accurate discrimination of pharmaceutical formulations of similar stoichiometry. Specifically, our results suggest that near-perfect classification can be obtained by employing suitable multivariate analysis on the acquired spectra, without prior removal of the continuum background. Indeed, we conjecture that pre-processing in the form of background removal may introduce spurious features in the signal. Our findings in this report significantly advance the prior results in time-integrated LIBS application and suggest the possibility of a portable, non-gated LIBS system as a process analytical tool, given its simple instrumentation needs, real-time capability and lack of sample preparation requirements.National Institute for Biomedical Imaging and Bioengineering (U.S.) (9P41EB015871-26A1

Effects of disordered microstructure and heat release on propagation of combustion front

Numerical experiments for diagnosis of combustion of actual heterogeneous systems are performed on a one-dimensional chain. The internal microstructure of actual heterogeneous systems is apriori unknown, various distributions like uniform, beta, and normal have been considered for distributing neighboring reaction cells. Two cases, for the nature of distribution of heat release of reaction cells are taken into account, one with identical heat release and the other with disordered heat release. Role of different random distributions in describing heterogeneous combustion process is established in present paper. Particularly, the normal distribution of arranging neighboring reaction cells has been found to be powerful methodology in explaining the combustion process of an actual heterogeneous system at higher ignition temperatures for both cases of distributing heat release. Validation of the developed model with the experimental data of combustion of the CMDB propellants, gasless Ti + xSi system, and different thermite mixtures is performed. Our results show that the experimental burning rates at higher ignition temperatures (ε > 0.32) of the heterogeneous system are better reproduced theoretically with the present model. We have also shown that different combustion limits for different thermite systems are the consequences of disordered heat release. Experimental data for thermite systems that have lower inflammability limits are analyzed in the view of disordered heat releases of cells. The model developed in the view of disordered heat releases reproduces the experimental burn rates and experimental combustion limit

Time-dependent intensity ratio-based approach for estimating the temperature of laser produced plasma

Reported here is a rapid and simplified approach for modeling the temporal evolution of the plasma temperature. The use of only two emission lines makes this technique simple, accurate, and fast. Usually, multiple emission lines are required for estimating plasma temperature using Boltzmann/Saha–Boltzmann plots. But, in several cases, either multiple emission lines are not available for every element and/or sufficient lines are not free from self-absorption effect. The proposed method greatly increases the possibility of plasma temperature estimation as it requires only two lines. A brass target was used to generate the plasma, using a conventional single-pulse nanosecond laser of ∼7 ns pulse duration at an excitation wavelength of 532 nm. The initial temperature of plasma and the radiation decay constant were estimated using a proposed intensity ratio model. The results were estimated using various combinations of emission lines, which show an excellent agreement with the values obtained using the previously reported method

Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection

Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the ‘curse of dimensionality’ have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers –based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations.National Institute for Biomedical Imaging and Bioengineering (U.S.) (9P41EB015871-27A1

Representative LIBS spectra acquired from the pharmaceutical formulation investigated in this report.

<p>(a) Cetirizine dihydrochloride; (b) Cipro pure; (c) Metformin hydrochloride; (d) Ciprofloxacin hydrochloride. Intensity on the y-axis is normalized with respect to the characteristic hydrogen emission peak at 656 nm.</p

Incorporation of Support Vector Machines in the LIBS Toolbox for Sensitive and Robust Classification Amidst Unexpected Sample and System Variability

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)when measurements from samples <i>not</i> included in the training set are incorporated in the test datahighlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis