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.

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

LIBS as a Spectral Sensor for Monitoring Metallic Molten Phase in Metallurgical Applications—A Review

This review article discusses the latest advances on molten phase monitoring in metallurgical processes by using Laser-Induced Breakdown Spectroscopy (LIBS). LIBS is an analytical laser-based technique, where a pulsed laser is focused on a sample to create a plasma. The optical emission from the plasma can be transferred through open-path optical configuration or via an optical fiber to a spectrometer to receive analytical information in the form of elemental composition. Thus, a relatively long-distance analysis can be performed using LIBS. Several modern experimental arrangements, patents and industrial notes are assessed, and the literature is reviewed. The review includes applications of LIBS to analyze steel, iron, aluminum, copper, slags, metal melts, and other materials. Temperature, pressure, and atmospheric composition are crucial parameters of any melting process. Hence, past studies on molten phases describing these parameters have been discussed. Finally, the review addresses the last technological advances for these types of applications. It also points out the need of development in some fields and some limitations to overcome. In addition, the review highlights the use of modern machine learning and data processing techniques to increase the effectiveness of calibration and quantification approaches. These developments are expected to improve the performance of LIBS systems already implemented at an industrial scale and ease the development of new applications in pyrometallurgical processes to address the stringent market and environmental regulations

Discrimination of pharmaceutical samples based on their LIBS spectra.

<p>(<b>A</b>) Scores plot corresponding to principal components 1, 2 and 3 for the spectral dataset acquired from the four samples. The data points corresponding to Cetirizine dihydrochloride, Cipro pure, Metformin hydrochloride and Ciprofloxacin hydrochloride are indicated by green squares, blue circles, black asterisks and yellow inverted triangles, respectively. (<b>B</b>) Hierarchical clustering using the dendrogram representation for LIBS spectra acquired from the 4 sets of pharmaceutical samples.</p

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

Plot of the first three principal components corresponding to the entire spectral dataset acquired for all classes.

<p>These three principal components, combined, explain 96.5% of the net variance in the dataset.</p

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

ANN classification results obtained from the test samples over 100 iterations.

<p>ANN classification results obtained from the test samples over 100 iterations.</p