Detection of explosives with laser-induced breakdown spectroscopy

Wang, Qianqian; Li, Kai; Zhao, Hua; Ge, Cong-hui; Huang, Zhiwen

doi:10.1007/s11467-012-0272-x

Cited by 53 publications

(26 citation statements)

References 18 publications

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“…Currently there are numerous applications for LPPs in a wide variety of fields, including laser-induced breakdown spectroscopy (LIBS) [1][2][3][4], pulsed-laser deposition (PLD) [5,6], distinguishing of explosives [7][8][9][10][11][12][13], environmental science [14][15][16], production of classical and novel materials [17][18], cultural heritage monitoring [19] or space exploration [20][21][22]. Beyond traditional applications of LIBS, where inorganic materials are mainly studied for analytical purposes, recent progresses in LIBS lead to analysis of organic and biological samples for detection of chemical and biological warfare agent materials [23][24][25][26][27][28], animal tissues studies [29][30][31][32][33][34][35][36][37], identification of bacteria [38][39][40][41][42][43][44][45]…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Time- and space-resolved spectroscopic characterization of laser-induced swine muscle tissue plasma

Camacho

Díaz

Martínez-Ramírez

et al. 2015

Spectrochimica Acta Part B: Atomic Spectroscopy

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The spatial-temporal evolution of muscle tissue sample plasma induced by a high-power transversely excited atmospheric (TEA) CO 2 pulsed laser at vacuum conditions (0.1-0.01 Pa) has been investigated using high-resolution optical emission spectroscopy (OES) and imaging methods.The induced plasma shows mainly electronically excited neutral Na, K, C, Mg, H, Ca, N and O Plasma parameters such as electron density and temperature were measured from the spatiotemporal analysis of different species. Average velocities of some plasma species were estimated.

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Section: Accepted M Manuscriptmentioning

confidence: 99%

Time- and space-resolved spectroscopic characterization of laser-induced swine muscle tissue plasma

Camacho

Díaz

Martínez-Ramírez

et al. 2015

Spectrochimica Acta Part B: Atomic Spectroscopy

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“…Recent work has revealed the effects of substrate and interferent dependence [171] on analyte classification and how selective sampling can enable surface sensitivity [172][173][174][175][176]. Effects caused by wind [177], thermal radiation [166,178,179], and different atmospheres [179][180][181] were also investigated.…”

Section: Laser-induced Breakdown Spectroscopymentioning

confidence: 99%

“…Linear analysis was found to be insufficient, necessitating multivariate analysis [190]. Partial-least-squares discriminant analysis [175,178,191] and machine-learning classifiers with supervised learning methods [172,189,192] are two of the more common techniques. Combining LIBS with Raman spectroscopy has increased the ability to identify unknown materials at stand-off distances through sensor-data fusion (Fig.…”

Section: Laser-induced Breakdown Spectroscopymentioning

confidence: 99%

Advances in explosives analysis—part II: photon and neutron methods

et al. 2015

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The number and capability of explosives detection and analysis methods have increased dramatically since publication of the Analytical and Bioanalytical Chemistry special issue devoted to Explosives Analysis [Moore DS, Goodpaster JV, Anal Bioanal Chem 395:245-246, 2009]. Here we review and critically evaluate the latest (the past five years) important advances in explosives detection, with details of the improvements over previous methods, and suggest possible avenues towards further advances in, e.g., stand-off distance, detection limit, selectivity, and penetration through camouflage or packaging. The review consists of two parts. Part I discussed methods based on animals, chemicals (including colorimetry, molecularly imprinted polymers, electrochemistry, and immunochemistry), ions (both ion-mobility spectrometry and mass spectrometry), and mechanical devices. This part, Part II, will review methods based on photons, from very energetic photons including X-rays and gamma rays down to the terahertz range, and neutrons.

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“…Laser-Induced Breakdown Spectroscopy (LIBS) has been proved a useful tool for qualitative and quantitative analysis of samples, since it can obtain results in a fast in-situ and non-destructive way. Thus, it has been widely applied in numerous fields, such as metals [1][2][3][4] , coal 5,6 , geochemical investigation [7][8][9][10] , archaeological findings 11 , plastics [12][13][14] and explosives [15][16][17] . In China, a lot of researchers have put effort in the development of this method in these years, involving the fundamentals, instrumentation, data processing and modeling applications 18 .…”

Section: Introductionmentioning

confidence: 99%

A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy

Zhang

Sun

Kong

et al. 2017

AOPC 2017: Optical Spectroscopy and Imaging

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Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.

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Detection of explosives with laser-induced breakdown spectroscopy

Cited by 53 publications

References 18 publications

Time- and space-resolved spectroscopic characterization of laser-induced swine muscle tissue plasma

Time- and space-resolved spectroscopic characterization of laser-induced swine muscle tissue plasma

Advances in explosives analysis—part II: photon and neutron methods

A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy

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