Laser-induced breakdown spectroscopy analysis of energetic materials

A number of energetic materials and explosives have been studied by laser-induced breakdown spectroscopy (LIBS). They include black powder, neat explosives such as TNT, PETN, HMX, and RDX (in various forms), propellants such as M43 and JA2, and military explosives such as C4 and LX-14. Each of these materials gives a unique spectrum, and generally the spectra are reproducible shot to shot. We observed that the laser-produced microplasma did not initiate any of the energetic materials studied. Extensive studies of black powder and its ingredients by use of a reference spectral library have demonstrated excellent accuracy for unknown identification. Finally, we observed that these nitrogen- and oxygen-rich materials yield LIBS spectra in air that have correspondingly different O:N peak ratios compared with air. This difference can help in the detection and identification of such energetic materials.     

Laser-induced breakdown spectroscopy (LIBS): a promising versatile chemical sensor technology for hazardous material detection

A series of laboratory experiments have been performed highlighting the potential of laser-induced breakdown spectroscopy (LIBS) as a versatile sensor for the detection of terrorist threats. LIBS has multiple attributes that provide the promise of unprecedented performance for hazardous material detection and identification. These include: 1) real-time analysis, 2) high sensitivity, 3) no sample preparation, and 4) the ability to detect all elements and virtually all hazards, both molecular and biological. We have used LIBS to interrogate a variety of different target samples, including explosives, chemical warfare simulants, biological agent simulants, and landmine casings. We have used the acquired spectra to demonstrate discrimination between different chemical warfare simulants, including those on soil backgrounds. A linear correlation technique permits discrimination between an anthrax surrogate and several other biomaterials such as molds and pollens. We also use broadband LIBS to identify landmine casings versus other plastics and environmental clutter materials. A new man-portable LIBS system developed as a collaborative effort between the U.S. Army Research Laboratory and Ocean Optics, Inc., is described and several other schemes for implementing LIBS sensors for homeland security and force protection are discussed.     

Identification of explosives with two-dimensional ultraviolet resonance Raman spectroscopy

The first two-dimensional (2D) resonance Raman spectra of TNT, RDX, HMX, and PETN are measured with an instrument that sequentially and rapidly switches between laser wavelengths, illuminating these explosives with forty wavelengths between 210 nm and 280 nm. Two-dimensional spectra reflect variations in resonance Raman scatter with illumination wavelength, adding information not available from single or few one-dimensional spectra, thereby increasing the number of variables available for use in identification, which is especially useful in environments with contaminants and interferents. We have recently shown that 2D resonance Raman spectra can identify bacteria. Thus, a single device that identifies the presence of explosives, bacteria, and other chemicals in complex backgrounds may be feasible.     

Analyzing Raman maps of pharmaceutical products by sample-sample two-dimensional correlation

Sample-sample (SS) two-dimensional (2D) correlation spectroscopy is applied in this study as a spectral selection tool to produce chemical images of real-world pharmaceutical samples consisting of two, three, and four components. The most unique spectra in a Raman mapping spectral matrix are found after analysis of the covariance matrix. (This is obtained by multiplying the original mapping data matrix by itself.) These spectra are identified by analyzing the slices of the covariance matrix at the positions where covariance values are at maxima. Chemical images are subsequently produced in a univariate fashion by visually selecting the wavenumbers in the extracted spectra that are least overlapped. The performance of SS 2D correlation is compared with principal component analysis in terms of highlighting the most prominent spectral differences across the whole data set (which typically comprises several thousand spectra) and determining the total number of species present. In addition, the selection of the unique spectra by SS 2D correlation is compared with the selection obtained by the orthogonal projection approach (OPA). Both comparisons are found to be satisfactory and demonstrate that a quite simple SS 2D correlation routine can be used for producing reliable images of unknown samples. The main benefit of using SS 2D correlation is that it is based on a few data processing commands that can be executed separately and produce results that are closely related to the chemical features of the system.     

Hierarchical Alignment and Full Resolution Pattern Recognition of 2D NMR Spectra: Application to Nematode Chemical Ecology

Nuclear magnetic resonance (NMR) is the most widely used nondestructive technique in analytical chemistry. In recent years, it has been applied to metabolic profiling due to its high reproducibility, capacity for relative and absolute quantification, atomic resolution, and ability to detect a broad range of compounds in an untargeted manner. While one-dimensional (1D) (1)H NMR experiments are popular in metabolic profiling due to their simplicity and fast acquisition times, two-dimensional (2D) NMR spectra offer increased spectral resolution as well as atomic correlations, which aid in the assignment of known small molecules and the structural elucidation of novel compounds. Given the small number of statistical analysis methods for 2D NMR spectra, we developed a new approach for the analysis, information recovery, and display of 2D NMR spectral data. We present a native 2D peak alignment algorithm we term HATS, for hierarchical alignment of two-dimensional spectra, enabling pattern recognition (PR) using full-resolution spectra. Principle component analysis (PCA) and partial least squares (PLS) regression of full resolution total correlation spectroscopy (TOCSY) spectra greatly aid the assignment and interpretation of statistical pattern recognition results by producing back-scaled loading plots that look like traditional TOCSY spectra but incorporate qualitative and quantitative biological information of the resonances. The HATS-PR methodology is demonstrated here using multiple 2D TOCSY spectra of the exudates from two nematode species: Pristionchus pacificus and Panagrellus redivivus. We show the utility of this integrated approach with the rapid, semiautomated assignment of small molecules differentiating the two species and the identification of spectral regions suggesting the presence of species-specific compounds. These results demonstrate that the combination of 2D NMR spectra with full-resolution statistical analysis provides a platform for chemical and biological studies in cellular biochemistry, metabolomics, and chemical ecology.     

Raman spectroscopy as a discovery tool in carbohydrate chemistry

Raman spectra of nine anomerically stable monosaccharides have been obtained in aqueous solution in the 700-1700 cm(-1) spectral range. Good-quality spectra are obtained of solutions with concentrations as low as 10 mM and volumes as small as 15 microL. Interestingly, the Raman spectra appear to be exquisitely sensitive to the configuration of the carbon centers; unique spectra are obtained of all nine monosaccharides. The unique Raman spectral fingerprint observed for each monosaccharide, and for each anomer of each monosaccharide, suggests that Raman spectroscopy may be a useful technique for the identification and characterization of biologically relevant oligosaccharides. To test this idea, Raman spectra of three unknown disaccharides were obtained in a single-blind study. Identification of the individual monosaccharide components and their anomeric configuration was completely successful. All of these results suggest that development of Raman spectroscopy as a fast, sensitive discovery tool in glycobiology and carbohydrate chemistry is straightforward.     

A matrix-based two-dimensional regularization algorithm for signal-to-noise ratio enhancement of multidimensional spectral data

We present a new spectral image processing algorithm, the "matrix maximum entropy method" (MxMEM), which offers efficient signal-to-noise ratio (SNR) enhancement of multidimensional spectral data. MxMEM is based upon two previous regularization methods that employ the maximum entropy concept. The first is a one-dimensional (1D) algorithm, which smoothes individual vectors, called the two-point maximum entropy method (TPMEM). The second is a two-dimensional (2D) form called 2D TPMEM, that smoothes images but processes them one vector at a time. MxMEM is a truly two dimensional image processing algorithm in that its "smoothing engine" performs two-dimensional processing in every iteration. We demonstrate that this matrix-based construction makes more effective use of two-dimensionally embedded information and thus confers significant advantages over other regularization approaches. In addition, we utilize the concept that individual related Raman spectra can be combined in a matrix to form an artificial Raman "image". We show that, when processed as an image, superior SNR enhancement is achieved compared to processing the same data by TPMEM one spectrum at a time.     

The effect of bacterial environmental and metabolic stresses on a laser-induced breakdown spectroscopy (LIBS) based identification of Escherichia coli and Streptococcus viridans

In this paper we investigate the effect that adverse environmental and metabolic stresses have on the laser-induced breakdown spectroscopy (LIBS) identification of bacterial specimens. Single-pulse LIBS spectra were acquired from a non-pathogenic strain of Escherichia coli cultured in two different nutrient media: a trypticase soy agar and a MacConkey agar with a 0.01% concentration of deoxycholate. A chemometric discriminant function analysis showed that the LIBS spectra acquired from bacteria grown in these two media were indistinguishable and easily discriminated from spectra acquired from two other non-pathogenic E. coli strains. LIBS spectra were obtained from specimens of a nonpathogenic E. coli strain and an avirulent derivative of the pathogen Streptococcus viridans in three different metabolic situations: live bacteria reproducing in the log-phase, bacteria inactivated on an abiotic surface by exposure to bactericidal ultraviolet irradiation, and bacteria killed via autoclaving. All bacteria were correctly identified regardless of their metabolic state. This successful identification suggests the possibility of testing specimens that have been rendered safe for handling prior to LIBS identification. This would greatly enhance personnel safety and lower the cost of a LIBS-based diagnostic test. LIBS spectra were obtained from pathogenic and non-pathogenic bacteria that were deprived of nutrition for a period of time ranging from one day to nine days by deposition on an abiotic surface at room temperature. All specimens were successfully classified by species regardless of the duration of nutrient deprivation.     

Robust validation of pattern classification methods for laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is an emerging technology that is suitable for a variety of material identification applications. For LIBS to successfully transition from the laboratory into field applications, the sensor must be paired with the appropriate algorithms for accurate and robust processing of the LIBS spectra. In this study we will report on the results of testing classification methods on eight distinct classification tasks using LIBS datasets. Results suggest that standard cross-validation techniques may not accurately estimate generalization performance and a proposed "leave-one-sample-out" approach to experiment design for classifier validation may provide a more robust measure of performance.     

Identification of inorganic pigments used in porcelain cards based on fusing Raman and X-ray fluorescence (XRF) data

Raman spectroscopy and X-ray fluorescence (XRF) spectroscopy are often used as complementary techniques that are well suited for the analysis of art objects because both techniques are fast, sensitive, and noninvasive and measurements can take place in situ. In most of these studies, both techniques are used separately, in the sense that the spectra are evaluated independently and single conclusions are obtained, considering both results. This paper presents a data fusion procedure for Raman and XRF data for the characterization of pigments used in porcelain cards. For the classification of the analyzed points of the porcelain cards principal component analysis (PCA) was used. A first attempt was made to develop a new procedure for the identification of the pigments using a database containing the fused Raman-XRF data of 24 reference pigments. The results show that the classification based on the fused Raman-XRF data is significantly better than the classifications based on the Raman data or the XRF data separately.     

Surface-enhanced Raman spectroscopy of soft-landed polyatomic ions and molecules

Surface-enhanced Raman spectroscopy (SERS) was used to detect and characterize polyatomic cations and molecules that were electrosprayed into the gas phase and soft-landed in vacuum on plasma-treated silver substrates. Organic dyes such as crystal violet and Rhodamine B, the nucleobase cytosine, and nucleosides cytidine and 2'-deoxycytidine were immobilized by soft landing on plasma-treated metal surfaces at kinetic energies ranging from near thermal to 200 eV. While enhancing Raman scattering 10(5)-10(6)-fold, the metal surface effectively quenches the fluorescence that does not interfere with the Raman spectra. SERS spectra from submonolayer amounts of soft-landed compounds were sufficiently intense and reproducible to allow identification of Raman active vibrational modes for structure assignment. Soft-landed species appear to be microsolvated on the surface and bound via ion pairing or pi-complexation to the Ag atoms and ions in the surface oxide layer. Comparison of spectra from soft-landed and solution samples indicates that the molecules survive soft landing without significant chemical damage even when they strike the surface at hyperthermal collision energies.     

Characterization of woody and herbaceous biomasses lignin composition with 1064 nm dispersive multichannel Raman spectroscopy

Biomass representing different classes of bioenergy feedstocks, including woody and herbaceous species, was measured with 1064 nm Raman spectroscopy. Pine, oak, poplar, kenaf, miscanthus, pampas grass, switchgrass, alfalfa, orchard grass, and red clover were included in this study. Spectral differences have been identified with an emphasis on lignin guaiacyl and syringyl monomer content and carotenoid compounds. The interpretation of the Raman spectra was correlated with (13)C-nuclear magnetic resonance cross-polarization/magic-angle spinning spectra of select biomass samples. Thioacidolysis quantification of guaiacyl and syringyl monomer composition and the library of Raman spectra were used as a training set to develop a principal component analysis model for classifying plant samples and a principal component regression model for quantifying lignin guaiacyl and syringyl composition. Raman spectroscopy with 1064 nm excitation offers advantages over alternative techniques for biomass characterization, including low spectral backgrounds, higher spectral resolution, short analysis times, and nondestructive analyses.     

Automatic correction of continuum background in laser-induced breakdown and Raman spectrometry

The goal of this work was the development and evaluation of an algorithm for the approximation and automatic subtraction of continuum backgrounds in laser-induced breakdown and Raman spectra. The background correction algorithm was applied to simple and complex spectra and its effect on identification accuracy was studied. Linear correlation was used for the identification of plastic samples using both laser-induced breakdown and Raman spectra. For both techniques, the algorithm successfully eliminated continuum background without compromising spectral integrity. A significant improvement in the percentage of correct plastic identifications was observed for Raman spectra. The approach should be applicable to a wide range of background correction problems in atomic and molecular spectroscopy.     

Extracting coal ash content from laser-induced breakdown spectroscopy (LIBS) spectra by multivariate analysis

Laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) analysis has been applied for the quantitative analysis of the ash content of coal in this paper. The multivariate analysis method was employed to extract coal ash content information from LIBS spectra rather than from the concentrations of the main ash-forming elements. In order to construct a rigorous partial least squares regression model and reduce the calculation time, different spectral range data were used to construct partial least squares regression models, and then the performances of these models were compared in terms of the correlation coefficients of calibration and validation and the root mean square errors of calibration and cross-validation. Afterwards, the prediction accuracy, reproducibility, and the limit of detection of the partial least squares regression model were validated with independent laser-induced breakdown spectroscopy measurements of four unknown samples. The results show that a good agreement is observed between the ash content provided by thermo-gravimetric analyzer and the LIBS measurements coupled to the PLS regression model for the unknown samples. The feasibility of extracting coal ash content from LIBS spectra is approved. It is also confirmed that this technique has good potential for quantitative analysis of the ash content of coal.     

Raman signal processing software for automated identification of mineral phases and biosignatures on Mars

Data from the ESA ExoMars Rover Mission will provide invaluable input for further studies in astro/exobiology. The search for mineral products as indicators of present and/or past biogenetic activities in Mars' surface and subsurface samples is the main objective of the compact Raman-laser-induced breakdown spectroscopy (LIBS) instrument. The inherent features of Raman spectroscopy and LIBS make the combined instrument a unique and very powerful tool in the search for biomarkers and hence it is regarded as the highest priority instrument for mineral analysis within the mission. We have developed a software package for the on-board processing of the instrument's data outputs, including spectral conditioning and search-match characterization of mineral phases and biomarkers. In this paper we show the mathematical and physical basis of the software package.     

Discrimination of bacteria and bacteriophages by Raman spectroscopy and surface-enhanced Raman spectroscopy

Detection of pathogenic organisms in the environment presents several challenges due to the high cost and long times typically required for identification and quantification. Polymerase chain reaction (PCR) based methods are often hindered by the presence of polymerase inhibiting compounds and so direct methods of quantification that do not require enrichment or amplification are being sought. This work presents an analysis of pathogen detection using Raman spectroscopy to identify and quantify microorganisms without drying. Confocal Raman measurements of the bacterium Escherichia coli and of two bacteriophages, MS2 and PRD1, were analyzed for characteristic peaks and to estimate detection limits using traditional Raman and surface-enhanced Raman spectroscopy (SERS). MS2, PRD1, and E. coli produced differentiable Raman spectra with approximate detection limits for PRD1 and E. coli of 10(9) pfu/mL and 10(6) cells/mL, respectively. These high detection concentration limits are partly due to the small sampling volume of the confocal system but translate to quantification of as little as 100 bacteriophages to generate a reliable spectral signal. SERS increased signal intensity 10(3) fold and presented peaks that were visible using 2-second acquisitions; however, peak locations and intensities were variable, as typical with SERS. These results demonstrate that Raman spectroscopy and SERS have potential as a pathogen monitoring platform.     

"Sequential order" rules in generalized two-dimensional correlation spectroscopy

The widely used "sequential order" rules in the generalized two-dimensional (2D) correlation spectroscopy were adopted from the mechanical perturbation-based 2D infrared, where dynamic spectral intensity variation must be a simple sinusoid. 2D correlation analysis is fundamentally a form of parametrization of the integrated or overall relationship between two variable quantities. In generalized 2D correlation spectroscopy, however, the dynamic spectral intensity variations are generally nonperiodic and monotonic, and spectral intensity changes are largely instantaneous. The sequential orders in generalized situations are therefore localized. It is naturally necessary and important to testify whether the analysis result obtained by using the sequential order rules is consistent with the local sequential order of events, which reflects the real sequential order in generalized situations. Unfortunately, this test was not done yet. In this report, the sequential order rules have been tested in the generalized situations using simulated spectra with different local sequential orders and assuming the intensity changes of bands take the exponential forms. It has been found that the sequential order rules correctly identify the local sequential order of two events when spectral intensities of two bands increase or decrease at the same direction but fail when spectral intensities change at different directions. In addition, 2D correlation analysis cannot distinguish the local sequential order from the rate difference of events. A theoretical analysis demonstrates that the synchronous and asynchronous spectra in the generalized 2D correlation spectroscopy may also indicate the linear/nonlinear relationship, in addition to the integrated or overall sequential order of events. The synchronous and asynchronous spectra in the generalized 2D correlation spectroscopy do not necessarily provide the information on the local sequential order or rate difference of events.     

On-line identification method in column liquid chromatography: UV resonance Raman spectroscopy.

Ultraviolet resonance Raman spectroscopy (RRS) is presented as a novel identification tool for conventional-size column liquid chromatography (LC). The on-line coupling was made using a standard Z-shaped flow cell. A continuous-wave frequency-doubled argon ion laser operating at a wavelength of 244 nm was used for excitation. "On-the-fly" resonance Raman spectra of four model compounds, fluorene, phenanthrene, fluoranthene, and pyrene, were recorded after a standard acetonitrile/water reversed-phase LC separation. When applying a large-volume-injection procedure (32 mL), detection limits were at the nanogram per milliliter level. The results indicate that UV-RRS gives detailed spectral information at an appropriate sensitivity level so that coupling with LC becomes feasible.     

Raman spectroscopic discrimination of cell response to chemical and physical inactivation

Raman spectroscopy was applied to study Escherichia coli and Staphylococcus epidermidis cells that were inactivated by different chemicals and stress conditions including starvation and high temperature. E. coli cells exposed to starvation conditions over several days lost viability at the same rate that spectral bands assigned to DNA and RNA bases decreased in intensity. Band intensities correlate with standard plate counts with R(2) = 0.99 and R(2) = 0.97, respectively. Principal components analysis and discriminant analysis multivariate statistical techniques were used to evaluate the spectral data collected. Significant changes were observed in the spectra of treated cells in comparison with their respective controls (samples without treatment). As a result, there was a significant differentiation between viable and non-viable cells (treated and non-treated cells) in the first and second principal component plots for all the treatments. Discriminant analysis was used along with PCA to estimate a classification rate based on viability status of the cells. Non-viable cells were differentiated from viable cells with classification rates that ranged between 60 and 90% for specific treatments (i.e., EDTA-treated cells versus control cells). The classification rate obtained considering all the treatments (non-viable cells) and controls (viable cells) at the same time for each of the species studied was 86%. The classification rate based on species differentiation when all the spectra (viable and non-viable) were used was 87%. These results suggest that Raman spectroscopy is a powerful tool that can be used to evaluate viability and to study metabolic changes in microorganisms. It is a robust method for bacterial identification even when high spectral variations are introduced.     

Qualitative analysis using Raman spectroscopy and chemometrics: a comprehensive model system for narcotics analysis

The rapid, on-site identification of illicit narcotics, such as cocaine, is hindered by the diverse nature of the samples, which can contain a large variety of materials in a wide concentration range. This sample variance has a very strong influence on the analytical methodologies that can be utilized and in general prevents the widespread use of quantitative analysis of illicit narcotics on a routine basis. Raman spectroscopy, coupled with chemometric methods, can be used for in situ qualitative and quantitative analysis of illicit narcotics; however, careful consideration must be given to dealing with the extensive variety of sample types. To assess the efficacy of combining Raman spectroscopy and chemometrics for the identification of a target analyte under real-world conditions, a large-scale model sample system (633 samples) using a target (acetaminophen) mixed with a wide variety of excipients was created. Materials that exhibit problematic factors such as fluorescence, variable Raman scattering intensities, and extensive peak overlap were included to challenge the efficacy of chemometric data preprocessing and classification methods. In contrast to spectral matching analyte identification approaches, we have taken a chemometric classification model-based approach to account for the wide variances in spectral data. The first derivative of the Raman spectra from the fingerprint region (750-1900 cm(-1)) yielded the best classifications. Using a robust segmented cross-validation method, correct classification rates of better than ～90% could be attained with regression-based classification, compared to ～35% for SIMCA. This study demonstrates that even with very high degrees of sample variance, as evidenced by dramatic changes in Raman spectra, it is possible to obtain reasonably reliable identification using a combination of Raman spectroscopy and chemometrics. The model sample set can now be used to validate more advanced chemometric or machine learning algorithms being developed for the identification of analytes such as illicit narcotics.