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High resolution applications of laser-induced breakdown spectroscopy for environmental and forensic applications
Spectrochimica Acta Part B 62 (2007) 1426 – 1432 www.elsevier.com/locate/sab
Technical note
High resolution applications of laser-induced breakdown spectroscopy for environmental and forensic applications ☆ Madhavi Z. Martin a,⁎, Nicole Labbé b , Nicolas André b , Ronny Harris c , Michael Ebinger c , Stan D. Wullschleger a , Arpad A. Vass d a
Environmental Sciences Division Oak Ridge National Laboratory, P.O. Box 2008 MS 6038, Oak Ridge TN 37831-6038, USA b Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996-4570, USA c Los Alamos National Laboratory, Los Alamos, NM 87545-J495, USA d Bio-Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008 MS 6422, Oak Ridge TN 37831-6422, USA Received 8 December 2006; accepted 23 October 2007 Available online 13 November 2007
Abstract Laser-induced breakdown spectroscopy (LIBS) has been used in the elemental analysis for a variety of environmental samples and as a proof of concept for a host of forensic applications. In the first application, LIBS was used for the rapid detection of carbon from a number of different soil types. In this application, a major breakthrough was achieved by using a multivariate analytical approach that has brought us closer towards a “universal calibration curve”. In a second application, it has been demonstrated that LIBS in combination with multivariate analysis can be employed to analyze the chemical composition of annual tree growth rings and correlate them to external parameters such as changes in climate, forest fires, and disturbances involving human activity. The objectives of using this technology in fire scar determinations are: 1) To determine the characteristic spectra of wood exposed to forest fires and 2) To examine the viability of this technique for detecting fire occurrences in stems that did not develop fire scars. These examples demonstrate that LIBS-based techniques are inherently well suited for diverse environmental applications. LIBS was also applied to a variety of proof of concept forensic applications such as the analysis of cremains (human cremation remains) and elemental composition analysis of prosthetic implants. © 2007 Elsevier B.V. All rights reserved. Keywords: Laser induced breakdown spectroscopy; Multivariate analysis; Soil carbon measurements; Human and animal bone chemical analysis; Forensic science
1. Introduction Laser-induced breakdown spectroscopy (LIBS) has proven to be a versatile technique to identify most elements present in any sample. This technique has been used for numerous applications in a host of research fields [1–5]. The advantages of LIBS include, identification of metals and non-metals in less than a second or longer depending on the number of averages per sample via a broadband spectrum (200–800 nm), remote instrument operation via fiber-optics to gather information ☆
This paper was presented at the 4th International Conference on Laser Induced Plasma Spectroscopy and Applications (LIBS 2006) held in Montreal, Canada, 5–8 September 2006, and is published in the Special Issue of Spectrochimica Acta Part B, dedicated to that conference. ⁎ Corresponding author. Tel.: +1 865 574 7828; fax: +1 865 576 8646. E-mail address: [email protected] (M.Z. Martin). 0584-8547/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.sab.2007.10.046
from a hazardous site, minimal sample preparation and no waste generation, continuous monitoring capability, and the ability to perform depth profiling and mapping using robust instrumentation. While many groups, worldwide, are developing LIBS instruments and databases for a variety of materials (e.g. glass, metals, inks, etc.) [6–8], previously LIBS technique has been used at ORNL for the quantification of elemental concentrations in environmental samples such as natural and engineered wood [9], wood affected by fire [10], soils [11,12], as well as human and animal bones [13] by using a multivariate model development approach. Wood is a heterogeneous material primarily comprised of macromolecular cellulose, hemicelluloses, lignin and lowmolecular-weight substances, i.e. extractives and minerals [14]. For trees which are grown on different sites, the chemical
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characteristics like lignin content vary even if the genetic origin is the same. This is a result of the influence of the environment on the physiology of trees. Trace elements are present in much lower concentrations. Calcium, as a major component of plant cell walls, and nitrogen, phosphorous, and magnesium, as constituents of proteins, amino acids, and other cellular constituents are dispersed throughout these biological matrices. The presence of lead, chromium and other heavy metals in wood have been identified in the field of dendrochemistry to document the presence of pollutants in the soil and aerial environment. The wood of tree trunks is an ideal archive for the identification of critical events such as volcanic eruptions and chemical changes over long periods of time [15,16]. The chemical composition of annual growth rings can be correlated to external parameters such as changes in climate, forest fires, and disturbances involving human activity. The spectral data obtained from tree rings have the potential to be used for biomonitoring environmental and ecological events such as forest fires. Multivariate methods are powerful approaches to analyze large and complex dataset such as LIBS spectra. Principal component analysis (PCA), one of the most common multivariate analyses, can be used to extract information from large datasets and to determine natural events in trees. This technique shows a great deal of promise for detecting past fire events in trees, even though visible fire scars may be absent. As a result, this technique may prove valuable for constructing more accurate fire histories for forest ecosystems. Soil carbon analysis is a very important aspect of carbon sequestration research. There has been an increase in international efforts to reduce anthropogenic emissions of greenhouse gases, particularly carbon dioxide (CO2), as the link between atmospheric greenhouse gases and climate change has been established. Knowledge that CO2 is stored within, and exchanged between, the atmosphere and vegetation and soils has led to the suggestion that soils and vegetation could be managed to increase their uptake and storage of CO2, and thus become ‘land carbon sinks’. There have been some concerns about the permanence of the carbon sinks and the accuracy with which they can be quantified and verified [17]. Additionally, knowing the total chemical composition of forensic evidence collected at a crime scene would give the added advantage of not only knowing the origin of the particular forensic samples e.g., wood or bones found, but also having the capability of total identification along with quantification of the chemistry, a critical element in forensic comparisons. The forensic applications research at ORNL includes the ability to identify bone, differentiate between the cremated remains of humans and to be able to fingerprint prosthetic implants based on their metallic content. We have used this technique to determine the presence of elements such as, C, N, Ca, Al, Fe, Ti, Si, Mg, Mn, and Na from the wood provided to us from Texas related to a murder investigation. Eleven logs of wood were sampled using the LIBS technique. The chemical fingerprint was found to be consistent for all the wood samples that were tested. This strongly suggested that (1) the wood logs came from the same type of tree species, and (2) the logs likely came
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from the same tree or from a group of trees growing in the same area [18]. Other applications of LIBS for forensic analysis have been done for analysis of glass [19]. 2. Materials and methods 2.1. Fire scar sampling method Concerning the fire scar sampling, a cookie core from longleaf pine (Pinus palustris) was analyzed in triplicate. The main goal of this study was to determine if a change in the dendrochemistry can be determined using the LIBS technique. The core was placed on an automated stage set to sample every 2.5 mm. Thus, a spectrum would be taken and then the stage would move the core 2.5 mm to the next sampling site. Spectra were taken along the length of the cookie from pith to bark. Each sample site was subjected to 10 laser shots which produced plasma containing the elements found in each spectrum (two spectra, one before the fire event and one soon after the fire event are shown in Fig. 1). This plasma emitted light in wavelengths specific to the elements found in that sample. The light was collected by a collection lens, delivered to a spectrometer, digitized by a detector, and downloaded to a computer where it was saved as a series of data points containing 30001 wavelengths (variables) for that sample spot. 2.2. Soil carbon sampling method Soils of different textures and carbon content were obtained from Los Alamos National Laboratory. A total of 38 soil samples were tested using LIBS. These soils were pelletized into 1/4″ thick and 1″ in diameter sized pellet using a pressure of 2500 psi to make consistent sampling configurations. The standard laboratory technique that was used to determine the concentrations of carbon in these soils was done by using the CN-elemental analyzer that heats the sample to a temperature of 1350 °C in the presence of oxygen. Combustion converts SOM containing nitrogen and carbon into N2, NOx, and CO2. Hydrogen and oxygen combine to form water vapor. The combustion gases are analyzed for CO2 (infrared spectroscopy)
Fig. 1. LIBS spectra for the wood tissue before and after the fire event.
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and N2 (thermal conductivity detector). After conducting a systematic LIBS study based on a number of experimental parameters, the optimal conditions to collect the data were 45 mJ of energy per pulse at an excitation wavelength of 532 nm. More comprehensive soil analysis results will be published in a later manuscript. For each soil sample, a LIBS spectrum was obtained by averaging 100 laser shots for a laser repetition rate of 10 Hz. Three spectra per sample were collected. Hence a total number of 114 spectra were obtained for the soil samples. 2.3. Forensic sampling method The forensic samples that were tested were two cremation ash samples obtained from a mortuary in Georgia. Both samples were analyzed by ICP-MS prior to the LIBS experiments. About 300 mg of the powdered cremation samples were pressed into pellets to obtain consistent sampling surfaces. We also obtained a sample of a prosthetic implant that was supposed to have been fabricated using titanium and some titanium oxide mixture. In this case there was a need to know if the sample was actually made of a titanium metal/oxide material. The prosthetic implant sample was directly tested at different points on the sample with 10 laser shots and compared to a pure titanium metal spectrum that was tested using the same protocol. 3. LIBS setup and experiment Laser pulses are delivered to the sample, creating a plasma, resulting in vaporization and atomization of small amounts (about nanograms) of the target material. Spectroscopic detection of the light released from the plasma contains emission spectra which permits identification of the elements through their unique spectral signatures with the signal being diagnostic of the total elemental concentration. An excitation source consisting of a pulsed laser is used for LIBS. The experimental setup consists of a pulsed laser Spectra physics model Indi-HG laser that is a Q-switched Nd:YAG laser with output wavelengths of 1064, 532, and 266 nm. For the data reported in this article, the laser was used at 532 nm with 45 mJ/ pulse. The light emitted by the plasma at the focal volume (FL of input lens is 100 mm) was collected by a set of collection optics and focused into a low O–H silica fiber bundle consisting of 19 fibers. The light is delivered to a 0.5 m Acton Research model SpectraPro-500 spectrometer, (spectral bandwidth = 40 nm for 1200 g/mm grating and slitwidth of spectrometer = 25 μm) which was then detected by an intensified charge coupled detector (ICCD) made by Andor Technologies using gate widths of 10 μm and delays of 1 μm. The detailed experimental set up used for all the measurements mentioned in this article is described in detail elsewhere [20]. 4. Multivariate analysis Multivariate models were used to provide quantitative measure and statistical evaluation of identification of critical events such as forest fires and carbon concentrations in soil
samples. The objective was to validate the LIBS technology by using chemometric methods such as principal component analysis (PCA), and partial least squares (PLS) [21]. If the spectral data contain information about the properties of interest, a reliable calibration model can be constructed. PLS analysis was performed to determine the concentration of carbon in soils of different textures. In order to understand the fire scar chemistry of the sampled growth rings, PCA was used to detect changes in the inorganic elements present before and after the fire event. The data obtained for the forensic samples is preliminary and extensive chemometric methods have not been used yet. Initial results help in fingerprinting and analyzing the chemical composition of human cremation remains. Multivariate analyses can be used to correlate the impacts of external parameters such as changes in the climate and/or disturbances involving human activity on the wood properties, and internal parameters such as nutritional intake, bone density and porosity for humans and animal bones. 5. Results and discussion A cross-section of Mountain pine (Pinaceae Pinus pungens Lamb.) was scanned using a translational stage to determine the differences in the chemical features both before and after a fire event. The line scan was not collected on the fire scar on purpose. The idea was to compare the elemental composition of the tissue grown post-fire to the pre-fire tissue. Collecting spectra on the scar would have shown changes in the elemental composition due the burning process. Fig. 1 shows a spectrum collected on the third ring (before the fire) and a spectrum collected on the 32nd ring (after the fire) along the line scan. Some differences between the elemental composition of the tissue before and after the fire can be directly observed from the spectra. One can easily assign the emission lines of sodium at 588.98 and 589.54 nm. Two emission lines at 422.66 and 396.82 nm are also observed in the two spectra with different intensities. These signals are due to the presence of calcium in the tissue. The spectrum of the tissue after the fire has a very strong emission line at 249.3 nm. This band is assigned to iron. Although chemical differences are obvious by direct visual analysis of the two spectra, comparing 20 spectra simultaneously is impossible. PCA was used to highlight the chemical differences between the spectra. PCA is a projection method that transforms large data sets into smaller ones and into more interpretable plots. For instance, the large matrix that is represented by the 20 samples (spectra) with 30001 wavelengths (variables) each is, by the PCA method, reduced to a 20 samples by 5 principal components matrix. The scores plots represent the samples in the principal components space. The loadings plots are used to explain the relationships between the original variables (spectra) space and the new principal components space. Fig. 2 shows the samples in the first principal component space (line scores plot). Samples with similar chemical characteristics (similar spectra) will be plotted close together while samples with different chemical features will be plotted away one from each other. This plot shows the effect of a fire on the elemental composition of wood tissue. It
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Fig. 2. The score plot shows the change in elemental concentration before, during, and after the fire event.
also showed that the fire affected tree did return to its original wood tissue chemistry after the occurrence of the fire event (Fig. 2). The samples need to be scanned at a higher resolution in order to determine the exact number of years the trees need to return to a “healthy state”. The loading plot (Fig. 3) shows which wavelengths are responsible for the differences between samples along the first principal component. If the score of a sample and the loading of a variable (wavelength) on a particular PC have the same sign, the sample has higher than average value for that variable and vice-versa [22]. By using this statement, one can deduce that sample 14 has a higher iron and calcium content and a lower sulfur and sodium content than the other samples. From these findings, it is therefore possible to presume that a fire will affect the chemical composition of the ring that grows just after the fire and that some elements content will increase and while some will decrease as a result of the fire. Consequently, by monitoring the elemental composition pattern of a tree and by looking for abrupt changes, one can reconstruct the disturbance history of a tree and a forest. Two different approaches were employed to develop a robust and rapid method to obtain carbon content in soil from LIBS data. After collecting the LIBS spectra on three different soil textures (sand, loam, and clay), a univariate and multivariate
Fig. 3. Plot of the loadings of principal component one, showing the chemical changes in the tree.
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Fig. 4. Model to predict carbon content obtained by the univariate approach (at 247.856 nm).
approaches were tested by building models that predict C content from the 38 soils. The univariate model was developed by correlating the intensity of the emission line at 247.856 nm to the “true” carbon content which was obtained by a standard laboratory technique, based on combustion of soils. The model was then used to predict the carbon content in the same samples.
Fig. 5. a. Model to predict carbon content in soil obtained by the multivariate approach. b. Significant variables for the multivariate model.
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Fig. 4 shows a plot of the predicted carbon content versus the measured carbon content with a correlation coefficient of 0.56. In parallel, Projection to Latent Structures (PLS) was used as the multivariate approach to correlate the spectral data to the carbon content. A good model was obtained with a correlation coefficient of 0.97 (Fig. 5a). In addition to the multivariate calibration model, PLS provides a regression coefficients plot (Fig. 5b) which represents the significant variables (wavelengths) in building the model. The emission lines at 230.341, 237.64, 247.86, 251.33 nm are critical to construct the model. All these lines except the one at 237.64 nm are known emission peaks of carbon. These lines have been obtained from the NIST standard reference database number 38. It is important to note
that most of the emission lines obtained when using a laser as an excitation source to generate a plasma are the same. Yet some emission lines are different from those published in this NIST database. The emission lines from the NIST database are usually detected in vacuum with X-ray as the excitation source. On the other hand, in our experiments, a high powered laser is used as the excitation source and the lines are detected under atmospheric conditions. Two PLS analyses were performed, one with the emission line at 237.64 nm and one without it. Both analyses did not show any significant changes in the regression coefficients and the correlation coefficient. The combination of LIBS and multivariate analysis has permitted to develop a robust calibration model to predict carbon content in
Fig. 6. a. LIBS data for two cremation samples with more Pb in one sample. (b). LIBS data for two cremation samples with more Cu in one sample. (c). LIBS data for two cremation samples with twice as much Pb and Cu in one sample.
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Fig. 6 (continued ).
soils samples with different textures. This experiment demonstrates the ability of LIBS to deal with heterogeneous materials such as soils with different porosity, ph, textures, etc. The forensic applications using the LIBS technique are illustrated in Fig. 6(a), (b) and (c). The spectra for two cremated human remain samples are shown in Fig. 6(a) and (b). In the two spectra, an excess of two elements, lead (Pb) and copper (Cu), is seen in sample labeled ICP2 as compared to sample ICP1. Both samples were also analyzed using the ICP-MS technique. The results were similar. The amount of copper seen by the ICP-MS technique is 0.08 ppm in sample ICP1 and 0.17 ppm in sample ICP2. Similarly the numbers for the concentration of lead in samples ICP1 and ICP2 as measured by the ICP-MS technique
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are 1.69 ppm and 2.56 ppm respectively. This shows that LIBS can be employed to fingerprint cremated remains. In Fig. 6(c), the data has been plotted using bar graphs to elucidate the LIBS spectral information in a semi-quantitative manner. This shows that the amount of Cu found in sample ICP2 is almost twice as much as the one found in sample ICP1. Similarly, the amount of Pb detected in sample ICP2 is also twice as much as in sample ICP1. In Fig. 7, a prosthetic implant was tested to determine if titanium metal was a major component of this sample. When the sample spectrum (260–570 nm) was compared to the spectrum derived from stainless steel (obtained from steels from two different sources), the spectral peak positions were identical but had different amounts of the major components e.g., Mn, Si, Cr, and Ni, depending on where the stainless steel was imported from (Germany or Pakistan). However, when the sample spectrum was compared to pure titanium metal, the major emission peaks were different. Major titanium emission peaks are 335.04, 336.22, 337.38, 338.47, 364.37, 365.45, 375.39, 395.93, 399.09, 399.98, 430.23, 430.71, 453.45, and 498.31 nm. This demonstrates that the prosthetic sample was not made of titanium or any alloy with the major component being titanium. The stainless steel implant caused leaching of metals into the body and caused build up of metal toxicity in the blood which affected patient health in an adverse manner. 6. Conclusion The LIBS technique has been shown to be very versatile in the detection of elements present in a variety of environmental and forensic applications. Multivariate analyses have been successfully used to develop quantitative models for their use in complex heterogeneous matrices, for example, the assessment
Fig. 7. LIBS spectra for prosthetic implant sample compared to two stainless steel metals derived from different sources. The bottom is a pure titanium metal sample for comparison. (The dashed lines identify major emission peaks of pure titanium metal).
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of the chemical profile of trees affected by fire, and carbon concentration in soils. The LIBS technique depends on the interaction of a laser source with the material of the sample under test. Preliminary proof of concept results have shown that LIBS can also be used to fingerprint and differentiate forensic samples. The detailed quantification of forensic samples is underway and will be reported in the next publication. Acknowledgements We would like to extend our thanks to Deanne Brice who helped with the LIBS measurements and made sure that the wood pellets were sampled in a reproducible and repeatable manner. She was also diligent in helping with the consistent preparation of forensic samples. This research has been sponsored by the Laboratory Directed Research and Development (LDRD) program of Oak Ridge National Laboratory, managed by University of Tennessee-Battelle, LLC for the U. S. Department of Energy (DOE) under contract number DEAC05-00OR22725. The soil LIBS research was supported by DOE-Terrestrial Carbon Sequestration program. References [1] J. Amador-Hernández, J.M. Fernández-Romero, M.D. Luque de Castro, In-depth characterization of screen-printed electrodes by laser-induced breakdown spectrometry and pattern recognition, Surf. Interface Anal. 31 (2001) 313–320. [2] A. Jurado-López, M.D. Luque de Castro, Rank correlation of laser-induced breakdown spectroscopic data for the identification of alloys used in jewelry manufacture, Spectrochim. Acta Part B 58 (2003) 1291–1299. [3] J.D. Hybl, G.A. Lithgow, S.G. Buckley, Laser-induced breakdown spectroscopy detection and classification of biological aerosols, Appl. Spectrosc. 57 (2003) 1207–1215. [4] O. Samsek, H.H. Telle, D.CS. Beddows, Laser-induced breakdown spectroscopy: a tool for real-time, in vitro and in vivo identification of carious teeth, BMC Oral Health 1 (2001) 1–9. [5] S. Sjostrom, P. Mauchien, Laser atomic spectroscopic techniques — The analytical performance for trace element analysis of solid and liquid samples, Spectrochim. Acta Part B 15 (1991) 153–180. [6] S.M. Angel, Dimitra N. Stratis, Kristine L. Eland, Tianshu Lai, Mark A. Berg, David M. Gold, LIBS using dual- and ultra-short laser pulses, Fres. J. Anal. Chem. 269 (2004) 320–327. [7] K. Song, Y.I. Lee, J. Sneddon, Applications of laser-induced breakdown spectrometry, Appl. Spectrosc. Rev. 32 (1997) 183–235.
[8] B. Dolgin, Y. Chen, V. Bulatov, I. Schechter, Use of LIBS for rapid characterization of parchment, Analy. Bioanaly. Chem. 386 (2006) 1535–1541. [9] M.Z. Martin, N. Labbé, T.G. Rials, S.D. Wullschleger, Analysis of preservative-treated wood by multivariate analysis of LIBS spectra, Spectrochim. Acta Part B 60 (2005) 1179–1185. [10] M.Z. Martin, S.D. Wullschleger, N. Labbé, N. André, T.G. Rials, A High Resolution Laser-Based Technique for Quantifying the Elemental Composition of Wood: Applications in Forest Fire Ecological Response, Central Hardwood Conference, Knoxville TN, February 27–March 1, 2006. [11] M. Martin, S. Wullschleger, C. Garten Jr., A. Palumbo, Laser-induced breakdown spectroscopy for the environmental determination of total carbon and nitrogen in soils, Appl. Opt. 42 (2003) 2072–2077. [12] M. Martin, S. Wullschleger, C. Garten Jr., in: Tuan Vo-Dinh, Stephanus Buttgenbach (Eds.), Laser-induced breakdown spectroscopy for environmental monitoring of soil carbon and nitrogen, Proceedings of SPIE, vol. 4576, 2002, pp. 188–195. [13] M.Z. Martin, S.D. Wullschleger, A. Vass, High Resolution applications of Laser-induced breakdown spectroscopy for Homeland Security and Forensic Applications, First Indo-US Workshop on Spectroscopy, (Invited Talk), Varanasi, India. Jan 9–12, 2006. [14] G. Tuskan, D. West, H.D. Bradshaw, D. Neale, M. Sewell, N. Wheeler, B. MeGraw, K. Jech, A. Wislogel, R. Evans, C. Elam, M. Davis, R. Dinus, Two high-throughput techniques for determining wood properties as part of a molecular genetics analysis of hybrid poplar and loblolly pine, Appl. Biochem. Biotechnol. 77–79 (1999) 55–65. [15] C. Pearson, S.W. Manning, M. Coleman, K. Jarvis, Can tree-ring chemistry reveal absolute dates for past volcanic eruptions? J. Archaeol. Sci. 32 (2005) 1265–1274. [16] C. Pearson, S.W. Manning, M. Coleman, K. Jarvis, A dendrochemical study of Pinus sylvestris from Siljansfors Experimental Forest, central Sweden, Appl. Geochem. 21 (2006) 1681–1691. [17] D. Read, D. Beerling, M. Cannell, P. Cox, P. Curran, J. Grace, P. Ineson, Y. Malhi, D. Powlson, J. Shepherd, I. Woodward, The role of land carbon sinks in mitigating global climate change, vol. 1–27, The Royal Society, London, 2001. [18] M.Z. Martin, S.D. Wullschleger, A.A. Vass, R.C. Martin, H. GrissinoMayer, High-resolution laser-induced breakdown spectroscopy used in homeland security and forensic applications, Bull. Las. Spectrosc. Soc. India (2006) 23–35 Special Issue No. 14. [19] J.R. Almirall, in: B. Caddy (Ed.), Forensic Examination of Glass and Paint, Taylor and Francis, London, 2001, pp. 65–80. [20] M.Z. Martin, M.D. Cheng, The detection of chromium aerosol using timeresolved laser-induced plasma spectroscopy, Appl. Spectrosc. 54 (2000) 1279–1285. [21] H. Martens, T. Naes, Multivariate calibration, Ed. John Wiley & Sons, 1991, NY. [22] K.H. Esbensen, Multivariate data analysis in pratice, CAMO Process AS, 2002.
Technical note
High resolution applications of laser-induced breakdown spectroscopy for environmental and forensic applications ☆ Madhavi Z. Martin a,⁎, Nicole Labbé b , Nicolas André b , Ronny Harris c , Michael Ebinger c , Stan D. Wullschleger a , Arpad A. Vass d a
Environmental Sciences Division Oak Ridge National Laboratory, P.O. Box 2008 MS 6038, Oak Ridge TN 37831-6038, USA b Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996-4570, USA c Los Alamos National Laboratory, Los Alamos, NM 87545-J495, USA d Bio-Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008 MS 6422, Oak Ridge TN 37831-6422, USA Received 8 December 2006; accepted 23 October 2007 Available online 13 November 2007
Abstract Laser-induced breakdown spectroscopy (LIBS) has been used in the elemental analysis for a variety of environmental samples and as a proof of concept for a host of forensic applications. In the first application, LIBS was used for the rapid detection of carbon from a number of different soil types. In this application, a major breakthrough was achieved by using a multivariate analytical approach that has brought us closer towards a “universal calibration curve”. In a second application, it has been demonstrated that LIBS in combination with multivariate analysis can be employed to analyze the chemical composition of annual tree growth rings and correlate them to external parameters such as changes in climate, forest fires, and disturbances involving human activity. The objectives of using this technology in fire scar determinations are: 1) To determine the characteristic spectra of wood exposed to forest fires and 2) To examine the viability of this technique for detecting fire occurrences in stems that did not develop fire scars. These examples demonstrate that LIBS-based techniques are inherently well suited for diverse environmental applications. LIBS was also applied to a variety of proof of concept forensic applications such as the analysis of cremains (human cremation remains) and elemental composition analysis of prosthetic implants. © 2007 Elsevier B.V. All rights reserved. Keywords: Laser induced breakdown spectroscopy; Multivariate analysis; Soil carbon measurements; Human and animal bone chemical analysis; Forensic science
1. Introduction Laser-induced breakdown spectroscopy (LIBS) has proven to be a versatile technique to identify most elements present in any sample. This technique has been used for numerous applications in a host of research fields [1–5]. The advantages of LIBS include, identification of metals and non-metals in less than a second or longer depending on the number of averages per sample via a broadband spectrum (200–800 nm), remote instrument operation via fiber-optics to gather information ☆
This paper was presented at the 4th International Conference on Laser Induced Plasma Spectroscopy and Applications (LIBS 2006) held in Montreal, Canada, 5–8 September 2006, and is published in the Special Issue of Spectrochimica Acta Part B, dedicated to that conference. ⁎ Corresponding author. Tel.: +1 865 574 7828; fax: +1 865 576 8646. E-mail address: [email protected] (M.Z. Martin). 0584-8547/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.sab.2007.10.046
from a hazardous site, minimal sample preparation and no waste generation, continuous monitoring capability, and the ability to perform depth profiling and mapping using robust instrumentation. While many groups, worldwide, are developing LIBS instruments and databases for a variety of materials (e.g. glass, metals, inks, etc.) [6–8], previously LIBS technique has been used at ORNL for the quantification of elemental concentrations in environmental samples such as natural and engineered wood [9], wood affected by fire [10], soils [11,12], as well as human and animal bones [13] by using a multivariate model development approach. Wood is a heterogeneous material primarily comprised of macromolecular cellulose, hemicelluloses, lignin and lowmolecular-weight substances, i.e. extractives and minerals [14]. For trees which are grown on different sites, the chemical
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characteristics like lignin content vary even if the genetic origin is the same. This is a result of the influence of the environment on the physiology of trees. Trace elements are present in much lower concentrations. Calcium, as a major component of plant cell walls, and nitrogen, phosphorous, and magnesium, as constituents of proteins, amino acids, and other cellular constituents are dispersed throughout these biological matrices. The presence of lead, chromium and other heavy metals in wood have been identified in the field of dendrochemistry to document the presence of pollutants in the soil and aerial environment. The wood of tree trunks is an ideal archive for the identification of critical events such as volcanic eruptions and chemical changes over long periods of time [15,16]. The chemical composition of annual growth rings can be correlated to external parameters such as changes in climate, forest fires, and disturbances involving human activity. The spectral data obtained from tree rings have the potential to be used for biomonitoring environmental and ecological events such as forest fires. Multivariate methods are powerful approaches to analyze large and complex dataset such as LIBS spectra. Principal component analysis (PCA), one of the most common multivariate analyses, can be used to extract information from large datasets and to determine natural events in trees. This technique shows a great deal of promise for detecting past fire events in trees, even though visible fire scars may be absent. As a result, this technique may prove valuable for constructing more accurate fire histories for forest ecosystems. Soil carbon analysis is a very important aspect of carbon sequestration research. There has been an increase in international efforts to reduce anthropogenic emissions of greenhouse gases, particularly carbon dioxide (CO2), as the link between atmospheric greenhouse gases and climate change has been established. Knowledge that CO2 is stored within, and exchanged between, the atmosphere and vegetation and soils has led to the suggestion that soils and vegetation could be managed to increase their uptake and storage of CO2, and thus become ‘land carbon sinks’. There have been some concerns about the permanence of the carbon sinks and the accuracy with which they can be quantified and verified [17]. Additionally, knowing the total chemical composition of forensic evidence collected at a crime scene would give the added advantage of not only knowing the origin of the particular forensic samples e.g., wood or bones found, but also having the capability of total identification along with quantification of the chemistry, a critical element in forensic comparisons. The forensic applications research at ORNL includes the ability to identify bone, differentiate between the cremated remains of humans and to be able to fingerprint prosthetic implants based on their metallic content. We have used this technique to determine the presence of elements such as, C, N, Ca, Al, Fe, Ti, Si, Mg, Mn, and Na from the wood provided to us from Texas related to a murder investigation. Eleven logs of wood were sampled using the LIBS technique. The chemical fingerprint was found to be consistent for all the wood samples that were tested. This strongly suggested that (1) the wood logs came from the same type of tree species, and (2) the logs likely came
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from the same tree or from a group of trees growing in the same area [18]. Other applications of LIBS for forensic analysis have been done for analysis of glass [19]. 2. Materials and methods 2.1. Fire scar sampling method Concerning the fire scar sampling, a cookie core from longleaf pine (Pinus palustris) was analyzed in triplicate. The main goal of this study was to determine if a change in the dendrochemistry can be determined using the LIBS technique. The core was placed on an automated stage set to sample every 2.5 mm. Thus, a spectrum would be taken and then the stage would move the core 2.5 mm to the next sampling site. Spectra were taken along the length of the cookie from pith to bark. Each sample site was subjected to 10 laser shots which produced plasma containing the elements found in each spectrum (two spectra, one before the fire event and one soon after the fire event are shown in Fig. 1). This plasma emitted light in wavelengths specific to the elements found in that sample. The light was collected by a collection lens, delivered to a spectrometer, digitized by a detector, and downloaded to a computer where it was saved as a series of data points containing 30001 wavelengths (variables) for that sample spot. 2.2. Soil carbon sampling method Soils of different textures and carbon content were obtained from Los Alamos National Laboratory. A total of 38 soil samples were tested using LIBS. These soils were pelletized into 1/4″ thick and 1″ in diameter sized pellet using a pressure of 2500 psi to make consistent sampling configurations. The standard laboratory technique that was used to determine the concentrations of carbon in these soils was done by using the CN-elemental analyzer that heats the sample to a temperature of 1350 °C in the presence of oxygen. Combustion converts SOM containing nitrogen and carbon into N2, NOx, and CO2. Hydrogen and oxygen combine to form water vapor. The combustion gases are analyzed for CO2 (infrared spectroscopy)
Fig. 1. LIBS spectra for the wood tissue before and after the fire event.
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and N2 (thermal conductivity detector). After conducting a systematic LIBS study based on a number of experimental parameters, the optimal conditions to collect the data were 45 mJ of energy per pulse at an excitation wavelength of 532 nm. More comprehensive soil analysis results will be published in a later manuscript. For each soil sample, a LIBS spectrum was obtained by averaging 100 laser shots for a laser repetition rate of 10 Hz. Three spectra per sample were collected. Hence a total number of 114 spectra were obtained for the soil samples. 2.3. Forensic sampling method The forensic samples that were tested were two cremation ash samples obtained from a mortuary in Georgia. Both samples were analyzed by ICP-MS prior to the LIBS experiments. About 300 mg of the powdered cremation samples were pressed into pellets to obtain consistent sampling surfaces. We also obtained a sample of a prosthetic implant that was supposed to have been fabricated using titanium and some titanium oxide mixture. In this case there was a need to know if the sample was actually made of a titanium metal/oxide material. The prosthetic implant sample was directly tested at different points on the sample with 10 laser shots and compared to a pure titanium metal spectrum that was tested using the same protocol. 3. LIBS setup and experiment Laser pulses are delivered to the sample, creating a plasma, resulting in vaporization and atomization of small amounts (about nanograms) of the target material. Spectroscopic detection of the light released from the plasma contains emission spectra which permits identification of the elements through their unique spectral signatures with the signal being diagnostic of the total elemental concentration. An excitation source consisting of a pulsed laser is used for LIBS. The experimental setup consists of a pulsed laser Spectra physics model Indi-HG laser that is a Q-switched Nd:YAG laser with output wavelengths of 1064, 532, and 266 nm. For the data reported in this article, the laser was used at 532 nm with 45 mJ/ pulse. The light emitted by the plasma at the focal volume (FL of input lens is 100 mm) was collected by a set of collection optics and focused into a low O–H silica fiber bundle consisting of 19 fibers. The light is delivered to a 0.5 m Acton Research model SpectraPro-500 spectrometer, (spectral bandwidth = 40 nm for 1200 g/mm grating and slitwidth of spectrometer = 25 μm) which was then detected by an intensified charge coupled detector (ICCD) made by Andor Technologies using gate widths of 10 μm and delays of 1 μm. The detailed experimental set up used for all the measurements mentioned in this article is described in detail elsewhere [20]. 4. Multivariate analysis Multivariate models were used to provide quantitative measure and statistical evaluation of identification of critical events such as forest fires and carbon concentrations in soil
samples. The objective was to validate the LIBS technology by using chemometric methods such as principal component analysis (PCA), and partial least squares (PLS) [21]. If the spectral data contain information about the properties of interest, a reliable calibration model can be constructed. PLS analysis was performed to determine the concentration of carbon in soils of different textures. In order to understand the fire scar chemistry of the sampled growth rings, PCA was used to detect changes in the inorganic elements present before and after the fire event. The data obtained for the forensic samples is preliminary and extensive chemometric methods have not been used yet. Initial results help in fingerprinting and analyzing the chemical composition of human cremation remains. Multivariate analyses can be used to correlate the impacts of external parameters such as changes in the climate and/or disturbances involving human activity on the wood properties, and internal parameters such as nutritional intake, bone density and porosity for humans and animal bones. 5. Results and discussion A cross-section of Mountain pine (Pinaceae Pinus pungens Lamb.) was scanned using a translational stage to determine the differences in the chemical features both before and after a fire event. The line scan was not collected on the fire scar on purpose. The idea was to compare the elemental composition of the tissue grown post-fire to the pre-fire tissue. Collecting spectra on the scar would have shown changes in the elemental composition due the burning process. Fig. 1 shows a spectrum collected on the third ring (before the fire) and a spectrum collected on the 32nd ring (after the fire) along the line scan. Some differences between the elemental composition of the tissue before and after the fire can be directly observed from the spectra. One can easily assign the emission lines of sodium at 588.98 and 589.54 nm. Two emission lines at 422.66 and 396.82 nm are also observed in the two spectra with different intensities. These signals are due to the presence of calcium in the tissue. The spectrum of the tissue after the fire has a very strong emission line at 249.3 nm. This band is assigned to iron. Although chemical differences are obvious by direct visual analysis of the two spectra, comparing 20 spectra simultaneously is impossible. PCA was used to highlight the chemical differences between the spectra. PCA is a projection method that transforms large data sets into smaller ones and into more interpretable plots. For instance, the large matrix that is represented by the 20 samples (spectra) with 30001 wavelengths (variables) each is, by the PCA method, reduced to a 20 samples by 5 principal components matrix. The scores plots represent the samples in the principal components space. The loadings plots are used to explain the relationships between the original variables (spectra) space and the new principal components space. Fig. 2 shows the samples in the first principal component space (line scores plot). Samples with similar chemical characteristics (similar spectra) will be plotted close together while samples with different chemical features will be plotted away one from each other. This plot shows the effect of a fire on the elemental composition of wood tissue. It
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Fig. 2. The score plot shows the change in elemental concentration before, during, and after the fire event.
also showed that the fire affected tree did return to its original wood tissue chemistry after the occurrence of the fire event (Fig. 2). The samples need to be scanned at a higher resolution in order to determine the exact number of years the trees need to return to a “healthy state”. The loading plot (Fig. 3) shows which wavelengths are responsible for the differences between samples along the first principal component. If the score of a sample and the loading of a variable (wavelength) on a particular PC have the same sign, the sample has higher than average value for that variable and vice-versa [22]. By using this statement, one can deduce that sample 14 has a higher iron and calcium content and a lower sulfur and sodium content than the other samples. From these findings, it is therefore possible to presume that a fire will affect the chemical composition of the ring that grows just after the fire and that some elements content will increase and while some will decrease as a result of the fire. Consequently, by monitoring the elemental composition pattern of a tree and by looking for abrupt changes, one can reconstruct the disturbance history of a tree and a forest. Two different approaches were employed to develop a robust and rapid method to obtain carbon content in soil from LIBS data. After collecting the LIBS spectra on three different soil textures (sand, loam, and clay), a univariate and multivariate
Fig. 3. Plot of the loadings of principal component one, showing the chemical changes in the tree.
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Fig. 4. Model to predict carbon content obtained by the univariate approach (at 247.856 nm).
approaches were tested by building models that predict C content from the 38 soils. The univariate model was developed by correlating the intensity of the emission line at 247.856 nm to the “true” carbon content which was obtained by a standard laboratory technique, based on combustion of soils. The model was then used to predict the carbon content in the same samples.
Fig. 5. a. Model to predict carbon content in soil obtained by the multivariate approach. b. Significant variables for the multivariate model.
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Fig. 4 shows a plot of the predicted carbon content versus the measured carbon content with a correlation coefficient of 0.56. In parallel, Projection to Latent Structures (PLS) was used as the multivariate approach to correlate the spectral data to the carbon content. A good model was obtained with a correlation coefficient of 0.97 (Fig. 5a). In addition to the multivariate calibration model, PLS provides a regression coefficients plot (Fig. 5b) which represents the significant variables (wavelengths) in building the model. The emission lines at 230.341, 237.64, 247.86, 251.33 nm are critical to construct the model. All these lines except the one at 237.64 nm are known emission peaks of carbon. These lines have been obtained from the NIST standard reference database number 38. It is important to note
that most of the emission lines obtained when using a laser as an excitation source to generate a plasma are the same. Yet some emission lines are different from those published in this NIST database. The emission lines from the NIST database are usually detected in vacuum with X-ray as the excitation source. On the other hand, in our experiments, a high powered laser is used as the excitation source and the lines are detected under atmospheric conditions. Two PLS analyses were performed, one with the emission line at 237.64 nm and one without it. Both analyses did not show any significant changes in the regression coefficients and the correlation coefficient. The combination of LIBS and multivariate analysis has permitted to develop a robust calibration model to predict carbon content in
Fig. 6. a. LIBS data for two cremation samples with more Pb in one sample. (b). LIBS data for two cremation samples with more Cu in one sample. (c). LIBS data for two cremation samples with twice as much Pb and Cu in one sample.
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Fig. 6 (continued ).
soils samples with different textures. This experiment demonstrates the ability of LIBS to deal with heterogeneous materials such as soils with different porosity, ph, textures, etc. The forensic applications using the LIBS technique are illustrated in Fig. 6(a), (b) and (c). The spectra for two cremated human remain samples are shown in Fig. 6(a) and (b). In the two spectra, an excess of two elements, lead (Pb) and copper (Cu), is seen in sample labeled ICP2 as compared to sample ICP1. Both samples were also analyzed using the ICP-MS technique. The results were similar. The amount of copper seen by the ICP-MS technique is 0.08 ppm in sample ICP1 and 0.17 ppm in sample ICP2. Similarly the numbers for the concentration of lead in samples ICP1 and ICP2 as measured by the ICP-MS technique
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are 1.69 ppm and 2.56 ppm respectively. This shows that LIBS can be employed to fingerprint cremated remains. In Fig. 6(c), the data has been plotted using bar graphs to elucidate the LIBS spectral information in a semi-quantitative manner. This shows that the amount of Cu found in sample ICP2 is almost twice as much as the one found in sample ICP1. Similarly, the amount of Pb detected in sample ICP2 is also twice as much as in sample ICP1. In Fig. 7, a prosthetic implant was tested to determine if titanium metal was a major component of this sample. When the sample spectrum (260–570 nm) was compared to the spectrum derived from stainless steel (obtained from steels from two different sources), the spectral peak positions were identical but had different amounts of the major components e.g., Mn, Si, Cr, and Ni, depending on where the stainless steel was imported from (Germany or Pakistan). However, when the sample spectrum was compared to pure titanium metal, the major emission peaks were different. Major titanium emission peaks are 335.04, 336.22, 337.38, 338.47, 364.37, 365.45, 375.39, 395.93, 399.09, 399.98, 430.23, 430.71, 453.45, and 498.31 nm. This demonstrates that the prosthetic sample was not made of titanium or any alloy with the major component being titanium. The stainless steel implant caused leaching of metals into the body and caused build up of metal toxicity in the blood which affected patient health in an adverse manner. 6. Conclusion The LIBS technique has been shown to be very versatile in the detection of elements present in a variety of environmental and forensic applications. Multivariate analyses have been successfully used to develop quantitative models for their use in complex heterogeneous matrices, for example, the assessment
Fig. 7. LIBS spectra for prosthetic implant sample compared to two stainless steel metals derived from different sources. The bottom is a pure titanium metal sample for comparison. (The dashed lines identify major emission peaks of pure titanium metal).
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of the chemical profile of trees affected by fire, and carbon concentration in soils. The LIBS technique depends on the interaction of a laser source with the material of the sample under test. Preliminary proof of concept results have shown that LIBS can also be used to fingerprint and differentiate forensic samples. The detailed quantification of forensic samples is underway and will be reported in the next publication. Acknowledgements We would like to extend our thanks to Deanne Brice who helped with the LIBS measurements and made sure that the wood pellets were sampled in a reproducible and repeatable manner. She was also diligent in helping with the consistent preparation of forensic samples. This research has been sponsored by the Laboratory Directed Research and Development (LDRD) program of Oak Ridge National Laboratory, managed by University of Tennessee-Battelle, LLC for the U. S. Department of Energy (DOE) under contract number DEAC05-00OR22725. The soil LIBS research was supported by DOE-Terrestrial Carbon Sequestration program. References [1] J. Amador-Hernández, J.M. Fernández-Romero, M.D. Luque de Castro, In-depth characterization of screen-printed electrodes by laser-induced breakdown spectrometry and pattern recognition, Surf. Interface Anal. 31 (2001) 313–320. [2] A. Jurado-López, M.D. Luque de Castro, Rank correlation of laser-induced breakdown spectroscopic data for the identification of alloys used in jewelry manufacture, Spectrochim. Acta Part B 58 (2003) 1291–1299. [3] J.D. Hybl, G.A. Lithgow, S.G. Buckley, Laser-induced breakdown spectroscopy detection and classification of biological aerosols, Appl. Spectrosc. 57 (2003) 1207–1215. [4] O. Samsek, H.H. Telle, D.CS. Beddows, Laser-induced breakdown spectroscopy: a tool for real-time, in vitro and in vivo identification of carious teeth, BMC Oral Health 1 (2001) 1–9. [5] S. Sjostrom, P. Mauchien, Laser atomic spectroscopic techniques — The analytical performance for trace element analysis of solid and liquid samples, Spectrochim. Acta Part B 15 (1991) 153–180. [6] S.M. Angel, Dimitra N. Stratis, Kristine L. Eland, Tianshu Lai, Mark A. Berg, David M. Gold, LIBS using dual- and ultra-short laser pulses, Fres. J. Anal. Chem. 269 (2004) 320–327. [7] K. Song, Y.I. Lee, J. Sneddon, Applications of laser-induced breakdown spectrometry, Appl. Spectrosc. Rev. 32 (1997) 183–235.
[8] B. Dolgin, Y. Chen, V. Bulatov, I. Schechter, Use of LIBS for rapid characterization of parchment, Analy. Bioanaly. Chem. 386 (2006) 1535–1541. [9] M.Z. Martin, N. Labbé, T.G. Rials, S.D. Wullschleger, Analysis of preservative-treated wood by multivariate analysis of LIBS spectra, Spectrochim. Acta Part B 60 (2005) 1179–1185. [10] M.Z. Martin, S.D. Wullschleger, N. Labbé, N. André, T.G. Rials, A High Resolution Laser-Based Technique for Quantifying the Elemental Composition of Wood: Applications in Forest Fire Ecological Response, Central Hardwood Conference, Knoxville TN, February 27–March 1, 2006. [11] M. Martin, S. Wullschleger, C. Garten Jr., A. Palumbo, Laser-induced breakdown spectroscopy for the environmental determination of total carbon and nitrogen in soils, Appl. Opt. 42 (2003) 2072–2077. [12] M. Martin, S. Wullschleger, C. Garten Jr., in: Tuan Vo-Dinh, Stephanus Buttgenbach (Eds.), Laser-induced breakdown spectroscopy for environmental monitoring of soil carbon and nitrogen, Proceedings of SPIE, vol. 4576, 2002, pp. 188–195. [13] M.Z. Martin, S.D. Wullschleger, A. Vass, High Resolution applications of Laser-induced breakdown spectroscopy for Homeland Security and Forensic Applications, First Indo-US Workshop on Spectroscopy, (Invited Talk), Varanasi, India. Jan 9–12, 2006. [14] G. Tuskan, D. West, H.D. Bradshaw, D. Neale, M. Sewell, N. Wheeler, B. MeGraw, K. Jech, A. Wislogel, R. Evans, C. Elam, M. Davis, R. Dinus, Two high-throughput techniques for determining wood properties as part of a molecular genetics analysis of hybrid poplar and loblolly pine, Appl. Biochem. Biotechnol. 77–79 (1999) 55–65. [15] C. Pearson, S.W. Manning, M. Coleman, K. Jarvis, Can tree-ring chemistry reveal absolute dates for past volcanic eruptions? J. Archaeol. Sci. 32 (2005) 1265–1274. [16] C. Pearson, S.W. Manning, M. Coleman, K. Jarvis, A dendrochemical study of Pinus sylvestris from Siljansfors Experimental Forest, central Sweden, Appl. Geochem. 21 (2006) 1681–1691. [17] D. Read, D. Beerling, M. Cannell, P. Cox, P. Curran, J. Grace, P. Ineson, Y. Malhi, D. Powlson, J. Shepherd, I. Woodward, The role of land carbon sinks in mitigating global climate change, vol. 1–27, The Royal Society, London, 2001. [18] M.Z. Martin, S.D. Wullschleger, A.A. Vass, R.C. Martin, H. GrissinoMayer, High-resolution laser-induced breakdown spectroscopy used in homeland security and forensic applications, Bull. Las. Spectrosc. Soc. India (2006) 23–35 Special Issue No. 14. [19] J.R. Almirall, in: B. Caddy (Ed.), Forensic Examination of Glass and Paint, Taylor and Francis, London, 2001, pp. 65–80. [20] M.Z. Martin, M.D. Cheng, The detection of chromium aerosol using timeresolved laser-induced plasma spectroscopy, Appl. Spectrosc. 54 (2000) 1279–1285. [21] H. Martens, T. Naes, Multivariate calibration, Ed. John Wiley & Sons, 1991, NY. [22] K.H. Esbensen, Multivariate data analysis in pratice, CAMO Process AS, 2002.