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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 7 — Mar. 1, 2012
  • pp: B155–B164

Automated interpretation of LIBS spectra using a fuzzy logic inference engine

Jeremy J. Hatch, Timothy R. McJunkin, Cynthia Hanson, and Jill R. Scott  »View Author Affiliations

Applied Optics, Vol. 51, Issue 7, pp. B155-B164 (2012)

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Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment.

OCIS Codes
(070.4790) Fourier optics and signal processing : Spectrum analysis
(100.5010) Image processing : Pattern recognition
(160.2120) Materials : Elements
(160.3900) Materials : Metals
(300.6365) Spectroscopy : Spectroscopy, laser induced breakdown

Original Manuscript: October 5, 2011
Revised Manuscript: January 5, 2012
Manuscript Accepted: January 5, 2012
Published: February 29, 2012

Jeremy J. Hatch, Timothy R. McJunkin, Cynthia Hanson, and Jill R. Scott, "Automated interpretation of LIBS spectra using a fuzzy logic inference engine," Appl. Opt. 51, B155-B164 (2012)

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  1. J. Anzano, B. Bonilla, B. Montull-Ibor, and J. Casas-González, “Plastic identification and comparison by multivariate techniques with laser-induced breakdown spectroscopy,” J. Appl. Polym. Sci. 121, 2710–2716 (2011). [CrossRef]
  2. S. J. Rehse, N. Jeyasingham, J. Diedrich, and S. Palchaudhuri, “A membrane basis for bacterial identification and discrimination using laser-induced breakdown spectroscopy,” J. Appl. Phys. 105, 102034 (2009). [CrossRef]
  3. J. L. Gottfried, F. C. De Lucia, C. A. Munson, and A. W. Miziolek, “Laser-induced breakdown spectroscopy for detection of explosives residues: a review of recent advances, challenges, and future prospects,” Anal. Bioanal. Chem. 395, 283–300 (2009). [CrossRef]
  4. A. Ramil, A. J. López, and A. Yáñez, “Application of artificial neural networks for the rapid classification of archaeological ceramics by means of laser induced breakdown spectroscopy (LIBS),” Appl. Phys. A: Mater. Sci. Process. 92, 197–202 (2008). [CrossRef]
  5. K. Novotný, J. Kaiser, M. Galiová, V. Konečná, J. Novotný, R. Malina, M. Liška, V. Kanický, and V. Otruba, “Mapping of different structures on large area of granite sample using laser-ablation based analytical techniques, an exploratory study,” Spectrochim. Acta Part B: Atom. Spectrosc. 63, 1139–1144 (2008). [CrossRef]
  6. G. Asimellis, N. Michos, I. Fasaki, and M. Kompitsas, “Platinum group metals bulk analysis in automobile catalyst recycling material by laser-induced breakdown spectroscopy,” Spectrochim. Acta Part B: Atom. Spectrosc. 63, 1338–1343 (2008). [CrossRef]
  7. K. Song, Y. I. Lee, and J. Sneddon, “Recent developments in instrumentation for laser induced breakdown spectroscopy,” Appl. Spectrosc. Rev. 37, 89–117 (2002). [CrossRef]
  8. X. D. Hou and B. T. Jones, “Field instrumentation in atomic spectroscopy,” Microchem. J. 66, 115–145 (2000). [CrossRef]
  9. A. P. M. Michel, “Review: applications of single-shot laser-induced breakdown spectroscopy,” Spectrochim. Acta Part B: Atom. Spectrosc. 65, 185–191 (2010). [CrossRef]
  10. W. Hübert and G. Ankerhold, “Elemental misinterpretation in automated analysis of LIBS spectra,” Anal. Bioanal. Chem. 400, 3273–3278 (2011). [CrossRef]
  11. R. S. Harmon, J. Remus, N. J. McMillan, C. McManus, L. Collins, J. L. Gottfried, F. C. DeLucia, and A. W. Miziolek, “LIBS analysis of geomaterials: geochemical fingerprinting for the rapid analysis and discrimination of minerals,” Appl. Geochem. 24, 1125–1141 (2009). [CrossRef]
  12. N. Mujezinovic, G. Schneider, M. Wildpaner, K. Mechtler, and F. Eisenhaber, “Reducing the haystack to find the needle: improved protein identification after fast elimination of non-interpretable peptide MS/MS spectra and noise reduction,” BMC Genomics 11 (Suppl. 1) S13–S18 (2010). [CrossRef]
  13. D. Verdegem, K. Dijkstra, X. Hanoulle, and G. Lippens, “Graphical interpretation of Boolean operators for protein NMR assignments,” J. Biomol. NMR 42, 11–21 (2008). [CrossRef]
  14. K. Klagkou, F. Pullen, M. Harrison, A. Organ, A. Firth, and G. J. Langley, “Approaches towards the automated interpretation and prediction of electrospray tandem mass spectra of non-peptidic combinatorial compounds,” Rapid Commun. Mass Spectrom. 17, 1163–1168 (2003). [CrossRef]
  15. C. Affolter, K. Baumann, J. T. Clerc, H. Schriber, and E. Pretsch, “Automatic interpretation of infrared spectra,” Mikrochim. Acta 14 (Suppl.), 143–147 (1997).
  16. T. Visser and J. H. van der Maas, “Systematic computer-aided interpretation of infrared and Raman vibrational spectra based on the crise program,” Anal. Chim. Acta 122, 347–355 (1980). [CrossRef]
  17. L. R. Crawford and J. D. Morrison, “Computer methods in analytical mass spectrometry: identification of an unknown compound in a catalog,” Anal. Chem. 40, 1464–1469 (1968). [CrossRef]
  18. F. W. McLafferty, “A century of progress in molecular mass spectrometry,” Ann. Rev. Anal. Chem. 4, 1–22 (2011). [CrossRef]
  19. W. L. Chen, “Chemoinformatics: past, present, and future,” J. Chem Inf. Model. 46, 2230–2255 (2006). [CrossRef]
  20. K.-P. Hinz, N. Erdmann, C. Gruning, and B. Spengler, “Comparative parallel characterization of particle populations with two mass spectrometric systems LAMPAS 2 and SPASS,” Int. J. Mass Spectrom. 258, 151–166 (2006). [CrossRef]
  21. A. Held, K.-P. Hinz, A. Trimborn, B. Spengler, and O. Klemm, “Chemical classes of atmospheric aerosol particles at a rural site in Central Europe during winter,” J. Aerosol Sci. 33, 581–594 (2002). [CrossRef]
  22. A. A. Gorbatenko, T. A. Labutin, A. M. Popov, and N. B. Zorov, “Reduction of the matrix influence on analytical signal in laser-enhanced ionization spectrometry with laser sampling,” Talanta 69, 1046–1048 (2006). [CrossRef]
  23. J. Vrenegor, R. Noll, and V. Sturm, “Investigation of matrix effects in laser-induced breakdown spectroscopy plasmas of high-alloy steel for matrix and minor elements,” Spectrochim. Acta Part B: Atom. Spectrosc. 60, 1083–1091 (2005). [CrossRef]
  24. C. Pasquini, J. Cortez, L. M. C. Silva, and F. B. Gonzaga, “Laser induced breakdown spectroscopy,” J. Brazil. Chem. Soc. 18, 463–512 (2007). [CrossRef]
  25. J. L. Gottfried, F. C. De Lucia, C. A. Munson, and A. W. Miziolek, “Double-pulse standoff laser-induced breakdown spectroscopy for versatile hazardous materials detection,” Spectrochim. Acta Part B: Atom. Spectrosc. 62, 1405–1411 (2007). [CrossRef]
  26. F. C. De Lucia, J. L. Gottfried, C. A. Munson, and A. W. Miziolek, “Multivariate analysis of standoff laser-induced breakdown spectroscopy spectra for classification of explosive-containing residues,” Appl. Opt. 47, G112–G121 (2008). [CrossRef]
  27. C. A. Munson, F. C. De, Jr. Lucia, T. Piehler, K. L. McNesby, and A. W. Miziolek, “Investigation of statistics strategies for improving the discriminating power of laser-induced breakdown spectroscopy for chemical and biological warfare agent simulants,” Spectrochim. Acta Part B: Atom. Spectrosc. 60, 1217–1224 (2005). [CrossRef]
  28. M. Barker and W. Rayens, “Partial least squares for discrimination,” J. Chemom. 17, 166–173 (2003). [CrossRef]
  29. C. Bohling, K. Hohmann, D. Scheel, C. Bauer, W. Schipper, S. J. Burgmeier, U. Willer, G. Holl, and W. Schade, “All-fiber-coupled laser-induced breakdown spectroscopy sensor for hazardous materials analysis,” Spectrochim. Acta Part B: Atom. Spectrosc. 62, 1519–1527 (2007). [CrossRef]
  30. J.-B. Sirven, B. Bousquet, L. Canioni, L. Sarger, S. Tellier, M. Potin-Gautier, and I. Le Hecho, “Qualitative and quantitative investigation of chromium-polluted soils by laser-induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256–262 (2006). [CrossRef]
  31. T. M. Cover, “Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition,” IEEE Trans. Electron. Comput. EC-14, 326–334 (1965). [CrossRef]
  32. O. Linda, M. Manic, and T. R. McJunkin, “Anomaly detection for resilient control systems using fuzzy-neural data fusion engine,” in Proceedings of the 4th International Symposium on Resilient Control Systems (ISRCS 2011) (IEEE, 2011), pp. 35–41.
  33. L. A. Zadeh, “Fuzzy sets,” Inf. Control 8, 338–353 (1965). [CrossRef]
  34. J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: the fuzzy c-means clustering algorithm,” Comput. Geosci. 10, 191–203 (1984). [CrossRef]
  35. S. Mitra, K. M. Konwar, and S. K. Pal, “Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation,” IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 32, 328–339 (2002). [CrossRef]
  36. K.-P. Hinz, M. Greweling, F. Drews, and B. Spengler, “Data processing in on-line laser mass spectrometry of inorganic, organic, or biological airborne particles,” J. Am. Soc. Mass Spectrom. 10, 648–660 (1999). [CrossRef]
  37. G. Yuan, J. Xiao, M. Horiike, C.-S. Kim, and C. Hirano, “Similarity between mass spectra of isomeric alkenols and their acetates,” Rapid Commun. Mass Spectrom. 12, 1287–1290 (1998). [CrossRef]
  38. G. Yuan, J. H. Xiao, G. J. Wang, M. Horiike, and C.-S. Kim, “Similarity between mass spectra of double-bond positional isomers of tetradecen-1-ols and their acetates,” Rapid Commun. Mass Spectrom. 11, 1699–1701 (1997). [CrossRef]
  39. G. Yuan, M. Y. He, and X. R. He, “Identification of aliphatic dienic alcohols and acetates by fuzzy similarity analysis/mass spectrometry,” Acta Chim. Sin. 54, 481–486 (1996).
  40. G. Yuan, M. Y. He, X. R. He, M. Horiike, C.-S. Kim, and C. Hirano, “Mass-spectrometric location of double-bond position in isomeric dodecenols, without chemical derivatization,” Rapid Commun. Mass Spectrom. 7, 591–593 (1993). [CrossRef]
  41. M. Horiike, G. Yuan, C- S. Kim, C. Hirano, and K. Shibuya, “Determination of the double-bond position in hexadecenols by mass-spectrometry without prior chemical modification,” Org. Mass Spectrom. 27, 944–948 (1992). [CrossRef]
  42. M. Horiike, G. Yuan, and C. Hirano, “Fuzzy classification of location of double-bonds in tetradecenyl acetates by electron-impact mass-spectrometry,” Agric. Biol. Chem. 55, 2521–2526 (1991). [CrossRef]
  43. Y. Gu, C. Hirano, and M. Horiike, “Fuzzy classificational analysis of continuously scanned mass spectra of binary mixtures of positionally isomeric tetradecenols,” Rapid Commun. Mass Spectrom. 5, 622–623 (1991). [CrossRef]
  44. M. Bieroza, A. Baker, and J. Bridgeman, “Classification and calibration of organic matter fluorescence data with multiway analysis methods and artificial neural networks: an operational tool for improved drinking water treatment,” Environmetrics 22, 256–270 (2011). [CrossRef]
  45. E. Karpushkin, A. Bogomolov, Y. Zhukov, and M. Boruta, “New system for computer-aided infrared and Raman spectrum interpretation,” Chemom. Intell. Lab. Syst. 88, 107–117 (2007). [CrossRef]
  46. M. C. Tutzó, R. Perez-Pueyo, M. J. Soneira, and S. R. Moreno, “Fuzzy logic: a technique to Raman spectra recognition,” J. Raman Spectrosc. 37, 1003–1011 (2006). [CrossRef]
  47. S. R. Ramakrishnan, R. Mao, A. A. Nakorchevskiy, J. T. Prince, W. S. Willard, W. J. Xu, E. M. Marcotte, and D. P. Miranker, “A fast coarse filtering method for peptide identification by mass spectrometry,” Bioinformatics 22, 1524–1531 (2006). [CrossRef]
  48. B. K. Alsberg, R. Goodacre, J. J. Rowland, and D. B. Kell, “Classification of pyrolysis mass spectra by fuzzy multivariate rule induction—comparison with regression, K-nearest neighbour, neural and decision-tree methods,” Anal. Chim. Acta 348, 389–407 (1997). [CrossRef]
  49. P. B. Harrington, C. Laurent, D. F. Levinson, P. Levitt, and S. P. Markey, “Bootstrap classification and point-based feature selection from age-staged mouse cerebellum tissues of matrix assisted laser desorption/ionization mass spectra using a fuzzy rule-building expert system,” Anal. Chim. Acta 599, 219–231 (2007). [CrossRef]
  50. P. D. Harrington, N. E. Vieira, P. Chen, J. Espinoza, J. K. Nien, R. Romero, and A. L. Yergey, “Proteomic analysis of amniotic fluids using analysis of variance-principal component analysis and fuzzy rule-building expert systems applied to matrix-assisted laser desorption/ionization mass spectrometry,” Chemom. Intell. Lab. Syst. 82, 283–293 (2006). [CrossRef]
  51. M. L. Ochoa and P. D. Harrington, “Chemometric studies for the characterization and differentiation of microorganisms using in situ derivatization and thermal desorption ion mobility spectrometry,” Anal. Chem. 77, 854–863 (2005). [CrossRef]
  52. P. J. Tandler, J. A. Butcher, H. Tao, and P. D. Harrington, “Analysis of plastic recycling products by expert-systems,” Anal. Chim. Acta 312, 231–244 (1995). [CrossRef]
  53. P. D. Harrington, “Minimal neural networks—concerted optimization of multiple decision planes,” Chemom. Intell. Lab. Syst. 18, 157–170 (1993). [CrossRef]
  54. T. R. McJunkin and J. R. Scott, “Application of fuzzy logic for automated interpretation of mass spectra,” in Fuzzy Logic Theory, Programming and Applications, R. E. Vargas, ed. (Nova Science, 2009), pp. 85–113.
  55. B. Yan, T. R. McJunkin, D. L. Stoner, and J. R. Scott, “Validation of fuzzy logic method for automated mass spectral classification for mineral imaging,” Appl. Surf. Sci. 253, 2011–2017 (2006). [CrossRef]
  56. J. R. Scott, T. R. McJunkin, and P. L. Tremblay, “Automated analysis of mass spectral data using fuzzy logic classification,” J. Assoc. Lab. Auto. 8, 61–63 (2003). [CrossRef]
  57. T. R. McJunkin, P. L. Tremblay, and J. R. Scott, “Automation and control of an imaging internal laser desorption Fourier transform mass spectrometer (I2LD-FTMS),” J. Lab. Auto. 7, 76–83 (2002). [CrossRef]
  58. C. D. Richardson, N. W. Hinman, T. R. McJunkin, J. M. Kotler, and J. R. Scott, “Exploring biosignatures associated with thenardite by geomatrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (GALDI-FTICR-MS),” Geomicrobiol. J. 25, 432–440 (2008). [CrossRef]
  59. J. R. Scott, B. Yan, and D. L. Stoner, “Spatially correlated spectroscopic analysis of microbe–mineral interactions,” J. Microbiol. Methods 67, 381–384 (2006). [CrossRef]
  60. J. R. Scott and P. L. Tremblay, “Highly reproducible laser beam scanning device for an internal source laser desorption microprobe Fourier transform mass spectrometer,” Rev. Sci. Instrum. 73, 1108–1116 (2002). [CrossRef]
  61. A. Bogaerts, Z. Chen, and D. Autrique, “Double pulse laser ablation and laser induced breakdown spectroscopy: a modeling investigation,” Spectrochim. Acta Part B: Atom. Spectrosc. 63, 746–754 (2008). [CrossRef]
  62. A. de Giacomo, R. Gaudiuso, M. Dell’Aglio, and A. Santagata, “The role of continuum radiation in laser induced plasma spectroscopy,” Spectrochim. Acta Part B: Atom. Spectrosc. 65, 385–394 (2010). [CrossRef]
  63. D. Steinley, “K-means clustering: a half-century synthesis,” Br. J. Math. Stat. Psychol. 59, 1–34 (2006). [CrossRef]

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