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Objective chemical fingerprinting of oil spills by partial least-squares discriminant analysis

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Abstract

An objective method based on partial least-squares discriminant analysis (PLS-DA) was used to assign an oil lump collected on the coastline to a suspected source. The approach is an add-on to current US and European oil fingerprinting standard procedures that are based on lengthy and rather subjective visual comparison of chromatograms. The procedure required an initial variable selection step using the selectivity ratio index (SRI) followed by a PLS-DA model. From the model, a “matching decision diagram” was established that yielded the four possible decisions that may arise from standard procedures (i.e., match, non-match, probable match, and inconclusive). The decision diagram included two limits, one derived from the Q-residuals of the samples of the target class and the other derived from the predicted y of the PLS model. The method was used classify 45 oil lumps collected on the Galician coast after the Prestige wreckage. The results compared satisfactorily with those from the standard methods.

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References

  1. Bentz AP (1976) Oil Spill Identification. Anal Chem 48(6):454A–472A

    Article  Google Scholar 

  2. US Coast Guard (1977) Oil Spill Identification System. USCG Report No (X-D-52-77, Task No 4243.2

  3. Boehm PD, Douglas GS, Burns WA, Mankiewicz PJ, Page DS, Bence AE (1997) Application of petroleum hydrocarbon chemical fingerprinting and allocation techniques after the Exxon Valdez oil spill. Mar Poll Bull 34(8):599–613

    Article  CAS  Google Scholar 

  4. Christensen JH, Hansen AB, Tomasi G, Mortensen J, Andersen O (2004) Integrated methodology for forensic oil spill identification. Environ Sci Technol 38(10):2912–2918

    Article  CAS  Google Scholar 

  5. Douglas GS, Bence AE, Prince RC, McMillen SJ, Butler EL (1996) Environmental stability of selected petroleum hydrocarbon source and weathering ratios. Environ Sci Technol 30(7):2332–2339

    Article  CAS  Google Scholar 

  6. Fernández-Varela R, Gómez-Carracedo MP, Ballabio D, Andrade JM, Consonni V, Todeschini R (2010) Self Organizing Maps for Analysis of Polycyclic Aromatic Hydrocarbons 3-Way Data from Spilled Oils. Anal Chem 82(10):4264–4271

    Article  Google Scholar 

  7. Wang Z, Stout SA (2007) Oil Spill environmental forensics: fingerprinting and source identification. Academic Press, United States of America

    Google Scholar 

  8. Wang Z, Fingas M, Sergy G (1994) Study of 22-year-old Arrow oil samples using biomarker compounds by GC–MS. Environ Sci Technol 28(9):1733–1746

    Article  CAS  Google Scholar 

  9. Wang Z, Fingas M, Page DS (1999) Oil Spill Identification. J Chromatogr A 843(1–2):369–411

    Article  CAS  Google Scholar 

  10. Wang Z, Fingas MF (2003) Development of oil hydrocarbon fingerprinting and identification techniques. Mar Pollut Bull 47(9–12):423–452

    Article  CAS  Google Scholar 

  11. Nordtest Method NT CHEM 001 (1991) Oil Spill Identification, 2nd Edition

  12. Daling PS, Faksness LG (2001) Laboratory and reporting instructions for the CEN/BT/TF 120 Oil Spill Identification Round Robin Test. Technical report, SINTEF report STF66 A02027

  13. Fasksness LG, Weiss H, Dailing PS (2002) Revision of the Nordtest methodology for oil spill identification. Technical Report, SINTEF Report STF66 A02028

  14. EUROCRUDE® (1995) European Crude Oil Identification System Final Report

  15. CEN/TR 15522–2 (2006) Oil Spill identification, Waterborne petroleum and petroleum products, Part 2: Analytical methodology and interpretation of results. Technical report

  16. ASTM D3328–06 (2006) Standard Test Methods for Comparison of Waterborne Petroleum Oils by Gas Chromatography. ASTM International, West Conshohocken, PA

    Google Scholar 

  17. ASTM D5739–06 (2006) Standard Practice for Oil Spill Source Identification by Gas Chromatography and Positive Ion Electron Impact Low Resolution Mass Spectrometry. ASTM International, West Conshohocken, PA

    Google Scholar 

  18. Method 8015B (1996) Total petroleum hydrocarbons (TPH) as Gasoline and Diesel. US-EPA SW-846, Revision 2

  19. Method 8270D (2007) Semivolatile organic compounds by gas chromatography/mass spectrometry (GC/MS). US-EPA SW-846, Revision 4

  20. Snape I, Harvey PMcA, Ferguson SH, Rayner JL, Revill AT (2005) Investigation of evaporation and biodegradation of fuel spills in Antarctica I A chemical approach using GC–FID. Chemosphere 61(10):1485–1494

    Article  CAS  Google Scholar 

  21. Snape I, Ferguson SH, Harvey P, McA RMJ (2006) Investigation of evaporation and biodegradation of fuel spills in Antarctica: II – Extent of natural attenuation at Casey Station. Chemosphere 63(1):89–98

    Article  CAS  Google Scholar 

  22. Lucas Z, MacGregor C (2006) Characterization and source of oil contamination on the beaches and seabird copses, Sable Island, Nova Scotia, 1996–2005. Mar Pollut Bull 52(7):778–789

    Article  CAS  Google Scholar 

  23. Christensen JH, Hansen AB, Karlson U, Mortensen J, Andersen O (2005) Multivariate statistical methods for evaluating biodegradation of mineral oil. J Chromatogr A1090(1–2):133–145

    Article  Google Scholar 

  24. González JJ, Viñas L, Franco MA, Fumega J, Soriano JA, Grueiro G, Muniategui S, López-Mahía P, Prada D, Bayona JM, Alzaga R, Albaigés J (2006) Spatial and temporal distribution of dissolved/dispersed aromatic hydrocarbons in seawater in the area affected by the Prestige oil spill. Mar Pollut Bull 53(5–7):250–259

    Article  Google Scholar 

  25. Franco MA, Viñas L, Soriano JA, de Armas D, González JJ, Beiras R, Salas N, Bayona JM, Albaigés J (2006) Spatial distribution and ecotoxicity of petroleum hydrocarbons in sediments from the Galicia continental shelf (NW Spain) after the Prestige oil spill. Mar Pollut Bull 53(5–7):260–271

    Article  CAS  Google Scholar 

  26. Ebrahimi D, Brynn Hibbert D (2008) Identification of sources of diesel oil spills using parallel factor analysis: A bridge between American society for testing and materials and Nordtest methods. J Chromatogr A 1198–1199:181–187

    Article  Google Scholar 

  27. Sun P, Bao M, Li G, Wang X, Zhao Y, Zhou Q, Cao L (2009) Fingerprinting and source identification of an oil spill in China Bohai Sea by gas chromatography–flame ionization detection and gas chromatography – mass spectrometry coupled with multi-statistical analyses. J Chromatogr A 1216(5):830–836

    Article  CAS  Google Scholar 

  28. Pasadakis N, Gidarakos E, Kanellopoulou G, Spanoudakis N (2008) Identifying Sources of Oil Spills in a Refinery by Gas Chromatography and Chemometrics: A Case Study. Environ Forensics 9(1):33–39

    Article  CAS  Google Scholar 

  29. Lavine BK, Brzozowski D, Moores AJ, Davidson CE, Mayfield HT (2001) Genetic algorithm for fuel spill identification. Anal Chim Acta 437(2):233–246

    Article  CAS  Google Scholar 

  30. Doble Ph, Sandercock M, Du Pasquier E, Petocz P, Roux C, Dawson M (2003) Classification of Premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks. Forensic Sci Int 132(1):26–39

    Article  CAS  Google Scholar 

  31. Urdal K, Vogt NB, Sporstøl SP, Lichtenthaler RG, Mostad H, Kolset K, Nordenson S, Esbensen K (1986) Classification of Weathered Crude Oils Using Multimethod Chemical Analysis, Statistical Methods and SIMCA Pattern Recognition. Mar Pollut Bull 17(8):366–373

    Article  CAS  Google Scholar 

  32. Gaines RB, Hall GJ, Frysinger GS, Grondlund WR, Juaire KL (2006) Chemometric Determination of Target Compounds Used to Fingerprint Unweathered Diesel Fuels. Environ Forensics 7(1):77–87

    Article  CAS  Google Scholar 

  33. Fonseca AM, Biscaya JL, Aires-de-Sousa J, Lobo AM (2006) Geographical classification of crude oils by Kohonen self-organizing maps. Anal Chim Acta 556(2):374–382

    Article  CAS  Google Scholar 

  34. Borges C, Gómez-Carracedo MP, Andrade JM, Duarte MF, Biscaya JL, Aires-de-Sousa J (2010) Geographical classification of weathered crude oil samples with unsupervised self-organizing maps and a consensus criterion. Chemom Intell Lab Syst 101(1):43–55

    Article  CAS  Google Scholar 

  35. Fernández-Varela R, Andrade JM, Muniategui S, Prada D, Ramírez-Villalobos F (2010) Identification of petroleum hydrocarbons using a reduced number of PAHs selected by Procrustes. Mar Pollut Bull 60(4):526–535

    Article  Google Scholar 

  36. Grueiro-Noche G, Andrade JM, Muniategui-Lorenzo S, López-Mahía P, Prada-Rodríguez D (2010) 3-Way pattern-recognition of PAHs from Galicia (NW Spain) seawater simples after the Prestige’s wreck. Environ Pollut 158(1):207–214

    Article  CAS  Google Scholar 

  37. Christensen JH, Hansen AB, Mortensen J, Andersen O (2005) Characterization and Matching of Oil Samples Using Fluorescence Spectroscopy and Parallel Factor Analysis. Anal Chem 7(77):2210–2217

    Article  Google Scholar 

  38. Arancibia JA, Boschetti CE, Olivieri AC, Escandar GM (2008) Screening of oil samples on the basis of excitation–emission room-temperature phosphorescence data and multi-way chemometric techniques. Introducing the second-order advantage in a classification study. Anal Chem 8(80):2789–2798

    Article  Google Scholar 

  39. Zorzetti BM, Harynuk JJ (2011) Using GC × GC–FID profiles to estimate the age of weathered gasoline samples. Anal Bioanal Chem 401(8):2423–2431

    Article  CAS  Google Scholar 

  40. Lobão MM, Cardoso JN, Mello MR, Brooks PW, Lopes CC, Lopes RSC (2010) Identification of source of a marine oil-spill using geochemical and chemometric techniques. Mar Pollut Bull 60(12):2263–2274

    Article  Google Scholar 

  41. Fernández-Varela R, Andrade JM, Muniategui S, Prada D, Ramírez-Villalobos F (2009) The comparison of two heavy fuel Oils in composition and weathering pattern, based on IR, GC–FID and GC–MS analyses: Application to the Prestige wreckage. Water Res 43(4):1015–1026

    Article  Google Scholar 

  42. Cozzolino D, Chree A, Scaife JR, Murray I (2005) Usefulness of Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics to Discriminate Fishmeal Batches Made with Different Fish Species. J Agric Food Chem 53(11):4459–4463

    Article  CAS  Google Scholar 

  43. Bishop CM (2006) Pattern Recognition and Machine Learning. Springer, New York

    Google Scholar 

  44. Wise BM, Gallagher N B, Bro R, Shaver JM, Windig W, Koch RS (2005) PLS-Toolbox Version 3.5 for use with MATLABTM, Eigenvector Research, Inc., Manson, WA, USA

  45. Duda RO, Hart PE, Stork DG (2001) Pattern Classification, 2nd edn. John Wiley & Sons, Inc., New York, NY

    Google Scholar 

  46. Sánchez MS, de la Cruz Ortiz M, Sarabia LA, Busto V (2010) Class-modelling techniques that optimize the probabilities of false noncompliance and false compliance. Chemom Intell Lab Syst 103(1):25–42

    Article  Google Scholar 

  47. Brereton RG (2006) Consequences of sample size, variable selection, and model validation and optimization, for predicting classification ability from analytical data. Trends Anal Chem 25(11):1103–1111

    Article  CAS  Google Scholar 

  48. Rajalahti T, Arneberg R, Berven FS, Myhr K-M, Ulvik RJ, Kvalheim OM (2009) Biomarker discovery in mass spectral profiles by means of selectivity ratio plot. Chemom Intell Lab Syst 95(1):35–48

    Article  CAS  Google Scholar 

  49. Rajalahti T, Arneberg R, Kroksveen AC, Berle M, Myhr D-M, Kvalheim OM (2009) Discriminating variable test and selectivity ratio plot: Quantitative tools for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles. Anal Chem 81(7):2581–2590

    Article  CAS  Google Scholar 

  50. Sinkov NA, Johnston BM, Sandercock PML, Harynuk JJ (2011) Automated optimization and construction of chemometric models based on highly variable raw chromatographic data. Anal Chim Acta 697:8–15

    Article  CAS  Google Scholar 

  51. Johnson KJ, Wright BW, Jarman KH, Synovec RE (2003) High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis. J Chromatogr A 996:141–155

    Article  CAS  Google Scholar 

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Acknowledgments

M.P.G-C acknowledges the Galician Government (Xunta de Galicia) for a postdoctoral “Ángeles Alvariño” Research Contract and a post-doc grant partially supported by the EU FEDER funds to stay at the URV. The Research Grant 07MDS031103PR (Xunta de Galicia) is recognized.

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Authors and Affiliations

  1. Department of Analytical Chemistry, University of A Coruña, Campus da Zapateira s/n, 15008, A Coruña, Spain

    M. P. Gómez-Carracedo, J. M. Andrade & R. Fernández-Varela

  2. Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007, Tarragona, Spain

    J. Ferré & R. Boqué

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  1. M. P. Gómez-Carracedo
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Correspondence to J. M. Andrade.

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Gómez-Carracedo, M.P., Ferré, J., Andrade, J.M. et al. Objective chemical fingerprinting of oil spills by partial least-squares discriminant analysis. Anal Bioanal Chem 403, 2027–2037 (2012). https://doi.org/10.1007/s00216-012-6008-5

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