A multivariate approach to oil hydrocarbon fingerprinting and spill source identification

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Standard

A multivariate approach to oil hydrocarbon fingerprinting and spill source identification. / Christensen, Jan H.; Tomasi, Giorgio.

Standard handbook oil spill environmental forensics: fingerprinting and source identification. ed. / Scott Stout; Zhendi Wang. 2. ed. Elsevier, 2016. p. 747-788.

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Christensen, JH & Tomasi, G 2016, A multivariate approach to oil hydrocarbon fingerprinting and spill source identification. in S Stout & Z Wang (eds), Standard handbook oil spill environmental forensics: fingerprinting and source identification. 2. edn, Elsevier, pp. 747-788. https://doi.org/10.1016/B978-0-12-803832-1.00016-7

APA

Christensen, J. H., & Tomasi, G. (2016). A multivariate approach to oil hydrocarbon fingerprinting and spill source identification. In S. Stout, & Z. Wang (Eds.), Standard handbook oil spill environmental forensics: fingerprinting and source identification (2. ed., pp. 747-788). Elsevier. https://doi.org/10.1016/B978-0-12-803832-1.00016-7

Vancouver

Christensen JH, Tomasi G. A multivariate approach to oil hydrocarbon fingerprinting and spill source identification. In Stout S, Wang Z, editors, Standard handbook oil spill environmental forensics: fingerprinting and source identification. 2. ed. Elsevier. 2016. p. 747-788 https://doi.org/10.1016/B978-0-12-803832-1.00016-7

Author

Christensen, Jan H. ; Tomasi, Giorgio. / A multivariate approach to oil hydrocarbon fingerprinting and spill source identification. Standard handbook oil spill environmental forensics: fingerprinting and source identification. editor / Scott Stout ; Zhendi Wang. 2. ed. Elsevier, 2016. pp. 747-788

Bibtex

@inbook{d7c8220523d34f6eaf1969f24756e7ef,
title = "A multivariate approach to oil hydrocarbon fingerprinting and spill source identification",
abstract = "Tiered approaches for oil spill fingerprinting have evolved rapidly since the 1990s. Chemometrics provides a large number of tools for pattern recognition, calibration, and classification that can increase the speed and the objectivity of the fingerprinting analysis and allow for more extensive use of the available data. A framework (integrated multivariate oil hydrocarbon fingerprinting - IMOF) for the use of chemometric approaches in tiered oil spill fingerprinting is presented in this chapter. It consists of four main steps where a suite of analytical instruments, data preprocessing and multivariate statistical methods, as well as data evaluation and visualization tools have been tested. IMOF is exemplified using parallel factor analysis of fluorescence excitation-emission spectra, and pixel-based analysis of gas chromatography - mass spectrometry selected ion chromatograms (GC-MS SICs). Its application to other data types such as GC-flame ionization detection, liquid chromatography-MS, and two-dimensional GC and LC are briefly discussed.",
keywords = "Chemometrics, Fluorescence spectroscopy, GC-FID, GC-MS, Multivariate data analysis, Oil classification, Oil matching, Pattern recognition, Pixel-based analysis",
author = "Christensen, {Jan H.} and Giorgio Tomasi",
year = "2016",
doi = "10.1016/B978-0-12-803832-1.00016-7",
language = "English",
isbn = "978-0-12-803832-1",
pages = "747--788",
editor = "Scott Stout and Zhendi Wang",
booktitle = "Standard handbook oil spill environmental forensics",
publisher = "Elsevier",
address = "Netherlands",
edition = "2.",

}

RIS

TY - CHAP

T1 - A multivariate approach to oil hydrocarbon fingerprinting and spill source identification

AU - Christensen, Jan H.

AU - Tomasi, Giorgio

PY - 2016

Y1 - 2016

N2 - Tiered approaches for oil spill fingerprinting have evolved rapidly since the 1990s. Chemometrics provides a large number of tools for pattern recognition, calibration, and classification that can increase the speed and the objectivity of the fingerprinting analysis and allow for more extensive use of the available data. A framework (integrated multivariate oil hydrocarbon fingerprinting - IMOF) for the use of chemometric approaches in tiered oil spill fingerprinting is presented in this chapter. It consists of four main steps where a suite of analytical instruments, data preprocessing and multivariate statistical methods, as well as data evaluation and visualization tools have been tested. IMOF is exemplified using parallel factor analysis of fluorescence excitation-emission spectra, and pixel-based analysis of gas chromatography - mass spectrometry selected ion chromatograms (GC-MS SICs). Its application to other data types such as GC-flame ionization detection, liquid chromatography-MS, and two-dimensional GC and LC are briefly discussed.

AB - Tiered approaches for oil spill fingerprinting have evolved rapidly since the 1990s. Chemometrics provides a large number of tools for pattern recognition, calibration, and classification that can increase the speed and the objectivity of the fingerprinting analysis and allow for more extensive use of the available data. A framework (integrated multivariate oil hydrocarbon fingerprinting - IMOF) for the use of chemometric approaches in tiered oil spill fingerprinting is presented in this chapter. It consists of four main steps where a suite of analytical instruments, data preprocessing and multivariate statistical methods, as well as data evaluation and visualization tools have been tested. IMOF is exemplified using parallel factor analysis of fluorescence excitation-emission spectra, and pixel-based analysis of gas chromatography - mass spectrometry selected ion chromatograms (GC-MS SICs). Its application to other data types such as GC-flame ionization detection, liquid chromatography-MS, and two-dimensional GC and LC are briefly discussed.

KW - Chemometrics

KW - Fluorescence spectroscopy

KW - GC-FID

KW - GC-MS

KW - Multivariate data analysis

KW - Oil classification

KW - Oil matching

KW - Pattern recognition

KW - Pixel-based analysis

U2 - 10.1016/B978-0-12-803832-1.00016-7

DO - 10.1016/B978-0-12-803832-1.00016-7

M3 - Book chapter

AN - SCOPUS:84969685662

SN - 978-0-12-803832-1

SP - 747

EP - 788

BT - Standard handbook oil spill environmental forensics

A2 - Stout, Scott

A2 - Wang, Zhendi

PB - Elsevier

ER -

ID: 172029276