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A Novel Method for Chemical Fingerprinting of Oil and Petroleum based products based on Electrospray Mass Spectrometry and Chemometrics

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Title: A Novel Method for Chemical Fingerprinting of Oil and Petroleum based products based on Electrospray Mass Spectrometry and Chemometrics


1
A Novel Method for Chemical Fingerprinting of Oil
and Petroleum based products based on
Electrospray Mass Spectrometry and Chemometrics
  • Ingvar Eide and Kolbjorn Zale
  • Energy and Fuels, 2006
  • Statoil Research Centre, Norway
  • Revised January 4, 2004

2
Overview
  • Intro
  • Petroleum
  • Mass Spectrometry
  • ESI-MS
  • Chemometrics
  • Methods
  • Results
  • Conclusions
  • Future Implications

3
Background Info.
  • Aim of Study Develop a quick and simple method
    for fingerprinting of oil products.
  • IR and GC-MS were studied.
  • IR Spectra cannot be resolved into individual
    mass numbers.
  • GC-MS
  • Interpretation of spectra was very complicated.
  • Large molecules with high boiling points.
  • Combined Platform of ESI-MS Methodology and
    Chemometrics.

4
Petroleum
  • 50-80 of Petroleum is Hydrocarbons
  • N-alkanes
  • Cycloalkanes
  • Aromatics
  • The amount of branched

    alkanes is equal to or greater

    than the n-alkane content in petroleum.
  • Branches are typically methyl groups
  • Cycloalkanes Largest group in Petroleum
  • Typically cyclopentanes and cylohexanes.
  • Seven and Eight member rings exist but

    in small amounts.
  • Aromatics 20 or less
  • Benzene, PAH, and Alkyl Substituted
  • Benzenes.
  • Sulfur, Nitrogen, Oxygen

5
Petroleum
6
Petroleum (Rock Oil)
  • Crude Oil Unrefined Petroleum
  • Petroleum is produced by deposition of organic
    matter on sedimentary rocks and burial and
    compaction of that matter over of millions of
    years
  • Petroleum is introduced in to the environment
    via
  • Dispersion
  • Evaporation
  • Dissolution
  • For low molecular weight, volatile,

    compounds evaporation is the

    most important.
  • API (American Petroleum Institute Gravity)
  • Measure of relative density

    between a petroleum liquid
    and water.
  • Crude Oil
  • Light - gt 31.1 API
  • Medium - 22.3 API - 31.1 API
  • Heavy - lt 22.3 API
  • gt 10---Floats on Water
  • lt 10---Heavier than Water

7
Mass Spectrometry(MS)
  • Invented by J.J. Thomson in the early 20th
    Century
  • Used to identify and quantify compounds present
    in complex organic mixtures.
  • A Substance is bombarded with an electron with
    sufficient energy to fragment the molecule.
  • Represent parent compounds that have been ionized
    into positive gaseous ions.
  • Compounds are separated by their

    mass to charge ratio or (m/z)
  • Example CO2 z1 so 44/1 44.

8
Mass Spectrometry
  • Quadrupole MS A Spectrum is achieved by a
    combination of radio frequency(RF) and Direct
    Current(DC) fields with opposite polarities on
    the opposing rods.
  • Oscillations by the RF field cause lighter ions
    to strike the positive poles and heavier ions to
    strike the negative poles.

9
Mass Spectrometry Detector
  • Ions pass into the detector and impact a
    dynode(High Negative Voltage). This release
    electrons which are drawn towards a more positive
    dynode.
  • Electon multipliers produce millions of electrons
    from one ion.

10
CO2 Mass Spectrum
11
Electrospray Mass Spectrometry
  • Electrospray Mass Spectrometry (ESI-MS)
  • Liquid Analyte
  • Capillary ? Metal Plate with a High Potential ?
    Charged droplets on Plate ? Evaporation with N2 ?
    Pin Size Hole in Plate leads to a Mass Analyzer
    in a high vacuum.
  • Can run from 65-3000 m/z range.

12
MS Spectrum
Figure 2. Expanded image of a portion of the mass
spectrum depicted in Figure 1.
Figure 1. Illustration of the full-scan mass
spectrometry without chromatographic separation.
13
ESI-MS Spectrum
14
Chemometrics
  • Chemometrics application of mathematical and
    statistical methods to chemical data
  • Projections to Latent Structures or Partial Least
    Squares (PLS)
  • Regression Modeling
  • Principal Component Analysis (PCA)
  • Statistical method of Identifying patterns in
    data in such a way as to highlight their
    differences and similarities.
  • Data Reduction Technique
  • Useful for large sets of interrelated variables
  • Good for MS because each compound has a
    characteristic mass and peaks are well defined.
  • PCA will find eigenvectors or eigenvalues
    relevant to the data using a covariance matrix.
  • Eigenvectors can be thought of as preferential
    directions or main patterns in a data set.
  • Higher Value Better representation of the data.

15
Principal Component Analysis
  • Steps
  • Creation of a Correlation/Covariance Matrix
  • Covariance is a linear relationship between two
    variables
  • C (1/(N-1)) (X-mean) )(XT-mean)
  • X Data Matrix
  • XT Transpose of Data Matrix
  • N Number of Columns
  • Eigenanalyis is performed
  • Eigenvalues are ordered in decreasing variance.
  • 2 Largest values are considered the principal
    components
  • Project both PCs onto a plane Score Plot

16
Principal Component Analysis
  • Example
  • Spectra of 15 Peaks.
  • Suspected to contain three components.
  • Reduced to two principal components
    with three
    constituents.

17
Methods
  • Four Crude Oils
  • A - Norwegian Sector in the North Sea (Light,
    API of 36.8)
  • B - Norwegian Sector in the North Sea (Medium,
    API of 31.9)
  • C - South America
  • D - Central Asia
  • ESI-MS
  • 65-1000 m/z range. Atmospheric pressure positive
    ionization.
  • Mobile Phase Acetonitrile and Ammonium Acetate
    9010
  • Two Experiments
  • Discriminate four crude oils
  • Discriminate the two Norwegian oils
  • Five Combinations of Oils
  • 0,25,50,75, and 100 of each
  • Dissolved in Dichloromethane(2mg/ml)
  • 1 ul samples were injected into a single
    quadrupole mass spectrometer (x10).
  • Spectra were tabulated to a row with numbers
    where each number represents the height of each
    spectral line.

18
Methods
  • Analysis
  • Matrix X 40 Rows x 931 Columns(1 per integer
    mass number).
  • Matrix X 50 Rows x 934 Columns
  • Matrix Y Combinations of A and B 50 rows and 2
    Columns
  • (Percentages are
    given in each column)
  • PCA was performed on Matrix X.
  • PLS was performed on Matrix Y.

19
Results - ESI-MS Spectra
Figure 3. Spectra of the four different crude
oils (A, B, C, and D).
20
Results - (PCA Score Plot)
Figure 4. Score plot obtained after
fingerprinting and multivariate pattern
recognition of the four crude oils (each sample
was analyzed 10 times).
21
Results - PLS
  • PLS Projections to Latent Structures.
  • R2 0.996
  • Q2(Goodness Prediction) 0.985

Figure 5. Observed versus predicted percentage of
oil A in a binary mixture of oils A and B (each
mixture was analyzed 10 times).
22
Conclusions
  • Confirm several specific spectral characteristics
    of hydrocarbon-containing mixtures.
  • Repetitive Spacing of 14 and 2 Da.
  • ESI-MS produces singly charged ions.
  • ESI-MS
  • Captures most of the information needed for
    pattern recognition
  • Commercially available
  • Number are increasing worldwide.
  • With the combination of ESI-MS and Chemometrics
    we can distinguish between oils and mixtures of
    oils with impressive repeatability.
  • Can be analyzes without pretreatment, except for
    dissolution in Dichloromethane.

23
Future Implications
  • (1) Characterization of crude oils, including
    heavy
  • crudes and petroleum products
  • (2) Estimation of the contribution of oil from
    various
  • reservoirs in commingled production scenarios
  • (3) Detection of the effects of well treatment
    chemicals
  • on crudes
  • (4) Source identification (e.g., of oil spills)
    by comparing
  • samples of spilled oil and candidate oils
  • (5) Identification of compounds that make samples
  • different (e.g., contaminants and additives)
  • (6) Time-based studies (hydrocarbon upgrading,
    degradation,
  • oil-in-water, oil weathering, etc.) and process
    control
  • (7) Correlation of chemical fingerprints with
    measured physicochemical or environmental oil
    properties

24
References/Thank You
  • Ingvar, Eide and Kolbjorn Zahlsen. A Novel Method
    for Chemical Fingerprinting of oil and Petroleum
    Products Based on Electrospray Mass Spectrometry
    and Chemometrics. Energy Fuels 2005, 19,
    964-967 2005. 1 Oct 2004.
  • Standardizing the Novel Method for Chemical
    Fingerprinting of Oil and Petroleum Products
    Based on Positive Electrospray Mass Spectrometry
    and Chemometrics. Energy Fuels 2006, 20,
    265-270. 1 Nov 2005.
  • Williamson, Keenneth L. Minard, Robert D., and
    Masters, Katherine M. Microscale and Marcroscale
    Organic Experiments Fifth Edition. Houghton
    Milfin. 2007
  • McLafferty, Fred W and Turecek, Frantisek.
    Interpretation of Mass Spectra Fourth Edition.
    University Science Books. 2003.
  • March and Hughes. Quadrupole Storage Mass
    Spectrometry. John Wiley and Sons. 1989.
  • Connel, Des W. Basic Concepts of Environmental
    Chemistry Second Edition. Taylor and Francis
    Group. 2005
  • Brereton, Richard G. Chemometrics Application of
    mathematics and statistics to laboratory systems.
  • Prof. Powers
  • President Haas

25
Questions
26
  • San Francisco Oil Spill - Nov 7th 2007
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