PNNL-13501 - Pacific Northwest National Laboratory

Probabilistic Methods Development for Comprehensive Analysis of Complex Spectral and
Chromatographic Data
Study Control Number: PN00076/1483
Kristin H. Jarman, Alan R. Willse, Karen L. Wahl, Jon H. Wahl
Techniques such as chromatography, spectrometry, and spectroscopy are being used in both basic research to improve
understanding of biological and biochemical processes, and in field portable instrumentation to perform rapid, on-site
chemical analysis in a complex environment. Under this project, new data analysis methods are being developed for
spectral and chromatographic data, providing algorithms that are automated, scientifically interpretable, and versatile
enough to analyze spectral or chromatographic data under complex backgrounds or varying experimental conditions.
Project Description
The purpose of this project was to develop new methods
for analysis of spectral or chromatographic data that apply
to a wide variety of instrumentation, and can be readily
adapted to either laboratory or field application. This
research draws from modern probabilistic reasoning and
statistics, where similarities between two data sets are
quantified based on the properties of the peaks within the
sets. The goal of this research was to advance the state of
the art by providing data analysis methods that are
automated, scientifically interpretable, and versatile
enough to analyze spectral or chromatographic data under
complex backgrounds or varying experimental conditions.
Methods developed are initially being applied to and
tested on specific problems in the area of matrix-assisted
laser desorption/ionization (MALDI) spectrometry and
gas chromatography.
Introduction
Analytical instrumentation techniques are evolving
rapidly. Various techniques are being developed for basic
research, while at the same time many of these techniques
are being developed and deployed into field portable
chemical or biological analysis tools. The tremendous
potential of new and existing analytical techniques has
resulted in rapid progress in the area of hardware and
methods development. In a research setting, it is now
possible to analyze sophisticated biochemical or physical
processes. In a field development setting, it is possible to
analyze samples in environments that generate complex,
nonhomogeneous background signals. In both cases, the
result is a complex spectrum and/or chromatogram.
Current data analysis methods, such as principalcomponents-based
methods (Seber 1984; Johnson and
Wichern 1992) tend to place nearly all of their emphasis
on peak intensities. This is appropriate for some
applications but not others. For example, intensity-based
methods are appropriate in gas chromatography, where
quantitation of analytes is often of interest. On the other
hand, mass spectrometry is often used to identify analytes
present in a sample based on the locations of the peaks
that appear. In this case, it can be difficult to obtain
reproducible relative peak intensities, and therefore the
presence or absence of peaks is of more interest than peak
heights. In this case, intensity-based data analysis
methods such as principal components analysis do not
reliably capture the features of interest in a spectrum and
a more subjective, manual interpretation tends to be used.
Under this project, we developed a unified statistical
framework through which many different types of
analytical data can be interpreted. This approach
incorporates recent research in both discrete and
continuous multivariate statistical methods. This model is
general enough to handle different types of analytical
data, where different spectral features are of interest. In
the first year of this project, methods for hypothesis
testing and process control of spectral data were
developed based on this model. A comparison against the
more traditional methods demonstrates that this approach
improved upon existing approaches.
Approach
The research under this project draws from a unique
mathematical model for spectral and chromatographic
data. The exact form of this multi-stage model is
specified by the spectral features of interest for a
particular application. Therefore, it is general enough to
handle mass spectral data, where peak locations are of
primary interest but intensities are not. It can also handle
chromatographic data, where both peak locations and
relative intensities may be of interest. In addition, this
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