Interactive Series Baseline Correction Algorithm

1
Interactive Series Baseline Correction Algorithm

• Andrey Bogomolova, Willem Windigb,
• Susan M. Geerc, Debra B. Blondellc,
• and Mark J. Robbinsc
• a ACD/Labs, Russian Chemometrics Society, Moscow,
  Russia
• b Eigenvector Research Inc., Rochester, NY, USA
• c Eastman Kodak Company, Rochester, NY, USA

2
Baseline (Background) Problem

• Baseline is an eternal issue in analytical data
  processing
• Baseline or background?
• no clear distinction
• baseline is associated with a smooth line
  reflecting a physical interference
• background tends to be used in a more general
  sense to designate ANY unwanted signal including
  noise and chemical components
• Our preference is given to the term baseline
  because smoothness of the background signal is
  the main assumption of the proposed correction
  algorithm

3
Classical Approach to the Baseline Correction
Problem

• Classical baseline correction algorithms with
  respect to single curve are almost exhaustively
  elaborated in the literature
• A baseline to be subtracted is fitted by a linear
  (polynomial) function to the nodes that belong to
  signal-free regions
• The nodes can be automatically detected by the
  software or manually placed by the user
• These methods are advantageous for half-automatic
  processing where software-generated results need
  to be revised by a human expert

4
Serial (Batch) Methods

• Development of two-dimensional spectroscopy and
  hyphenated techniques demanded new methods
  applicable to data matrices
• Early works in this direction applied automated
  baseline correction algorithms to every
  individual curve in a matrix dataset
• The main problem with this approach is that it
  neglects internal (inter-spectral) correlations
• Instead of the expected rank reduction it may
  introduce additional variance into the dataset
• It is a black-box routine that is difficult to
  control

5
Multivariate Background Correction

• Multivariate data analysis produced a
  revolutionary impact onto the baseline problem in
  general
• The paradigmatic shift from hard-
  (knowledge-driven) to soft- or self-
  (data-driven) modeling has opened new horizons
  and introduced new concepts
• PLS introduces the means to address the
  background without its subtraction in the
  calibration context
• OSC by S. Wold turns the problem inside out
  eliminating the variance that is irrelevant for
  calibration (orthogonal to Y) from the data (X)
• A number of other excellent algorithms

6
Our Objectives

• The researchers are typically concentrated at the
  development of fully automated background
  correction methods
• Statement fuzzy character of the baseline
  problem in general puts in doubt the feasibility
  of automated (expert-free) baseline correction
  routines
• In contrast, we present an alternative approach
  that tends to maximize the means of control for a
  human operator
• simplicity
• visualization
• interactive stepwise algorithm

7
The Method

• The method is applied to a series of curves
  (e.g., spectra or chromatograms)
• The method consists of two distinct steps
• First, a prototype baseline is constructed from
  linear segments by selecting a set of nodes
• To aid in the node selection the mean values are
  calculated to represent the entire series
• Second, the prototype baseline is used to
  construct individual baselines to be subtracted
  from the series curves by adjusting the nodes
  vertically to the corrected curve

8
HPLC/DAD Sample Data
9
2nd Derivative for Node Selection
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Baseline Correction for Curve Resolution

• Baseline correction is an application-specific
  preprocessing technique
• The present baseline correction algorithm has
  been developed to improve the performance of
  SIMPLISMA (SIMPLe-to-use Interactive
  Self-modeling Mixture Analysis) curve resolution
  technique
• The algorithm has been used at Eastman Kodak
  Company over 10 years for routine analysis of
  TGA/IR data that represent a challenging case for
  curve resolution
• a lot of components
• high degree of overlap
• intensive background signal

11
TGA/IR Sample Data
Reprinted with permission from Eastman Kodak
Company, 2005
12
Baseline Nature in TGA/IR

• The most common reasons for TGA/IR baseline
  drift
• Temperature fluctuations over time
• Instrument drift
• Material scattering
• Impurities
• Inappropriate background, etc.
• In the present dataset - miscellaneous reasons
• Spectral domain is more suitable for series
  baseline correction because of narrow peaks and
  explicit baseline areas

13
TGA/IR Baseline Correction
Reprinted with permission from Eastman Kodak
Company, 2005
14
TGA/IR Corrected Data Map
Reprinted with permission from Eastman Kodak
Company, 2005
15
TGA/IR SIMPLISMA Curve Resolution
Reprinted with permission from Eastman Kodak
Company, 2005
16
IR Library Identification
Reprinted with permission from Eastman Kodak
Company, 2005
17
Conclusions

• A new interactive approach to the baseline
  correction problem has been suggested
• It allows for adapting traditional automated
  single-scan baseline correction routines or for
  performing manual correction on matrix data as if
  they were a single curve
• Advantages of the method include transparency
  of the process and the means for extensive
  operator interaction
• The method has passed long-term testing in an
  industrial laboratory and was integrated into a
  professional software package
• In spite of the simplicity of the algorithm, it
  allows for successful elimination of baselines
  even in complex cases such as TGA/IR data

18
Acknowledgements

• Antony Williams for his friendly support, and
• Michel Hachey for his help and valuable ideas