PNNL-13501 - Pacific Northwest National Laboratory

model is designed to handle other types of data where
additional peak properties such as width and shape may
be important.
Results and Accomplishments
Model Construction and Hypothesis Testing Procedure
The multi-stage model for spectral and chromatographic
data was constructed. The model is currently able to
handle data types where peak presence, location, and
intensity are important. A hypothesis testing algorithm
was developed based on this model. The hypothesis
testing algorithm is a likelihood ratio based test for simple
or composite hypotheses.
Process Control Procedure
A multivariate cumulative sum procedure for control of
analytical processes based on the model presented here was
developed. We assume the process follows some
prescribed nominal behavior until time k>1, called the
change point, at which time the process behavior changes.
This multivariate approach considers the sequence Zi =
g(Xi) - c, where c is some constant, and g(⋅) is a function of
the incoming spectra constructed from the spectral model.
We let Sn = ∑1≤j≤n Zj, and define the test statistic to be Cn =
Sn - min1≤j≤n {Sj} for n≥1 with C0 = 0. Then Cn can be
formulated recursively by the relation Cn+1 = max{0,Cn +
Zn+1}. This process is repeated for incoming observations
until Cn ≥ A for some constant A, at which time the process
is declared to be out of control.
Comparison to Existing Methods
A comparison of our process control technique to the
more traditional method proposed in Nijhuis et al. (1997)
was made for two application areas. The first application
area involves control of routine analysis in gas
chromatography, where peak presence, location, and
intensity are important. In this case, both the traditional
approach and the new approach identified an out-ofcontrol
gas chromatography process. The second
application area involves bacterial analysis using MALDI
mass spectrometry, where peak presence and location are
of interest. In this case, the new approach performed
much better than the traditional approach. The results of
the MALDI mass spectrometry comparison are provided
here.
The goal was to determine if the new approach could
better detect contamination of an E. coli bacterial culture
by Shewanella alga (S. alga) using MALDI mass
spectrometry than the existing method. Contamination
452 FY 2000 Laboratory Directed Research and Development Annual Report
was simulated by using a mixture of the two bacteria.
Figure 1 plots MALDI mass spectra for a pure E. coli
culture and for a mixture of E. coli and S. alga. Figure 2
compares the two different process control procedures,
where the first 29 samples are of pure E. coli and the
last 30 samples contain a mixture of the two bacteria.
Intensity
Intensity
a) MALDI-MS for pure E. coli culture
1200
1000
800
600
400
200
2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000
b) MALDI-MS for E. coli, S. alga mixture
1400
1200
1000
800
600
400
200
2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000
Mass/Charge Ratio (m/z)
Figure 1. Typical MALDI mass spectra for bacterial
cultures
Figure 2a shows the results of the traditional method,
where the process control test statistic is plotted. Test
statistic values that lie above the threshold (horizontal
bold line at 0.1) result in a conclusion that the culture is
pure. Test statistic values that fall below the horizontal
line result in a conclusion that contamination is present.
Figure 2b shows the results of new approach developed
under this project. In this case, the test statistic is plotted
and compared to an upper threshold marked by the bold
horizontal line at 4.6. Test statistic values that remain
below the threshold result in a conclusion that the culture
is pure, whereas values that hit the threshold result in the
conclusion that the culture is contaminated. Figure 2
clearly shows that the new approach developed under this
project detects the bacterial contamination whereas the
more traditional approach fails to identify any of the
contaminated samples.
Summary and Conclusions
A novel mathematical model for spectral/
chromatographic data was developed. Methods for
hypothesis testing and process control of analytical
instrumentation were developed based on this model.
Comparison with more traditional methods indicates that
this approach performs as well as or better than the
traditional methods for the applications tested here. A
more complete comparison using different applications