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Introduction toIntroduction toparameter optimizationparameter optimization
Sabine Beulke, Central Science Laboratory, York, UK
Kinetic Evaluation according to Recommendations by the FOCUS Work Group on Degradation Kinetics
Washington, January 2006
Curve fittingCurve fitting
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measured
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measured SFO
OptimizationOptimization
Least squares method:
Minimizes the sum of squared residuals (RSS)
Calculated line
Residual = deviation between calculated and measured data
Measured datapoint
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OptimizationOptimization
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Calculatecurve
Initial guess(starting value)
CalculateRSS
Modifyparameter 0
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Automatic optimizationAutomatic optimization
Stops when:
Convergence criteria are metComparison between RSS for actual and previous runs. Convergence reached if difference is smaller than user-specified difference
Termination criteria are metFor example, when maximum number of runs has been carried out (user-specified)
Good fit not guaranteed!
Non-uniquenessNon-uniqueness
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Time
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measured FOMC
M0 92.48 DT50 7.2alpha 957.220 DT90 24.1beta 10004.3
139.277 Residual Sum of Squares
M0 92.47 DT50 7.2alpha 6696.536 DT90 24.1beta 70030.3
139.120 Residual Sum of Squares
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Time
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measured FOMC
Non-uniquenessNon-uniqueness
Parameter correlationParameters strongly related
Effects on RSS of changes in one parameter can be compensated by changes in another parameter
Inadequate modelFor example, selection of bi-phasic model not warranted if data follow SFO
Global versus local minimumGlobal versus local minimum
The optimisation may find a local “valley” in the RSS surface, but not the absolute, global minimum.
Different parameter combinations may be returned for different starting values.
Good fit not guaranteed!
From: http://www.ssg-surfer.com/
RSS as a function ofchanges in 2 parameters
FOCUS recommendationsFOCUS recommendations
Always evaluate the visual fit
Avoid over-parameterisation
Aim at finding reasonable starting values
Always use different starting values
Constrain parameter ranges if appropriate
Plausibility checks for parameters and endpoints
Stepwise fitting where necessary
Be aware of differences between software packages
Residual plot
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Time
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SFO
Goodness of fit - visual assessmentGoodness of fit - visual assessment
Concentration vs. time plot
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measured
SFO
Goodness of fit - statistical criteriaGoodness of fit - statistical criteria
2 test
whereC = calculated valueO = observed value = mean of all observed valueserr = measurement error percentage
If calculated 2 > tabulated 2 then the model is not appropriate at the chosen level of significance
Error percentage unknown Calculate error level at which 2 test is passed
2
22
)O x 100/err(
)OC(
2
2
2tabulated O
OC
χ
1100err
Confidence in parameter estimates
Calculate e.g. from ModelMaker output
A parameter is significantly different from zero if p (t) < alpha
Others (e.g. model efficiency, F-test)
iparameteroferrordardtans
iparameterofestimateat
i
i
Goodness of fit - statistical criteriaGoodness of fit - statistical criteria
FOCUS optimization procedureFOCUS optimization procedure
Initial guess(starting values)
Enter measureddata
Evaluate:Visual fitStatistics
ParametersEndpoints
Optimize
Select kinetic model& parameters
Elim
inat
e o
utl
iers
, wei
gh
tin
g?
Ch
ang
e m
od
el, f
ix p
aram
eter
s?
Ch
ang
e st
arti
ng
val
ues