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Numerical algorithms to select the optimal weight parameters are provided based on a calibration sample.
The creation and integration of a calibration sample into the assays permitted their calibration across experiments.
The best predictions were obtained using K-means clustering for calibration sample selection.
An experimental design approach was used in generating a 5-level (%, w/w) calibration sample set that included 125 samples.
The topics covered include instrument design and calibration, sample size choice, sample and gas purity, isotherm measurement procedure and issues associated with data reduction.
Further testing of the models demonstrated that the 2-level models were as effective as the 5-level ones, which reduced the calibration sample number to 50.
We present initial results of in situ 14C system blank and calibration sample measurements obtained using the in situ 14C extraction scheme developed at the University of Cologne.
The results of the SEM test using the calibration sample revealed an adequate fit to the data (CFI = 0.95, RMSEA = 0.04, Δχ2/Δdf = 2.99).
Although PCR appeared less sensitive than PLSR and SVMR to calibration sample size, SVMR outperformed PLSR and PCR with higher R2 and RPIQ values and lower RMSEP.
The time-to-peak feature divided by the thickness of a calibration sample is linearly related to wall thickness within 60% of the calibration sample [49].
As a calibration sample, a carbon layer containing H was used.
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