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Errors were propagated through the calculated parameters using a bootstrap analysis.
Quantitative analysis on samples was carried out by subtracting the spectrum of water in the same geometry, while uncertainty on peaks (k = 1, 68 % level of confidence) was calculated propagating the combined error over the efficiency fit previously determined with the counting error.
Standard error was calculated by propagating the standard deviation of each QM throughout subsequent calculations.
Errors in peak areas were calculated by propagating the peak parameter errors.
Errors in NOE values were calculated by propagating the error in the respective peak intensities.
Standard errors in the θ tip angles were calculated by propagating the error in measuring the positions of each tip of the tube.
Uncertainty propagation technique is used to calculate how the uncertainties in each of the measured variables propagate into the value of the calculated quantities.
The far field mobility of the shell for each propagating circumferential modenis calculated using a residue method.
After propagating the current values of these parameters over a multilayer neural network, the corresponding output is compared to the desired throughput to calculate the training error.
The calibration result for each thermocouple was used to correct the offset error and the slope error was propagated to calculate the measurement uncertainty (approximately ±0.2 °C), represented by the x-axis error bars shown in Fig. 4b.
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