Hi Sophie,
I do still wonder if these spikes will cause a significant issue with the output data for those samples.
It depends on how many data points you have in your selection and how many datapoints comprise the spikes. If the spikes are very short (i.e. one or two data points) and you have quite a few datapoints in your selection, the spikes will be removed automatically when iolite calculates the stats for each selection. That is because iolite, by default, does outlier rejection before calculating the mean. An outlier in this case is anything beyond 3 SD of the mean. iolite removes these datapoint and then calculates the mean and 2SE for the selection.
Note that you can change this behaviour in iolite's Preferences in the Stats section: there is an option for outlier rejection for baseline selections, and another for RM and sample selections.
Unless you have very short selections (e.g. less than 30 data points) or your spikes are quite wide, I would guess that they will rejected as outliers.
Of course, for those that wish to be very thorough: you can save out the individual datapoints for each selection and calculate the mean and uncertainty yourself. You can do this in the Export to File options by checking the "Time Series" option which creates a new worksheet (if exporting to Excel; a new csv file if exporting to csv) for each selection. It reports each time-resolved datapoint for the selection, with a value each of the selected channels. Note that you don't have to do this, I just mention it in case you wanted to explore the process. 😃
-Bence