However, a more advanced analysis can be performed on a dataset that has particle labels matched across images. As a result of this, it was possible to track grey-level change in specific particles at different SoC. For every particle in the dataset, the mean grey-level change relative to its mean grey-level in the pristine state was tracked, or ∆ Īprist . (Again, when calculating the mean particle grey-levels the ROI of each particle excluded pixels at the edge of the particles due to their phase contrast enhancement, see figure .b). Calculating the mean ∆ Īprist of a set of particles gives the 'mean of mean particle grey-level change vs. pristine' or ∆ Īprist . This is subtly different from simply tracking the Ī as in figure .a and is generally more precise, as shown in 8.b. where the associated standard error is lower. This is because this method normalises out the large variation in mean particle grey-levels within the sample, thereby reducing sampling error. Tracking the ∆ Īprist is particularly useful when comparing different subsets of data. This is because when doing so a low Ī does not necessarily indicate that a drop in grey-level has occurred in this subset as the initial state is unknown (i.e. the subset may have started with a low Ī in the pristine state).