Initially, a 1-4 drift FWHM trendline was applied to the XylE cIM multi-pass data to assess the effect of trendlines set below all detected peaks throughout the mobility distribution (Figure ). As expected, the 1-4 trendline resulted in a 13% reduction in peptide identification relative to default processing: likely owing to a relative increase in undersmoothing of IM data. The trendline was then sequentially shifted upward throughout the mobility distribution to assess the effect of using increasingly wide kernel smoothers on peptide identification. Moreover, the trendline gradient was kept consistent and relatively flat to provide similar degrees of kernel smoothing throughout the mobility distribution within each individual processing variant. Raising the drift FWHM trendline to 3-6, 5-8, 7-10, and 11-14 resulted in peptide identification increases of 2, 10, 13, and 15%, respectively (Figure ). While the increase in 3-6 and 5-8 was expected (as they more closely resemble the Apex3D auto-calculated trendlines), the further increase observed in 7-10 and 11-14 was unexpected and suggests that oversmoothing of IM data is less detrimental than undersmoothing. This is likely due to the high proportion of unimodal distributions that are seen when generating extracted ion mobiligrams from less complex samples such as purified proteins. Thus, the detrimental effects from adjacent peak merging appear limited relative to what would be seen in more complex samples. Despite this, further increase of trendline to 41-44, 81-84, and 151-154 resulted in sequential decrease (Figure ), thereby demonstrating that oversmoothing can still negatively impact peptide identification of less complex samples if trendlines are set too high. Thus, the 11-14 trendline (and similar trendlines within this region) appear optimal in accommodating wider drift FWHM at earlier drift times without detrimentally oversmoothing multi-pass IM data. As such, we advise employing trendlines that sit just above all detected ions in multi-pass mobility distributions to minimise undersmoothing while simultaneously maintaining an appropriate degree of oversmoothing for less complex data. In contrast to multi-pass cIM, use of identical drift FWHM trendlines on XylE cIM 1-pass data resulted in decreasing peptide identifications ranging from 2 to 19% relative to default processing (Figure ). This suggests that, due to the positive correlation between drift time and drift FWHM in cIM 1-pass data, the Apex3D auto-calculated trendline is optimal, and that manual modification of trendline only serves to detrimentally increase oversmoothing and/or undersmoothing of IM data. This is further exemplified by the fact that the 3-6 and 5-8 trendlines, which most closely resemble the auto-calculated trendline, were the variants with the highest numbers of peptide identification. Consequently, Apex3D autocalculated trendlines were continued to be applied to all cIM 1-pass data.