In contrast to cIM 1-pass, multi-pass routines have the potential to exhibit "wrap-around" phenomena, whereby slower ion populations are overtaken by higher mobility ions within the cyclic mobility device over multiple passes . Thus, depending on the relative geometric positioning of each ion population when ejection from the mobility device is triggered, it is possible for slower ions with longer periodic drift time to exit the device ahead of speedier ions with shorter periodic drift times. Consequently, in cIM multi-pass data, the linear positive correlation between recorded drift time and drift FWHM is lost (Figure and Figure ), as ions with any drift FWHM width can now potentially be found in any drift time bin. Prior to peak detection, the Apex3D algorithm in PLGS smooths IM data using a kernel smoother. For appropriate selection of smoothing kernel, approximate determination of drift peak width is required; as smoothing wide peaks with narrow kernels results in undersmoothing and retention of noise (resulting in peak splitting), whilst smoothing narrow peaks with wide kernels results in oversmoothing (and merging of adjacent peaks). Rather than measure drift peak widths on an individual basis, Apex3D autonomously samples ~700 extracted ion mobiligrams and, using a linear regression model, calculates a drift peak FWHM vs drift time trendline to approximate peak width at any given drift time bin. Owing to the linear relationship between drift time and drift FWHM in cIM 1-pass data, a single compromise drift FWHM trendline with high R 2 value is sufficient to provide appropriate smoothing of all IM data (Figure ). However, for cIM multi-pass data, the wrap-around-derived loss of direct relationship between drift time bin number and drift FWHM results in drift FWHM trendlines with low positive correlation and R 2 values (Figure ). Based on this observation, we hypothesised that Apex3D autocalculated trendlines are insufficient to appropriately smooth all IM data across the multi-pass mobility distribution, and that manual input of trendlines to accommodate wider drift FWHM at earlier drift times could improve results.