RD‐PMA: A Rigid‐Deformable Point‐Matching Algorithm for Two‐Dimensional Calibration of GC‐IMS

ABSTRACT Gas chromatography‐ion mobility spectrometry (GC‐IMS) is a powerful analytical technique for characterizing volatile organic compounds (VOCs). However, variability in retention and drift times frequently complicates peak alignment and downstream analyses. To address this challenge, we introduce RD‐PMA, a Rigid‐Deformable Point‐Matching Algorithm derived from and extending an established point‐set registration framework. RD‐PMA is specifically designed for GC‐IMS data and incorporates dimension‐dependent modeling, where rigid translation is applied in the drift‐time dimension and nonlinear deformation is employed in the retention‐time dimension. Robustness is further enhanced through an iterative outlier‐removal procedure. Extensive evaluations across multiple GC‐IMS datasets, including both homogeneous and heterogeneous conditions, as well as same‐parameter and different‐parameter scenarios, demonstrate that RD‐PMA consistently outperforms conventional alignment approaches, such as internal reference‐based shifting and other point‐matching methods. The algorithm preserves structural consistency while delivering high alignment accuracy and resilience to noise and parameter variability. Collectively, these findings establish RD‐PMA as a reliable and scalable solution for GC‐IMS peak alignment, with broad applicability to large‐scale VOC profiling in chemometrics, food science, and clinical research.