Quantitative analysis of volatile organic compounds (VOCs) in biological matrices remains challenging due to matrix-dependent headspace partitioning, the presence of endogenous analytes, and the intrinsically non-linear response of certain detectors. In this context, static headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) offers high sensitivity for VOC profiling, but these combined effects complicate calibration and quantitative interpretation. In this work, a practical framework for quantitative VOC analysis in urine using HS-GC-IMS is presented, in which calibration is performed in a synthetic urine surrogate matrix and subsequently adapted to real urine samples. The approach preserves the inherent non-linear detector response and accounts for matrix-dependent partitioning effects through an affine adjustment of the concentration axis. The resulting matrix-adapted calibration model can be applied to individual urine samples without requiring patient-specific recalibration. The methodology was evaluated using three colorectal cancer–related VOCs (anisole, 2-heptanone, and 2-pentanone) over a 0–30 ppb concentration range. Matrix adaptation substantially improved quantitative accuracy within the transferable dynamic range, particularly for compounds strongly affected by matrix-dependent partitioning, while highlighting fundamental limitations when endogenous concentrations place the instrument response near saturation. By explicitly addressing the interplay between headspace partitioning and non-linear detector behavior, the proposed strategy allows reliable surrogate-matrix calibration in complex biological samples and is applicable to other headspace-based analytical platforms affected by matrix effects.