Standardized Frameworks for n‐ Alkane Compound‐Specific Isotope Analysis: Assessments and Analytical Platform for Data Processing Workflows and Uncertainty Estimation

Abstract Post‐acquisition processing of plant wax n ‐alkane isotope data in compound‐specific isotope analysis (CSIA) remains variable and often lacks transparency. To improve inter‐laboratory comparability and data reliability, we present standardized, reproducible workflows for CSIA that address key challenges in drift correction, scale normalization, and uncertainty propagation. We evaluate and compare a variety of approaches for correcting drift and scale compression effects, offering simulated and empirical assessments of the success and validity of common correction methods. These include global and compound‐specific estimations of drift and regression‐based scale normalization techniques that integrate multiple compounds. We find that analyte‐dependent nonlinear estimations of drift generally yield lower error across the tested drift scenarios. Our analyses also suggest that regression‐based scale corrections which incorporate all available compounds within a standard mixture best minimize scale expansion and compression artifacts that lead to bias. We consider the influence of applying these corrections on propagated uncertainty, finding that correction‐derived standard deviations contribute small but systematic increases in estimated error. Monte Carlo simulations, controlled standard sequences, and empirical data sets confirm that these methods can reduce systematic bias and stabilize isotope calibrations in multi‐compound data sets. The workflows presented here are implemented in SINC (Standardization and Isotope Normalization for CSIA), an open‐source MATLAB platform designed for standardized n‐ alkane δD and δ 13 C processing.