Dynamic headspace sampling is an important technique for the analysis of consumer products, the study of biological samples and environmental water analyses. This paper shows the influence of experimental conditions, such as the sampling time, sampling flow rate, headspace volume, liquid volume and Henry coefficient on the measured average concentration values. A corresponding closed expression as function of these variables is introduced in order to quantify the deviation of the initial headspace concentration. The proposed bi-exponential function embeds different current existing models for recovery calculation in dynamic sampling analyses in one single expression. A fully automated and user-friendly Excel* file to investigate or to model the dynamic headspace sampling results is added to everyone's easy use.