Total Diet Studies to estimate dietary exposure to food contaminants need to evaluate laboratory measurements data variance. In this process it is critical that data from analytical methods are reliable to correctly scrutinize and compare values over time and between countries. In Europe it is widely recognized that the evaluation of measurement uncertainty is an important parameter when assessing the sources of analytical data variability. Two approaches are considered to estimate uncertainty in analytical measurement. Arsenic, Lead, Chromium and Cadmium, content in several food matrix determined by Inductively Coupled Mass Spectrometry (ICP-MS) microwave digestion assisted , are used as examples. The aim of the present research work is to compare both approaches accepted by Eurolab and GUM: Mathematical modeling to assess uncertainty components based on a classical model (bottom up) and an empirical method (top down), based on either experimental data obtained from a single laboratory validation data or inter-laboratory data from Proficiency Testing schemes. Relative expanded uncertainty calculated by both approaches agree when U (%) < 20%. These values are concordant with RSDR reported in collaborative studies of EN 15763: 2009, which were assumed as target uncertainty. The top down approach described is simple and easy to use when compared with the mathematical modeling approach therefore providing considerable benefits to those who assess data produced by several laboratories.