Sample homogeneity/inhomogeneity

Last, but not least, it should be borne clearly in mind that even the most careful sample preparation cannot compensate for a wrong sampling procedure resulting, for example, in inhomogeneous and/or not representative samples. This is especially true in the field of food analysis, since food matrices can be extremely diverse. Therefore, prior to analysis sample homogenization is mandatory. 

It is always a matter of concern when analysis of duplicate samples produces significantly different results. One of the key reasons for this is that the analyte is unevenly distributed through the sample matrix. Sample inhomogeneity is the term used to describe situations where the analyte is unevenly distributed through the sample matrix (see also heterogeneous, Section 1.2). Similarly, sample homogeneity is the term used to describe how uniformly the analyte is distributed through the sample. 

The sampling variance of the material determined at a certain mass and the number of repetitive analyses can be used for the calculation of a sampling constant, K, a homogeneity factor, Hg or a statistical tolerance interval (m A) which will cover at least a 95 % probability at a probability level of r - a = 0.95 to obtain the expected result in the certified range (Pauwels et al. 1994). The value of A is computed as A = k 2R-s, a multiple of Rj, where is the standard deviation of the homogeneity determination,. The value of fe 2 depends on the number of measurements, n, the proportion, P, of the total population to be covered (95 %) and the probability level i - a (0.95). These factors for two-sided tolerance limits for normal distribution fe 2 can be found in various statistical textbooks (Owen 1962). The overall standard deviation S = (s/s/n) as determined from a series of replicate samples of approximately equal masses is composed of the analytical error, R , and an error due to sample inhomogeneity, Rj. As the variances are additive, one can write (Equation 4.2) ... 

Figure 2.5 illustrates the state of affairs, and shows that heterogeneous material may be characterized by an inhomogeneous (C) or homogeneous (D) concentration function dependent on the relation between the total variation of concentration and the uncertainty of measurement on the one hand and the sample amount (or microprobe diameter in case of distribution-analytical investigations) on the other. 

The sample A is homogeneous both from the physicochemical and the analytical-chemical point of view because the variation of the concentration is within the uncertainty of the analytical measurement unc(x). In contrast, sample B is homogeneous from the physico-chemical but not from the analytical-chemical viewpoint because the systematical change of the concentration exceeds unc(x). Whereas sample C is heterogeneous from the physico-chemical point of view and inhomogenous from the analytical-chemical viewpoint (the concentration deviations plainly exceed unc(x)), sample D is heterogeneous from the physico-chemical standpoint. The an-... 

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