This paper presents a practical application of Monte Carlo simulation packages, i.e., GESPECOR and LabSOCS, for computing self-attenuation and coincidence-summing correction factors in gamma-ray spectrometry analysis of foods.  Analysis of gamma emitters in foods is routinely performed in large-sample volume and close-to-detector geometry, where the counting efficiency subjects to significant self-attenuation and coincidence summing due to density variations and complex decay scheme.  The required corrections are difficult to derive experimentally without appropriate physical standards which are expensive and not commonly available.  To overcome such limitation, a computational technique with the knowledge of detector characteristics, matrix composition, decay scheme, shielding material, and counting geometry is first applied to identify the key variables that dominate self-attenuation and coincidence-summing effects.  Then, the values of self-attenuation and coincidence-summing correction factors relating to the identified key variables are computed.  The results from a validation study comparing computational technique with physical standards method will be discussed.

Applying self-attenuation and coincidence-summing corrections for gamma-ray spectrometric analysis of foods using GESPECOR and LabSOCS

245th National Meeting (2013)

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