Gamma spectrometry is a non-destructive method used to identify and quantify the activity of gamma emitting radionuclides in a wide variety of samples, including environmental, waste, and radio pharmaceutical. Quantification of the activity of radionuclides in gamma spectrometry depends mostly on three inputs: efficiency calibration, peak area calculation and nuclide decay data. All three can present significant challenges to an accurate analysis. Self-consistency checks based on calculating the activity of a single radionuclide using more than one gamma emission energy are a powerful tool to reveal problems with the inputs. Traditionally, this self-consistency check has relied on calculating the line activity for a radionuclide using the assumption that all the counts in the peak at the gamma emission energy of interest originates from the radionuclide. This approach breaks down when two or more radionuclides contribute to the same peak in the spectrum.
A new approach has been developed based on the consistency of the measured peak area and the peak area that is accounted for from the activities of all identified radionuclides in the spectrum. Using the consistency of the peak areas instead of the line activities makes it possible to use self-consistency for samples containing more than one radionuclide contributing to the same peak. This self-consistency check can reveal incorrect shape of the efficiency calibration, missing interferences in the nuclide decay data, and point to peaks where the peak area calculation needs to be optimized. Because this method relies on the consistency of quantities calculated using multiple inputs, it can be applied to samples with unknown activities. During this presentation the peak area consistency evaluation (PACE) method will be presented and examples will be shown that demonstrates how it can be used to find inconsistencies in the gamma spectrometry analysis. This algorithm will be implemented as a report deployed in a future release of the Genie gamma spectrometry software, which can be used to identify discrepancies in activities and demonstrate how the impact on reported results may range from minimal to significant. Applying this method can identify and rectify sub-optimal inputs, obtaining more accurate results and ensuring quality data.