The data reduction in PySILLS is strongly influenced by the workflow from its precursor SILLS. The user has to measure external standards, also called standard reference materials, before and after taking sample measurements via LA-ICP-MS.
In PySILLS, the user will define different calculation intervals, that correspond to the nature of the acquired signal: background, matrix and inclusion signal. The background signal occurs before, after and in between shooting the sample. However, it may happen that the average of the background signal does not stay at the same level, e.g., 100 cps. The user could either wait, until the background signal has been stabilized itself, or the user should consider only the background signal before shooting the sample. Since the background signal can be interpreted as a systematic error, it contributes to all other types of signal. Therefore, it is necessary to correct the matrix and inclusion signal by the background. These background-corrected signal intensities of the matrix can be used for the quantification of the matrix composition. The further analysis of the inclusion composition does require more sophisticated algorithms.
This chapter would like to highlight the theoretical concepts behind the data reduction of complex datasets from LA-ICP-MS experiments.