Abstract

In this paper the detection limit was estimated when signals were affected by two error contributions, namely instrumental errors and operational-non-instrumental errors. The detection limit was theoretically obtained following the hypothesis testing schema implemented with the calibration curve methodology. The experimental calibration design was based on J standards measured I times with non-instrumental errors affecting each standard systematically but randomly among the J levels. A two-component variance regression was performed to determine the calibration curve and to define the detection limit in these conditions. The detection limit values obtained from the calibration at trace levels of 41 elements by ICP-MS resulted larger than those obtainable from a one component variance regression. The role of the reagent impurities on the instrumental errors was ascertained and taken into account. Environmental pollution was studied as source of non-instrumental errors. The environmental pollution role was evaluated by Principal Component Analysis technique (PCA) applied to a series of nine calibrations performed in fourteen months. The influence of the seasonality of the environmental pollution on the detection limit was evidenced for many elements usually present in the urban air particulate. The obtained results clearly indicated the need of using the two-component variance regression approach for the calibration of all the elements usually present in the environment at significant concentration levels.

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