Paving the way for new and challenging matrix reference materials—particle suspensions at the core of material processing providing RMs for method development and method validation

Abstract

AbstractSufficient homogeneity of the certified parameter(s) over the whole fill series of a matrix reference material (RM) is a fundamental quality criterion. In practice, the heterogeneity of the target parameter is evaluated, whereby a relative value can be calculated of how much the target parameter is varying over the RM-batch. A high degree of homogeneity (low heterogeneity) is an inherent quality mark of a good RM. Here, we report how challenging matrix RMs were produced by using particle suspensions at the core of the material processing step. The examples of matrix RMs produced span from whole water reference materials for persistent organic pollutants, PM2.5-like atmospheric dust certified for specific ions to microplastic RMs. Most of these RMs were subsequently used in different phases of analytical method development or for method validation. Common to all these matrices is that they cannot be easily mixed, handled, or dosed to prepare larger sample batches. In all cases, a continuously stirred suspension of particles was used during material processing. In general, relative between-bottle heterogeneities from 1.6 to 6% were achieved for the target parameters in these matrix presentations. Concerning developments of new CRMs in emerging fields, the co-dependence between the availability of validated analytical methods with good repeatability and testing materials with a known and high homogeneity of the target parameter(s) becomes particularly challenging. This situation is an RM/Method causality dilemma. To overcome that hurdle, strategies are proposed for stepwise processes where RM producers and a network of analytical method developers could work hand in hand. In addition, development of a portfolio of inexpensive and well-homogenised common samples coupled with a reporting interface is suggested. This would benefit method developers and RM producers alike. As more and more data is compiled for a specific matrix, it paves the way for new and challenging RMs that can later be used by a wider community.