Which particles to select, and if yes, how many? Subsampling methods for Raman microspectroscopic analysis of very small microplastic

Micro- and nanoplastic contamination is becoming a growing concern for environmental protection and food safety. Therefore, analytical techniques need to produce reliable quantification to ensure proper risk assessment. Raman microspectroscopy (RM) offers identification of single particles, but to ensure that the results are reliable, a certain number of particles has to be analyzed. For larger MP, all particles on the Raman filter can be detected, errors can be quantified, and the minimal sample size can be calculated easily by random sampling. In contrast, very small particles might not all be detected, demanding a window-based analysis of the filter. A bootstrap method is presented to provide an error quantification with confidence intervals from the available window data. In this context, different window selection schemes are evaluated and there is a clear recommendation to employ random (rather than systematically placed) window locations with many small rather than few larger windows. Ultimately, these results are united in a proposed RM measurement algorithm that computes confidence intervals on-the-fly during the analysis and, by checking whether given precision requirements are already met, automatically stops if an appropriate number of particles are identified, thus improving efficiency.

Automated summary

Micro- and nanoplastic contamination is a growing concern for environmental protection and food safety, requiring reliable quantification through analytical techniques. Raman microspectroscopy allows for identification of single particles, with larger particles easily quantified through random sampling while smaller particles require window-based analysis. A bootstrap method is introduced for error quantification, recommending random window selection schemes for improved efficiency.