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. To provide quality control in the MP quantification by Raman microspectroscopy, a window subsampling and bootstrap protocol is presented, which can provide confidence intervals that enable the assessment of the reliability of the data. This is brought together with a proposed on-the-fly algorithm that assesses the precision during the measurement and stops at the optimal point.
Keywords: Automation; Bootstrap; Chemometrics; Microplastic; Nanoplastic; Raman microspectroscopy.