Identification and visualisation of microplastics / nanoplastics by Raman imaging (iii): algorithm to cross-check multi-images.

Abstract We recently developed the Raman mapping image to visualise and identify microplastics / nanoplastics (Fang et al. 2020, Sobhani et al. 2020). However, when the Raman signal is low and weak, the mapping uncertainty from the Raman peak intensity increases and may lead to images with false positive or negative features. For real samples, even the Raman signal is high, a low signal-noise ratio still occurs and leads to the mapping uncertainty due to the high spectrum background when: the target plastic is dispersed within another material with interfering Raman peaks; materials are present that exhibit broad Raman peaks; or, materials are present that fluoresce when exposed to the excitation laser. In this study, in order to increase the mapping certainty, we advance the algorithm to combine and merge multi-images that have been simultaneously mapped at the different characteristic peaks from the Raman spectra, akin imaging via different mapping channels simultaneously. These multi-images are merged into one image via algorithms, including colour off-setting to collect a signal with a higher ratio of signal-noise, logic-OR to pick up more signal, logic-AND to eliminate noise, and logic-SUBTRACT to remove image background. Specifically, two or more Raman images can act as “parent images”, to merge and generate a “daughter image” via a selected algorithm, to a “granddaughter image” via a further selected algorithm, and to an “offspring image” etc. More interestingly, to validate this algorithm approach, we analyse microplastics / nanoplastics that might be generated by a laser printer in our office or home. Depending on the toner and the printer, we might generate millions of microplastics and nanoplastics when we print a single A4 document.