What Happens in the Particle Analysis Phase?

This week of research (and the next few weeks of my work) is all about lasers and particles, which I find quite fun, though monotonous. I sit for approximately 4 hours in front of the laser spectroscope and find particles on microscope slides, focus the laser on the particle, apply the protective covering like a lead shield on an x-ray patient, and then let the machine run for ~3.5 minutes. During the processing time I sit and wait while listening to music or podcasts to keep me busy and attentive. You've heard some of this from previous posts, but I haven't focused on the final step of this task, which is what happens with the data. I was able to get a great picture example for this week's topic and thought it was a sign to write about the final step: particle/spectral matching.

In a previous post I talked about how the data I collect from the laser scatter microscope is related to how the laser bounces off the particles it contacts during the 3.5 minutes. The data are compiled by the machine into a document and then turned into a spectrum (graph) that I can analyze at the lab or at home, but the data are raw and unfiltered. Although the data are palatable, the particle matching process works best when we refine the spectra and remove laser scatter patterns caused by dust and other particulates. Similarly, we can refine the rigidity of the spectra and smooth out the peaks and valleys to remove some of the variation within the data before we assess what type of particle we have in front of us. These two processes together are called baseline correction and smoothing.

The final step in the particle/spectral matching process happens when I upload the refined spectra to an analysis software. For my research, I have had success with Open Specy, which is a free program developed by a group of microplastics researchers wanting to reduce the monetary obstacle between scientists and polymer analysis (note: paid versions of this software often starts at $1000). The software hosts a database of particle spectra sourced from the primary literature, labs around the world, and scientists willing to share their data, and it is this database that Open Specy uses to identify my polymers. The software compares the uploaded/processed data to the database of materials and determines how closely related the data are to reference materials. In the example here, you can see that Open Specy has identified this material as polyester, with a correlation coefficient of 0.79, and it also cites the work where the reference spectrum came from. You can see that Open Specy also provides 100 entries in decreasing correlation order in case you want to know how your spectrum compares to other known materials. In this image, the top 5 spectra were all plastic polymers. When I run a comparison and Open Specy provides no polymer matches, I can confidently claim that the material is not plastic and I can recalculate the numbers of microplastics in my samples.

At a talk I recently gave, a researcher asked me if I thought that this particle analysis process, including laser scatter and spectral matching, could be automated. While I am confident that we can teach a computer to find particles that resemble plastics and expose them to laser scatter, and compare the resulting data, I think we still are years away from such advances, so for the time being, I will be manually shooting lasers, listening to my podcasts, and tabulating microplastics data by hand.

I will not be posting next week, as we are off for break, but I will be back on the 24th with more new information about microplastics and research at FSU.

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