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Research data for Microplastic quantification and chemical characterization in salt samples, using stereomicroscopy, smartphone camera and supervised machine learning tools.
Summary
Scientists developed a new, low-cost method to detect and count tiny plastic bits (microplastics) hiding in salt using a smartphone camera, a basic microscope, and smart computer software instead of expensive lab equipment. This matters because salt is something almost everyone eats daily, and having a cheaper, more accessible way to check it for plastic contamination could help researchers and food safety agencies track this potential health concern more widely. Note that this particular resource is the underlying dataset and code shared for other scientists to verify and reuse the method, rather than a report of new findings about how much microplastic is actually in salt.
Research data for the article "Microplastic quantification and chemical characterization in salt samples, using stereomicroscopy, smartphone camera and supervised machine learning tools.". This repository contains the datasets and processing files used in the microplastic detection workflow developed in this study. The files included allow reproducibility of the image analysis pipeline. The repository contains: (i) raw microscopy images (.png) corresponding to the validation dataset used to evaluate the trained models; (ii) trained Ilastik project files (.ilp) for the two classifiers developed in this work (filament and fragment detection), which can be opened directly in the Ilastik software* to reproduce the image probability segmentation step and; (iii) FIJI macros (.ijm) used for image preprocessing and automated particle analysis viaFiji.