Research data for Microplastic quantification and chemical characterization in salt samples, using stereomicroscopy, smartphone camera and supervised machine learning tools.

Description

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.

Steps to reproduce

The workflow operates as follows: raw microscopy images can be pretreated with the Fiji macro "SM1-FIJI Macro- images pretreatment" in order to scale the image to 2280x2280 and be processed using the provided Ilastik project files to generate segmented probability maps of detected microplastic particles. These segmented images can then be analyzed using the provided FIJI macros "SM3-FIJI Macro- Optimized automated count-Filaments" and "SM5-FIJI Macro- Optimized automated count-Fragments", which apply automated thresholding followed by particle quantification using the “Analyze Particles” function to determine particle counts and size measurements.

Institutions

• Universidad de Colima

  Colima, Colima

Categories

Licence