Supervised classification combined with genetic algorithm variable selection for a fast identification of polymeric microdebris using infrared reflectance

Abstract

Pollution caused by plastics and, in particular, microplastics has become a source of environmental concern for Society. Their ubiquity, with millions of tons of plastic debris spilled in both land and sea, requires efficient technological improvements in the ways residues are collected, handled, characterized and recycled. For reliable decision-making, dependable chemical information is essential to assess both the nature of the plastics found in the environment and their fate. In this work an efficient method to identify the polymeric composition of microplastic fragments is proposed. It combines infrared reflectance spectra and chemometric methods. A breakthrough result is that the models include polymers weathered under both dry (shoreline) and submerged (in sea water) conditions and, hence, they are very promising as a starting point for eventual practical applications. In addition, no spectral processing is required after the initial measurement. SYNOPSIS: This approach to identify microplastics in aquatic environments combines infrared measurements and multivariate data analysis to fight against (micro)plastic pollution.

Keywords: ATR; Genetic algorithm; Infrared spectrometry; Microplastics; Reflectance; Supervised classification.