Microplastic detection and identification by Nile red staining: Towards a semi-automated, cost- and time-effective technique

Microplastic pollution is an issue of concern due to the accumulation rates in the marine environment combined with
the limited knowledge about their abundance, distribution and associated environmental impacts. However, surveying
and monitoring microplastics in the environment can be time consuming and costly. The development of cost- and
time-effective methods is imperative to overcome some of the current critical bottlenecks in microplastic detection
and identification, and to advance microplastics research. Here, an innovative approach for microplastic analysis is
presented that combines the advantages of high-throughput screening with those of automation. The proposed approach
used Red Green Blue (RGB) data extracted from photos of Nile red-fluorescently stained microplastics
(50–1200 μm) to train and validate a ‘Plastic Detection Model’ (PDM) and a ‘Polymer Identification Model’ (PIM).
These two supervised machine learning models predicted with high accuracy the plastic or natural origin of particles
(95.8%), and the polymer types of the microplastics (88.1%). The applicability of the PDM and the PIM was demonstrated
by successfully using the models to detect (92.7%) and identify (80%) plastic particles in spiked environmental
samples that underwent laboratorial processing. The classification models represent a semi-automated, highthroughput
and reproducible method to characterize microplastics in a straightforward, cost- and time-effective yet
reliable way.