A Benchmarking on Optofluidic Microplastic Pattern Recognition: A Systematic Comparison Between Statistical Detection Models and ML-Based Algorithms

Microplastics, small particles of plastic found in the environment, have become an increasingly worrying topic in recent years. This paper compares a statistical detection model to classifiers from various supervised learning paradigms in order to detect microplastics. The objective of this paper is to present a benchmark for detecting microplastics using statistical and machine learning models. The main goal is to assess and compare their performance when the defined parameters deviate from the optimal solution of the respective model. Results are presented in terms of probability error, comparing the performance of the machine learning techniques to the statistical model. The study considers a range of signal-to-noise ratios and a priori event probabilities, focusing on the classifiers’ ability to handle amplitude variability and threshold variation. Results show that as the number of analyzed particles in the flow increases, the detection performance improves, with Support Vector Machine, Linear Discriminant Analysis and Naive Bayes standing out from the other methods.