A portable and low-cost optical device for pigment-based taxonomic classification of microalgae using machine learning

Abstract

The proliferation of certain phytoplankton species may lead to harmful algal blooms (HABs) that can affect living resources and human health. Therefore, an accurate identification of phytoplankton populations is essential for the sustainable management of some activities relevant for the blue economy, such as aquaculture, being also relevant for environmental monitoring and marine research purposes. Microalgae taxonomic discrimination, based on their pigment composition, is a versatile and promising technique to detect and identify potential HABs. In this work, a portable and low-cost device for taxonomic identification of microalgae, based on the pigment composition of 16 species belonging to 6 different phyla, was developed. It uses the fluorescence intensity signal emitted by each species at three wavelengths (575 nm, 680 nm and 730 nm) when excited at five wavelengths (405 nm, 450 nm, 500 nm, 520 nm and 623 nm) to create a fluorescence signature for each species. Furthermore, several machine learning classifiers were studied using this fluorescence signature as features to train and classify each species according to their respective taxonomic group. The Extreme Gradient Boosting (XGBoost) classifier was able to correctly predict microalgae monocultures with 97 % accuracy at the phylum level and 92 % accuracy at the order level. The obtained results confirm the potential of this technique for fast, accurate and low-cost identification of microalgae.