Toward new tools for biodiversity studies: the use of portable near-infrared spectroscopy combined with machine learning to identify species of Decapoda

Abstract

Context Accuracy in species identification is a crucial factor for the quality of biodiversity studies and species management. Ensuring high accuracy is challenging for diverse taxonomic groups, including those with fishery importance such as Decapoda. Aims The objective of the present study was to use portable near-infrared spectroscopy combined with machine learning through a neural network (ANN) to identify species of Decapoda. Methods We propose an ANN application that rapidly and accurately emulates the results that would be obtained by a specialist. We used 124 specimens from seven marine Decapoda species as a dataset to fit the model. Key results The ANN was able to correctly learn (classify) all the patterns of the species (100% accuracy), with an overall mean probability of 0.97 ± 0.068. Conclusions The results obtained using portable near-infrared spectroscopy combined with machine learning (ANN) demonstrated that this method can be used with high accuracy to distinguish Decapoda species. Implications Studies aiming at comparisons among species may consider the use of this technique for the precise and inexpensive separation among species by non-specialists or for species that require the identification of a large number of individuals.