## 1 Introduction  
Knowledge- and resilience-based management of marine ecosystem services is strongly required to cope with the pressures and impacts of the Anthropocene society [1]. To this aim, a constant and long-term monitoring of several environmental marine variables is needed to assess marine ecosystem health [2, 3]. The study of phytoplankton species and their density estimation is a routine task in marine scientific research, since under certain conditions, the proliferation of algae can have a negative impact on the environment, fishing activities and the public health. This phenomenon is known as a harmful algal bloom (HAB) and can be as diverse as the species that originates it. For instance, blooms of high biomass of phytoplankton can drive the depletion of oxygen from the water, causing massive indiscriminate marine death. Another type of HAB consists of microalgae that, under certain conditions, are capable of producing toxins, and for some species, only a few hundreds of cells per litre is sufficient for the presence of toxins to be manifested in bivalves [4]. Other events are so massive that the ocean colour blends with the pigment coloration of the species, and \"red tides\" can be observed [5]. Therefore, it is of extreme importance to study the population dynamics of phytoplankton communities [6], enabling knowledge about the potentially harmful algae present in aquatic marine systems. Local or regional authorities routinely perform this task by collecting phytoplankton and shellfish samples, analysing their toxicity and regulating the safe harvesting of seafood. The available methods for phytoplankton identification and quantification are based on optical and electronic microscopy, flow cytometry, image analysis, pigment analysis through high performance liquid chromatography (HPLC) and molecular methods [7; 8]. However, these tasks can be expensive, time or reagent consuming, and require highly trained personnel, which can be limiting for in situ and real time HAB monitoring [9]. Efforts are being made to develop swift and precise measurement techniques for in situ automated measurements, such as FlowCAM, CytoBuoy, Imaging FlowCytboot or Laser Optical Plankton Counter [10; 11], but these are bulky and very expensive (about EUR 26,000-130,000). Furthermore, these devices are mainly based on imaging methods which provide information about phytoplankton size, shape and cross-sectional area. However, to obtain precise taxonomic classification, advanced hardware and software and computer-assisted human experts are required to complement the complex data processing. Efforts for obtaining automated recognition of images using machine-learning techniques are in progress, but still have to be complemented with taxonomic expertise, which limit their in-situ application. A comparison of several optical and imaging methods already available and their operating capabilities can be found in [12]. Several photosynthetic pigments, such as chlorophylls a, b and c, carotenes, xanthophylls and phycobilins, are present in phytoplankton. Chlorophyll a is the main pigment of all organisms that performs photosynthesis with oxygen release, being widely used to estimate autotrophic phytoplankton biomass in aquatic ecosystems [13]. Distinct phytoplankton communities have specific marker pigments that can be used for taxonomic discrimination [14]. For instance, the main secondary pigment for most dinoflagellates is peridinin, while for diatoms fucoxanthin is the representative secondary pigment. These variations in pigment compositions produce different spectral characteristics across taxa that can be harnessed for phytoplankton identification [15]. The fluorometric methods are widely used for quantifying the photosynthetic pigments due their high sensitivity, high specificity and simplicity. However, most fluorometric system use expensive and bulky equipment and, mainly, cannot be applied for in situ detection.