ALBERT

Description: Occurrence of tetrodotoxin (TTX), a potent natural neurotoxin, in marine organisms and seafood from the European Union has been of increasing concern due to its relatively recent detection in bivalve mollusks and gastropods. Following a request of the European Food Safety Authority (EFSA) to EU Member States to obtain more data on TTX occurrence, this study collected 117 samples of bivalve mollusks, including mussels (Mytilus galloprovincialis), oysters (Crassostrea gigas) and clams (Ruditapes philippinarum and Donax spp.), from the South and Southwest Portuguese coast between May and October 2018, for TTX determination and microbiological analysis. The analyses carried out by hydrophilic interaction liquid chromatography tandem mass spectrometry (HILIC-MS/MS) did not detect TTX in any sample, and microbiological analysis did not reveal high concentrations of Vibrio spp., which has been linked to the presence of TTX. Although preliminary, results from this study, the first investigating the presence of TTX in bivalve mollusks from the Portuguese coast, suggest that TTX may not represent a risk for human consumption of bivalve mollusks.

Description: Harmful algal blooms are responsible worldwide for the contamination of fishery resources, with potential impacts on seafood safety and public health. Most coastal countries rely on an intense monitoring program for the surveillance of toxic algae occurrence and shellfish contamination. The present study investigates the use of near infrared (NIR) spectroscopy for the rapid in situ determination of cell concentrations of toxic algae in seawater. The paralytic shellfish poisoning (PSP) toxin-producing dinoflagellate Gymnodinium catenatum was selected for this study. The spectral modeling by partial least squares (PLS) regression based on the recorded NIR spectra enabled the building of highly accurate (R2 = 0.92) models for cell abundance. The models also provided a good correlation between toxins measured by the conventional methods (high-performance liquid chromatography with fluorescence detection (HPLC-FLD)) and the levels predicted by the PLS/NIR models. This study represents the first necessary step in investigating the potential of application of NIR spectroscopy for algae bloom detection and alerting.

Description: Natural high proliferations of toxin-producing microorganisms in marine and freshwater environments result in dreadful consequences at the socioeconomically and environmental level due to water and seafood contamination. Monitoring programs and scientific evidence point to harmful algal blooms (HABs) increasing in frequency and intensity as a result of global climate alterations. Among marine toxins, the okadaic acid (OA) and the related dinophysistoxins (DTX) are the most frequently reported in EU waters, mainly in shellfish species. These toxins are responsible for human syndrome diarrhetic shellfish poisoning (DSP). Fish, like other marine species, are also exposed to HABs and their toxins. However, reduced attention has been given to exposure, accumulation, and effects on fish of DSP toxins, such as OA. The present review intends to summarize the current knowledge of the impact of DSP toxins and to identify the main issues needing further research. From data reviewed in this work, it is clear that exposure of fish to DSP toxins causes a range of negative effects, from behavioral and morphological alterations to death. However, there is still much to be investigated about the ecological and food safety risks related to contamination of fish with DSP toxins.

Description: Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.