A machine learning-based forecasting system for shellfish safety

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Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks

Publication . Cruz, Rafaela C.; Reis Costa, Pedro; Krippahl, Ludwig; Lopes, Marta B.

Harmful algal blooms (HABs) and the consequent contamination of shellfish are complex processes depending on several biotic and abiotic variables, turning prediction of shellfish contamination into a challenging task. Not only the information of interest is dispersed among multiple sources, but also the complex temporal relationships between the time-series variables require advanced machine methods to model such relationships. In this study, multiple time-series variables measured in Portuguese shellfish production areas were used to forecast shellfish contamination by diarrhetic she-llfish poisoning (DSP) toxins one to four weeks in advance. These time series included DSP con-centration in mussels (Mytilus galloprovincialis), toxic phytoplankton cell counts, meteorological, and remotely sensed oceanographic variables. Several data pre-processing and feature engineering methods were tested, as well as multiple autoregressive and artificial neural network (ANN) models. The best results regarding the mean absolute error of prediction were obtained for a bivariate long short-term memory (LSTM) neural network based on biotoxin and toxic phytoplankton measurements, with higher accuracy for short-term forecasting horizons. When evaluating all ANNs model ability to predict the contamination state (below or above the regulatory limit for contamination) and changes to this state, multilayer perceptrons (MLP) and convolutional neural networks (CNN) yielded improved predictive performance on a case-by-case basis. These results show the possibility of extracting relevant information from time-series data from multiple sources which are predictive of DSP contamination in mussels, therefore placing ANNs as good candidate models to assist the production sector in anticipating harvesting interdictions and mitigating economic losses.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

2022-12Journal article

Open Access

Bivalve shellfish safety in Portugal: variability of faecal levels, metal contaminants and marine biotoxins during the last decade (2011–2020)

Publication . Braga, Ana Catarina; Rodrigues, Susana Margarida; Lourenço, Helena Maria; Reis Costa, Pedro; Pedro, Sónia

Bivalves are a high-value product whose production has markedly increased, reaching 9863 tonnes in Portugal in 2021. Bivalves' habitats-lagoons, estuaries and coastal waters-are exposed to biological and anthropogenic contaminants, which can bioaccumulate in these organisms and pose a significant public health risk. The need to obtain a safe product for human consumption led to the implementation of standardised hygiene regulations for harvesting and marketing bivalve molluscs, resulting in routine monitoring of bivalve production areas for microbial quality, metal contaminants, and marine biotoxins. While excessive levels of biotoxins and metal contamination lead to temporary harvesting bans, high faecal contamination leads to area reclassification and impose post-harvest treatments. In this study, the seasonal and temporal variability of these parameters were analysed using historical data generated by the monitoring programme during the last decade. Moreover, the impact of the monitoring program on bivalve harvesting from 2011 to 2020 was assessed. This program presented a considerable improvement over time, with an increase in the sampling effort and the overall program representativeness. Finally, contamination risk, revising control measures, and defining recommendations for risk mitigation measures are given in the light of ten years' monitoring.

2023Journal article

Open Access