Fish quality has a direct impact on market price and its accurate assessment and prediction are of main importance to set prices, increase competitiveness, resolve conflicts of interest and prevent food wastage due to conservative product shelf-life estimations. In this work we present a general methodology to derive predictive models of fish freshness under different storage conditions. The approach makes use of the theory of optimal experimental design, to maximize data information and in this way reduce the number of experiments. The resulting growth model for specific spoilage microorganisms in hake (Merluccius merluccius) is sufficiently informative to estimate quality sensory indexes under time-varying temperature profiles. In addition it incorporates quantitative information of the uncertainty induced by fish variability. The model has been employed to test the effect of factors such as fishing gear or evisceration, on fish spoilage and therefore fish quality. Results show no significant differences in terms of microbial growth between hake fished by long-line or bottom-set nets, within the implicit uncertainty of the model. Similar conclusions can be drawn for gutted and un-gutted hake along the experiment horizon. In addition, whenever there is the possibility to carry out the necessary experiments, this approach is sufficiently general to be used in other fish species and under different stress variables.

Fish wastage and market prices highly depend on accurate and reliable predictions of product shelf life and quality. The Quality Index Method (QIM) and EU grading criteria for whitefish (Council Regulation(EC) No 2406/96, 1996) are established sensory methods used in the market to monitor fish quality. Each assessment requires the consultation of a panel of trained experts. The indexes refer exclusively to the current state of the fish without any predictions about its evolution in the following days. This work proposes the development of a smart quality sensor which enables to measure quality and to predict its progress through time. The sensor combines information of biochemical and microbial spoilage indexes with dynamic models to predict quality in terms of the QIM and EU grading criteria. Besides, the sensor can account for the variability inside the batch if spoilage indexes are measured in more than one fish sample. The sensor is designed and tested to measure quality in fresh cod (Gadus morhua) under commercial ice storage conditions. Only two spoilage indexes, psychrotrophic counts and total volatile base-nitrogen content, were required to get accurate estimations of the two usual established sensory methods. The sensor is able to account for biological variability as shown with the validation and demonstration data sets. Moreover, new research and technologies are in course to make these measurements faster and non-destructive, what would allow having at hand a smart non-intrusive fish quality sensor.

ATP-derived products are typically used as early indicators of fish quality loss during storage. In this work, we explore different biochemical routes that are potentially relevant in contributing to nucleotide degradation in hake (Merluccius merluccius). A major motivation of this study is to get more insight on the biochemical degradation mechanisms of nucleotide catabolites in hake muscle at fish storage and transport conditions. This requires the identification of its relevant pathways. To that purpose, different degradation routes proposed in the literature are considered and a mathematical model for the degradation process is derived. First order kinetics are assumed for all the reactions and temperature dependence is taken into account through the Arrhenius equation. Unknown model parameters, namely activation energies and preexponential Arrhenius coefficients, are estimated via fitting to experimental data. From the estimation results, relevant routes are identified. The kinetic study is performed on sterile fish juice to avoid coupling with microbial degradation mechanisms or possible interferences of the food matrix that might hide biochemical interactions. The proposed scheme adequately describes biochemical changes in nucleotide catabolites under variable temperature profiles. It also reveals a pathway which at least seems relevant for nucleotide degradation in hake.

In this work we develop a model describing the evolution of the adenosine triphosphate (ATP) degradation products, typically used as early indicators of fish quality loss, during storage and transport conditions. The model is constructed following a modular approach that includes essentially three mechanisms: (1) enzymatic transformation of inosine 5'monophosphate (IMP), inosine (Ino) and hypoxanthine (Hx) with some reactions catalyzed by bacteria; (2) bacterial growth and (3) nucleotide diffusion through the food matrix. This approach allows us to combine the different underlying mechanisms to account for other fish species and conditions. We compare alternative mechanisms explaining the catalytic effect of Pseudomonas and Shewanella populations on the reaction linking IMP, Ino and Hx. The selection is carried out in terms of the Akaike Information Criteria. The predictive capabilities of the selected model are demonstrated with experiments.

Emerging risk identification is a priority for the European Food Safety Authority (EFSA). The goal of the Galician Emerging Food Safety Risks Network (RISEGAL) is the identification of emerging risks in foods produced and commercialized in Galicia (northwest Spain) in order to propose prevention plans and mitigation strategies. In this work, RISEGAL applied a systematic approach for the identification of emerging food safety risks potentially affecting bivalve shellfish. First, a comprehensive review of scientific databases was carried out to identify hazards most quoted as emerging in bivalves in the period 2016–2018. Then, identified hazards were semiquantitatively assessed by a panel of food safety experts, who scored them accordingly with the five evaluation criteria proposed by EFSA: novelty, soundness, imminence, scale, and severity. Scores determined that perfluorinated compounds, antimicrobial resistance, Vibrio parahaemolyticus, hepatitis E virus (HEV), and antimicrobial residues are the emerging hazards that are considered most imminent and severe and that could cause safety problems of the highest scale in the bivalve value chain by the majority of the experts consulted (75%). Finally, in a preliminary way, an exploratory study carried out in the Galician Rías highlighted the presence of HEV in mussels cultivated in class B production areas.