Quality and shelf-life prediction for retail fresh hake (Merluccius merluccius)

Abstract

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.

Smart sensor to predict retail fresh fish quality under ice storage

“…with spoilage indexes The models of QIM dynamics found in the literature were inadequate for the purpose of this work. Nuin et al (2008) proposed a model independent of any spoilage indexes whereas the works by García et al (2015) and Giuffrida et al (2013) were developed for different fish species and considering only microbial counts. Similar formulations were tested using psychrotrophic counts and TVB-N content re-sulting in inaccurate QIM predictions.…”

“…Confidence in the prediction due to fish-to-fish variability, commonly known as core predictions (García et al, 2015), is evaluated using a Monte Carlo sampling approach. Here we use the model to simulate a large number of solutions for different realizations of the parameter confidence intervals and the measured initial fish-to-fish variability.…”

“…First attempts to estimate quality proposed the use of nonestablished sensory indexes (Nuin et al, 2008;Tsironi et al, 2009). García et al (2015) proposed a model that is able to correlate Pseudomonas and Shewanella growth with the SQM. However, these indexes were insufficient to predict QIM.…”

“…It is, therefore, desirable to accompany most probable quality dynamics with the associated variability. García et al (2015) and Koutsoumanis et al (2002) explored that variability in the SQM and shelf life respectively.…”

Modeling Quality Changes in Brined Bream (Megalobrama amblycephala) Fillets During Storage: Comparison of the Arrhenius Model, BP, and RBF Neural Network

“…Modeling quality indexes of bream under constant storage temperatures is a standard and classical contribution, which establishes the foundation for its use outside our experimental conditions where quality or shelf-life had to be estimated under fluctuations in temperature, or under other relevant variables (García et al 2015;Taoukis et al 1999). The Arrhenius model is usually selected to predict quality under fluctuating temperatures Peleg 2006, 2007), but it resulted in significant discrepancies between predicted and experimental values under non-isothermal storage condition.…”