2015
DOI: 10.1016/j.ijfoodmicro.2015.05.012
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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… Show more

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Cited by 37 publications
(36 citation statements)
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“…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.…”
Section: A Correlation Of Quality Index Methods (Qim)mentioning
confidence: 99%
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“…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.…”
Section: A Correlation Of Quality Index Methods (Qim)mentioning
confidence: 99%
“…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.…”
Section: Sensor Validationmentioning
confidence: 99%
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“…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.…”
Section: Evaluation Of the Arrhenius Model Bp-nn And Rbf-nnmentioning
confidence: 99%