Fish Freshness Meter (Torrymeter) used to Predict Estimate of Freshness Index in Farmed Sea Bream

Title	Design of Predictive Tools to Estimate Freshness Index in Farmed Sea Bream (Sparus aurata) Stored in Ice
Article Written by	Juan Calanche, Selene Pedrós, Pedro Roncalés, José Antonio Beltrán
Research Done at	University of Zaragoza, Spain University of Orient, Venezuela University College of Dublin, Ireland
Published Date	Jan. 8, 2020
Fish Species	Sea bream
Key Words	partial least square regression; modelling; fish quality tools; shelf life

What the research is about

This research studied sea bream freshness evolution through storage time in ice by determining different quality parameters and sensory profiles. Predictive models for freshness index, storage time, and microbial counts were designed from these data. Physico–chemical parameters were assessed to evaluate the quality of fish; microbial growth was controlled to ensure food safety, and sensory analyses were carried out to characterize quality deterioration. Predictive models were developed and improved with the aim of being used as tools for quality management in the seafood industry. Validation was conducted in order to establish the accuracy of models. There was a good relationship between the physico–chemical and microbiological parameters. Sensory analysis and microbial counts allowed for the establishment of a shelf-life of 10 days, which corresponded to a poor quality (according to the European Community’s system of grading fish for marketing purposes), with a freshness index lower than 50%. Sensory profiles showed that gill and flesh texture were the most vulnerable attributes during storage in ice related to spoilage. The predictive models for the freshness index (%) and ice storage time (h) exhibited an accuracy close to 90% following practical validation.

Research conclusion

Predictive models were developed for the freshness index (%), ice storage time (h) and microbial counts; all of these models exhibited an accuracy of close to 90% following practical validation. The findings of this study suggest that the predictive tools designed may be proposed as a valuable alternative to monitor spoilage by assessing freshness as a key aspect of seabream quality. These tools allow for the possibility, too, of avoiding the use of sensory experts and microbiological analysis for estimating freshness degree, time of storage in ice, and microbial load. In a food safety management system, the proposed tools could be employed for quality control and assessment within the cold chain of seabream, as well as for determining freshness loss during logistic and marketing operations. Combining standard traditional methods with these new tools could help decision making and provide additional benefits such as less time consumption, easiness to perform, sensitivity, automation, and the use of non-destructive and non-invasive techniques.