Open AccessDevelopment of a predictive model for the shelf-life of Atlantic mackerel (<em>Scomber scombrus</em>)
Author(s) -
Filippo Giarratana,
Felice Panebianco,
Luca Nalbone,
Graziella Ziino,
Davide Valenti,
Alessandro Giuffrida
Publication year - 2022
Publication title -
italian journal of food safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 13
ISSN - 2239-7132
DOI - 10.4081/ijfs.2022.10019
Subject(s) - scomber , food spoilage , mackerel , shelf life , food science , fishery , index method , fish <actinopterygii> , environmental science , biology , mathematics , bacteria , production (economics) , genetics , macroeconomics , economics
Despite its commercial value, the shelflife of the Atlantic mackerel (Scomber scombrus) during refrigerated storage was poorly investigated. In this regard, the Quality Index Method (QIM) was proposed as a suitable scoring system for freshness and quality sensorial estimation of fishery products. This study aims to develop a deterministic mathematical model based on dynamic temperatures conditions and a successive statistical analysis of the results obtained. This model will be exploited to predict the shelf-life of the Atlantic mackerel based on specific storage temperatures. A total of 60 fresh fishes were subdivided into two groups and respectively stored in ice for 12 days at a constant temperature of 1 0.5 C (Group A) and a fluctuating temperature ranging between 1 and 7 C (Group B). Microbiological analysis and sensory evaluation through the QIM were performed on each fish at regular time intervals. A critical value of 6 Log cfu/g of spoilage bacteria (mainly psychotropic) associated with a significant decay of the sensorial characteristics was exceeded after 9 days of storage for Group A and 3 days for Group B. A reliable prediction of fish freshness was obtained by modelling the QIM as a function of the spoilage bacteria behaviour. A coefficient β of correlation was determined to convert the spoilage bacteria load into a Quality Index score. The adoption of mathematical predictive models to assess microbial behaviour under different environmental conditions is an interesting tool for food industries to maximize production and reduce waste.