Development of a predictive model for the shelf-life of Atlantic mackerel (Scomber scombrus)

Italian Journal of Food Safety

Alessandro Giuffrida¹  Davide Valenti²  Graziella Ziino³  Luca Nalbone³  Filippo Giarratana³  Felice Panebianco⁴ 

[1] CNR-IRIB, Consiglio Nazionale delle Ricerche - Istituto per la Ricerca e l’Innovazione Biomedica, Palermo;Department of Physics and Chemistry, University of Palermo, Group of Interdisciplinary Theoretical Physics and CNISM, Palermo Unit;Department of Veterinary Science, University of Messina;Department of Veterinary Sciences, University of Turin, Grugliasco;

关键词: Scomber scombrus;    Quality Index Method;    Spoilage bacteria;    Predictive model;   

DOI  :  10.4081/ijfs.2022.10019

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown