Giarratana Filippo, Panebianco Felice, Nalbone Luca, Ziino Graziella, Valenti Davide, Giuffrida Alessandro
Department of Veterinary Science, University of Messina.
Department of Veterinary Sciences, University of Turin, Grugliasco.
Ital J Food Saf. 2022 Feb 23;11(1):10019. doi: 10.4081/ijfs.2022.10019. eCollection 2022 Feb 22.
Despite its commercial value, the shelflife of the Atlantic mackerel () 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.
尽管大西洋鲭鱼具有商业价值,但其在冷藏储存期间的货架期却鲜有研究。在这方面,质量指数法(QIM)被提议作为一种适用于渔业产品新鲜度和质量感官评估的评分系统。本研究旨在基于动态温度条件和对所得结果的连续统计分析,开发一个确定性数学模型。该模型将用于根据特定储存温度预测大西洋鲭鱼的货架期。总共60条新鲜鱼被分成两组,分别在1±0.5°C的恒定温度下(A组)和1至7°C的波动温度下(B组)在冰中储存12天。每隔一定时间间隔对每条鱼进行微生物分析和通过QIM进行感官评估。A组储存9天后,B组储存3天后,与感官特性显著下降相关的腐败细菌(主要是嗜冷菌)的临界值超过了6 Log cfu/g。通过将QIM建模为腐败细菌行为的函数,获得了对鱼新鲜度的可靠预测。确定了一个相关系数β,以将腐败细菌负荷转换为质量指数得分。采用数学预测模型来评估不同环境条件下的微生物行为,对于食品行业来说是一个有趣的工具,可以最大限度地提高产量并减少浪费。