Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia.
Institute for General and Physical Chemistry, Studentski trg 12/V, 11000 Belgrade, Serbia.
Int J Environ Res Public Health. 2022 Dec 13;19(24):16727. doi: 10.3390/ijerph192416727.
Consumption of raw or undercooked meat is responsible for 2.3 million foodborne illnesses yearly in Europe alone. The greater part of this illness is associated with beef meat, which is used in many traditional dishes across the world. Beneath the low microbiological quality of beef lies (pathogenic) bacterial contamination during primary production as well as inadequate hygiene operations along the farm-to-fork chain. Therefore, this study seeks to understand the microbiological quality pathway of minced beef processed for fast-food restaurants over three years using an artificial neural network (ANN) system. This simultaneous approach provided adequate precision for the prediction of a microbiological profile of minced beef meat as one of the easily spoiled products with a short shelf life. For the first time, an ANN model was developed to predict the microbiological profile of beef minced meat in fast-food restaurants according to meat and storage temperatures, butcher identification, and working shift. Predictive challenges were identified and included standardized microbiological analyses that are recommended for freshly processed meat. The obtained predictive models (an overall of 0.867 during the training cycle) can serve as a source of data and help for the scientific community and food safety authorities to identify specific monitoring and research needs.
仅在欧洲，每年就有 230 万人因食用生的或未煮熟的肉而患病。这种疾病的大部分与牛肉有关，牛肉被广泛用于世界各地的许多传统菜肴中。牛肉的微生物质量较低，原因是在初级生产过程中存在（病原体）细菌污染，以及在从农场到餐桌的整个链条中卫生操作不足。因此，本研究旨在使用人工神经网络 (ANN) 系统，在三年的时间内，了解快餐店加工的绞碎牛肉的微生物质量途径。这种同步方法为预测绞碎牛肉的微生物特征提供了足够的精度，绞碎牛肉是保质期短、容易变质的产品之一。首次根据肉和储存温度、屠夫身份和工作班次，开发了一个 ANN 模型来预测快餐店中牛肉绞碎肉的微生物特征。确定了预测方面的挑战，包括推荐用于新鲜加工肉类的标准化微生物分析。所获得的预测模型（在训练周期中的总体为 0.867）可作为数据来源，帮助科学界和食品安全当局确定具体的监测和研究需求。
