Kremer Frederico Schmitt, Rodrigues Rafaela da Silva, Omori Wellington Pine, de Oliveira Rafael Rodrigues, de Oliveira Gabriel Alves Silva, Nero Luís Augusto
Pelotas Federal University, Center of Technological Development, Laboratory of Bioinformatics, Campus Universitário, 96160-000 Capão do Leão, RS, Brazil.
Federal University of Viçosa, Department of Veterinary Medicine, InsPOA - Laboratory of Food Inspection, Campus Universitário, 36570-900 Viçosa, MG, Brazil; Federal University of Viçosa, BIOAGRO - Institute of Biotechnology Applied to Agriculture, BIOMOLVET - Laboratory of Veterinary Molecular Biology, Campus Universitário, 36570-900 Viçosa, MG, Brazil.
Int J Food Microbiol. 2025 Nov 2;442:111375. doi: 10.1016/j.ijfoodmicro.2025.111375. Epub 2025 Jul 31.
The preservation of vacuum-packaged beef products is essential for maintaining shelf life. However, the occurrence of blown pack phenomenon, characterized by the expansion of packaging due to gas production by spoilage microorganisms, is still a challenge. In the present work, we demonstrate that microbiome analysis using next generation sequencing (NGS) and machine learning might be useful in the analysis, modeling and prediction of spoilage and blown pack in vacuum-packaged beef. Beef systems (n = 10) were vacuum-packed, stored at 4 and 15 °C, and their populations were monitored based on NGS at 0 h and 7, 14, 21 and 28 days. Our analysis allowed the prediction of blown pack based on information of the initial microbiome in beef and storage conditions, identification of the relationship of different bacteria genera associated with spoilage along with temperature, which were consistent with differential abundance analysis, and estimate the relationship of temperature and blown pack. Using SHAP (Shapley Additive Explanations) to interpret the XGBoost model, we identified temperature as the most influential factor in blown pack prediction when considering microbiome data from day zero. Additionally, SHAP analysis of Random Forest and XGBoost models based on OTU Spearman correlation and linear regression, computed about time, highlighted Peptoniphilus as the most important bacterial genus, followed by Hafnia and Peptostreptococcus. Additional studies might extend these methods for other types of meat, cuts and including additional storage conditions, allowing a better modeling of the dynamics in the microbiome associated with the blown pack phenomenon.
真空包装牛肉产品的保鲜对于维持货架期至关重要。然而，出现胀袋现象（其特征为因腐败微生物产气导致包装膨胀）仍是一个挑战。在本研究中，我们证明使用下一代测序（NGS）和机器学习进行微生物群落分析可能有助于对真空包装牛肉的腐败和胀袋进行分析、建模和预测。将牛肉系统（n = 10）进行真空包装，在4℃和15℃下储存，并在0小时以及7、14、21和28天时基于NGS监测其菌群。我们的分析能够基于牛肉初始微生物群落信息和储存条件预测胀袋，识别与腐败相关的不同细菌属与温度之间的关系（这与差异丰度分析一致），并估计温度与胀袋之间的关系。使用SHAP（Shapley值加法解释）来解释XGBoost模型，当考虑第零天的微生物群落数据时，我们确定温度是胀袋预测中最具影响力的因素。此外，基于OTU斯皮尔曼相关性和线性回归对随机森林和XGBoost模型进行的SHAP分析（计算时间相关）突出显示了嗜胨菌属是最重要的细菌属，其次是哈夫尼亚菌属和消化链球菌属。进一步的研究可能会将这些方法扩展到其他类型的肉类、切块，并纳入更多储存条件，从而更好地对与胀袋现象相关的微生物群落动态进行建模。
