Department of Food Science and Technology, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
Food Safety Research Department, Nestlé Research, PO BOX44, CH-1000 Lausanne 26, Switzerland.
Food Microbiol. 2023 Jun;112:104230. doi: 10.1016/j.fm.2023.104230. Epub 2023 Jan 25.
Anoxybacillus flavithermus and Bacillus licheniformis are among the predominant spore-formers of heat-processed foods. To our knowledge, no systematic analysis of growth kinetic data of A. flavithermus or B. licheniformis is currently available. In the present study, the growth kinetics of A. flavithermus and B. licheniformis in broth at various temperature and pH conditions were studied. Cardinal models were used to model the effect of the above-mentioned factors on the growth rates. The estimated values for the cardinal parameters T,T,T,pH and pH for A. flavithermus were 28.70 ± 0.26, 61.23 ± 0.16 and 71.52 ± 0.32 °C, 5.52 ± 0.01 and 5.73 ± 0.01, respectively, while for B. licheniformis they were 11.68 ± 0.03, 48.05 ± 0.15, 57.14 ± 0.01 °C, 4.71 ± 0.01 and 5.670 ± 0.08, respectively. The growth behaviour of these spoilers was also investigated in a pea beverage at 62 and 49 °C, respectively, to adjust the models to this product. The adjusted models were further validated at static and dynamic conditions and demonstrated good performance with 85.7 and 97.4% of predicted populations for A. flavithermus and B. licheniformis, respectively, being within the -10%-10% relative error (RE) zone. The developed models can be useful tools in assessing the potential of spoilage of heat-processed foods including plant-based milk alternatives.
黄杆菌和地衣芽孢杆菌是热加工食品中主要的孢子形成菌。据我们所知，目前还没有关于黄杆菌和地衣芽孢杆菌生长动力学数据的系统分析。在本研究中，研究了黄杆菌和地衣芽孢杆菌在不同温度和 pH 值条件下的肉汤中的生长动力学。使用基本模型来模拟上述因素对生长速率的影响。估计的黄杆菌的基本参数 T、T、T、pH 和 pH 值分别为 28.70±0.26、61.23±0.16 和 71.52±0.32°C、5.52±0.01 和 5.73±0.01，而地衣芽孢杆菌的相应参数分别为 11.68±0.03、48.05±0.15、57.14±0.01°C、4.71±0.01 和 5.670±0.08。还分别在 62 和 49°C 下在豌豆饮料中研究了这些腐败菌的生长行为，以调整模型以适应该产品。调整后的模型在静态和动态条件下进一步进行了验证，对于黄杆菌和地衣芽孢杆菌，分别有 85.7%和 97.4%的预测种群落在 -10%-10%相对误差 (RE) 范围内，表明模型表现良好。开发的模型可以成为评估包括植物性牛奶替代品在内的热加工食品腐败潜力的有用工具。
