Maktabdar Maryam, Wemmenhove Ellen, Gkogka Elissavet, Dalgaard Paw
Food Microbiology and Hygiene, DTU National Food Institute (DTU Food), Technical University of Denmark, Kongens Lyngby, Denmark.
Arla Foods Ingredients Innovation Center, Arla Foods Ingredients, Videbæk, Denmark.
Front Microbiol. 2025 Mar 21;16:1553885. doi: 10.3389/fmicb.2025.1553885. eCollection 2025.
Guidelines for combinations of product characteristics to prevent unacceptable growth of in foods are lacking, and models are therefore valuable for predicting these responses. isolates of dairy origin were used to generate a comprehensive dataset to develop two cardinal parameter growth and growth boundary models for mesophilic and psychrotolerant , respectively. Each model incorporated the inhibitory effect of 11 environmental factors, i.e., temperature, pH, NaCl/a, organic acids (acetic, benzoic, citric, lactic, and sorbic), phosphate salts (orthophosphate, diphosphate, and triphosphate), and the effect of interactions between these factors. Cardinal parameter values for mesophilic and psychrotolerant strain cocktails were estimated using 231 and 203 maximum specific growth rates ( values), respectively, generated in a standard liquid laboratory medium (BHI broth). Furthermore, an additional 113 and 100 values were generated for the two strain cocktails using a dairy-specific liquid medium (an ultra-filtration permeate from whey) to evaluate growth responses obtained in BHI broth. Cardinal parameter values for the two extensive growth boundary models were selected conservatively using data from BHI broth or UF permeate, such that the widest growth range was obtained for each environmental factor. The studied cocktail of six vegetative mesophilic isolates exhibited greater acid tolerance in UF permeate than in BHI broth with lower ( values of 4.75 versus 4.98), higher minimum inhibitory concentrations () of undissociated lactic acid ( of 2.99 versus 2.34 mM) and total citric acid ( of 169.1 versus 82.5 mM). The psychrotolerant cocktail also had lower and higher values for and in UF permeate than in BHI broth. The remaining cardinal parameter values were determined from growth rates in BHI broth. The two new models can predict the combined effect of storage temperature and a wide range of dairy product characteristics, including combinations of organic acids and phosphate melting salts. These growth and growth boundary models can support the evaluation and management of the two subgroups in various dairy products. However, product validation of the two predictive models is required to determine their performance and range of applicability.
目前缺乏关于食品中防止[微生物名称]不可接受生长的产品特性组合指南，因此模型对于预测这些反应很有价值。使用源自乳制品的[微生物名称]分离株生成了一个综合数据集，分别为嗜温菌和耐冷菌开发了两个基本参数生长模型和生长边界模型。每个模型都纳入了11种环境因素的抑制作用，即温度、pH值、NaCl/a、有机酸（乙酸、苯甲酸、柠檬酸、乳酸和山梨酸）、磷酸盐（正磷酸盐、焦磷酸盐和三磷酸盐）以及这些因素之间相互作用的影响。嗜温菌和耐冷菌菌株混合物的基本参数值分别使用在标准液体实验室培养基（脑心浸液肉汤）中产生的231个和203个最大比生长速率（μ值）进行估计。此外，使用特定于乳制品的液体培养基（乳清超滤渗透物）为这两种菌株混合物生成了另外113个和100个μ值，以评估在脑心浸液肉汤中获得的生长反应。使用来自脑心浸液肉汤或超滤渗透物的数据保守地选择了两个广泛生长边界模型的基本参数值，以便为每个环境因素获得最宽的生长范围。所研究的六种嗜温营养型[微生物名称]分离株的混合物在超滤渗透物中比在脑心浸液肉汤中表现出更高的耐酸性，pH值更低（分别为4.75和4.98），未离解乳酸的最低抑菌浓度（MIC）更高（分别为2.99和2.34 mM），总柠檬酸的最低抑菌浓度（MIC）更高（分别为169.1和82.5 mM）。耐冷菌混合物在超滤渗透物中的pH值也比在脑心浸液肉汤中更低，MIC和μ值更高。其余基本参数值由脑心浸液肉汤中的生长速率确定。这两个新模型可以预测储存温度和多种乳制品特性的综合影响，包括有机酸和磷酸盐熔盐的组合。这些生长模型和生长边界模型可以支持对各种乳制品中这两种[微生物名称]亚群的评估和管理。然而，需要对这两个预测模型进行产品验证，以确定它们的性能和适用范围。
