The fate of Listeria monocytogenes during ripening of artisanal Minas semi-hard cheese, as influenced by cheese intrinsic properties and by autochthonous (naturally present) or intentionally-added anti-listerial lactic acid bacteria (LAB) was modeled. Selected LAB strains with anti-listerial capacity were added or not to raw or pasteurized milk to prepare 4 cheese treatments. Counts of LAB and L. monocytogenes, pH, temperature and water activity were determined throughout cheese ripening (22 days, 22±1ᵒC). Different approaches were adopted to model the effect of LAB on L. monocytogenes: an independent approach using the Huang primary model to describe LAB growth and the linear decay model to describe pathogen inactivation; the Huang-Cardinal [pH] model using the effect of pH variation in a dynamic tertiary approach; and the Jameson-effect with N_{max tot} model which simultaneously describes L. monocytogenes and LAB fate. L. monocytogenes inactivation occurred in both treatments with added LAB and inactivation was faster in raw milk cheese (−0.0260 h⁻¹) vs. pasteurized milk cheese (−0.0182 h⁻¹), as estimated by the linear decay model. Better goodness-of-fit was achieved for the cheeses without added LAB when the Huang primary model was used. A faster and great pH decline was detected for cheeses with added LAB, and the Huang-Cardinal [pH] model predicted higher pathogen growth rate in cheese produced with raw milk, but greater L. monocytogenes final concentration in pasteurized milk cheese. The Jameson-effect model with N_{max tot} predicted that LAB suppressed pathogen growth in all treatments, except in the treatment with pasteurized milk and no LAB addition. The Huang-Cardinal [pH] model was more accurate in modeling L. monocytogenes kinetics as a function of pH changes than was the Jameson-effect model with N_{max tot} as a function of LAB inhibitory effect based on the goodness-of-fit measures. The Jameson-effect model may however be a better competition model since it can more easily represent L. monocytogenes growth and death. This study presents crucial kinetic data on L. monocytogenes behavior in the presence of competing microbiota in Minas semi-hard cheese under dynamic conditions.

对手工米纳斯半硬奶酪成熟期间李斯特菌的命运进行了建模，其受奶酪内在特性和土生（天然存在）或有意添加的抗李斯特氏乳酸菌（LAB）的影响。将选定的具有抗李斯特菌能力的LAB菌株添加到生乳或巴氏灭菌奶中，也可以不添加，以制备4种奶酪处理方法。在奶酪成熟期间（22天，22±1ᵒC）确定LAB和单核细胞增生李斯特菌，pH，温度和水活度的计数。采用了不同的方法来模拟LAB对单核细胞增生李斯特菌的影响：一种独立的方法，使用Huang初级模型描述LAB生长，使用线性衰变模型描述病原体失活；在动态第三级方法中使用pH变化的影响的Huang-Cardinal [pH]模型；和詹姆森效应（N _{max tot）}模型同时描述了单核细胞增生李斯特菌和LAB的命运。单增李斯特菌灭活发生在添加了LAB和失活两种治疗是原料奶奶酪更快（-0.0260 ^ h ^-1）与巴氏杀菌奶奶酪（-0.0182 ^ h ^-1），由线性衰减模型估算。当使用Huang初级模型时，不添加LAB的奶酪具有更好的拟合度。添加了LAB的奶酪的pH下降更快且更大，而Huang-Cardinal [pH]模型预测用生奶生产的奶酪中病原体的生长速率更高，但巴氏灭菌奶奶酪中的单核细胞增生李斯特菌终浓度更高。N _{max tot}的詹姆森效应模型预测，在所有处理中，LAB均能抑制病原体的生长，除了巴氏杀菌牛奶和不添加LAB的处理。Huang-Cardinal [pH]模型在建模单核细胞增生李斯特菌动力学随pH变化的函数方面比具有N _{max tot}的詹姆森效应模型更准确基于拟合优度度量的LAB抑制作用的函数。然而，詹姆逊效应模型可能是更好的竞争模型，因为它可以更轻松地代表单核细胞增生李斯特菌的生长和死亡。这项研究提供了动态条件下米纳斯半硬奶酪中存在竞争微生物群时单核细胞增生李斯特菌行为的关键动力学数据。