This study tested the hypothesis that growth of Listeria monocytogenes in processed cheese with added nisin can be predicted from residual nisin A concentrations in the final product after processing. A LC-MS/MS method and a bioassay were studied to quantify residual nisin A concentrations and a growth and growth boundary model was developed to predict the antilisterial effect in processed cheese. 278 growth rates were determined in broth for 11 L. monocytogenes isolates and used to determine 13 minimum inhibitory concentration (MIC) values for nisin between pH 5.5 and 6.5. To supplement these data, 67 MIC-values at different pH-values were collected from the scientific literature. A MIC-term was developed to describe the effect of pH on nisin MIC-values. An available growth and growth boundary model (doi: 10.1016/j.fm.2019.103255) was expanded with the new MIC-term for nisin to predict growth in processed cheese. To generate data for model evaluation and further model development, challenge tests with a total of 45 growth curves, were performed using processed cheese. Cheese were formulated with 11.2 or 12.0 ppm of nisin A and heat treated to obtain residual nisin A concentrations ranging from 0.56 to 5.28 ppm. Below 15°C, nisin resulted in extended lag times. A global regression approach was used to fit all growth curves determined in challenge tests. This was obtained by combining the secondary growth and growth boundary model including the new term for the inhibiting effect of nisin on μ_max with the primary logistic growth model with delay. This model appropriately described the growth inhibiting effect of residual nisin A and showed that relative lag times depended on storage temperatures. With residual nisin A concentrations, other product characteristics and storage temperature as input the new model correctly predicted all observed growth and no-growth responses for L. monocytogenes. This model can support development of nisin A containing recipes for processed cheese that prevent growth of L. monocytogenes. Residual nisin A concentrations in processed cheese were accurately quantified by the developed LC-MS/MS method with recoveries of 83 to 110 % and limits of detection and quantification being 0.04 and 0.13 ppm, respectively. The tested bioassay was less precise and nisin A recoveries varied for 53% to 94%.

这项研究检验了以下假设：可以从加工后最终产品中残留的乳酸链球菌素A浓度预测添加了乳酸链球菌素的加工奶酪中单核细胞增生李斯特菌的生长。研究了LC-MS / MS方法和生物测定法来定量残留的乳链菌肽A浓度，并建立了生长和生长边界模型来预测加工奶酪的抗李斯特菌作用。确定了11种单核细胞增生李斯特菌在肉汤中的278种生长速率分离并用于测定pH 5.5至6.5之间的乳链菌肽的13种最小抑菌浓度（MIC）值。为了补充这些数据，从科学文献中收集了67个不同pH值的MIC值。开发了一个MIC术语来描述pH对乳链菌肽MIC值的影响。使用新的乳链菌肽MIC术语扩展了可用的生长和生长边界模型（doi：10.1016 / j.fm.2019.103255），以预测加工奶酪的生长。为了生成用于模型评估和进一步模型开发的数据，使用加工奶酪进行了总共45条生长曲线的挑战测试。用11.2或12.0 ppm的乳链菌肽A配制奶酪，并进行热处理以获得0.56至5.28 ppm的残留乳链菌肽A浓度。低于15°C，乳链菌肽导致滞后时间延长。全局回归方法用于拟合挑战测试中确定的所有增长曲线。这是通过结合次级生长和生长边界模型（包括乳链菌肽抑制作用的新术语）获得的μ_最大与延迟主物流的增长模式。该模型适当地描述了残留乳链菌肽A的生长抑制作用，并表明相对滞后时间取决于储存温度。以残留的乳链菌肽A浓度，其他产品特性和储存温度为输入，新模型正确预测了单核细胞增生李斯特菌的所有观察到的生长和无生长反应。该模型可以支持乳链菌肽A的开发，该乳链菌素的配方可防止单核细胞增生李斯特氏菌的生长。通过开发的LC-MS / MS方法可准确定量加工奶酪中的乳酸链球菌素A残留浓度，回收率为83％至110％，检出限和定量限分别为0.04和0.13 ppm。测试的生物测定法不太精确，乳链菌肽A的回收率在53％至94％之间变化。