Div. de Biotecnología y Alimentos, Inst. Tecnológico y de Estudios Superiores de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Col. Tecnológico, Monterrey, NL 64849, México.
J Food Sci. 2011 Aug;76(6):E495-502. doi: 10.1111/j.1750-3841.2011.02265.x. Epub 2011 Jul 5.
Consumer demand for food safety and quality improvements, combined with new regulations, requires determining the processor's confidence level that processes lowering safety risks while retaining quality will meet consumer expectations and regulatory requirements. Monte Carlo calculation procedures incorporate input data variability to obtain the statistical distribution of the output of prediction models. This advantage was used to analyze the survival risk of Mycobacterium avium subspecies paratuberculosis (M. paratuberculosis) and Clostridium botulinum spores in high-temperature short-time (HTST) milk and canned mushrooms, respectively. The results showed an estimated 68.4% probability that the 15 sec HTST process would not achieve at least 5 decimal reductions in M. paratuberculosis counts. Although estimates of the raw milk load of this pathogen are not available to estimate the probability of finding it in pasteurized milk, the wide range of the estimated decimal reductions, reflecting the variability of the experimental data available, should be a concern to dairy processors. Knowledge of the C. botulinum initial load and decimal thermal time variability was used to estimate an 8.5 min thermal process time at 110 °C for canned mushrooms reducing the risk to 10⁻⁹ spores/container with a 95% confidence. This value was substantially higher than the one estimated using average values (6.0 min) with an unacceptable 68.6% probability of missing the desired processing objective. Finally, the benefit of reducing the variability in initial load and decimal thermal time was confirmed, achieving a 26.3% reduction in processing time when standard deviation values were lowered by 90%.
In spite of novel technologies, commercialized or under development, thermal processing continues to be the most reliable and cost-effective alternative to deliver safe foods. However, the severity of the process should be assessed to avoid under- and over-processing and determine opportunities for improvement. This should include a systematic approach to consider variability in the parameters for the models used by food process engineers when designing a thermal process. The Monte Carlo procedure here presented is a tool to facilitate this task for the determination of process time at a constant lethal temperature.
消费者对食品安全和质量的要求不断提高，再加上新法规的出台，这就要求加工商确定其对降低安全风险同时保持质量的处理过程有足够的信心，确保其能够满足消费者的期望和监管要求。蒙特卡罗计算程序将输入数据的可变性纳入其中，以获得预测模型输出的统计分布。该优势被用于分析分别在高温短时间（HTST）牛奶和罐装蘑菇中，禽分枝杆菌亚种副结核分枝杆菌（M. paratuberculosis）和肉毒梭菌孢子的存活风险。结果表明，15 秒 HTST 处理过程实现至少 5 个数量级副结核分枝杆菌计数减少的概率估计为 68.4%。虽然目前还无法估计这种病原体在生奶中的负荷量，从而估计其在巴氏杀菌奶中的存在概率，但由于可用实验数据的变异性，估计的十进制减少量范围很广，这应该引起奶制品加工商的关注。肉毒梭菌初始负荷和十进制热时间可变性的知识被用于估计在 110°C 下对罐装蘑菇进行 8.5 分钟的热加工时间，从而将风险降低到每容器 10⁻⁹孢子/容器，置信度为 95%。这一数值远高于使用平均值（6.0 分钟）估计的数值（68.6%的概率无法达到预期的加工目标）。最后，通过降低初始负荷和十进制热时间的变异性，确认了降低加工时间的好处，当标准偏差值降低 90%时，处理时间缩短了 26.3%。
尽管有新技术商业化或正在开发中，但热加工仍然是提供安全食品的最可靠和最具成本效益的选择。然而，应评估处理过程的严格程度，以避免处理不足和过度，并确定改进的机会。这应包括系统地考虑食品加工工程师在设计热加工过程时使用的模型参数的可变性。这里介绍的蒙特卡罗程序是确定在恒定致死温度下的加工时间的工具。
