肉品品质变化的蒙特卡罗方法仿真

目的采用蒙特卡罗方法仿真研究检测间距对检测深度的影响以及光子在不同肉品组织中的迁移规律。方法使用Windows界面的蒙特卡罗仿真软件对肉品的不同品质、不同检测间距的光子迁移分布进行仿真。结果随着检测器与光源之间间距的增加,检测深度逐渐增加;对于不同新鲜度的样品,散射系数在0.4~1.5 mm~(-1)范围内,随着散射系数的增大,光子在组织中的迁移路径长度逐渐减小,检测器可以捕获更多的光子包。结论对不同检测间距下检测深度的蒙特卡罗仿真实验结果与实际测定结果基本相同,因此蒙特卡罗仿真可以为光学在肉品无损检测方案的研究提供有效理论支撑。     

Investing in a robotic milking system: a Monte Carlo simulation analysis

This paper uses Monte Carlo simulation methods to estimate the breakeven value for a robotic milking system (RMS) on a dairy farm in the United States. The breakeven value indicates the maximum amount that could be paid for the robots given the costs of alternative milking equipment and other important factors (e.g., milk yields, prices, length of useful life of technologies). The analysis simulates several scenarios under three herd sizes, 60, 120, and 180 cows. The base-case results indicate that the mean breakeven values are $192,056, $374,538, and $553,671 for each of the three progressively larger herd sizes. These must be compared to the per-unit RMS cost (about $125,000 to $150,000) and the cost of any construction or installation of other equipment that accompanies the RMS. Sensitivity analysis shows that each additional dollar spent on milking labor in the parlor increases the breakeven value by $4.10 to $4.30. Each dollar increase in parlor costs increases the breakeven value by $0.45 to $0.56. Also, each additional kilogram of initial milk production (under a 2x system in the parlor) decreases the breakeven by $9.91 to $10.64. Finally, each additional year of useful life for the RMS increases the per-unit breakeven by about $16,000 while increasing the life of the parlor by 1 yr decreases the breakeven value by between $5,000 and $6,000.     

蒙特卡罗模拟在微米木纤维热磨中的应用

分析总结了木纤维热磨加工中功耗较大的原因,提出了微米级纤维切削的新方法,并提出用蒙特卡罗统计分析纤维热磨过程中的相关参数变化,从而得出合理参数估计值的设想。     

Development of a Monte Carlo simulation model to predict pasteurized fluid milk spoilage due to post-pasteurization contamination with gram-negative bacteria

Psychrotolerant gram-negative bacteria introduced as post-pasteurization contamination (PPC) are a major cause of spoilage and reduced shelf life of high-temperature, short-time pasteurized fluid milk. To provide improved tools to (1) predict pasteurized fluid milk shelf life as influenced by PPC and (2) assess the effectiveness of different potential interventions that could reduce spoilage due to PPC, we developed a Monte Carlo simulation model that predicts fluid milk spoilage due to psychrotolerant gram-negative bacteria introduced as PPC. As a first step, 17 gram-negative bacterial isolates frequently associated with fluid milk spoilage were selected and used to generate growth data in skim milk broth at 6°C. The resulting growth parameters, frequency of isolation for the 17 different isolates, and initial concentration of bacteria in milk with PPC, were used to develop a Monte Carlo model to predict bacterial number at different days of shelf life based on storage temperature of milk. This model was then validated with data from d 7 and 10 of shelf life, collected from commercial operations. The validated model predicted that the parameters (1) maximum growth rate and (2) storage temperature had the greatest influence on the percentage of containers exceeding 20,000 cfu/mL standard plate count on d 7 and 10 (i.e., spoiling due to PPC), which indicates that accurate data on maximum growth rate and storage temperature are important for accurate predictions. In addition to allowing for prediction of fluid milk shelf life, the model allows for simulation of “what-if” scenarios, which allowed us to predict the effectiveness of different interventions to reduce overall fluid milk spoilage due to PPC through a set of proof-of-concept scenario (e.g., frequency of PPC in containers reduced from 100% to 10%; limiting distribution temperature to a maximum of 6°C). Combined with other models, such as previous models on fluid milk spoilage due to psychrotolerant spore-forming bacteria, the data and tools developed here will allow for rational, digitally enabled, fluid milk shelf life prediction and quality enhancement.     

Monte Carlo模拟对面制品中铝膳食暴露风险的概率评估

结合常见市售面制品中铝的残留量监测结果,采用Crystal Ball软件建立非参数概率评估模型,对居民面制品来源的铝膳食暴露量进行了初步评估。结果表明,所有面制品样品的铝残留量均值为15.5 mg/kg,最高检出值为1437.7 mg/kg,超标率为8.5%。不同类别面制品中铝的残留量差异具有统计学意义(p<0.05),以油炸类面制品的铝残留量均值最高,为22.7 mg/kg。不同年龄人群面制品来源的铝膳食日暴露量的P99百分位数分别为青少年(6~17岁):男性103.89/女性116.47 μg/(kg·d);青年(18~44岁):男性66.36/女性79.36 μg/(kg·d);中年(45~59岁):男性68.23/女性72.41 μg/(kg·d);老年(≥60岁):男性73.80/女性78.89 μg/(kg·d),低于每日可耐受摄入量(ADI)285.7 μg/(kg·d),风险指数最大值为0.41,小于1。面制品来源的铝膳食暴露风险在可接受范围内,普通居民通过面制品摄入铝的健康风险较低。     

蒙特卡洛法在甘蔗种质资源研究中的应用展望

甘蔗种质资源是甘蔗育种的物质基础,也是研究甘蔗及其近缘属植物起源、进化和遗传多样性的物质基础。蒙特卡洛法是一种有效的统计试验方法,本文通过分析总结其在大豆、野生利马豆、马铃薯、明党参等植物种质资源研究中的应用,对蒙特卡洛法在甘蔗种质资源研究中的应用作了展望。     

牵伸区浮游纤维动态行为的随机模拟

通过各种模拟手段得到牵伸区浮游纤维动态行为的规律是牵伸研究的重要内容之一。在考虑了牵伸纱条中纤维长度分布的情况下,利用前人提出的纤维变速点的抛物线密度函数族方法,模拟纱条在牵伸区变细曲线的随机状态,讨论了在不同纤维长度分布下以及不同牵伸工艺参数下,纱条变细曲线的模拟结果。给出在快速牵伸区和慢速牵伸区纤维比例的模拟曲线,给出了在已知长度分布下,牵伸纱条在前罗拉钳口下纱条截面纤维根数变异系数的模拟,此模拟结果与已有模拟结果相比具有良好的相关性,为实现牵伸后须条不匀的预测打下了基础。     

基于蒙特卡洛法的溶解性总固体测量不确定度评定

目的研究出一种基于蒙特卡洛法的溶解性总固体测量不确定度评定的方法。方法按照溶解性总固体实验过程,建立对应的数学模型。根据蒙特卡洛法分布传播和总结过程,得出蒙特卡洛法的评定结果。同时使用Guide 98-3:2008《测量不确定度表示指南》进行评定,得出对比结果。结果使用蒙特卡洛法所得结果为208.0 mg/L,扩展不确定度为5.2 mg/L。区间为197.82~218.18 mg/L。指南法评定结果为208.6 mg/L,扩展不确定度为5.6 mg/L,计算结果为(208.6±5.6)mg/L。结论蒙特卡洛法评定结果与测量不确定度表示指南法评定的数据相近,可以用于计算溶解性总固体的测量不确定度,为实验室工作人员提供了一种实用的测量不确定度评定方法。     

Prediction of time to growth of Listeria monocytogenes using Monte Carlo simulation or regression analysis,influenced by sublethal heat and recovery conditions

Stochastic models, including the variability in extent and probability of microbial growth, are useful for estimating the risk of foodborne illness (i.e. Nauta, 2000). Risk assessment typically has to embrace all sources of variability. In this paper, a stochastic approach to evaluate growth of heat damaged Listeria monocytogenes cells influenced by different stresses (pH and presence of eugenol) was performed, using an individual-based approach of growth through OD measurements. Both the lag phase duration and the “work to be done” (h₀ parameter) were derived from the growth curves obtained. From results obtained histograms of the lag phase were generated and distributions were fitted. Histograms showed a shift to longer lag phases and an increase in variability with high stress levels. Using the distributions fitted, predictions of time to unacceptable growth (10² cfu/g) of L. monocytogenes were established by Monte Carlo simulation and they were compared with results from statistical methods. It was evidenced that both methods (Monte Carlo and regression analysis) gave a good indication of the probability of a certain level of growth other than the average. Tornado plots were obtained to establish a sensitivity analysis of the influence of the conditions tested (heat, pH, eugenol) applied to the microorganism and their combinations.     

基于蒙特卡罗模拟技术的食用菌中二氧化硫膳食暴露风险评估

通过基于蒙特卡罗模拟技术的@Risk软件对我国主要消费的10种食用菌中二氧化硫残留量进行模拟分布分析,并运用风险商理论对其膳食消费风险进行评估。结果显示,干制食用菌二氧化硫残留及超标情况远超新鲜食用菌,新鲜及干制食用菌中二氧化硫平均残留量分别为2.94?mg/kg及43.70?mg/kg,香菇及银耳等部分干制食用菌存在超标风险。膳食暴露评估结果显示,经新鲜及干制食用菌途径摄入二氧化硫的风险商在各百分位点均远低于1,最大值为0.007 3,且在10%位点以上经干制食用菌途径摄入二氧化硫的风险商均高出新鲜食用菌1个数量级。因此,中国普通居民通过食用菌摄入二氧化硫的风险较低。研究结果可为我国食用菌的质量安全管理提供科学依据。     

蒙特卡洛方法及在二维随机游动问题中的应用初探

概述蒙特卡洛方法的产生与发展,并结合射击问题的求解阐述了蒙特卡洛方法的基本特点,最后就蒙特卡洛方法在二维随机游动问题中的应用进行讨论,指出可将此用在油墨印刷牢度的评价上。     

The variable nature of Biot numbers in food drying

Biot numbers (Bi) relate internal and external resistances to mass or heat fluxes and are useful to identify controlling mechanisms. Their variation during drying received little attention despite its importance in model selection. Therefore, variable Bi were analysed in drying of wheat, a low-moisture product, and in the formation of a low-calorie, dehydrated apple-leather (LCAL), a high moisture product. Wheat drying was predicted with a numerical solution from a m.c. of 0.35 kg water/kg dry matter, to find a variable mass transfer Bi that shifted the controlling mechanism from internal-external to internal. From a lower m.c. of 0.205 kg water/kg dry matter, the entire process was internally controlled. Drying of shrinking LCAL was solved with the energy balance. The mass transfer Bi varied from 0.5 to 1600, while the heat transfer Bi, only from 0.25 to 0.5, and a modified version, from 0.25 to 0.     

Power density control in microwave assisted air drying to improve quality of food

Currently, the new trend is the development of microwave hot air drying control in order to solve the problem of hot spots on the product. So in this study, a microwave/hot air drying system with the ability of automatic power density control was developed for tomato drying. Three power densities (3, 5 and 7 W/g) at 40 °C air temperature were applied and the output, reflected microwave power and the mass of the sample were registered online. Two different drying strategies were implemented. The first consisted of a microwave drying without specific power control. The second one introduced power control. A feedback control based on specific power related to the actual sample mass resulted in the best power control and the improvement of product quality compared to the dry sample without power control.     

计算机数值模拟在食品干燥过程中的应用进展

由于食品干燥过程牵涉到相变、传热、传质、收缩等诸多变化,为预见食品真实的干燥情况,为生产应用提供参考,一种直观、快速、经济的研究方法——计算机数值模拟应运而出。从数值模拟常用的数学解析方法、模拟分析语言和软件三个方面介绍了计算机数值模拟的工具,并阐明了其一般流程,概述了该方法在食品干燥过程中的应用和发展前景。     

数值模拟方法在食品微波干燥机理与过程研究中的应用

在对微波干燥原理及特点进行分析的基础上,对农产品、食品的微波干燥机理与工艺研究中采用的数值模拟方法进行了归纳,重点分析说明了数值模拟方法在微波干燥过程中物料吸收微波能大小、水分扩散、物料收缩等方面的研究现状,并对该方法在微波干燥中的进一步研究进行了展望。     

Monte Carlo Simulation to establish the effect of pH, temperature and heating time on the final load of Bacillus stearothermophilus spores

Bacillus stearothermophilus heat treatment studies at different pH levels were carried out in order to predict the final microbiological load as affected by the temperature and pH treatment. For that, distribution functions were obtained for the initial number of microorganisms, the decimal reduction coefficient, the z _T and the z _pH parameters, and Monte Carlo Simulation was used as a tool to determine this microbial load after a heat treatment introducing in this way the variability and uncertainty. The mean value of the predictions made by Monte Carlo Simulation, when compared with independent data, rendered Accuracy Factors close to 1, values close to 1 indicated good predictions. Bias values were close to 1, but some values were below 1 and others above 1. The tornado graphs indicated that the main factor contributing to the final load after the heat treatment was the initial number of microorganisms followed by the temperature and pH.