预测食品微生物学概述及应用

为了解预测食品微生物学的基本内容,综述了预测微生物学在食品中的应用.预测食品微生物学通过数学模型来预测微生物在不同环境条件下生长或死亡的数据.预测模型的分类有多种方法,根据微生物生长或失活的情况将预测模型分为生长模型和失活/存活模型.预测微生物模型已经广泛应用于食品安全质量管理和生产工艺中.     

冷鲜猪肉中细菌总数Gompertz生长预测模型的建立

研究目的是建立细菌总数在冷鲜肉中的生长预测模型。冷鲜肉在不同温度下,即0、4、8、12、16、20、24℃的基础上获得细菌总数的生长数据,数据对数转换后进行分析。决定系数范围为0.9657到0.9932,结果发现随着温度的增加肉品中微生物的最大比生长速率增加,迟滞期降低。温度对细菌的生长存在显著的影响,二级模型中,最大比生长速率和迟滞期与温度的线性决定系数分别为0.9233和0.9543。通过验证实验我们发现预测模型的偏差因子和准确性因子分别为1.2695和1.3376。我们从实验中得到的数据可以预测时间和温度对微生物生长的影响。     

卤制鸭头中乳酸菌生长预测模型的建立与验证

为快速预测和监控卤制鸭头中微生物的生长,建立和验证卤制鸭头基质上5～25℃温度条件下乳酸菌的生长预测模型.结果表明:利用Gompertz模型建立不同温度下卤制鸭头中乳酸菌的一级模型,得到的乳酸菌一级生长预测模型的偏差因子和准确因子都在1左右;利用平方根模型描述温度与最大比生长速率和延滞期的关系,得到乳酸菌生长预测的二级模型,R2分别为0.9923和0.9031,模型的总体显著,初步说明生长预测模型能有效地预测5～25℃下卤制鸭头中乳酸菌的生长.     

基于神经网络的微生物生长预测模型

鉴于现有大多数预测模型都是经验型模型,含有过多没有生物解释的参数,提出一个基于神经网络的非经验型的微生物生长预测模型,并以李斯特菌为研究实例,利用其试验环境的温度、pH值和Aw值建立BP神经网络二级生长模型,在不同环境条件下拟合微生物的生长速率和倍增时间,结合微生物初始浓度对一级模型的时间与微生物生长情况进行预测,最后利用李斯特菌生长数据对模型进行仿真测试。试验结果证明,该模型可以对微生物生长的各个时期进行有效预测,相对于经验模型,该模型更加适用于微生物生长动力学预测,有效地解决了经验型模型的参数问题。     

基于不同样本容量构建原料乳中金黄色葡萄球菌的预测生长模型

为建立金黄色葡萄球菌在原料乳中的生长模型,比较不同样本容量条件下预测模型的适用性,测定10,15,20,25,30,37℃条件下牛奶中金黄色葡萄球菌的生长数据,拟合建立最大比生长速率与温度之间的预测模型一;结合Com Base数据库中收录的相似环境试验数据,建立预测模型二。根据主要评价参数R2、Af、Bf等,对所建模型进行内部和外部验证。内部验证结果显示模型能够较好地预测微生物生长状况,而在外部验证中模型二的Af值,Bf值均优于模型一。一个简单的预测生长模型能够很好地预测相似条件下的微生物生长状况,然而存在普适性不高的问题。一个适用性高的可靠微生物预测模型应建立在大样本容量基础上。     

冷鲜猪排骨假单胞菌生长预测模型的建立与验证

为快速预测和监控冷鲜猪肉中微生物的生长,建立和验证冷鲜排骨中0℃～20℃温度条件下假单胞菌的生长预测模型.结果表明:Gompertz方程能很好地描述不同温度下假单胞菌的生长,得到的假单胞菌一级生长预测模型,且其偏差因子和准确因子都在1左右;利用平方根模型描述温度与最大比生长速率和延滞期的关系,且呈现良好的线性关系,R2分别为0.9934和0.9263,从而得到假单胞菌生长预测的二级模型.初步说明生长预测模型能有效地预测0℃～20℃冷鲜猪排骨中假单胞菌的生长.     

预测微生物学在肉品工业中的应用

预测微生物学是建立于计算机基础上的对食品中微生物的生长、残存和死亡进行的数量化预测方法,具有简便、高效、预测等优点,现已广泛应用于食品安全质量管理和生产工艺之中.本文简述了预测微生物学的概念、常用方法、模型分类和验证、开发的软件及其特点;介绍了预测微生物学在肉品工业中的应用和存在问题,并对其未来发展趋势进行了展望.     

微生物预测模型研究及其在肉品工业中的应用

预测微生物的数学模型可以对食品中微生物的生长、残存和死亡进行数量化预测.简述了预测微生物的数学模型研究食品微生物行为的理由.介绍了微生物预测模型的研究概况及其在肉类工业中的应用情况.对目前存在的问题和未来的发展进行了分析和总结.     

托盘包装鲟鱼中腐败希瓦氏菌和总菌的生长动力学及货架期预测

为研究托盘包装鲟鱼中特定腐败菌腐败希瓦氏菌和总菌的生长规律,用不同的微生物生长模型进行拟合,以此为基础建立并评价了货架期预测模型。以修正的Gompertz方程为一级模型,平方根方程为二级模型,建立腐败希瓦氏菌和总菌在0~20℃的生长预测模型和货架期预测模型。进一步通过托盘包装鲟鱼片在8℃和波动温度下贮藏数据对模型进行验证,结果显示腐败希瓦氏菌生长预测模型的准确度Af为1.28、1.35,偏差度Bf为0.91、1.08,货架期预测相对误差为5.23%、3.83%;而总菌的生长预测模型的Af为1.45、1.30,Bf为0.92、0.96,货架期预测相对误差为-4.40%、2.02%。以上结果表明根据两类微生物生长动力学建立的货架期预测模型对0~20℃贮藏的托盘包装鲟鱼货架期预测效果好,具有一定的实用价值。     

运用Real-time quantification PCR方法建立副溶血性弧菌在即食虾中的生长预测模型

运用Real-time quantification PCR(real-time qPCR)方法建立副溶血性弧菌在即食虾中生长预测模型.首先构建质粒标准品,梯度稀释后建立标准曲线,然后用Real-time qPCR方法检测虾中副溶血性弧菌的数量,最后建立37℃下即食虾中副溶血性弧菌生长预测模型,并与传统涂布计数方法进行比较.结果表明,Real-time qPCR方法和传统计数方法均可建立Gmopertz模型、Logistic模型和Richards模型,模型拟合的相关系数R2均在0.9以上.基于Real-time qPCR方法省时省力、特异性好等优点,用Real-time qPCR方法建立微生物预测模型是未来预测微生物学领域的一种发展趋势.     

Modeling microbial competition in food: application to the behavior of Listeria monocytogenes and lactic acid flora in pork meat products

Competition between background microflora and microbial pathogens raises questions about the application of predictive microbiology in situ, i.e., in non-sterile naturally contaminated foods. In this article, we present a review of the models developed in predictive microbiology to describe interactions between microflora in foods, with a special focus on two approaches: one based on the Jameson effect (simultaneous deceleration of all microbial populations) and one based on the Lotka-Volterra competition model. As an illustration of the potential of these models, we propose various modeling examples in estimation and in prediction of microbial growth curves, all related to the behavior of Listeria monocytogenes with lactic acid bacteria in three pork meat products (fresh pork meat and two types of diced bacon).     

Shelf life of modified atmosphere packed cooked meat products: a predictive model

The effect of temperature, concentration of dissolved CO2 and water activity on the growth of Lactobacillus sake was investigated by developing predictive models for the lag phase and the maximum specific growth rate of this specific spoilage organism for gas-packed cooked meat products. Two types of predictive model were compared: an extended Ratkowsky model and a response surface model. In general, response surface models showed a slightly better correlation, but the response surface model for the maximum specific growth rate showed illogical predictions at low water activities. The concentration of dissolved CO2 proved to be a significant independent variable for the maximum specific growth rate as well as for the lag phase of L. sake. Synergistic actions on the shelf life-extending effect were noticed between temperature and dissolved CO2, as well as between water activity and dissolved CO2. The developed models were validated by comparison with the existing model of Kant-Muermans et al. (1997) and by means of experiments in gas-packed cooked meat products. Both developed models proved to be useful in the prediction of the microbial shelf life of gas-packed cooked meat products.     

Quantifying the robustness of a broth-based model for predicting Listeria monocytogenes growth in meat and poultry products

Given the importance of Listeria monocytogenes as a risk factor in meat and poultry products, there is a need to evaluate the relative robustness of predictive growth models applied to meat products. The U.S. Department of Agriculture-Agricultural Research Service Pathogen Modeling Program is a tool widely used by the food industry to estimate pathogen growth, survival, and inactivation in food. However, the robustness of the Pathogen Modeling Program broth-based L. monocytogenes growth model in meat and poultry application has not, to our knowledge, been specifically evaluated. In the present study, this model was evaluated against independent data in terms of predicted microbial counts and covered a range of conditions inside and outside the original model domain. The robustness index was calculated as the ratio of the standard error of prediction (root mean square error of the model against an independent data set not used to create the model) to the standard error of calibration (root mean square error of the model against the data set used to create the model). Inside the calibration domain of the Pathogen Modeling Program, the best robustness index for application to meat products was 0.37; the worst was 3.96. Outside the domain, the best robustness index was 0.40, and the worst was 1.22. Product type influenced the robustness index values (P < 0.01). In general, the results indicated that broth-based predictive models should be validated against independent data in the domain of interest; otherwise, significant predictive errors can occur.     

Microbiological predictive modeling and risk analysis based on the one-step kinetic integrated Wiener process

The actual growth-monitoring data of microbial hazards in food are characterized by uncertainty, accumulation, discreteness, and nonlinearity, and thus it is difficult to accurately predict and analyze food safety microbiological risks in real time. Hence, we propose an approach of microbiological predictive modeling and risk analysis based on the one-step kinetic integrated Wiener process (OS-WP). First, the microbial tertiary prediction model was directly constructed through one-step kinetic analysis. Then, the WP was integrated with a tertiary model for predictive modeling of the actual microbial stochastic growth. Second, an indicator, “remaining safety life” (RSL), was introduced to analyze the potential microbiological risk on the basis of the established prediction models. Finally, the maximum likelihood estimation was used obtaining the model parameters online, and for calculating the RSL value in real time. The OS-WP approach was applied to a case study of the mixed mildew hazard during wheat storage. For different datasets, the root mean square error (RMSE) of the microbiological predictive model was less than 1.5; the relative RMSE of the RSL prediction reached 6.77%; the running time was less than 0.6 s. The result showed that the proposed approach is effective and feasible in modeling the actual growth of microbial hazards in food and can achieve online risk analysis. It can provide valuable microbiological early warning information to risk-management and decision-making departments for ensuring food safety.     

肉制品预测微生物模型的研究进展

肉制品贮藏过程的预测微生物模型是预测微生物学的重要分支。简述了肉制品货架期与特定腐败菌的相关性,综述了预测微生物模型的发展过程及其最新研究进展,分析预测微生物模型实际应用于肉制品质量监测的可行性,介绍几种有关食品微生物的数据库和预测软件,讨论了预测微生物模型存在的问题,展望了预测微生物模型的发展方向和应用前景。     

低温肉制品微生物控制与预测模型应用研究进展

低温肉制品营养丰富,是我国肉制品未来的发展方向。本文介绍了低温肉制品中的主要微生物来源、种类以及控制措施;综述了低温肉制品中微生物预测的各级模型,分析了其优缺点;简述了预测模型在实际生产中的应用。     

预测微生物模型的评价方法

在预测微生物模型构建过程中,模型验证是必不可少的步骤,其可靠性和适用性得到合理评价后才具有应用价值。本文综述预测微生物模型的常用评价方法及对应的参数,着重归纳总结相关评价参数的基本概念及意义,并结合前期工作的论文数据阐述演示各常用评价参数的应用方法及评价依据。     

Predictive modeling of microbial single cells: A review

In practice, food products tend to be contaminated with food-borne pathogens at a low inoculum level. However, the huge potential risk cannot be ignored because microbes may initiate high-speed growth suitable conditions during the food chain, such as transportation or storage. Thus, it is important to perform predictive modeling of microbial single cells. Several key aspects of microbial single-cell modeling are covered in this review. First, based on previous studies, the techniques of microbial single-cell data acquisition and growth data collection are presented in detail. In addition, the sources of microbial single-cell variability are also summarized. Due to model microbial growth, traditional deterministic mathematical models have been developed. However, most models fail to make accurate predictions at low cell numbers or at the single-cell level due to high cell-to-cell heterogeneity. Stochastic models have been a subject of great interest; and these models take into consideration the variability in microbial single-cell behavior.     

Development of an integrated model for heat transfer and dynamic growth of Clostridium perfringens during the cooling of cooked boneless ham

Numerous small meat processors in the United States have difficulties complying with the stabilization performance standards for preventing growth of Clostridium perfringens by 1 log10 cycle during cooling of ready-to-eat (RTE) products. These standards were established by the Food Safety and Inspection Service (FSIS) of the US Department of Agriculture in 1999. In recent years, several attempts have been made to develop predictive models for growth of C. perfringens within the range of cooling temperatures included in the FSIS standards. Those studies mainly focused on microbiological aspects, using hypothesized cooling rates. Conversely, studies dealing with heat transfer models to predict cooling rates in meat products do not address microbial growth. Integration of heat transfer relationships with C. perfringens growth relationships during cooling of meat products has been very limited. Therefore, a computer simulation scheme was developed to analyze heat transfer phenomena and temperature-dependent C. perfringens growth during cooling of cooked boneless cured ham. The temperature history of ham was predicted using a finite element heat diffusion model. Validation of heat transfer predictions used experimental data collected in commercial meat-processing facilities. For C. perfringens growth, a dynamic model was developed using Baranyi's nonautonomous differential equation. The bacterium's growth model was integrated into the computer program using predicted temperature histories as input values. For cooling cooked hams from 66.6 degrees C to 4.4 degrees C using forced air, the maximum deviation between predicted and experimental core temperature data was 2.54 degrees C. Predicted C. perfringens growth curves obtained from dynamic modeling showed good agreement with validated results for three different cooling scenarios. Mean absolute values of relative errors were below 6%, and deviations between predicted and experimental cell counts were within 0.37 log10 CFU/g. For a cooling process which was in exact compliance with the FSIS stabilization performance standards, a mean net growth of 1.37 log10 CFU/g was predicted. This study introduced the combination of engineering modeling and microbiological modeling as a useful quantitative tool for general food safety applications, such as risk assessment and hazard analysis and critical control points (HACCP) plans.     

植物多酚对肉制品蛋白氧化的抑制机理及其延长货架期的应用

蛋白氧化和微生物污染是导致肉与肉制品在贮藏期间腐败变质的主要因素。植物多酚具有良好的抗氧化和抑菌活性,在肉与肉制品中添加植物多酚是防止其变质的有效方法之一。本文综述了植物多酚的种类及其对肉与肉制品蛋白质氧化和微生物污染的抑制机理;介绍了多酚以作为反应性物质清除剂、非自由基衍生物清除剂、过渡金属离子螯合剂和高铁肌红蛋白还原剂4 种方式抑制蛋白氧化,并通过与细菌细胞壁组分和细胞膜相互作用、防止和抑制生物膜形成、合成生物大分子及抑制细菌酶活性来延长肉与肉制品货架期;此外,还阐述了植物多酚在抑制肉制品氧化及微生物方面的应用,并对其研究前景进行了展望。