量化栅栏技术和创建生长/非生长界面模型对食品保藏的意义

通过温度与水分活度(aw)和温度与pH对微生物生长速率联合作用的预测模型分析栅栏效应,由于食品中联合栅栏作用下,存在着微生物生长/非生长界面,探讨通过微生物生长动力模型来限定微生物生长/非生长界面的可能性。由于联合栅栏效应在不同的条件下,作用是不同的,有时是叠加的,有时是协同的。分析食品中栅栏作用就需要量化栅栏技术,量化栅栏技术可能为开发一种最低限度影响产品质量的新一代食品保藏技术提供一些新的思路。     

环境因子及接种量对单增李斯特菌生长/非生长界面的影响

单增李斯特菌在实验设定的环境条件下,通过10倍梯度稀释将菌悬液分别稀释到101、103、105、107CFU/m L四个接种水平,然后接种到TSB-YE肉汤中,培养基置于恒温培养箱中进行培养,然后通过肉眼观察培养基浊度并结合涂布TSA-YE平板对其生长/非生长情况进行判定,通过Logistics多项式回归模型对处理的数据建立了单增李斯特菌生长/非生长的界面模型。实验结果表明不同生长温度,p H和盐度的交互作用对单增李斯特菌的生长/非生长界面的影响较大,接种量的大小也会影响单增李斯特菌生长/非生长过渡区域的具体位置,但具体原因和作用机制还有待进一步研究。该研究为抑制单增李斯特菌生长的环境因子条件范围和实际产品中的污染严重程度提供一定的参考依据,对于有潜在单增李斯特菌污染的产品来说,这为加强产品的栅栏因子,优化工艺条件以提高其安全度也提供了重要的参考。     

pH、水分活度和NaCl对腐败希瓦氏菌生长/非生长界限及生长动力学参数的影响

以有氧贮藏养殖大黄鱼的特定腐败菌腐败希瓦氏菌为研究对象,通过p H、Aw、Na Cl和温度调控其生长/非生长状况,并分析其对生长动力学参数的影响,为有效抑制特定腐败菌增殖,延长产品货架期提供支持。将腐败希瓦氏菌接种于TSB中培养,测定不同调控因子下OD动态数据,确定生长/非生长界限,在生长范围内用修正的Gompertz方程进行拟合,对模型的拟合优度及调控因子对动力学参数的影响进行研究。实验表明,15℃时,p H≤5.0或Aw≤0.930或Na Cl≥7%时,腐败希瓦氏菌不生长;25℃时,p H≤5.0或Aw≤0.920或Na Cl≥12%时,腐败希瓦氏菌不生长;37℃时,腐败希瓦氏菌均不生长。腐败希瓦氏菌生长模型拟合显示,不同p H、Aw和Na Cl对其最大比生长速率和迟滞期有较大影响,模型的R2、偏差度和准确度接均接近1,均方根误差接近0,表明模型能较好地拟合不同条件下的腐败希瓦氏菌的生长。     

软烤贻贝中蜡样芽孢杆菌生长/非生长界面模型的构建

基于不同贮藏温度、pH值和水分活度,使用R软件建立软烤贻贝中蜡样芽孢杆菌的生长/非生长界面logistic回归模型。分析结果得到,R2-Nagelkerke=0.979,Hosmer-Lemeshow检验中χ~2=0.0189,P=1,表明logistic回归模型拟合度高。此外,贮藏温度、水分活度、pH值及其交互作用对蜡样芽孢杆菌的生长/非生长情况影响显著(P0.05),并且贮藏温度、水分活度或pH值越低,蜡样芽孢杆菌受到的抑制越强。随着温度、水分活度或pH值的升高,它们对蜡样芽孢杆菌生长概率的主要影响因素地位被其余两个因素的交互作用所取代,而且交互作用中两因素的影响中一个增强,另一个减弱。建立的生长/非生长界面模型对实际生产有重要的指导意义,可以量化栅栏因子,并结合其交互效应,保证微生物的安全,及产品本身的营养和感官品质。     

耐药性副溶血性弧菌生长异质性比较研究

目的在不同温度(10、20、30、37℃)下,比较17株不同耐药性、耐药表型及基因型不一致的副溶血性弧菌在碱性蛋白胨水(3%NaCl)中最大比生长速率及生长快慢之间的差异。方法应用Bioscreen C全自动微生物生长曲线分析仪测定副溶血性弧菌的最大比生长速率,构建不同温度下生长曲线,采用修正后的Gompertz模型进行拟合得出生长动力学参数。结果不同耐药性副溶血性弧菌μmax存在一定差异,差异随着温度的降低而增加,抗生素耐药种类越多,其μmax越大;同一温度下,耐药表型与基因型不一致的副溶血性弧菌其生长参数存在一定的差异,但是菌株的生长参数的差异与菌株耐药表型与基因型不一致之间没有明显的对应关系。结论菌株之间的微生物生长动力学参数的异质性将会对风险评估的准确性带来影响,此结果可为多重耐药副溶血性弧菌风险评估提供基础数据支持。     

骨肉相连中腐败菌动力学生长参数对货架期的影响

为了研究调理鸡肉制品骨肉相连中腐败菌动力学参数对货架期的影响,将骨肉相连托盘包装在5℃、10℃、15℃、20℃、25℃贮藏,应用修正的Gompertz方程描述假单胞菌和乳酸菌不同温度下的生长动力学参数,研究动力学参数与感官货架期的相关性;结果表明,随着温度的升高,假单胞菌和乳酸菌的生长速率逐渐增大,迟滞期逐渐缩短。在10℃、15℃、20℃、25℃下,假单胞菌和乳酸菌的迟滞期分别为0.93、0.28、1.87、0.23 h;33.05、6.38、1.69、4.16 h;5℃假单胞菌和乳酸菌的迟滞期较长,分别为28.98、89.17 h。乳酸菌和假单胞菌的生长与样品品质相关性均较好,动力学生长参数迟滞期λ(h)与货架期相关性最好,相关系数分别为0.929、0.987。因此,为了延长骨肉相连货架期,在其加工贮藏及流通过程中通过严格控制环境温度,可以有效延长微生物迟滞期,进而抑制微生物生长。     

冷藏三文鱼片微生物生长动力学模型适用性分析及货架期模型的建立

为研究不同冷藏温度(0,4,10℃)下三文鱼片中菌落总数、明亮发光杆菌、乳酸菌、假单胞菌以及产H2S细菌的生长情况,用不同的微生物生长动力学模型对其微生物生长动态进行非线性拟合,探究动力学模型对冷藏三文鱼片微生物生长的适用性,建立其剩余货架期模型。分别以一级化学反应动力学模型、修正的Gompetz模型、Baranyi and Roberts模型作为一级微生物生长动力学模型,描述微生物在恒定温度下随时间的生长规律;分别以Arrhenius方程和Belehradek方程(平方根模型)为二级模型,描述贮藏温度对微生物生长曲线的最大比生长速率(μmax)及延滞时间(λ)的影响。结果显示:Baranyi and Roberts模型方程能更好地描述冷藏三文鱼片中微生物的生长动态,拟合效果优于一级化学反应动力学模型和修正的Gompertz模型。Baranyi and Roberts模型方程所得参数用Belehradek方程拟合,结果发现冷藏三文鱼片微生物生长的最大比生长速率和延滞时间与贮藏温度呈良好的线性关系。由Belehradek方程建立的冷藏三文鱼片剩余货架期模型相对误差在(±18.90%)内,比Arrhenius方程建立的货架期预测模型预测更准确,可很好地描述菌落总数、假单胞菌、产H2S细菌及明亮发光杆菌随贮藏时间和温度的变化规律,预测冷藏三文鱼片的货架期。     

软烤贻贝中蜡样芽孢杆菌生长/非生长界面模型建立与评价

建立软烤贻贝中蜡样芽孢杆菌标准菌株(ATCC49064与DSMZ 4312)在不同贮藏温度(T)、pH、水分活度(Aw)下生长/非生长界面模型,对其拟合情况和来自软烤贻贝蜡样芽孢杆菌(YB001)的验证情况进行分析和评价,并与已建立的脑心浸出液肉汤(BHI)中蜡样芽孢杆菌生长/非生长界面模型进行比较。所建模型总方程为Lopit(P)=-208.457-2.167·T+35.304·pH+705.573·Bw+1.117·T·pH-7.072·T·Bw-174.946·pH·Bw,其中R2-Nagelkerke=0.979和χ~2=0.019(df=8,P=1)显示拟合度较高,而且其预测一致率明显高于BHI培养基中建立的模型,表明该模型在预测软烤贻贝中蜡样芽孢杆菌的生长/非生长情况有很高的精确度和很好的适用性。此外,贮藏温度、水分活度、pH及其交互作用显著影响蜡样芽孢杆菌的生长/非生长(P0.05)。因此可以通过所建生长/非生长界面模型量化温度、水分活度、pH值等栅栏因子并结合其交互效应来确保软烤贻贝的高品质与安全性。     

紫外线处理鲜切辣椒的微生物二级模型建立

通过研究紫外线处理不同时间对鲜切辣椒中菌落总数的影响,做出不同贮藏温度条件下微生物的生长曲线并建立预测微生物一级和二级模型。结果表明,紫外线处理15 min能很好的控制微生物的生长;试验中所建立的Gompertz模型能很好地拟合在不同贮藏温度下鲜切辣椒中菌落总数的变化;建立的微生物预测模型能有效预测不同贮藏时间和贮藏温度下鲜切辣椒中的菌落总数,从而为评估鲜切辣椒的微生物安全性,提供一个方便有效的方法。     

草鱼肉酸化条件优化及其对微生物生长的影响

栅栏技术是近年来成功应用于鲜肉防腐保鲜的新技术,其中酸化(pH)是一个重要的栅栏因子。在栅栏技术理论的基础上,通过单因素试验和正交试验研究缓冲液类型、pH值、浓度和浸泡时间等酸化处理条件对草鱼肉贮藏过程中微生物生长的影响。结果表明,草鱼肉最佳酸化条件:以浓度为0.4 mol/L,pH为3.0的乳酸/乳酸钠缓冲液浸泡6min,该条件下,鱼肉低温贮藏6d时微生物菌落平均总数为5.85×104 CFU/g,远低于草鱼二级品微生物指标要求。     

Effect of chemicals on the microbial evolution in foods

In contrast with most chemical hazardous compounds, the concentration of food pathogens changes during processing, storage, and meal preparation, making it difficult to estimate the number of microorganisms or the concentration of their toxins at the moment of ingestion by the consumer. These changes are attributed to microbial proliferation, survival, and/or inactivation and must be considered when exposure to a microbial hazard is assessed. The number of microorganisms can also change as a result of physical removal, mixing of food ingredients, partitioning of a food product, or cross-contamination (M. J. Nauta. 2002. Int. J. Food Microbiol. 73:297-304). Predictive microbiology, i.e., relating these microbial evolutionary patterns to environmental conditions, can therefore be considered a useful tool for microbial risk assessment, especially in the exposure assessment step. During the early development of the field (late 1980s and early 1990s), almost all research was focused on the modeling of microbial growth over time and the influence of temperature on this growth. Later, modeling of the influence of other intrinsic and extrinsic parameters garnered attention. Recently, more attention has been given to modeling of the effects of chemicals on microbial inactivation and survival. This article is an overview of different applied strategies for modeling the effect of chemical compounds on microbial populations. Various approaches for modeling chemical growth inhibition, the growth-no growth interface, and microbial inactivation by chemicals are reviewed.     

Kinetic Modeling of Food Quality: A Critical Review

ABSTRACT: This article discusses the possibilities to study relevant quality aspects of food, such as color, nutrient content, and safety, in a quantitative way via mathematical models. These quality parameters are governed by chemical, biochemical, microbial, and physical changes. It is argued that the modeling of such quality aspects is in fact kinetic modeling. Therefore, attention is paid to chemical kinetics, and its possibilities and limitations are discussed when applied to changes occurring in foods. The discussion is illustrated with examples from the literature. A major difficulty is that principles from chemical kinetics are strictly speaking only valid for simple elementary reactions, and foods are all but simple. Interactions in the food matrix and variability are 2 complicating factors. It is discussed how this difficulty can be tackled, and research priorities are suggested to come to better models in food science, and thereby to a better control of food quality.     

真空包装盐水鹅在不同温度条件下的贮藏特性及其货架期预测

为探明真空包装盐水鹅在不同贮藏温度条件条件下的贮藏特性和货架期。通过分析贮藏在4、25℃和30℃真空包装盐水鹅的感官品质、pH值、aw、挥发性盐基氮(total volatile basic nitrogen,TVB-N)、TBARS、菌落总数等指标的动态变化及其相关性,并结合回归方程预测货架期。结果表明:在不同贮藏温度、时间条件条件下,各项指标变化差异显著;贮藏温度与pH值、TVB-N、TBARS、菌落总数、感官指标均呈显著的相关性;通过回归方程得到其货架期分别为398d(4℃)、83d(25℃)和20d(30℃),经验证预测贮藏期与实际贮藏期较为相符。     

Quantifying the hurdle concept by modelling the bacterial growth/no growth interface

The hurdle concept described eloquently over many years by Professor Leistner and his colleagues draws attention to the interaction of factors that affect microbial behaviour in foods. Under some circumstances these effects are additive. Under others the implication is that synergistic interactions lead to a combined effect of greater magnitude than the sum of constraints applied individually. Predictive modelling studies on the combined effects of temperature and water activity and temperature and pH suggest that the effect of these combinations on growth rate is independent. Where the effect of the two factors is interactive rather than independent is at the point where growth ceases--the growth/no growth interface. An interesting and consistent observation is that a very sharp cut off occurs between conditions permitting growth and those preventing growth, allowing those combinations of factors to be defined precisely and modelled. Growth/no growth interface models quantify the effects of various hurdles on the probability of growth and define combinations at which the growth rate is zero or the lag time infinite. Increasing the stringency of one or more hurdles at the interface by only a small amount will significantly decrease the probability of an organism growing. Understanding physiological processes occurring near the growth/no growth interface and changes induced by moving from one side of the interface to the other may well provide insights that can be exploited in a new generation of food preservation techniques with minimal impact on product quality.     

A quasi-chemical model for the growth and death of microorganisms in foods by non-thermal and high-pressure processing

Predictive microbial models generally rely on the growth of bacteria in laboratory broth to approximate the microbial growth kinetics expected to take place in actual foods under identical environmental conditions. Sigmoidal functions such as the Gompertz or logistics equation accurately model the typical microbial growth curve from the lag to the stationary phase and provide the mathematical basis for estimating parameters such as the maximum growth rate (MGR). Stationary phase data can begin to show a decline and make it difficult to discern which data to include in the analysis of the growth curve, a factor that influences the calculated values of the growth parameters. In contradistinction, the quasi-chemical kinetics model provides additional capabilities in microbial modelling and fits growth-death kinetics (all four phases of the microbial lifecycle continuously) for a general set of microorganisms in a variety of actual food substrates. The quasi-chemical model is differential equations (ODEs) that derives from a hypothetical four-step chemical mechanism involving an antagonistic metabolite (quorum sensing) and successfully fits the kinetics of pathogens (Staphylococcus aureus, Escherichia coli and Listeria monocytogenes) in various foods (bread, turkey meat, ham and cheese) as functions of different hurdles (a_w, pH, temperature and anti-microbial lactate). The calculated value of the MGR depends on whether growth-death data or only growth data are used in the fitting procedure. The quasi-chemical kinetics model is also exploited for use with the novel food processing technology of high-pressure processing. The high-pressure inactivation kinetics of E. coli are explored in a model food system over the pressure (P) range of 207–345 MPa (30,000–50,000 psi) and the temperature (T) range of 30–50 °C. In relatively low combinations of P and T, the inactivation curves are non-linear and exhibit a shoulder prior to a more rapid rate of microbial destruction. In the higher P, T regime, the inactivation plots tend to be linear. In all cases, the quasi-chemical model successfully fit the linear and curvi-linear inactivation plots for E. coli in model food systems. The experimental data and the quasi-chemical mathematical model described herein are candidates for inclusion in ComBase, the developing database that combines data and models from the USDA Pathogen Modeling Program and the UK Food MicroModel.     

COMPUTER SIMULATION of MICROBIAL GROWTH DURING FREEZING and FROZEN FOOD STORAGE

A computer simulation model was developed to predict the time for temperature equilibration as well as microbial growth within a food product during freezing and the equilibration to frozen storage conditions. Theoretical results indicate that freezing medium temperature, surface heat transfer coefficient and product size influence the equilibration time significantly. Storage conditions influenced the equilibration time during storage and significantly influenced the growth of microorganisms. Microbial growth is a function of the freezing time. Slow freezing of a food product from a high initial temperature and stored at a relatively high temperature can provide conditions for microbial growth as compared to very rapid freezing processes. the model is a useful tool for approximate indications of effects of freezing conditions on microbial growth within a food product.     

Monitoring and modelling of headspace-gas concentration changes for shelf life control of a glass packaged perishable food

Gas concentrations in the headspace of hermetically sealed glass packages of seasoned soybean sprouts were monitored and related to their microbial quality, i.e., the change in aerobic bacterial count, in order to examine the potential for using package gas changes as a primary quality index for shelf life control. Aerobic bacterial count, CO₂ and O₂ concentrations were measured from packages stored at four different temperatures: 0, 5, 10 and 15 °C. The CO₂ concentration increased and the O₂ concentration decreased with microbial growth. The microbial growth and CO₂ concentration change were described by a logistic function to yield kinetic parameters, and their temperature dependence was analysed by a square-root model. The kinetic parameters for microbial growth and CO₂ production differed in their magnitude and temperature dependence. The lag time observed for the increase in CO₂ concentration could be used as a shelf life index that corresponds to the time to reach a given microbial limit (here, 10⁷ CFU/g) under different temperature conditions, particularly under conditions of temperature abuse.     

Ripening and spoilage of sugar salted herring with and without nitrate.

The technological effect of nitrate and the possible role of this compound in the formation of volatile nitrosamines during ripening and spoilage of sugar salted herring have been examined. In total, 600 barrels of fish with various amounts of nitrate added to the curing salt were prepared and stored under commercial conditions. During storage (18 months) the organoleptic, microbiological and chemical changes were studied. Additionally, a number of model experiments were carried out to evaluate the effect of nitrate on microbial growth and chemical changes. It was found that addition of nitrate significantly influences the colour of the fish while texture and flavour are not affected. Based on both the practical experiments and the model studies it is concluded that nitrate per se does not inhibit any microbial growth. Growth of strict anaerobes does not occur until the substrate is depleted of TMAO, and nitrate is effective in delaying the most common type of spoilage by delaying the TMAO reduction.
Small amounts of nitrosodimethylamine—up to 2.9 ppb has been detected—was formed in the sugar salted herring, but the addition of nitrate to the curing salt had no influence.     

动力学模型预测板鸭货架寿命

研究不同温度下贮藏过程中板鸭的感官性质、细菌总数、酸价、水分含量、过氧化值及挥发性盐基氮(TVB-N值)随存放时间的变化规律,确定引起贮藏过程中板鸭品质下降的关键因子是板鸭中脂肪的氧化酸败,并建立了酸价、过氧化值与贮藏时间、贮藏温度之间的动力学模型,以预测板鸭在贮藏过程中的品质变化和货架期。并求出了酸价变化反应的Ea和K0分别为66.1kJ/mol和2.173×1010,过氧化物生成反应的Ea(活化能)和K0分别为103.96kJ/mol和1.016×1017。     

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.