CN111324935A - PH value-based quantitative risk identification method for staphylococcus aureus in milk - Google Patents
PH value-based quantitative risk identification method for staphylococcus aureus in milk Download PDFInfo
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- CN111324935A CN111324935A CN201811437506.3A CN201811437506A CN111324935A CN 111324935 A CN111324935 A CN 111324935A CN 201811437506 A CN201811437506 A CN 201811437506A CN 111324935 A CN111324935 A CN 111324935A
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Abstract
The invention relates to a quantitative risk identification method of staphylococcus aureus in milk based on a PH value, which comprises the following steps: step S1: loading a data source, and dividing the data into a plurality of groups according to the PH value of sample milk in the data source; step S2: for each group, fitting according to the temperature of each sample and the concentration data of staphylococcus aureus to respectively obtain the functional relation between the growth rate and the temperature of staphylococcus aureus in milk with different pH values and the functional relation between the growth stagnation period and the temperature; step S3: and respectively calculating the growth conditions of the staphylococcus aureus in the heat treatment consumption path and the direct consumption path of the processing plant based on the obtained functional relationship, and outputting the optimal consumption path of the milk with each pH value. Compared with the prior art, the invention recommends the consumption approach of the milk according to different pH values, and has the effect of improving food safety.
Description
Technical Field
The invention relates to the field of food safety, in particular to a quantitative risk identification method of staphylococcus aureus in milk based on a PH value.
Background
The food-borne pathogenic bacteria are one of the main factors influencing global food safety and endangering public human health, and the risk assessment of the food-borne pathogenic bacteria is an important means for guaranteeing food safety and promoting food trade. Quantitative Microbial Risk Assessment (QMRA) can be used to assess microbial risk and factors controlling food-borne hazards. It can help to develop more effective Hazard Analysis and Critical Control Point (HACCP) programs and Good Manufacturing Practices (GMP) programs to protect consumers from pathogens and toxins. Staphylococcus aureus is a common food-borne pathogenic bacterium in nature and widely exists in various foods including milk and dairy products.
Milk can provide a plurality of important nutrients for human bodies and becomes an important component in the dietary structure of people. Meanwhile, milk is a natural culture medium of a plurality of microorganisms, is extremely easy to be polluted by staphylococcus aureus in production and transportation, and food-borne diseases related to milk and dairy products occur every year, and staphylococcus aureus is only one of pathogenic bacteria which endanger the safety of the dairy products. Therefore, quantitative risk assessment of the consumption process of milk is significant.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a quantitative risk identification method for staphylococcus aureus in milk based on pH value.
The purpose of the invention can be realized by the following technical scheme:
a quantitative risk identification method of staphylococcus aureus in milk based on pH value comprises the following steps:
step S1: loading a data source, and dividing the data into a plurality of groups according to the PH value of sample milk in the data source;
step S2: for each group, fitting according to the temperature of each sample and the concentration data of staphylococcus aureus to respectively obtain the functional relation between the growth rate and the temperature of staphylococcus aureus in milk with different pH values and the functional relation between the growth stagnation period and the temperature;
step S3: and respectively calculating the growth conditions of the staphylococcus aureus in the heat treatment consumption path and the direct consumption path of the processing plant based on the obtained functional relationship, and outputting the optimal consumption path of the milk with each pH value.
The data source in the step S1 is experimental data.
The data source in step S1 is public data, and the process of loading the data source specifically includes:
step S11: searching multiple published documents about staphylococcus aureus in milk by using a document search platform;
step S12: the experimental samples in the literature were extracted as samples in the data source.
The function relationship of the growth rate and the temperature is as follows:
μ＝(a·T-b)2
wherein: μ is the growth rate, a is the growth slope parameter, T is the temperature, b is the growth translation parameter.
The function relationship between the growth stagnation period and the temperature is as follows:
λ＝1/(c·T-d)2
wherein: λ is the growth lag phase, c is the lag slope parameter, T is the temperature, and d is the lag translation parameter.
The step S3 specifically includes:
step S31: respectively acquiring the temperature and time of each link of a heat treatment consumption path and a direct consumption path of a processing plant;
step S32: respectively calculating the growth conditions of staphylococcus aureus in a heat treatment consumption way and a direct consumption way of a processing plant according to the corresponding functional relation of the milk with different pH values;
step S33: the consumption route with the expected smaller number of staphylococcus aureus is taken as the preferred consumption route for milk with different pH values.
Compared with the prior art, the invention has the following beneficial effects:
1) the milk is recommended according to different pH values, and the food safety is improved.
2) The data source is transparent, concise and convenient to disclose, and has universality for scientific research.
3) The linear formula fitted according to the data in the Combase database is used for the growth model of the staphylococcus aureus for the first time, and the method has innovation.
4) The risk assessment method has innovation for the risk assessment of staphylococcus aureus in milk in the consumption process from farms to dining tables in China under different pH values for the first time.
Drawings
FIG. 1 is a schematic flow chart of the main steps of the method of the present invention;
FIG. 2 is a schematic flow diagram of a consumption pathway.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Microbial predictive models are commonly applied in the microbial risk profiling stage of QMRA to assess the impact of a particular hazard on a particular population. Predictive microbiology is a discipline that uses mathematical models to quantitatively describe the dynamics of growth, survival and death of microorganisms under specific environmental conditions. The microorganism prediction model is divided into 3 levels of a primary model, a secondary model and a tertiary model. Wherein, the primary model is used for describing the functional relationship between the growth and inactivation of the microorganism and time under certain growth conditions; the secondary model expresses a functional relationship between the parameters obtained by the primary model and the environmental factors; the tertiary model is mainly a computer software program established on the basis of the primary and secondary models and used for predicting the growth or inactivation condition of the same microorganism under the same or different environmental conditions. The primary models most applied to domestic QMRA comprise a Gompertz model and a modified model thereof and a Baranyi model, and are also the most common models in the field of microorganism prediction, but the model does not consider the influence of a lag phase on the growth of microorganisms. In addition, the square root model in the second-level model is one of the most commonly used second-level models because the square root model is convenient to use and has single parameter, and can well predict the influence of environmental factors such as temperature on the maximum growth rate and the lag phase of microorganisms. In the three-level model, the forecast software developed in the world is more than ten, wherein the biggest forecast microbiology information database 'Com Base' established by pathogen model program PMP developed by the United states department of agriculture and scientists in America and England is the most famous.
A method for identifying quantitative risk of staphylococcus aureus in milk based on pH value is shown in figure 1 and comprises the following steps:
step S1: loading a data source, and dividing the data into a plurality of groups according to the pH value of sample milk in the data source, wherein the data source can be experimental data or obtained from public data, and the process of loading the data source specifically comprises the following steps:
step S11: searching a plurality of documents in public data by using a database;
step S12: the experimental samples in the literature were extracted as samples in the data source.
Data relating to the prevalence and concentration of staphylococcus aureus in chinese starting milk was collected using a literature review and used to calculate initial contamination levels. The predictive microbiology information database (ComBase) model was then used to predict the growth rate and lag phase of Staphylococcus aureus in milk at different pH values, different storage temperatures. Two major consumption pathways are considered, the processing plant thermal treatment consumption pathway and the direct consumption pathway for risk exposure assessment. Finally the cumulative probability of staphylococcus aureus in the consumption pathway was calculated using monte carlo simulations performed by the @ Risk software.
The total number of samples is 4062 parts, the positive samples (detected staphylococcus aureus) are 1137 parts, and the detection rate is 27.99 percent by using monitoring data in 21 documents retrieved by the Notification network
Beta distribution was used to describe the prevalence of staphylococcus aureus in raw milk and the actual contamination level was calculated using the Jarvis formula: d ═ - (2.303/V) Log (Z/N)
Step S2: for each group, fitting according to the temperature of each sample and the concentration data of staphylococcus aureus to respectively obtain the functional relation between the growth rate and the temperature of staphylococcus aureus in milk with different pH values and the functional relation between the growth stagnation period and the temperature;
the growth rate as a function of temperature is:
μ＝(a·T-b)2
wherein: μ is the growth rate, a is the growth slope parameter, T is the temperature, b is the growth translation parameter.
The function relationship of growth lag phase and temperature is as follows:
λ＝1/(c·T-d)2
wherein: λ is the growth lag phase, c is the lag slope parameter, T is the temperature, and d is the lag translation parameter.
Download from Combase database of 0.5 physiological status, AWThe maximum growth rate data of staphylococcus aureus in milk at 10 ℃, 15 ℃, 20 ℃, 25 ℃ and 30 ℃ respectively is 0.98, the growth rate of staphylococcus aureus in milk at different pH values is fitted with linear functions of temperature and growth stagnation period and temperature, and the specific fitting results are shown in table 1.
TABLE 1
Step S3: based on the obtained functional relationship, respectively calculating the growth conditions of staphylococcus aureus in a heat treatment consumption path and a direct consumption path of a processing plant shown in fig. 2, and outputting the optimal consumption path of milk with various pH values, specifically comprising:
step S31: respectively acquiring the temperature and time of each link of a heat treatment consumption path and a direct consumption path of a processing plant;
step S32: aiming at milk with different pH values, respectively calculating the growth conditions of staphylococcus aureus in a heat treatment consumption way and a direct consumption way of a processing plant according to corresponding functional relations, and specifically calculating the accumulation probability of staphylococcus aureus in two consumption ways of milk with pH value of 6.4 by adopting Monte Carlo simulation iteration 10000 times through @ Risk software;
step S33: the consumption route with the expected smaller number of staphylococcus aureus is taken as the preferred consumption route for milk with different pH values.
Claims (6)
1. A quantitative risk identification method of staphylococcus aureus in milk based on pH value is characterized by comprising the following steps:
step S1: loading a data source, and dividing the data into a plurality of groups according to the PH value of sample milk in the data source;
step S2: for each group, fitting according to the temperature of each sample and the concentration data of staphylococcus aureus to respectively obtain the functional relation between the growth rate and the temperature of staphylococcus aureus in milk with different pH values and the functional relation between the growth stagnation period and the temperature;
step S3: and respectively calculating the growth conditions of the staphylococcus aureus in the heat treatment consumption path and the direct consumption path of the processing plant based on the obtained functional relationship, and outputting the optimal consumption path of the milk with each pH value.
2. The method for identifying the quantitative risk of staphylococcus aureus in milk based on pH value of claim 1, wherein the data source in the step S1 is experimental data.
3. The method for identifying the quantitative risk of staphylococcus aureus in milk based on PH value of claim 1, wherein the data source in the step S1 is public data, and the process of loading the data source specifically comprises:
step S11: searching multiple published documents about staphylococcus aureus in milk by using a document search platform;
step S12: the experimental samples in the literature were extracted as samples in the data source.
4. The method for quantitative risk identification of staphylococcus aureus in milk based on PH value of claim 1, wherein the functional relationship between growth rate and temperature is:
μ＝(a·T-b)2
wherein: μ is the growth rate, a is the growth slope parameter, T is the temperature, b is the long-term parameter.
5. The method for quantitative risk identification of staphylococcus aureus in milk based on PH value of claim 1, wherein the functional relationship between growth lag phase and temperature is as follows:
λ＝1/(c·T-d)2
wherein: lambda is the growth lag phase, c is the lag slope parameter, T is the temperature, and d is the lag phase parameter.
6. The method for identifying the quantitative risk of staphylococcus aureus in milk based on pH value according to claim 1, wherein the step S3 specifically comprises:
step S31: respectively acquiring the temperature and time of each link of a heat treatment consumption path and a direct consumption path of a processing plant;
step S32: respectively calculating the growth conditions of staphylococcus aureus in a heat treatment consumption way and a direct consumption way of a processing plant according to the corresponding functional relation of the milk with different pH values;
step S33: the consumption route with the expected smaller number of staphylococcus aureus is taken as the preferred consumption route for milk with different pH values.
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| CN112342265A (en) * | 2020-11-10 | 2021-02-09 | 南京工业大学 | Method for using growth model based on salmonella in beef under variable temperature condition |
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| CN112342265A (en) * | 2020-11-10 | 2021-02-09 | 南京工业大学 | Method for using growth model based on salmonella in beef under variable temperature condition |
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