CN111127239A - Method for establishing staphylococcus aureus growth prediction model in spinach juice - Google Patents
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Abstract
The invention belongs to the technical field of food, and particularly relates to a method for establishing a growth prediction model of staphylococcus aureus with different initial concentrations in spinach juice. And fitting Gompertz models with different initial bacterium concentrations at 25 ℃ by matlab software to obtain corresponding model parameters, and making a secondary model according to the relevant parameters to obtain the relations between the inoculation concentration and the maximum specific growth rate and the lag phase respectively. The method has wide application prospect in the aspects of food quality management, risk early warning and risk assessment.
Description
Technical Field
The invention belongs to the technical field of food, and particularly relates to a method for establishing a growth prediction model of staphylococcus aureus with different initial concentrations in spinach juice.
Background
Staphylococcus aureus (s. aureus) is widely found in nature, such as air, water, dust, and human and animal excreta, and is one of the major pathogenic bacteria causing human and animal infections and food poisoning. Food poisoning caused by staphylococcal enterotoxins produced by it has also become a worldwide health problem. In recent years, food poisoning caused by staphylococcus aureus accounts for a large proportion of bacterial food poisoning, both in developed and developing countries, and has a tendency to increase year by year. According to the report of the American center for disease prevention and control, food poisoning caused by Staphylococcus aureus is located at the 2 nd position, second only to Escherichia coli. The enterotoxin produced by staphylococcus aureus is still active in the environment with higher temperature and lower pH, is a worldwide sanitary problem and can quickly cause food poisoning symptoms such as nausea, severe vomiting, diarrhea and the like.
The staphylococcus aureus does not have too strict requirements on the growth environment of the staphylococcus aureus, which indicates that the staphylococcus aureus has strong viability and can quickly grow and reproduce without special nutrition. The existence of the protein can be detected in the external environment such as water, food (such as dairy products, meat products or eggs), leftovers, smoked products, even biological excrement, skin mucosa and the like. Staphylococcus aureus can produce bacterial viruses, which are harmful to humans. Staphylococcus aureus also produces a pathogenic enterotoxin, an alkaline protein difficult to eliminate at high temperatures, classified as SEA, SEB, SEC, SED, SEE five sera. The five kinds of serum can cause the human body to generate rapid toxic reaction. In recent years, food poisoning events have frequently occurred. There are a few of these food poisoning events caused by staphylococcus aureus. Staphylococcus aureus has now become a critical microorganism in food poisoning that threatens the life, health and safety of consumers. A plurality of food poisoning events occur in 2015 years in China, which account for 53.7 percent of the national poisoning events all year round, wherein the poisoning rate caused by the toxicity of staphylococcus aureus is as high as the first four. The european food safety agency made a report showing that food poisoning by staphylococcus was as high as 386 in 2013 alone, accounting for 7.3% of the total number of european union epidemics, and a considerable portion was caused by staphylococcus aureus. It is estimated in the united states that more than 240000 food poisoning by staphylococcus aureus occurs each year and results in over 1000 hospitalizations. Can seriously result in toxic death of people. In conclusion, the research and control on staphylococcus aureus is very slow and can not be ignored.
The spinach is not consumed until various steps such as planting, processing, storing, transporting and selling are carried out, any step in the whole process is polluted by staphylococcus aureus, staphylococcal enterotoxin is generated, and potential harm is caused to the health condition of consumers, so that the food pollution caused by the spinach and the spinach enterotoxin causes wide attention of departments such as import and export inspection and health inspection in China. At present, the detection of staphylococcus aureus is listed as a food health legal detection project by countries in the world.
The prediction microbiology is a prediction method for rapidly evaluating the food safety by judging the dynamic changes of death, survival and increment of main pathogenic bacteria and putrefactive microorganisms in the food in the whole process through a computer and matched software thereof on the premise of not carrying out microorganism detection and analysis according to a characteristic detail information base of various food microorganisms under different processing, storage and circulation conditions. The growth model of microorganisms in food is a model describing the growth, survival and life state of microorganisms. According to the mathematical model classification, the growth model comprises a primary model, a secondary model and a tertiary model. The primary model is used to describe the growth of microorganisms under a specific condition with respect to time. The secondary model is used to describe how changes in a single environmental factor affect parameters in the primary model. And integrating the primary model and the secondary model into computer software to form a tertiary model.
Currently, several studies have evaluated the growth of staphylococcus aureus when stored at different temperatures in several food systems, but few have compared the growth of staphylococcus aureus at different inoculum concentrations.
Disclosure of Invention
The invention aims to overcome the technical problems and provides a method for establishing a growth prediction model of staphylococcus aureus with different initial concentrations in spinach juice, aiming at determining the growth condition of the staphylococcus aureus in the fresh spinach juice under different inoculation concentrations and establishing a corresponding primary model and a corresponding secondary model, thereby monitoring and predicting the quantity change of the staphylococcus aureus in the fresh spinach juice. According to the obtained conclusion, the suitable conditions in the actual production and transportation process and the limit of the corresponding environmental conditions can be determined, and an idea is provided for the management of the whole food supply chain. The harm of staphylococcus aureus to the food quality in the food circulation process is reduced, so that the food quality is improved; also provides a theoretical basis for inhibiting the growth of staphylococcus aureus in the fresh spinach juice.
The technical scheme for solving the technical problems is as follows:
the method for establishing the staphylococcus aureus growth prediction model in the spinach juice comprises the following steps: respectively at a concentration of 106，105，104，103，102The method comprises the steps of inoculating the CFU/ml staphylococcus aureus in spinach juice at 25 ℃, measuring the number of bacteria, carrying out nonlinear regression analysis on the number of bacteria by using Matlab software, establishing a Gompertz first-level model, and fitting and solving growth parameters (shown in the following table 1) according to the derivation of a function formula, wherein the parameters are respectively as follows: and establishing a secondary model according to the obtained parameters by using the LPD in the lag phase and the maximum specific growth rate U. Wherein the maximum specific growth rate U-ac/e (h)-1) Means a tangent value at an inflection point in a logarithmic phase, and a lag phase LPD ═ (b-1)/c (h) of microbial growth means an intersection value of a tangent line at the inflection point and an x axis.
Table 1 growth parameters of different concentrations of s.aureus suspensions obtained using Gompertz model at 25 ℃
The maximum growth rate U and the growth retardation LPD are both reduced along with the increase of the bacteria inoculation concentration.
Further, the general form of the Gompertz primary model is:
y＝a·exp[-exp(b-cx)]
with time t as abscissa, ln (N), respectivelyt/N0) AsA vertical coordinate;
wherein N is0Initial number of bacteria (CFU/ml), NtThe number of bacteria at time t.
Further, the second-level model describes the influence of the initial concentration of the bacterial liquid on the parameters of the first-level model, and comprises the following steps: 1) establishing a second-order square root model of the maximum specific growth rate under different initial concentrations;
2) and establishing a second-order square root model of the lag phase under different initial concentrations.
Further, the second-order square root model of the maximum specific growth rate at different initial concentrations is established, and the square root equation is as follows:
a is-0.2 to-0.1, b is 1 to 2;
preferably, a is-0.1381 and b is 1.451.
More specifically, the second-order square root model of the lag phase at different initial concentrations is established, and the square root equation is as follows:
a is-0.3 to-0.2, and b is 2 to 3;
preferably, a is-0.2711 and b is 2.164.
According to the two square root equations, the influence of different inoculum concentrations on the maximum growth rate U and the growth retardation LPD can be accurately reflected.
That is, C in the above two equations is the inoculation concentration, and the maximum growth rate U and the growth retardation LPD corresponding to different inoculation concentrations can be accurately calculated by substituting the inoculation concentration into the equations.
The invention has the beneficial effects that:
the invention successfully establishes the staphylococcus aureus growth prediction model in the spinach juice, and can predict the staphylococcus aureus growth in the spinach juiceGrowth in spinach juice. At 25 ℃, the maximum growth rate decreases with increasing initial concentration, i.e. at 25 ℃, the lower the initial concentration, the more rapidly the staphylococcus aureus grows; the growth lag phase lpd (h) decreased with increasing initial concentration. The lag phase decreases with increasing initial concentration at 25 ℃. Because the temperature is the same, the higher the initial bacterium concentration is, the faster the bacterium enters the logarithmic growth phase, and the shorter the lag phase is; then, establishing a second-order square root model according to the maximum specific growth rate obtained in the first-order model under different initial concentrations, describing the growth influence of the initial concentrations on staphylococcus aureus, and obtaining a second-order square root equation as follows:
wherein R is20.9214, the model is shown to be able to more accurately reflect the effect of different initial concentrations on growth rate. Establishing a second-order square root model according to the lag phase obtained in the first-order model under different initial concentrations, describing the influence of the initial concentrations on the growth of staphylococcus aureus, and obtaining a second-order square root equation as follows:
wherein R is20.8593, although the linearity degree is not as high as that of the maximum growth rate secondary model, the influence of the initial concentration of different inoculum in fresh spinach juice on the lag phase can be reflected well.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a graph (A)10 showing the growth curve of S.aureus in fresh spinach juice at different concentrations, obtained by the method of the present invention at 25 ℃ by using the Gompertz model2，(B)103，(C)104，(D)105，(E)106；
FIG. 2 is a maximum specific growth rate second-order square root model of S.aureus;
figure 3 is a lag phase two-stage square root model of s.
Detailed Description
Example 1:
1) the standard strain used was staphylococcus aureus ATCC 13565 strain. After incubation of single colonies obtained by streaking dilution in Tryptic Soy Broth (TSB), 1ml of TSB and 50% glycerol was added to the centrifuge tube. The culture was frozen and stored at-80 ℃ until use.
2) Streaking on a soybean casein agar (TSA) sterile plate in parallel, culturing at 37 ℃ for 24-48 h for activation, selecting a single colony, inoculating the single colony in a conical flask containing 100mL sterile pancreatic soybean broth (TSB), fully mixing, putting the conical flask into a 36 ℃ incubator, culturing for 16.5h until the initial colony is approximately 106CFU/mL, namely a raw bacterial liquid, and storing at 4 ℃ for later use.
3) The original bacterial liquid was centrifuged at 8000g for 20min at 4 ℃ to obtain cell particles. Cleaning with 95% physiological saline twice, and diluting with sterilized fresh juice of herba Spinaciae to 106，105，104，103，102CFU/ml, which were stored at 4 deg.C, 10 deg.C, 25 deg.C, respectively. And taking out the subpackaged test tubes at proper time intervals to measure the concentration of the staphylococcus aureus. 1ml of sample solution is taken and fully shaken in 9ml of normal saline, and then the sample solution is continuously diluted step by using the normal saline. 0.1ml of the diluted bacterial solution was applied to a TSA solid plate and placed in a 37 ℃ incubator for 24 hours to obtain the total number of staphylococci.
4) And carrying out nonlinear regression analysis by using Matlab software, and fitting growth curves at different temperatures and different inoculation concentrations by using a Gompertz model to establish a primary growth model. Wherein the general form of the Gompertz model is: y ═ a, exp [ -exp (b-cx)]At times t and ln (N), respectively(t)/N0) Are the abscissa and ordinate. N is a radical of0Is the initial number of bacteria (CFU/mL), NtAnd (3) analyzing the growth data of staphylococcus aureus in fresh spinach juice at different temperatures and different initial concentrations by utilizing Matlab, and solving 3 parameters (mum, lambda, A) for describing the characteristics of a microorganism growth curve according to the derivation of a functional formula, wherein the parameters are as follows: maximum specific growth rate μm-ac/e (h)-1) Means the tangent at the inflection point of the logarithmic phase, the lag phase lambda of the microbial growth(b-1)/c (h) is the intersection value of the tangent line at the intersection point and the x-axis, and a ═ ln (N)max/N0) Nmax refers to the number of microorganisms in stationary phase (CFU/mL).
The experimental results are shown in figure 1, and the growth curves of staphylococcus aureus with different concentrations in fresh spinach juice are fitted by a Gompertz model at 25 ℃. The lag phase λ (h) of microbial growth refers to the value of the intersection of the tangent at this inflection point with the x-axis. As can be seen from FIG. 1, the initial bacterial concentration was 102，103，104The higher the initial bacteria concentration is, the shorter the lag phase of the bacteria is, the faster the bacteria enters the logarithmic phase, and as the initial bacteria concentration increases, the curve tends to be gentle, and the lag phase time increases. Maximum specific growth rate μm (h)-1) Refers to the tangent at the inflection point of the log phase, and the growth rate of the log phase decreases with increasing bacterial concentration.
5) A Gompertz model of the growth of staphylococcus aureus in fresh spinach juice is fitted by Matlab software to obtain corresponding growth parameters of a maximum specific growth rate U (lgCFU/mL. h) and a growth lag phase LPD (h).
The experimental results are shown in table 1, and the maximum growth rate decreases with increasing initial concentration at 25 ℃; the lag phase decreased with increasing initial concentration at 25 ℃. Because the temperature is the same, the higher the initial bacterial concentration, the faster the bacteria enters the logarithmic growth phase, and the shorter the lag phase. The Gompertz model has good fitting effect, and the correlation coefficients are all above 0.99. The method has wide application prospect in the aspects of food quality management, risk early warning and risk assessment.
Table 1 growth parameters of different concentrations of s.aureus suspensions obtained using Gompertz model at 25 ℃
6) The first-level model can only describe the relation between the growth quantity change of the microorganisms and time and cannot describe the influence of the change of environmental factors on the growth of the microorganisms, so that the second-level square root model is established according to the maximum specific growth rate obtained in the first-level model under different initial concentrations(As shown in FIG. 2, FIG. 2 is based on Table 1
A profile fitted to the data relating to inoculum concentration) describing the effect of initial concentration on the growth of staphylococcus aureus. The temperature growth rate curve was fitted using a square root model. The square root equation is as follows:
in the formula: t is the temperature, TminB is a constant of the equation.
The results are shown in FIG. 2, from which it can be seen that the initial concentration is 102～106Within the range of (A) and (B),
a linear fit to the initial concentration is good. The second-order square root equation obtained by model fitting is,
wherein R is20.9214, the model is shown to be able to more accurately reflect the effect of different initial concentrations on growth rate.
7) According to the first-order model to obtain 102～106And establishing a lag phase square root model in the growth lag phase under different initial concentrations.
The experimental results are shown in fig. 3, and the equation is obtained by fitting:
wherein R is20.8593, although the linearity degree is not as high as that of the maximum growth rate secondary model, the influence of the initial concentration of different inoculum in fresh spinach juice on the lag phase can be reflected well.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications and equivalent variations of the above embodiment according to the present invention are within the scope of the present invention.
Claims (9)
1. The method for establishing the staphylococcus aureus growth prediction model in the spinach juice is characterized by comprising the following steps of: respectively at a concentration of 106，105，104，103，102Inoculating the CFU/ml staphylococcus aureus in 25 ℃ spinach juice, measuring the bacterial number, carrying out nonlinear regression analysis on the bacterial number by using Matlab software, establishing a Gompertz first-level model, and fitting and solving growth parameters according to the derivation of a function formula, wherein the parameters are respectively as follows: and establishing a secondary model according to the obtained parameters by using the LPD in the lag phase and the maximum specific growth rate U.
2. The method for establishing the staphylococcus aureus growth prediction model in the spinach juice as claimed in claim 1, wherein the general form of the Gompertz primary model is as follows:
y＝a·exp[-exp(b-cx)]
with time t as abscissa, ln (N), respectivelyt/N0) As a ordinate;
wherein N is0Initial number of bacteria (CFU/ml), NtThe number of bacteria at time t.
3. The method for establishing the staphylococcus aureus growth prediction model in the spinach juice as claimed in claim 1, wherein the secondary model describes the influence of the initial concentration of the bacterial liquid on the parameters of the primary model, and comprises: 1) establishing a second-order square root model of the maximum specific growth rate under different initial concentrations;
2) and establishing a second-order square root model of the lag phase under different initial concentrations.
4. The method for building the staphylococcus aureus growth prediction model in the spinach juice as claimed in claim 3, wherein the maximum specific growth rate second-order square root model is built at different initial concentrations, and the square root equation is as follows:
a is-0.2 to-0.1, and b is 1 to 2.
5. The method for building the staphylococcus aureus growth prediction model in the spinach juice as claimed in claim 4, wherein a is-0.1381C and b is 1.451.
6. The method for establishing the staphylococcus aureus growth prediction model in the spinach juice as claimed in claim 3, wherein the second-order square root model of the lag phase is established under different initial concentrations, and the square root equation is as follows:
a is-0.3 to-0.2, and b is 2 to 3.
7. The method for building the staphylococcus aureus growth prediction model in the spinach juice as claimed in claim 6, wherein a is-0.2711 and b is 2.164.
8. The method for establishing the staphylococcus aureus growth prediction model in the spinach juice as claimed in claim 1, wherein the maximum growth rate U and the growth retardation LPD are both reduced as the bacteria inoculation concentration of the bacteria increases.
9. The method for establishing the staphylococcus aureus growth prediction model in the spinach juice as claimed in claim 1, wherein the method can accurately reflect the influence of different inoculation concentrations on the maximum growth rate U and the growth lag phase LPD.
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