CN110718266B - Establishment and application method of prediction model for evaluating safety of lactobacillus fermented food - Google Patents
Establishment and application method of prediction model for evaluating safety of lactobacillus fermented food Download PDFInfo
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Abstract
The invention relates to the technical field of food microorganism safety detection and control, in particular to a method for establishing and applying a prediction model for evaluating the safety of lactobacillus fermented foods. The prediction model can be applied to fermented foods taking lactobacillus as a main starter. According to the initial concentration of the added starter lactobacillus, the concentration of the food-borne pathogenic bacteria which can be infected initially, the fermentation temperature and the fermentation time, the model can predict the growth of the starter lactobacillus and the growth and inactivation dynamic process of each pathogenic bacteria in the whole fermentation process, so that the safety of food fermentation under certain fermentation conditions is determined, and powerful technical support is provided for the microbial safety control of fermented foods in the fermentation process. Meanwhile, the model can be used for optimizing fermentation parameters to ensure that the fermented food reaches the optimal safety standard.
Description
Technical Field
The invention belongs to the technical field of food microorganism safety detection and control, and particularly relates to a method for establishing and applying a prediction model for evaluating the safety of lactobacillus fermented foods.
Background
In recent years, with the rapid development of economy, the change of food consumption structure in China is aggravated, the demand for finished products of the food industry is rapidly increased, the industrialization level of fermented foods in China is also improved year by year, and the fermented foods such as yoghurt, fermented bean curd, fermented soybean, fermented (cured) sausage and the like have taken an important place in the food market in China.
Due to the metabolism of beneficial microorganisms, organic acid, ethanol and other substances for inhibiting the growth of pathogenic bacteria can be generated in the fermented food, and meanwhile, the pH value can be reduced due to the generation of the organic acid, so that the fermented food is acidic, and the growth of mixed bacteria is further inhibited. Thus, in most cases, fermented foods are considered to have high edible safety. However, fermented foods are not necessarily safe and reliable. The great growth of starter strains in fermented food can cause the sensory characteristics of taste, color, texture and the like of the food to be greatly different from those of the original food, and whether spoilage or pathogenic bacteria grow can not be judged on sense organs. Moreover, many fermented foods generally do not require consumption by means of heating or refrigeration. From this point of view, the safety risk of fermented foods is greater. Recent European and American reports of infectious pathogenic events of multiple food-borne pathogens (such as Listeria monocytogenes or Escherichia coli O157: H7) in fermented sausage or cheese also knock off the alarm for the safety problems of fermented foods.
At present, many traditional fermented foods in China, such as fermented bean curd, fermented soya beans, preserved meat and the like, have relatively lack of methods for evaluating microbial safety. Moreover, since the conventional microorganism detection process is complicated, time-consuming and costly, and fermentation itself is a complex dynamic process, it is a tedious, time-consuming and labor-consuming task to perform a more complete microorganism safety evaluation on the fermented food. With the popularization of food industrialization and expansion of new food markets, more and more food enterprises start to improve traditional fermented foods, so that the fermented foods develop to more diversification, such as low-salt fermented foods, fermented foods with shortened fermentation time, and the like. The need for more accurate and more convenient methods for microbial safety assessment of these new fermented foods is urgent.
Disclosure of Invention
The invention aims to provide a method for establishing and applying a prediction model for evaluating the safety of lactobacillus fermented foods. The prediction model can be applied to fermented foods taking lactobacillus as a main starter. According to the initial concentration of the added starter lactobacillus, the concentration of the food-borne pathogenic bacteria which can be infected initially, the fermentation temperature and the fermentation time, the model can predict the growth of the starter lactobacillus and the growth and inactivation dynamic process of each pathogenic bacteria in the whole fermentation process, so that the safety of food fermentation under certain fermentation conditions is determined, and powerful technical support is provided for the microbial safety control of fermented foods in the fermentation process. Meanwhile, the model can be used for optimizing fermentation parameters to ensure that the fermented food reaches the optimal safety standard.
The invention firstly provides a method for establishing a prediction model for evaluating the safety of lactobacillus fermented foods, which comprises the following steps:
1. the maximum concentration N of the starter lactobacillus and each food-borne pathogenic bacteria in the raw materials to be fermented is respectively obtained through preliminary experiments max And theoretical minimum growth temperature T min 。
The method specifically comprises the following steps: inoculating starter lactobacillus and food-borne pathogenic bacteria into the raw materials to be fermented, and culturing at constant temperature; quantitatively taking out fermentation raw materials at intervals, diluting, coating on a selective plate corresponding to each strain, and counting colonies after single colony grows to obtain strain concentration N at different culture times t The method comprises the steps of carrying out a first treatment on the surface of the Fitting a growth curve by using a formula (A) to obtain the maximum concentration N of the starter lactobacillus and each food-borne pathogenic bacteria in the fermentation raw material respectively max And a maximum specific growth rate mu max The method comprises the steps of carrying out a first treatment on the surface of the Obtaining theoretical minimum growth temperature T of starter lactobacillus and food-borne pathogenic bacteria in fermentation raw materials respectively by using a formula (B) min 。
Wherein t is the strain culture time (h), t lag Is the lag phase (h), N of the strain 0 For initial strain concentration (logCFU/g), N t For the strain concentration at time t (logCFU/g), N max The maximum concentration of the strain (logCFU/g), μ is the maximum specific growth rate of the strain (1/h).
μ max ＝b(T-T min ) ^2 (B)
Wherein mu max Is the maximum specific growth rate of the strain, T is the strain culture temperature, T min Is the theoretical minimum growth temperature of the strain.
The food-borne pathogenic bacteria include, but are not limited to, bacillus cereus, escherichia coli, listeria monocytogenes, salmonella, staphylococcus aureus, and yersinia enterocolitica.
2. Establishing a first-order model of interaction between lactobacillus and each food-borne pathogenic bacteria to obtain a temperature T ref The maximum specific growth rate mu of each strain in the fermentation raw material max Delay period t lag And a competing factor gamma.
2.1 inoculating lactobacillus and each food-borne pathogenic bacteria into raw materials to be fermented respectively according to a certain initial concentration, culturing at a constant temperature, quantitatively taking out the fermentation raw materials respectively in different culturing time, diluting, coating on a selective plate corresponding to each bacterium, counting colonies after single colony grows, and obtaining colony concentration N of the lactobacillus and the food-borne pathogenic bacteria in different culturing time t 。
2.2 expressing the growth curve of the formula (C) by Euler's method, fitting the growth curve to the data obtained in the step 2.1, and establishing a primary model of the interaction between lactic acid bacteria and each food-borne pathogenic bacteria to obtain a temperature T ref The maximum specific growth rate mu of each strain in the fermentation raw material max Delay period t lag And a competing factor gamma.
t<t lag-LAB ,
t≥t lag-LAB ,
Wherein PB (pathogenic bacteria) represents a certain food-borne pathogenic bacterium, LAB (Lactic acid bacteria) represents starter lactic acid bacteria; t is time, t lag Is the delay phase of the strain, mu t Mu, for specific growth rate of strain at time t max For the maximum specific growth rate of the strain, N t For the concentration of the strain at time t, N max Is the maximum concentration of the strain.
The formula introduces a competing factor gamma which can simulate the lactic acid bacteria in the starter to reach the maximum concentration N max After (entering the stationary phase), the food-borne pathogenic bacteria in the same environment continue to grow slightly (gamma)<1) Or inactivation (gamma)>1) Or unchanged (γ=1).
By the maximum specific growth rate mu obtained max And delay period t lag The relative delay RLT (Relative Lag Time) is calculated by the formula:
3. obtaining μ at other temperatures (T) using a secondary model of maximum specific growth rate versus temperature max-T And t lag-T The method is used for predicting growth and inactivation dynamics of lactic acid bacteria and food-borne pathogenic bacteria at other temperatures (T).
The formula of the secondary model of the relation between the maximum specific growth rate and the temperature is that
Wherein T is ref The temperature and mu in the constant temperature fermentation in the step 2.1 max-ref At T ref Maximum specific growth rate of strain obtained at temperature, T min Is the theoretical minimum growth temperature of the strain, T is the new temperature and mu max-T Is the maximum specific growth rate of the strain at the new temperature T.
Calculating the delay period T of each strain at the temperature T from the relative delay period RLT value lag-T The formula is as follows:
deducing the minimum growth temperature T of lactobacillus and each food-borne pathogenic bacteria by using a secondary model formula (E) min And a maximum specific growth rate between the optimal growth temperatures, thereby determining the growth and inactivation of each species when fermentation is performed between these two temperatures.
4. Under the conditions of appointed fermentation temperature, fermentation time, initial inoculation amount of starter lactic acid bacteria and each food-borne pathogenic bacteria, the concentration of each food-borne pathogenic bacteria at the end of fermentation and the growth and inactivation conditions of the lactic acid bacteria and each food-borne pathogenic bacteria in the fermentation process are predicted.
4.1 substituting the appointed fermentation temperature (T) into a secondary model formula (E) to obtain the maximum specific growth rate mu of the starter lactobacillus and each food-borne pathogenic bacteria at the temperature max-T And delay period t lag-T ；
4.2 the maximum specific growth rate μ obtained in 4.1 max-T And delay period t lag-T Substituting formula (C) and substituting initial inoculum size of starter lactobacillus and each food-borne pathogenic bacteria to predict growth curves of the lactobacillus and each food-borne pathogenic bacteria;
4.3, judging whether each food-borne pathogenic bacteria is completely inactivated in a specific time in the fermentation process through the growth curve analysis obtained in the step 4.2, thereby achieving the purpose of judging whether the fermented food is safe.
The invention also provides application of the prediction model in evaluating safety of lactobacillus fermented foods.
The application method comprises the following steps:
(1) Inputting the fermentation temperature, fermentation time, concentration of lactic acid bacteria and food-borne pathogenic bacteria of the fermenting agent before fermentation into a prediction model;
(2) The model predicts the concentration of lactobacillus and each food-borne pathogenic bacteria after fermentation is finished, and the respective growth and inactivation curves;
(3) According to the growth curve predicted by the model, whether each food-borne pathogenic bacteria is completely inactivated or not is analyzed and judged at a specific time in the fermentation process, so that the aim of judging whether the fermented food is safe or not is fulfilled.
The fermented food can be food rich in protein such as fish, pork, beef, bean product, etc.
According to the invention, the interaction relation between the lactic acid bacteria and each food-borne pathogenic bacteria in the food fermentation process is predicted by establishing a prediction model, so that the microbial safety of the fermented food in the fermentation process is evaluated. The method has the advantages that the method can quickly evaluate the safety of the fermented food after fermentation for a plurality of days without using traditional microorganism detection means and only knowing the preset fermentation temperature, fermentation time, inoculated starter concentration and initial concentration of naturally polluted food-borne pathogenic bacteria in the fermented food, and gives guidance for safety control, namely, how long the fermentation can ensure that all the food-borne pathogenic bacteria in the fermented food are completely inactivated, the fermented food achieves the safety condition, and the conventional pathogenic bacteria detection method only can detect the content of pathogenic bacteria after the long-time fermentation is completed, is time-consuming and labor-consuming and has lag results, and cannot give guidance advice on how to enable the fermented food to be safe.
The method can accurately evaluate the safety of the fermented food, realizes the artificial intelligent supervision and management, effectively saves the cost, improves the efficiency, and is the preferred method for controlling the quality safety of the fermented food.
Drawings
Fig. 1: (a), (b) and (c) are model fitting growth curves of starter lactobacillus during constant-temperature fermentation at 10 ℃, 20 ℃ and 30 ℃ respectively; (d) Fitting a graph for a maximum specific growth rate-temperature secondary model;
fig. 2: model fitting growth curve graphs of lactobacillus and each food-borne pathogenic bacteria in the fermented salmon during constant-temperature fermentation at 25 ℃;
fig. 3: an application process display diagram of the prediction model; wherein (a) is to input the fermentation temperature, fermentation time, the concentration of lactic acid bacteria of the fermenting agent before fermentation and each pathogenic bacteria into a model; (b) The concentration value of each pathogenic bacteria after fermentation predicted by the model is divided into two possible cases, namely, the pathogenic bacteria have a delay period, and the pathogenic bacteria have no delay period (most dangerous case); (c) A growth curve of pathogenic bacteria predicted by the model when the pathogenic bacteria have a delay period; (d) A growth curve of pathogenic bacteria predicted by the model when no delay (most dangerous condition) exists;
fig. 4: comparing the measured and predicted values of the concentration of the strains of the escherichia coli and salmonella in the fermented salmon at different fermentation times; wherein (a) is Escherichia coli and (b) is Salmonella.
Detailed Description
The invention is further described below in connection with specific embodiments.
Food-borne pathogenic bacteria described in the examples of the present invention include, but are not limited to, bacillus cereus (Bacillus cereus), escherichia coli (Escherichia coli), listeria monocytogenes (Listeria monocytogenes), salmonella (Salmonella spp.), staphylococcus aureus (Staphylococcus aureus), and yersinia enterocolitica (Yersinia enterocolitica).
The food-borne pathogenic bacteria selected in the embodiment of the invention are provided by the national food institute of Danish, wherein Listeria monocytogenes is a mixture of four bacteria of 94-203D,95-54A, 95-4472A and 94-167B; salmonella is a mixture of two species of Enteritidis I2 and Welterreden I3; the staphylococcus aureus is mixed by two strains of M2 and M7; coli is a mixture of four bacteria of MS 21811, MS 21812, MS 21813 and MS 21815; yersinia enterocolitica is a mixture of four bacteria of F78261, F80460, R123 and S274; bacillus cereus is a mixture of three bacteria NVH 0861-00, 10329 and 10480.
The agar plate for detecting the lactobacillus is a Rogosa agar plate (Oxoid CM 0627); detecting bacillus cereus as bacillus cereus selective agar plate (Oxoid CM 0617); e.coli was detected as RAPID' E.coli 2 agar plates (Bio-Rad 356-4024); detection of Listeria monocytogenes as a Palcam agar plate (Oxoid CM 0877); detection of Salmonella as XLD agar plates (Oxoid CM 0469); the staphylococcus aureus was detected as Baird Parker agar plates (Oxoid CM 0275); detection of Yersinia enterocolitica Yersinia enterocolitica Selective agar plate (Oxoid CM 0653)
Example 1 application of the method of the invention in the evaluation of safety of fermented salmon products
1. Establishment of predictive model
1. The maximum concentration N of the strains of the starter lactobacillus and the food-borne pathogenic bacteria in the salmon to be fermented is obtained through a preliminary experiment max Theoretical minimum growth temperature T min 。
Inoculating lactobacillus and food-borne pathogenic bacteria into salmon to be fermented, culturing, and culturing at constant temperature; quantitatively taking out fermented salmon samples respectively in different culture time, diluting, coating on a selective plate corresponding to each strain, and counting after single colony grows out to obtain strain concentration N in different culture time t The method comprises the steps of carrying out a first treatment on the surface of the Fitting of growth curves (operating by microsoft Excel and programming solution loading) using formula (a) to obtain maximum concentration N of starter lactic acid bacteria and food-borne pathogenic bacteria in fermented salmon, respectively max And a maximum specific growth rate mu max The method comprises the steps of carrying out a first treatment on the surface of the Obtaining theoretical minimum growth temperature T of starter lactobacillus and food-borne pathogenic bacteria in fermented salmon respectively by using formula (B) min ；
Wherein t is strain culture time, t lag N is the delay of the strain 0 For initial strain concentration (logCFU/g), N t For the strain concentration at time t (logCFU/g), N max The maximum concentration of the strain (logCFU/g), mu is the maximum specific growth rate of the strain (1/h);
μ max ＝b(T-T min ) ^2 (B)
wherein mu max Is the maximum specific growth rate of the strain, T is the strain culture temperature, T min Is the theoretical minimum growth temperature of the strain.
The maximum concentration N of lactobacillus in salmon to be fermented will be described in detail below by taking lactobacillus as starter max And theoretical minimum growth temperature T min These two parametersNumber acquisition process.
The method comprises the following specific steps:
(1) Inoculating lactobacillus into salmon to be fermented with initial inoculation amount of 10 4 CFU/g, respectively culturing at 10deg.C, 20deg.C and 30deg.C, extracting 10g salmon sample every 1-2h, and taking 2 parallel samples at each time point; the obtained sample is diluted and then coated on an MRS flat plate, and after single colony grows out, counting is carried out, so that the actual measurement value of the strain concentration of the lactobacillus at different fermentation time is obtained;
(2) Then using a formula (A) to fit the concentration of the strain at different fermentation times, obtaining the sum of squares (Residual sum of square, RSS) of the residual errors of the measured value and the fitted value, using a Microsoft Excel loading item programming solving function to target the minimum RSS value, and t lag And (3) fitting a growth curve of the lactobacillus under the constraint condition of 0 or more, as shown in (a), (b) and (c) of figure 1.
Obtaining maximum concentration N in salmon when lactobacillus is cultured at 10deg.C, 20deg.C and 30deg.C max And a maximum specific growth rate μ, with specific results shown in table 1. After averaging, the maximum concentration of lactic acid bacteria in the fermented salmon was 9.46log cfu/g.
TABLE 1 maximum concentration and maximum specific growth rate of lactic acid bacteria in fermented salmon at different temperatures
Obtaining the theoretical minimum growth temperature T of lactobacillus in salmon by using the formula (B) min Is 3.7 ℃.
With reference to the same method, the maximum concentration N of each food-borne pathogenic bacterium in the fermented salmon is obtained max And theoretical minimum growth temperature T min The specific results are shown in Table 2.
TABLE 2 maximum concentration N of lactic acid bacteria and food-borne pathogenic bacteria max And theoretical minimum growth temperature T min
2. Establishing a first-order model of interaction between lactobacillus and each food-borne pathogenic bacteria to obtain a temperature T ref Maximum specific growth rate mu of each strain in fermented salmon max Delay period t lag And a competing factor gamma.
2.1 lactic acid bacteria and food-borne pathogenic bacteria were mixed at a predetermined initial concentration (the inoculation amount of lactic acid bacteria was 10) 6 CFU/g, pathogenic bacteria inoculation amount of 10 3 CFU/g) are respectively inoculated into salmon to be fermented, and the salmon is cultivated at constant temperature; and respectively taking out the fermented salmon samples quantitatively in different culture time, diluting, coating the fermented salmon samples on a selective plate corresponding to each strain, and counting the bacterial colonies after single bacterial colonies grow out to obtain the concentration of the bacterial strain in different culture time.
The method comprises the following specific steps:
(1) Melting frozen salmon small meat blocks at 4deg.C overnight, adding 1.5% sugar, 0.5% salt, 0.25% citric acid, and 1% mixed flavoring agent, and stirring;
(2) Inoculating lyophilized starter culture of lactococcus lactis subspecies butanedione variant (Lactococcus lactis ssp. Lactis biovar diacetylactis) at an initial concentration of 10 6 About CFU/g;
(3) Inoculating each food-borne pathogenic bacteria to make its initial infection concentration be 10 3 About CFU/g; dividing the fermented salmon samples into 2 groups, wherein listeria monocytogenes, salmonella and staphylococcus aureus are combined and inoculated into a first group of samples, and escherichia coli, yersinia enterocolitica and bacillus cereus are combined and inoculated into a second group of samples;
(4) Culturing at constant temperature in a constant temperature cabinet at 25 ℃ for every 1-2 hours, extracting 10g salmon samples, and taking 2 parallel samples at each time point. Samples were diluted with 10-fold peptone physiological saline (0.85% sodium chloride, 0.1% peptone), homogenized on a slapping homogenizer for 1 minute, and serial 10-fold dilutions of peptone physiological saline were made. For different dilution concentrations, 0.1ml of the dilution was spread on agar plates corresponding to each strain for cultivation. And (3) counting the bacterial colonies after single bacterial colonies grow out, and obtaining the actual measurement values of the bacterial strain concentration of the starter lactobacillus and each food-borne pathogenic bacteria at different fermentation times.
2.2 expressing the growth curve of the formula (C) by using an Euler's method, fitting the growth curve to the measured concentration values of the lactic acid bacteria and the strains of the food-borne pathogens obtained in the step 2.1 (the tool used for fitting is a program solving loading item of Microsoft Excel), and establishing a first-order model of the interaction between the lactic acid bacteria and the food-borne pathogens to obtain the temperature T ref Maximum specific growth rate mu of each strain in fermented salmon max Delay period t lag And a competing factor γ;
wherein PB (pathogenic bacteria) represents a food-borne pathogenic bacterium, LAB (Lactic acid bacteria) represents starter lactic acid bacteria; t is time, t lag Mu, the lag phase of the strain t Mu, for specific growth rate of strain at time t max N, the maximum specific growth rate of the strain t For strain concentration at time t, N max Is the maximum concentration of the strain.
The biological significance of the model formula (C) is that the lactic acid bacteria and the food-borne pathogenic bacteria inhibit the growth of each other to the same extent as they inhibit the growth of themselves, i.e. when the dominant bacteria reach the maximum concentration N max Thereafter, other bacteria also cease to grow at the same time.
The formula introduces a competing factor gamma which can simulate the lactic acid bacteria in the starter to reach the maximum concentration N max After (entering the stationary phase), the food-borne pathogenic bacteria in the same environment continue to grow slightly (gamma)<1) Or inactivation (gamma)>1) Or unchanged (γ=1).
By the maximum specific growth rate mu obtained max And delay period t lag The relative delay RLT (Relative Lag Time) is calculated by the formula:
the method comprises the following specific steps:
expressing the growth curve of the first-order model formula (C) by Euler's method, wherein the time step is 1 min, and substituting the initial measured concentration of lactobacillus and each food-borne pathogenic bacteria into N t (i.e., N when t=0) t ＝N 0 )，N max-PB And (3) fitting a growth curve to the measured concentration values of the lactic acid bacteria and the strains of the food-borne pathogenic bacteria, which are measured in the step (2.1), by using the data obtained in the table 2, and establishing a primary model of the interaction between the lactic acid bacteria and the food-borne pathogenic bacteria. The fitted growth curve is shown in fig. 2, all food-borne pathogenic bacteria grow rapidly together with starter lactic acid bacteria within the first 12 hours, but after the lactic acid bacteria grow to the maximum concentration, the inhibitory inactivation effect on each pathogenic bacteria begins to occur.
Obtaining the maximum specific growth rate mu of each strain in the fermented salmon through a primary model of the interaction between the lactic acid bacteria and each food-borne pathogenic bacteria max Delay period t lag The relative delay RLT and the competing factors γ are detailed in table 3.
TABLE 3 parameter values and B of each strain in the model obtained by fitting the measured values f And A f Value of
B f Is Bias factor, A f These two parameters are used to indicate the proximity between the predicted (or fitted) and measured values of the model, and to verify the Accuracy of the modeled model, as an Accuracy factor (Accury factor). B (B) f And A f The calculation formula (G) of (a) is as follows:
wherein X is predicted To predict or fit values, X observed The measured value is n, which is the number of measurements.
When the deviation factor B f Between 0.95 and 1.11, and an accuracy factor A f Within 1.10, the prediction effect of the representation model is good; when the deviation factor B f Between 0.87-0.95 or 1.1-1.43, and accurate factor A f Within 1.22, the predictive effect of the representation model is acceptable; when both factors are outside the above range, this indicates that the predictive effect is not acceptable.
As can be seen from the data in Table 3, the present invention fits the growth curve for the measured concentration values of lactic acid bacteria and each food-borne pathogenic bacteria, all deviation factors B f Are all between 0.95 and 1.11, and the accuracy factor A f The fitting effect of the first-order model of the interaction between the lactic acid bacteria and each food-borne pathogenic bacteria established by the invention is good.
3. Obtaining μ at other temperatures (T) using a secondary model of maximum specific growth rate versus temperature max-T And t lag-T Is used for predicting the growth and inactivation dynamics of lactic acid bacteria and food-borne pathogenic bacteria at other temperatures (T).
The formula of the secondary model of the relation between the maximum specific growth rate and the temperature is as follows:
wherein T is ref The temperature and mu in the constant temperature fermentation in the step 2.1 max-ref At T ref Maximum specific growth rate of strain obtained at temperature, T min Is the theoretical minimum growth temperature of the strain, T is the new temperature and mu max-T Maximum specific growth rate for the species at the new temperature T;
calculating the delay period T of each strain at the temperature T from the relative delay period RLT value lag-T The formula (F) is:
deducing the minimum growth temperature T of lactobacillus and each food-borne pathogenic bacteria by using a secondary model formula (E) min And a maximum specific growth rate between the optimal growth temperatures, thereby determining the growth and inactivation of each strain when fermentation is performed between the two temperatures;
4. under the conditions of appointed fermentation temperature, fermentation time, initial inoculation amount of starter lactic acid bacteria and each food-borne pathogenic bacteria, the concentration of each food-borne pathogenic bacteria at the end of fermentation and the growth and inactivation conditions of the lactic acid bacteria and each food-borne pathogenic bacteria in the fermentation process are predicted.
4.1 substituting the appointed fermentation temperature (T) into a secondary model formula (E) to obtain the maximum specific growth rate mu of the starter lactobacillus and each food-borne pathogenic bacteria at the temperature max-T And delay period t lag-T ；
4.2 the maximum specific growth rate μ obtained in 4.1 max-T And delay period t lag-T Substituting formula (C) and substituting initial inoculum size of starter lactobacillus and each food-borne pathogenic bacteria to predict growth curves of the lactobacillus and each food-borne pathogenic bacteria;
4.3, judging whether each food-borne pathogenic bacteria is completely inactivated in a specific time in the fermentation process through the growth curve analysis obtained in the step 4.2, thereby achieving the purpose of judging whether the fermented food is safe.
2. Safety assessment of fermented salmon using established predictive models
The application process of the prediction model is shown in fig. 3;
(1) Inputting fermentation condition parameters and the concentration of lactic acid bacteria and pathogenic bacteria before fermentation into a model;
the natural infection amount of each food-borne pathogenic bacteria is as follows according to the historical detection data of the fermented salmon product: coli and staphylococcus aureus are within 300CFU/g, salmonella and bacillus cereus are within 100CFU/g, and other pathogenic bacteria are within 10 CFU/g. Fermenting the maximum infection concentration of the pathogenic bacteriaInitial concentration of lactic acid bacteria 10 6 CFU/g, fermentation temperature of 30 ℃ and fermentation time of 216h are input into a model; (as shown in FIG. 3 (a))
(2) The model predicts the concentration of each pathogenic bacteria after fermentation, and the two possible conditions are that the pathogenic bacteria have a delay period and the pathogenic bacteria have no delay period (most dangerous condition); (as shown in FIG. 3 (b))
(3) The model predicts the growth curve of pathogenic bacteria in a delay period; (as shown in FIG. 3 (c))
(4) The model predicts the growth curve of pathogenic bacteria without delay; (as shown in FIG. 3 (d))
As can be seen from fig. 3 (c) and (d), all pathogenic bacteria grow rapidly with starter lactic acid bacteria within the first 12 hours; only after the growth of the lactic acid bacteria reaches the maximum concentration, the inhibiting and inactivating effects of the lactic acid bacteria on each pathogenic bacteria can be generated. With the extension of fermentation time, the concentration of the lactobacillus tends to be stable, and the concentration of each pathogenic bacteria continuously decreases. When there is a delay, bacillus cereus is inactivated at 35.9h, listeria monocytogenes is inactivated at 57.3h, yersinia enterocolitica is inactivated at 60h, staphylococcus aureus is inactivated at 73.8h, salmonella is inactivated at 215.7h, and escherichia coli is inactivated at 216 h; in the absence of delay, listeria monocytogenes was inactivated at 35.7h, bacillus cereus at 37.2h, yersinia enterocolitica at 67.1h, staphylococcus aureus at 85.1h, salmonella and Escherichia coli at 216 h.
Therefore, according to the prediction of the model disclosed by the invention, the lactobacillus fermentation time of salmon at the constant temperature of 30 ℃ is not less than 9 days, so that all pathogenic bacteria can be inactivated to 0log CFU/g, and the safest purpose is achieved.
3. The accuracy of the prediction result is verified by adopting a conventional fermentation and pathogenic bacteria detection method
In order to verify whether the prediction result of the model is accurate, the applicant adopts the fermentation condition same as that of the prediction model to perform salmon fermentation: the initial infection concentration of pathogenic bacteria is 300CFU of Escherichia coli and Staphylococcus aureusPer gram, salmonella and bacillus cereus 100CFU/g, other pathogenic bacteria at 10CFU/g; starter lactobacillus initial concentration of 10 6 CFU/g; fermenting at 30deg.C.
The escherichia coli and salmonella have strong resistance to starter lactic acid bacteria, so that the inactivation rate is obviously slower than that of other pathogenic bacteria, and the escherichia coli and salmonella are key bacteria for influencing the safety of fermented foods. Thus, during salmon fermentation, samples were taken on days 2, 4, 6, 8 and 10, respectively, and 2 parallel samples were taken at each time point to detect the concentration of E.coli and Salmonella strains in fermented salmon, respectively, while other pathogenic bacteria were only detected on day 10 of fermentation.
The experimental results are shown in FIG. 4, in which the concentration of E.coli and Salmonella strains in salmon rapidly increased and then slowly decreased in a short period of time during fermentation. Wherein, on the 8 th day of fermentation, the concentration of the escherichia coli strain is reduced to 1.0log CFU/g, so as to meet the food safety requirement (less than or equal to 2.0log CFU/g), and the concentration of the salmonella strain is reduced to 0log CFU/g; at day 10 of fermentation, both E.coli and Salmonella strains were inactivated at 0log CFU/g.
It can also be seen from FIG. 4 that the trend of the actual measured concentration values of the strains of E.coli and Salmonella completely matches the predicted trend. Wherein, E.coli bias factor B f And accuracy factor A f Salmonella bias factor B of 1.02 and 1.06, respectively f And accuracy factor A f Deviation factor B of 1.09 and 1.10, respectively f And accuracy factor A f The results of (2) are all within good limits.
Moreover, according to the actual detection results, all four pathogenic bacteria of bacillus cereus, listeria monocytogenes, staphylococcus aureus and yersinia enterocolitica were not detected at the 10 th day of salmon fermentation, indicating that all the four pathogenic bacteria were inactivated at the 10 th day of fermentation.
The result shows that the prediction result of the model is completely consistent with the actual detection result, and the accuracy is high.
The method provided by the method is not only applied to the evaluation of the safety of the fermented salmon. According to the interaction of lactobacillus and various food pathogenic bacteria in food and the inherent rules of growth and inactivation of the lactobacillus and various food pathogenic bacteria, the method provided by the invention can be applied to any fermented food taking lactobacillus as a starter, such as pork, beef, fish, bean products and the like, so that the microbial safety problem of the fermented food can be accurately predicted, and the application prospect is wide.
Claims (5)
1. A method of establishing a predictive model for assessing the safety of lactic acid bacteria fermented food products, the method comprising the steps of:
(1) The maximum concentration N of the starter lactobacillus and each food-borne pathogenic bacteria in the raw materials to be fermented is respectively obtained through preliminary experiments max And theoretical minimum growth temperature T min ；
Wherein t is the strain culture time h, t lag Is the lag phase h, N of the strain 0 For initial strain concentration logCFU/g, N t For strain concentration log CFU/g, N at time t max The maximum concentration log CFU/g of the strain is shown, and mu is the maximum specific growth rate of the strain of 1/h;
μ max ＝h(T-T min ) ^2 (B)
wherein mu max Is the maximum specific growth rate of the strain, T is the strain culture temperature, T min The theoretical minimum growth temperature of the strain;
(2) Establishing a first-order model of interaction between lactobacillus and each food-borne pathogenic bacteria to obtain a temperature T ref The maximum specific growth rate mu of each strain in the fermentation raw material max Delay period t lag And a competing factor γ;
(1) inoculating lactobacillus and food-borne pathogenic bacteria into the raw materials to be fermented respectively according to certain initial concentration, culturing at constant temperature, taking out the fermentation raw materials quantitatively respectively in different culture time, diluting, and coating onOn a selective plate corresponding to each strain, counting the bacterial colonies after single bacterial colonies grow out, and obtaining the bacterial colony concentration N of the lactic acid bacteria and the food-borne pathogenic bacteria at different culture times t ；
(2) Expressing the growth curve of the formula (C) by using Euler's method, fitting the growth curve to the data obtained in the step (1), and establishing a primary model of the interaction between lactobacillus and each food-borne pathogenic bacteria to obtain the temperature T ref The maximum specific growth rate mu of each strain in the fermentation raw material max Delay period t lag And a competing factor γ;
wherein PB represents a certain food-borne pathogenic bacterium, and LAB represents starter lactic acid bacteria; t is time, t lag Is the delay phase of the strain, mu t Mu, for specific growth rate of strain at time t max For the maximum specific growth rate of the strain, N t For the concentration of the strain at time t, N max Is the maximum concentration of the strain;
by the maximum specific growth rate mu obtained max And delay period t lag And calculating a relative delay period RLT, wherein the formula is as follows:
(3) Obtaining μ at other temperatures T using a secondary model of maximum specific growth rate versus temperature max-T And t lag-T The method is used for predicting growth and inactivation dynamics of lactic acid bacteria and food-borne pathogenic bacteria at other temperatures T;
the formula of the secondary model of the relation between the maximum specific growth rate and the temperature is as follows:
wherein T is ref The temperature and mu in the constant temperature fermentation in the step 2.1 max-ref At T ref Maximum specific growth rate of strain obtained at temperature, T min Is the theoretical minimum growth temperature of the strain, T is the new temperature and mu max-T Maximum specific growth rate for the species at the new temperature T;
calculating the delay period T of each strain at the temperature T from the relative delay period RLT value lag-T The formula is as follows:
deducing the minimum growth temperature T of lactobacillus and each food-borne pathogenic bacteria by using a secondary model formula (E) min And a maximum specific growth rate between the optimal growth temperatures, thereby determining the growth and inactivation of each species when fermentation is performed between the two temperatures;
(4) Under the conditions of appointed fermentation temperature, fermentation time, initial inoculation amount of starter lactic acid bacteria and each food-borne pathogenic bacteria, the concentration of each food-borne pathogenic bacteria at the end of fermentation and the growth and inactivation conditions of the lactic acid bacteria and each food-borne pathogenic bacteria in the fermentation process are predicted;
(1) substituting the appointed fermentation temperature T into a secondary model formula (E) to obtain the maximum specific growth rate mu of the starter lactobacillus and each food-borne pathogenic bacteria at the temperature max-T And delay period t lag-T ；
(2) The maximum specific growth rate mu obtained in the step (1) is calculated max-T And delay period t lag-T Substituting formula (C) and substituting initial inoculum size of starter lactobacillus and each food-borne pathogenic bacteria to predict growth curves of the lactobacillus and each food-borne pathogenic bacteria;
(3) and (3) judging whether each food-borne pathogenic bacteria is completely inactivated at a specific time in the fermentation process through the growth curve analysis obtained in the step (2), so that the aim of judging whether the fermented food is safe is fulfilled.
2. The method according to claim 1, wherein the specific steps of step (1) in the method are: inoculating starter lactobacillus and food-borne pathogenic bacteria into the raw materials to be fermented, and culturing at constant temperature; quantitatively taking out fermentation raw materials at intervals, diluting, coating on a selective plate corresponding to each strain, and counting colonies after single colony grows to obtain strain concentration N at different culture times t The method comprises the steps of carrying out a first treatment on the surface of the Fitting a growth curve by using a formula (A) to obtain the maximum concentration N of the starter lactobacillus and each food-borne pathogenic bacteria in the fermentation raw material respectively max And a maximum specific growth rate mu max The method comprises the steps of carrying out a first treatment on the surface of the Obtaining theoretical minimum growth temperature T of starter lactobacillus and food-borne pathogenic bacteria in fermentation raw materials respectively by using a formula (B) min 。
3. The method of claim 1 or 2, wherein the food-borne pathogenic bacteria is bacillus cereus, escherichia coli, listeria monocytogenes, salmonella, staphylococcus aureus, or vibrio parahaemolyticus.
4. Use of a predictive model established by the method according to any one of claims 1-3 for the evaluation of the safety of lactic acid bacteria fermented food products.
5. The application of claim 4, wherein the application comprises the steps of:
(1) inputting the fermentation temperature, fermentation time, concentration of lactic acid bacteria and food-borne pathogenic bacteria of the fermenting agent before fermentation into a prediction model;
(2) the model predicts the concentration of lactobacillus and each food-borne pathogenic bacteria after fermentation is finished, and the respective growth and inactivation curves;
(3) according to the growth curve predicted by the model, whether each food-borne pathogenic bacteria is completely inactivated or not is analyzed and judged at a specific time in the fermentation process, so that the aim of judging whether the fermented food is safe or not is fulfilled.
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