Construction method of growth prediction model of burkholderia gladioli in soaked black fungus and application of constructed model

Abstract

The invention discloses a construction method of a growth prediction model of burkholderia gladioli in soaked black fungus and application of the constructed model, the method comprises the following steps: inoculating burkholderia gladioli containing rifampicin resistance into a soaked black fungus sample, putting the soaked black fungus sample into a plurality of incubators at different temperatures, and culturing; calculating the colony count of rifampicin-resistant burkholderia gladioli, and determining the growth curve of the burkholderia gladioli; respectively fitting the three first-level models and the two second-level models at different temperatures by utilizing statistical analysis software; by comparing comprehensive evaluation parameters, an optimal primary model and an optimal secondary model are selected, SGompertz is screened out as the optimal primary model, a quadratic polynomial model is screened out as the secondary model, the models are applied to prediction of the growth condition of burkholderia gladioli in soaked black fungus, the shelf life of the black fungus and the quantitative microbial risk can be predicted, and the quality guarantee period of the black fungus can be predicted. And the food quality can be improved.

Description

Technical Field

[0001] The present invention relates to the field of food, and particularly to a method for constructing a growth prediction model of Burkholderia gladioli in soaked black fungus and the application of the constructed model. Background Art

[0002] Black fungus is rich in various bioactive substances and has edible and medicinal values. It is the third largest edible mushroom in terms of global planting area. Black fungus is usually sold and preserved in a dried form and needs to be fully soaked before consumption to supplement moisture. This process creates favorable conditions for the proliferation of foodborne pathogens. Among them, Burkholderia gladioli is extremely easy to grow and reproduce during the soaking process of black fungus and produces the biotoxins bongkrekic acid and toxoflavin. Moreover, the bongkrekic acid toxin is highly heat-resistant and cannot be eliminated by washing, boiling, or cooking, which can cause highly lethal food poisoning. Therefore, how to predict the growth of Burkholderia gladioli in black fungus and determine reasonable consumption conditions is a technical problem that urgently needs to be solved in this field.

[0003] Therefore, the technical personnel in this field are committed to developing a method for predicting the growth of Burkholderia gladioli in black fungus, so as to better evaluate the edible safety risk of black fungus. Summary of the Invention

[0004] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to provide a model for predicting the growth of Burkholderia gladioli in black fungus to better evaluate the edible safety risk of black fungus and its application in the production, preservation, and consumption of black fungus.

[0005] To achieve the above object, the present invention provides a method for constructing a growth prediction model of Burkholderia gladioli in soaked black fungus, which is characterized in that the method comprises the following steps:

[0006] Step 1: Inoculate Burkholderia gladioli with rifampicin resistance into soaked black fungus samples, place them in incubators at multiple different temperatures for cultivation, calculate the colony number of rifampicin-resistant Burkholderia gladioli, and measure the growth curve of Burkholderia gladioli.

[0007] Step 2: Use statistical analysis software to fit three primary models at different temperatures respectively.

[0008] Step 3: Select the optimal primary model by comparing comprehensive evaluation parameters.

[0009] Step 4: Select two secondary models to describe the influence of temperature on the growth rate μ max and the lag phase λ of Burkholderia gladioli.

[0010] Step 5: Select the most suitable secondary model through comprehensive evaluation of multiple parameters.

[0011] In a preferred embodiment of the present invention, the multiple different temperatures in Step 1 are 4°C, 10°C, 15°C, 20°C, 26°C, 30°C, and 36°C respectively.

[0012] In another preferred embodiment of the present invention, the three primary models in Step 2 are: SGompertz model, SLogistic model, and Baranyi model.

[0013] In another preferred embodiment of the present invention, the comprehensive evaluation parameters in Step 3 are: coefficient of determination, i.e., R 2 , root mean square error, i.e., RMSE, and Akaike information criterion, i.e., AIC. The selection criteria for the primary model are: R 2 is closest to 1, the RMSE parameter is closer to 0, and the AIC value is the smallest.

[0014] In another preferred embodiment of the present invention, the two secondary models in Step 4 are the square root model and the quadratic polynomial model.

[0015] In another preferred embodiment of the present invention, the multiple parameters in Step 5 are: R 2 , RMSE, and accuracy factor A f , bias factor B f , and the selection criteria for the secondary model are: R 2 is closest to 1, the RMSE parameter is closer to 0, and the values of A f and B f are between 1.06 and 1.15.

[0016] In another preferred embodiment of the present invention, the RMSE parameter is 0.05 - 0.15.

[0017] In another preferred embodiment of the present invention, the construction method further includes verification of the model. When verifying the model, the predicted bacterial concentration and the observed bacterial concentration are compared.

[0018] The present invention also provides the application of the model constructed by the above construction method in predicting the growth of Burkholderia gladioli in soaked black fungus.

[0019] In a preferred embodiment of the present invention, the model includes a primary model and a secondary model, wherein the primary model is the SGompertz model, specifically as shown in Equation 1:

[0020] Equation 1

[0021] lg(N t / N0) = a×exp{-exp[-k×(t - x c )]}

[0022] μ max = a×k / e

[0023] λ = x c -1 / k

[0024] In Equation 1, t is the bacterial growth time, with the unit of h; N t is the bacterial concentration corresponding to t, with the unit of Log CFU / g; N0 is the initial bacterial concentration, with the unit of Log CFU / g; a is the difference between the maximum number of bacteria N max and the initial number of bacteria N0; x c is the time required to reach the relative maximum growth rate; k is the relative growth rate of x c ; μ max is the maximum growth rate of microorganisms, with the unit of h -1 ; λ is the bacterial growth lag time, with the unit of h;

[0025] The secondary model is a quadratic polynomial model, as shown in Equation 2:

[0026] Equation 2

[0027] μ max or λ = a + bT + cT 2

[0028] In the equation, T is the storage temperature, with the unit of °C; μ max is the maximum specific growth rate of bacteria, with the unit of h -1 ; λ is the bacterial growth lag time, with the unit of h; a, b, and c are model parameters.

[0029] Technical effects

[0030] The present invention uses predictive microbiology to describe the growth of Burkholderia gladioli in Auricularia auricula-judae, and for the first time constructs a growth prediction model of Burkholderia gladioli in soaked Auricularia auricula-judae, which can simulate the growth of Burkholderia gladioli in soaked Auricularia auricula-judae under different temperature conditions, providing a necessary tool for predicting the shelf life of Auricularia auricula-judae and quantitative microbial risk assessment.

[0031] According to the growth model constructed by the present invention, the suitable conditions in the actual process of soaking Auricularia auricula-judae can be determined, providing ideas for the management of the entire food supply chain, reducing the harm caused by Burkholderia gladioli during the soaking process of Auricularia auricula-judae, and thus improving the quality of food.

[0032] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, features and effects of the present invention. Description of the Drawings

[0033] Figure 1 is the growth curve of Burkholderia gladioli in Auricularia auricula-judae of a preferred embodiment of the present invention at different temperatures (15 - 36 °C);

[0034] Figure 2 is a schematic diagram showing the relationship between temperature and the maximum growth rate μ max and the lag phase λ of a preferred embodiment of the present invention. Detailed Embodiments

[0035] The following introduces multiple preferred embodiments of the present invention with reference to the drawings of the specification to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0036] The materials used in the embodiments of the present invention include:

[0037] Strain

[0038] The Burkholderia gladioli used in this embodiment was isolated from Auricularia auricula-judae by the microbial research group of the Institute of Quality Standards of the Shanghai Academy of Agricultural Sciences and was identified as Burkholderia gladioli through the national standard of the People's Republic of China (GB4789.29 - 2020) and stored in a -80 °C refrigerator for standby.

[0039] Materials and Reagents

[0040] Auricularia auricula-judae, Mudanjiang City, Heilongjiang Province; Difco TM Potato Dextrose Agar (PDA) medium, BD Company, USA; Brain Heart Infusion Broth (BHI), Beijing Land Bridge Technology Co., Ltd.; Phosphate Buffered Saline (PBS), Beijing Land Bridge Technology Co., Ltd.; Rifampicin (analytical pure) is a product of Solarbio Company, China.

[0041] Main Instruments and Equipment

[0042] Autoclave (SX-500), Tomy Digital Biology Co., Ltd., Japan; Biological safety cabinet (Type 1300SERIESA2), Thermo Company, USA; Incubator (Medcenter Einrichtungen GmbH type), Friocell Company, Germany; Constant temperature shaking incubator (TQZ-312 type), Shanghai Jinghong Experimental Equipment Co., Ltd.; Electronic balance (AL104), Mettler Toledo Company, Switzerland.

[0043] The methods used in the embodiments of the present invention include:

[0044] Induction of rifampicin-resistant strains

[0045] Inoculate Burkholderia gladioli into BHI liquid medium and culture it overnight at 37 °C on a shaker at 180 rpm. Then inoculate it into BHI liquid medium containing rifampicin at a ratio of 1:100 for tolerance induction. The rifampicin mass concentrations are 0, 2.5 mg / L, 5 m / L, 10 mg / L, and 20 mg / L. Stabilize it at 10 mg / L and store it in a -80 °C refrigerator. Before each experiment, activate the strain by streaking on a PDA plate containing rifampicin resistance (PDA / R) and store it in a 4 °C refrigerator.

[0046] Inoculation, culture, and counting of Auricularia auricula

[0047] Inoculation of Auricularia auricula

[0048] Before inoculation, inoculate Burkholderia gladioli with rifampicin resistance into 5 mL of BHI liquid medium containing 10 mg / rifampicin and culture it overnight at 37 °C on a shaker at 180 rpm for 20 - 22 h. Centrifuge it at 4 °C (9000 rpm, 3 min), discard the supernatant, add PBS buffer, shake and wash, and then centrifuge again under the same conditions. After discarding the supernatant, add PBS buffer to ensure an initial inoculation amount of approximately 3 Log CFU / g.

[0049] Weigh an appropriate amount of Auricularia auricula, soak Auricularia auricula and sterile water in a ratio of 1:20 for 40 min, drain the water, and weigh 10 g of Auricularia auricula for each portion and place it in a sterile homogenization bag for standby. Aspirate 1 mL of the bacterial suspension with rifampicin resistance and evenly inoculate it onto the surface of the above-treated Auricularia auricula to ensure an initial inoculation amount of approximately 3 Log CFU / g.

[0050] Culture and counting

[0051] Place the prepared Auricularia auricula samples in an incubator at a constant temperature of 4°C, 10°C, 15°C, 20°C, 26°C, 30°C, and 36°C respectively. Set different sampling times for different temperatures. Take out two samples at a specific culture time. Open each sample aseptically, add 90 mL of PBS buffer, and beat with a sterile homogenizer at 8 times / s for 2 minutes. Then, respectively pipette 200 μL of the serial dilution and spread it onto the PDA / R plate. Place it in an incubator at 36°C for 24 hours and then count. Each experiment is repeated.

[0052] In the embodiments of the present invention, for the production processes, experimental methods, or detection methods involved, unless otherwise specified, those skilled in the art can understand the conventional process steps according to the name and apply the corresponding equipment, and implement them under conventional conditions or the conditions recommended by the manufacturer.

[0053] For various instruments, equipment, raw materials, or reagents used in the embodiments of the present invention, there are no special restrictions on the sources. They are all conventional products that can be obtained through regular commercial channels, or can also be prepared according to the conventional methods well-known to those skilled in the art.

[0054] Example 1

[0055] Establishment process of the growth prediction model

[0056] Establishment of the primary model

[0057] Burkholderia gladioli shows a classic three-stage growth, namely, the lag phase, the exponential phase, and the stationary phase. In this example, three commonly used primary growth models are selected: the modified Gompertz model (SGompertz model) (Equation 1-1), the modified Logistic model (SLogistic model) (Equation 1-2), and the Baranyi model (Equation 1-3) to fit the growth of Burkholderia gladioli in the soaked Auricularia auricula. The SGompertz and SLogistic models come from the Origin-2022 software, and the Baranyi model comes from the IPMP-2013 software.

[0058] SGompertz model:

[0059] lg(N t / N0) = a × exp{-exp[-k × (t - x c )]}

[0060] μ max = a × k / e

[0061] λ = x c -1 / k (1-1)

[0062] where t is the bacterial growth time (h); N t is the bacterial concentration corresponding to t (Log CFU / g); N0 is the initial bacterial concentration (Log CFU / g); a is the difference between the maximum number of bacteria N max and the initial number of bacteria N0; x c is the time required to reach the relative maximum growth rate; k is the relative growth rate of x c ; μ max is the maximum growth rate of microorganisms (h -1 ); λ is the bacterial growth lag time (h);

[0063] SLogistic model:

[0064] lg(N t / N0) = a / {1 + exp[-k×(t - x c )]}

[0065] μ max = a×k / 4

[0066] λ = x c - 2 / k (1 - 2)

[0067] where t is the bacterial growth time (h); N t is the bacterial concentration corresponding to t (Log CFU / g); N0 is the initial bacterial concentration (Log CFU / g); a is the difference between the maximum number of bacteria and the initial number of bacteria N0; x c is the time required to reach the relative maximum growth rate; k is the relative growth rate of x c ; μ max is the maximum growth rate of microorganisms (h -1 ); λ is the bacterial growth lag time (h);

[0068] Baranyi model:

[0069]

[0070] λ = h0 / μ max (1 - 3)

[0071] where t is the time (h); Y(t) is the concentration of the bacterial population at time t (Ln CFU / g); Y0 is the initial concentration of the bacterial population (Ln CFU / g); Y max is the maximum population density (Ln CFU / g); A(t) is the regulatory function representing the physiological state of Burkholderia gladioli cells to define the lag phase; μ max is the maximum specific growth rate of bacteria (h -1 ); λ is the duration of the lag phase (h); h0 is the physiological state of microorganisms.

[0072] Establishment of the secondary model

[0073] The square root model (Equation 1-4) and the quadratic polynomial model (Equation 1-5) were used to analyze the effects of temperature on the maximum specific growth rate (μ max ) and the lag phase (λ).

[0074]

[0075]

[0076] μ max or λ = a + bT + cT 2 (1-5)

[0077] where T is the storage temperature (°C); T min is the theoretical minimum growth temperature of bacteria (°C); μ max is the maximum specific growth rate of bacteria (h -1 ); λ is the duration of the lag phase (h); a, b, and c are regression coefficients;

[0078] Comparison of goodness-of-fit parameters

[0079] The accuracy of the model was evaluated by the coefficient of determination (R 2 )(Equation 1-6), root mean square error (RMSE) (Equation 1-7), Akaike information criterion (AIC) (Equation 1-8), and accuracy factor (A f )(Equation 1-9), bias factor (B f )(Equation 1-10).

[0080]

[0081]

[0082] where n is the number of experiments, m is the number of parameters of the model, SSE is the sum of squared errors, and SST is the sum of squares. μ pre refers to the predicted μ max (h -1 ), μ obs refers to the experimental μ max (h -1 ); n refers to the number of experimental growth data.

[0083] Validation of the model

[0084] To obtain a growth curve to validate the model, the temperature of 28 °C was not used in model development. Then, the root mean square error (RMSE) value was calculated to indicate the model performance. The closer the RMSE value is to 0, the better. A value between 0.2 - 0.5 belongs to the normal experimental error range. RMSE (Equation 1 - 11) is a number determined using the difference between the observed and predicted values. Where μ pre is the predicted bacterial concentration at this temperature, μ obs is the observed bacterial concentration at this temperature, and n is the number of observation points.

[0085]

[0086] Model data statistics and graph plotting

[0087] In this example, Excel was used to perform statistical analysis on the data; IPMP - 2013 and Origin2022 software were used to fit the model.

[0088] Example 2

[0089] Establishment of a first - order model for predicting the growth of Burkholderia gladioli in soaked black fungus

[0090] IPMP - 2013 and Origin 2022 software were used to fit three first - order models at 15, 20, 26, 30, and 36 °C respectively. The fitting curves are as Figure 1 shown. The observed values of Burkholderia gladioli at each temperature are close to the model predicted values, indicating that the three prediction models (SGompertz, SLogistic, and Baranyi models) all successfully describe the growth of Burkholderia gladioli on black fungus. In this example, three comprehensive parameters were introduced: coefficient of determination (R 2 ), root mean square error (RMSE), and Akaike information criterion (AIC) to accurately fit the growth of Burkholderia gladioli in black fungus. As can be seen from Table 1, the R 2 of the three first - order models is not much different and is all above 0.98. Therefore, the optimal first - order model was selected by comparing the other two comprehensive evaluation parameters, RMSE and AIC. The comprehensive model evaluation results show that the RMSE and AIC parameters of the SGompertz model are the smallest, being 0.0874 to 0.1858 and - 57.7693 to - 38.8949 respectively. An RMSE value close to 0 indicates that the model prediction is close to the experimental data. A model with a lower AIC is considered to be of better quality, and R 2Generally considered as the overall metric of the predictions calculated by the developed model, it can assume values between 0 and 1, and a value of 1 for this metric indicates the best performance of the model (Table 1). Therefore, in this embodiment, the SGompertz model is selected as the main growth model for Burkholderia gladioli. The fitting results of the SGompertz model show that with the increase in temperature, the growth rate μ of Burkholderia gladioli max increases from 0.0486 h -1 to 0.4113 h -1 ; the lag phase shortens from 18.8222 h to 3.9671 h. It can be seen from this that temperature is the main factor affecting the growth rate of Burkholderia gladioli.

[0091] Table 1 Fitting parameters of the primary model of Burkholderia gladioli in Auricularia auricula

[0092]

[0093] Example 3

[0094] Establishment and evaluation of the secondary model

[0095] Two secondary models, namely the square root model and the quadratic polynomial model, are selected to describe the effects of temperature on the growth rate μ of Burkholderia gladioli max and the lag phase λ ( Figure 2 ). Through the comprehensive evaluation of the parameters R 2 , RMSE, and A f , B f , the most suitable secondary model is selected. It can be seen from Table 2 that the R 2 values of the two secondary models of Burkholderia gladioli are both greater than 0.95 and close to 1, and both can be accepted. However, compared with the square root model, the quadratic polynomial model has a better fitting effect on the growth rate μ of Burkholderia gladioli max and the lag phase λ, with higher R 2 values (0.9993 and 0.9913 respectively) and lower RMSE values (0.0059 and 0.5115 respectively). The A max and λ values of the quadratic polynomial model are 1.0254 and 1.0651 respectively, and the B f values are 1.0131 and 0.9978 respectively, both of which are closer to 1. A f represents the accuracy between the predicted value and the measured value, and B f represents the deviation degree between the predicted value and the measured value. According to the established criteria, A f and B f and B fValues in the range of 0.9 to 1.05 are considered ideal, and those between 1.06 and 1.15 are considered acceptable; values outside these ranges are considered unacceptable. Therefore, the quadratic polynomial model serves as the secondary model for Burkholderia gladioli. In addition, the results of this quadratic model indicate that the lowest temperature for the growth of Burkholderia gladioli in soaked black fungus is 12.9 °C. According to this study, it is recommended to keep the temperature below 12.9 °C during the soaking process of black fungus, which can effectively inhibit the growth of Burkholderia gladioli and thus reduce the risk of foodborne diseases.

[0096] Table 2 Fitting parameters of the secondary model

[0097]

[0098] The present invention comprehensively evaluates the parameters R 2 , RMSE, and AIC, and uses the modified Gompertz model (SGompertz) to fit and describe the growth of Burkholderia gladioli on soaked black fungus at different storage temperatures. A quadratic polynomial model is constructed through the primary model parameters to describe the effects of temperature on the growth rate and lag phase of Burkholderia gladioli. Through R 2 , RMSE, and A f , B f , the quadratic polynomial model is selected as the secondary model. The results of the secondary model show that the lowest growth temperature of Burkholderia gladioli is 12.9 °C, which is consistent with the results of the preliminary experiment; under the conditions of 15 - 36 °C, as the temperature increases, the growth rate μ max of Burkholderia gladioli increases from 0.0486 h -1 to 0.4113 h -1 , and the lag phase shortens from 18.8222 h to 3.9671 h. This indicates that temperature is a key factor affecting the growth rate and lag phase of Burkholderia gladioli. The predicted growth model is verified by the growth curve observed at 28 °C. The verified RMSE value is 0.205, indicating that the model can accurately fit the growth of Burkholderia gladioli in soaked black fungus.

[0099] As can be seen from the above embodiments, the growth prediction model constructed by the present invention can accurately fit the growth of Burkholderia gladioli on soaked black fungus. The present invention uses the parameter R 2, RMSE and AIC comprehensive evaluation determined that the SGompertz model was the main model to describe the growth kinetics of Burkholderia gladioli in Auricularia auricula. The model results showed that at 15, 20, 26, 30 and 36 °C, the maximum growth rates of Burkholderia gladioli were 0.0486, 0.1440, 0.2624, 0.3300 and 0.4113 Log CFU / g / h respectively. -1 . The secondary model was constructed by the primary model parameters to describe the effects of temperature on the growth rate and lag phase of Burkholderia gladioli. The quadratic polynomial model effectively served as the secondary model of Burkholderia gladioli. The results of the secondary model showed that the minimum growth temperature of Burkholderia gladioli was 12.9 °C, which was consistent with the pre-experiment results. The verification parameter RMSE was 0.205, indicating that the prediction was quite accurate, and the model could accurately fit the growth of Burkholderia gladioli in Auricularia auricula. This model clarified the minimum growth temperature of Burkholderia gladioli and provided a basic tool for evaluating the growth and safety risks of Burkholderia gladioli in food.

[0100] Through the model of the present invention, it can be specifically applied to the industrial production of Auricularia auricula as follows:

[0101] Temperature control suggestion: Strictly control the soaking and storage temperatures below 12.9 °C to significantly inhibit bacterial growth.

[0102] Dynamic adjustment strategy: If it is impossible to achieve low temperature due to equipment limitations, the bacterial proliferation rate at different temperatures can be predicted through the model. For example, in an environment of 20 °C, the predicted safe storage time of Auricularia auricula is about 24 hours; while at 26 °C, the safe time is shortened to 12 hours.

[0103] Risk assessment and hierarchical control: Use the model to quantify the microbial risks of the storage and transportation temperatures of soaked Auricularia auricula. In one embodiment, during storage and transportation, if the temperature fluctuates to 15 °C (μ max = 0.0486 h-1), the model predicts that after 48 hours, the microbial content in the soaked Auricularia auricula is still within the safe value range; while when the temperature rises to 26 °C (μ max = 0.2624 h-1), the microbial concentration will reach 5.8 Log CFU / g within the same time, exceeding the safety standard. Based on such analysis results, it can be determined that key monitoring measures need to be implemented in high-risk links such as normal temperature transportation of soaked Auricularia auricula to ensure its safety and quality.

[0104] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by those skilled in the art in this technical field based on the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art shall fall within the protection scope determined by the claims.

Claims

1. A method for constructing a growth prediction model of Burkholderia gladioli in soaked black fungus, characterized in that The method includes the following steps: Step 1: Inoculate Burkholderia gladioli with rifampicin resistance into the soaked Auricularia auricula samples, place them in incubators at multiple different temperatures for cultivation, calculate the colony count of rifampicin-resistant Burkholderia gladioli, and measure the growth curve of Burkholderia gladioli; Step 2: Use statistical analysis software to fit three first-order models at different temperatures respectively; Step 3: Select the optimal first-order model by comparing comprehensive evaluation parameters; Step 4: Select two secondary models to describe the effects of temperature on the growth rate μ max and the lag phase λ of Burkholderia gladioli; Step 5: Select the most suitable second-order model through the comprehensive evaluation of multiple parameters.

2. The method for constructing a prediction model according to claim 1, wherein In Step 1, the multiple different temperatures are 4°C, 10°C, 15°C, 20°C, 26°C, 30°C, and 36°C respectively.

3. The method for constructing a prediction model according to claim 1, wherein The three first-order models in Step 2 are: SGompertz model, SLogistic model, and Baranyi model.

4. The method for constructing the prediction model according to claim 1, characterized in that The comprehensive evaluation parameters in step 3 are: coefficient of determination, i.e., R 2 , root mean square error, i.e., RMSE, and Akaike information criterion, i.e., AIC. The selection criteria for the primary model are: R 2 is closest to 1, the closer the RMSE parameter is to 0, the better, and the AIC value is the smallest.

5. The method for constructing a prediction model according to claim 1, wherein The two second-order models in Step 4 are the square root model and the quadratic polynomial model.

6. The method for constructing a prediction model according to claim 1, wherein The multiple parameters in step 5 are: R 2 , RMSE, and accuracy factor A f , bias factor B f , and the selection criteria for the secondary model are: R 2 is closest to 1, the closer the RMSE parameter is to 0, the better, A f and B f have values between 1.06 and 1.

15.

7. The method for constructing a prediction model according to claim 4 or 6, characterized in that The RMSE parameter is 0.05 - 0.

15.

8. The method for constructing a prediction model according to claim 1, characterized in that The construction method further includes the verification of the model. When verifying the model, compare the predicted bacterial concentration and the observed bacterial concentration.

9. Application of the model constructed by the construction method according to any one of claims 1 - 8 in predicting the growth of Burkholderia gladioli in soaked Auricularia auricula.

10. The application according to claim 9, characterized in that, The model includes a first-order model and a second-order model. The first-order model is the SGompertz model, specifically as shown in Equation 1: Equation 1 lg(N t / N0) = a×exp{-exp[-k×(t - x c )]} μ max = a × k / e λ = x c -1 / k In Equation 1, t is the bacterial growth time, with the unit of h; N t is the bacterial concentration corresponding to t, with the unit of Log CFU / g; N0 is the initial bacterial concentration, with the unit of Log CFU / g; a is the difference between the maximum bacterial number N max and the initial bacterial number N0; x c is the time required to reach the relative maximum growth rate; k is the relative growth rate of x c ; μ max is the maximum growth rate of microorganisms, with the unit of h -1 ; λ is the bacterial growth delay time, with the unit of h; The second-order model is the quadratic polynomial model, as shown in Equation 2: Equation 2 μ max or λ = a + bT + cT 2 where T is the storage temperature in °C; μ max is the maximum specific growth rate of bacteria in h -1 ; λ is the lag time of bacterial growth in h; a, b, and c are model parameters.

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