An algorithm model for predicting the thermal inactivation efficiency of microorganisms in biohazardous wastewater and application thereof

Abstract

The application belongs to the technical field of biology, and discloses a construction method of an algorithm model for predicting the heat inactivation efficiency of microorganisms in biohazard wastewater. By monitoring the temperature change of the sterilization medium, the model calculates the inactivation efficiency of the microorganisms in the water heat sterilization system in real time based on an integral algorithm. The method has the dual functions of process optimization and safety evaluation, can not only optimize the water heat sterilization process parameters, but also provide key environmental risk evaluation basis for the discharge of the live toxic wastewater after heat sterilization.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology and relates to a predictive model for the thermal inactivation efficiency (disinfect rate) of microorganisms in wastewater, especially for the real-time quantitative prediction of the disinfect rate of biohazardous wastewater in the fields of medical treatment, vaccine production and biosafety. Background Technology

[0002] High-temperature hydrothermal inactivation is an effective technology for microbial inactivation in wastewater, widely used in the treatment of biohazardous wastewater in fields such as medical treatment, vaccine production, and biosafety. This technology primarily utilizes high temperatures and the thermal properties of water to disinfect microorganisms. Existing research shows that high-temperature treatment can effectively destroy the cell structure of microorganisms, leading to their inactivation. Traditional thermal inactivation technologies often employ preset non-linear time-temperature curves. While this method allows for some adjustment of sterilization conditions based on experience, it cannot accurately assess sterilization efficiency and effectiveness, making real-time monitoring and precise control of the sterilization effect difficult. This method not only risks wasting energy and time due to excessively prolonged sterilization time but also struggles to ensure comprehensive sterilization effectiveness.

[0003] In treating biohazardous wastewater, the commonly used quantitative method for microbial thermal inactivation efficiency is based on a first-order thermal inactivation mechanics model or a more advanced Bigelow model. This method integrates time, temperature, and microbial survival curves to predict the microbial thermal inactivation process, establishing a sterilization time-temperature integral model. By calibrating the model parameters based on abundant experimental data, accurate predictions of thermal inactivation mechanics can be made, calculating the time required to reach a set sterilization rate at a specific temperature, or the real-time sterilization rate. The application of this technology can significantly improve the efficiency and safety of thermal inactivation, reduce energy and time waste, and ensure the comprehensiveness and reliability of sterilization effects. This is not only of great significance for medical wastewater treatment but also plays a positive role in promoting environmental protection and public health safety.

[0004] Thermophilic Bacillus stearothermophilus ( Geobacillus stearothermophilus Due to its significant high heat resistance, the spores of [a specific organism] exhibit excellent stability and repeatability in the biovalidation of high-temperature hydrothermal sterilization processes, making it a standard specification for validating the effectiveness of high-temperature inactivation processes worldwide. G. stearothermophilus Spores are recognized as a precise microbial indicator, and using them as a quantitative benchmark can reliably ensure the complete elimination of highly resistant microorganisms during the sterilization process. Summary of the Invention

[0005] This invention proposes a mathematical algorithm model that can predict the thermal inactivation efficiency of biohazardous wastewater in real time by analyzing the temperature change curve during the thermal inactivation process. According to this algorithm model, the sterilization time and temperature of the system can be adjusted in a timely manner based on the sterilization efficiency, avoiding over-sterilization and effectively reducing energy consumption and operating costs during the wastewater thermal inactivation process. Furthermore, the parameters of the algorithm model can be appropriately corrected based on factors that may affect the inactivation efficiency in biohazardous wastewater (such as pH, organic matter, ion concentration, etc.), ensuring the reliability of the algorithm's prediction results. This invention can significantly improve the efficiency of the thermal sterilization process in the treatment of biohazardous wastewater, promoting the scientific improvement of wastewater thermal sterilization technology in fields such as medical treatment, biosafety, and vaccine production. In addition, by combining with traditional biological indicators, this algorithm model can also conduct environmental risk assessment and determination of wastewater discharge after thermal sterilization, providing a guarantee for the efficient treatment and safe discharge of biohazardous wastewater.

[0006] The present invention is achieved through the following technical solution.

[0007] The principle of the algorithm model for predicting the thermal inactivation efficiency of microorganisms in biohazardous wastewater is to process dynamic temperature changes by integrating the temperature change over time during the inactivation process. This allows for accurate prediction of the logarithmic reduction (LR) or Log Reduction of specific microorganisms under different temperature and time conditions. This is used to quantitatively predict the efficiency and effectiveness of the thermal inactivation process for biohazardous wastewater in real time, and to conduct environmental risk assessment and determination of wastewater discharge after thermal disinfection in hospitals and biosafety laboratories. Further, the prediction algorithm model is as follows:

[0008]

[0009] This includes the heat resistance parameter D of microorganisms at standard sterilization temperatures. ref (min), which is the time required for the survival rate of microorganisms to decrease to 1 / 10 of their original value, and the standard sterilization temperature T. ref (°C), heat inactivation time t (min), real-time temperature of the system T (t) (°C), temperature change Z (°C) required to reduce the microbial heat resistance parameter D by one order of magnitude (i.e., 90%).

[0010] The algorithm model according to claim 1 or 2 is characterized in that the parameter D ref The fitting of the Z value was performed using standard thermostable model microorganisms as a reference.

[0011] The algorithm model according to claim 1 or 2 is characterized in that the thermoresistant model microorganism is *Bacillus stearothermophilus* ATCC7953.

[0012] Furthermore, the preparation steps of the *Bacillus thermophilus* ATCC7953 include:

[0013] (1) Take the sample stored at -80℃ G. stearothermophilus The ATCC7953 strain was activated by culturing it in nutrient broth at 56°C for 10-16 hours.

[0014] (2) The activated bacterial solution was diluted 10-10000 times, spread on solid enrichment medium, and cultured at 56℃ for 24-48h, resulting in 20-100 colonies with a diameter of 0.5-1.5 mm.

[0015] (3) Select a single colony with clear boundaries and disperse it in 1000 μL of sterile water. Dilute it 10-10000 times and spread it on the surface of a solid sporulation medium. Incubate at 60℃ for 60-150 h to produce sporulation. Collect the spores on the surface of the plate using sterile water.

[0016] The algorithm model according to any one of claims 1-4 is characterized in that the algorithm model is established through four steps:

[0017] (1) Standard spores were heat-inactivated at different temperatures and times, and the changes in spore survival rate were determined;

[0018] (2) Based on the data from step (1) of Formula 1, obtain the heat resistance parameter D of the spores at a temperature of T (°C). T (min);

[0019] (Formula 1)

[0020] Among them, the number of original spores N0 (mL) -1 The number of spores N after heat treatment for t minutes t (mL) -1 ).

[0021] (3) Fit the heat resistance parameter D of the spores in step (2) according to formula 2. T The relationship between (min) and sterilization temperature T (°C), i.e., the heat resistance parameter D T Reduce the change in T (°C) by one logarithmic order (i.e., reduce it by 90%);

[0022] (Formula 2)

[0023] Among them, the heat resistance parameter D of spores at T ℃ T (min), Real-time sterilization temperature T (°C), Reference sterilization temperature T ref (°C), the heat resistance parameter D of spores at the reference sterilization temperature. ref(min), the temperature change Z (°C) required for the heat resistance parameter of spores to decrease by one logarithm at the reference sterilization temperature;

[0024] (4) Based on Formula 3, the LR of spores at any time point during the dynamic heating process is predicted by integrating the actual residence time t (min) at the real-time sterilization temperature T (°C). (Formula 3)

[0025] Furthermore, parameter D T Calibration can be performed based on the influence of wastewater pH on thermal inactivation efficiency.

[0026] Furthermore, the algorithm model performs real-time quantitative prediction of the thermal disinfection rate of biohazardous wastewater generated by hospitals and biosafety laboratories, and conducts environmental risk assessment and determination of wastewater discharge after thermal disinfection.

[0027] A method for predicting the thermal inactivation efficiency of microorganisms in biohazardous wastewater, characterized in that the method comprises:

[0028] Step 1) Establishment of the inactivation efficiency prediction algorithm model:

[0029] (1) The number of surviving standard heat-resistant microorganisms inactivated by heat at different temperatures and times was fitted using Formula 1 to obtain the spore heat resistance parameter D dependent on temperature T (°C). T (min);

[0030] (Formula 1)

[0031] Among them, the number of original spores N0 (mL) -1 The number of spores N after heat treatment for t minutes t (mL) -1 );

[0032] (2) Fit the heat resistance parameter D of the spores in step (2) according to formula 2. T The relationship between (min) and sterilization temperature T (°C), i.e., the heat resistance parameter D T The change in T (°C) required to reduce the temperature by one logarithmic order (i.e., a 90% reduction);

[0033] (Formula 2)

[0034] Among them, the heat resistance parameter D of spores at T ℃ T (min), Real-time sterilization temperature T (°C), Reference sterilization temperature T ref (°C), the heat resistance parameter D of spores at the reference sterilization temperature. ref (min), the temperature change Z (°C) required for the heat resistance parameter of spores to decrease by one logarithm at the reference sterilization temperature;

[0035] (3) Based on Formula 3, integrate the actual residence time t (min) at the real-time sterilization temperature T (°C) to calculate the log reduction value (LR) at any time point during the dynamic heating sterilization process, and predict the real-time inactivation rate of spores;

[0036] (Formula 3);

[0037] Step 2) Preparation of standard spores of G. stearothermophilus ATCC7953:

[0038] Store at -80℃ G. stearothermophilus ATCC7953 spore-forming glycerol tubes were activated in nutrient broth and cultured at 56°C and 180 rpm for 12 h to obtain a bacterial suspension. The suspension was diluted and spread onto the surface of solid enrichment medium, incubated at 56°C for 24-48 h until 20-100 colonies with a diameter of 1 mm appeared. Single colonies with clear boundaries were picked, mixed in 1000 μL of sterile water, diluted to an appropriate multiple, and spread onto the surface of solid sporulation medium. The medium was incubated at 60°C for 5 days, and the bacterial growth on the plate surface was collected with sterile water to obtain a mixture of spores and vegetative cells. The spore suspension was centrifuged at 4°C and 8000 rpm for 10 min, the supernatant was discarded, and the precipitate was resuspended in sterile water. The precipitate was then heated in a 99.9°C water bath for 20 min to inactivate the vegetative cells, and immediately cooled in an ice bath for 5 min. This centrifugation and resuscitation process was repeated twice, separating the spore and bacterial cell precipitate layers during the process. The resulting spore suspension was stored at 4°C.

[0039] Step 3) Model parameter fitting:

[0040] Five groups of capillaries were prepared by injecting 50 μL of standard spore suspension into quartz capillaries and heat-sealing both ends of the capillaries. One group was not sterilized, and the other four groups were exposed to specific sterilization conditions. At least one group had a spore count decrease of at least 4lg after sterilization, and the sterilization conditions of the other three groups were between those of the two groups mentioned above. After the capillaries were treated with an oil bath, they were immediately removed and placed in an ice bath at 4°C for 5 min to cool. The residual oil on the outer wall of the capillaries was cleaned with a detergent and then soaked in sodium hypochlorite solution for 15 min. The residual sodium hypochlorite solution on the outer wall was washed away with sterile water. The capillaries were then opened at both ends and blown 20 times with 5 mL of sterile water to minimize the spore residue on the tube wall. After dilution to the optimal counting concentration, the capillaries were cultured on tryptic soy peptone agar at 56°C for 24 h before counting.

[0041] Formula 1 was used to predict the inactivation of standard spores at different temperatures over time. At different temperatures, the logarithm of surviving spores showed a linear decreasing trend with increasing inactivation time. Furthermore, within the same treatment time, higher treatment temperatures resulted in fewer surviving spores, i.e., the standard spore heat resistance parameter D... T The temperature decreases as it increases; an environment with pH=8.86 and a standard sterilization temperature of 121℃ was established. G. stearothermophilus The ATCC7953 standard spore heat inactivation prediction model is as follows:

[0042]

[0043] Step 4) The effect of the pH of the sterilization hydrothermal medium on the standard spore D value shows a low-high-low trend, with the D value reaching its highest near pH=8; by fitting regression, the D value is estimated based on the actual pH of the hydrothermal medium. 121 The equation is:

[0044] D T = -0.0065x 3 + 0.0853x 2 - 0.1695x + 2.0295 (R) 2 =0.9907)

[0045] Where x is the pH value, which ranges from 4 to 10.

[0046] The beneficial effects achieved by this invention mainly include, but are not limited to, the following aspects:

[0047] 1. Compared with qualitative methods using biological and chemical indicators, this invention can quantitatively predict disinfection efficiency in real time, providing a possibility for dynamically and intelligently adjusting sterilization parameters during the sterilization process to optimize sterilization effects. This dynamic adjustment not only improves the effectiveness of sterilization but also prevents over-sterilization, thereby protecting the quality of the sterilized items.

[0048] 2. The integral calculation algorithm calculates LR in real time. Compared with the D value of the reference microorganism, this method can more accurately reflect the inactivation efficiency of microorganisms under different operating temperatures and time accumulation conditions.

[0049] 3. By integrating real-time temperature sensor data acquisition with real-time sterilization rate calculation based on model algorithms, it is easy to integrate and apply in existing sterilization systems. Attached Figure Description

[0050] Figure 1 Thermophilic lipid-lowering Bacillus G. stearothermophilus Micrographs of spore and cell suspensions of ATCC7953. Spores appear blue, and cells appear red.

[0051] Figure 2 Different thermal inactivation temperatures G. stearothermophilus Experimental values ​​of ATCC7953 standard spore viability (log CFU / mL) and fitted curves. Where · represents the measured value, and A, B, C, and D represent heat inactivation temperatures of 119℃, 121℃, 123℃, and 125℃, respectively.

[0052] Figure 3 : Different thermal inactivation temperatures G. stearothermophilus The logarithmic value of the heat resistance parameter lgD of ATCC7953 standard spores T The fitting result with temperature T is shown in the graph. Where · represents the measured value.

[0053] Figure 4 pH value for heat inactivation and G. stearothermophilus ATCC7953 spore heat resistance parameter D 121 Values ​​and fitted curves. Where · represents the measured values. Detailed Implementation

[0054] The embodiments of the present invention are described in detail below. It should be understood that the described embodiments are only some examples of the present invention. Based on the embodiments described herein, other embodiments that can be deduced by those skilled in the art without inventive effort are also within the protection scope of the present invention.

[0055] In practical applications, this model is implemented through a system combining the following hardware and software. This system includes:

[0056] (1) Temperature sensor: armored platinum resistance probe, in contact with the medium in the heating equipment, with an accuracy of 0.1°C, and a matching temperature transmitter with a data acquisition frequency of 10-15 Hz.

[0057] (2) Data acquisition and processing system: PC, serial port temperature data transmission method.

[0058] (3) Temperature acquisition, storage, model algorithm, and result output interface are implemented using Python or C++ programming.

[0059] Example 1

[0060] This invention utilizes an algorithmic model to calculate the log reduction (LR) value of microorganisms in biohazardous wastewater during the thermal inactivation process in real time. The model collects temperature data at specified intervals, calculates the LR value in real time, and quantitatively reflects the thermal inactivation efficiency of the system. Specific steps include: establishing the model, preparing standard model heat-resistant spores, performing parameter fitting, and calibrating and validating the model. Specific implementation steps include:

[0061] 1. Establishment of the inactivation efficiency prediction algorithm model

[0062] (1) The number of surviving standard heat-resistant microorganisms inactivated by heat at different temperatures and times was fitted using Formula 1 to obtain the spore heat resistance parameter D dependent on temperature T (°C). T (min);

[0063] (Formula 1)

[0064] Among them, the number of original spores N0 (mL) -1 The number of spores N after heat treatment for t minutes t (mL) -1 ).

[0065] (2) Fit the heat resistance parameter D of the spores in step (2) according to formula 2. T The relationship between (min) and sterilization temperature T (°C), i.e., the heat resistance parameter D T The change in T (°C) required to reduce the temperature by one logarithmic order (i.e., a 90% reduction);

[0066] (Formula 2)

[0067] Among them, the heat resistance parameter D of spores at T ℃ T (min), Real-time sterilization temperature T (°C), Reference sterilization temperature T ref (°C), the heat resistance parameter D of spores at the reference sterilization temperature. ref (min), the temperature change Z (°C) required for the heat resistance parameter of spores to decrease by one logarithm at the reference sterilization temperature;

[0068] (3) Based on Formula 3, integrate the actual residence time t (min) at the real-time sterilization temperature T (°C) to calculate the log reduction value (LR) at any time point during the dynamic heating sterilization process, and predict the real-time inactivation rate of spores;

[0069] (Formula 3)

[0070] 2. G. stearothermophilus Preparation of ATCC7953 standard spores

[0071] Store at -80℃ G. stearothermophilusATCC7953 spore-forming glycerol tubes were activated in nutrient broth and cultured at 56°C and 180 rpm for 12 h to obtain a bacterial suspension. The suspension was diluted and spread onto the surface of solid enrichment medium and incubated at 56°C for 24-48 h until 20-100 colonies with a diameter of approximately 1 mm appeared. Single colonies with clear boundaries were picked, mixed with 1000 μL of sterile water, diluted to an appropriate multiple, and spread onto the surface of solid sporulation medium. The plates were incubated at 60°C for 5 days, and the bacterial growth on the surface was collected using sterile water to obtain a mixture of spores and vegetative cells. Centrifuge the spore suspension at 4 ℃ and 8000 rpm for 10 min, discard the supernatant, resuspend the precipitate in sterile water, heat in a 99.9 ℃ water bath for 20 min to inactivate the vegetative cells, and immediately cool in an ice bath for 5 min. Repeat the centrifugation and resuspension process twice, separating the spore precipitate layer and the bacterial cell precipitate layer during the process. Store the prepared spore suspension at 4 ℃ (Appendix). Figure 1 The enrichment medium consisted of 3.0 g / L soluble starch, 10.0 g / L tryptone, 10.0 g / L sodium chloride, and 15.0 g / L agar, with a pH of 7.0 ± 0.2. The sporulation medium consisted of 3.0 g / L yeast extract, 10.0 g / L tryptone, 10.0 g / L sodium chloride, 0.022 g / L manganese sulfate monohydrate, 0.246 g / L magnesium sulfate heptahydrate, 0.110 g / L anhydrous calcium chloride, 0.962 g / L potassium chloride, and 15.0 g / L agar, with a pH of 7.0 ± 0.2.

[0072] 3. Model parameter fitting

[0073] Inject 50 μL of standard spore suspension into a 0.9-1.1×150 mm quartz capillary and heat-seal both ends of the capillary to prepare five sets of capillary tubes (no less than three tubes in each set). One set is not sterilized, and the other four sets are exposed to specific sterilization conditions (e.g., constant temperature oil bath at 119±0.5℃ for 6, 10, 14, and 18 min). At least one set has a spore count that decreases by no less than 4lg after sterilization, and the sterilization conditions of the other three sets are between those of the two sets mentioned above. After the capillary tubes were treated with an oil bath, they were immediately removed and placed in an ice bath at 4°C for 5 minutes to cool. The residual oil on the outer wall of the capillary tubes was cleaned with a detergent, and then soaked in a 525 ppm sodium hypochlorite solution for 15 minutes. The residual sodium hypochlorite solution on the outer wall was washed away with sterile water. The capillary tubes were then opened at both ends and blown about 20 times with 5 mL of sterile water to minimize the spore residue on the tube wall. After diluting to the optimal counting concentration, the capillary tubes were cultured on tryptic soy peptone agar (TSA) at 56°C for 24 hours before counting.

[0074] Formula 1 was used to predict the inactivation of standard spores at different temperatures over time (Appendix). Figure 2At different temperatures, the logarithm of surviving spores decreased linearly with increasing inactivation time. Furthermore, within the same treatment time, higher treatment temperatures resulted in fewer surviving spores, i.e., the standard spore heat resistance parameter D... T It decreases as the temperature rises.

[0075] Table 1. Different sterilization temperatures G. stearothermophilus The heat resistance parameter D of ATCC7953 standard spores T pH value (8.86)

[0076]

[0077] The heat resistance parameter D of spores in Table 1 was fitted using Formula 2. T The relationship with the change in temperature T was calculated. G. stearothermophilus The Z-value of ATCC7953 standard spores is 7.13℃ (see attached image). Figure 3 Based on this, an environment with a standard sterilization temperature of 121℃ and a pH of 8.86 was established. G. stearothermophilus The ATCC7953 standard spore heat inactivation prediction model is as follows:

[0078]

[0079] 4. Correction of model parameter D under the condition that pH changes in wastewater affect the efficiency of spore heat inactivation.

[0080] Standard spore suspensions were diluted in aqueous solutions at pH values ​​of 4, 6, 8, 10, and 12. 50 μL of the spore suspension at a specific pH was injected into a 0.9–1.1 × 150 mm quartz capillary tube, and both ends of the capillary were heat-sealed. After incubating the capillary in an oil bath at 121 ± 0.5 °C for a specific time, it was immediately removed and cooled in an ice bath at 4 °C for 5 min. Residual silicone oil on the outer wall of the capillary was cleaned with a detergent, and the tube was then immersed in a 525 ppm sodium hypochlorite solution for 15 min. The sodium hypochlorite residue on the outer wall was washed away with sterile water. The capillary was then opened at both ends and agitated approximately 20 times with 5 mL of sterile water to minimize spore residue on the tube wall. After diluting to the optimal counting concentration, the tube was incubated on tryptic soy agar (TSA) at 56 °C for 24 h before counting.

[0081] The effect of pH on the spore D value of the sterilization hydrothermal medium showed a low-high-low trend, with the D value reaching its highest value near pH=8 (see appendix). Figure 4 By fitting regression, D 121 The correction equation is:

[0082] D T = -0.0065x 3 + 0.0853x 2- 0.1695x + 2.0295 (R) 2 =0.9907)

[0083] Where x is the pH value, which ranges from 4 to 10.

[0084] Example 2

[0085] The model of this invention was applied and its reliability was determined. The model should be used to calculate the real-time inactivation efficiency (LR) of microorganisms at time point t (min) after the start of sterilization. t The data was compared and analyzed with actual sampling data. It is known that at a sterilization environment pH=8.86, the spore density was experimentally measured... 121 The time is 2.7021 min. Substituting pH=8.86 into the pH correction equation, we get: D （121，pH=8.86） =2.7030 min, the two values ​​are very close, with a deviation of only 0.03%. The sterilization condition parameters are input into the algorithm model to predict LR, and the error (ɛ) and error rate () between the experimentally measured LR values ​​are calculated. ).

[0086] Table 2 Comparison of actual sterilization effects and model prediction effects under different sterilization conditions

[0087]

[0088] The results show that, under different heat inactivation conditions, the error rate between the prediction results and the actual measurement results of the model described in this invention is controlled within 20%, and the error is controlled within 0.3 logarithms.

[0089] The smaller the sample size, the higher the error rate, and the calculated error rate may have greater randomness and instability. Furthermore, the error rate calculation only considers the correctness of the result, without taking into account the specific difference between the predicted and actual values. Therefore, in some cases, a large error rate may exist with a small error, or vice versa.

[0090] Because the model algorithm assumes that sterilization temperature, time, and other parameters are constant, various uncertainties exist in actual applications. Changes in factors such as the operator's skill level, equipment precision, water quality, and initial spore count can all lead to deviations between actual and predicted values, which constitute errors. If we assume the experimental temperature fluctuates within ±0.5℃ of the set sterilization temperature, and accordingly change the model's sterilization temperature range, the predicted LR range obtained through reanalysis is shown in the table. Comparison of the data in the table shows that the experimentally measured LRs are all within the predicted LR range, achieving a prediction accuracy of 100%.

[0091] While the specific embodiments of the present invention have been described above in conjunction with examples, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that any modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection of the present invention.

Claims

1. A method for constructing a biohazardous wastewater microorganism thermal inactivation efficiency prediction algorithm model, characterized by, The method comprises the following steps: (1) The survival number of heat-resistant model microorganisms under different temperatures and time is determined, and formula 1 is used for fitting: (Formula 1) Wherein, T is the treatment temperature, unit ℃; N0 is the original spore number, unit mL -1 ; N t is the spore number after heat treatment t min, unit mL -1 ; D T is the heat-resistant parameter of spore, unit min; (2) D is fitted according to Formula 2 T the relationship with the change in sterilization temperature T, i.e. the heat resistance parameter D T the value of the change in T required to reduce by one logarithmic unit, i.e. by 90% (Formula 2) wherein T ref is the standard reference sterilization temperature in °C; D ref is the heat resistance parameter of the spores at the standard reference sterilization temperature, in min; Z is the temperature change in °C required to decrease the D ref value by one logarithmic unit at T ref . (3) The actual residence time t at the real-time sterilization temperature T is integrated, the log reduction LR at any time point in the dynamic temperature rising sterilization process is calculated, and a prediction algorithm model is formed, see formula 3, which is used for predicting the real-time inactivation rate of spores; (Formula 3).

2. The construction method of claim 1, wherein, Parameter D T The influence of pH on the efficiency of heat inactivation was calibrated.

3. The construction method according to claim 1 or 2, characterized in that, The heat-resistant microorganism is Geobacillus stearothermophilus ATCC 7953, i.e. Geobacillus stearothermophilus ATCC 7953.

4. The construction method according to claim 3, characterized in that, The spore suspension preparation step of the Geobacillus stearothermophilus ATCC7953 is as follows: The strain stored in a glycerol tube at-80 DEG C is activated and cultured in a nutrient broth at 56 DEG C for 12 hours to obtain a bacterial suspension; the bacterial suspension is diluted and coated on a solid enrichment culture medium; the solid enrichment culture medium is cultured at 56 DEG C for 24-48 hours until 20-100 colonies with a diameter of 1 mm appear; a single colony with clear boundaries is picked up in 1000 microliters of sterile water, mixed and diluted, and coated on the surface of a spore-producing solid culture medium; the spore-producing solid culture medium is cultured at 60 DEG C for 5 days; the bacterial layer on the surface of the plate is collected in sterile water to obtain a mixture of spores and bacterial cells; the spore and bacterial cell mixture is centrifuged at 4 DEG C, the precipitate is collected and resuspended in sterile water, and the bacterial cells are inactivated by heating in a 100 DEG C water bath for 20 minutes; the mixture is immediately transferred to an ice bath for cooling; the centrifugation-resuspension operation is repeated twice, and the recovered spore layer is resuspended to obtain a spore suspension, which is stored at 4 DEG C.

5. The construction method according to claim 4, characterized in that, The solid enrichment culture medium comprises soluble starch 3.0 g / L, tryptone 10.0 g / L, sodium chloride 10.0 g / L, and agar 15.0 g / L, and has a pH value of 7.0±0.2; the spore-producing solid culture medium comprises yeast extract powder 3.0 g / L, tryptone 10.0 g / L, sodium chloride 10.0 g / L, manganese sulfate monohydrate 0.022 g / L, magnesium sulfate heptahydrate 0.246 g / L, anhydrous calcium chloride 0.110 g / L, potassium chloride 0.962 g / L, and agar 15.0 g / L, and has a pH value of 7.0±0.

2.

6. The construction method of claim 5, wherein, The parameter fitting step in the model is as follows: five groups of sealed quartz capillary tubes containing 50 μL standard spore suspension are prepared; one group is used as a blank control and is not treated, and the other four groups are treated by oil bath heating and inactivation; after the capillary tubes are treated by oil bath heating, they are immediately transferred to a 4°C ice bath for cooling for 5 min, the residual oil on the outer wall of the capillary tube is cleaned with a cleaning agent, and the capillary tube is immersed in sodium hypochlorite solution for 15 min, the residual sodium hypochlorite solution on the outer wall is washed away with sterile water, the two ends of the capillary tube are opened, the capillary tube is blown in 5 mL sterile water for 20 times, and the spores on the tube wall are reduced as much as possible; the spore suspension is collected and diluted, spread on trypticase soy protein peptone agar medium, incubated at 56°C for 24 h, and counted; the treatment conditions need to ensure that after heat inactivation treatment, the number of surviving spores in at least one of the four groups is reduced by not less than 4 log units, i.e., not less than 4 lg values; the inactivation effects of the other three groups are between the blank control group and the group with a reduction in the number of surviving spores of not less than 4 lg values; formula 1 is used to predict the heat inactivation of the standard spores, and the logarithmic value of the surviving spores at different temperatures is linearly and negatively correlated with the inactivation time; the heat-resistant parameter D T value calculated by the model decreases with the increase of the inactivation temperature.

7. The construction method of claim 6, wherein, Under the conditions of a standard sterilization temperature of 121 DEG C and a sterilization hot water medium with a pH value of 8.86, the heat inactivation prediction model of the standard spores of the Geobacillus stearothermophilus ATCC7953 is shown in formula 4: (Formula 4) Wherein, LR is the log reduction value of the active Geobacillus stearothermophilus ATCC7953 standard spores in the dynamic temperature rising sterilization process; T is the real-time sterilization temperature, in DEG C; t is the actual residence time of the system at the temperature T, in minutes.

8. The construction method of claim 7, wherein, Standard Spore D 121 Values are calculated using Equation 5; D 121 = -0.0065x 3 + 0.0853x 2 - 0.1695x + 2.0295 (Formula 5) Wherein, x is the actual pH value of the hot water medium around the spores at 121 DEG C, and the value range is 4-10.

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