CN111027749A - Method for establishing and applying shelf life prediction model of chilled fresh meat - Google Patents
Method for establishing and applying shelf life prediction model of chilled fresh meat Download PDFInfo
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Abstract
The invention aims to disclose a method for establishing and applying a chilled fresh meat shelf life prediction model. The model can predict the growth condition of the psychrophilic putrefying bacteria according to the initial quantity of the psychrophilic putrefying bacteria on the surface of the meat, the packaging method (common, vacuum and air-conditioned) of the chilled meat, and the temperature change in the storage and cold chain transportation processes, and then the maximum detection concentration of the psychrophilic putrefying bacteria is 6.5logCFU/cm2The shelf life of the cold fresh meat is predicted in real time. The model is suitable for any chilled meat product, and provides powerful technical support for quality control of chilled meat.
Description
Technical Field
The invention belongs to the technical field of meat product storage and cold-chain logistics monitoring, and particularly relates to a method for predicting shelf lives of cold fresh meat using different packaging modes at fluctuating temperatures.
Background
Chilled meat is more and more popular among consumers due to its excellent taste and safety. At present, the chilled fresh meat in the market of China only accounts for 20-30% of the total consumption of pork, and has huge market development potential compared with 90% of the level of developed countries such as Europe, America and Japan.
Microorganisms are a critical factor in causing deterioration of chilled fresh meat. When microorganisms naturally polluted on the surface of the meat grow and breed to a certain amount, the cold fresh meat has peculiar smell and discolor, and the quality and the edible safety are seriously influenced. The growth of microorganisms is influenced by a number of factors, but temperature is the most critical growth limiting factor for processed chilled meat. The standard cold chain control requires the temperature to be controlled between 0 and 4 ℃, but the improper temperature control can occur in the actual cold chain logistics process of storage, transportation and distribution, so that the microorganisms can be rapidly proliferated, and the deterioration of the chilled fresh meat is accelerated. The traditional detection method needs to carry out sampling microorganism detection on cold fresh meat, is time-consuming and labor-consuming, and results need to be known after 48 hours or more, so that serious delay is caused. The prediction model can perform simulation operation on the quantity change trend according to the growth rule of microorganisms in the chilled fresh meat under different temperature conditions, so that the aim of predicting the shelf life of the chilled fresh meat in real time is fulfilled.
Chinese patent documents CN102650632B, CN104293878A and CN104298868A disclose methods for predicting microbial growth and sensory changes in chilled meat, respectively, thereby predicting shelf life. However, all of the growth of mesophilic spoilage bacteria (microorganisms detected by culturing at 30 ℃ or 37 ℃) is predicted in these inventions. Whereas the cold chain temperature conditions are controlled essentially in the range of 0-4 ℃ and occasionally rise to 8-10 ℃ in a short time. Therefore, the deterioration of chilled meat is judged by mesophilic spoilage bacteria with a great deviation. This is why these patents, in addition to predicting the growth of mesophilic spoilage bacteria, also build models to specifically predict sensory indexes, and find no clear corresponding relationship between spoilage bacteria and sensory indexes (i.e. key amount of spoilage bacteria is different at different temperatures), which results in too complicated modeling and large error of prediction result.
Research results of the research institute of Denmark (national food institute of Denmark, meat research institute of Denmark) show that the key microorganism causing the spoilage of cold fresh meat under the control of cold chain temperature conditions is psychrophilic spoilage bacteria, not mesophilic spoilage bacteria. Addiction to foodThe cold spoilage bacteria are particularly the spoilage bacteria which can grow on the cold fresh meat in a cold chain control temperature range (0-5 ℃), and are a general term for the spoilage bacteria including pseudomonas, lactobacillus, spoilage bacillus, partial enterobacter psychrophilus and the like. Different from mesophilic putrefying bacteria, the putrefaction of cold fresh meat is judged by the growth amount of psychrophilic putrefying bacteria, and a clear corresponding relation is provided. According to the research result of the research organization in Denmark, the psychrophilic putrefying bacteria reaches 6.5log CFU/cm2Later, the cold fresh meat has obvious putrefaction characteristics, so most of the meat enterprises in Denmark have the psychrophilic putrefactive bacteria of 6.5log CFU/cm2The limit of the shelf life of the chilled fresh meat is defined.
It is currently common in europe and america to extend the shelf life of chilled meat by means of vacuum or modified atmosphere packaging. The consumption mode of the fresh meat in China is in a severe change period, and particularly the development of the fresh electricity supplier inevitably pushes the consumption of the cold fresh meat in China to develop towards the sale mode of the pre-packaging in Europe and America. Numerous studies have demonstrated that vacuum or modified atmosphere packaging can effectively inhibit the growth of microorganisms in chilled meat, but no quantitative relationship has been found to the effect of vacuum or modified atmosphere packaging on the growth of psychrophilic spoilage bacteria. According to the invention, the rapid prediction of the quantity of the psychrophilic spoilage bacteria under the dynamic temperature condition of cold chain logistics is realized by establishing a growth model of the psychrophilic spoilage bacteria in the cold fresh meat which is common (or equivalent to no package), air-conditioned and vacuum-packaged, so that the purpose of predicting the shelf life of the cold fresh meat is achieved, and a rapid, simple and convenient quality monitoring method is provided for the logistics distribution process.
Disclosure of Invention
The invention aims to provide a method for establishing and applying a model for predicting the shelf life of chilled fresh meat. The model can predict the growth condition of the psychrophilic putrefying bacteria according to the initial quantity of the psychrophilic putrefying bacteria on the surface of the meat, the packaging method (common, vacuum and air-conditioned) of the chilled meat, and the temperature change in the storage and cold chain transportation processes, and then the maximum detection concentration of the psychrophilic putrefying bacteria is 6.5logCFU/cm2The shelf life of the cold fresh meat is predicted in real time. The model is suitable for any chilled meat product, and provides powerful technical support for quality control of chilled meat.
The invention firstly provides a method for establishing a prediction model of the shelf life of chilled fresh meat, which comprises the following steps:
(1) respectively placing cold fresh meat samples of different packages at different temperatures for constant-temperature storage, and respectively sampling at regular time to determine the number of psychrophilic putrefying bacteria in the cold fresh meat by adopting a standard destructive sampling method and a microbial detection method;
(2) fitting the concentration data of the psychrophilic spoilage bacteria of the chilled fresh meat measured in the step (1) under different constant-temperature storage conditions in different packaging modes by using a Baranyi and Roberts first-class model (formula A) to obtain the maximum specific growth rate mu of the psychrophilic spoilage bacteria under the conditionsmaxAnd a lag period tlag；
Wherein:
wherein t is time, N0Is the initial seed concentration, NtConcentration of the seed at time t, NmaxThe maximum concentration to which the strain can grow, mumaxIs the maximum specific growth rate of the strain, h0The strain physiological state parameters;
(3) the maximum specific growth rate μ obtained in the first-order modelmaxFitting to obtain μ with a square root two-level model (formula C)maxThe change relation with the temperature T is calculated by the lowest root mean square deviation of the measured value and the fitting value
(RMSE) to obtain constants b and Tmin(formula D) was chosen to describe the maximum specific growth rate μmaxAnd a lag period tlagAnd passes tlagFind h0；
μmax＝b×(T-Tmin)2(C)
Wherein T is temperature, mumaxFor maximum specific growth rate of the strain at temperature T, TminIs the theoretical minimum growth temperature, b is a constant, tlagIs the lag phase of the strain;
the formula for root mean square difference (RMSE) is:
wherein VMeasured in factIs a measured value, VFittingFor model fit values, n is the total number of measured values, and p is the number of parameters to be found in the model formula (here only two parameters b and TminAnd thus p is 2).
The packaging mode in the step (1) comprises common packaging, modified atmosphere packaging or vacuum packaging.
The common package means that the cold fresh meat is covered by a common plastic preservative film.
The modified atmosphere packaging means that the chilled meat is packaged by adopting a plastic tray and a plastic film which have good air tightness and oxygen isolation, and the air in the package is replaced by mixed gas.
The vacuum packaging means that the chilled fresh meat is packaged by a plastic film with good sealing property and oxygen isolation property and then is vacuumized.
The invention also provides application of the prediction model in predicting the shelf life of the chilled fresh meat, which comprises the following specific steps:
(1) actually measuring the initial amount of psychrophilic putrefying bacteria on the surface of the chilled fresh meat before cooling and acid discharge;
(2) recording the initial amount of the psychrophilic putrefying bacteria, the temperature and the time consumption of each link of acid discharge, cutting, packaging, storage and cold chain transportation and the packaging mode into the prediction model, and predicting the strain concentration of the psychrophilic putrefying bacteria on the surface of the chilled fresh meat at different times;
(3) according to the maximum detection concentration of psychrophilic putrefying bacteria of 6.5logCFU/cm2The shelf life of the cold fresh meat is predicted in real time.
The prediction model can predict different uses under the fluctuating temperatureAnd (3) the growth condition of psychrophilic putrefying bacteria in the cold fresh meat in a packaging mode is adopted, and then the shelf life of the cold fresh meat is predicted in real time by combining a prediction model with a cold chain monitoring system. The method has the advantages that: firstly, the traditional microorganism detection means is not needed, and the remaining shelf life of the product can be directly predicted by only knowing the adopted packaging mode, the temperature change condition and the used time of a cold chain in the processing, storage and transportation processes and the initial quantity of psychrophilic putrefying bacteria. The traditional detection method needs to sample the chilled fresh meat for microbial detection, and the result can be known only after 48 hours, so that the hysteresis is serious; secondly, the key microorganism causing the spoilage of the cold fresh meat under the cold chain condition is psychrophilic spoilage bacteria which reach 6.5log CFU/cm2After-cooling the fresh meat, obvious spoilage characteristics are produced. The modeling process for predicting the shelf life of the chilled meat directly through the growth amount of the psychrophilic putrefying bacteria is simple, the prediction error is small, and the shelf life can be predicted without distinguishing the types of the psychrophilic putrefying bacteria. The existing prediction model only predicts the growth of mesophilic microorganisms or only predicts pseudomonas psychrophilic putrefying bacteria, only the pseudomonas is not enough for predicting the shelf life, a model is also required to be established for predicting the growth of the mesophilic microorganisms, a clear corresponding relation between the putrefying bacteria and the sensory indexes cannot be found, and the result error of complex modeling prediction is large.
The prediction model can accurately predict the shelf life of the chilled fresh meat in different packaging modes at fluctuating temperature, real-time monitoring and prediction results can be mastered at any time, artificial intelligent monitoring is realized, and after the results are directly presented to consumers through a traceability system, the consumers can have visual understanding on the product quality, and the confidence of the consumers on the food safety is improved.
Drawings
FIG. 1 shows the maximum specific growth rate mu of psychrophilic putrefying bacteria measured at different temperaturesmaxAnd a lag period tlagComparing with the corresponding fitting value of the secondary model, ◇ is measured value, the solid line is predicted value, (a) the maximum specific growth rate mu of common packagemax(best fit results were obtained after square root conversion of the data); (b) lag period t of ordinary packaginglag(data obtained by natural logarithm transformation of best fit results); (c) maximum specific growth rate mu of modified atmosphere packagingmax(ii) a (d) Lag time t of modified atmosphere packaginglag(data obtained by natural logarithm transformation of best fit results); (e) vacuum packed lag phase tlag；
FIG. 2 is a graph showing the actual measurement result of the growth of psychrophilic putrefying bacteria in the whole process of processing and transporting a batch of air-conditioned packaged chilled fresh meat products, compared with the predicted result, (a) the processing and transporting process and the corresponding temperature fluctuation change of the product are shown, which is the parameter input interface of the prediction software, (b) the actual measurement result of the growth of psychrophilic putrefying bacteria is compared with the predicted result, (◇) the actual measurement value is shown, the solid line is the predicted value, and the dotted line is the predicted value of +/-1.0 log CFU/cm2Acceptable prediction horizon of; wherein 5 repeated samples were tested in total at the beginning (time 0 point), and the average value was taken to obtain an initial psychrophilic spoilage bacteria amount of 2.45log CFU/cm 22 replicate samples were measured at each of the remaining time points;
FIG. 3 is a graph showing the actual measurement result of the growth of psychrophilic putrefying bacteria in the whole process of processing and transporting a batch of vacuum-packed chilled fresh meat products and comparing the actual measurement result with the predicted result, (a) the processing and transporting process and the corresponding temperature fluctuation change of the product, which are the parameter input interface of the prediction software, (b) the actual measurement result of the growth of psychrophilic putrefying bacteria is compared with the predicted result, (◇) the actual measurement value is shown, the solid line is the predicted value, and the dotted line is the predicted value of +/-1.0 log CFU/cm2Acceptable prediction horizon of; wherein a total of 5 replicate samples were tested initially (time 0) and averaged to give an initial psychrophilic spoilage bacteria count of 2.75log CFU/cm2For each of the remaining time points, 2 replicate samples were tested.
Detailed Description
The process of the present invention is described in detail below with reference to specific examples.
The chilled fresh meat used in the embodiment of the invention is prepared by slaughtering pork of a certain brand according to the standard process of a slaughterhouse, cleaning, cutting into meat blocks of about 200g (mainly leg meat, each block has a circular sampling area with the radius of 2.5 cm), placing in a sterile plastic bag, placing on ice, and transporting to a laboratory.
The packaging material adopted by the invention is a PP rigid plastic tray of Faerch Plast company and a complete oxygen-barrier packaging film of Cryovac company. The gas in the package was replaced with a MAP Mix 9001 modified atmosphere system from Dansensor. The vacuum packaging machine is Komet X200.
Example 1 establishment and application of chilled fresh meat shelf life prediction model using different packaging modes at fluctuating temperatures
Firstly, establishing a prediction model
1. The concentration of psychrophilic spoilage bacteria was determined for modeling when the chilled meat samples were stored under different constant temperature storage conditions using different packaging regimes.
1.1 sample treatment
All the meat blocks are put into the same large-sized sterile plastic bag and manually turned and mixed, so that the aim of making the number of the polluted bacteria on the surfaces of all the meat bodies more uniform is fulfilled. 5 meat samples were taken for detection of psychrophilic spoilage bacteria (this is the initial psychrophilic spoilage bacteria count). The meat pieces are then grouped in preparation for packaging.
1.2 sample packaging
Plain packaging (equivalent to no packaging): each meat sample was placed in a rigid plastic tray and covered with a common monolayer of plastic wrap. The ordinary package and the non-package both make the flesh directly contact with natural air, and have no difference on the growth influence of microorganisms, so the ordinary package and the non-package share the same model parameters.
Modified atmosphere packaging: placing each meat sample into a rigid plastic tray, packaging with a complete oxygen-barrier packaging film, and introducing 80% O gas into the package2+20％CO2Is replaced by the gas (c).
And (3) vacuum packaging: each meat sample was placed in a rigid plastic tray, and packed with a complete oxygen barrier packaging film at a vacuum strength of-683 mbar.
1.3 growth culture test
Storing the cold fresh meat packaged by different packaging schemes in constant temperature boxes of-1 ℃, 2 ℃, 5 ℃ and 8 ℃ respectively, taking out samples and opening the packages for detecting the quantity of psychrophilic putrefying bacteria after a proper time interval. 2 replicate samples were taken at each time point. In order to verify the repeatability of the results and enable the model to have higher fault-tolerant capability, 2 batches of meat blocks slaughtered on different days are respectively subjected to growth culture experiments. This produced 2 separate growth curve results for each packaging method at the same temperature.
1.4 detection of psychrophilic spoilage bacteria
Sampling of flesh surface the flesh surface was sampled according to the destructive sampling method of ISO 17604 using a circular stainless steel sampler with an inner radius of 2.5cm and a scalpel. Detection of spoilage bacteria psychrophilic bacteria microorganism detection method according to ISO 4833, specifically culturing for 10 days at 6.5 deg.C using PCA solid medium.
2. Baranyi and Roberts primary models (formula A) were used to predict the growth change of psychrophilic putrefying bacteria at a constant temperature over time as follows:
wherein the content of the first and second substances,
wherein t is time. N is a radical of0At the initial seed concentration. N is a radical oftThe concentration of the bacterial species at time t. N is a radical ofmaxIs the maximum concentration to which the strain can grow. Mu.smaxThe maximum specific growth rate of the strain. h is0The physiological state parameter of the strain is a constant.
The method comprises the following specific steps:
the method comprises the steps of carrying out Baranyi and Roberts first-class model fitting on the concentration data of the psychrophilic spoilage bacteria of the chilled fresh meat measured in the step 1 under different constant-temperature storage conditions in different packaging modes, and completing the fitting through DMFit software published by Combase (www.combase.cc) to obtain the maximum specific growth rate mu of the psychrophilic spoilage bacteria under the conditionsmaxAnd a lag period tlag。
Maximum growth concentration NmaxThe value of (A) was directly set from the average of more historical accumulated data, 9.0log CFU/cm for normal and modified atmosphere packaging2For vacuum packaging, 8.0log CFU/cm2. The results are shown in Table 1. Wherein the psychrophilic spoilage bacteria directly grow under vacuum packaging condition, and no lag phase is found, so that all lag phases t are observed under vacuum packaging conditionlagAre both 0.
TABLE 1 maximum specific growth rate μ of psychrophilic spoilage bacteria measured at different packaging methods and different temperaturesmaxAnd a lag period tlag。
3. The square root secondary model (formula C) is selected to predict the maximum specific growth rate mumaxSelecting strain physiological state parameter h according to the variation relation of temperature0To describe the lag period tlagGrowth rate mu according to maximum ratiomaxChange (formula D).
μmax＝b×(T-Tmin)2(C)
Wherein T is temperature, mumaxFor maximum specific growth rate of the strain at temperature T, TminIs the theoretical minimum growth temperature, b is a constant, tlagIs the lag phase of the strain.
The method comprises the following specific steps:
the maximum specific growth rate μ obtained in the first-order modelmaxAnd a lag period tlagFitting the data to a two-stage model (formula C), and calculating the minimum root mean square difference (RMSE) between the measured and fitted values to obtain constants b and Tmin. And passes through tlagFind h0(formula D). This is done via a Solver plug-in from Microsoft Excel. The formula of the root mean square difference (RMSE) is as follows:
wherein VMeasured in factIs a measured value, VFittingFor model fit values, n is the total number of measured values, and p is the number of parameters to be found in the model formula (here only two parameters b and TminAnd thus p is 2).
For model prediction result evaluation under the fluctuating temperature, the invention adopts an evaluation method aiming at the fluctuating temperature environment, which is commonly used in the mainstream academic circles in the world at present, and an acceptable prediction range (acceptable prediction zone) evaluation method, namely: the predicted value is +/-1.0 log CFU/cm2The range of (2) is set as an acceptable prediction range, and if more than 80% of the measured values are within the acceptable prediction range, the model prediction result is qualified. As shown in Table 2, the RMSE values were all below 0.2, indicating that the fitting results were all good.
Measured maximum specific growth rate mumaxAnd a lag period tlagThe model fit values compared to their counterparts are shown in FIG. 1.
TABLE 2 Secondary model parameters obtained by fitting the measured values
4. And (3) combining the primary model and the secondary model, and predicting the growth condition of the psychrophilic spoilage bacteria according to the initial concentration of the psychrophilic spoilage bacteria on the surface of the chilled fresh meat, the temperature in the storage and cold chain transportation processes and the packaging mode.
(1) Actually measuring the initial amount of psychrophilic putrefying bacteria on the surface of the chilled fresh meat before cooling and acid discharge;
(2) recording the initial amount of the psychrophilic putrefying bacteria, the temperature and the time consumption of each link of acid discharge, cutting, packaging, storage and cold chain transportation and the packaging mode into the model, and predicting the strain concentration of the psychrophilic putrefying bacteria on the surface of the chilled fresh meat at different times;
(3) according to the maximum detection concentration of psychrophilic putrefying bacteria of 6.5logCFU/cm2The shelf life of the cold fresh meat is predicted in real time.
Second, the shelf life evaluation and verification of the chilled fresh meat in different packaging modes by using a prediction model
1. Shelf life evaluation and verification of chilled fresh meat in modified atmosphere packaging mode
To verify the accuracy of the prediction results of the model of the invention, the applicant tested a batch of air-conditioned chilled fresh meat products for psychrophilic spoilage bacteria after undergoing a series of processing and transportation processes, and sampled at day 0 (5 repeated samples at time 0 to determine the initial value of psychrophilic spoilage bacteria, out of consideration), day 2, day 5, day 7, day 9, day 11, and day 13, and two replicates at each time point. And (3) displaying an actual detection result: the concentration of psychrophilic spoilage organisms on day 9 is less than 6.5log CFU/cm2On day 11, the concentration of psychrophilic spoilage bacteria is more than 6.5log CFU/cm2. Thus, the shelf life of the product should be determined to be 10 days, as measured.
FIG. 2(a) is an input interface of a prediction model, and applicants record the initial amount of psychrophilic putrefying bacteria in the product, the temperature and time consumption of each link of acid discharge, cutting, packaging, storage and cold chain transportation, and the packaging mode into the prediction model of the invention to predict the strain concentration of psychrophilic putrefying bacteria on the surface of the chilled fresh meat of the product at different times. As a result, as shown in FIG. 2(b), the concentration of psychrophilic putrefying bacteria was less than 6.5log CFU/cm on day 102After 10.4 days, the growth of the psychrophilic putrefying bacteria exceeds 6.5log CFU/cm2So the model predicts that the product should have a shelf life of 10 days.
Furthermore, as shown in FIG. 2(b), the comparison between the actual measurement and the predicted result shows that only 1 point among 12 actual measurement points is not within. + -. 1.0logCFU/cm2Within the acceptable prediction range, the prediction accuracy reaches 11/12-91.7%, which is higher than 80%, and the prediction result is good.
The results show that the prediction result of the model is completely consistent with the actual detection result, and the accuracy is high.
2. Shelf life evaluation and verification of chilled fresh meat in vacuum packaging mode
In order to verify whether the prediction result of the model is accurate, the applicant detects the psychrophilic spoilage bacteria of a batch of vacuum-packaged chilled fresh meat products after a series of whole processing and transportation processes, and 5 repeated samples at the 0 th day (time 0 point) are respectively used for determining the psychrophilic spoilage bacteriaInitial values, not considered), day 3, day 6, day 9, day 12, day 15, day 17, day 21 samples were taken in duplicate at each time point. And (3) displaying an actual detection result: the concentration of psychrophilic spoilage organisms on day 15 is less than 6.5log CFU/cm2The concentration of psychrophilic putrefying bacteria at day 17 substantially reached 6.5log CFU/cm2Thus, the shelf life of the product should be set to 17 days.
FIG. 3(a) is a prediction software input interface, and applicants input the initial amount of psychrophilic putrefying bacteria in the product, the temperature and time consumption of each link of acid discharge, cutting, packaging, storage and cold chain transportation, and the packaging mode into the prediction model of the invention, and predict the strain concentration of psychrophilic putrefying bacteria on the surface of the chilled fresh meat of the product at different times. As shown in FIG. 3(b), at day 17, the concentration of psychrophilic putrefying bacteria was less than 6.5log CFU/cm2The growth of the psychrophilic putrefying bacteria exceeds 6.5log CFU/cm after 17.6 days2So the model predicts that the product should have a shelf life of 17 days.
Furthermore, as shown in FIG. 3(b), the comparison between the actual measurement and the predicted result shows that only 1 point of the 14 actual measurement points is not within. + -. 1.0logCFU/cm2Within the acceptable prediction range, the prediction accuracy reaches 13/14, 92.9 percent and is higher than 80 percent, which indicates that the prediction result is good.
The results show that the prediction result of the model is completely consistent with the actual detection result, and the accuracy is high.
The prediction model established by the invention can predict the shelf lives of the chilled fresh meat in three different packaging modes, and accords with the consumption mode of the chilled fresh meat product at present. Meanwhile, the model provides powerful technical support for quality control of the cold fresh meat in the whole cold chain process from acid discharge after slaughter to supermarket retail.
Claims (5)
1. A method for establishing a chilled fresh meat shelf life prediction model is characterized by comprising the following steps:
(1) respectively placing cold fresh meat samples of different packages at different temperatures for constant-temperature storage, and respectively sampling at regular time to determine the number of psychrophilic putrefying bacteria in the cold fresh meat by adopting a standard destructive sampling method and a microbial detection method;
(2) fitting the concentration data of the psychrophilic spoilage bacteria of the chilled fresh meat measured in the step (1) under different constant-temperature storage conditions in different packaging modes by using a Baranyi and Roberts first-class model (formula A) to obtain the maximum specific growth rate mu of the psychrophilic spoilage bacteria under the conditionsmaxAnd a lag period tlag；
Wherein:
wherein t is time, N0Is the initial seed concentration, NtConcentration of the seed at time t, NmaxThe maximum concentration to which the strain can grow, mumaxIs the maximum specific growth rate of the strain, h0The strain physiological state parameters;
(3) the maximum specific growth rate μ obtained in the first-order modelmaxFitting to obtain μ with a square root two-level model (formula C)maxThe constants b and T are obtained by calculating the minimum root mean square difference (RMSE) between the measured value and the fitting value in relation to the change of the temperature Tmin(formula D) was chosen to describe the maximum specific growth rate μmaxAnd a lag period tlagAnd passes tlagFind h0；
μmax＝b×(T-Tmin)2(C)
Wherein T is temperature, mumaxFor maximum specific growth rate of the strain at temperature T, TminIs the theoretical minimum growth temperature, b is a constant, tlagIs the lag phase of the strain;
the formula for root mean square difference (RMSE) is:
wherein VMeasured in factIs a measured value, VFittingFor model fit values, n is the total number of measured values, and p is the number of parameters to be found in the model formula (here only two parameters b and TminAnd thus p is 2).
2. The method of claim 1, wherein the packaging in step (1) comprises normal packaging, modified atmosphere packaging or vacuum packaging.
3. A chilled fresh meat shelf life prediction model, wherein the prediction model is created using the method of claim 1 or 2.
4. Use of the predictive model of claim 3 to predict shelf life of chilled fresh meat.
5. The use according to claim 4, characterized in that it comprises the following steps:
(1) actually measuring the initial amount of psychrophilic putrefying bacteria on the surface of the chilled fresh meat before cooling and acid discharge;
(2) recording the initial amount of the psychrophilic putrefying bacteria, the temperature and the time consumption of each link of acid discharge, cutting, packaging, storage and cold chain transportation and the packaging mode into the prediction model, and predicting the strain concentration of the psychrophilic putrefying bacteria on the surface of the chilled fresh meat at different times;
(3) according to the maximum detection concentration of psychrophilic putrefying bacteria of 6.5logCFU/cm2The shelf life of the cold fresh meat is predicted in real time.
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