CN102650632A - Method for evaluating shelf life of cooling pork at fluctuating temperature - Google Patents

Abstract

The invention relates to a method for evaluating the shelf life of chilled pork under fluctuating temperatures, comprising the following steps: (1) sample microorganism and sensory analysis; (2) microbial growth prediction model; (3) application of the prediction model under fluctuating temperatures and verify. The advantages of the present invention are: the present invention uses the method of predicting microbiology, by analyzing the growth of aerobic bacteria in the cooled pork at different time points under different temperature conditions, and establishing a microbial prediction model for the cooled pork under different storage temperatures, so as to evaluate The edible safety of chilled pork, the established predictive microbial kinetic model can effectively predict the shelf life of chilled pork under the condition of 4~15 ℃, which provides a basis for real-time monitoring and tracking of the shelf life of chilled pork under the environmental conditions of temperature changes. Strong technical support.

Description

A method for evaluating the shelf life of chilled pork under fluctuating temperatures

technical field

The invention relates to the field of meat product storage, in particular to a method for evaluating the shelf life of cooled pork under fluctuating temperatures.

Background technique

Chilled pork is welcomed by consumers because of its good taste and safety. As the main meat consumption food of Chinese consumers, the safety of pork products has received extensive attention and attention. Therefore, the development of technical methods that can quickly and effectively evaluate the shelf life and safety of chilled pork is of vital importance not only for food consumers, but also for retailers and the meat industry.

In the case of proper temperature control, the chilled pork is always at a low temperature of 0-4°C, which inhibits the growth of microorganisms in the meat to a certain extent. However, if the cold chain system is not perfect, improper control occurs in the process of storage, transportation, and sales, resulting in changes or failures in the temperature control system to increase the temperature, microorganisms will proliferate rapidly, and accelerate the deterioration of fresh meat, which will affect public health. constitute a potential threat. Therefore, mastering the growth law of microorganisms in meat products under different temperature conditions, so as to predict their changing laws, can achieve the purpose of evaluating the shelf life of meat products. Traditional product testing is to conduct sampling inspections on meat products, which is time-consuming and labor-intensive, and the results need to be judged after 24 or 48 hours, which has a certain hysteresis and cannot play a predictive role. Using mathematical model predictions can overcome these limitations. Facilitate the adoption of effective preventive measures.

In the production, transportation, storage, and consumption stages of the actual meat industry chain, the ambient temperature of chilled pork products often fluctuates, and the higher the ambient temperature, the faster the proliferation of microorganisms in chilled pork. Fast, may reduce the safety of chilled pork, shorten the shelf life, bring food safety hazards to consumers, and bring economic losses to producers and operators.

Food predictive microbiology is a new technology developed in recent years. Its characteristic is that without microbial analysis, if the temperature process experienced by the food is known, the microbial prediction model can be used to calculate the microbial concentration in the food. Quantity, and use this as an indicator to estimate the shelf life and safety of food. Although the microbial prediction model is convenient and practical, the initial establishment process requires a lot of basic work and a large number of microbial analyzes to establish a mathematical model. However, after the model is established and verified to be feasible, it can provide a powerful tool for food quality monitoring. Technical Support.

Chinese patent document CN101576553A discloses a shelf life prediction model for chilled pork. Experimental studies have been carried out on pork stored at 0°C, 5°C, 10°C and 20°C from the sensory and physical and chemical aspects. Based on the analysis, the reaction order of volatile base nitrogen was established as level 1. Based on its good correspondence with sensory value, it was set as the freshness index of pork, and a prediction model for the shelf life of chilled meat was established based on this index. Chinese patent document CN101949870A discloses a method for predicting the freshness and quality of refrigerated carp. Through the research on the sensory, chemical and microbiological qualities of refrigerated carp, the shelf life of the product is determined, and the exponential relative spoilage rate equation model is used to describe the relationship between temperature and fish freshness. Relationship. However, there is no report on a microbial prediction model applied to evaluate the shelf life of chilled pork under fluctuating temperatures.

Contents of the invention

The purpose of the present invention is to provide a method for evaluating the shelf life of chilled pork under fluctuating temperatures aimed at the deficiencies in the prior art.

In order to achieve the above object, the technical solution adopted by the present invention is: a method for evaluating the shelf life of cooled pork under fluctuating temperatures, comprising the following steps:

(1) Microbiological and sensory analysis of samples: put the cooled pork at 4°C, 7°C, 10°C, 15°C and two fluctuating temperatures, namely 4°C/12h~7°C/12h and 4°C/12h~10°C/6h Store at ~15°C/6h, and conduct microbial count and sensory evaluation after appropriate time intervals;

(2) Microbial growth prediction model: ① first-level model, using a three-stage linear model to describe the changes in the number of aerobic bacteria in chilled pork at different storage temperatures over time; ② second-level model, using linear equations to describe μ The change of _max with temperature and the change of A with temperature, the power equation is used to describe the change of λ with temperature;

(3) Application and verification of the prediction model under fluctuating temperatures: Combine the first-level model and the second-level model to predict the number of aerobic bacteria in chilled pork under two groups of fluctuating temperatures, and compare with the actual measured values , to evaluate the accuracy of the model.

The method for measuring the number of microorganisms in the step (1) is to take samples from samples stored at different temperatures, and use plate count agar to measure the total number of aerobic bacteria.

The three-stage linear model in the step (2) is:

Among them, t is the time (h), y is the number of bacteria at t (log ₁₀ CFU/g), A is the maximum number of bacteria (log ₁₀ CFU/g), μ _max is the maximum specific growth rate (h ^-1 ), λ is the lag period (h) of microbial growth.

The secondary model in the step (2) is:

Kinetic parameters regression equation μ _max (1/h) y = 0.0082*t - 0.019 lambda (h) y = 97*t ^ (-1.5) A (log ₁₀ CFU/g) A = 0.03*t +8.4

Among them, t is the time (h), y is the number of bacteria at t (log ₁₀ CFU/g), A is the maximum number of bacteria (log ₁₀ CFU/g), μ _max is the maximum specific growth rate (h ^-1 ), λ is the lag period (h) of microbial growth.

The present invention has the advantage that:

The invention applies the method of predicting microbiology, by analyzing the growth of aerobic bacteria in cooled pork at different time points under different temperature conditions, and establishing a microbial prediction model for cooled pork at different storage temperatures, so as to evaluate the eating safety of cooled pork The established predictive microbial kinetics model can effectively predict the shelf life of chilled pork at 4-15°C, providing strong technical support for real-time monitoring and tracking of the shelf life of chilled pork under environmental conditions of temperature fluctuations .

Description of drawings

Accompanying drawing 1 is the change of the number of aerobic bacteria in chilled pork with time under different storage temperatures.

Detailed ways

The specific embodiments provided by the present invention will be described in detail below in conjunction with the accompanying drawings.

The purpose of the present invention is to apply the method of predictive microbiology, by analyzing the growth of aerobic bacteria in cooled pork at different time points under different temperature conditions, and establish a microbial prediction model for cooled pork under different storage temperatures, so as to evaluate the cooling effect of pork. The food safety of pork.

Example

1 Materials and methods

1.1 Test material

Chilled pork: Chilled pork (thigh meat) of a certain brand 12 hours after slaughter. After purchase, put them in an insulating foam box with ice packs and transport them back to the laboratory within 2 hours.

1.2 Test method

1.2.1 Sample pretreatment

Microbiological and sensory analysis: Aseptically cut the purchased cooled pork into 25g tissue blocks with a thickness of about 2-3cm after removing tendon and fat, packed in plastic wrap trays, put them in a high-precision low-temperature incubator, and placed them in a high-precision low-temperature incubator. Store at 7°C, 10°C, 15°C and two fluctuating temperatures (4°C/12h~7°C/12h, 4°C/12h~10°C/6h~15°C/6h), after an appropriate time interval, respectively Determination of microbial count and sensory evaluation.

1.2.2 Determination of the total number of aerobic bacteria

Samples stored at different temperatures were taken at 0, 1, 2, 3, 4, 5, and 6 days (in the 15°C storage experiment, samples were taken every 0.5 days), operated according to GB/T 4789.2-2003, using a flat plate Counting agar to determine the total number of aerobic bacteria in chilled pork.

1.2.3 Sensory evaluation

According to the GB 2707-2005 standard, a six-person sensory evaluation team was established, and simple training was given to them in advance. For each sample, the panelists scored the color, smell, texture, surface state, flavor, softness, tissue juices, and broth color of raw and cooked meat, each of which was the sum of the scores scored by the sensory panelists. average value. The evaluation criteria for sensory evaluation are listed in Table 1. If the comprehensive score is below 6 (including 6), it indicates that the chilled pork has reached the point of sensory rejection.

Table 1 Sensory evaluation table of chilled pork

1.3 Microbial growth prediction model

1.3.1 Level 1 Model

The classic three-stage linear model was used to simulate the change of the number of aerobic bacteria in chilled pork at different storage temperatures over time. The three-stage linear model is as follows:

Among them, t is the time (h), y is the number of bacteria at t (log ₁₀ CFU/g), A is the maximum number of bacteria (log ₁₀ CFU/g), μ _max is the maximum specific growth rate (h ^-1 ), λ is the lag period (h) of microbial growth.

1.3.2 Secondary Model

In order to evaluate the relationship between temperature and growth parameters, the relationship between μ _max , λ and A, linear equations are used to describe the variation of μ _max with temperature and the variation of A with temperature, and power equations are used to describe the variation of λ with temperature Variety.

1.3.3 Application and verification of prediction model under fluctuating temperature

The invention combines the established first-level model with the second-level model to predict the number of aerobic bacteria in the cooled pork under two groups of fluctuating temperatures, and compares it with the actual measured value to evaluate the accuracy of the model. The above data processing was completed by using the software Microsoft Excel 2003 and Origin 7.5.

2 Result Analysis

2.1 Sensory shelf life of chilled pork at constant and fluctuating temperatures

According to the change of the sensory score of chilled pork over time at different storage temperatures, linear regression is performed, and the results are shown in the table below. When the sensory score is 6, the chilled pork reaches the sensory end point.

Table 2 Sensory shelf life of chilled pork under constant temperature and fluctuating temperature

Storage temperature (℃) linear regression equation Sensory shelf life (days) 4°C y = 8.07425 - 0.018*x 4.80 7°C y = 8.52186 - 0.02666*x 3.94 10°C y = 7.64881 - 0.02158*x 3.18 15°C y = 7.9626 - 0.07352*x 1.11 4℃/12h ~ 7℃/12h y = 7.95575 - 0.02031*x 4.01 4℃/12h~10℃/6h~15℃/h y = 7.51246 - 0.01992*x 3.16

2.2 Application of prediction model to predict shelf life of chilled pork under constant and fluctuating temperature

Through microbial analysis, the total number of aerobic bacteria in chilled pork at different time points under storage conditions of 4, 7, 10, and 15°C can be known. Sensory evaluation of chilled pork was performed concurrently with microbial enumeration. When the sensory score reaches 6, it is the sensory rejection point, that is, the cooled pork reaches the end of the sensory shelf life. Taking this time into the established first-level predictive microbial model, the minimum spoilage level of total aerobic bacteria in chilled pork can be obtained at the end of the sensory shelf life: when the number of aerobic bacteria is about 10 ⁷ CFU/g (10 ^6.89 CFU/g), the cooled pork reaches the sensory end point, so the present invention sets the microbial index at the end of the shelf life of the cooled pork as log ₁₀ 7CFU/g.

2.2.1 Primary and secondary models of microorganisms

The results of storage experiments under constant temperature conditions showed that the established microbial kinetic equation could well describe the effect of temperature on the growth of total aerobic bacteria in chilled pork in the range of 4-15 °C. In order to evaluate the reliability of the established model, it needs to be applied to predict the shelf life of chilled pork under fluctuating temperature conditions of 4-15°C. And through statistical analysis to evaluate the prediction effect of the model (Figure 1).

Table 3 Secondary model

Kinetic parameters regression equation μ _max (1/h) y = 0.0082*t - 0.019 lambda (h) y = 97*t ^ (-1.5) A (log ₁₀ CFU/g) A = 0.03*t +8.4

The verification method of the model under fluctuating temperature conditions is as follows: the initial microbial count is the measured value obtained through the plate count test, and the maximum growth rate, lag period and maximum bacterial count are obtained through the established secondary model. If the temperature change occurs within the lag period of microorganisms, the calculation of the number of bacteria applies the first-stage equation of the three-stage linear model; if the temperature change occurs within the logarithmic growth period of microorganisms, the calculation of the number of bacteria applies the second stage of the three-stage linear model Equation, when the number of microorganisms grows to reach the maximum number of bacteria, the stable period is reached, and the number of bacteria is calculated by the third-stage equation of the three-stage linear model. Combining the first-level model and the second-level model established to predict the shelf life of chilled pork under four groups of constant temperatures and two groups of fluctuating temperatures, and compared with the sensory shelf life, the prediction effect of the model was evaluated. The results are as follows:

Table 4 Comparison of sensory shelf life and model predicted shelf life of pork chilled at constant and fluctuating temperatures

Storage temperature/℃ Sensory shelf life (days) Microbial prediction model shelf life (days) 4°C 4.80 8.92 7°C 3.94 2.92 10°C 3.18 2.56 15°C 1.11 1.51 4℃/12h ~ 7℃/12h 4.01 4.77 4℃/12h~10℃/6h~15℃/h 3.16 2.84

Table 5 Analysis of variance for comparison of sensory shelf life and model predicted shelf life of chilled pork

source degrees of freedom sum of square mean square F value p-value Model 1 0.918533333 0.918533333 0.21109 0.65574 error 10 43.5137333 4.35137333 the the

The results showed that there was no significant difference between the two methods for evaluating the shelf life of chilled pork at the 0.05 level.

3 Conclusion

The predictive microbial kinetics model established by the present invention can effectively predict the shelf life of chilled pork at 4-15°C, and provides strong technical support for real-time monitoring and tracking of the shelf life of chilled pork under environmental conditions of temperature fluctuations .

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the method of the present invention, some improvements and supplements can also be made, and these improvements and supplements should also be considered Be the protection scope of the present invention.

Claims (4)

1. a method that is applied to estimate under the fluctuating temperature chilled pork shelf life is characterized in that, may further comprise the steps:

(1) sample microorganism and organoleptic analysis: with chilled pork in 4 ℃, 7 ℃, 10 ℃, 15 ℃ and two fluctuating temperatures; I.e. 4 ℃/12h ~ 7 ℃/12h and 4 ℃/12h ~ 10 ℃/6h ~ 15 ℃/6h storage down; Through reasonable time at interval, carry out The determination and subjective appreciation respectively;

(2) growth of microorganism forecast model: 1. one-level model, adopt quantity that three stage linear models describe aerobic bacteria in the chilled pork under the different reserve temperatures over time; 2. second-level model selects for use linear equation to describe μ respectively _MaxWith variation of temperature, select for use the power equation to describe λ with variation of temperature and A with variation of temperature;

(3) application of forecast model and checking under the fluctuating temperature: the one-level model of being set up is combined with second-level model, predict the aerobic bacteria number in the chilled pork under two groups of fluctuating temperatures, and compare, with the accuracy of evaluation model with actual measured value.

2. method according to claim 1 is characterized in that, the method for The determination is with the sample that is housed under the different temperatures in the described step (1), and sampling respectively adopts plate count agar to measure total aerobic bacteria.

3. method according to claim 1 is characterized in that, three stage linear models in the described step (2) are:

Wherein, t is time (h), the bacterium number (log when y is t ₁₀CFU/g), A is maximum bacterium number (log ₁₀CFU/g), μ _MaxBe maximum specific growth rate (h ^-1), λ is the lag phase (h) of growth of microorganism.

4. method according to claim 1 is characterized in that, the second-level model in the described step (2) is:

Kinetic parameter Regression equation μ _max (1/h) y = 0.0082*t - 0.019 λ (h) y = 97*t ^ (-1.5) A (log ₁₀CFU/g) A = 0.03*t +8.4

Wherein, t is time (h), the bacterium number (log when y is t ₁₀CFU/g), A is maximum bacterium number (log ₁₀CFU/g), μ _MaxBe maximum specific growth rate (h ^-1), λ is the lag phase (h) of growth of microorganism.

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