CN102650632A - Method for evaluating shelf life of cooling pork at fluctuating temperature - Google Patents
Method for evaluating shelf life of cooling pork at fluctuating temperature Download PDFInfo
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Abstract
The invention relates to a method for evaluating the shelf life of cooling pork at fluctuating temperature. The method comprises the following steps of: (1) sample microorganism and sensory analysis; (2) microorganism growth prediction models; and (3) application and verification of the prediction models at the fluctuating temperature. The method has the advantages that a predictive microbiology method is applied to analyze the growth situations of aerobic bacteria in the cooling pork at different time points under different temperature conditions and establish the microorganism prediction models of the cooling pork at different storage temperatures so as to evaluate the edible safety of the cooling pork; the established prediction microorganism dynamical-models can effectively predict the shelf life of the cooling pork at the temperature of 4-15 DEG C; and a strong technical support is provided for monitoring and tracking the shelf life of the cooling pork under the environment condition with the fluctuating temperature in real time.
Description
Technical field
The present invention relates to meat products storage field, specifically, is a kind of method that is applied to estimate under the fluctuating temperature chilled pork shelf life.
Background technology
Chilled pork receives consumers in general's welcome because of its good mouthfeel and security, and as the main consumption of meat food of Chinese Consumer's, the security of pork product has received to be paid attention to widely and pay close attention to.So exploitation can be estimated the technical method of chilled pork shelf life and security quickly and efficiently,, all be vital for retailer and meat industry not only for the food consumption person.
Under the proper situation of temperature control, chilled pork is in 0 ~ 4 ℃ the low temperature all the time, makes that microbial growth has received certain inhibition in the meat.Yet if the cold chain system is perfect inadequately, it is improper control in processes such as storage, transportation and sale, to occur; Cause the variation or the failure of temperature control system to elevate the temperature; Microorganism is propagation rapidly, quickens spoilage of fresh meat, thereby can constitute potential threat to public health.So, grasp the growth rhythm of microorganism under condition of different temperatures in the meat, thereby its Changing Pattern is predicted, can reach the purpose of estimating the meat shelf life.It is that meat is surveyed sample that traditional product detects; Not only waste time and energy, and the result need could judge to have certain hysteresis quality after 24 or 48 hours; Do not have the effect of prevision, use mathematical model prediction can overcome these restrictions and help taking proper prophylactic methods.
Production, transportation, storage, consumer phase in the meat industrial chain of reality; The situation that fluctuation takes place the residing environment temperature of chilled pork product often occurs, and residing environment temperature is high more, the growth rate of microorganism can be fast more in the chilled pork; Possibly make the security of chilled pork reduce; Shelf life shortens, and brings food security hidden danger to the consumer, brings economic loss to the operator.
Food prediction microbiology is the new technology that developed recently gets up; Its characteristics are and can carrying out under the prerequisite of microbiological analysis; If the temperature course that food experienced is known; Then can use the microorganism forecast model to calculate microbial numbers in this food, and estimate the shelf life and the security of food as index.Though; The microorganism forecast model is convenient, practical, but the initial stage set up a large amount of element task of process need, need carry out a large amount of microbiological analysiss and set up mathematical model; But modelling and verify its feasible after, the quality monitoring that just can be food provides strong technical support.
Chinese patent document CN101576553A discloses a kind of shelf life forecasting model of chilled pork; From sense organ and physics and chemistry aspect the pork that is housed under 0 ℃, 5 ℃, 10 ℃ and 20 ℃ has been carried out experimental study; Through the Functional Analysis of corresponding quality energy level, establishing the VBN order of reaction is 1 grade, based on the good correspondence of itself and sense organ value; Be established as the index of fish freshness of pork, and set up chilled pork shelf life prediction model according to this index.Chinese patent document CN101949870A discloses a kind of refrigeration carp freshness quality prediction method; Through carp sense organ, chemistry and microbiology Study on Quality are cultured in refrigeration; Confirm the product shelf life, adopt the relation of index corrupt relatively rate equation model description temperature and fish products freshness.But also do not appear in the newspapers at present about a kind of microorganism forecast model that is applied to evaluation chilled pork shelf life under the fluctuating temperature.
Summary of the invention
The objective of the invention is to deficiency of the prior art, a kind of method that is applied to estimate under the fluctuating temperature chilled pork shelf life is provided.
For realizing above-mentioned purpose, the technical scheme that the present invention takes is: a kind of method that is applied to estimate under the fluctuating temperature chilled pork shelf life 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.
The method of 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.
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.
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.
The invention has the advantages that:
The microbiological method of applied forcasting of the present invention; Through analyzing the growth pattern of the aerobic bacteria in the different time points chilled pork under the condition of different temperatures; Set up the microorganism forecast model of chilled pork under the different reserve temperatures; Estimate the edible safety of chilled pork with this; The prediction microbial kinetics model of being set up can be predicted the shelf life of chilled pork under 4 ~ 15 ℃ of conditions effectively, for monitoring in real time and the shelf life of following the tracks of the chilled pork under the environmental baseline of temperature change provide strong technical support.
Description of drawings
Accompanying drawing 1 is that the aerobic bacteria number is over time in the chilled pork under the different reserve temperatures.
Embodiment
Below in conjunction with accompanying drawing embodiment provided by the invention is elaborated.
The objective of the invention is to; The microbiological method of applied forcasting; Through analyzing the growth pattern of the aerobic bacteria in the different time points chilled pork under the condition of different temperatures, set up the microorganism forecast model of chilled pork under the different reserve temperatures, estimate the edible safety of chilled pork with this.
Embodiment
1 materials and methods
1.1 test material
Chilled pork: certain brand is killed back 12h chilled pork (round).Purchase is placed on and in 2h, transports the laboratory in the heat barrier foam box that ice bag is housed back.
1.2 test method
1.2.1 sample pretreatment
Microorganism and organoleptic analysis: after sterile working removes the chilled pork of buying to the tendon degrease; Be divided into 25g, the piece of tissue of thick about 2 ~ 3cm is put into the high precision low temperature incubator after the preservative film pallet packing; In (4 ℃/12h ~ 7 ℃/12h of 4 ℃, 7 ℃, 10 ℃, 15 ℃ and two fluctuating temperatures; 4 ℃/12h ~ 10 ℃/6h ~ 15 ℃/6h) storage down, through reasonable time at interval, carry out The determination and subjective appreciation respectively.
1.2.2 total aerobic bacteria is measured
With the sample that is housed under the different temperatures,,, adopt plate count agar to measure the total aerobic bacteria in the chilled pork according to GB/T 4789.2-2003 operations respectively at 0,1,2,3,4,5,6 d sampling (in 15 ℃ of storage experiments, sampling in per 0.5 day).
1.2.3 subjective appreciation
Press GB 2707-2005 standard, set up six people's subjective appreciation groups, in advance it is carried out simple training.For each sample, the group member will be to color, smell, structural state, surface state, local flavor, the pliability of raw meat and cold cuts, organize juice, meat soup color to mark, every mean value that must be divided into sense organ number that the group member marks.The evaluation criterion of subjective appreciation is seen table 1.If comprehensive grading (is comprising 6) below 6, show that then chilled pork has arrived sense organ refusal point.
Table 1 chilled pork subjective appreciation table
1.3 growth of microorganism forecast model
1.3.1 one-level model
Adopt three classical stage linear models to simulate in the chilled pork under the different reserve temperatures aerobic bacteria number over time, three stage linear models are following:
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.
1.3.2 second-level model
In order to concern μ between evaluation temperature and the growth parameter(s)
Max, the relation between λ and the A 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.
1.3.3 the application of forecast model and checking under the fluctuating temperature
The present invention combines the one-level model of being set up with second-level model, predict the aerobic bacteria number in the chilled pork under two groups of fluctuating temperatures, and compares with actual measured value, with the accuracy of evaluation model.Above data processing all uses software Microsoft Excel 2003 and Origin7.5 accomplishes.
2 interpretations of result
2.1 the sense organ shelf life of chilled pork under constant and the fluctuating temperature
According to the sensory evaluation scores of chilled pork under the different reserve temperatures over time, carry out linear regression, result such as following table, when sensory evaluation scores was 6, chilled pork reached the sense organ terminal point.
The sense organ shelf life of chilled pork under table 2 constant temperature and the fluctuating temperature
| Reserve temperature (℃) | Equation of linear regression | The sense organ shelf life (my god) |
| 4℃ | y = 8.07425 - 0.018*x | 4.80 |
| 7℃ | y = 8.52186 - 0.02666*x | 3.94 |
| 10℃ | y = 7.64881 - 0.02158*x | 3.18 |
| 15℃ | 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 the applied forcasting model prediction is constant and fluctuating temperature under the shelf life of chilled pork
Through microbiological analysis, can learn the total aerobic bacteria of different time points chilled pork under 4,7,10,15 ℃ of holding conditions.In microorganism count, chilled pork is carried out subjective appreciation.When sensory evaluation scores reaches 6, be sense organ refusal point, promptly chilled pork reaches sense organ shelf life terminal point.Bring this time into set up prediction microorganism one-level model respectively, get final product when sense organ shelf life terminal point, the minimum corrupt level of total aerobic bacteria in the chilled pork: when the aerobic bacteria number is about 10
7CFU/g (10
6.89CFU/g) time, chilled pork reaches the sense organ terminal point, so the microbiological indicator that the present invention sets chilled pork shelf life terminal point is log
107CFU/g.
2.2.1 one-level model and the second-level model of microorganism
Storage experiment result by under the constant temperature shows that the microbial kinetics equation of being set up can be described in the influence that temperature is grown to total aerobic bacteria in the chilled pork in 4 ~ 15 ℃ of scopes well.To set up the reliability of model in order estimating, need to be applied to predict the shelf life of chilled pork under 4 ~ 15 ℃ of fluctuating temperature conditions.And come the prediction effect (Fig. 1) of evaluation model through statistical analysis.
Table 3 second-level model
| 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 |
Verification of model method under the fluctuating temperature condition is following: Initial microorganisms quantity is to test resulting measured value through plate count, and maximum growth rate, and lag phase and maximum bacterium number then are to pass through the second-level model gained set up.If temperature variation occurs in the lag phase of microorganism; Bacterium is counted the phase one equation of three stage of computing application linear model; If temperature variation occurs in the exponential phase of microorganism, bacterium is counted the subordinate phase equation of three stage of computing application linear model, when the maximum bacterium of micro organism quantity growth arrival is counted; Then reach stationary phase, the bacterium number calculates with the phase III equation of three stage linear models.Predict the shelf life of chilled pork under four groups of steady temperatures and the two groups of fluctuating temperatures in conjunction with the one-level model of being set up and second-level model, and compare that the prediction effect of model is estimated, and the result is following with the sense organ shelf life:
The sense organ shelf life of chilled pork and the comparison of model prediction shelf life under the constant and fluctuating temperature of table 4
| Reserve temperature/℃ | The sense organ shelf life (my god) | Microorganism forecast model shelf life (my god) |
| 4℃ | 4.80 | 8.92 |
| 7℃ | 3.94 | 2.92 |
| 10℃ | 3.18 | 2.56 |
| 15℃ | 1.11 | 1.51 |
| 4℃/12h ~ 7℃/12h | 4.01 | 4.77 |
| 4℃/12h~10℃/6h~15℃/h | 3.16 | 2.84 |
The variance analysis of the sense organ shelf life of table 5 chilled pork and the comparison of model prediction shelf life
| The source | Degree of freedom | Quadratic sum | All square | The F value | The P value |
| Model | 1 | 0.918533333 | 0.918533333 | 0.21109 | 0.65574 |
| |
10 | 43.5137333 | 4.35137333 | ? | ? |
The result shows that two kinds of methods of estimating the chilled pork shelf life are at 0.05 horizontal there was no significant difference.
3 conclusions
The prediction microbial kinetics model that the present invention set up can be predicted the shelf life of chilled pork under 4 ~ 15 ℃ of conditions effectively, for monitoring in real time and the shelf life of following the tracks of the chilled pork under the environmental baseline of temperature change provide strong technical support.
The above only is a preferred implementation of the present invention; Should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the inventive method; Can also make some improvement and replenish, these improvement and replenish and also should be regarded as 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:
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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| CN103995965A (en) * | 2014-05-14 | 2014-08-20 | 东南大学 | Shelf life early-warning system and method based on microorganism predication model |
| CN104330539A (en) * | 2014-10-28 | 2015-02-04 | 浙江大学 | Chilled fresh pork shelf life span forecasting method based on internet of things architecture |
| CN108254513A (en) * | 2017-12-22 | 2018-07-06 | 北京农业信息技术研究中心 | A kind of judgment method and device of fish shelf life |
| CN108344841A (en) * | 2018-02-08 | 2018-07-31 | 杭州汇健科技有限公司 | A method of prediction refrigeration pasteurize fresh milk shelf life |
| CN109738600A (en) * | 2018-12-22 | 2019-05-10 | 河南农业大学 | A kind of construction method of cold chain meat products microorganism intermittent dynamic prediction model |
| CN111027784A (en) * | 2019-12-26 | 2020-04-17 | 上海市农业科学院 | Method for predicting shelf life of cold fresh chicken |
| CN111159635A (en) * | 2020-01-13 | 2020-05-15 | 青岛蔚蓝生物股份有限公司 | Establishment and application method of shelf life prediction model of liquid probiotic |
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| CN112342265A (en) * | 2020-11-10 | 2021-02-09 | 南京工业大学 | Method for using growth model based on salmonella in beef under variable temperature condition |
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| CN103712704A (en) * | 2013-12-23 | 2014-04-09 | 朱宇尘 | Recorder for storage temperature change times |
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| CN103995965B (en) * | 2014-05-14 | 2017-02-22 | 东南大学 | Shelf life early-warning system and method based on microorganism predication model |
| CN104330539A (en) * | 2014-10-28 | 2015-02-04 | 浙江大学 | Chilled fresh pork shelf life span forecasting method based on internet of things architecture |
| CN108254513A (en) * | 2017-12-22 | 2018-07-06 | 北京农业信息技术研究中心 | A kind of judgment method and device of fish shelf life |
| CN108344841A (en) * | 2018-02-08 | 2018-07-31 | 杭州汇健科技有限公司 | A method of prediction refrigeration pasteurize fresh milk shelf life |
| CN109738600A (en) * | 2018-12-22 | 2019-05-10 | 河南农业大学 | A kind of construction method of cold chain meat products microorganism intermittent dynamic prediction model |
| CN111027784A (en) * | 2019-12-26 | 2020-04-17 | 上海市农业科学院 | Method for predicting shelf life of cold fresh chicken |
| CN111027784B (en) * | 2019-12-26 | 2023-04-18 | 上海市农业科学院 | Method for predicting shelf life of cold fresh chicken |
| CN111159635A (en) * | 2020-01-13 | 2020-05-15 | 青岛蔚蓝生物股份有限公司 | Establishment and application method of shelf life prediction model of liquid probiotic |
| CN111159635B (en) * | 2020-01-13 | 2023-08-29 | 青岛蔚蓝生物股份有限公司 | Method for establishing and applying liquid probiotic shelf life prediction model |
| CN111257234A (en) * | 2020-03-25 | 2020-06-09 | 杭州御佳金属材料有限公司 | Microorganism concentration measuring method |
| CN112342265A (en) * | 2020-11-10 | 2021-02-09 | 南京工业大学 | Method for using growth model based on salmonella in beef under variable temperature condition |
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