CN110533250B - Method for predicting food shelf life through dimensional analysis - Google Patents
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Abstract
The invention discloses a method for predicting food shelf life through dimension analysis. The method provided by the invention predicts the microbial growth condition by using a model established by a dimension analysis method, and is further applied to the prediction of the food shelf life, so that the method has important value for controlling the food quality and guaranteeing the food safety. The invention is suitable for various foods with shelf life, not only can be applied to the prediction of the constant-temperature shelf life, but also can be applied to the prediction of the food shelf life in the range from low temperature to normal temperature. The method is simple, the initial quantity of target microorganisms in the food is not required to be known, the calculation is simple, and the popularization and the application of the method are greatly facilitated.
Description
Technical Field
The invention relates to the technical field of dimensional analysis and microbiological thermodynamics, in particular to a method for predicting food shelf life through dimensional analysis.
Background
Microorganisms are an important factor causing spoilage of foods. In order to rapidly predict the safety and shelf life of food, the management and monitoring of raw material processing, storage and sales links are realized, and the prediction of food microbiology is generated. The predictive food microbiology (Predictive Food Microbiology) is a rising discipline which combines microbiology, statistics, mathematics and application computer science to predict the growth dynamics of microorganisms in food, reflect the complex relationship between the predictive microorganisms and the food, and achieve the purpose of effectively predicting shelf life. The traditional method in the meat industry adopts a method for detecting samples, however, the method cannot guarantee the safety of overall consumption, and the charm of microbial prediction is to predict the future development trend by utilizing existing data, so that the method has important practical significance in monitoring the actual production and circulation processes of foods.
Growth models are classified into three types according to the classification methods of Whiting and Buchanan: primary, secondary, and tertiary models. The primary model is a model that varies over time under specific conditions depending on the number of microorganisms, and the growth curve is generally "S" shaped, which includes four parts: delay, log, stationary and aging. The most widely used models in the primary model are the Logistic model and the Gompertz model. The Gompertz model obtained through Zwiering correction can better describe growth dynamics under different temperature conditions. The secondary model is constructed on the basis of the primary model to describe the response of one or more parameters in the primary model to environmental conditions (e.g., temperature, pH, aw), mainly describing the temperature dependence of the model parameters, since temperature is considered to be the most important influencing factor. The more commonly used secondary models are the Arrhenius model and the square root model and their correction models, the simple Arrhenius model is usually used for predictive microbiology, but it is accurate only in a limited temperature range where microorganisms grow, so that the corrected version that has been developed can be better applied to extreme temperatures, but is still complex in use. The three-level model is built on the basis of the primary model and the secondary model by using a prediction software built by a computer, and the prediction software can be regarded as an interface between scientists and end users, and the end users can input a set of characteristics of the product, so that growth prediction parameters can be obtained. However, only a few three-level predictive models are available for use in industry. The first-level model and the second-level model only consider the influence of 2-3 factors, the third-level model is an expert system, a user is required to have certain expertise, the application range and the application condition of the system are known, and the user has certain difficulty in mastering the expert system. There is a need to develop new methods for use in food microorganism prediction. There is a wide range of methods in the disciplines of physical and chemical principles, food engineering principles, etc., namely dimension analysis, which can describe the relationship between complex physical phenomena, especially from small to large, and from this to the other, a classical example of this is that in chemical principles or food engineering principles, the relationship between friction coefficient and reynolds number and relative roughness is found by dimension analysis. However, since microorganisms are living, many factors affect their growth, but there are few methods for analyzing test cases to predict microbial growth.
There are many patent applications in the chinese patent database that relate to predicting microorganisms, but there are many that relate to predicting microbial growth in foods. For example, 2009102174276, "method for predicting staphylococcus aureus growth and toxicity in milk", is to predict staphylococcus aureus growth using predictive microbiology principles; for example, 2013800105543, a method for predicting proliferation of microorganism number, is to use probability density function to build a prediction model; further, according to 2014105217749, the chilled meat shelf life is predicted by a chilled meat shelf life prediction model; and 2016102892182, a method for predicting the growth of microorganisms contained in food in the process of production and circulation, is to establish a fuzzy relation matrix of input and output variables to obtain a predicted value of the microorganism number. Although a scholars report a patent CN201810949548.9, "a method for predicting food microorganisms by dimension planning model", the method only aims at microbial prediction under constant temperature condition, and does not predict in a certain temperature range, and the model needs to be improved appropriately. The patent is further advanced on the basis of the patent, expands the application of dimension analysis and prediction of microorganisms from constant temperature to variable temperature, and is more practical.
Disclosure of Invention
Aiming at the defects of the existing invention materials, the invention provides a method for predicting the shelf life of food through dimension analysis, which is simple, does not need to know the initial quantity of target microorganisms in food, is simple in calculation, and is greatly convenient for popularization and application.
In order to achieve the above purpose, the invention is realized by the following technical scheme: a method for predicting the shelf life of a food product by dimensional analysis comprising the steps of:
(1) Preparation of detection targets: taking out the food to be detected, and curing for 30-40 hours in a refrigeration environment; adding the pickled materials into a processing material for rolling and kneading, and filling; steaming at 80deg.C for 2 hr, cooling, slicing, vacuum packaging, sterilizing, and cooling;
(2) Establishing a model for predicting microbial growth by a dimensional analysis method:
wherein NT represents the amount of microorganisms in the food per unit mass at time T, θ represents the temperature, cp represents the specific heat capacity, T represents the time, ST represents the food surface area at time T, pHT represents the food pH at time T, awT represents the water activity at time T, K is the undetermined constant;
(3) And (3) detecting the detection target prepared in the step (1) according to the required parameters in the model in the step (2), and then carrying the detected parameters into a microbial growth model to predict the microbial growth condition of food in the shelf life, so as to predict the shelf life of the food.
The curing material adopted in the curing in the step (1) comprises, by mass, 1-3 parts of salt, 0.01-0.03 part of sodium nitrite, 0.05-0.07 part of sodium erythorbate, 0.1-0.3 part of sodium polyphosphate, 0.1-0.3 part of sodium pyrophosphate, 0.6-0.8 part of white granulated sugar, 20-40 parts of distilled water and 0.02-0.04 part of nisin
The processing material in the step 1) comprises, by mass, 0.3-0.5 part of salt, 0.4-0.6 part of monosodium glutamate, 0.5-07 part of black pepper powder, 2.0-2.5 parts of soybean protein, 6-7 parts of starch, 0.2-0.3 part of xanthan gum, 0.3-0.5 part of carrageenan, 0.01-0.02 part of monascus red, 0.1-0.3 part of smoke liquid C and 0.4-0.6 part of smoke liquid C.
Compared with the prior art, the method provided by the invention predicts the growth condition of microorganisms by using the model established by the dimensional analysis method, and is further applied to the prediction of the food shelf life, so that the method has important value for controlling the food quality and guaranteeing the food safety. The invention is suitable for various foods with shelf life, not only can be applied to the prediction of the constant-temperature shelf life, but also can be applied to the prediction of the food shelf life in the range from low temperature to normal temperature. The method is simple, the initial quantity of target microorganisms in the food is not required to be known, the calculation is simple, and the popularization and the application of the method are greatly facilitated.
Detailed Description
Examples: the method for predicting the shelf life of food through dimensional analysis takes western-style smoked and boiled ham as a detection example.
1 preparation of materials and methods
1.1 materials
The pork leg meat is purchased from Qian five good fortune. Salt, white granulated sugar, monosodium glutamate, black pepper powder, starch and the like are purchased in a local supermarket. The curing formula comprises 2.0% of salt, 0.015% of sodium nitrite, 0.06% of sodium erythorbate, 0.1% of sodium polyphosphate, 0.2% of sodium pyrophosphate, 0.8% of white granulated sugar, 30% of distilled water and 0.02% of nisin. The processing formula comprises, by mass, 0.4% of salt, 0.5% of monosodium glutamate, 0.5% of black pepper powder, 2.2% of soy protein, 6.5% of starch, 0.2% of xanthan gum, 0.3% of carrageenan, 0.01% of monascus red, 100.2% of smoke liquid C and 0.5% of smoke liquid C. Plate Count Agar (PCA) medium: 5.0g of tryptone, 2.5g of yeast extract, 1.0g of glucose, 15.0g of agar and 1000mL of distilled water, and the pH value is 7.0+/-0.2.
1.2 Western style smoked and boiled ham production
1.2.1 formulations
The pickling formula comprises, by mass, 2.0% of salt, 0.015% of sodium nitrite, 0.06% of sodium erythorbate, 0.1% of sodium polyphosphate, 0.2% of sodium pyrophosphate, 0.8% of white granulated sugar, 30% of distilled water and 0.02% of nisin; the processing formula comprises, by mass, 0.4% of salt, 0.5% of monosodium glutamate, 0.5% of black pepper powder, 2.2% of soy protein, 6.5% of starch, 0.2% of xanthan gum, 0.3% of carrageenan, 0.01% of monascus red, 100.2% of smoke liquid C and 0.5% of smoke liquid C.
1.2.2 operating points
(1) Checking, trimming and cutting raw meat
The pork leg meat is used as raw material, skin, bone, connective tissue, tendon and bond, lymph, fat and sundries are removed, so that the pork leg meat becomes pure lean meat, and then the pure lean meat is cut into meat blocks with the length of 3 multiplied by 2 multiplied by 1 cm.
(2) Pickling
Preparing a pickling solution according to a pickling formula, pouring the pickling solution into the cut meat, fully and uniformly mixing, and pickling for 36 hours at the temperature of 0-4 ℃.
(3) Vacuum rolling and kneading
Chopping small parts of the cured meat blocks into meat blocks, putting the meat blocks, the processing formula and the meat blocks into a vacuum rolling machine for rolling, and adopting intermittent vacuum rolling for 6 hours (working for 30 minutes and intermittent for 10 minutes).
(4) Quantitative sausage
And filling the well-settled raw meat by a filling machine, and then performing compression molding.
(5) Steaming and boiling
And (5) steaming and boiling the mixture for 2 hours at the constant temperature of 80 ℃ in a water bath kettle.
(6) Cooling and packaging
Cooling with flowing water, and vacuum packaging (with air extraction time of 25s, heating time of 1.5s, cooling time of 1.5s, and heating temperature).
Application principle and application of dimension analysis in predicting food shelf life
Principle of
The food is rich in nutrition, is suitable for microorganism growth, and can satisfy microorganism growth before food spoilage. Then the microbial growth N of the food is affected T The factors of (1) are mainly temperature θ, specific heat capacity Cp, time T and sphericity
Microorganism growth N per unit mass per unit surface area T /L T 2 (N T The microbial biomass CFU/g in the food per unit mass at the T moment is N because the microbial biomass can be expressed as mass T Is dimensionless per se (means the number of microorganisms per unit mass of food, since the mass of individual microorganisms is constant, for example 1 cfu=2×10 of the total number of colonies -12 g, CFU/g can be considered dimensionless); s is S T Surface area of food at time T), pH at time T T Moisture activity aw at time T and percentage of oxygen O in the gas space at time T of food storage 2 Etc. Namely->
From the dimensional analysis, a fiducial number can be established
In addition, pH, aw and O 2 The method is dimensionless and can be regarded as a standard number. The following expression can be written according to pi theorem:
in the method, in the process of the invention,
sphericity of V p Is the volume of non-spherical particles S p Surface area of non-spherical particles, d ev Let be the equivalent sphere diameter of a volume equal to the volume of the non-spherical particles. T is time d, K, n 1 、n 2 、n 3 、n 4 Is a pending constant. Specific heat capacity C of Western style fumigation and boiling p Is 1.1 kJ/(kg. DEG C), which is substantially unchanged during storage.
Considering western-style smoked ham vacuum packaging, the effect of a small amount of oxygen on the growth of microorganisms is basically considered to be unchanged, so that the effect of oxygen is temporarily not considered in the subsequent prediction of western-style smoked ham microorganisms, and then the expression obtained by dimensional analysis becomes:
y＝N T ，
B＝a wT ，C＝pH T . Logarithm of two sides of the equation (2) is obtained by linear function: log=log+n 1 LogA+n 2 LogB+n 3 LogC③
Application method of dimensional analysis in predicting food microorganisms
Ham slices were divided into five groups and stored at temperatures of 4,10,15,20 and 25 ℃ respectively, and the total number of colonies, the surface area per 20g, sphericity, pH and water activity of the five groups of samples were measured periodically (every 4 d). At each sampling, 3 bags were drawn for each group of samples for repeated experiments.
Analytical detection
Colony count N T CFU/g as measured according to GB 4789.2-2016; since Western smoked ham is cylindrical, S is obtained by measuring the diameter and height of the sample T ，m 2 . The pH was measured by an insert pH meter. a, a w The measurement was performed by a water activity meter.
2.3.1 Dimension analysis prediction model and verification at 4 DEG C
Table 1 quality index of western-style smoked and boiled ham stored at θ=4deg.C
The data of Table 1 were calculated according to equation (3) and the results are shown in Table 2.
TABLE 2 results of the operations according to equation (3)
As the linear regression equation running results were analyzed by SPSS software:
Logy＝2.979+0.381LogA-30.909LogB-3.539LogC(R 2 =0.960, adjust R 2 ＝0.945)
Due to pH, a w Is not significant, so the regression equation becomes
Logy＝0.755+0.455LogA(R 2 =0.948, adjust R 2 ＝0.943)
The model is as follows:
as can be obtained by the formula (4),
/>
so that the number of the parts to be processed,
table 3 4 ℃ shelf life actual and predicted values
As can be seen from table 3, the growth laws followed by the microorganisms at the early stage and the later stage are different, and the microorganisms at the early stage are basically in the conditioning stage, and the later stage after the conditioning stage is in the normal growth stage. The data is processed in stages, and since the microbial spoilage of the food is certainly after the conditioning phase, we focus here on fitting the growth phase after the conditioning phase. Thus, we re-fit the data after 13 days (> 13 d) according to table 3 (table 2), yielding: log=0.697+0.460 log a (R 2 =0.948, adjust R 2 ＝0.947)
TABLE 4 actual and predicted values for shelf life at 4℃after adjustment
Dimension analysis model and verification at 10 DEG C
Table 5 quality index of western-style smoked and boiled ham stored at θ=10℃
The data of Table 3 were calculated according to equation (3) and the results are shown in Table 4.
TABLE 6 results of the operations according to equation (3)
As the linear regression equation running results were analyzed by SPSS software:
Logy＝2.813+0.351LogA-48.748LogB-3.992LogC(R 2 =0.964, adjust R 2 ＝0.950)
Due to pH, a w Is not significant, so the regression equation becomes
Logy＝0.537+0.481LogA(R 2 =0.946, adjust R 2 ＝0.940)
The model is as follows:
as can be obtained by the formula (9),
/>
so that the number of the parts to be processed,
the data after 13 days (> 13 d) according to table 6 (table 2) were re-fitted in the same way to give:
Logy＝-0.474+0.652LogA(R 2 =0.993, adjust R 2 ＝0.992)
TABLE 7 actual and predicted values for shelf life at 10 ℃ after adjustment
Dimension analysis model and verification at 15 DEG C
Table 8 quality index of western-style smoked and boiled ham stored at θ=15℃
The data from table 8 were calculated according to equation (3) and the results are shown in table 9.
TABLE 9 results of the operations according to equation (3)
As the linear regression equation running results were analyzed by SPSS software:
Logy＝3.883+0.343LogA-23.907LogB-4.369LogC(R 2 =0.977, adjust R 2 ＝0.966)
Due to pH, a w Is not significant, so the regression equation becomes
Logy＝0.714+0.453LogA(R 2 =0.955, adjust R 2 ＝0.949)
The model is as follows:
as can be obtained from the r,
so that the number of the parts to be processed,
the data after 5 days (> 5 d) were similarly re-fitted (Table 9) to give:
Logy＝0.039+0.566LogA(R 2 =0.967, adjust R 2 ＝0.961)
Table 10 actual and predicted values of 15 ℃ shelf life after adjustment
Dimension analysis model and verification at 20 DEG C
Table 11 quality index of western-style smoked and boiled ham stored at θ=20deg.C
The data from table 11 were calculated according to equation (3) and the results are shown in table 12.
Table 12 results of the operation according to equation (3)
As the linear regression equation running results were analyzed by SPSS software:
Logy＝-5.413+0.465LogA-77.423LogB+5.387LogC(R 2 =0.974, adjust R 2 ＝0.948)
As the regression coefficients were not significant, the data after 5 days (> 5 d) (table 12) were re-fitted in combination with other temperature results to give:
Logy＝0.012+0.653LogA(R 2 =0.997, adjust R 2 ＝0.996)
Table 13 actual and predicted values of 20 ℃ shelf life after adjustment
Dimension analysis model and verification at 25 DEG C
Table 14 quality index of western-style smoked and boiled ham stored at θ=25℃
The data from table 14 were calculated according to equation (3) and the results are shown in table 15.
TABLE 15 results of the operations according to equation (3)
As the linear regression equation running results were analyzed by SPSS software:
Logy＝11.371+0.649LogA+85.984LogB-11.145LogC(R 2 =0.974, adjust R 2 ＝0.935)
Because the regression coefficients were not significant, the data of table 15 (> 1 d) were re-fitted in combination with other temperature results, log= -0.371+0.713log a (R 2 =0.999, adjust R 2 ＝0.998)
The model is as follows:
as can be obtained from the above-mentioned method,
so that the number of the parts to be processed,
table 16 actual and predicted values of 25 ℃ shelf life after adjustment
Dimension analysis model and verification at 4-25 DEG C
The data reflecting the growth after the conditioning period were fitted according to the western smoked ham quality index stored at θ= 4,10,15,20, 25 ℃ in combination with the equation (3). Under the constant temperature condition of 4-25 ℃, a w Neither difference in pH was significant, so analysis of the linear regression equation run results by SPSS software:
Logy＝-0.571+0.7LogA(R 2 =0.915, adjust R 2 ＝0.913)
The model is as follows:
so that the number of the parts to be processed,
table 17. Table 4-25 ℃ combined actual and shelf life model predicted values
It is evident from table 17 that the relative error between the actual and predicted values by the fitted model is smaller at 4,10,15 c, thus suggesting modeling in the low, non-low temperature range.
(1) Low temperature range (4-15 ℃ C.)
Similarly, the running result of the linear regression equation is analyzed by SPSS software:
Logy＝0.045+0.573LogA(R 2 =0.959, adjust R 2 ＝0.957)
The model is as follows:
/>
so that the number of the parts to be processed,
table 18 actual values for low temperature range (4-15 ℃ C.) and shelf life model predictive values
(2) Non-low temperature range (20-25 ℃ C.)
Similarly, the running result of the linear regression equation is analyzed by SPSS software:
Logy＝-0.273+0.696LogA(R 2 =0.997, adjust R 2 ＝0.997)
The model is as follows:
so that the number of the parts to be processed,
TABLE 19 actual values for non-Low temperature Range (20-25 ℃) and shelf life model predictions
From the sectional models of Western ham, which are established based on dimensional analysis, at constant temperature of 4 ℃,10 ℃,20 ℃, 25 ℃ and low and non-low temperatures, R of the established models is known 2 All are larger than 0.94, the relative error is small, especially the minimum relative error of the segmented model is 1.87%, and the maximum relative error is only 27.72%, which shows that the model established by using the dimension analysis method has high suitability and reliability, and can be applied to the prediction of the food shelf life.
Thus, the shelf life of the food under the actual storage condition can be predicted by constructing a microorganism growth model under each temperature interval through dimensional analysis by combining the time temperature change condition during the storage period of the food.
The dimension analysis model is reliable and simple, the initial putrefying bacteria number is not needed to be measured, the initial colony number of the foods which are normally packaged and good is difficult to detect, and the initial bacteria are needed to be predicted by the common prediction model, so that the detection is difficult. The model established by the dimension analysis method does not need to detect the initial putrefying bacteria content, which brings great convenience and practicability to application. But also the method is suitable for predicting the shelf life of liquid foods or other foods in other states.
In the above method, only K, n, n2, n3 are constants to be determined, i.e. obtained by calculation, the others are actually measured, phi is calculated by a given formula, vp and sp are actually measured.
The above embodiments are merely specific examples of the present invention, and it is apparent that the implementation of the present invention is not limited by the above-described manner. It is within the scope of the present invention to apply the inventive concept and technical solution directly to other situations, as long as insubstantial improvements are made by the inventive method concept and technical solution, or not improved.
Claims (3)
1. A method for predicting the shelf life of a food product by dimensional analysis, characterized by: the method comprises the following steps:
(1) Preparation of detection targets: taking out the food to be detected, and curing for 30-40 hours in a refrigeration environment; adding the pickled materials into a processing material for rolling and kneading, and filling; steaming at 80deg.C for 2 hr, cooling, slicing, vacuum packaging, sterilizing, and cooling;
(2) By passing throughThe dimension analysis method establishes a model for predicting the growth of microorganisms:
wherein N is T The microbial mass in the food per unit mass at the time T is represented by θ, cp, the specific heat capacity, T, the time and S T The surface area of the food at time T and pH T Indicating the pH value of the food at time T, a wT Represents the water activity at time T, K, n 1 、n 2 、n 3 For the undetermined constant, +.>
Sphericity of V p Is the volume of non-spherical particles S p Surface area of non-spherical particles, d ev Equivalent sphere diameter for a volume equal to the volume of the non-spherical particles;
(3) Detecting the detection targets prepared in the step (1) according to the required parameters in the model of the step (2), periodically measuring the total number of colonies, the surface area/20 g, the sphericity, the pH value and the water activity of the detection targets, then bringing the detected parameters into a microorganism growth model, and predicting the microorganism growth condition of food in the shelf life, thereby predicting the food shelf life;
respectively constructing a constant-temperature prediction microorganism growth model and a low-temperature and non-low-temperature range prediction microorganism growth model.
2. The method of predicting food shelf life by dimensional analysis of claim 1, wherein: the curing material adopted in the curing in the step (1) comprises, by mass, 1-3 parts of salt, 0.01-0.03 part of sodium nitrite, 0.05-0.07 part of sodium erythorbate, 0.1-0.3 part of sodium polyphosphate, 0.1-0.3 part of sodium pyrophosphate, 0.6-0.8 part of white granulated sugar, 20-40 parts of distilled water and 0.02-0.04 part of nisin.
3. The method of predicting food shelf life by dimensional analysis of claim 1, wherein: the processing material in the step 1) comprises, by mass, 0.3-0.5 part of salt, 0.4-0.6 part of monosodium glutamate, 0.5-07 part of black pepper powder, 2.0-2.5 parts of soybean protein, 6-7 parts of starch, 0.2-0.3 part of xanthan gum, 0.3-0.5 part of carrageenan, 0.01-0.02 part of monascus red, 100.1-0.3 part of smoke liquid C and 120.4-0.6 part of smoke liquid C.
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| CN109055478B (en) * | 2018-08-20 | 2021-09-07 | 贵州大学 | Method for predicting food microorganisms through dimensional planning model |
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| Laping He.Influences of pulsed light-UV treatment on the storage period of dry-cured meat and shelf life prediction by ASLT method.《Journal of food science and technology》.2019,1744-1756. * |
| 何腊平.基于假单胞菌生长模型预测冷却牛肉的货架期.《中国酿造》.2017,114-119. * |
| 杨潇.基于电子鼻的猪肉冷冻储藏期的无损检测方法.《食品与发酵工业》.2017,247-252. * |
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