A method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables

Abstract

The invention discloses a method for predicting the growth of fluorescent pseudomonas in bagged fresh-cut vegetables, and belongs to the technical field of food safety. The method establishes a gas exchange model, combines the fluorescent pseudomonas growth prediction model, and obtains a coupling model of gas exchange and fluorescent pseudomonas growth, thereby better predicting the growth of fluorescent pseudomonas in fresh-cut vegetables. Under the premise of not destroying the packaging of fresh-cut vegetable products, only the respiratory parameters of fresh-cut vegetables, the fluorescent pseudomonas parameters themselves, the gas permeability parameters of the packaging materials, and the elapsed time need to be known to quickly use the model to predict the number of fluorescent pseudomonas in fresh-cut vegetables, thereby real-time monitoring of the shelf life of fresh-cut vegetables, saving time and effort and with high accuracy.

Description

Technical Field

[0001] The invention relates to a method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables, and belongs to the technical field of food safety. Background Art

[0002] Fresh-cut vegetables, also known as minimally processed vegetables, semi-processed vegetables, lightly processed vegetables, etc., are ready-to-eat or ready-to-use vegetable products made from fresh vegetables, which are processed through a series of processes such as grading, cleaning, trimming, peeling, cutting, preservation, and packaging, and then transported at low temperatures into refrigerators for sale. Fresh-cut vegetables not only maintain the original freshness of the vegetables, but also undergo processing to make the products clean and hygienic. They belong to the category of clean vegetables, are natural, nutritious, fresh, convenient, and highly available (100% edible), and can meet people's needs for a natural, nutritious, fast-paced lifestyle. Among them, romaine lettuce, fruit cucumbers, and cherry radishes are all rich in beta-carotene, antioxidants, dietary fiber, trace elements, and multiple vitamins, and have the effects of promoting gastrointestinal motility, increasing appetite, aiding digestion, and losing weight. They are common ready-to-use vegetable products.

[0003] With the increasing demand of consumers for fresh, healthy and convenient ready-to-eat products and the establishment of fruit and vegetable distribution centers across the country, the market for fresh-cut fruits and vegetables will continue to grow. However, there are many safety hazards in the complete supply chain from farm to table, the most common of which is microbial contamination. Pseudomonas fluorescens, as a psychrophilic bacterium, is the most common spoilage bacteria in fresh-cut vegetables. Infection in humans can cause sepsis, septic shock, intravascular coagulation and other consequences.

[0004] There are many methods for predicting the growth of Pseudomonas fluorescens in fresh-cut vegetables, which can be roughly divided into probability statistical models and kinetic models. The former cannot clearly simulate the growth status of microorganisms, which limits its comprehensive application in the field of microbial prediction; the latter includes three different levels, namely primary models, secondary models and tertiary models. The most commonly used primary models include Logistic model, Modified Gomperz model, Huang model and Baranyi model. For example, Patent KR20110125196 introduces a food quality prediction method using a microbial growth model. Without measuring the number of microorganisms, the microbial growth model is used to provide users with predicted microbial numbers; Patent CN201410521774.9 introduces a chilled meat shelf life prediction method for cold chain logistics. On the basis of the Modified Gomperz model, a chilled meat shelf life prediction model is constructed in combination with physical and chemical indicators and sensory evaluation results, thereby predicting the shelf life of chilled meat in cold chain logistics; Patent CN201210185603.4 introduces a method for constructing a growth prediction model for Vibrio parahaemolyticus in white shrimp. A three-stage linear model is used to simulate the changes of Vibrio parahaemolyticus in white shrimp at different storage temperatures over time, and a linear relationship between the maximum specific growth rate and temperature is established; Patent CN201810127335.8 introduces a method for predicting the shelf life of refrigerated pasteurized fresh milk. First, the microbial growth is measured, and the Modified Gomperz model is used to predict the shelf life of chilled meat in cold chain logistics. The Gomperz model is used to describe the change of the total number of microorganisms over time, and the Rotkowsk equation is used to describe the linear relationship between microbial growth parameters and temperature. The total number of microorganisms in refrigerated pasteurized milk is predicted by these two equations, and compared with the actual measured values ​​to evaluate the accuracy of the model. Finally, the shelf life prediction model of fresh milk is established through the microbial growth prediction model to predict the shelf life of fresh milk at different temperatures. Up to now, most of the existing models only consider the effect of temperature on microbial growth parameters, which is one-sided and not accurate enough.

[0005] Use taking into account CO 2 and O 2 The mathematical model of the effect of microbial growth on the microbial safety of food inside the package is an emerging topic that has not been studied yet. As Chaix et al. (Chaix E, Couvert O, Guillaume C, et al. Predictive microbiology coupled with gas (O 2 / CO 2)Transfer information / packaging systems: how to develop an efficient decision support tool for food packaging dimensioning[J]. Comprehensive Reviews in Food Science and Food Safety, 2015, 14(1): 1-21.) As described in [1], only a few predictive microbiology models have considered CO 2 Effects on bacterial growth, most commonly during food storage CO 2 The concentration is considered constant. As described in the studies of Devlieghere et al. (Devlieghere F, Geeraerd A, Versyck K, et al. Growth of Listeria monocytogenes in modified atmosphere packed cooked meat products: a predictive model [J]. Food microbiology, 2001, 18 (1): 53-66.) or Mejlholm et al. (Mejlholm O, Dalgaard P. Modeling and predicting the growth boundary of Listeria monocytogenes in lightly preserved seafood [J]. Journal of food protection, 2007, 70 (1): 70-84.), this constant value may be due to CO 2 Partial pressure or dissolved CO 2 However, in any modified atmosphere packaging, the dynamic transfer of gas cannot be ignored. 2 The transfer of O will affect the amount of dissolved carbon dioxide in food, thereby affecting the growth of microorganisms. 2 The influence of CO2 is also indispensable for simulating the growth of aerobic bacteria such as Pseudomonas fluorescens. Pseudomonas fluorescens is the main spoilage bacteria in the refrigerated storage of fresh-cut vegetables. It is also a strict aerobic bacterium, so the CO2 content inside the packaging must be considered. 2 and O 2 The dynamic effect of O 2 The situation is even worse than that of CO 2is less common, only Geysen et al. (Geysen S, Escalona V, Verlinden B, et al. Validation of predictive growth models describing superatmospheric oxygen effects on Pseudomonas fluorescens and Listeria innocua on fresh-cut lettuce[J]. International journal of food microbiology, 2006, 111(1):48-58.), Alfaro et al. (Alfaro B, Hernández I, Le Marc Y, et al. Modelling the effect of the temperature and carbon dioxide on the growth of spoilage bacteria in packed fish products[J]. Food control, 2013, 29(2):429-437.), Farber et al. (Farber J, Cai Y, Ross W. Predictive modeling of the growth of Listeria monocytogenes in CO2 environments[J]. International Journal of Food Microbiology, 1996, 32(1-2):133-144.), Pin et al. (Pin C, Baranyi J, De Fernando GG. Predictive model for the growth of Yersinia enterocolitica under modified atmospheres[J]. Journal of applied microbiology, 2000, 88(3):521-530.), Guillard et al. (Guillard V, Couvert O, Stahl V, et al. Validation of a predictive model coupling gas transfer and microbial growth in fresh food packed under modified atmosphere[J].Food microbiology, 2016, 58: 43-55.), Dolan et al (Dolan K, Meredith H, Bolton D, et al. Coupling the dynamics of diffused gases and microbial growth in modified atmosphere packaging [J]. International journal of food microbiology, 2019, 292: 31-38.), Chaix et al (Chaix E, Broyart B, Couvert O, et al. Mechanistic coupling model Seven modeling attempts made by gas exchange dynamics and Listeriamonocytogenes growth in modified atmosphere packaging of non respiring food[J].Food microbiology,2015,51:192-205.) considered O. 2 However, these studies did not integrate the effects of CO 2 and O 2 The dynamic effects on microbial growth are also not integrated, nor is the dynamic transport of gas through the packaging material. Summary of the invention

[0006] In order to solve the problem that existing models and software do not integrate internal CO 2 and O 2 In order to solve the problems of dynamic influence on the growth of Pseudomonas fluorescens and dynamic transmission of unintegrated gas through packaging materials, the present invention provides a method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables, the method comprising:

[0007] Step 1: Sample processing;

[0008] Step 2: Based on enzyme kinetics theory, establish the respiration model of fresh-cut vegetables and Pseudomonas fluorescens;

[0009] Step 3: Measure the growth of Pseudomonas fluorescens and the composition of headspace gas in fresh-cut vegetables;

[0010] Step 4: Establish a gas exchange model based on the measured data in step 3;

[0011] Step 5: Establish a growth prediction model for Pseudomonas fluorescens based on the measured data in step 3;

[0012] Step 6: Based on the gas exchange model established in step 3 and the Pseudomonas fluorescens growth prediction model established in step 5, a coupling model of gas exchange and Pseudomonas fluorescens growth is established;

[0013] Step 7: Use the coupling model of gas exchange and Pseudomonas fluorescens growth established in step 6 to predict the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables.

[0014] Optionally, the step 2 includes:

[0015] Step S2.1: Determine the respiration rate of fresh-cut vegetables, where the respiration rate refers to the rate of oxygen consumption and carbon dioxide production;

[0016]

[0017]

[0018] Among them, R O2 O 2 Consumption rate, mL O 2 / (kg*h); R CO2 For CO 2 Production rate, in mL CO 2 / (kg*h);y O2 O 2 Concentration volume ratio, %; y CO2 For CO 2 Concentration volume ratio, %; t is the storage time, unit is h; Δt is the time difference between two gas measurements, unit is h; V F is the headspace volume of the transparent airtight can used to measure the respiration rate of fresh-cut vegetables, in L; M is the sample mass, in kg;

[0019] Step S2.2: Using CO 2 As O 2 The Michaelis-Menten equation of the noncompetitive inhibitor was fitted, and the O determined in step S2.1 was used 2 and CO 2 concentration and respiratory rate, and the parameters of the noncompetitive inhibition Michaelis-Menten equation including V were calculated using a multiple linear regression model. m , K m and K i , establish a respiration model for fresh-cut vegetables:

[0020]

[0021] Among them, V m For fresh cut vegetables 2 The maximum consumption rate or CO 2 The maximum generation rate, unit is mL / (kg*h); Km is the Michaelis constant, unit: %O 2 ; K i For CO 2 As O 2 Michaelis constant of noncompetitive inhibitor, unit: %O 2 ;

[0022] Step S2.3: Establish the Pseudomonas fluorescens respiration model:

[0023]

[0024]

[0025] Among them, R P.f Pseudomonas fluorescens O 2 The consumption rate or CO 2 The generation rate, unit is mL / (CFU*h); V m,P.f Pseudomonas fluorescens O 2 The maximum consumption rate or CO 2 The maximum generation rate, unit is mL / (CFU*h); K m,P.f is the Michaelis constant; K i,P.f For CO 2 As O 2 Michaelis constant of competitive inhibitor; y O2 and CO2 O 2 and CO 2 Concentration volume ratio, unit: %.

[0026] Optionally, the step 4 includes:

[0027] Assumptions:

[0028] ⑥ The gas inside and outside the packaging bag for fresh-cut vegetables is evenly distributed, and the gas is all ideal gas;

[0029] ⑦ The gas permeability of the film used in the packaging bag remains constant;

[0030] ⑧The exchange of all gases through the membrane is independent of each other;

[0031] ⑨The gas exchange process of the packaging bag is a constant temperature process;

[0032] ⑩The total pressure of gas inside and outside the packaging bag is equal;

[0033] According to Fick's law and the above assumptions, when using packaging film to store fresh-cut vegetables, the total volume change of gas in the packaging bag is the sum of the volume change of gas that permeates the film, the volume change caused by the respiration of fresh-cut vegetables and the respiration of Pseudomonas fluorescens;

[0034] Therefore, the gas exchange model of the original film packaging is:

[0035]

[0036]

[0037] Among them, V f Q is the headspace volume inside the packaging bag, mL; g,j is the permeability of the membrane used in the packaging bag to gas component j, in cm 3 / (m 2 ·24h·0.1MPa), gas component j refers to O 2 or CO 2 ; are the volume fractions of gas component j outside and inside the packaging bag, respectively, %;

[0038] P 0 is the pressure under standard conditions, 0.1MPa; R O2 For fresh cut vegetables 2 Consumption rate, mL O 2 / (kg*h); R CO2 CO for fresh cut vegetables 2 Production rate, mL CO 2 / (kg*h); N(t) is the number of microorganisms, unit is CFU / g; M is the mass of fresh-cut vegetables, unit is kg.

[0039] Optionally, the step five includes:

[0040] Two primary models were selected as the growth prediction models for Pseudomonas fluorescens, namely the Huang model and the Baranyi model. The mathematical description of the Huang model is as follows:

[0041]

[0042] The mathematical description of the Baranyi model is as follows:

[0043]

[0044] Among them, N(t), N max and N 0 They represent the number of microorganisms at time t, the maximum number of bacterial colonies and the initial number of microorganisms, respectively, in ln cfu / g; h; λ is the hysteresis time, in h; μ max is the maximum specific growth rate of microorganisms, in h -1 ; α is the hysteresis phase change coefficient, which is 4.

[0045] Optionally, the step six includes:

[0046] The gas exchange model established in step 3 and the two Pseudomonas fluorescens growth prediction models established in step 5 are coupled respectively to establish a coupling model of gas exchange and Pseudomonas fluorescens growth;

[0047] The coupling model of gas exchange and Pseudomonas fluorescens growth obtained by coupling with the Huang model is:

[0048]

[0049] Among them, Q(t) is related to microbial physiology and is expressed as:

[0050]

[0051] The coupling model of gas exchange and Pseudomonas fluorescens growth obtained by coupling with the Baranyi model is:

[0052]

[0053] Among them, Q(t) is related to microbial physiology and is expressed as:

[0054]

[0055] Among them, N(t), N max and N 0 They represent the number of microorganisms at time t, the maximum number of bacterial colonies and the initial number of microorganisms, respectively, in ln cfu / g; in h; λ is the hysteresis time, in h; μ max is the maximum specific growth rate of microorganisms, h -1 ;

[0056] CO 2 max-diss , is the maximum dissolved CO that allows the growth of Pseudomonas fluorescens 2 concentration;

[0057] O 2 min-diss , is the minimum dissolved O that allows the growth of Pseudomonas fluorescens 2 concentration.

[0058] Optionally, in step 6, for Pseudomonas fluorescens, the growth of dissolved CO 2 The maximum concentration of CO 2 max-diss Take 40% to allow growth of dissolved O 2 The minimum concentration of O 2 min-diss Take 0.25%.

[0059] Optionally, the method selects iceberg lettuce, fruit cucumber and cherry radish for leafy vegetables, fruit vegetables and root vegetables, respectively, to prepare fresh-cut vegetable samples.

[0060] The present application also provides a method for selecting packaging materials for fresh-cut vegetables. The method uses the above-mentioned method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables to predict the growth of Pseudomonas fluorescens in fresh-cut vegetables packaged with different packaging materials, so as to determine the packaging materials used for packaging fresh-cut vegetables.

[0061] The present application also provides an application method of the above-mentioned method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables in the transportation and storage of vegetables.

[0062] The beneficial effects of the present invention are:

[0063] By improving the characterization of environmental factors that affect microbial growth parameters, a method for predicting the growth of fluorescent Pseudomonas in bagged fresh-cut vegetables is provided. By establishing a gas exchange model and combining it with a fluorescent Pseudomonas growth prediction model, a coupling model of gas exchange and fluorescent Pseudomonas growth is obtained, thereby better predicting the growth of fluorescent Pseudomonas in fresh-cut vegetables. Without destroying the packaging of fresh-cut vegetable products, only the respiratory parameters of fresh-cut vegetables, the parameters of fluorescent Pseudomonas itself, the gas permeability parameters of the packaging materials, and the elapsed time can be used to quickly use the model to predict the number of fluorescent Pseudomonas in fresh-cut vegetables, thereby real-time monitoring of the shelf life of fresh-cut vegetables, saving time and effort with high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0065] Figure 1 A flow chart of a method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables provided in one embodiment of the present application.

[0066] Figure 2 This is a model diagram of the respiration rate of fresh-cut lettuce.

[0067] Figure 3 This is a diagram showing the growth kinetics model of Pseudomonas fluorescens in fresh-cut lettuce in different packages.

[0068] Figure 4 This is a comparison chart of the predicted values ​​and measured values ​​of the gas composition in the package of fresh-cut lettuce using the coupling model.

[0069] Figure 5This is a comparison chart of the predicted values ​​and measured values ​​of the coupling model for the growth of Pseudomonas fluorescens in fresh-cut lettuce.

[0070] Figure 6 This is the respiratory rate model diagram of fresh-cut cucumbers.

[0071] Figure 7 This is a diagram showing the growth kinetics model of Pseudomonas fluorescens in fresh-cut cucumbers in different packages.

[0072] Figure 8 This is a comparison chart of the predicted values ​​and measured values ​​of the gas composition in the package of fresh-cut cucumbers using the coupling model.

[0073] Fig. 9 This is a comparison chart of the predicted values ​​and measured values ​​of the coupling model for the growth of Pseudomonas fluorescens in fresh-cut cucumbers.

[0074] Fig.10 This is a model diagram of the respiration rate of fresh-cut radish.

[0075] Fig.11 This is a diagram showing the growth kinetics model of Pseudomonas fluorescens in fresh-cut radishes in different packages.

[0076] Fig.12 This is a comparison chart of the predicted values ​​and measured values ​​of the gas composition in the package of fresh-cut radish using the coupling model.

[0077] Fig.13 This is a comparison chart of the predicted value and measured value of the coupling model for the growth of Pseudomonas fluorescens in fresh-cut radish. DETAILED DESCRIPTION

[0078] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0079] First, the sources of the gas analyzer and fresh vegetables involved in the present invention are described as follows:

[0080] Source of gas analyzer: purchased from Shenzhen Yuante Technology Instrument Co., Ltd., model SKY6000-S4, equipped with infrared sensor and electrochemical sensor, capable of simultaneously measuring oxygen and carbon dioxide content.

[0081] Fresh vegetables come from Shanghai Yinlong Vegetable Base. They are immediately transported to the laboratory through cold chain after being picked on the same day, and then placed in a 4℃ refrigerator for later use. They are all fresh vegetables with no mechanical damage and basically the same maturity.

[0082] Embodiment 1:

[0083] This embodiment provides a method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables. Figure 1 , the method comprising:

[0084] Step 1: Sample processing;

[0085] Step 2: Based on enzyme kinetics theory, establish the respiration model of fresh-cut vegetables and Pseudomonas fluorescens;

[0086] Step 3: Measure the growth of Pseudomonas fluorescens and the composition of headspace gas in fresh-cut vegetables;

[0087] Step 4: Establish a gas exchange model based on the measured data in step 3;

[0088] Step 5: Establish a growth prediction model for Pseudomonas fluorescens based on the measured data in step 3;

[0089] Step 6: Based on the gas exchange model established in step 3 and the Pseudomonas fluorescens growth prediction model established in step 5, a coupling model of gas exchange and Pseudomonas fluorescens growth is established;

[0090] Step 7: Use the coupling model of gas exchange and Pseudomonas fluorescens growth established in step 6 to predict the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables.

[0091] Vegetables can be basically divided into five categories: leafy vegetables, cauliflower, fruit vegetables, root vegetables and stem vegetables. Each category has its own biological characteristics. There are many fresh-cut vegetables that can be used as clean vegetables. Among them, cauliflower such as broccoli and stem vegetables such as celery all need to be blanched, which are not within the scope of raw food we studied. Therefore, according to the difference in respiration intensity, this application subsequently selected the representative of leafy vegetables - heart lettuce, the representative of fruit vegetables - fruit cucumber and the representative of root vegetables - cherry radish for experiments. These three vegetables have good flavor and taste, and can provide a variety of nutrients to the human body, and are favored by consumers.

[0092] Embodiment 2

[0093] This embodiment provides a method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables. Figure 2 , the method comprising:

[0094] Step 1: Sample processing;

[0095] 1.1 Preparation of fresh-cut romaine lettuce

[0096] Select leaves of moderate maturity, intact, free of mechanical damage, pests and diseases, and emerald green as experimental materials. Select fully expanded leaves from the middle of the lettuce head, and remove damaged outer leaves and immature inner leaves. First wash with tap water to remove surface debris, and then use a stainless steel knife disinfected with 75% alcohol to cut into pieces of about 3cm×3cm. Soak the cut lettuce in 5℃, 100ppm NaClO solution for 5min, and then wash away residual chlorine with deionized water. After drying the surface moisture, put it in a ziplock bag and refrigerate it at 4℃ for use.

[0097] 1.2 Preparation of fresh-cut cucumber fruit

[0098] Select cucumbers of moderate maturity, intact, free of mechanical damage, pests and diseases, and emerald green color as experimental materials, remove the roots and heads, wash with running tap water for 2 minutes, remove surface debris, soak in 5℃, 100ppm NaClO solution for 5 minutes, wash with deionized water to remove residual chlorine, then wipe dry, irradiate with ultraviolet light for 30 minutes in a biological safety cabinet, and then cut into 5mm slices with a handheld slicer disinfected with 75% alcohol, put in a ziplock bag and refrigerate at 4℃ for use.

[0099] 1.3 Preparation of fresh-cut cherry radish

[0100] Select radishes of moderate maturity, intact, free of mechanical damage, pests and diseases, and bright red in color as experimental materials, remove the roots and heads, wash with running tap water for 2 minutes, remove surface debris, soak in 5℃, 100ppm NaClO solution for 5 minutes, wash with deionized water to remove residual chlorine, then wipe dry, irradiate with ultraviolet light for 30 minutes in a biological safety cabinet, and then cut into 5mm slices with a handheld slicer disinfected with 75% alcohol, put in a ziplock bag and refrigerate at 4℃ for use.

[0101] 1.4 Activation, inoculation and packaging

[0102] Activate the fluorescent Pseudomonas stored at 28℃ and prepare a concentration of 10 8 cfu / mL of the initial inoculum, and gradient dilution to about 10 4 cfu / mL and then inoculated into fresh-cut vegetables (i.e., fresh-cut heart lettuce, fresh-cut fruit cucumber, and fresh-cut cherry radish), so that the initial fluorescent Pseudomonas inoculation concentration of fresh-cut vegetables was about 10 3 ~10 4 cfu / mL.

[0103] The inoculated fresh-cut vegetables were placed in a clean air dryer for 15 minutes and then immediately placed in packaging bags with a specification of 200mm×160mm, with 20g in each bag. The samples were divided into four groups according to the different packaging materials. The packaging material parameters are shown in Table 1 below.

[0104] Regarding packaging materials, this application combines the current status of fresh-keeping materials on the market in recent years and selects four types of packaging fresh-keeping bags from different manufacturers according to the different oxygen permeabilities of packaging materials. They are marked as Ⅰ: OTR5, Ⅱ: OTR48, Ⅲ: OTR2058, and Ⅳ: OTR3875, respectively. A certain amount of air is filled into the bags through an air pump and then heat-sealed.

[0105] Table 1 Parameters of different packaging film materials

[0106]

[0107]

[0108] Step 2: Based on enzyme kinetics theory, the respiration model of three fresh-cut vegetables and Pseudomonas fluorescens was established;

[0109] 2.1 The respiration rate of fresh-cut vegetables was measured by the static closed method (see: Shapawi ZI A, Ariffin SH, Shamsudin R, et al. Modeling respiration rate of fresh-cut sweet potato (Anggun) stored indifferent packaging films [J]. Food Packaging and Shelf Life, 2021, 28: 100657.). In this application, the respiration rate refers to the rate of oxygen consumption and carbon dioxide production;

[0110] First, the gas analyzer was used to measure O 2 and CO 2 The concentration is calculated by combining the volume of fresh-cut vegetables and the volume of the transparent airtight jar used for sampling. Specifically, the rates of oxygen consumption and carbon dioxide production are calculated according to the following formula:

[0111]

[0112]

[0113] Among them, R O2 O 2 Consumption rate, mL O 2 / (kg*h); R CO2 For CO 2 Production rate, in mL CO 2 / (kg*h);

[0114] y O2 O 2 Concentration volume ratio, %; y CO2 For CO2 Concentration volume ratio, %;

[0115] t is the storage time, in h; Δt is the time difference between two gas measurements, in h; V F is the headspace volume of the airtight tank, in L; M is the sample mass, in kg.

[0116] 2.2 Based on the enzyme kinetics theory, CO 2 As O 2 The Michaelis-Menten equation for the non-competitive inhibitor of was fitted (Peppelenbos HW, Tijskens LMM, van't Leven J, et al. Modelling oxidative and fermentative carbon dioxide production of fruits and vegetables [J]. Postharvest Biology and Technology, 1996, 9 (3): 283-295.) and the O measured in step 2.1 was used to 2 and CO 2 concentration, and the parameters of the Michaelis-Menten equation for noncompetitive inhibition were calculated using a multiple linear regression model including V m , K m and K i , establish a respiration model for fresh-cut vegetables:

[0117]

[0118] Among them, R O2 O 2 Consumption rate, mL O 2 / (kg*h); R CO2 For CO 2 Production rate, in mL CO 2 / (kg*h);

[0119] y O2 O 2 Concentration volume ratio, %; y co2 For CO 2 Concentration volume ratio, %;

[0120] V m O 2 The maximum consumption rate or CO 2 The maximum generation rate, unit is mL / (kg*h); K m is the Michaelis constant, unit: %O 2 ; K i For CO 2 As O2 Michaelis constant of noncompetitive inhibitor, unit: %O 2 ;

[0121] 2.3 Based on the enzyme kinetics theory, the parameters obtained in the study of Thiele et al. (Thiele T, Kamphoff M, Kunz B. Modeling the respiration of Pseudomonas fluorescens on solid-state lettuce-juice agar [J]. Journal of food engineering, 2006, 77 (4): 853-857.) include V m , K m and K i , establish the Pseudomonas fluorescens respiration model:

[0122]

[0123]

[0124] Among them, R P.f Pseudomonas fluorescens O 2 The consumption rate or CO 2 The generation rate, unit is mL / (CFU*h); V m,P.f O 2 The consumption rate or CO 2 The generation rate, unit is mL / (CFU*h); K m,P.f is the Michaelis constant; K i,P.f For CO 2 As O 2 Michaelis constant of competitive inhibitor; y O2 and CO2 O 2 and CO 2 Concentration volume ratio, unit: %.

[0125] It should be noted that the respiratory entropy of Pseudomonas fluorescens is 1, which means that 2 The consumption rate is equal to CO 2 Spawn rate.

[0126] Step 3: Measure the growth of Pseudomonas fluorescens and the composition of headspace gas in fresh-cut vegetables;

[0127] 3.1 The four groups of treated samples were placed in a 4°C refrigerator for 15 days. The number of Pseudomonas fluorescens and the composition of the headspace gas were measured every 24 hours. The measurements were repeated three times and the average value was taken.

[0128] 3.2 Take 20g of sample and add it to a homogenizing bag containing 180mL of sterile saline. Homogenize for 4min (beat the front and back sides for 2min each), and then dilute it 10 times in a gradient. Select 1mL of the appropriate gradient dilution and apply it to the Pseudomonas CFC selective medium. Make 2 parallels for each dilution, for a total of three dilutions. After culturing at 28℃ for 48h, count the colonies in log CFU / g.

[0129] Step 4: Establish a gas exchange model based on the measured data in step 3;

[0130] To facilitate the analysis of gas exchange theory, the following assumptions are made:

[0131] The gas inside and outside the packaging bag is evenly distributed, and the gas is all ideal gas;

[0132] The permeability of the film to the gas remains constant;

[0133] The exchange of all gases through the membrane is independent of each other;

[0134] The gas exchange process of the packaging system is a constant temperature process;

[0135] The total pressure of gas inside and outside the package is equal;

[0136] According to Fick's law and the above assumptions, when using packaging film to store fresh-cut vegetables, the total volume change of gas in the packaging bag is the sum of the gas change that passes through the film, the gas change caused by the respiration of fresh-cut vegetables and the respiration of Pseudomonas fluorescens.

[0137] Therefore, the gas exchange model of the original film packaging can be obtained as follows:

[0138]

[0139]

[0140] Among them, V f In this application, it is the headspace volume inside the package, mL; Q g,j is the permeability of the membrane to gas component j, cm 3 / (m 2 ·24h·0.1MPa), gas component j refers to O 2 or CO 2 ; are the volume fractions of gas component j outside and inside the package, respectively, %; P 0 is the pressure under standard conditions, 0.1MPa; R O2 For fresh cut vegetables2 Consumption rate, mL O 2 / (kg*h); R CO2 CO for fresh cut vegetables 2 Production rate, mL CO 2 / (kg*h); R P.f Pseudomonas fluorescens O 2 The consumption (generation) rate or CO 2 The generation rate is mL / (CFU*h); N(t) is the number of microorganisms, CFU / g; M is the mass of fresh-cut vegetables, kg.

[0141] Step 5: Establish a growth prediction model for Pseudomonas fluorescens;

[0142] The primary model is a mathematical equation that describes the number of microorganisms or other microbial response parameters such as maximum growth rate, hysteresis time and maximum bacterial colony number that change over time. The commonly used models are Modified Gompertz model, Logistic model, Huang model and Baranyi model. The Logistic model does not include a hysteresis period. The Modified Gomperz model is basically an empirical model with inflection points and requires the image to be strictly symmetrical. The Huang model and the Baranyi model are both complete continuous growth models, which cover the entire range from the lag period, through the exponential growth period, and finally to the stable period. Therefore, this application uses the Huang model (Huang L. Optimization of a new mathematical model for bacterial growth [J]. Food Control, 2013, 32 (1): 283-288.) or the Baranyi model (Baranyi J, Roberts T AA dynamic approach to predicting bacterial growth in food [J]. International journal of food microbiology, 1994, 23 (3-4): 277-294.) as the primary model to simulate the changes of Pseudomonas fluorescens in fresh-cut vegetables in different packages with storage time.

[0143] The mathematical description of Huang's model is as follows:

[0144]

[0145] The mathematical description of the Baranyi model is as follows:

[0146]

[0147] Among them, N(t), N max and N 0 They represent the number of microorganisms at time t, the maximum number of bacterial colonies, and the initial number of microorganisms, respectively, in units of ln cfu / g; t is time, h; λ is the hysteresis time, h; μ max is the maximum specific growth rate of microorganisms, h -1 ; α is the hysteresis phase change coefficient, which is taken as 4.

[0148] Step 6: Based on the gas exchange model established in step 3 and the Pseudomonas fluorescens growth prediction model established in step 5, a coupling model of gas exchange and Pseudomonas fluorescens growth is established;

[0149] Coupling of gas mass transfer with the above Huang model:

[0150]

[0151]

[0152] Among them, Q(t) is related to microbial physiology and is expressed as:

[0153]

[0154] Coupling of gas mass transfer with the above Baranyi model:

[0155]

[0156] Among them, Q(t) is related to microbial physiology and is expressed as:

[0157]

[0158] Among them, N(t), N max and N 0 They represent the number of microorganisms at time t, the maximum number of bacterial colonies, and the initial number of microorganisms, respectively, in units of ln cfu / g; t is time, h; λ is the hysteresis time, h; μ max is the maximum specific growth rate of microorganisms, h -1 ;

[0159] CO 2 max-diss , is the dissolved CO that allows growth 2 The maximum concentration is 40% for Pseudomonas fluorescens;

[0160] O 2 min-diss , is the dissolved O that allows growth 2The minimum concentration for Pseudomonas fluorescens is 0.25%. Both parameters are taken from the study of Guillard et al. (Guillard V, Couvert O, Stahl V, et al. Validation of a predictive model coupling gas transfer and microbial growth in fresh foodpacked under modified atmosphere[J]. Food microbiology, 2016, 58: 43-55.).

[0161] Step 7: Use the coupling model of gas exchange and Pseudomonas fluorescens growth established in step 6 to predict the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables.

[0162] According to formula (10), (11) or (12), (13) and combined with formula (6) and (7), the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables can be predicted. The growth of Pseudomonas fluorescens accompanied by gas transfer inside the fresh-cut vegetable packaging can be predicted, which provides certain theoretical guidance for predicting the shelf life of fresh-cut fruits and vegetables.

[0163] It should be noted that my country currently does not have a clear standard for the limit of microorganisms in clean vegetables. Some Western countries have a limit of 10 microorganisms in fresh-cut fruits and vegetables. 6 CFU / g, and then the microbial limit value is input into the model established in this application, and the time required to reach the microbial limit, that is, the shelf life of fresh-cut vegetables, can be output. This application does not further explain this subsequent calculation.

[0164] Secondly, the model established in this application can also predict the internal O 2 and CO 2 content, providing a theoretical basis for the development of new prediction software.

[0165] Finally, the model can also be used as a decision support tool for the design of modified atmosphere packaging systems and help food manufacturers choose suitable packaging materials through a demand-driven approach in a more rational and sustainable way than trial and error, saving costs and time.

[0166] In order to verify the prediction accuracy of the model established in this application and determine the optimal prediction model corresponding to different types of vegetables, this application uses the established coupling model of gas exchange and Pseudomonas fluorescens growth to predict the growth of Pseudomonas fluorescens in fresh-cut vegetables in different packages at 4°C, and compares it with the actual measured values ​​to evaluate the accuracy of the model.

[0167] The evaluation parameters are:

[0168] Adjusted correlation coefficient:

[0169]

[0170] Root Mean Square Error:

[0171]

[0172] DIF:

[0173]

[0174] Deviation Factor:

[0175]

[0176] Where n is the number of observations, i.e. the number of data points; s is the number of parameters to be fitted; SSE is the sum of squared errors; SST is the total sum of squared errors; N i-pre is the model prediction value; N i-obs is the observed value, i.e. the experimental value.

[0177] The bias factor measures whether the predicted value overestimates or underestimates the measured value, indicating the structural bias of the model. The precision factor measures the average error between the predicted value and the measured value. A value equal to 1 indicates that the predicted value is completely consistent with the measured value and the prediction is very accurate. Ross recommends that the spoilage bacteria B f 0.85~1.25 is acceptable, A f The closer it is to 1, the better the model is. f A value greater than 1.5 indicates poor model performance and is unacceptable.

[0178] ASZ is the ratio (%) of the number of samples falling within the acceptable prediction interval to the total number of samples. Here, ±0.5logcfu / g is taken as the acceptable prediction interval. ASZ greater than 70% indicates that the model prediction performance is good.

[0179] Specifically, the specific process and data of the experiment on the representative of leafy vegetables, i.e., heart lettuce, the representative of fruit vegetables, i.e., fruit cucumber, and the representative of root vegetables, i.e., cherry radish, are shown in the following Examples 3, 4, and 5 respectively:

[0180] Embodiment 3

[0181] This embodiment provides a method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables. For the representative leafy vegetable, romaine lettuce, different films are used to package the fresh-cut romaine lettuce. The gas composition includes oxygen and carbon dioxide concentrations. The method comprises:

[0182] Step 1: Sample processing;

[0183] Prepare fresh-cut romaine lettuce by referring to step 1.1 in Example 2;

[0184] Activate the fluorescent Pseudomonas stored at 28℃ and prepare a concentration of 10 8 cfu / mL of the initial inoculum, and gradient dilution to about 10 4 cfu / mL and then inoculated onto fresh-cut lettuce. The initial inoculation concentration of Pseudomonas fluorescens on fresh-cut lettuce was 2.1×10 4 cfu / mL. The inoculated fresh-cut lettuce was placed in a clean air dryer for 15 minutes, and then immediately placed in a packaging bag with a specification of 200mm×160mm, with 20g in each bag. The sample treatment was divided into four groups. According to the different oxygen permeabilities of the packaging materials, the treated samples were marked as Ⅰ:OTR5, Ⅱ:OTR48, Ⅲ:OTR2058, Ⅳ:OTR3875, and a certain amount of air was filled in through an air pump and heat-sealed.

[0185] Step 2: Based on enzyme kinetics theory, establish the respiration model of fresh-cut lettuce and Pseudomonas fluorescens;

[0186] The static closed method was used for measurement. The closed system used a 2.5L transparent airtight can. The container was filled with air. The lettuce sample was 200±2g and repeated three times. The density of the lettuce was measured by the water displacement method to be 0.97699g / mL. A handheld headspace gas analyzer was used to continuously measure the gas composition inside the airtight can. The results were reported as the expected percentage of air composition. The measurement was performed every 2 hours and each measurement was repeated three times. The volume ratio of oxygen concentration and the volume ratio of carbon dioxide concentration are Figure 2 (A);

[0187] Using CO 2 As O 2 The Michaelis-Menten equation of the noncompetitive inhibitor was fitted, and the measured O 2 and CO 2 concentration, and the respiratory model parameter V of fresh-cut lettuce was calculated using a multiple linear regression model. m , K m and K i , see Table 2; Comparison of the model predicted values ​​and measured values ​​of the respiration rate of fresh-cut lettuce is shown in Figure 2 (B);

[0188] The uninhibited Michaelis-Menten equation was used for fitting, and the respiratory model parameter V of Pseudomonas fluorescens was m , K m They are 0.289 mL / [(1.7×10 7 CFU)*h], 1.906%.

[0189] Step 3: Measure the growth of Pseudomonas fluorescens and the composition of headspace gas in fresh-cut lettuce;

[0190] The four groups of treated samples were stored in a 4°C refrigerator for 15 days. The number of Pseudomonas fluorescens and the composition of the headspace gas were measured every 24 hours. The measurements were repeated three times and the average values ​​were taken.

[0191] Fluorescent Pseudomonas assay method: Take 20g of sample and add it to a homogenizing bag containing 180mL of sterile saline, homogenize for 4min (beat the front and back sides for 2min each), and then dilute it 10 times in a gradient. Select 1mL of the appropriate gradient dilution and apply it to the Pseudomonas CFC selective medium. Make 2 parallels for each dilution, for a total of three dilutions. After culturing at 28℃ for 48h, count the colonies in log CFU / g.

[0192] Method for determining headspace gas composition: headspace gas analyzer.

[0193] Step 4: Establish a growth prediction model for Pseudomonas fluorescens;

[0194] According to formula (9), the fluorescent Pseudomonas data measured in step 3 were nonlinearly fitted to obtain the Baranyi-based prediction model for the growth of fluorescent Pseudomonas in fresh-cut lettuce. The fitting effect of fluorescent Pseudomonas growth is shown in Figure 3 The growth kinetic parameters and model evaluation obtained by fitting are shown in Table 3.

[0195] Step 5: Based on the Pseudomonas fluorescens growth prediction model, a coupling model of gas exchange and Pseudomonas fluorescens growth is established, and the model is verified and evaluated by comparing the predicted results with the actual values;

[0196] For fresh-cut lettuce, first substitute formula (3) and (4) into formula (6) and (7), then combine formula (6) and (7) with formula (12) and (13), and use MATLAB programming (Runge-Kutta algorithm) to calculate the O of the sample in different packaging bags at any time predicted by the gas mass transfer coupling Baranyi model. 2 and CO 2 The gas concentration and the number of fluorescent Pseudomonas can also be obtained, which are then compared with the measured values ​​of the samples during different packaging and storage processes to verify the coupling model. The results are shown in Figure 4 and Figure 5 ; Using R 2 ,RMSE,A f , B f The model was evaluated by ASZ and the gas exchange model evaluation results are shown in Table 4. The growth kinetic parameters of Pseudomonas fluorescens in fresh-cut lettuce in different packages after coupling gas mass transfer and the coupling model evaluation results are shown in Table 5.

[0197] Embodiment 4

[0198] This embodiment provides a method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables, targeting the representative of fruits and vegetables - fruit cucumbers, and using different films to package the fresh-cut fruit cucumbers. The gas composition includes oxygen and carbon dioxide concentrations.

[0199] The method comprises:

[0200] Step 1: Sample processing;

[0201] Prepare fresh-cut cucumber fruit by referring to step 1.2 in Example 2;

[0202] Activate the fluorescent Pseudomonas stored at 28℃ and prepare a concentration of 10 8 cfu / mL of the initial inoculum, and gradient dilution to about 10 4 cfu / mL and then inoculated into fresh-cut cucumbers, the initial fluorescent Pseudomonas inoculation concentration of fresh-cut cucumbers was 1.02×10 3 cfu / mL. The inoculated fresh-cut cucumbers were placed in a clean air dryer for 15 minutes, and then immediately placed in a packaging bag with a specification of 200mm×160mm, with 20g in each bag. The samples were divided into four groups. According to the different oxygen permeabilities of the packaging materials, the treated samples were marked as Ⅰ:OTR5, Ⅱ:OTR48, Ⅲ:OTR2058, Ⅳ:OTR3875, and filled with a certain amount of air through an air pump and heat-sealed.

[0203] Step 2: Based on enzyme kinetics theory, the respiration model of fresh-cut cucumber and Pseudomonas fluorescens was established;

[0204] The static closed method was used for measurement. The closed system used a 2.5L transparent airtight jar with air in the container. The cucumber sample was 400±2g and repeated three times. The density of the cucumber was measured by the water displacement method to be 1.0413g / mL. A handheld headspace gas analyzer was used to continuously measure the gas composition inside the airtight jar. The results were reported as the expected percentage of air composition. The measurement was performed every 2 hours and each measurement was repeated three times. The volume ratio of oxygen concentration and the volume ratio of carbon dioxide concentration are as follows: Figure 6 (A);

[0205] Using CO 2 As O 2 The Michaelis-Menten equation of the noncompetitive inhibitor was fitted, and the measured O 2 and CO 2 concentration, and the respiratory model parameter V of fresh-cut cucumber was calculated using the multiple linear regression model. m , K m and K i , see Table 2; Comparison of the model predicted values ​​and measured values ​​of the respiration rate of fresh-cut cucumbers Figure 6 (B);

[0206] Using CO2 As O 2 The Michaelis-Menten equation of the competitive inhibitor was fitted, and the respiratory model parameter V of Pseudomonas fluorescens was m , K m and K i They are 0.27902mL / [(1.7×10 7 CFU)*h), 0.00023%, 0.00195%.

[0207] Step 3: Measure the growth of Pseudomonas fluorescens and the composition of headspace gas in fresh-cut cucumbers;

[0208] The four groups of treated samples were stored in a 4°C refrigerator for 15 days. The number of Pseudomonas fluorescens and the composition of the headspace gas were measured every 24 hours. The measurements were repeated three times and the average values ​​were taken.

[0209] Fluorescent Pseudomonas assay method: Take 20g of sample and add it to a homogenizing bag containing 180mL of sterile saline, homogenize for 4min (beat the front and back sides for 2min each), and then dilute it 10 times in a gradient. Select 1mL of the appropriate gradient dilution and apply it to the Pseudomonas CFC selective medium. Make 2 parallels for each dilution, for a total of three dilutions. After culturing at 28℃ for 48h, count the colonies in log CFU / g.

[0210] Method for determining headspace gas composition: headspace gas analyzer.

[0211] Step 4: Establish a growth prediction model for Pseudomonas fluorescens;

[0212] According to formula (8), the fluorescent Pseudomonas data measured in step 3 were nonlinearly fitted to obtain the fluorescent Pseudomonas growth prediction model of fresh-cut lettuce based on Huang. The fluorescent Pseudomonas growth fitting effect is shown in Figure 7 The growth kinetic parameters and model evaluation obtained by fitting are shown in Table 3.

[0213] Step 5: Based on the Pseudomonas fluorescens growth prediction model, a coupling model of gas exchange and Pseudomonas fluorescens growth is established, and the model is verified and evaluated;

[0214] For fresh-cut cucumbers, first substitute formulas (3) and (5) into formulas (6) and (7), then combine formulas (6) and (7) with formulas (12) and (13), and use MATLAB programming (Runge-Kutta algorithm) to calculate the O of samples in different packaging bags at any time predicted by the gas mass transfer coupling Huang model. 2 and CO 2The gas concentration and the number of fluorescent Pseudomonas can also be obtained, which are then compared with the measured values ​​of the samples during different packaging and storage processes to verify the coupling model. The results are shown in Figure 8 and Fig. 9 ; Using R 2 ,RMSE,A f , B f The model was evaluated by ASZ and the gas exchange model evaluation results are shown in Table 4. The growth kinetic parameters of Pseudomonas fluorescens in fresh-cut cucumbers with different packages and the coupled model evaluation results are shown in Table 5 after coupling gas mass transfer.

[0215] Embodiment 5

[0216] This embodiment provides a method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables. The experiment is conducted on cherry radish, a representative of root vegetables. The gas composition in fresh-cut cherry radish packaged with different films includes oxygen and carbon dioxide concentrations. The method comprises:

[0217] Step 1: Sample processing;

[0218] Prepare fresh-cut cherry radish by referring to step 1.3 of Example 2; activate Pseudomonas fluorescens stored at 28°C to prepare a 10 8 cfu / mL of the initial inoculum, and gradient dilution to about 10 4 cfu / mL and then inoculated into fresh-cut cherry radish, the initial fluorescent Pseudomonas inoculation concentration of fresh-cut cherry radish was 2.54×10 4 cfu / mL. The inoculated fresh-cut cherry radish was placed in a clean air dryer for 15 minutes, and then immediately placed in a packaging bag with a specification of 200mm×160mm, with 20g in each bag. The sample treatment was divided into four groups. According to the different oxygen permeabilities of the packaging materials, the treated samples were marked as Ⅰ:OTR5, Ⅱ:OTR48, Ⅲ:OTR2058, Ⅳ:OTR3875, and a certain amount of air was filled in through an air pump and heat-sealed.

[0219] Step 2: Based on enzyme kinetics theory, the respiration model of fresh-cut cherry radish and Pseudomonas fluorescens was established;

[0220] The static closed method was used for measurement. The closed system used a 2.5L transparent airtight can. The container was filled with air. The sample volume of radish was 400±2g, and three samples were repeated. The density of radish was measured by the water displacement method to be 1.1172g / mL. A handheld headspace gas analyzer was used to continuously measure the gas composition inside the airtight can. The results were reported as the expected percentage of air composition. The measurement was performed every 2 hours, and each measurement was repeated three times. The volume ratio of oxygen concentration and the volume ratio of carbon dioxide concentration are Fig.10 (A);

[0221] Using CO2 As O 2 The Michaelis-Menten equation of the noncompetitive inhibitor was fitted, and the measured O 2 and CO 2 concentration, and the respiratory model parameter V of fresh-cut radish was calculated using a multiple linear regression model. m , K m and K i , see Table 2; Comparison of the model predicted values ​​and measured values ​​of the respiration rate of fresh-cut radish Fig.10 (B);

[0222] The uninhibited Michaelis-Menten equation was used for fitting, and the respiratory model parameter V of Pseudomonas fluorescens was m , K m They are 0.289 mL / [(1.7×10 7 CFU)*h), 1.906%.

[0223] Step 3: Measure the growth of Pseudomonas fluorescens and the composition of headspace gas in fresh-cut vegetables;

[0224] The four groups of treated samples were stored in a 4°C refrigerator for 15 days. The number of Pseudomonas fluorescens and the composition of the headspace gas were measured every 24 hours. The measurements were repeated three times and the average values ​​were taken.

[0225] Fluorescent Pseudomonas assay method: Take 20g of sample and add it to a homogenizing bag containing 180mL of sterile saline, homogenize for 4min (beat the front and back sides for 2min each), and then dilute it 10 times in a gradient. Select 1mL of the appropriate gradient dilution and apply it to the Pseudomonas CFC selective medium. Make 2 parallels for each dilution, for a total of three dilutions. After culturing at 28℃ for 48h, count the colonies in log CFU / g.

[0226] Method for determining headspace gas composition: headspace gas analyzer.

[0227] Step 4: Establish a growth prediction model for Pseudomonas fluorescens;

[0228] According to formula (9), the fluorescent Pseudomonas data measured in step 3 were nonlinearly fitted to obtain the Baranyi-based prediction model for the growth of fluorescent Pseudomonas in fresh-cut lettuce. The fitting effect of fluorescent Pseudomonas growth is shown in Fig.11 The growth kinetic parameters and model evaluation obtained by fitting are shown in Table 3.

[0229] Step 5: Based on the Pseudomonas fluorescens growth prediction model, a coupling model of gas exchange and Pseudomonas fluorescens growth is established, and the model is verified and evaluated;

[0230] For fresh-cut radish, first substitute formula (3) and (4) into formula (6) and (7), then combine formula (6) and (7) with formula (12) and (13), and use MATLAB programming (Runge-Kutta algorithm) to calculate the O of the sample in different packaging bags at any time predicted by the gas mass transfer coupling Baranyi model. 2 and CO 2 The gas concentration and the number of fluorescent Pseudomonas can also be obtained, which are then compared with the measured values ​​of the samples during different packaging and storage processes to verify the coupling model. The results are shown in Fig.12 and Fig.13 ; Using R 2 ,RMSE,A f , B f The model was evaluated by ASZ and the gas exchange model evaluation results are shown in Table 4. The growth kinetic parameters of Pseudomonas fluorescens in fresh-cut radishes with different packages and the coupled model evaluation results are shown in Table 5 after coupling gas mass transfer.

[0231] The specific data of the experiments conducted in the above-mentioned Embodiment 3, Embodiment 4 and Embodiment 5 respectively on the representative of leafy vegetables, i.e., heart lettuce, the representative of fruit vegetables, i.e., fruit cucumber, and the representative of root vegetables, i.e., cherry radish, are as follows:

[0232] Table 2 Parameters of Michaelis-Menten equation model for different fresh-cut vegetables (4℃)

[0233]

[0234] Table 3 Growth kinetic parameters and model evaluation of Pseudomonas fluorescens in fresh-cut vegetables (4℃)

[0235]

[0236] Table 4 Gas exchange model parameters in original film packaging of fresh-cut vegetables (4℃)

[0237]

[0238]

[0239] Table 5 Growth kinetic parameters and model evaluation of Pseudomonas fluorescens in fresh-cut vegetables after coupled gas mass transfer (4℃)

[0240]

[0241] From Table 2, we can see that the R 2 All above 0.90, combined Figure 2 , Figure 6 and Fig.10It can be seen that all the respiratory models can predict the respiratory rate of fresh-cut vegetables well, indicating that a gas exchange model can be established on this basis.

[0242] Table 3 shows the growth kinetic parameters of Pseudomonas fluorescens in fresh-cut vegetables obtained by fitting the Huang or Baranyi growth prediction model. It can be seen from the table that the R 2 The bias factor measures whether the predicted value overestimates or underestimates the measured value, and the precision factor measures the average error between the predicted value and the measured value. The closer the value is to 1, the better the model. f The range of 0.85 to 1.25 indicates that it is acceptable. Table 3 shows that the B value of the growth prediction model of Pseudomonas fluorescens in all fresh-cut vegetables is f All between 0.97 and 1.02, A f All between 1.01 and 1.06, combined with Figure 3 , Figure 7 and Fig.11 It can be seen that all growth prediction models can well predict the growth of Pseudomonas fluorescens in fresh-cut vegetables, indicating that a model coupled with gas mass transfer can be established on this basis.

[0243] From Table 4, we can see that the R 2 The prediction results of fresh-cut lettuce membranes III and IV were all above 0.90, except for those of fresh-cut lettuce membranes III and IV, which were slightly worse. 2 It is also above 0.78. Figure 4 , Figure 8 and Fig.12 It can be seen that almost all gas exchange models of fresh-cut vegetables can predict the respiration rate of fresh-cut vegetables well, except Figure 3 The prediction effects of fresh-cut lettuce membrane III and membrane IV are slightly worse, but still within an acceptable range. This shows that the gas mass transfer and fluorescent Pseudomonas growth coupling model established in this application can better predict the changes in the internal gas composition of different packages of fresh-cut vegetables, thereby providing certain theoretical guidance for selecting suitable packaging materials or predicting the shelf life of products.

[0244] Table 5 shows the parameters for the growth of Pseudomonas fluorescens that need to be input into the coupling model. Some of the values ​​may be slightly different from the Pseudomonas fluorescens growth prediction model because the standard deviation is taken into account. f All between 0.85 and 1.25, A fThe ASZ values ​​were all between 1.11 and 1.48, and they were all greater than 75%, indicating that the established gas mass transfer and Pseudomonas fluorescens growth coupling model had excellent performance and could well predict the growth of Pseudomonas fluorescens in fresh-cut vegetables with different packaging. Figure 5 , Fig. 9 and Fig.13 The results shown are consistent. By inputting the microbial limit value into the model established in this application, the time required to reach the microbial limit, that is, the shelf life of fresh-cut vegetables, can be output, thereby providing certain theoretical guidance for predicting the shelf life of fresh-cut fruits and vegetables.

[0245] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0246] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables. It is characterized in that The method comprises: Step 1: Sample processing; Step 2: Based on enzyme kinetics theory, establish the respiration model of fresh-cut vegetables and Pseudomonas fluorescens; Step 3: Measure the growth of Pseudomonas fluorescens and the composition of headspace gas in fresh-cut vegetables; Step 4: Establish a gas exchange model based on the measured data in step 3; Step 5: Establish a growth prediction model for Pseudomonas fluorescens based on the measured data in step 3; Step 6: Based on the gas exchange model established in step 3 and the Pseudomonas fluorescens growth prediction model established in step 5, a coupling model of gas exchange and Pseudomonas fluorescens growth is established; Step 7: Use the coupling model of gas exchange and Pseudomonas fluorescens growth established in step 6 to predict the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables.

2. The method according to claim 1, It is characterized in that The second step comprises: Step S2.1: Determine the respiration rate of fresh-cut vegetables, where the respiration rate refers to the rate of oxygen consumption and carbon dioxide production; Among them, R O2 O 2 Consumption rate, mL O 2 / (kg*h); R CO2 For CO 2 Production rate, in mL CO 2 / (kg*h);y O2 O 2 Concentration volume ratio, %; y CO2 For CO 2 Concentration volume ratio, %; t is the storage time, unit is h; Δt is the time difference between two gas measurements, unit is h; V F is the headspace volume of the transparent airtight can used to measure the respiration rate of fresh-cut vegetables, in L; M is the sample mass, in kg; Step S2.2: Using CO 2 As O 2 The Michaelis-Menten equation of the noncompetitive inhibitor was fitted, and the O determined in step S2.1 was used 2 and CO 2 concentration and respiratory rate, and the parameters of the noncompetitive inhibition Michaelis-Menten equation including V were calculated using a multiple linear regression model. m , K m and K i , establish a respiration model for fresh-cut vegetables: Among them, V m For fresh cut vegetables 2 The maximum consumption rate or CO 2 The maximum generation rate, unit is mL / (kg*h); K m is the Michaelis constant, unit: %O 2 ; K i For CO 2 As O 2 Michaelis constant of noncompetitive inhibitor, unit: %O 2 ; Step S2.3: Establish the Pseudomonas fluorescens respiration model: Among them, R P.f Pseudomonas fluorescens O 2 The consumption rate or CO 2 The generation rate, unit is mL / (CFU*h); V m,P.f Pseudomonas fluorescens O 2 The maximum consumption rate or CO 2 The maximum generation rate, unit is mL / (CFU*h); K m,P.f is the Michaelis constant; K i,P.f For CO 2 As O 2 Michaelis constant of competitive inhibitor; y O2 and CO2 O 2 and CO 2 Concentration volume ratio, unit: %.

3. The method according to claim 2, It is characterized in that The fourth step comprises: Assumptions: ① The gas inside and outside the packaging bag for fresh-cut vegetables is evenly distributed, and the gas is all ideal gas; ② The gas permeability of the film used in the packaging bag remains constant; ③ The exchange of all gases through the membrane is independent of each other; ④The gas exchange process of the packaging bag is a constant temperature process; ⑤The total gas pressure inside and outside the packaging bag is equal; According to Fick's law and the above assumptions, when using packaging film to store fresh-cut vegetables, the total volume change of gas in the packaging bag is the sum of the volume change of gas that permeates the film, the volume change caused by the respiration of fresh-cut vegetables and the respiration of Pseudomonas fluorescens; Therefore, the gas exchange model of the original film packaging is: Among them, V f Q is the headspace volume inside the packaging bag, mL; g,j is the permeability of the membrane used in the packaging bag to gas component j, in cm 3 / (m 2 ·24h·0.1MPa), gas component j refers to O 2 or CO 2 ; are the volume fractions of gas component j outside and inside the packaging bag, respectively, %; P 0 is the pressure under standard conditions, 0.1MPa; R O2 For fresh cut vegetables 2 Consumption rate, mL O 2 / (kg*h); R CO2 CO for fresh cut vegetables 2 Production rate, mL CO 2 / (kg*h); N(t) is the number of microorganisms, unit is CFU / g; M is the mass of fresh-cut vegetables, unit is kg.

4. The method according to claim 3, It is characterized in that The step five comprises: Two primary models were selected as the growth prediction models for Pseudomonas fluorescens, namely the Huang model and the Baranyi model. The mathematical description of the Huang model is as follows: The mathematical description of the Baranyi model is as follows: Among them, N(t), N max and N 0 They represent the number of microorganisms at time t, the maximum number of bacterial colonies and the initial number of microorganisms, respectively, in ln cfu / g; h; λ is the hysteresis time, in h; μ max is the maximum specific growth rate of microorganisms, in h -1 ; α is the hysteresis phase change coefficient, which is 4.

5. The method according to claim 4, It is characterized in that The step six comprises: The gas exchange model established in step 3 and the two Pseudomonas fluorescens growth prediction models established in step 5 are coupled respectively to establish a coupling model of gas exchange and Pseudomonas fluorescens growth; The coupling model of gas exchange and Pseudomonas fluorescens growth obtained by coupling with the Huang model is: Among them, Q(t) is related to microbial physiology and is expressed as: The coupling model of gas exchange and Pseudomonas fluorescens growth obtained by coupling with the Baranyi model is: Among them, Q(t) is related to microbial physiology and is expressed as: Among them, N(t), N max and N 0 They represent the number of microorganisms at time t, the maximum number of bacterial colonies and the initial number of microorganisms, respectively, in ln cfu / g; in h; λ is the hysteresis time, in h; μ max is the maximum specific growth rate of microorganisms, h -1 ; CO 2 max-diss , is the maximum dissolved CO that allows the growth of Pseudomonas fluorescens 2 concentration; O 2 min-diss , is the minimum dissolved O that allows the growth of Pseudomonas fluorescens 2 concentration.

6. The method according to claim 5, It is characterized in that In step 6, for Pseudomonas fluorescens, the dissolved CO that allows growth 2 The maximum concentration of CO 2 max-diss Take 40% to allow growth of dissolved O 2 The minimum concentration of O 2 min-diss Take 0.25%.

7. The method according to claim 6, It is characterized in that The method selects romaine lettuce, fruit cucumber and cherry radish for leafy vegetables, fruit vegetables and root vegetables respectively to prepare fresh-cut vegetable samples.

8. A method for selecting packaging materials for fresh-cut vegetables. It is characterized in that The method uses the method described in any one of claims 1 to 7 to predict the growth of Pseudomonas fluorescens in fresh-cut vegetables packaged with different packaging materials, so as to determine the packaging materials used for packaging the fresh-cut vegetables.

9. Application of the method for predicting the growth of Pseudomonas fluorescens in bagged fresh-cut vegetables according to any one of claims 1 to 7 in the transportation and storage of vegetables.

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