Mechanisms of food microbial growth and quality changes under low temperature storage conditions

With the continuous development of modern agricultural technology, people have put forward higher requirements for the preservation and storage of fresh food. As a common method, cryogenic storage is widely used in the preservation treatment of fresh foods such as fruits and vegetables, meat and poultry, and seafood [1-4]. Low-temperature storage is a method of preserving fresh food for a long time by utilizing a low-temperature environment. On the one hand, low temperature can slow down the growth rate of microorganisms in food. Microorganisms are one of the main causes of food spoilage, and the low-temperature environment can slow down the growth rate of microorganisms, thus prolonging the shelf life of food [5-8]. Generally speaking, microorganisms multiply significantly slower at lower temperatures, which is why we often store food in refrigerators [9-11]. On the other hand, low temperature can slow down the activity of enzymes in food. Enzymes are a kind of biological catalysts, which can accelerate the chemical reactions in food and lead to food deterioration. However, at low temperatures, the activity of enzymes is significantly reduced, which slows down the deterioration of food [12-15]. In addition, low temperature reduces water loss and slows down the rate of oxidative reactions in food. Moisture loss is one of the most important factors leading to drying and deterioration of food, whereas the rate of moisture loss in food will be slowed down under low temperature environment, thus maintaining the taste and freshness of food [16-19]. Whereas oxidation reaction is one of the important causes of food deterioration, the rate of oxidation reaction in food will be slowed down under low temperature environment, thus prolonging the shelf life of food. However, while using low-temperature storage, attention should also be paid to the control of the storage environment and the monitoring of the food in order to fully utilize its freshness [20-23].

Literature [24] examined the effect of storage temperature on the quality and microbial growth of dried pet food, noting that dried pet food can be safely stored at room temperature or up to 35°C without compromising its quality. Literature [25] assessed the effect of prolonged freezing and refrigeration on the microbial quality of sheep milk, noting that frozen milk did not show a significant increase in bacterial counts after thawing, and that psychrotolerant protein-hydrolyzing bacteria grew slowly in comparison to the total number of bacteria and psychrotolerant bacteria. Literature [26] explored the effect of pressure, post-harvest low-pressure treatment delay on the storage quality of strawberries and developed a detailed description of the results, the findings were favorable for the development of commercially available low-pressure technology for perishable and perishable fruits and vegetables. Literature [27] explored the effect of low-temperature storage on quality changes of shrimp and described the prospects for the application of non-protein technologies in shrimp preservation quality analysis based on physicochemical properties, sensory evaluation, blackening disease evaluation and microbiological analysis. Literature [28] evaluated the quality of Atlantic salmon by adopting the low-temperature, high-pressure storage (HS/LT) method, revealing that HS/LT slows down the growth of spoilage microorganisms and is a physicochemical parameter capable of controlling the microbial activity of fish muscle parts. Literature [29], in order to explore the potential application of static magnetic field (SMF) treatment in marine fish preservation, examined the quality changes of black sea bass during cold storage in terms of total viable counts, pH, and color, which indicated that SMF maintains the quality of the fish by inhibiting the growth of microorganisms and and enhancement of the stability of protein structure. Literature [30] examined the blackening, quality characteristics and bacterial growth of shrimp during frozen storage, describing that frozen storage retarded bacterial growth and putrescine accumulation in shrimp, but frozen storage aggravated blackening and lipid oxidation. Literature [31] discussed the effects of washing, waxing and cold storage on apples based on amplicon sequencing technology and showed that post-harvest treatments and cold storage had a large impact on both fungal and bacterial diversity and community composition in apples. Literature [32] concluded that seafood, although having greater nutritional value, is susceptible to spoilage and explored the current knowledge related to the effects of seafood spoilage flora, volatile organic compounds, and storage temperature on the quality of seafood products. Literature [33] introduced cold atmospheric plasma (CAP) and examined the effect of CAP treatment on the natural microbiota and quality of pork stored at low temperatures, mentioning that CAP is a very promising technology with the ability to prolong the microbial shelf life of pork during cold storage. Literature [34] discussed the microbiota that can be cultured during cryogenic storage of a variety of sea bass products and the effect of primary processing on the microbial community of fish products, stating that primary processing and storage can have an effect on the microbial community of gutted and filleted fish. Literature [35] describes a loT-based cold storage management system designed to cope with the difficulties of food quality and quantity tracking, which is able to accurately detect the quantity of food, and immediately sends an alert when the food is degraded in order to ensure the quality of the food. The above study illustrates from the point of view of shrimp, fish, pork and other food products, food cryogenic storage is an important link in the process of food from production to consumption, is as long as possible to maintain the nutritional value of food, color and aroma, as well as good organoleptic properties of the important means of food, and its by reducing the growth of microorganisms, in order to effectively reduce the microbial damage, to avoid the deterioration of the food.

As a common means of preservation, low-temperature storage can effectively inhibit the growth of microorganisms in food and prolong the shelf life of food. In this paper, we take different dairy products as an example and set up relevant experiments to study the growth pattern of microorganisms and their relationship with food quality changes under low-temperature storage conditions. The quantitative changes of five microorganisms, including coliform bacteria, were analyzed under different low-temperature conditions and different storage dates. A secondary model for the growth of the total number of bacteria was established by integrating the factors of temperature, O₂ concentration and CO₂ concentration. To explore whether there is a correlation between the growth kinetic parameters of the total number of bacterial colonies and the shelf-life of food, and to provide theoretical support for the low-temperature preservation of food.

2

Mathematical modeling of microbial growth

In order to accurately describe the growth pattern of microorganisms under low-temperature storage conditions, the construction of mathematical models is crucial. In this chapter, the construction methods and advantages and disadvantages of different microbial growth models will be introduced in detail, and the choice of constructing a square root model as the microbial growth kinetic model for this study was finally determined.

2.1

Microbial growth modeling

According to the characteristics of exponential growth of microorganisms, the logarithm of the number of microorganisms with the change of time to get a “S”-shaped curve, the drawn growth curve is divided into hysteresis, logarithmic, stable and senescence. Figure 1 shows the growth curve of microorganisms. Mathematical equations Logistic and Gompertz equations are effective in describing microbial growth and are easy to use and are nonlinear regressions called Gompertz functions.

The Gompertz function growth model is: (1) $N_{t} = N_{0} + a_{1} \exp {- \exp [- a_{2} (t - τ)]}$ ${N_t} = {N_0} + {a_1}\exp \left\{ { - \exp \left[ { - {a_2}(t - \tau )} \right]} \right\}$

Where: N_t, N₀ - the number of microbial cell populations at t and at the initial time expressed in logarithmic units $(\log_{10} c f u / m l)$ $\left( {{{\log }_{10}}cfu/ml} \right)$, respectively;

a₁ - the difference between the number of microorganisms at stabilization and at the time of inoculation (logarithmic value log₁₀cfu/ml);

a₂ - the slope;

τ - time at the point of bending of the function curve.

Logistic equations, especially 4-parameter logistic equations also model bacterial growth better. The logistic equations are as follows: (2) $\begin{array}{rcl} \log N (t) & = & \log N_{\min} \\ + {1 + (N_{\max} - N_{\min}) / [1 + E x p (- μ_{\max} (t - t_{i}))]} \end{array}$ $ $\begin{array}{rcl} {\log N(t)}& = &{\log {N_{\min }}} \\ {}&{}&{ + \left\{ {1 + \left( {{N_{\max }} - {N_{\min }}} \right)/\left[ {1 + Exp\left( { - {\mu _{\max }}\left( {t - {t_i}} \right)} \right)} \right]} \right\}} \end{array}$ $

t is the time, N(t) is the number of bacteria at moment t, N_max, N_min are the maximum and minimum number of bacteria (logarithmic values), and μ_max is the maximum specific growth rate. These equations are mostly derived from the Gompertz function.

The modified Gompertz equations obtained better describe the growth dynamics under different temperature conditions. (3) $\begin{array}{rcl} N (t) & = & N_{0} + (N_{\max} - N_{0}) \\ \times E x p {- E x p [μ_{\max} \times 2.717 / (N_{\max} - N_{0}) \times (λ - t) + 1]} \end{array}$ $\begin{array}{rcl} N(t) &=& {N_0} + \left( {{N_{\max }} - {N_0}} \right) \\ && \times Exp\left\{ { - Exp\left[ {{\mu_{\max }} \times 2.717/\left( {{N_{\max }} - {N_0}} \right) \times (\lambda - t) + 1} \right]} \right\} \\ \end{array}$

Where: N(t) is the number of microorganisms $[\log_{10} (c f u / g)]$ $\left[ {{{\log }_{10}}(cfu/g)} \right]$ at t; N₀ is the initial number of microorganisms $[\log_{10} (c f u / g)]$ $\left[ {{{\log }_{10}}(cfu/g)} \right]$ at t = 0; N_max is the maximum number of microorganisms at the time of increase to the stabilization period $[\log_{10} (c f u / g)]$ $\left[ {{{\log }_{10}}(cfu/g)} \right]$; μ_max is the maximum specific growth rate of microbial growth $(h^{- 1})$ $\left( {{h^{ - 1}}} \right)$; λ is the hysteresis time for the growth of microorganisms $(h)$ $\left( h \right)$; t is the time.

The modified Gompertz function can be widely used to describe the growth of food microorganisms to derive the three main parameters of hysteresis time, specific growth rate and total growth during microbial growth, which effectively predicts the growth of microorganisms.

2.2

Kinetic modeling of microbial growth

Kinetic modeling of microbial growth is based on kinetics, which is the process of building data on microbial growth in terms of fit S shaped functions or curves, and then simulating the effects of various environmental factors such as: temperature functions, pH or a_w, to build a kinetic model that explains the response of time to a particular growth. Additional variables include gas composition, redox potential, biological structure, relative humidity, nutrient content, and biocide properties, etc. Data are collected to investigate the effects of extrinsic and intrinsic parameters such as temperature, pH, or a_w on microbial growth. Kinetic models are useful in that they can be used to predict changes in microbial populations over time, regardless of whether the variables controlled can affect growth. The creation of information bases in microbial prediction techniques requires the consideration of many fenestration factors related to food safety and quality. Although not all fence factors can be included in a simple prediction model, general prediction models include several major fence factors such as temperature, pH, and preservatives and their interactions. Producers and processors can predict the storability of products and the microorganisms that may grow and reproduce according to the fences provided by computerized databases. A reasonable combination of fence factors can not only determine the microbial stability of food products, but also improve the organoleptic quality and nutritive properties of products, and enhance economic efficiency. In the process of computerization of food design, the existing physicochemical and microbiological data can be collected to establish a computer software with a database, through the computer to put forward the rationalization of the formula, process flow and packaging mode of the proposal, at least theoretically, so that the microbial stability of the product is guaranteed, in addition to the application of computer software to continuously improve the instability of the product.

Secondary growth modeling focuses on determining the parameters of the primary model for different environmental factors. There are three mathematical ways to deal with the primary growth model, namely the response surface equation or the Arrhennius relational equation and the square root model.

Response surface models are regression equations which can be linear, quadratic, cubic and inverse equations. Below is a linear response surface model: (4) $k = k_{0} (1 + a T)$ $k = {k_0}\left( {1 + aT} \right)$

where k is the rate of spoilage at a specific temperature T, k₀ is the rate of spoilage at zero degrees, and a is a constant. Although the growth of bacteria in food products is affected by many factors, in most cases, for a specific product, whose intrinsic factors are basically determined, temperature is the most important factor affecting the quality and determining the growth rate and delay time of microorganisms. In the actual modeling process, temperature is mostly used as the main variable to explore the effect of environmental factors on bacterial growth.

The differences in the growth of various microorganisms at different temperatures are mainly measured by the maximum specific growth rate $(μ_{\max})$ $\left( {{\mu_{\max }}} \right)$ and the latency time $(λ)$ $\left( \lambda \right)$, while the effect of temperature on the kinetic parameters of microbial growth μ_max and the latency period is mostly described by the Arrhenius model or the Belehradek model (square root model).

The classical Arrhenius equation describes the relationship between the chemical reaction rate constant K and the absolute temperature T: (5) $K = A * \exp (- E / R T)$ $K = A^*\exp ( - E/RT)$

Here E is the activation energy, A is the collision coefficient, and R is the general gas constant. The equation was modified to better fit the effect of temperature on microbial growth: (6) $I n k = C_{0} + C_{1} / T + C_{2} / T^{2}$ $Ink = {C_0} + {C_1}/T + {C_2}/{T^2}$

The effects of other factors pH, a_w etc. were considered together to study their effect on microbial growth. The following is the Arrhennius relationship between growth rate $(k)$ $\left( k \right)$ and absolute temperature $(T)$ $\left( T \right)$ and water activity $(A w)$ $\left( {Aw} \right)$: (7) $\ln k = C_{0} + C_{1} / T + C_{2} / T^{2} + C_{w} + C {(a_{w})}^{2}$ $\ln k = {C_0} + {C_1}/T + {C_2}/{T^2} + {C_w} + C{\left( {{a_w}} \right)^2}$

where E is the heat function, R is the gas constant, and C is the model parameter.

This equation is another variation of the Arrhenius equation, combining the effects of pH, a_w gives a rather complex nonlinear equation with 6 parameters, which is very complicated. Although it can be fitted very well to data from both ends of the microbial growth temperature region, it is cumbersome to use.

The square root equation was used to describe the growth of microorganisms at different temperatures. A simple empirical model based on the linear relationship that exists between the square root of the inverse of the growth rate or latency and temperature of microorganisms at low temperatures. The relational equation is given below: (8) $k^{1 / 2} = a (T - T_{0})$ ${k^{1/2}} = a\left( {T - {T_0}} \right)$

T is the Celsius temperature (°C), T₀ is a hypothetical concept that refers to the temperature at which microorganisms are not metabolically active, i.e., the temperature at which the maximum specific growth rate is zero, and a is a constant of the equation.

Square root equations are widely studied and used. Evaluating the effect of different constant storage temperatures on the growth of a wide range of microorganisms in food or simulated systems, the square root equation is valid. Its greatest advantage is its simplicity, both in modeling and in the use of the model. In this paper, after setting up a low-temperature storage test for food products, we use relevant data such as microbial growth, combined with the square root equation, to establish a second-level model for the growth of the total number of bacterial colonies.

3

Experimental process and analysis of results

This part designs low temperature storage experiments to analyze the growth changes of microorganisms at different temperatures. Milk tofu and milk skin were selected as the research objects, and they were inoculated with common microorganisms such as coliforms, molds, lactic acid bacteria, etc., and sampled and analyzed at different time points. The inhibitory effect of low-temperature storage on the growth of microorganisms and its effect on food quality were investigated through the determination of microbial counts and DNA analysis.

3.1

Materials and Methods

3.1.1

Materials

Milk tofu, milk skin: commercially available; E. coli ATCC 25922, Mycobacteria ATCC 6631, Lactobacillus ATCC 8008, Staphylococcus aureus ATCC 25923, Salmonella ATCC 14028: American Mycobacteria Conservation Center ATCC; Nutrient broth CM106, Plate counting agar medium CM101, Crystalline Violet Neutral Red Bile Salt Agar CM115, Baird-Parker agar base CM302: Beijing Luqiao Technology Co., Ltd. SHP-250 biochemical incubator: Shanghai Senxin Company; Themmo 1300 II A2 type biological safety cabinet, etc.

3.1.2

Experimental methods

Strain activation: Frozen strains of Escherichia coli ATCC 25922, Mycobacterium sp. ATCC 6631, Lactobacillus sp. ATCC 8008, Staphylococcus aureus ATCC 25923, and Salmonella sp. ATCC 14028 were received into the nutrient broth at the additive amount of 1% $(v / v)$ $\left( {v/v} \right)$, respectively, and then activated for 24h at 38℃, and then placed at 0℃ for spare.

Bacterial suspension preparation: the activated strains of nutrient broth were gradient dilution, select the appropriate dilution (107) of the dilution as a bacterial suspension added to the sample.

Sample preparation: the samples were made into a number of homogeneous portions, 30g / portion, placed in a sterilization bag, autoclaved at 120 ℃ for 10min to eliminate the influence of background microorganisms, each sample was accessed to 0.5mL of each strain of bacterial suspension, and each sample was accessed to 1mL of each strain of bacterial suspension to be tested; the samples were synchronized into a 0 ℃ refrigerator for preservation, respectively, at 0, 1, 6, 12, 18, 35, 65, One sample was taken at 0, 1, 6, 12, 18, 35, 65, 95d for determination.

3.1.3

Cultivation and Enumeration

Determination of culture and count of coliform bacteria: refer to GB4789.3-2010 plate count method; culture and count of Staphylococcus aureus: refer to GB4789.10-2010 plate count method; determination of culture and count of mycobacterial flora: refer to GB4789.3-2010 plate count method; determination of culture and count of salmonella flora: refer to GB4789.3-2010 plate count method; determination of culture and count of lactic acid bacteria flora: refer to GB4789.10-2010 plate count method; determination of total number of colonies. GB4789.3-2010 plate count method; Determination of culture and count of lactic acid bacteria flora: refer to GB4789.10-2010 plate count method; Determination of total colony count: refer to GB4789.2-2010.

3.1.4

Data processing

SPSS 13.0 software was used to statistically analyze the experimental data on different dates.

3.2

Test procedure

3.2.1

Extraction of total bacterial DNA from samples

Strictly in accordance with the requirements of aseptic operation, 30g of milk tofu and milk skins that had been refrigerated for 3 days, 5 days, 7 days and 10 days were taken into a Stomacher bag in an aseptic environment, 230mL of sterilized protein-veined saline was added, and tapped with a Stomacher tapper (Bagmixer 400, France) for 1min. 30mL of the filtrate was taken, and the supernatant was removed by centrifugation at 2005g ( Eppendorf 5415D, Germany) for 2min to remove impurities and take the supernatant, then centrifuged at 13,000g for 2min to take the bacterial body. Bacterial DNA was extracted by DNA extraction kit (Bacterial Genomic DNA Kit, Sigma, USA). The extracted total bacterial DNA was dissolved in 150uL of TE buffer, detected by 2% agarose gel electrophoresis, and then stored in the refrigerator at -25℃ for reserve.

3.2.2

PCR amplification

PCR amplification of the V5~V7 region of bacterial 16SrDNA was performed. The upstream primer was band U968 (including GC clamp): 5′CGC CCG GGG CGC GCC CCG GGC GGG GCG GGG GCA CGG GGG GAA CGC GAA GAA CCT TAC3′; the downstream primer L1401 was: 5′CGG TGT GTA CAA GAC CC3′. The above primers were synthesized by Shanghai Bioengineering Co.

PCR amplification was performed on a gradient PCR instrument (Bio-Rad, USA). The PCR reaction system was 55 uL, including: buffer (MgCl₂) 10 uL, dNTP (2 mM, Toyobo, Japan) 10 uL, forward and reverse primers (10 pmol) 2 uL each, TaqDNA polymerase 1 uL (Toyobo, Japan), 5uL of DNA template, and 34uL of DEPC-treated water.

PCR amplification program: pre-denaturation at 95°C for 5 min, 25 cycles (denaturation at 95°C for 35 s, annealing temperature from 60°C to 50°C, annealing for 35 s, extension at 73°C for 36 s), and then 15 cycles at constant annealing temperature (denaturation at 95°C for 35 s, annealing at 50°C for 35 s, extension at 73°C for 35 s), and final extension at 73°C for 15 min. PCR products were analyzed by 2% agarose gel electrophoresis for detection, and every three parallel samples were combined into one sample, which was purified by DNA purification kit (SolarbioR, China) and stored at -25°C in the refrigerator for backup.

3.2.3

Denaturing gradient gel electrophoresis analysis of PCR products

The PCR products were concentrated to detect the DNA concentration (GeneQuant100, g&e) and adjusted the DNA concentration around 0.3ug/uL, and analyzed using the DCodeTMDGGE electrophoresis system from Bio-Rad. 1)

Preparation of denaturing gels: 7% polyacrylamide gels (acrylamide to methylenedioxybisacrylamide mass ratio 37:1) with denaturant concentrations ranging from 50% to 70% (100% denaturant was 6 mol/L urea and 45% formamide) were prepared using a gradient gel preparation device, in which the concentration of denaturant was increased from the top of the gel to the bottom (Sambrook et al., 2002). ).

2)

Spiking of PCR samples: After the gel has completely solidified, the plate is placed into a device containing 1× TAE electrophoresis buffer, and 40 uL of sample with 20 uL of 6× bromophenol blue xylene cyanide solution is added to each spiking well.

3)

Electrophoresis and staining: electrophoresis was performed at a constant temperature of 65°C for 25 min at 120 V, followed by electrophoresis at 80 V for 25 h. After electrophoresis was completed, the DGGE film was immersed in 1×TAE (containing 0.5 mg/LEB) for 20 min, and the soaking solution was discarded and then immersed in ddH2O for 25 min (YuZ et al., 2004).

4)

Observation and photography: the stained gels were analyzed by a gel imaging analysis system, and the electrophoretic bands of each sample were observed and photographed, and analyzed by Quantity one (Bio-rad) analysis software.

3.2.4

DNA Recovery and Sequencing

The EB-stained DGGE film was placed under UV light, and the bands at different positions were cut off and put into 2 mL centrifuge tubes, respectively, and 45 uL of ddH₂O was added, and the DNA was recovered and purified with DNA purification kit (Solarbio, China) at 0°C overnight. The recovered DNA was used as a template for PCR amplification, and the bacterial primers were U968 and L1401 (without GC clip). The PCR amplification procedure was the same as that for total bacterial DNA. The amplification products were ligated with vector PMD18-T and cloned. PCR amplification was carried out by taking 2uL of bacterial solution, and the primers were RV-M: GAG CGG ATA ACA ATT TCA CAC AGG; M13R: GTC GTG ACT GGG AAA ACC CTG GCG. The PCR amplification program was as follows: pre-denaturation at 90℃ for 10min; 35 cycles (95℃ for 2min, annealing at 55℃ for 35s, 73℃ for 2min); The final extension was 15 min at 73°C. After detection by 2% agarose gel electrophoresis, the cloned products were sent to Shanghai Sangong Biotechnology Service Co. for sequencing. The sequences were logged into NCBI and compared with the known sequences in the database.

3.3

Visualization of test results

3.3.1

Coliform count test results

The traditional milk tofu and milk skins were stored at temperatures of -6, -12, -18, and -36°C, respectively, for 0, 1, 6, 12, 18, 35, 65, and 95 d. The number of flora except lactic acid bacteria was basically stable at 18 days, so only the number of flora at 0, 1, 6, 12, and 18 days were analyzed in the graph visualization display.

Figure 2 shows the results of coliform count test in milk tofu. Figure 3 shows the results of coliform count test in milk skin. The maximum values of coliforms in milk tofu and milk skins were 21CFU/g and 72CFU/g at -6℃ for 18d, and the minimum values were 0CFU/g and 15CFU/g at 0d. According to the maximum limit of coliforms ≤105CFU/g of the standard as a reference and analyzed based on the test data, the coliforms in the samples did not exceed the limit value of the local standard. Limit value.

3.3.2

Mold count test results

Figure 4 shows the results of mold count test in milk tofu. Figure 5 shows the results of mold count test in milk skin. The number of molds in milk tofu was within the standard range from 0 to 6d, and the number of molds exceeded the standard range from the 12th day, and the number of molds was elevated at -6 and -12℃, which shows that the higher the temperature, the more the number of molds. The longer the time, the higher the number of molds. Milk skin mold maximum value is the 18th day of -6 ℃ for 4.5 × 10CFU / g, the minimum value is 0d for 0CFU / g. According to the standard maximum limit of mold ≤ 85CFU / g as a reference, the milk skin in the -36 ℃ temperature under the preservation of 1 ~ 6d, in addition to other conditions under the number of molds exceed the limit value of the local standard, so beyond these two ranges are not suitable for the conditions of the Therefore, any condition beyond these two ranges is not suitable for milk skin storage.

3.3.3

Lactobacillus test results

Table 1 shows the results of lactic acid bacteria in milk tofu and milk skin over 35 days at -36°C. The least value of lactic acid bacteria was measured in milk tofu at 0 d, which was 1.2×10⁵CFU/g. The number of lactic acid bacteria was increasing gradually with the extension of time and the increase of temperature. At 95d and -36°C, the number of lactic acid bacteria reached 7.5×10⁵CFU/g.

Table 1.

-36℃ after 35 days of lactic acid bacteria determination results

Storage days t/d	Ordinary bouillon			Skin on milk
Storage days t/d	Coliform number / CFU·g^-1	Mold count / CFU·g^-1	Lactic acid bacteria number / CFU·g-1	Coliform number / CFU·g^-1	Mold count / CFU·g^-1
35	<10	20	1.0*10⁵	<10	52
65	<10	25	3.6*10⁵	<10	55
95	<10	35	7.5*10⁵	<10	60

3.3.4

Staphylococcus aureus count and Salmonella count test results

Milk tofu and milk skins were placed in different temperatures and different days, and no Staphylococcus aureus and Salmonella were detected, which were in accordance with the standard. The results of this microbiological test can obviously reflect the suitable storage temperature and time period for milk tofu and milk skin, which can provide reference and basis for the storage of milk tofu and milk skin. It will ensure their edible quality.

4

Secondary modeling of total bacterial population growth

Based on the analysis of microbial growth data, the secondary model of total colony growth was further established. Combined with the previous chapter 3 of the number of microorganisms in different low-temperature storage conditions and time conditions, in the low-temperature range of dairy storage studied in this paper, using the square root formula to calculate the existence of a relationship between the temperature and the total number of bacterial colony growth kinetic parameter μ_max is shown in Equation (9), that is: (9) $\sqrt{μ_{\max}} = b_{T} \times (T - T_{\min})$ $\sqrt {{\mu_{\max }}} = {b_T} \times \left( {T - {T_{\min }}} \right)$

On the basis of obtaining the relationship between temperature and the growth kinetic parameters of total bacterial population, this chapter will continue to analyze the effects of environmental factors such as O₂ concentration and CO₂ concentration on microbial growth kinetic parameters by introducing these factors. Validation data were selected to verify the validity of the established secondary model and to analyze the correlation between microbial growth parameters and shelf life of dairy products.

4.1

Growth Dynamics Parameter Acquisition

The Matlab 2011b software was used to fit the predictive microbiology square root model primary to the microbial growth data obtained from the experiments to obtain the model fit correlation coefficients as well as the relevant microbial growth kinetic parameters. Table 2 shows the growth kinetic parameters of the total number of bacterial colonies under different combinations of O₂ concentration and CO₂ concentration packages. Figure 6 shows the conditional effects of O₂ concentration and CO₂ concentration on the variation of the kinetic parameters of total colony growth μ_max. It can be seen that, taking the experimental error into account, no matter the CO₂ concentration is 1.7%, 22%, 31%, 43%, the fold lines are basically parallel to each other, and no matter the value of the O₂ concentration is 2%, 6.5%, 8.7%, 10.5%, 30.5%, 47.5%, the effect law of CO₂ on _{μ_max} is not affected, indicating that the O₂ concentration and CO₂ concentration in the growth of total colony growth There was no interaction between O₂ concentration and CO₂ concentration in the action of kinetic parameter μ_max. And in the effect on microbial growth, there is no interaction between temperature and atmosphere concentration or the interaction is very small can be assumed to be ignored. From the previous experimental studies, it is clear that the effect of temperature on microbial growth parameters can be well described by the temperature square root equation. When the oxygen concentration varied in a wide range, there was no significant effect on the maximum specific growth rate and the hysteresis period of the colonies; when the oxygen concentration varied in a small range, there was a certain effect on the growth kinetic parameters of the colonies. And the carbon dioxide square root model (Eq.) can well describe the effect of the initial concentration in the package on the maximum specific growth rate μ of the growth of the total number of colonies in the sample. Therefore, when establishing a secondary model of total number of colonies, segmentation is considered according to the concentration.

Table 2.

Kinetic parameters of total colony growth at different concentrations

O₂ concentration/%	CO₂ concentration/%	N₀ /In(CFU/mL)	μ_max/h^-1	λ/h	N_max /In(CFU/mL)	R²	RMSE
2	1.7	11.7	0.032	187.43	17.03	0.9578	0.5115
	22.0	11.51	0.02538	236.3	17.21	0.9905	0.215
	31.0	11.4	0.02272	263.96	17.41	0.9965	0.1134
	43.0	11.51	0.02021	296.73	16.52	0.9898	0.1576
6.5	1.7	11.61	0.0362	165.69	17.27	0.9895	0.2814
	22.0	11.51	0.02927	204.91	17.02	0.9969	0.1471
	31.0	11.61	0.02642	227	17.03	0.985	0.2732
	43.0	11.52	0.02263	265.01	16.55	0.9908	0.1691
8.7	1.7	11.73	0.03704	161.93	17.16	0.9726	0.448
	22.0	11.61	0.02943	203.79	17.12	0.9946	0.1808
	31.0	11.66	0.02673	224.37	17.18	0.9853	0.2761
	43.0	11.54	0.02315	259.06	16.69	0.9904	0.181
10.5	1.7	11.5	0.03714	161.5	17.54	0.9814	0.4026
	22.0	11.51	0.03072	195.24	17.85	0.9974	0.1451
	31.0	11.55	0.0269	222.96	17.56	0.9639	0.4348
	43.0	11.53	0.02334	256.95	18.09	0.9861	0.1907
30.5	1.7	11.53	0.03915	153.21	17.45	0.9946	0.2162
	22.0	11.47	0.03093	193.91	18.02	0.995	0.1942
	31.0	11.69	0.02718	220.66	17.98	0.9274	0.6463
	43.0	11.43	0.02344	255.85	18.4	0.9959	0.1463
47.5	1.7	11.57	0.03913	153.29	17.39	0.9926	0.2488
	22.0	11.47	0.03069	195.43	17.15	0.9912	0.2534
	31.0	11.73	0.02799	214.28	17.79	0.9016	0.7696
	43.0	11.39	0.02364	253.69	18.03	0.9959	0.148

4.2

Validation data

The validity of the secondary model of colony total change was tested by using the data related to the total number of bacteria at 0℃, and Table 3 shows the relevant data. Analyzing the data in Table 3, it can be seen that the deviation of the model fitting is 0.998, and the accuracy is 1.002, indicating that the obtained secondary model of colony total prediction can well and accurately predict the growth rate of colony total under the low temperature storage conditions, which has the value of application, and it can be used to carry out the next step of the correlation analysis between the microbial growth parameter and the shelf-life, and to provide reference data for ensuring the quality and safety of the foodstuffs.

Table 3.

Verification data of the prediction model (0℃)

O₂ concentration/%	CO₂ concentration/%	√ μ_max Predicted value/h^-1/2	√ μ_max Measured value/h^-1/2	A_f	B_f	Residual
4.5	1.7	0.1858	0.1841	1.002	0.998	-0.0016
4.5	22.0	0.1671	0.1617			-0.0053
6.5	1.7	0.1893	0.1902			0.0008
	22.0	0.1703	0.171			0.0006
	31.0	0.1615	0.1625			0.0009
	43.0	0.1497	0.1504			0.0006
8.7	1.7	0.1914	0.1924			0.0009
8.7	43.0	0.1513	0.1521			0.0007
31.5	1.7	0.1928	0.1978			0.0049
31.5	31.0	0.1644	0.1648			0.0003
47.5	22.0	0.1734	0.1751			0.0016
47.5	43.0	0.1524	0.1537			0.0012

4.3

Correlation analysis between microbial growth parameters and shelf life

There are differences between microbiological shelf-life and sensory shelf-life. Microbiological shelf-life refers to shelf-life criteria based on microbial count criteria, usually not exceeding 106 bacteria, which varies from food to food. Microbial shelf-life is the primary measure of food safety. Sensory shelf-life is an indicator that the sensory quality of a food does not exceed the maximum tolerance level of the consumer. In general, the storage period of food should be lower than the microbial shelf-life to ensure the safety of the food, and lower than the sensory shelf-life to meet the needs of consumers to consume the food. In this study microbial shelf-life was assessed and Table 4 shows the results of microbial and sensory shelf-life assessment of dairy products such as milk tofu and milk skin in different storage groups. From Table 4, it can be seen that the microbial shelf-life of groups A, B, C, D, E, and F were 19, 12, 6, 19, 7, and 5 d, respectively, and the score values of the sensory shelf-life, which were based on the criterion of not exceeding 6, were 18, 11, 6, 17, 6, and 5 d, respectively.

Table 4.

Microbiological and sensory shelf life of different storage groups

Storage group	Microbial shelf life /d	Sensory shelf life /d
A	19	18
B	12	11
C	6	6
D	19	17
E	7	6
F	5	5

The deterioration of dairy products is mainly due to the growth and reproduction of microorganisms contaminated in dairy products, which gradually cause dairy products to deteriorate. At the initial stage of deterioration of dairy products, microorganisms can only utilize low molecular weight substances, so the concentration of glucose in dairy products is a very important factor in determining the time of deterioration and the type of deteriorating microorganisms. As deterioration progresses, when the supply of glucose cannot meet the growth needs of deteriorating microorganisms, they degrade amino acids and produce malodorous by-products. Different types of microorganisms due to differences in their biological characteristics, and therefore their metabolic characteristics, metabolic rate and the utilization of the components in the dairy product is different, resulting in different metabolites, resulting in dairy products deterioration time, different types of deterioration, and different organoleptic changes. The shelf-life of microorganisms in dairy products is determined by the metabolic pattern of microorganisms, and has nothing to do with the maximum number of microorganisms growing at the end stage of deterioration. The metabolic patterns of different types of microorganisms grown in dairy products can be described by growth parameters, and the correlation between microbial metabolic patterns and shelf life can be determined by correlation analysis of microbial growth parameters and shelf life of dairy products.

Staphylococcus aureus and Salmonella were not analyzed for correlation with these two flora because they were not measured in the previous tests. The correlation coefficients of the growth parameters of colony counts, coliforms, molds, and lactic acid bacteria with the microbial shelf life and sensory shelf life of the dairy products are presented in Table 5. The hysteresis phases of the determined microorganisms all showed good correlation (p<0.01) with microbial shelf life and sensory shelf life. Among them, the correlation coefficients of hysteresis phase with microbial shelf-life and sensory shelf-life of Lactobacillus spp. were 0.9894 and 0.9848, respectively. The maximum growth rate of microorganisms was negatively correlated with microbial shelf-life and sensory shelf-life. The correlation coefficients of the growth rate of coliforms with the shelf-life were significant (p<0.01), and the correlation coefficients with the microbial and sensory shelf-life were -0.9582 and -0.9694, respectively. Lactobacilli and molds showed a better correlation with the shelf-life (p<0.05).

Table 5.

Shows the relationship between growth parameters and shelf life

Microorganism	Growth parameter	Correlation coefficient (r)
Microorganism	Growth parameter	Microbial shelf life	Sensory shelf life
Total flora	N₀	-0.2374	-0.1963
	N_max	0.7302	0.6785
	μ	-0.7343	-0.7016
	λ	0.8924**	0.9128**
Coliform bacteria	N₀	-0.8273*	-0.7976*
	N_max	0.6852	0.6725
	μ	-0.9582**	-0.9694**
	λ	0.7592*	0.7195
Mold	N₀	-0.7024	-0.6546
	N_max	0.6899	0.6236
	μ	-0.8374*	-0.8056*
	λ	0.9813**	0.9829**
Lactic acid bacteria	N₀	-0.7679*	-0.7651*
	N_max	-0.0763	-0.1425
	μ	-0.7290	-0.7016
	λ	0.9894**	0.9848**

Note: ** p<0.01 *p<0.05 The number of samples (n) was 8.

The initial bacterial counts of lactic acid bacteria, coliforms and molds all showed better correlation (p<0.05) with microbial shelf-life and sensory shelf-life. This indicates that the higher the initial microbial counts the shorter the shelf life of the dairy products. This is consistent with the traditional view of microbial shelf-life, which is that the lower the number of microorganisms originally present in the dairy product, the better, and if there are too many microorganisms, the faster deterioration occurs. It has been shown that the shelf life of microorganisms in dairy products is determined by the metabolic pattern of the microorganisms and is not related to the maximum number of microorganisms growing at the end stage of spoilage. In this study, we showed that the _{N_max} of all three microorganisms were weakly correlated with the microbial shelf life and sensory shelf life with a correlation coefficient of r<0.65.

5

Conclusion

In this paper, we set up a microbial population change test under low-temperature conditions to establish a secondary model for the growth of the total number of flora and to study the technology of low-temperature preservation of food. The five types of microorganisms inoculated in milk tofu and milk skins, including coliforms, molds, lactobacilli, Staphylococcus aureus, and Salmonella, reached their maximum growth at -6℃ for more than 18 days of storage. In line with the “higher the temperature, the longer the time, the more food microorganisms, the more likely to deteriorate” storage law. The difference in microbial counts between milk tofu and milk skin was analyzed. For example, the number of molds in milk tofu exceeded the standard range from the 12th day, while the number of molds in milk skins exceeded the standard range under all other conditions, except for the condition of storing at -36℃ for 1~6 days. It shows that there are some differences in the microbial growth of different types of food when stored at low temperatures. In the actual storage, need to target the adjustment of storage conditions.

Setting four values of CO₂ concentration: 1.7%, 22%, 31%, 43%, and six values of O₂ concentration: 2%, 6.5%, 8.7%, 10.5%, 30.5%, 47.5%, the law of influence of CO₂ on the kinetic parameters of the total number of bacterial colonies’ growth was not affected no matter how the values of O₂ concentration were changed. It indicates that although O₂ concentration and CO₂ concentration affect the microbial growth change situation when food products are packaged in combination, they do not cause influence on each other. When establishing the secondary model of total colony growth, temperature, O₂ concentration and CO₂ concentration were considered separately as different influencing factors. The model was validated by using the storage data related to 0℃, and it was found that the deviation of the model fitting was 0.998 and the accuracy was 1.002, which could well predict the microbial growth under the low-temperature storage conditions.

The hysteresis phase, maximum growth rate, and initial bacterial number of the measured microorganisms all showed good correlation with the microbial shelf-life and sensory shelf-life (all p less than 0.05). Microbial N_max (the maximum number of microorganisms when increasing to the stable phase) were all weakly correlated with the microbial shelf-life and sensory shelf-life, with correlation coefficients of r<0.65. This indicates that in order to obtain the best-quality food products, not only is it necessary to carry out low-temperature storage of food products, but also it is necessary to take into account the thoroughness of sterilization of food products prior to packaging, the stimulation of the external environment in the process of low-temperature storage, and so on, and optimize the technology of food low-temperature preservation as an inseparable whole. An inseparable whole for optimization, effective control of food quality and safety.

Langue:: Anglais

Périodicité:: 1 fois par an
Sujets de la revue:: Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres

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Mechanisms of food microbial growth and quality changes under low temperature storage conditions

Xian Liu

Chun Bian

Yanfeng Qu

Publié en ligne: 25 sept. 2025

Reçu: 16 janv. 2025

Accepté: 25 avr. 2025

DOI: https://doi.org/10.2478/amns-2025-1016

Mots clésLow-temperature storage, Microbial growth parameters, Quality change, Square root model, Shelf life

© 2025 Xian Liu, Chun Bian and Yanfeng Qu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Low-temperature storage, Microbial growth parameters, Quality change, Square root model, Shelf life