Bean product fermentation strain screening method and system

Abstract

The invention provides a bean product fermentation strain screening method and system, and relates to the technical field of fermentation strain screening, and the specific steps are as follows: extracting microorganisms from soybean milk, diluting, inoculating to an agar culture medium, observing and recording bacterial colonies, naming as candidate strains, and numbering; observing the cellular morphology of the candidate strain, carrying out a staining experiment, and preliminarily judging the strain type; respectively carrying out soybean milk fermentation experiments on the candidate strains; after fermentation, carrying out sensory evaluation and physicochemical index test on the soybean milk to obtain data such as color, smell and taste; processing the sensory characteristics and the physicochemical index data to generate a quality evaluation coefficient; and judging the optimal fermentation strain according to the numerical value of the evaluation coefficient. According to the method, excellent fermentation strains can be effectively identified, the accuracy and reliability of strain screening can be improved, the screening period is shortened, and compared with the prior art, the method has obvious advantages.

Description

Technical Field

[0001] The invention relates to the technical field of fermentation strain screening, and in particular to a method and system for screening fermentation strains of soy products. Background Art

[0002] The screening method for fermented soy products refers to the identification and selection of microbial strains with excellent fermentation characteristics and quality for the production and improvement of soy products through a series of systematic experimental and analytical methods. These methods aim to improve the flavor, texture, nutritional value and shelf life of fermented products while ensuring the safety and controllability of the production process.

[0003] Soy milk can be regarded as a product of soy products. Taking soy milk as an example, in the production process of soy milk, the selection of fermentation strains plays a decisive role in the texture, flavor and nutritional content of the final product. The screening methods in the prior art usually rely on experience and intuitive observation, which often leads to the randomness of the screening process and the uncertainty of the results. This method not only lacks scientific basis, but also may lead to a low match between the fermentation performance of the selected strains and the target product, which ultimately affects the quality and market competitiveness of soy milk; in addition, traditional methods usually take a long time to complete the isolation, cultivation and evaluation of strains, which increases production costs and time costs. Although the efficiency of strain screening has been improved in recent years with the help of molecular biology technology, it still fails to solve the problem of how to comprehensively evaluate the performance of strains in a complex fermentation environment.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method and system for screening soy product fermentation bacteria to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for screening soy product fermentation strains, comprising the following specific steps:

[0008] S1. Take a sample from soy milk, extract the microorganisms therein, dilute the sample, inoculate it into an agar medium, observe and record the colonies on the medium, name them as candidate strains, and record the numbers of the candidate strains respectively;

[0009] S2. Observe the cell morphology of candidate strains with different numbers, obtain the cell shape, size and arrangement, and perform a staining experiment. When the experimental results show that the candidate strains are all Gram-positive, the candidate strains are preliminarily judged to be Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis, and Bacillus based on the cell shape, size and arrangement of the candidate strains;

[0010] S3. Conducting a fermentation experiment of soy milk on Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus, taking samples from agar plates of candidate strains with different numbers, inoculating them into a pre-prepared and numbered soy milk matrix, placing the inoculated soy milk in an incubator for fermentation, and setting fermentation parameters respectively. After the experiment, sensory evaluation of the fermented soy milk with different numbers was performed to obtain the color, smell and taste characteristics of the fermented soy milk, and physical and chemical index tests were performed to obtain the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milk;

[0011] S4. The color, smell and taste characteristics of the fermented soy milk with different numbers are processed to generate the sensory coefficient of the fermented soy milk, the fermentation time, temperature, pH value, acidity and sugar content of the fermented soy milk with different numbers are processed to generate the influence coefficient of the sensory coefficient of the fermented soy milk, and the sensory coefficient and influence coefficient of the fermented soy milk with different numbers are processed and correlated to generate a quality evaluation coefficient for evaluating the overall quality of the fermented soy milk;

[0012] S5. According to the numerical values ​​of the quality evaluation coefficients of the fermented soy milks with different numbers, the best fermentation bacteria among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus subtilis are determined.

[0013] Furthermore, observe and record the colonies on the culture medium. The specific process is as follows:

[0014] To sample fresh soy milk, first ensure the sterility of the sampling tools and containers to avoid contamination by exogenous microorganisms, select an appropriate amount of soy milk sample, and then transfer it to a sterilized centrifuge tube;

[0015] Under a sterile operating environment, the soy milk sample is processed by shaking or gently shaking to fully disperse the microorganisms in the soy milk, and then diluted;

[0016] Prepare agar medium suitable for culturing microorganisms in soy milk, and ensure that all culture media have been sterilized before inoculation;

[0017] Use a sterilized inoculation loop or inoculation needle to take an appropriate amount of each diluted soy milk sample for inoculation. Gently draw a line on the agar surface with the inoculation loop to ensure a uniform colony distribution. Each dilution sample can be inoculated on different culture media for subsequent comparison.

[0018] Place the inoculated agar medium in a suitable incubator and set the appropriate temperature and time to promote the growth and reproduction of microorganisms;

[0019] After the incubation period, regularly observe the colonies on the agar medium, including color, shape, size and texture.

[0020] Furthermore, they are named as candidate strains, and the numbers of the candidate strains are recorded respectively. The specific process is as follows:

[0021] A unique number is assigned to each candidate strain. The candidate strain number is Cn, where n is the serial number of the candidate strain and the value range of n is 1, 2, 3, ...

[0022] Furthermore, the specific process of step S2 is as follows:

[0023] Use an optical microscope to observe the cell morphology of candidate strains with different numbers. The observation contents include:

[0024] Cell shape: record the morphological characteristics of cells, including rod-shaped, ellipsoidal, or spherical;

[0025] Cell size: Measure the average length and width of cells to obtain the cell dimensions;

[0026] Arrangement: Observe the arrangement of cells, including single scattered, pairs, chains or clusters;

[0027] Gram staining experiments were performed to determine the Gram reaction of the candidate bacteria, and the stained cells were observed again using a microscope. It was observed that the cells appeared purple, indicating that they were Gram-positive;

[0028] Record the cell morphology, size and arrangement of each candidate bacterial species, and preliminarily determine the category of the candidate bacterial species:

[0029] C1: Lactobacillus acidophilus, rod-shaped, arranged in pairs or chains;

[0030] C2: Lactobacillus plantarum, oval, scattered or clustered;

[0031] C3: Lactobacillus lactis, slender rod-shaped, chain-like;

[0032] C4: Bacillus, spherical or short rod-shaped, alone or in chains.

[0033] Furthermore, the specific process of step S3 is as follows:

[0034] Take samples from agar plates with different numbers using a sterile inoculation loop;

[0035] Inoculate each strain into the prepared soy milk matrix. The soy milk samples after inoculation of each strain should be marked as:

[0036] D1: soy milk fermented with Lactobacillus acidophilus;

[0037] D2: soy milk fermented with Lactobacillus plantarum;

[0038] D3: soy milk fermented with Lactobacillus lactis;

[0039] D4: soy milk fermented with Bacillus;

[0040] The sampled bacteria were inoculated into the corresponding soy milk at an inoculation rate of 10%;

[0041] Place the inoculated soy milk into the incubator and set the fermentation parameters:

[0042] Temperature: Lactic acid bacteria grow best between 30°C and 40°C, but different strains are optimized at different temperatures;

[0043] Time: Determine the best time for fermentation, conduct preliminary tests from 6 to 48 hours, and observe the fermentation conditions at different time points;

[0044] pH value: Different strains have different sensitivities to pH values. Proper adjustment of pH value can improve the fermentation effect.

[0045] After the experiment, the fermented soy milks with different numbers were subjected to sensory evaluation to obtain the color, smell and taste characteristics of the fermented soy milks, as follows:

[0046] Color: The color of soy milk changes, recorded as white, milky white, and light yellow;

[0047] Aroma: Evaluate the aroma after fermentation and record it as off-flavor, sour aroma, and bean aroma characteristics;

[0048] Taste: Taste was evaluated by tasting and recorded as acidity, smoothness, and sweetness;

[0049] Quantify the color, smell, and taste characteristics of fermented soy milk and obtain the corresponding coefficients:

[0050] Turbid is 1, milky is 2, and transparent is 3;

[0051] Strong odor is 1, weak odor is 2, and bean aroma is 3;

[0052] Slightly sour, slightly rough, and slightly sweet is 1, moderately sour, moderately smooth, and moderately sweet is 2, and no sourness, very smooth, and very sweet is 3.

[0053] Furthermore, the color, smell, and taste characteristics of fermented soy milk with different numbers are processed to generate the sensory coefficient of fermented soy milk according to the following formula:

[0054]

[0055] Among them, GS (i) is the sensory coefficient of the i-th fermented soy milk, S A is the color coefficient of the i-th fermented soy milk, Q B is the odor coefficient of the i-th fermented soybean milk, K C is the taste coefficient of the i-th fermented soy milk, ω S is the weight coefficient of color feature, ω Q is the weight coefficient of odor feature, ω K is the weight coefficient of taste characteristics, i is the index of fermented soy milk sample, the value range of i is D1, D2, …, Dm, m is the total number of fermented soy milk samples, 1>ω K >ω Q >ω Q >0,ω K +ω Q +ω Q =1.

[0056] Furthermore, the fermentation time, temperature, pH value, acidity, and sugar content data of fermented soy milk with different numbers are processed to generate the influence coefficient of the sensory coefficient of the fermented soy milk, according to the following formula:

[0057]

[0058] YS (i), =max(0,min(1,[YS (i) ]))

[0059] Among them, YS (i) is the influence coefficient of the i-th fermented soybean milk before constraint, YS (i), is the influence coefficient of the i-th fermented soy milk after constraint, T (i) is the fermentation time of the i-th fermented soy milk, Temp (i) is the temperature of the i-th fermented soy milk, T opt The best fermentation time for fermented soy milk is Temp. opt is the optimal temperature for fermenting soybean milk, A (i) is the acidity of the i-th fermented soy milk, pH (i) is the pH value of the i-th fermented soy milk, pH opt is the optimal pH value, U (i) is the sugar content of the i-th fermented soy milk, σ is the exponential coefficient of fermentation time, σ≥0, μ is the exponential coefficient of fermentation temperature, μ≥0, θ is the adjustment coefficient, θ∈R, is the influence coefficient of pH value, k is the influence coefficient of sugar content, k≥0.

[0060] Furthermore, the sensory coefficients and influence coefficients of fermented soy milk with different numbers were subjected to data processing and correlation analysis to generate a quality evaluation coefficient for evaluating the overall quality of fermented soy milk, based on the following formula:

[0061]

[0062] Among them, PS (i) is the quality evaluation coefficient of the i-th fermented soy milk, ρ is the exponential coefficient of the sensory coefficient, τ is the weight coefficient of the sensory coefficient, 0<τ<1, ε is the balance factor to prevent the denominator from being zero, ε>0.

[0063] Furthermore, according to the numerical values ​​of the quality evaluation coefficients of the fermented soy milks with different numbers, the best fermentation bacteria among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus are determined. The specific process is as follows:

[0064] Since D1 is fermented soy milk with Lactobacillus acidophilus, D2 is fermented soy milk with Lactobacillus plantarum, D3 is fermented soy milk with Lactobacillus lactis, and D4 is fermented soy milk with Bacillus subtilis, the corresponding PS (1) is the quality evaluation coefficient of Lactobacillus acidophilus fermented soy milk, PS (2) is the quality evaluation coefficient of soy milk fermented by Lactobacillus plantarum, PS (3) is the quality evaluation coefficient of soy milk fermented by Lactobacillus lactis, PS (4) is the quality evaluation coefficient of soy milk fermented by Bacillus;

[0065] PS (1) ,PS (2) ,PS (3) ,PS (4) Compare and select the maximum value;

[0066] When PS (1) When it is the maximum value, the corresponding Lactobacillus acidophilus is the best fermentation bacteria;

[0067] When PS (2) When it is the maximum value, the corresponding Lactobacillus plantarum is the best fermentation strain;

[0068] When PS (3) When it is the maximum value, the corresponding Lactobacillus lactis is the best fermentation strain;

[0069] When PS (4) When it is the maximum value, the corresponding Bacillus is the best fermentation strain;

[0070] When PS (1) ,PS (2) ,PS (3) ,PS(4) When any two or more of the above are equal, the best fermentation bacteria is their combination.

[0071] A bean product fermentation strain screening system, the system is used to perform any of the above-mentioned bean product fermentation strain screening methods, comprising:

[0072] The data collection and screening module is used to take samples from soybean milk, extract microorganisms therein, dilute the samples, inoculate them into agar medium, observe and record the colonies on the medium, name them as candidate strains, and record the numbers of the candidate strains respectively;

[0073] The feature analysis module is used to observe the cell morphology of candidate bacterial species with different numbers, obtain the cell shape, size and arrangement, and perform staining experiments. When the experimental results show that the candidate bacterial species are all Gram-positive, the candidate bacterial species are preliminarily judged to be Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis, and Bacillus based on the cell shape, size and arrangement of the candidate bacterial species;

[0074] The fermentation and testing module is used to conduct fermentation experiments on soy milk with Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus, respectively taking samples from agar plates of candidate strains with different numbers, inoculating them into a pre-prepared and numbered soy milk matrix, placing the inoculated soy milk in an incubator for fermentation, and setting fermentation parameters respectively. After the experiment, the fermented soy milks with different numbers are subjected to sensory evaluation to obtain the color, smell and taste characteristics of the fermented soy milk, and conducting physical and chemical index tests to obtain the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milk;

[0075] A data processing and analysis module, for processing the color, smell and taste characteristics of fermented soy milks with different numbers to generate the sensory coefficient of the fermented soy milk, processing the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milks with different numbers to generate the influence coefficient of the sensory coefficient of the fermented soy milk, performing data processing and correlation analysis on the sensory coefficient and influence coefficient of the fermented soy milks with different numbers to generate a quality evaluation coefficient for evaluating the overall quality of the fermented soy milk;

[0076] The judgment module is used to judge the best fermentation bacteria among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus according to the numerical values ​​of the quality evaluation coefficients of fermented soy milks with different numbers.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] The present invention extracts microorganisms from soybean milk and performs a systematic preliminary screening. Then, by observing the cell morphology and staining experiments, the types of candidate strains are preliminarily identified, thereby narrowing the screening range. Subsequently, fermentation experiments are conducted on different strains, and sensory evaluation and physical and chemical index tests are combined to comprehensively evaluate the performance of each strain under actual production conditions. The implementation of this series of methods can not only effectively identify excellent fermentation strains, but also improve the accuracy and reliability of strain screening and shorten the screening cycle, which has obvious advantages over the prior art.

[0079] By establishing quality evaluation coefficients, producers can scientifically evaluate the impact of different fermentation strains on the quality of soy products and thus select the best fermentation strains, which not only optimizes the flavor and texture of soy products, but also enhances their nutritional value and market competitiveness, providing a solid scientific foundation for the production of soy products. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0081] Figure 2 This is a block diagram of the module composition of the present invention. DETAILED DESCRIPTION

[0082] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0083] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0084] Embodiment 1:

[0085] See also Figure 1 , the present invention provides a technical solution:

[0086] A method for screening soy product fermentation strains, comprising the following specific steps:

[0087] S1. Take a sample from soy milk, extract the microorganisms therein, dilute the sample, inoculate it into an agar medium, observe and record the colonies on the medium, name them as candidate strains, and record the numbers of the candidate strains respectively;

[0088] S2. Observe the cell morphology of candidate strains with different numbers, obtain the cell shape, size and arrangement, and perform a staining experiment. When the experimental results show that the candidate strains are all Gram-positive, the candidate strains are preliminarily judged to be Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis, and Bacillus based on the cell shape, size and arrangement of the candidate strains;

[0089] S3. Conducting a fermentation experiment of soy milk on Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus, taking samples from agar plates of candidate strains with different numbers, inoculating them into a pre-prepared and numbered soy milk matrix, placing the inoculated soy milk in an incubator for fermentation, and setting fermentation parameters respectively. After the experiment, sensory evaluation of the fermented soy milk with different numbers was performed to obtain the color, smell and taste characteristics of the fermented soy milk, and physical and chemical index tests were performed to obtain the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milk;

[0090] S4. The color, smell and taste characteristics of the fermented soy milk with different numbers are processed to generate the sensory coefficient of the fermented soy milk, the fermentation time, temperature, pH value, acidity and sugar content of the fermented soy milk with different numbers are processed to generate the influence coefficient of the sensory coefficient of the fermented soy milk, and the sensory coefficient and influence coefficient of the fermented soy milk with different numbers are processed and correlated to generate a quality evaluation coefficient for evaluating the overall quality of the fermented soy milk;

[0091] S5. According to the numerical values ​​of the quality evaluation coefficients of the fermented soy milks with different numbers, the best fermentation bacteria among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus subtilis are determined.

[0092] Based on the above embodiment, the bacterial colonies on the culture medium were observed and recorded. The specific process was as follows:

[0093] To sample fresh soy milk, first ensure the sterility of the sampling tools and containers to avoid contamination by exogenous microorganisms, select an appropriate amount of soy milk sample (e.g., 10 ml), and then transfer it to a sterilized centrifuge tube;

[0094] In a sterile operating environment (such as a biosafety cabinet), the soy milk sample is processed, and the microorganisms in the soy milk are fully dispersed by shaking or gently mixing, and then diluted: the sampled soy milk sample is graded diluted, and the dilution ratios of 1:10, 1:100 and 1:1000 can usually be selected. The specific operation is: take 1 ml of soy milk sample, add 9 ml of sterile saline, mix well, and obtain a 1:10 dilution; then, take 1 ml of the 1:10 dilution, add 9 ml of sterile saline, and obtain a 1:100 dilution, and so on, until the desired dilution is obtained;

[0095] Prepare agar medium suitable for culturing microorganisms in soy milk, such as nutrient agar or selective medium (e.g. lactic acid bacteria selective medium), and ensure that all culture media have been sterilized before inoculation;

[0096] Use a sterile inoculation loop or inoculation needle to take an appropriate amount (e.g. 100 μl) from each diluted soy milk sample for inoculation. Gently streak the inoculation loop on the agar surface to ensure a uniform colony distribution. Each dilution sample can be inoculated on different culture media for subsequent comparison.

[0097] Place the inoculated agar medium in a suitable incubator and set the appropriate temperature (e.g., 30°C to 37°C) and time (usually 24 to 48 hours) to promote the growth and reproduction of microorganisms;

[0098] After the incubation period, regularly observe the colonies on the agar medium:

[0099] Color: record the color of the colony, which may be white, yellow, red, etc. The color can reflect the type of strain and its metabolic characteristics;

[0100] Shape: observe the shape of the colonies, which may include round, irregular, filamentous, etc. The difference in shape helps with preliminary classification;

[0101] Size: Measure the diameter of the colony and record its size (less than 1 mm is a small colony, 1-5 mm is a medium colony, and larger than 5 mm is a large colony). The size is usually related to the growth rate and metabolic characteristics of the microorganism.

[0102] Texture: Describes the texture of the colony, such as smooth, rough, wet or dry. Texture characteristics help further identify the microorganism.

[0103] On the basis of the above embodiment, they are named as candidate strains, and the numbers of the candidate strains are recorded respectively. The specific process is as follows:

[0104] Each candidate strain is assigned a unique number (for example, C1, C2, C3, etc.), the candidate strain number is Cn, n is the serial number of the candidate strain, and the value range of n is (1, 2, 3, ...).

[0105] Based on the above embodiment, the specific process of step S2 is as follows:

[0106] Use an optical microscope to observe the cell morphology of candidate strains with different numbers. The observation contents include:

[0107] Cell shape: record the morphological characteristics of cells, such as rod-shaped, oval, or spherical;

[0108] Cell size: Measure the average length and width of cells to get the cell dimensions;

[0109] Arrangement: Observe the arrangement of cells, such as single scattered, pairs, chains or clusters;

[0110] Gram staining experiments were performed to determine the Gram reaction of the candidate bacteria, and the stained cells were observed again using a microscope. It was observed that the cells appeared purple, indicating that they were Gram-positive;

[0111] Record the cell morphology, size and arrangement of each candidate bacterial species, and preliminarily determine the category of the candidate bacterial species:

[0112] C1: probably Lactobacillus acidophilus, rod-shaped, arranged in pairs or chains;

[0113] C2: probably Lactobacillus plantarum, oval, scattered or clustered;

[0114] C3: probably Lactobacillus lactis, slender rod-shaped, chain-like;

[0115] C4: May be Bacillus, spherical or short rod-shaped, singly or in chains.

[0116] Based on the above embodiment, the specific process of step S3 is as follows:

[0117] Take samples from agar plates with different numbers using a sterile inoculation loop;

[0118] Inoculate each strain into the prepared soy milk matrix. The soy milk samples after inoculation of each strain should be marked as:

[0119] D1: soy milk fermented with Lactobacillus acidophilus;

[0120] D2: soy milk fermented with Lactobacillus plantarum;

[0121] D3: soy milk fermented with Lactobacillus lactis;

[0122] D4: soy milk fermented with Bacillus;

[0123] Inoculate the sampled bacteria into the corresponding soy milk, usually at a rate of 10% (e.g. 10 ml inoculated into 100 ml soy milk);

[0124] Place the inoculated soy milk into the incubator and set the fermentation parameters:

[0125] Temperature: Generally, lactic acid bacteria grow best between 30°C and 40°C, but different strains may be optimized at different temperatures;

[0126] Time: Determine the best time for fermentation, such as conducting preliminary tests from 6 to 48 hours to observe the fermentation conditions at different time points;

[0127] pH value: Different strains of bacteria have different sensitivities to pH values. Appropriate adjustment of pH values ​​may help improve fermentation results.

[0128] After the experiment, the fermented soy milks with different numbers were subjected to sensory evaluation to obtain the color, smell and taste characteristics of the fermented soy milks, as follows:

[0129] Color: The color changes of soy milk, recorded as white, milky white, light yellow, etc.;

[0130] Odor: Evaluate the odor after fermentation and record characteristics such as off-flavor, sour aroma, and bean aroma;

[0131] Taste: Evaluate the taste by tasting, recording as acidity, smoothness, sweetness, etc.

[0132] Quantify the color, smell, and taste characteristics of fermented soy milk and obtain the corresponding coefficients:

[0133] Turbid is 1, milky is 2, and transparent is 3;

[0134] Strong odor is 1, weak odor is 2, and bean aroma is 3;

[0135] Slightly sour, slightly rough, and slightly sweet is 1, moderately sour, moderately smooth, and moderately sweet is 2, and no sourness, very smooth, and very sweet is 3.

[0136] On the basis of the above embodiment, the color, smell and taste characteristics of fermented soy milk with different numbers are processed to generate the sensory coefficient of fermented soy milk according to the following formula:

[0137]

[0138] Among them, GS (i) is the sensory coefficient of the i-th fermented soy milk, S A is the color coefficient of the i-th fermented soy milk, Q B is the odor coefficient of the i-th fermented soybean milk, K Cis the taste coefficient of the i-th fermented soy milk, ω S is the weight coefficient of color feature, ω Q is the weight coefficient of odor feature, ω K is the weight coefficient of taste characteristics, i is the index of fermented soy milk sample, the value range of i is D1, D2, …, Dm, and m is the total number of fermented soy milk samples.

[0139] The value range of sensory coefficient GS is [1,5].

[0140] When the value of sensory coefficient GS is close to 1, it means that the quality of soy milk is poor;

[0141] When the value of the sensory coefficient GS is close to 5, it means that the soy milk has good quality.

[0142] The larger the sensory coefficient GS, the better the quality of the fermented soy milk, the better the color, smell and taste characteristics, the more pleasant the overall sensory experience, and the relatively higher consumer satisfaction and acceptance.

[0143] Color is one of the first features that consumers notice when choosing a product. Good color is usually associated with high quality and can increase consumers' desire to buy. If the soy milk has a bright color, it usually leads to a higher sensory coefficient GS. Therefore, color and sensory coefficient GS are positively correlated.

[0144] Smell has a significant impact on the attractiveness and acceptance of food. Positive smells (such as fresh and mellow bean aroma) can enhance consumers' taste experience and thus improve the overall sensory coefficient GS. Therefore, smell and sensory coefficient GS are positively correlated.

[0145] Taste directly affects consumers' drinking experience. A smooth, delicate, and balanced taste is usually associated with a high sensory coefficient GS. Improving taste will directly enhance consumers' overall satisfaction with the product. Therefore, taste and sensory coefficient GS are positively correlated.

[0146] The reason why the color, smell, taste characteristics and sensory coefficient ωK of fermented soy milk are expressed by the above function expression is as follows:

[0147] The sensory characteristics of fermented soy milk include color, smell and taste, which are indispensable for the overall sensory evaluation of the product.

[0148] By setting the exponential form (e.g. ), which can reflect the nonlinear effects of each feature in sensory evaluation. Different features affect consumers' perception in different ways, and the exponential form can better simulate this complex relationship. For example, when the improvement of color has a more significant impact on the overall sensory experience, its importance can be controlled by adjustment.

[0149] The weight coefficient of each feature allows the user to adjust the importance of different sensory features according to the actual situation. For example, in some cases, smell may be more important than color, so ω can be increased. Q This design makes the formula highly adaptable and can be adjusted according to different evaluation criteria and market demands.

[0150] By adding The formula takes into account the interaction between the three. This term can capture the positive impact on the overall sensory coefficient GS when color, smell and taste perform well at the same time. In other words, this term can provide additional bonus to the final score when the scores of each feature are high, reflecting the comprehensive effect.

[0151] The contribution of all features is smoothed by weighting and square root taking, and finally divided by 3 to keep the sensory coefficient GS within a reasonable range.

[0152] Usually, the weight of taste ω K The biggest one is taste. For most foods and beverages, consumers’ final experience is often based on taste. Good taste can greatly improve product acceptance and satisfaction.

[0153] The weight of the smell ω Q Smell is also very important. Usually, its weight is greater than color. Smell can directly affect consumers' first impression of the product and produce strong sensory stimulation before drinking.

[0154] Color weight ω S It is usually the lowest. This is because although color visually attracts consumers at the first sight, it has less impact on the actual experience of the product than smell and taste. Consumers tend to pay more attention to the smell and taste of the product when trying it.

[0155] Therefore, usually, the weight of taste ω K Greater than the weight of the smell ω Q , the weight of the smell ω Q Greater than the weight of color ω S , that is, 1>ω K >ω Q >ω Q >0, without the influence of other parameters, ω K +ω Q +ω Q =1.

[0156] On the basis of the above embodiment, the fermentation time, temperature, pH value, acidity and sugar content data of fermented soy milk with different numbers are processed to generate the influence coefficient of the sensory coefficient of the fermented soy milk, according to the following formula:

[0157]

[0158] YS (i), =max(0,min(1,[YS (i) ]))

[0159] Among them, YS (i) is the influence coefficient of the i-th fermented soybean milk before constraint, YS (i), is the influence coefficient of the i-th fermented soy milk after constraint, T (i) is the fermentation time of the i-th fermented soy milk, Temp (i) is the temperature of the i-th fermented soy milk, T opt The best fermentation time for fermented soy milk is Temp. opt is the optimal temperature for fermenting soybean milk, A (i) is the acidity of the i-th fermented soy milk, pH (i) is the pH value of the i-th fermented soy milk, pH opt is the optimal pH value, U (i) is the sugar content of the i-th fermented soy milk, σ is the exponential coefficient of fermentation time, σ≥0, μ is the exponential coefficient of fermentation temperature, μ≥0, θ is the adjustment coefficient, θ∈R, is the influence coefficient of pH value, k is the influence coefficient of sugar content, k≥0.

[0160] Influence coefficient YS (i), The value range of is [0,1].

[0161] When the influence coefficient YS (i), When the value is close to 0, it means that the fermentation conditions are ideal and the soymilk quality is high;

[0162] When the influence coefficient YS (i), When the value is close to 1, it means that the fermentation conditions are not ideal, the soymilk quality is poor, and the fermentation conditions need to be adjusted;

[0163] Influence coefficient YS (i), The smaller it is, the better the quality of the fermented soy milk is and the closer the fermentation conditions are to the optimal state.

[0164] The reason why the above function expression is used to express the fermentation time, temperature, pH value, acidity, sugar content data and the influence coefficient YS of fermented soy milk is (i) The reasons are as follows:

[0165] Fermentation time Y that is too long or too short will lead to changes in the quality of soymilk. A fermentation time T that is too long may increase the bitterness, while a fermentation time T that is too short may cause the soymilk to fail to ferment completely and lack flavor.

[0166] Appropriate temperature Temp is conducive to the growth of probiotics, while too high or too low temperature Temp will inhibit fermentation and even lead to the growth of undesirable microorganisms.

[0167] The pH value affects the acidity and flavor of soy milk. The optimal pH value helps maintain the taste and flavor of soy milk. Deviating from this value will affect the quality of fermentation.

[0168] Appropriate acidity A can increase the umami flavor and taste of soy milk, making it more delicious. Too high acidity A may make the soy milk taste too sour and astringent, affecting consumer acceptance, while too low acidity A may make the soy milk lack flavor and taste bland. Acidity A is closely related to pH. During the fermentation process, a decrease in pH is usually accompanied by an increase in acidity A. Therefore, maintaining an appropriate pH helps ensure that acidity A is within the ideal range, thereby enhancing the overall sensory quality of soy milk.

[0169] A certain sugar content U can balance the acidity A, increase the sweetness of soy milk, and improve the taste. Consumers usually prefer soy milk with a rich taste and a balance of sweetness and sourness.

[0170] In the formula The results show that the increase of sugar content U inhibits the effect of acidity A on sensory quality, that is, when sugar content U increases, the contribution of acidity A to sensory quality decreases, and vice versa. This relationship indicates that the sensory quality can be optimized by adjusting sugar content U during the production process.

[0171] Adjustment coefficient By adjusting the ratio of acidity A to sugar content U, producers can flexibly optimize the sensory quality of soy milk. For example, in the target market, consumers may prefer a certain sweet-sour balance, and producers can meet this demand by adjusting the ratio of sugar content U to acidity A.

[0172] |T (i) -Topt| σ and |Temp (i) -Temp opt | μ The form of deviation makes the impact of deviation on quality nonlinear, which means that small deviations may not significantly affect the quality, but once it deviates too much from the optimal conditions, the quality will drop rapidly. This reminds producers to strictly control the fermentation time and temperature during the production process to avoid the critical point that affects the quality.

[0173] Through the analysis of historical production data, the effects of different fermentation time, temperature, pH value, acidity and sugar content on sensory quality are analyzed, and the model coefficients σ, μ, θ, k.

[0174] Therefore, it is feasible to use the above function expression to express the relationship between the fermentation time, temperature, pH value, acidity, sugar content data and the influence coefficient of fermented soy milk.

[0175] Construct a coefficient optimization model including the influence coefficient of fermented soy milk. The entire coefficient optimization model is divided into a training set and a validation set. 80% of the data is used for training and 20% of the data is used for validation. The fermentation time, temperature, pH value, acidity, and sugar content are used as inputs, and the corresponding fermented soy milk influence coefficient is used as the output label to train the coefficient optimization model. The back propagation algorithm is used to train σ, μ, θ, k is optimized and the mean square error is used as the loss function. When the mean square error is in the range of [0,0.1], the training of the coefficient optimization model is completed. Once the training is completed, the obtained σ, μ, θ, k can be used to predict the impact coefficient of new fermented soy milk.

[0176] On the basis of the above embodiment, the sensory coefficients and influence coefficients of fermented soybean milk with different numbers are subjected to data processing and correlation analysis to generate a quality evaluation coefficient for evaluating the overall quality of fermented soybean milk, based on the following formula:

[0177]

[0178] Among them, PS (i) is the quality evaluation coefficient of the i-th fermented soy milk, ρ is the exponential coefficient of the sensory coefficient, τ is the weight coefficient of the sensory coefficient, 0<τ<1, ε is the balance factor to prevent the denominator from being zero, ε>0.

[0179] Quality evaluation coefficient PS (i) The higher it is, the better the overall quality of the i-th fermented soy milk in terms of color, smell and taste.

[0180] GS represents the sensory quality of fermented soy milk, with a value range of [1,5]. The higher the value, the better the quality.

[0181] ρ is the exponential coefficient of the sensory coefficient, and its value range is generally [1,3]. The introduction of this power term indicates that the importance of sensory quality to the overall quality is nonlinear. For example, when ρ>1, the improvement of sensory quality will lead to a nonlinear growth of PS, emphasizing that even a small improvement can significantly improve the overall quality.

[0182] τ is the weight coefficient of the sensory coefficient, which is usually small ([0.01, 0.1]). This term ensures that the improvement of the sensory coefficient GS will have a direct and positive impact on the overall quality. The role of this term is to emphasize the direct contribution of sensory quality in the overall quality score, ensuring that even if other conditions remain unchanged, the improvement of sensory quality will still improve the quality evaluation coefficient.

[0183] YS is the influence coefficient, which indicates the influence of fermentation conditions on quality. Its value range is [0,1]. The smaller the value, the closer the fermentation conditions are to the optimal ones.

[0184] This item is introduced to reflect the impact of fermentation conditions on quality. The more ideal the fermentation conditions are (i.e., the smaller the YS is), the greater the value of this item will be, thereby improving the overall quality PS. Conversely, if the fermentation conditions are not ideal (i.e., the larger the YS is), the overall quality PS will decrease.

[0185] Similarly, a coefficient optimization model including the quality evaluation coefficient is constructed. The entire coefficient optimization model is divided into a training set and a validation set. 80% of the data is used for training and 20% of the data is used for validation. The sensory coefficient and influence coefficient of fermented soy milk are taken as input, and the corresponding quality evaluation coefficient of fermented soy milk is taken as output label to train the coefficient optimization model. The back propagation algorithm is used to optimize ρ and τ, and the mean square error is used as the loss function. When the mean square error is in the range of [0,0.1], the training of the coefficient optimization model is completed. Once the training is completed, the obtained ρ and τ can be used to predict the quality evaluation coefficient of new fermented soy milk.

[0186] On the basis of the above embodiment, according to the numerical values ​​of the quality evaluation coefficients of fermented soybean milk with different numbers, the best fermentation strain among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus is judged, and the specific process is as follows:

[0187] Since D1 is fermented soy milk with Lactobacillus acidophilus, D2 is fermented soy milk with Lactobacillus plantarum, D3 is fermented soy milk with Lactobacillus lactis, and D4 is fermented soy milk with Bacillus subtilis, the corresponding PS (1) is the quality evaluation coefficient of Lactobacillus acidophilus fermented soy milk, PS (2) is the quality evaluation coefficient of soy milk fermented by Lactobacillus plantarum, PS (3) is the quality evaluation coefficient of soy milk fermented by Lactobacillus lactis, PS (4) is the quality evaluation coefficient of soy milk fermented by Bacillus;

[0188] PS (1) ,PS (2) ,PS (3) ,PS (4) Compare and select the maximum value;

[0189] When PS (1) When it is the maximum value, the corresponding Lactobacillus acidophilus is the best fermentation bacteria;

[0190] When PS (2) When it is the maximum value, the corresponding Lactobacillus plantarum is the best fermentation strain;

[0191] When PS (3) When it is the maximum value, the corresponding Lactobacillus lactis is the best fermentation strain;

[0192] When PS (4) When it is the maximum value, the corresponding Bacillus is the best fermentation strain;

[0193] When PS (1) ,PS (2) ,PS (3) ,PS (4) When any two or more of the above are equal, the best fermentation bacteria is their combination.

[0194] See also Figure 2 , the present invention also provides a technical solution:

[0195] A bean product fermentation strain screening system, the system is used to perform any of the above-mentioned bean product fermentation strain screening methods, comprising:

[0196] The data collection and screening module is used to take samples from soybean milk, extract microorganisms therein, dilute the samples, inoculate them into agar medium, observe and record the colonies on the medium, name them as candidate strains, and record the numbers of the candidate strains respectively;

[0197] The feature analysis module is used to observe the cell morphology of candidate bacterial species with different numbers, obtain the cell shape, size and arrangement, and perform staining experiments. When the experimental results show that the candidate bacterial species are all Gram-positive, the candidate bacterial species are preliminarily judged to be Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis, and Bacillus based on the cell shape, size and arrangement of the candidate bacterial species;

[0198] The fermentation and testing module is used to conduct fermentation experiments on soy milk with Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus, respectively taking samples from agar plates of candidate strains with different numbers, inoculating them into a pre-prepared and numbered soy milk matrix, placing the inoculated soy milk in an incubator for fermentation, and setting fermentation parameters respectively. After the experiment, the fermented soy milks with different numbers are subjected to sensory evaluation to obtain the color, smell and taste characteristics of the fermented soy milk, and conducting physical and chemical index tests to obtain the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milk;

[0199] A data processing and analysis module, for processing the color, smell and taste characteristics of fermented soy milks with different numbers to generate the sensory coefficient of the fermented soy milk, processing the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milks with different numbers to generate the influence coefficient of the sensory coefficient of the fermented soy milk, performing data processing and correlation analysis on the sensory coefficient and influence coefficient of the fermented soy milks with different numbers to generate a quality evaluation coefficient for evaluating the overall quality of the fermented soy milk;

[0200] The judgment module is used to judge the best fermentation bacteria among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus according to the numerical values ​​of the quality evaluation coefficients of fermented soy milks with different numbers.

[0201] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0202] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0203] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0204] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

[0205] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0206] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A method for screening soy product fermentation strains, characterized in that: The specific steps include: S1. Take a sample from soy milk, extract the microorganisms therein, dilute the sample, inoculate it into an agar medium, observe and record the colonies on the medium, name them as candidate strains, and record the numbers of the candidate strains respectively; S2. Observe the cell morphology of candidate strains with different numbers, obtain the cell shape, size and arrangement, and perform a staining experiment. When the experimental results show that the candidate strains are all Gram-positive, the candidate strains are preliminarily judged to be Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis, and Bacillus based on the cell shape, size and arrangement of the candidate strains; S3. Conducting a fermentation experiment of soy milk on Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus, taking samples from agar plates of candidate strains with different numbers, inoculating them into a pre-prepared and numbered soy milk matrix, placing the inoculated soy milk in an incubator for fermentation, and setting fermentation parameters respectively. After the experiment, sensory evaluation of the fermented soy milk with different numbers was performed to obtain the color, smell and taste characteristics of the fermented soy milk, and physical and chemical index tests were performed to obtain the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milk; S4. The color, smell and taste characteristics of the fermented soy milk with different numbers are processed to generate the sensory coefficient of the fermented soy milk, the fermentation time, temperature, pH value, acidity and sugar content of the fermented soy milk with different numbers are processed to generate the influence coefficient of the sensory coefficient of the fermented soy milk, and the sensory coefficient and influence coefficient of the fermented soy milk with different numbers are processed and correlated to generate a quality evaluation coefficient for evaluating the overall quality of the fermented soy milk; S5. According to the numerical values ​​of the quality evaluation coefficients of the fermented soy milks with different numbers, the best fermentation bacteria among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus subtilis are determined.

2. The method for screening soy product fermentation strains according to claim 1, characterized in that: Observe and record the colonies on the culture medium. The specific process is as follows: To sample fresh soy milk, first ensure the sterility of the sampling tools and containers to avoid contamination by exogenous microorganisms, select an appropriate amount of soy milk sample, and then transfer it to a sterilized centrifuge tube; Under a sterile operating environment, the soy milk sample is processed by shaking or gently shaking to fully disperse the microorganisms in the soy milk, and then diluted; Prepare agar medium suitable for culturing microorganisms in soy milk, and ensure that all culture media have been sterilized before inoculation; Use a sterilized inoculation loop or inoculation needle to take an appropriate amount of each diluted soy milk sample for inoculation. Gently draw a line on the agar surface with the inoculation loop to ensure a uniform colony distribution. Each dilution sample can be inoculated on different culture media for subsequent comparison. Place the inoculated agar medium in a suitable incubator and set the appropriate temperature and time to promote the growth and reproduction of microorganisms; After the incubation period, regularly observe the colonies on the agar medium, including color, shape, size and texture.

3. The method for screening soy product fermentation strains according to claim 2, characterized in that: Name them as candidate strains and record their numbers respectively. The specific process is as follows: A unique number is assigned to each candidate strain. The candidate strain number is Cn, where n is the serial number of the candidate strain and the value range of n is 1, 2, 3, ...

4. The method for screening soy product fermentation strains according to claim 3, characterized in that: The specific process of step S2 is as follows: Use an optical microscope to observe the cell morphology of candidate strains with different numbers. The observation contents include: Cell shape: record the morphological characteristics of cells, including rod-shaped, ellipsoidal, or spherical; Cell size: Measure the average length and width of cells to obtain the cell dimensions; Arrangement: Observe the arrangement of cells, including single scattered, pairs, chains or clusters; Gram staining experiments were performed to determine the Gram reaction of the candidate bacteria, and the stained cells were observed again using a microscope. It was observed that the cells appeared purple, indicating that they were Gram-positive; Record the cell morphology, size and arrangement of each candidate bacterial species, and preliminarily determine the category of the candidate bacterial species: C1: Lactobacillus acidophilus, rod-shaped, arranged in pairs or chains; C2: Lactobacillus plantarum, oval, scattered or clustered; C3: Lactobacillus lactis, slender rod-shaped, chain-like; C4: Bacillus, spherical or short rod-shaped, alone or in chains.

5. The method for screening fermentation strains of soybean products according to claim 4, characterized in that: The specific process of step S3 is as follows: Take samples from agar plates with different numbers using a sterile inoculation loop; Inoculate each strain into the prepared soy milk matrix. The soy milk samples after inoculation of each strain should be marked as: D1: soy milk fermented with Lactobacillus acidophilus; D2: soy milk fermented with Lactobacillus plantarum; D3: soy milk fermented with Lactobacillus lactis; D4: soy milk fermented with Bacillus; The sampled bacteria were inoculated into the corresponding soy milk at an inoculation rate of 10%; Place the inoculated soy milk into the incubator and set the fermentation parameters: Temperature: Lactic acid bacteria grow best between 30°C and 40°C, but different strains are optimized at different temperatures; Time: Determine the best time for fermentation, conduct preliminary tests from 6 to 48 hours, and observe the fermentation conditions at different time points; pH value: Different strains have different sensitivities to pH values, so adjusting the pH value can improve the fermentation effect; After the experiment, the fermented soy milks with different numbers were subjected to sensory evaluation to obtain the color, smell and taste characteristics of the fermented soy milks, as follows: Color: The color of soy milk changes, recorded as white, milky white, and light yellow; Aroma: Evaluate the aroma after fermentation and record it as off-flavor, sour aroma, and bean aroma characteristics; Taste: Taste was evaluated by tasting and recorded as acidity, smoothness, and sweetness; Quantify the color, smell, and taste characteristics of fermented soy milk and obtain the corresponding coefficients: Turbid is 1, milky is 2, and transparent is 3; Strong odor is 1, weak odor is 2, and bean aroma is 3; Slightly sour, slightly rough, and slightly sweet is 1, moderately sour, moderately smooth, and moderately sweet is 2, and no sourness, very smooth, and very sweet is 3.

6. The method for screening soy product fermentation strains according to claim 5, characterized in that: The color, smell, and taste characteristics of fermented soy milk with different numbers were processed to generate the sensory coefficient of fermented soy milk, based on the following formula: Among them, GS (i) is the sensory coefficient of the i-th fermented soy milk, S A is the color coefficient of the i-th fermented soy milk, Q B is the odor coefficient of the i-th fermented soybean milk, K C is the taste coefficient of the i-th fermented soy milk, ω S is the weight coefficient of color feature, ω Q is the weight coefficient of odor feature, ω K is the weight coefficient of taste characteristics, i is the index of fermented soy milk sample, the value range of i is D1, D2, …, Dm, m is the total number of fermented soy milk samples, 1>ω K >ω Q >ω Q >0,ω K +ω Q +ω Q =1.

7. The method for screening soy product fermentation strains according to claim 6, characterized in that: The fermentation time, temperature, pH value, acidity, and sugar content of fermented soy milk with different numbers were processed to generate the influence coefficient of the sensory coefficient of fermented soy milk, based on the following formula: Y.S. (i)’ =max(0,min(1,[YS (i) ])) Among them, YS (i) is the influence coefficient of the i-th fermented soybean milk before constraint, YS (i)’ is the influence coefficient of the i-th fermented soy milk after constraint, T (i) is the fermentation time of the i-th fermented soy milk, Temp (i) is the temperature of the i-th fermented soy milk, T opt The best fermentation time for fermented soy milk is Temp. opt is the optimal temperature for fermenting soybean milk, A (i) is the acidity of the i-th fermented soy milk, pH (i) is the pH value of the i-th fermented soy milk, pH opt is the optimal pH value, U (i) is the sugar content of the i-th fermented soy milk, σ is the exponential coefficient of fermentation time, σ≥0, μ is the exponential coefficient of fermentation temperature, μ≥0, θ is the adjustment coefficient, θ∈R, is the influence coefficient of pH value, k is the influence coefficient of sugar content, k≥0.

8. The method for screening soy product fermentation strains according to claim 7, characterized in that: The sensory coefficients and influence coefficients of fermented soy milk with different numbers were processed and correlated to generate a quality evaluation coefficient for evaluating the overall quality of fermented soy milk. The formula is as follows: Among them, PS (i) is the quality evaluation coefficient of the i-th fermented soy milk, ρ is the exponential coefficient of the sensory coefficient, τ is the weight coefficient of the sensory coefficient, 0<τ<1, ε is the balance factor to prevent the denominator from being zero, ε>0.

9. The method for screening soy product fermentation strains according to claim 8, characterized in that: According to the numerical values ​​of the quality evaluation coefficients of fermented soy milk with different numbers, the best fermentation strains among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus are judged. The specific process is as follows: Since D1 is fermented soy milk with Lactobacillus acidophilus, D2 is fermented soy milk with Lactobacillus plantarum, D3 is fermented soy milk with Lactobacillus lactis, and D4 is fermented soy milk with Bacillus subtilis, the corresponding PS (1) is the quality evaluation coefficient of Lactobacillus acidophilus fermented soy milk, PS (2) is the quality evaluation coefficient of soy milk fermented by Lactobacillus plantarum, PS (3) is the quality evaluation coefficient of soy milk fermented by Lactobacillus lactis, PS (4) is the quality evaluation coefficient of soy milk fermented by Bacillus; PS (1) ,PS (2) ,PS (3) ,PS (4) Compare and select the maximum value; When PS (1) When it is the maximum value, the corresponding Lactobacillus acidophilus is the best fermentation bacteria; When PS (2) When it is the maximum value, the corresponding Lactobacillus plantarum is the best fermentation strain; When PS (3) When it is the maximum value, the corresponding Lactobacillus lactis is the best fermentation strain; When PS (4) When it is the maximum value, the corresponding Bacillus is the best fermentation strain; When PS (1) ,PS (2) ,PS (3) ,PS (4) When any two or more of the above are equal, the best fermentation bacteria is their combination.

10. A bean product fermentation strain screening system, the system being used to perform a bean product fermentation strain screening method according to any one of claims 1 to 9, characterized in that: include: The data collection and screening module is used to take samples from soybean milk, extract microorganisms therein, dilute the samples, inoculate them into agar medium, observe and record the colonies on the medium, name them as candidate strains, and record the numbers of the candidate strains respectively; The feature analysis module is used to observe the cell morphology of candidate bacterial species with different numbers, obtain the cell shape, size and arrangement, and perform staining experiments. The experimental results show that the candidate bacterial species are all Gram-positive. Based on the cell shape, size and arrangement of the candidate bacterial species, the candidate bacterial species are preliminarily judged to be Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis, and Bacillus. The fermentation and testing module is used to conduct fermentation experiments on soy milk with Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus, respectively taking samples from agar plates of candidate strains with different numbers, inoculating them into a pre-prepared and numbered soy milk matrix, placing the inoculated soy milk in an incubator for fermentation, and setting fermentation parameters respectively. After the experiment, the fermented soy milks with different numbers are subjected to sensory evaluation to obtain the color, smell and taste characteristics of the fermented soy milk, and conducting physical and chemical index tests to obtain the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milk; A data processing and analysis module, for processing the color, smell and taste characteristics of fermented soy milks with different numbers to generate the sensory coefficient of the fermented soy milk, processing the fermentation time, temperature, pH value, acidity and sugar content data of the fermented soy milks with different numbers to generate the influence coefficient of the sensory coefficient of the fermented soy milk, performing data processing and correlation analysis on the sensory coefficient and influence coefficient of the fermented soy milks with different numbers to generate a quality evaluation coefficient for evaluating the overall quality of the fermented soy milk; The judgment module is used to judge the best fermentation bacteria among Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus lactis and Bacillus according to the numerical values ​​of the quality evaluation coefficients of fermented soy milks with different numbers.

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