JP2007312738A - Method for predicting growth of microorganism in food and drink - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and a system for constructing a prediction model for the growth of microorganisms and predicting the growth of the microorganisms in a soy sauce-containing food and drink on the basis of the model according to a method for judging and analyzing using qualitative data related to component analytical values in the food and drink and the presence or absence of the growth of the microorganisms in the food and drink. <P>SOLUTION: The method for predicting the growth of the microorganisms in the food and drink is provided. The method for predicting the growth of the microorganisms from the component analytical values of the food and drink on the basis of the prediction model is carried out as follows. The component analytical values are obtained as quantitative data for the food and drink and the presence or absence of the growth of the microorganisms is further obtained as the qualitative data of either of the presence of the proliferation of the microorganisms and the absence of the proliferation of the microorganisms. Both the obtained data are analyzed according to the method for judging and analyzing and the prediction model for predicting the growth of the microorganisms from the analytical values of the food and drink is constructed. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a method for discriminating the growth of microorganisms in food and drink.

  Conventionally, the risk of microbial growth in food and drink has been predicted from component analysis of food and drink.

  In the conventional technology, the growth limit of a factor that has a large influence on the growth of microorganisms, such as pH, salt concentration, and water activity, is investigated and used for growth prediction to determine the growth limit. In many cases, a combination of two factors having a large influence is mapped into a two-dimensional graph to map the growth of microorganisms, visualized, and used for prediction of growth (see Non-Patent Document 1, etc.). However, since various factors are involved in the growth of harmful microorganisms in actual foods, as represented by the hurdle theory, there are many ways to make highly accurate predictions using the above-mentioned conventional technology. There are insufficient factors. There are methods that use mathematical models such as logistic curves and Gompertz curves as growth models for microorganisms, but these are prediction methods based on the model growth rate. If you know, you can predict the number of bacteria after a certain time. Therefore, when it is desired to expand even when the analytical value of food is different, it is necessary to investigate the growth rate of the microorganism at the analytical value to be predicted. Is difficult to predict (see Non-Patent Document 2 and Non-Patent Document 3). Shinta et al. (See Non-Patent Document 4) proposed a model that predicts the growth of microorganisms by multiple regression analysis using “tsuyu” with different analytical values such as salt, sugar, soy sauce-derived nitrogen content, and pH. However, since it is necessary to check the growth rate in order to create these models, the number of bacteria must be measured over time to calculate the growth rate, and the number of tests becomes enormous. Ratkowsky et al. (See Non-Patent Document 5) have proposed a mathematical model for predicting the growth of microorganisms under different water activity, pH, and temperature. In order to create this model, it is necessary to examine the boundary line of the growth limit (presence or absence of growth) in advance for each component analysis value, but the accuracy of this boundary line affects the prediction accuracy of the mathematical model. Therefore, the number of tests inevitably increases as the number of components increases. In addition, since this mathematical model is not derived based on theory, there is no confirmation that can be always applied to the food, and it is necessary to confirm in advance. In addition, as the number of types of analysis values increases, the mathematical model becomes complicated, and it is necessary to confirm whether the extended mathematical model can be applied to the food. Model creation was very difficult. For the reasons described above, currently, factors that have a relatively large effect on the growth of microorganisms in foods that have been tested by experts in the past (pH, Aw, preservative concentration, etc.) and the growth potential at that time In many cases, the viability of the microorganism in an unknown sample has been determined empirically using the relationship with availability. However, this method has poor prediction accuracy and objectivity.

NAKAMURA Naruhisa et al., “On the microbiological safety evaluation method for soy sauce-related seasonings”, Soken Lab., Vol.23, No.1, 1997, p.1-8 Shimizu Shio, published by Sachi Shobo, "Food Microorganism 1-Basics, Science of Food Microbiology" p.162-170 (Predictive Microbiology) Shimizu Shio, “Predictive Microbiology”, Edible Magazine Vol.42, No.6, p317-323 Shintaji et al., “Numericalization of microbial resistance of straight soup products and its application”, Soken Vol.31, No.1, 2005, p.17-21 D.A.Ratkowsky et al., `` Modeling the bacterial growth / no growth interface '' Letters in Applied Microbaiology 1995, 20, 29-33

  The present invention uses a component analysis value in food and drink and qualitative data on the presence or absence of microbial growth in the food and drink to construct a microorganism growth predictive model by a discriminant analysis method. The object is to provide a method for predicting the growth of microorganisms in products.

  The present inventors examined a method for quickly and accurately predicting the growth of specific harmful microorganisms in various foods and drinks. As a result, we focused on the discriminant analysis method as a method for analyzing data of a large number of analysis values simultaneously, and created a discriminant using this method. By using this, if there is a lot of data on foods with known growth of specific microorganisms, it is possible to objectively predict the growth of microorganisms with relatively high accuracy in consideration of multiple factors in foods and drinks. The headline and the present invention were completed.

  That is, the present invention relates to analytical values that are considered to have an effect on the growth of microorganisms and the possibility of growth at that time for foods whose growth is known when inoculated with harmful microorganisms that can cause harm in food. From this relationship, a discriminant that associates the component analysis value of food and drink with the presence or absence of growth of microorganisms is obtained, is used as a prediction model, and this discriminant is stored in the storage device. There is a method for predicting the growth of a microorganism characterized in that the analysis value of a food or beverage sample whose microorganism growth is unknown is examined and calculated using the above discriminant to predict the growth of the microorganism.

Specifically, the present invention is as follows.
[1] A method for predicting the growth of microorganisms in foods and drinks, wherein the component analysis values of the foods and drinks are obtained as quantitative data, and the presence or absence of the growth of microorganisms is determined with or without the growth of microorganisms. Is obtained as one of the qualitative data, and both the obtained data are analyzed by a discriminant analysis method, and a prediction model for predicting the growth of microorganisms from the food and drink analysis value is constructed, and the food and drink based on the prediction model Of predicting microbial viability of food and drink from component analysis values of food.
[2] The method for predicting the microbial growth of a food or drink according to [1], wherein the food or drink is a soy sauce-containing liquid food or drink.

[3] A method for predicting microbial growth of a food or drink according to [1] or [2], wherein qualitative data on the presence or absence of microbial growth is obtained by the following steps:
(i) Inoculate the food and drink with microorganisms, and after a certain temperature and a certain period, determine the presence or absence of growth of microorganisms from the change in appearance, and if growth of microorganisms is observed, determine that there is growth of microorganisms,
(ii) Inoculate the food and drink with microorganisms, and after a certain temperature and period, determine the presence or absence of growth of microorganisms from the change in appearance, and if the growth of microorganisms is not observed, further measure the number of bacteria, If it exceeds 10 times the initial number of bacteria, it is determined that the microorganism has proliferated,
(iii) If it is not determined in the steps (i) and (ii) that the microorganism has grown, it is determined that the microorganism has not grown.

[4] Component analysis values for foods and drinks are three or more values selected from the group consisting of pH value, salt concentration, Brix, water activity value, alcohol concentration, total nitrogen content and acetic acid concentration in foods and drinks. A method for predicting the growth of microorganisms in a food or drink according to any one of [1] to [3].
[5] The method for predicting the growth of microorganisms in a food or drink according to [4], wherein the component analysis values for the food or drink are the pH value, salt concentration, Brix, and alcohol concentration in the food and drink.
[6] The food or drink according to any one of [1] to [5], wherein the microorganism used for constructing the prediction model is one selected from the group consisting of yeast, lactic acid bacteria, mold, spore bacteria, Staphylococcus aureus, and Escherichia coli. Of predicting the growth of microorganisms.

[7] A method for predicting the growth of microorganisms in a soy sauce-containing liquid food or drink, wherein the component analysis values for the pH value, salt concentration, Brix, and alcohol concentration in the food or drink for the soy sauce-containing liquid food or drink Qualitative data about the presence or absence of microbial growth, obtained as quantitative data,
(i) Inoculate the food and drink with microorganisms, and after a certain temperature and period, judge the presence or absence of growth of microorganisms from the change in appearance, and if proliferation of microorganisms is observed, determine that there is proliferation of microorganisms,
(ii) Inoculate the food and drink with microorganisms, and after a certain temperature and period, determine the presence or absence of growth of microorganisms from the change in appearance, and if the growth of microorganisms is not observed, further measure the number of bacteria, If it exceeds 10 times the initial number of bacteria, it is determined that the microorganism has proliferated,
(iii) If it is not determined that the microorganism has proliferated in the steps (i) and (ii), it is determined that the microorganism has not proliferated.
And analyzing both of the acquired data by discriminant analysis, constructing a prediction model for predicting the growth of microorganisms from the component analysis value of the soy sauce-containing liquid food and drink, and based on the prediction model A method for predicting microbial growth of a soy sauce-containing liquid food or drink from component analysis values in the soy sauce-containing liquid food or drink.

[8] A system for predicting microbial growth in a food or drink for performing a method for predicting the growth of microorganisms in the food or drink according to any one of [1] to [6],
(a) means for inputting component analysis values of foods and drinks whose microbial growth is unknown;
(b) storage means for storing the constructed microbial growth prediction model;
(c) Data processing for predicting the presence or absence of microbial growth by applying the component analysis value input using the input means of (a) to the microbial growth prediction model stored in the storage means of (b) Means, and
(d) an output means for outputting the presence or absence of the predicted microbial selectivity,
For predicting the growth of microorganisms in foods and drinks.

  By the method of the present invention, the viability of harmful microorganisms in foods and drinks such as soy sauce-containing foods and drinks can be predicted with high accuracy.

  In the conventional method, for example, since a prediction model is constructed using the growth rate of microorganisms as a variable, a long test period and an enormous number of experiments are required, and the construction of the prediction model is difficult. Since the method of the present invention obtains the growth of microorganisms as qualitative data indicating whether or not there is proliferation, and constructs a prediction model, a prediction system can be easily constructed. In addition, many ingredients are included in food and drink, and it is difficult to determine which ingredients affect the growth of microorganisms. To create an accurate prediction model, trial and error is necessary. is necessary. Since the present invention can construct a prediction model by acquiring qualitative data of microorganisms without measuring the growth rate of microorganisms, a large number of data can be easily acquired in a short period of time. Model building is also easy.

  As shown in the Examples, the prediction accuracy of the prediction according to the method of the present invention is remarkably high, and enables reliable prediction of microbial growth.

  In the present invention, foods and drinks include soy sauce-containing foods and drinks, retort foods (container and packaging, pressurized and heat sterilized foods), tea drinks such as tea and green tea, coffee, fruit juice drinks, carbonated drinks, sports drinks, amino acid drinks and the like. This includes all products distributed as dairy products such as beverages, beverages for hot vendors, alcoholic beverages, milk beverages, lactic acid bacteria beverages, refrigerated foods and beverages. Among them, soy sauce-containing foods and drinks, particularly soy sauce-containing liquid foods and drinks are preferable, and soy sauce, noodle soup (straight soy sauce products and the like), dashi, maple, sauce, ponzu, soy sauce with soup, ramen soup, seasoning and the like are included.

  In the present invention, a component of a food or drink means a factor that can affect the growth of microorganisms. The components to be analyzed in the method of the present invention are components (microorganism control factors) that can affect the growth of microorganisms, and vary depending on the type of food or drink, but pH, salt (NaCl) concentration, Brix, alcohol concentration, total nitrogen Minute, water activity (Aw), acetic acid concentration, sugar concentration, effective acidity and the like. When the food or drink is a liquid food or drink containing soy sauce, pH, sodium chloride (NaCl) concentration, Brix, water activity value, alcohol concentration, total nitrogen content and acetic acid concentration are important. In the method of the present invention, at least three of these components are analyzed, quantitative data is acquired as numerical data, and is used to construct a prediction model. These values can be measured by a known method, for example, based on the soy sauce test method (Japan Soy Sauce Research Institute, 1985).

  The type of microorganism used when constructing the prediction model differs depending on the type of food and drink, and a harmful microorganism that may cause a problem in growth may be used in each food and drink. As specific microorganisms, yeasts such as film-producing yeast, lactic acid bacteria, fungi, spore bacteria (Cereus bacteria, Bacillus subtilis, etc.), Staphylococcus aureus, Escherichia coli, and the like can be used. When the food or drink is a soy sauce-containing liquid food or drink, a yeast such as a film-producing yeast is preferred.

  In the method of the present invention, first, it is necessary to construct a prediction model. In order to construct a prediction model, first, a plurality of test foods and drinks are prepared. Examples of the plurality of test foods and drinks include the same kind of foods and drinks having different specifications. The same kind of foods and drinks with different specifications refers to foods and drinks manufactured by changing the types of components such as flavor and salt concentration or the content of each component. For example, when building a prediction model for soy sauce-containing liquid foods and drinks, soy sauce containing light mouth soy sauce, dark mouth soy sauce, low salt soy sauce, asa salt soy sauce, somen soup, soba soup, udonashi, grilled meat sauce, yakitori sauce Sauce, bowl of sauce, ponzu, dairy soy sauce, various seasoning liquids containing soy sauce are targeted.

  These aseptically treated test foods and drinks are dispensed into aseptically sealable containers, and a certain amount (a certain number of bacteria) is inoculated there. The aseptic processing method of the test food or drink is not limited, but may be performed by heating, for example. There is no limitation on the amount of food and drink used at this time and the inoculation amount of microorganisms, which can be appropriately determined. The container is then sealed and placed at a constant temperature for a certain period. Although there is no limitation on the period, the temperature is the most suitable temperature for the growth of microorganisms when the temperature is limited on the circulation, the temperature suitable for the growth of microorganisms to be used when not limited to the normal temperature circulation, for example, 30 ° C. to 37 ° C. In addition, the period to be set is a period sufficient for the microorganisms to be used to grow, and can be appropriately determined depending on the microorganisms. For example, it may be several days to several months.

  After being placed at a constant temperature for a certain period of time, the growth of microorganisms in the food and drink is determined. Microorganisms are first determined by a test for confirming changes in appearance by visual observation or the like. The determination of the presence or absence of microbial growth differs depending on the type of microorganism, but the presence of colonies in food and drink, the color change of food and drink, the presence or absence of turbidity such as white turbidity of food and drink, the generation of gas, and the smell of alcohol It can be determined by the presence / absence of the occurrence of, the presence / absence of a film or the like peculiar to microorganisms. In addition, some microorganisms may produce acid, and in this case, the change in pH of the food or drink may be measured. If the growth of the microorganism is recognized by the above determination, it is determined that the microorganism has grown (+).

  If the growth of microorganisms is not recognized by the above determination, the number of microorganisms in the food or drink is further measured. The microorganism can be measured by various known methods depending on the type of the microorganism. For example, the microorganism may be measured using a plate medium method. In this examination, when the number (density) of the measured microorganism is 10 times or more of the initial number at the time of inoculation, it is determined that the microorganism has proliferated (+).

  If it is not determined that the microorganisms have grown even by visual sensory tests or counting, it is determined that the microorganisms have not grown (-). For the convenience of later data processing, different symbols are assigned depending on whether the microorganism is grown or not. Since the prediction model construction is performed by statistical analysis, it is preferable to assign a numerical value. For example, “1” is assigned when microorganisms are grown, and “2” is assigned when microorganisms are not grown. .

  In the method of the present invention, it is not necessary to measure the growth rate of microorganisms. In the present invention, data relating to the growth of microorganisms is obtained as alternative qualitative data with or without the growth of microorganisms. That is, the growth of microorganisms is qualitatively determined.

  Through such steps, qualitative data of microorganisms is assigned to the analysis value of each food and drink. Using these data, a prediction model is constructed by multifactor analysis of three or more factors using a discriminant analysis technique.

  Discriminant analysis is a method of obtaining sample data extracted from two or more populations and examining which population the sample data belongs to when there is unknown sample data belonging to which population. In the invention, multivariate data (x1, x2, X3,...) Consisting of component analysis values of food and drink are used as explanatory variables, and the presence or absence of the growth of microorganisms is set as an objective variable (reference variable). That is, it can be determined whether the microorganism grows or does not grow.

  Such discriminant analysis techniques are well known, and the most typical discriminant analysis includes discrimination by a linear discriminant function. This can be done using commercially available statistical analysis software.

By discriminant analysis, a discriminant (discriminant function) Z = a ₁ · x ₁ + a ₂ · x 2 +... + _An · xn + a ₀ can be obtained as a boundary line from which the objective variable can be discriminated. The discriminant can be used as a prediction model (prediction equation). Since this equation is a boundary line between two areas to be distinguished, Z = 0 on this boundary line. If an analysis value that is not on the boundary line is substituted into the discriminant, a value of 0> Z or 0 <Z is obtained. When the analysis value of the food or drink to be examined is substituted, the area to which it belongs can be predicted depending on whether Z takes a positive value or a negative value. Note that the larger the absolute value of Z, the higher the possibility of belonging to the area. The discriminant can be evaluated by a discriminant probability. In the prediction model of the present invention, the discriminant predictive value is 80% or more, preferably 85% or more. If the discriminant predictive value is low, remove components that do not contribute to discrimination, if the components used for discrimination interact, do not use either component for discriminant analysis, remove abnormal values, etc. It is possible to increase the discriminatory probability by performing.

  Next, acquiring component analysis data of foods and beverages for which the growth of microorganisms is unknown and predicting the growth of microorganisms, and applying the analysis data to the prediction model, that is, substituting it into the variables of the discriminant The prediction result can be obtained from the discrimination score obtained by the above. The prediction result is “1” when the growth of the microorganism is predicted, and “2” when the growth is not predicted.

  The food and drink used for the prediction can be actually inoculated with microorganisms to determine whether or not the microorganisms can grow, and the prediction accuracy can be obtained. It is very important in the present invention to examine the prediction accuracy. The minimum required prediction accuracy varies depending on the purpose of use, but in the method of the present invention, the prediction accuracy is preferably 80% or more.

  Hereinafter, taking as an example the case where the food or drink is a soy sauce-containing seasoning liquid, the prediction of the growth of microorganisms of the present invention will be described in more detail.

  First, the seasoning solution is aseptically dispensed into a sterilized colorless and transparent film-like bag container, PT pouch (Sakami Medical Instrument Co.), and a certain amount of microorganisms is inoculated there. Thereafter, the opening is sealed with a sealer, and the presence or absence of growth of microorganisms is confirmed for a certain temperature and for a certain period.

  FIG. 1 is a schematic diagram of a test (hereinafter referred to as a viability test) in which microorganisms are inoculated experimentally in a seasoning liquid, stored at a constant temperature for a fixed time, and checked for the presence or absence of microbial growth.

  The method for determining the presence or absence of growth is as follows: (1) When there is a change in appearance such as production of film, cloudiness, gas generation, etc., if there is growth, and if they are not confirmed, (2) Measure and judge. When the number of bacteria is measured, if the number exceeds 1O times the number of the first bacteria, it is regarded as proliferating, and if it does not exceed 10 times, it is regarded as no growth.

  A number of tests as described above were conducted, and the analysis values (pH, salt concentration, Brix, water activity, alcohol concentration, etc.) of the seasoning liquid at that time and the viability of microorganisms corresponding thereto (Yes = 1, No =) Extract 2) into a list, and analyze the relationship between these data by discriminant analysis. In discriminant analysis, discrimination by a linear discriminant function is used. The resulting discriminant (discriminant function) is a straight line that separates the presence or absence of growth, and when the analytical value of a sample with unknown growth is input to the above discriminant, the value (discriminant score) obtained at that time grows. Whether it is viable or not is determined depending on whether it is classified as an area with or without growth. When this function is input to a personal computer and an analysis value is input, a system for automatically predicting the growth of microorganisms from the calculation of the discrimination score can be constructed, and a simple operation can be realized.

  The present invention includes a system for predicting the growth of microorganisms by the method for predicting the growth of microorganisms of the present invention.

The system for predicting microbial growth in foods and beverages of the present invention,
(a) means for inputting component analysis values of foods and drinks whose microbial growth is unknown;
(b) storage means for storing the constructed microbial growth prediction model;
(c) Data processing for predicting the presence or absence of microbial growth by applying the component analysis value input using the input means of (a) to the microbial growth prediction model stored in the storage means of (b) Means, and
(d) an output means for outputting the presence or absence of the predicted microbial selectivity,
It is a system including.

  The means for inputting the analysis value in (a) includes a keyboard or an external storage device storing the component analysis value. The storage means (b) includes a hard disk or the like. The data processing means receives the prediction model from the storage means, processes the input component analysis value, sends the processing result to the output means, and the processing result is displayed by the output means. The data processing means includes a central processing unit (CPU) that performs arithmetic processing on data. The output means includes a monitor and a printer for displaying the result.

  The system for predicting microbial growth in foods and drinks according to the present invention can be constructed using a commercially available personal computer or the like.

  Hereinafter, the present invention will be described in more detail based on examples.

Example 1 Discrimination of the viability of yeast in various seasonings The outline of the test method is shown in FIG. About 75 kinds of seasoning liquids shown in Table 1, pH, salt concentration, Brix, and alcohol concentration were measured in advance as analytical values before being subjected to the test. The characteristics of the analysis values are shown in Table 2. The test was performed by aseptically dispensing the seasoning liquid into a PT pouch and inoculating the film-forming yeast Zygosaccharomyces rouxii. The yeast is a bacterium that has a high salt tolerance and grows well even in a seasoning solution having a high salt concentration. After inoculation with the film-forming yeast, it was stored at 30 ° C., and after one month, the presence or absence of growth of the film-forming yeast was observed, and the number of bacteria in the liquid was measured. The test results were summarized as shown in Table 1 with 1 being grown and 2 being not grown.

  Discriminant analysis (linear discriminant) of the results obtained as described above for each analysis value (pH, salt concentration, Brix, alcohol concentration) of the seasoning liquid and the viability result of the yeast (growable, not viable) in that liquid The discriminant Z represented by the following formula 1 was obtained. In the analysis, commercially available statistical analysis software (SPSS Base12.O; SPS Corporation) was used.

Discriminant Formula 1
Z = -O.186 × pH + O.281 × NaCl + O.092 × Brix + 0.916 × A1c-9.465

  About evaluation of the obtained discriminant, it evaluated by the discriminant middle rate. An example of calculating the discriminant probability is shown in FIG. When the discriminant probability of the obtained discriminant was calculated, it showed a high value of 88% (Table 3).

  In order to verify the prediction accuracy of the obtained discriminant for an unknown sample, verification was performed using a sample different from the sample used for the discriminant.

  Table 4 shows the analysis values of the samples used, the prediction results based on the discriminant, and the actual test results.

  As a result, the prediction accuracy was as high as 84.6%, and it was confirmed that the prediction was highly reliable.

It is a figure which shows the outline of the inoculation test method of a harmful microorganism. It is a figure which shows the schematic diagram of a discriminant analysis, and the example of calculation of discriminant intermediate rate.

Claims (8)

  A method for predicting the growth of microorganisms in foods and drinks, wherein the component analysis values for foods and drinks are obtained as quantitative data, and the presence or absence of the growth of microorganisms is either with or without proliferation of microorganisms Qualitative data, analyze both of the acquired data by discriminant analysis, build a prediction model for predicting the growth of microorganisms from the analysis value of food and drink, and analyze the ingredients of food and drink based on the prediction model A method for predicting the microbial viability of food and drink from the value.   The method for predicting microbial growth of a food or drink according to claim 1, wherein the food or drink is a soy sauce-containing liquid food or drink. The method for predicting the microbial growth of a food or drink according to claim 1 or 2, wherein qualitative data on the presence or absence of microbial growth is obtained by the following steps:
(i) Inoculate the food or drink with microorganisms, and after a certain temperature and period, determine the presence or absence of growth of microorganisms from changes in appearance, and if growth of microorganisms is observed, determine that microorganisms have grown,
(ii) Inoculate the food and drink with microorganisms, and after a certain temperature and period, determine the presence or absence of growth of microorganisms from the change in appearance, and if the growth of microorganisms is not observed, further measure the number of bacteria, If it exceeds 10 times the initial number of bacteria, it is determined that the microorganism has proliferated,
(iii) If it is not determined in the steps (i) and (ii) that the microorganism has grown, it is determined that the microorganism has not grown.   The component analysis value for the food or drink is three or more values selected from the group consisting of pH value, salt concentration, Brix, water activity value, alcohol concentration, total nitrogen content and acetic acid concentration in the food and drink. The method of estimating the growth property of the microorganisms of the food-drinks of any one of 1-3.   The method for predicting the growth of microorganisms in food and drink according to claim 4, wherein the component analysis values of the food and drink are pH value, salt concentration, Brix and alcohol concentration in the food and drink.   The microorganism used for construction of the prediction model is one selected from the group consisting of yeast, lactic acid bacteria, mold, spore bacteria, Staphylococcus aureus, and Escherichia coli. A method for predicting the growth of microorganisms. A method for predicting the growth of microorganisms in soy sauce-containing liquid foods and drinks, and for the soy sauce-containing liquid foods and drinks, the component analysis values for the pH value, salt concentration, Brix, and alcohol concentration in the foods and drinks are used as quantitative data Qualitative data about the presence or absence of microbial growth
(i) Inoculate the food or drink with microorganisms, and after a certain temperature and period, determine the presence or absence of growth of microorganisms from changes in appearance, and if growth of microorganisms is observed, determine that microorganisms have grown,
(ii) Inoculate the food and drink with microorganisms, and after a certain temperature and period, determine the presence or absence of growth of microorganisms from the change in appearance, and if the growth of microorganisms is not observed, further measure the number of bacteria, If it exceeds 10 times the initial number of bacteria, it is determined that the microorganism has proliferated,
(iii) If it is not determined in the steps (i) and (ii) that the microorganism has grown, it is determined that the microorganism has not grown.
And analyzing both of the acquired data by discriminant analysis, constructing a prediction model for predicting the growth of microorganisms from the component analysis value of the soy sauce-containing liquid food and drink, and based on the prediction model A method for predicting microbial growth of a soy sauce-containing liquid food or drink from component analysis values in the soy sauce-containing liquid food or drink. A system for predicting microbial growth in foods and drinks for performing the method for predicting the growth of microorganisms in foods and drinks according to any one of claims 1 to 6,
(a) means for inputting component analysis values of foods and drinks whose microbial growth is unknown;
(b) storage means for storing the constructed microbial growth prediction model;
(c) Data processing for predicting the presence or absence of microbial growth by applying the component analysis value input using the input means of (a) to the microbial growth prediction model stored in the storage means of (b) Means, and
(d) an output means for outputting the presence or absence of the predicted microbial selectivity,
For predicting the growth of microorganisms in foods and drinks.

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