WO2013129655A2 - Method for predicting proliferation of microbial count - Google Patents

Method for predicting proliferation of microbial count Download PDF

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WO2013129655A2
WO2013129655A2 PCT/JP2013/055700 JP2013055700W WO2013129655A2 WO 2013129655 A2 WO2013129655 A2 WO 2013129655A2 JP 2013055700 W JP2013055700 W JP 2013055700W WO 2013129655 A2 WO2013129655 A2 WO 2013129655A2
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bacteria
growth
damaged
healthy
model
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French (fr)
Japanese (ja)
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WO2013129655A3 (en
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悟 大島
智典 土方
尚美 高橋
裕樹 松原
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株式会社明治
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Priority to CN201380010554.3A priority Critical patent/CN104136624B/en
Priority to JP2013546483A priority patent/JP5456219B1/en
Publication of WO2013129655A2 publication Critical patent/WO2013129655A2/en
Publication of WO2013129655A3 publication Critical patent/WO2013129655A3/en
Priority to HK15102240.7A priority patent/HK1201882A1/en

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • G01N33/54366—Apparatus specially adapted for solid-phase testing

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  • the present invention relates to a method for predicting the growth of the number of microorganisms.
  • the degree of spoilage and deterioration due to contaminating microorganisms in foods and beverages progresses in proportion to the increase in the number of contaminating microorganisms, but in reality, the nature and type of products, microbial species, storage temperature, elapsed time after contamination, etc. Affected by.
  • Patent Document 1 Japanese Patent Application Laid-Open No. 2007-312738 is a method for predicting the growth of microorganisms in foods and drinks, obtaining component analysis values for the foods and drinks as quantitative data, Predictive model that acquires the presence or absence of growth as qualitative data with or without the growth of microorganisms, analyzes both acquired data by discriminant analysis, and predicts the growth of microorganisms from food and beverage analysis values And predicting the microbial viability of the food or drink from the component analysis value of the food or drink based on the prediction model. It has been shown that this method can predict the growth of harmful microorganisms in foods and drinks such as soy sauce-containing foods and drinks with high accuracy.
  • non-patent literature examples of developing computer programs that predict growth of microorganisms under various temperature conditions using growth behavior data and mathematical models of Escherichia coli, Salmonella, Staphylococcus aureus, etc. are also known (non-patent literature). 1).
  • An object of the present invention is to provide a method for predicting the growth of the number of microorganisms in foods and drinks in consideration of the growth rate of damaged bacteria damaged by external stress. More specifically, an object of the present invention is to provide a method for predicting the growth of the number of microorganisms at an arbitrary temperature and an arbitrary elapsed time, taking into account the extended period of the induction period due to external stress of contaminating microorganisms in food and drink. To do.
  • the present inventors examined a method for predicting microbial growth in consideration of growth behavior data of damaged bacteria in a conventional mathematical model (a model for predicting microbial growth) obtained from healthy bacteria.
  • a conventional mathematical model a model for predicting microbial growth
  • the probability density function of growth of healthy and damaged bacteria and the extended time data of the induction period of damaged bacteria against healthy bacteria are obtained, and the parameter values of the new logistic model are calculated from the growth data of microorganisms in the target sample
  • To create a temperature fluctuation type growth prediction model obtain a probabilistic microbial growth prediction model from the probability density function and the temperature fluctuation type growth prediction model, and select any initial number of existing bacteria, any temperature, any Elapsed time is input to the probabilistic microbial growth prediction model, the probabilistic prediction method is used to calculate the number of each proliferating cell corresponding to the initial number of existing bacteria, and the total number is estimated to predict the proliferating cell count.
  • a method was found and the present invention was completed.
  • the invention according to claim 1 of the present invention is a method for predicting the growth of the number of microorganisms in food and drink, and predicting the growth in consideration of the extended time of the induction period of the damaged bacteria with respect to healthy bacteria. Is the method.
  • the invention according to claim 2 is characterized by subtracting the probability density function of growth of damaged bacteria from the probability density function of growth of healthy bacteria to obtain an extension time of the induction period of the damaged bacteria for the healthy bacteria.
  • the invention according to claim 3 is the method according to claim 2, characterized in that the probability density function of the growth of healthy and damaged bacteria is obtained using a digital microscope type bacteria detection apparatus.
  • the invention according to claim 4 is the method according to any one of claims 1 to 3, comprising the following steps.
  • Step of obtaining probability density function of growth of healthy bacteria and damaged bacteria and extended time data of induction period of damaged bacteria against healthy bacteria (2) Obtaining growth data of microorganisms in the target sample (3) Probability density function in (1) and growth in (2) to calculate a parameter value by fitting a new logistic model to the model and apply the parameter value to the new logistic model to obtain a temperature fluctuation type growth prediction model
  • Step of obtaining a probabilistic microbial growth prediction model from the prediction model (4) An arbitrary initial number of existing bacteria, an arbitrary temperature, and an arbitrary elapsed time are input to the model of (3), and the probabilistic prediction method The process of obtaining the number of proliferating bacteria in food and drink by calculating the number of each proliferating bacteria corresponding to the initial number of existing bacteria and adding them up
  • the invention according to claim 5 is the method according to claim 4, wherein the probabilistic prediction method is a Monte Carlo simulation method.
  • the degree of damage that microorganisms are unevenly affected by external stress can be quantitatively evaluated at the individual microorganism level, and at the same time, the growth behavior in food can be predicted by a temperature fluctuation type growth model. It was possible to objectively predict the number of bacteria that reflected the nature of contaminating bacteria and their behavior in food.
  • FIG. 1 is a diagram showing changes in colony detection of heat-damaged bacteria and healthy bacteria in Example 1.
  • FIG. 2 is a diagram showing changes in colony detection of heat-damaged bacteria and healthy bacteria in Example 2.
  • the method of the present invention includes the steps (1) to (4).
  • Step (1) In the method of the present invention, first, probability density functions of growth of healthy bacteria and damaged bacteria, and extended time data of the induction period of damaged bacteria against healthy bacteria are acquired. Damaged bacteria are generally defined as bacteria that are exposed to various external stresses such as heat and chemicals and are in a semi-lethal damage state. When damaged bacteria contaminate a product, it may behave differently from healthy bacteria. In order to evaluate the quality / safety and storage stability of a product, it is necessary to consider the behavior of the damaged bacteria. In the present invention, attention is paid to the fact that the induction period of damaged bacteria is delayed from the induction period of healthy bacteria. And the extended time data of the induction
  • a damaged bacterium artificially applied with external stress is used for the prediction model.
  • External stress includes, for example, all thermal treatments as physical stresses, especially heat treatment, high-pressure treatment, ultrasonic treatment, etc. in the sterilization process in production lines, and chemical stresses include chlorinated drugs, alcohol, bacteriocin, etc. Contains antibacterial agents.
  • drying of the production environment, hunger stress, and the like are included, and the factors that damage microorganisms are not particularly limited.
  • the external stress to be applied may be one stress or a plurality of stresses.
  • the induction phase means the time from the start of culture until individual cells start to proliferate, and when the growth curve is obtained, it substantially corresponds to the time from the start of culture to the logarithmic growth phase.
  • the extended time data of the induction period of the damaged bacteria relative to the healthy bacteria can be obtained by determining the difference between the induction period (induction time) of the healthy bacteria and the induction period (induction time) of the damaged bacteria. Acquisition of the extended time data of the induction period of the damaged bacterium with respect to the healthy bacterium can be performed using, for example, a device such as a digital microscope type bacteria detection apparatus.
  • the digital microscopic bacteria detection apparatus is an apparatus that can automatically measure colony formation in plate culture over time at a microscopic level, and is sold, for example, as Biomatic DMCS (trade name; registered trademark) by Microbio Inc. This device was developed for the purpose of rapid measurement of bacterial tests, and the bacteria detection technology related to this device is a known technology. However, there is no known example using a digital microscopic bacteria detection apparatus for probabilistic prediction of the time required for damage recovery of damaged bacteria, that is, the extension time of the induction period due to the stress damage of microorganisms.
  • the extension of the induction period is taken as the time required for the damage caused to the cells to recover and to start growing.
  • the induction period varies with individual cells even in healthy bacteria.
  • the degree of damage may differ among individual cells, and the induction period of damaged bacteria varies among individual cells. Therefore, in the present invention, the probability density function (colony formation rate curve) of the growth of both bacteria is determined for each of the healthy bacteria and the damaged bacteria in order to take into account variations in the induction period in individual cells.
  • the induction period extension time is obtained from the obtained two probability density functions, that is, the induction period extension time from the difference between the distribution of damaged bacteria and the distribution of healthy bacteria. Since the digital microscope type bacteria detection apparatus measures individual colony formation, the induction period focusing on individual cells can be easily obtained.
  • a probability density function of growth of healthy and damaged bacteria for example, culture is performed at a constant temperature, colony formation is automatically measured over time at a constant time interval, and the obtained data And a histogram of the numbers of healthy and damaged colonies newly detected per unit time is generated and analyzed by appropriate software operating on a computer.
  • the histograms created for each of the healthy and damaged bacteria indicate the distribution of the induction period of each cell, and the probability density function of proliferation is obtained from the distribution. Examples of software for performing such analysis include @RISK (Palisade), Crystal Ball (Oracle), and the like.
  • the extension period (X) of the induction period is obtained as a time difference (Y) between the distribution (function) in the damaged bacteria and the distribution (function) in the healthy bacteria.
  • Step (2) the growth data of the microorganism in the sample to be predicted is acquired, the new logistic model is fitted to the data, the parameter value is calculated, and the parameter value is converted into the new logistic model.
  • a fitting temperature fluctuation type growth prediction model is obtained.
  • the new logistic model is a microbial growth model described in Fujikawa et al. (Food Hygiene Journal, 44, 155-160, 2003). Specifically, using a sample to be predicted, healthy bacteria are cultured at any temperature (preferably three or more types of temperatures are set), and numerical analysis is performed based on the measured values of the obtained microbial growth curve. To solve the differential equation of the new logistic model using the fourth-order Runge-Kutta method.
  • Nmax maximum number of bacteria
  • Nmin minimum number of bacteria
  • the slope of the logarithm is the slope of the approximate curve of the logarithmic growth phase.
  • the values of m and n which are adjustment parameters of the new logistic model, are obtained by a solver function of (registered trademark, Microsoft Corporation).
  • the rate constant k is predicted by a square root model from the growth rate at each culture temperature obtained by fitting growth data.
  • the target sample is not particularly limited as long as it is expected to grow microorganisms.
  • the prediction target sample is, for example, soft drinks such as tea and juice, dairy products such as milk, yogurt, and ice cream, and can be meat such as beef, pork, and chicken.
  • Step (3) a probabilistic microbial growth prediction model is obtained from the probability density function of growth of healthy and damaged bacteria and the temperature fluctuation type growth prediction model.
  • the probability density function of healthy bacteria, the probability density function of damaged bacteria, and the temperature-variable growth prediction model are mathematically connected, and the number of initial existing bacteria is arbitrarily set, and the induction period extension time (damage For example, by using a stochastic prediction method such as Monte Carlo simulation, and applying the value to a temperature-variable growth prediction model. The number of proliferating bacteria after elapse of time can be predicted.
  • the temperature-variable growth prediction model in the step (2) is a model that performs simulation based on a growth curve using healthy bacteria, and includes the initial number of bacteria in the prediction target sample, an arbitrary temperature, and an arbitrary elapsed time ( By inputting T), the model predicts the number of bacteria that grow in the sample to be predicted when stored at that temperature.
  • bacteria in actual foods are damaged bacteria that have already been damaged by heat treatment or the like at the start of storage, and as described above, the time until growth begins is delayed unlike healthy bacteria.
  • Step (3) is a step that takes into account the delay until the start of growth in damaged bacteria. In this process, a temperature-variable growth prediction model is acquired after correcting the extension time of the induction period for healthy bacteria. To do.
  • a time obtained by subtracting the extension period (X) of the induction period from any set time (T) ( t) is the elapsed time in healthy bacteria, and the number of bacteria growing in the elapsed time is simulated to obtain a probabilistic microbial growth prediction model.
  • the probabilistic microbial growth prediction model obtained here is expressed by a probabilistic model of how many damaged bacteria grow in an arbitrary set time (T). That is, it is probabilistically predicted whether one damaged bacterium will grow to 1000, for example, 1100, or 900.
  • Step of (4) the total number of contaminating microorganisms in the food and drink at an arbitrary temperature and an arbitrary elapsed time (T) (prediction) is obtained by adding together the predicted growth numbers obtained for each initial number of existing bacteria. Number of bacteria).
  • the probabilistic microbial growth prediction model obtained in the step (3) is a probabilistic model for obtaining the number of proliferating bacteria after an elapsed time (T) when one bacterium is input.
  • T elapsed time
  • the initial number of bacteria is one
  • the estimated number of bacteria for the elapsed time (T) is obtained as the probability theory. Therefore, if there are a plurality of initial bacterial counts, simulation is performed a plurality of times according to the probabilistic microbial growth prediction model, and the bacterial counts obtained in each simulation are added together to obtain the predicted bacterial count.
  • the method of the present invention is characterized by predicting the growth of the number of microorganisms in food and drink taking into account the extended period of the induction period of damaged bacteria against healthy bacteria, and extending the induction period of damaged bacteria against healthy bacteria.
  • the prediction method is not limited to the above as long as it is a method that predicts the growth of the number of microorganisms in the food and drink taking time into consideration.
  • a test bacterium Enterobacter cloacae B-855 was activated with trypticase soy broth (TSB), and then washed with sterile phosphate buffered saline to prepare a bacterial solution. The same saline solution was put into a test tube, and it was confirmed that the temperature was raised to 52 ° C. in a hot water bath. Then, a bacterial solution was added and held for 20 minutes to give heat treatment (damaged bacteria). Healthy bacteria (unheated bacteria) and heat-treated bacteria were smeared on a standard agar medium, cultured at 30 ° C. in DMCS S-12 (MicroBio), and colony formation was automatically measured over time at 30-minute intervals.
  • TTB trypticase soy broth
  • the obtained data was analyzed, and a histogram of colonies of healthy bacteria and heat-treated bacteria (heat-damaged bacteria) newly detected per unit time was created (FIG. 1).
  • the probability density function of this histogram is one of the functions of @RISK (Palisade), and is a RiskGeneral function that expresses a continuous distribution using existing sample values (each point has a value x and a weight of its probability) N (x, p) pairs indicated by p are obtained as a general density function of a probability distribution defined between a minimum value and a maximum value of x). Then, the probability density function of healthy bacteria was subtracted from the probability density function of damaged bacteria to obtain the extension time of the induction period.
  • the rate constant k was determined by a square root model (Formula 2) representing the relationship between the growth of microorganisms and the temperature.
  • the probability density function of healthy bacteria obtained above, the probability density function of damaged bacteria, and the obtained temperature fluctuation type growth prediction model were mathematically connected to create a probabilistic microorganism growth prediction model. Specifically, the difference in colony detection time between damaged and healthy bacteria is determined for the initial number of bacteria that are set arbitrarily, and the value is applied to the temperature-variable growth prediction model. It was made possible to determine the number of proliferating bacteria after the lapse.
  • Bacteria count prediction As an example of bacterial count prediction, the initial bacterial count in milk was assumed to be 10 cfu / L, the storage temperature was set to 10 ° C., and a Monte Carlo simulation with 10 trials was performed. As a result, the number of Enterobacter cloacae B-855 after 10 days of storage was predicted to be 740000 cfu / mL.
  • a test bacterium Pseudomonas fluorescence B-125 was activated with trypticase soy broth (TSB) and then washed with sterile phosphate buffered saline to prepare a bacterial solution. The same saline solution was put into a test tube, and it was confirmed that the temperature was raised to 46 ° C. in a hot water bath. Healthy bacteria (unheated bacteria) and heat-treated bacteria were smeared on a standard agar medium, cultured at 10 ° C. in DMCS S-12 (Microbio), and colony formation was automatically measured over time at intervals of 3 hours.
  • the obtained data was analyzed, and a histogram of colonies of healthy bacteria and heat-treated bacteria (heat-damaged bacteria) newly detected per unit time was created (FIG. 2).
  • the probability density function of this histogram is one of the functions of @RISK (Palisade), and is a RiskGeneral function that expresses a continuous distribution using existing sample values (each point has a value x and a weight of its probability) N (x, p) pairs indicated by p are obtained as a general density function of a probability distribution defined between the minimum value and the maximum value of x). Then, the probability density function of healthy bacteria was subtracted from the probability density function of damaged bacteria to obtain the extension time of the induction period.
  • Bacteria count prediction As an example of bacterial count prediction, the initial bacterial count in milk was assumed to be 8 cfu / L, the storage temperature was set to 10 ° C., and a Monte Carlo simulation with 8 trials was performed. As a result, the number of Pseudomonas fluorescence B-125 bacteria after 6 days of storage was estimated to be 12000 cfu / mL.
  • the present invention is not limited to these bacteria, and may cause health damage such as Escherichia coli, Salmonella, Staphylococcus aureus, and food quality degradation.
  • the present invention can be applied not only to bacteria having a characteristic, but also to bacteria used in probiotics such as lactic acid bacteria and bifidobacteria.
  • the degree of damage that microorganisms are unevenly affected by external stress can be quantitatively evaluated at the individual microorganism level, and at the same time, the growth behavior in food can be predicted by a temperature fluctuation type growth model. It was possible to objectively predict the number of bacteria that reflected the nature of contaminating bacteria and their behavior in food. If a probabilistic microorganism prediction model is created based on the target microbial species and damage conditions, the bacteria in foods under any temperature and time conditions can be used without carrying out a storage test inoculated with microorganisms to which external stress has been applied. Since the number can be instantaneously predicted by simulation, it can be used for product quality design, setting of expiry date, verification, production process quality design, verification, employee education tools, etc.

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Abstract

[Problem] To provide a method for predicting proliferation of microbial count in food. [Solution] This problem is addressed by a method provided with the steps described below. (1) A step in which a probability density function for proliferation of normal healthy bacteria and damaged bacteria, and extension time data for the induction phase of damaged bacteria with respect to normal health bacteria, are obtained. (2) A step in which proliferation data for microbes in a prediction sample is obtained, a new logistic model is fitted to that data to calculate parameter values, and those parameter values are applied to a new logistic model to obtain a temperature-variable proliferation prediction model. (3) A step in which a stochastic microbe proliferation prediction model is obtained from the probability density function of (1) and the proliferation prediction model of (2). (4) A step in which arbitrarily-defined initially present microbial colony counts, arbitrarily-defined temperatures, and arbitrarily-defined elapsed times are input into the model from step (3), the microbial colony counts corresponding to the initially present microbial colony counts are determined using a stochastic prediction procedure, and these are added together to obtain the number of microbial colonies in foods and beverages.

Description

微生物数の増殖を予測する方法Methods for predicting microbial count growth
　本発明は、微生物数の増殖を予測する方法に関する。 The present invention relates to a method for predicting the growth of the number of microorganisms.
　飲食品中の汚染微生物による腐敗や変敗の程度は、汚染微生物数の増殖に比例して進行するが、実際には、製品の性質や種類、微生物種、保存温度、汚染後の経過時間等の影響を受ける。飲食品中の汚染微生物数の増殖数を予め知るためには、対象となる製品に微生物を接種した保存試験の実施が必要となる。保存試験は、一定の微生物種、及び一定の温度で実施されるため、対象とする微生物毎、あるいは、対象とする温度毎に保存試験を実施しなければならず、膨大な時間、労力、費用を必要とする。また、実際の製品流通時のように複雑に保存温度が変化する場合は、製品の温度履歴からは、対象となる汚染微生物の増殖を予測することは困難である。このため、保存試験のように膨大な時間、労力、費用を必要とせずに、実際の流通保存条件で、飲食品中の微生物数の増殖を予測する方法が求められており、様々な検討がなされている。 The degree of spoilage and deterioration due to contaminating microorganisms in foods and beverages progresses in proportion to the increase in the number of contaminating microorganisms, but in reality, the nature and type of products, microbial species, storage temperature, elapsed time after contamination, etc. Affected by. In order to know in advance the number of contaminated microorganisms in food and drink, it is necessary to conduct a storage test in which the target product is inoculated with microorganisms. Since the storage test is performed at a certain microbial species and at a certain temperature, the storage test must be performed for each target microorganism or for each target temperature, which requires enormous time, labor, and cost. Need. In addition, when the storage temperature changes in a complicated manner as in actual product distribution, it is difficult to predict the growth of the target contaminating microorganisms from the product temperature history. Therefore, there is a need for a method for predicting the growth of the number of microorganisms in foods and drinks under actual distribution and storage conditions without requiring enormous time, labor, and expense as in the storage test. Has been made.
　例えば、特開２００７－３１２７３８号公報（特許文献１）には、飲食品中での微生物の生育性を予測する方法であって、飲食品について成分分析値を定量データとして取得し、さらに微生物の生育性の有無を微生物の増殖有または微生物の増殖無のいずれかの定性データとして取得し、取得した両データを判別分析法により解析し、飲食品分析値から微生物の生育性を予測する予測モデルを構築し、該予測モデルに基づいて飲食品の成分分析値から飲食品の微生物生育性を予測する方法が開示されている。本方法により、醤油含有飲食品等の飲食品における危害微生物の生育性を高い精度で予測することができることが示されている。他に、大腸菌、サルモネラ菌、黄色ブドウ球菌などの増殖挙動データと数学的モデルとを用い、各種の温度条件下での微生物増殖を予測するコンピュータープログラムを開発した例も知られている（非特許文献１）。 For example, Japanese Patent Application Laid-Open No. 2007-312738 (Patent Document 1) is a method for predicting the growth of microorganisms in foods and drinks, obtaining component analysis values for the foods and drinks as quantitative data, Predictive model that acquires the presence or absence of growth as qualitative data with or without the growth of microorganisms, analyzes both acquired data by discriminant analysis, and predicts the growth of microorganisms from food and beverage analysis values And predicting the microbial viability of the food or drink from the component analysis value of the food or drink based on the prediction model. It has been shown that this method can predict the growth of harmful microorganisms in foods and drinks such as soy sauce-containing foods and drinks with high accuracy. In addition, examples of developing computer programs that predict growth of microorganisms under various temperature conditions using growth behavior data and mathematical models of Escherichia coli, Salmonella, Staphylococcus aureus, etc. are also known (non-patent literature). 1).
特開２００７－３１２７３８号公報JP 2007-312738 A
　これまでの予測方法は微生物が生育する環境条件に着目されており、賦活培養した無傷の健常菌から求められた増殖モデルを用いて、種々の因子を考慮して微生物数の増殖が予測されているのが実情である。しかしながら、飲食品中の微生物は、熱処理や飲食品のｐＨ等により外的ストレスを受けており、健常菌と同様の増殖曲線を描くとは限らず、このことが予測された結果との間に誤差を生じる原因と考えられる。 Previous prediction methods have focused on the environmental conditions under which microorganisms grow, and growth of microorganisms has been predicted in consideration of various factors using a growth model determined from intact healthy bacteria that have been activated and cultured. The fact is. However, microorganisms in foods and drinks are subjected to external stress due to heat treatment and pH of foods and drinks, and do not always draw the same growth curve as healthy bacteria. This is considered to be the cause of error.
　本発明は、外的ストレスにより損傷を受けた損傷菌の増殖速度を考慮した、飲食品中の微生物数の増殖を予測する方法を提供することを課題とする。より具体的には、飲食品中の汚染微生物の外的ストレスによる誘導期の延長時間を踏まえながら、任意の温度及び任意の経過時間における微生物数の増殖を予測する方法を提供することを課題とする。 An object of the present invention is to provide a method for predicting the growth of the number of microorganisms in foods and drinks in consideration of the growth rate of damaged bacteria damaged by external stress. More specifically, an object of the present invention is to provide a method for predicting the growth of the number of microorganisms at an arbitrary temperature and an arbitrary elapsed time, taking into account the extended period of the induction period due to external stress of contaminating microorganisms in food and drink. To do.
　上記課題を解決するために、本発明者らは、健常菌から得られる従来の数学的モデル（微生物の増殖予測モデル）に損傷菌の増殖挙動データを考慮した微生物増殖の予測方法を検討した。その結果、健常菌及び損傷菌の増殖の確率密度関数、及び健常菌に対する損傷菌の誘導期の延長時間データを取得し、予測対象試料での微生物の増殖データから新ロジスティックモデルのパラメーター値を算出して温度変動型増殖予測モデルを作成し、当該確率密度関数と当該温度変動型増殖予測モデルから、確率論的微生物増殖予測モデルを取得し、任意の初期存在菌数、任意の温度、任意の経過時間を当該確率論的微生物増殖予測モデルに入力し、確率論的予測方法により、初期存在菌数に対応してそれぞれの増殖菌数を求め、それを合算することで増殖菌数を予測する方法を見出し、本発明を完成させた。 In order to solve the above problems, the present inventors examined a method for predicting microbial growth in consideration of growth behavior data of damaged bacteria in a conventional mathematical model (a model for predicting microbial growth) obtained from healthy bacteria. As a result, the probability density function of growth of healthy and damaged bacteria and the extended time data of the induction period of damaged bacteria against healthy bacteria are obtained, and the parameter values of the new logistic model are calculated from the growth data of microorganisms in the target sample To create a temperature fluctuation type growth prediction model, obtain a probabilistic microbial growth prediction model from the probability density function and the temperature fluctuation type growth prediction model, and select any initial number of existing bacteria, any temperature, any Elapsed time is input to the probabilistic microbial growth prediction model, the probabilistic prediction method is used to calculate the number of each proliferating cell corresponding to the initial number of existing bacteria, and the total number is estimated to predict the proliferating cell count. A method was found and the present invention was completed.
　すなわち、本発明の請求項１に記載の発明は、飲食品中の微生物数の増殖を予測する方法であって、健常菌に対する損傷菌の誘導期の延長時間を考慮して、増殖を予測する方法である。 That is, the invention according to claim 1 of the present invention is a method for predicting the growth of the number of microorganisms in food and drink, and predicting the growth in consideration of the extended time of the induction period of the damaged bacteria with respect to healthy bacteria. Is the method.
　請求項２に記載の発明は、健常菌の増殖の確率密度関数から損傷菌の増殖の確率密度関数を減算して、前記健常菌に対する損傷菌の誘導期の延長時間を求めることを特徴とする、請求項１に記載の方法である。 The invention according to claim 2 is characterized by subtracting the probability density function of growth of damaged bacteria from the probability density function of growth of healthy bacteria to obtain an extension time of the induction period of the damaged bacteria for the healthy bacteria. The method according to claim 1.
　請求項３に記載の発明は、健常菌及び損傷菌の増殖の確率密度関数を、ディジタル顕微鏡方式細菌検出装置を用いて取得することを特徴とする、請求項２に記載の方法である。 The invention according to claim 3 is the method according to claim 2, characterized in that the probability density function of the growth of healthy and damaged bacteria is obtained using a digital microscope type bacteria detection apparatus.
　請求項４に記載の発明は、下記の工程を備える、請求項１～３の何れか一項に記載の方法、である。
（１）健常菌及び損傷菌の増殖の確率密度関数、及び健常菌に対する損傷菌の誘導期の延長時間データを取得する工程
（２）予測対象試料での微生物の増殖データを取得し、そのデータに新ロジスティックモデルをフィッティングさせてパラメーター値を算出し、そのパラメーター値を新ロジスティックモデルに当てはめて温度変動型増殖予測モデルを取得する工程
（３）（１）の確率密度関数と（２）の増殖予測モデルから、確率論的微生物増殖予測モデルを取得する工程
（４）任意の初期存在菌数、任意の温度、任意の経過時間を（３）のモデルに入力し、確率論的予測方法により、初期存在菌数に対応したそれぞれの増殖菌数を求め、それを合算することで飲食品中の増殖菌数を得る工程 The invention according to claim 4 is the method according to any one of claims 1 to 3, comprising the following steps.
(1) Step of obtaining probability density function of growth of healthy bacteria and damaged bacteria and extended time data of induction period of damaged bacteria against healthy bacteria (2) Obtaining growth data of microorganisms in the target sample (3) Probability density function in (1) and growth in (2) to calculate a parameter value by fitting a new logistic model to the model and apply the parameter value to the new logistic model to obtain a temperature fluctuation type growth prediction model Step of obtaining a probabilistic microbial growth prediction model from the prediction model (4) An arbitrary initial number of existing bacteria, an arbitrary temperature, and an arbitrary elapsed time are input to the model of (3), and the probabilistic prediction method The process of obtaining the number of proliferating bacteria in food and drink by calculating the number of each proliferating bacteria corresponding to the initial number of existing bacteria and adding them up
　請求項５に記載の発明は、確率論的予測方法が、モンテカルロシミュレーション法である、請求項４に記載の方法、である。 The invention according to claim 5 is the method according to claim 4, wherein the probabilistic prediction method is a Monte Carlo simulation method.
　本発明により、外的ストレスにより微生物が不均一に受ける損傷程度を、微生物個々のレベルで定量的に評価でき、同時に、温度変動型の増殖モデルにより食品中での増殖挙動を予測できるため、実際の汚染菌の性質と食品中での挙動を反映した客観的な菌数予測が可能となった。 According to the present invention, the degree of damage that microorganisms are unevenly affected by external stress can be quantitatively evaluated at the individual microorganism level, and at the same time, the growth behavior in food can be predicted by a temperature fluctuation type growth model. It was possible to objectively predict the number of bacteria that reflected the nature of contaminating bacteria and their behavior in food.
図１は、実施例１における、加熱損傷菌と健常菌のコロニー検出の変化を示した図である。FIG. 1 is a diagram showing changes in colony detection of heat-damaged bacteria and healthy bacteria in Example 1. 図２は、実施例２における、加熱損傷菌と健常菌のコロニー検出の変化を示した図である。FIG. 2 is a diagram showing changes in colony detection of heat-damaged bacteria and healthy bacteria in Example 2.
　本発明の方法は上記（１）から（４）の工程を有する。
　（１）の工程
　本発明の方法においては、まず、健常菌及び損傷菌の増殖の確率密度関数、及び健常菌に対する損傷菌の誘導期の延長時間データを取得する。損傷菌とは、一般に、加熱や薬剤等の様々な外的ストレスにさらされ、半致死的な損傷状態にある菌と定義される。損傷菌が製品に汚染した場合、健常菌とは異なる挙動を示す可能性が考えられ、製品の品質・安全性や保存性の評価を行うには、損傷菌の挙動を考慮する必要がある。本発明では、損傷菌の誘導期が健常菌の誘導期よりも遅れることに着目した。そして、健常菌に対する損傷菌の誘導期の延長時間データを予測モデルに取り込むこととした。 The method of the present invention includes the steps (1) to (4).
Step (1) In the method of the present invention, first, probability density functions of growth of healthy bacteria and damaged bacteria, and extended time data of the induction period of damaged bacteria against healthy bacteria are acquired. Damaged bacteria are generally defined as bacteria that are exposed to various external stresses such as heat and chemicals and are in a semi-lethal damage state. When damaged bacteria contaminate a product, it may behave differently from healthy bacteria. In order to evaluate the quality / safety and storage stability of a product, it is necessary to consider the behavior of the damaged bacteria. In the present invention, attention is paid to the fact that the induction period of damaged bacteria is delayed from the induction period of healthy bacteria. And the extended time data of the induction | guidance | derivation period of the damage microbe with respect to a healthy microbe was taken in into the prediction model.
　実際の環境下では損傷菌は種々の外的ストレスが付与され、個々の損傷菌の損傷状態は異なる。本発明では、予測モデルのために、人為的に外的ストレスを付与した損傷菌が用いられる。外的ストレスは、例えば、物理的ストレスとして、あらゆる熱的処理、特に生産ラインにおける殺菌工程の熱処理、高圧処理、超音波処理等を含み、化学的ストレスとして、塩素系薬剤、アルコール、バクテリオシン等の抗菌剤等を含んでいる。その他に、製造環境の乾燥、飢餓ストレス等も含まれており、微生物に損傷を与える要因は特に限定されないことが特徴である。また、付与する外的ストレスは１つのストレスでもよく、また、複数のストレスでもよい、 In the actual environment, the damaged bacteria are subjected to various external stresses, and the damaged state of each damaged bacteria is different. In the present invention, a damaged bacterium artificially applied with external stress is used for the prediction model. External stress includes, for example, all thermal treatments as physical stresses, especially heat treatment, high-pressure treatment, ultrasonic treatment, etc. in the sterilization process in production lines, and chemical stresses include chlorinated drugs, alcohol, bacteriocin, etc. Contains antibacterial agents. In addition, drying of the production environment, hunger stress, and the like are included, and the factors that damage microorganisms are not particularly limited. Moreover, the external stress to be applied may be one stress or a plurality of stresses.
　本発明において、誘導期とは、培養開始後、個々の細胞が増殖しはじめるまでの時間を意味し、増殖曲線を得た場合において培養を始めてから対数増殖期に至るまでの時間にほぼ相当する。健常菌に対する損傷菌の誘導期の延長時間データは、健常菌の誘導期（誘導時間）と損傷菌の誘導期（誘導時間）の差を求めることで得られる。健常菌に対する損傷菌の誘導期の延長時間データの取得は、例えばディジタル顕微鏡方式細菌検出装置等の機器を用いて行うことができる。 In the present invention, the induction phase means the time from the start of culture until individual cells start to proliferate, and when the growth curve is obtained, it substantially corresponds to the time from the start of culture to the logarithmic growth phase. . The extended time data of the induction period of the damaged bacteria relative to the healthy bacteria can be obtained by determining the difference between the induction period (induction time) of the healthy bacteria and the induction period (induction time) of the damaged bacteria. Acquisition of the extended time data of the induction period of the damaged bacterium with respect to the healthy bacterium can be performed using, for example, a device such as a digital microscope type bacteria detection apparatus.
　ディジタル顕微鏡方式細菌検出装置とは、平板培養でのコロニー形成を顕微鏡レベルで経時的に自動計測できる装置であり、例えばマイクロバイオ社よりＢｉｏｍａｔｉｃ　ＤＭＣＳ（商品名；登録商標）として販売されている。本装置は細菌検査の迅速測定を目的に開発されたもので、本装置に係る細菌検出技術は既知の技術である。しかし、損傷菌の損傷回復に要する時間、すなわち、微生物のストレス損傷による誘導期の延長時間について、その確率論的な予測に、ディジタル顕微鏡方式細菌検出装置を用いた例は知られていない。 The digital microscopic bacteria detection apparatus is an apparatus that can automatically measure colony formation in plate culture over time at a microscopic level, and is sold, for example, as Biomatic DMCS (trade name; registered trademark) by Microbio Inc. This device was developed for the purpose of rapid measurement of bacterial tests, and the bacteria detection technology related to this device is a known technology. However, there is no known example using a digital microscopic bacteria detection apparatus for probabilistic prediction of the time required for damage recovery of damaged bacteria, that is, the extension time of the induction period due to the stress damage of microorganisms.
　誘導期の延長は、菌体に生じた損傷が回復し増殖を開始するのに必要な時間として捉えられる。誘導期は健常菌でも個々の細胞で異なる。また、一定条件の損傷を与えてもその損傷程度が個々の細胞で異なることもあって、損傷菌の誘導期は個々の細胞で異なる。そこで、本発明では、健常菌及び損傷菌のそれぞれについて、個々の細胞における誘導期のバラツキを考慮すべく、両菌の増殖の確率密度関数（コロニー形成速度曲線）を求める。そして、得られた２つの確率密度関数から誘導期の延長時間、すなわち、損傷菌の分布と健常菌の分布の差とから誘導期の延長時間を求める。ディジタル顕微鏡方式細菌検出装置は個々のコロニー形成を計測していることから、個々の細胞に着目した誘導期を容易に求めることができる。 The extension of the induction period is taken as the time required for the damage caused to the cells to recover and to start growing. The induction period varies with individual cells even in healthy bacteria. In addition, even if damage is caused under certain conditions, the degree of damage may differ among individual cells, and the induction period of damaged bacteria varies among individual cells. Therefore, in the present invention, the probability density function (colony formation rate curve) of the growth of both bacteria is determined for each of the healthy bacteria and the damaged bacteria in order to take into account variations in the induction period in individual cells. Then, the induction period extension time is obtained from the obtained two probability density functions, that is, the induction period extension time from the difference between the distribution of damaged bacteria and the distribution of healthy bacteria. Since the digital microscope type bacteria detection apparatus measures individual colony formation, the induction period focusing on individual cells can be easily obtained.
　本発明の方法において、健常菌及び損傷菌の増殖の確率密度関数を取得するには、例えば一定温度で培養を行い、一定の時間間隔で経時的にコロニー形成を自動計測し、得られたデータを解析し、単位時間あたりに新たに検出された健常菌及び損傷菌のコロニー数のヒストグラムを作成し、これをコンピューター上で動作する適切なソフトウェアで解析すれば得られる。健常菌及び損傷菌のそれぞれで作成されたヒストグラムが、個々の細胞の誘導期の分布を示し、当該分布から増殖の確率密度関数が取得される。このような解析を行うソフトウェアとしては、例えば＠ＲＩＳＫ（Ｐａｌｉｓａｄｅ社）や、Ｃｒｙｓｔａｌｌ　Ｂａｌｌ（Ｏｒａｃｌｅ社）等が挙げられる。誘導期の延長時間（Ｘ）は、損傷菌における分布（関数）と健常菌における分布（関数）の時間差（Ｙ）として求められる。 In the method of the present invention, in order to obtain a probability density function of growth of healthy and damaged bacteria, for example, culture is performed at a constant temperature, colony formation is automatically measured over time at a constant time interval, and the obtained data And a histogram of the numbers of healthy and damaged colonies newly detected per unit time is generated and analyzed by appropriate software operating on a computer. The histograms created for each of the healthy and damaged bacteria indicate the distribution of the induction period of each cell, and the probability density function of proliferation is obtained from the distribution. Examples of software for performing such analysis include @RISK (Palisade), Crystal Ball (Oracle), and the like. The extension period (X) of the induction period is obtained as a time difference (Y) between the distribution (function) in the damaged bacteria and the distribution (function) in the healthy bacteria.
　（２）の工程
　次に、本発明の方法では、予測対象試料での微生物の増殖データを取得し、そのデータに新ロジスティックモデルをフィッティングさせパラメーター値を算出し、そのパラメーター値を新ロジスティックモデルに当てはめ温度変動型増殖予測モデルを取得する。新ロジスティックモデルとは、藤川らの文献（食品衛生学雑誌、４４、１５５－１６０、２００３）に記載されている微生物増殖モデルである。具体的には、予測対象試料を用いて、任意の温度（３種類以上の温度を設定することが望ましい）で健常菌を培養し、得られた微生物増殖曲線の実測値を基に、数値解析により新ロジスティックモデルの微分方程式を４次のルンゲ・クッタ法で解く。Ｎｍａｘ（最大菌数）は定常期の菌数の値、Ｎｍｉｎ（最小菌数）は初発菌数の値、対数の傾きは対数増殖期の近似曲線の傾きを入力し、例えばコンピューターソフトウェアであるＥｘｃｅｌ（登録商標、マイクロソフト社）のソルバー機能等により、新ロジスティックモデルの調整パラメーターであるｍとｎの値を求める。速度定数ｋは増殖データのフィッティングより求めた各培養温度での増殖速度から、平方根モデルにより予測する。以上の方法により、予測対象試料における温度変動型増殖予測モデルが取得できる。 Step (2) Next, in the method of the present invention, the growth data of the microorganism in the sample to be predicted is acquired, the new logistic model is fitted to the data, the parameter value is calculated, and the parameter value is converted into the new logistic model. A fitting temperature fluctuation type growth prediction model is obtained. The new logistic model is a microbial growth model described in Fujikawa et al. (Food Hygiene Journal, 44, 155-160, 2003). Specifically, using a sample to be predicted, healthy bacteria are cultured at any temperature (preferably three or more types of temperatures are set), and numerical analysis is performed based on the measured values of the obtained microbial growth curve. To solve the differential equation of the new logistic model using the fourth-order Runge-Kutta method. Nmax (maximum number of bacteria) is the value of the number of bacteria in the stationary phase, Nmin (minimum number of bacteria) is the value of the initial number of bacteria, and the slope of the logarithm is the slope of the approximate curve of the logarithmic growth phase. The values of m and n, which are adjustment parameters of the new logistic model, are obtained by a solver function of (registered trademark, Microsoft Corporation). The rate constant k is predicted by a square root model from the growth rate at each culture temperature obtained by fitting growth data. By the above method, a temperature fluctuation type growth prediction model in the prediction target sample can be acquired.
　予測対象試料は微生物の増殖が予想されるものであれば特に制限されない。予測対象試料は、例えば、お茶やジュースなどの清涼飲料水であり、牛乳やヨーグルト、アイスクリームなどの乳製品であり、牛肉や豚肉、鶏肉などの食肉類でもあり得る。 The target sample is not particularly limited as long as it is expected to grow microorganisms. The prediction target sample is, for example, soft drinks such as tea and juice, dairy products such as milk, yogurt, and ice cream, and can be meat such as beef, pork, and chicken.
　（３）の工程
　本発明の方法では、次に、健常菌及び損傷菌の増殖の確率密度関数と、温度変動型増殖予測モデルから、確率論的微生物増殖予測モデルを取得する。具体的には、健常菌の確率密度関数、及び損傷菌の確率密度関数、温度変動型増殖予測モデルを数学的に連結させ、任意に設定する初期存在菌数から、誘導期の延長時間（損傷菌と健常菌のコロニー検出時間の差）を考慮して、例えばモンテカルロシミュレーション等の確率論的予測方法により予測して、その値を温度変動型増殖予測モデルに適用して、任意の温度における任意の時間経過後の増殖菌数を予測できるようにする。（２）の工程における温度変動型増殖予測モデルは、健常菌を用いた増殖曲線を元にしてシミュレーションを行うモデルであり、予測対象試料における初期存在菌数、任意の温度、任意の経過時間（Ｔ）を入力することで、当該温度で保存した場合に予測対象試料中に増殖する菌数を予測するモデルである。ところが、実際の食品中の細菌は、保存開始時点において既に加熱処理等により損傷を受けた損傷菌であって、上記のように健常菌とは異なり増殖を始めるまでの時間が遅れる。（３）の工程は、損傷菌におけるこの増殖開始までの遅れを考慮する工程であり、当該工程において、健常菌に対して誘導期の延長時間を補正した上で温度変動型増殖予測モデルを取得する。具体的には、損傷菌は健常菌よりも誘導期の延長時間だけ遅れて増殖することになるので、設定された任意の時間（Ｔ）から誘導期の延長時間（Ｘ）を差し引いた時間（ｔ）を、健常菌における経過時間として、当該経過時間に増殖する菌数をシミュレートして、確率論的微生物増殖予測モデルを取得する。ここで得られる確率論的微生物増殖予測モデルは、１個の損傷菌が、設定された任意の時間（Ｔ）に何個に増殖するかという確率論的モデルで表される。つまり、１個の損傷菌が例えば１０００個に増殖するのか、１１００個に増殖するのか、９００個に増殖するのかが確率論的に予測される。 Step (3) In the method of the present invention, next, a probabilistic microbial growth prediction model is obtained from the probability density function of growth of healthy and damaged bacteria and the temperature fluctuation type growth prediction model. Specifically, the probability density function of healthy bacteria, the probability density function of damaged bacteria, and the temperature-variable growth prediction model are mathematically connected, and the number of initial existing bacteria is arbitrarily set, and the induction period extension time (damage For example, by using a stochastic prediction method such as Monte Carlo simulation, and applying the value to a temperature-variable growth prediction model. The number of proliferating bacteria after elapse of time can be predicted. The temperature-variable growth prediction model in the step (2) is a model that performs simulation based on a growth curve using healthy bacteria, and includes the initial number of bacteria in the prediction target sample, an arbitrary temperature, and an arbitrary elapsed time ( By inputting T), the model predicts the number of bacteria that grow in the sample to be predicted when stored at that temperature. However, bacteria in actual foods are damaged bacteria that have already been damaged by heat treatment or the like at the start of storage, and as described above, the time until growth begins is delayed unlike healthy bacteria. Step (3) is a step that takes into account the delay until the start of growth in damaged bacteria. In this process, a temperature-variable growth prediction model is acquired after correcting the extension time of the induction period for healthy bacteria. To do. More specifically, since the damaged bacteria grow later than the healthy bacteria by an extension period of the induction period, a time obtained by subtracting the extension period (X) of the induction period from any set time (T) ( t) is the elapsed time in healthy bacteria, and the number of bacteria growing in the elapsed time is simulated to obtain a probabilistic microbial growth prediction model. The probabilistic microbial growth prediction model obtained here is expressed by a probabilistic model of how many damaged bacteria grow in an arbitrary set time (T). That is, it is probabilistically predicted whether one damaged bacterium will grow to 1000, for example, 1100, or 900.
　（４）の工程
　次に、初期存在菌数個々について得られた増殖予測菌数を合算して、任意の温度及び任意の経過時間（Ｔ）における、飲食品中の汚染微生物数の総数（予測菌数）とする。前記（３）の工程で得られた確率論的微生物増殖予測モデルは、１個の細菌が入力された経過時間（Ｔ）後に増殖菌数を得る確率論的モデルである。このモデルでは、初期存在菌数が１個の場合に、確率論として経過時間（Ｔ）の増殖推定菌数が得られる。従って、初期存在菌数が複数の場合であれば、当該確率論的微生物増殖予測モデルに従って複数回シミュレートして、各シミュレーションで得られた菌数を合算して、予測菌数とする。 Step of (4) Next, the total number of contaminating microorganisms in the food and drink at an arbitrary temperature and an arbitrary elapsed time (T) (prediction) is obtained by adding together the predicted growth numbers obtained for each initial number of existing bacteria. Number of bacteria). The probabilistic microbial growth prediction model obtained in the step (3) is a probabilistic model for obtaining the number of proliferating bacteria after an elapsed time (T) when one bacterium is input. In this model, when the initial number of bacteria is one, the estimated number of bacteria for the elapsed time (T) is obtained as the probability theory. Therefore, if there are a plurality of initial bacterial counts, simulation is performed a plurality of times according to the probabilistic microbial growth prediction model, and the bacterial counts obtained in each simulation are added together to obtain the predicted bacterial count.
　この結果、外的ストレスを受けた細菌が予測対象試料中で増殖した結果、どの程度の菌数に増えるかを予測できる。つまり、加熱処理を受けた食品、例えば製品である乳飲料が保存された場合、食品中に残った細菌がどの程度の菌数に増殖するか、実態により近い状態で推定できる。また、本発明の方法は、健常菌に対する損傷菌の誘導期の延長時間を考慮して飲食品中の微生物数の増殖を予測することに特徴があり、健常菌に対する損傷菌の誘導期の延長時間を考慮して飲食品中の微生物数の増殖を予測する方法であれば上記の予測方法に限られない。 As a result, it is possible to predict how many bacteria will increase as a result of the growth of bacteria that have undergone external stress in the sample to be predicted. That is, when food that has been subjected to heat treatment, for example, a milk drink that is a product, is stored, it can be estimated in a state closer to the actual condition how many bacteria remain in the food. In addition, the method of the present invention is characterized by predicting the growth of the number of microorganisms in food and drink taking into account the extended period of the induction period of damaged bacteria against healthy bacteria, and extending the induction period of damaged bacteria against healthy bacteria The prediction method is not limited to the above as long as it is a method that predicts the growth of the number of microorganisms in the food and drink taking time into consideration.
　以下、実施例に基づいて、本発明をより具体的に説明する。なお、この実施例は、本発明を限定するものではない。 Hereinafter, the present invention will be described more specifically based on examples. In addition, this Example does not limit this invention.
　〔健常菌と損傷菌の確率密度関数の定義〕
　供試菌であるＥｎｔｅｒｏｂａｃｔｅｒ　ｃｌｏａｃａｅ　Ｂ－８５５をトリプチケースソイブロス（ＴＳＢ）で賦活した後、滅菌リン酸緩衝生理食塩水で洗浄して菌液を調製した。試験管に同食塩水を入れ、湯浴中で５２℃に昇温したのを確認して、菌液を添加し２０分間保持して、加熱処理を与えた（損傷菌）。健常菌（未加熱菌）と加熱処理菌を標準寒天培地に塗抹して、ＤＭＣＳ　Ｓ－１２（マイクロバイオ社）で３０℃培養を行い、３０分間隔で経時的にコロニー形成を自動計測した。 [Definition of probability density function of healthy and damaged bacteria]
A test bacterium Enterobacter cloacae B-855 was activated with trypticase soy broth (TSB), and then washed with sterile phosphate buffered saline to prepare a bacterial solution. The same saline solution was put into a test tube, and it was confirmed that the temperature was raised to 52 ° C. in a hot water bath. Then, a bacterial solution was added and held for 20 minutes to give heat treatment (damaged bacteria). Healthy bacteria (unheated bacteria) and heat-treated bacteria were smeared on a standard agar medium, cultured at 30 ° C. in DMCS S-12 (MicroBio), and colony formation was automatically measured over time at 30-minute intervals.
　得られたデータを解析し、単位時間あたりに新たに検出された健常菌及び加熱処理菌（加熱損傷菌）のコロニーのヒストグラムを作成した（図１）。このヒストグラムの確率密度関数を、＠ＲＩＳＫ（Ｐａｌｉｓａｄｅ社）の関数のうちの１つであり、既存のサンプル値を使って連続分布を表現するＲｉｓｋＧｅｎｅｒａｌ関数（各ポイントが値ｘとその確率の重みを示すｐで示された、n個の（ｘ，ｐ）ペアがｘの最小値から最大値の間に定義された確率分布の一般密度関数）として得た。そして、損傷菌の確率密度関数から健常菌の確率密度関数を減算し、誘導期の延長時間を求めた。 The obtained data was analyzed, and a histogram of colonies of healthy bacteria and heat-treated bacteria (heat-damaged bacteria) newly detected per unit time was created (FIG. 1). The probability density function of this histogram is one of the functions of @RISK (Palisade), and is a RiskGeneral function that expresses a continuous distribution using existing sample values (each point has a value x and a weight of its probability) N (x, p) pairs indicated by p are obtained as a general density function of a probability distribution defined between a minimum value and a maximum value of x). Then, the probability density function of healthy bacteria was subtracted from the probability density function of damaged bacteria to obtain the extension time of the induction period.
　〔温度変動型増殖予測モデルの作成〕
　上記と同様の方法で供試菌であるＥｎｔｅｒｏｂａｃｔｅｒ　ｃｌｏａｃａｅ　Ｂ－８５５の菌液を調製し、牛乳に系内１００ｃｆｕ／ｍＬになるよう接種した後、１０℃、２０℃、３０℃で培養した。経時的に試料を抜き取り、標準寒天培地培養法にて生菌数を測定した。次いで、得られた増殖データ（培養時間と菌数との関係データ）に新ロジスティックモデル（数式１）をフィッティングさせ、新ロジスティックモデルの調整パラメーターであるｍとｎの値を求めた。 [Creation of temperature fluctuation type growth prediction model]
A bacterial solution of Enterobacter cloacae B-855, which is a test bacterium, was prepared in the same manner as described above, and inoculated into milk at 100 cfu / mL in the system, and then cultured at 10 ° C., 20 ° C., and 30 ° C. Samples were withdrawn over time, and the number of viable bacteria was measured by a standard agar culture method. Next, a new logistic model (Formula 1) was fitted to the obtained growth data (relation data between the culture time and the number of bacteria), and values of m and n, which are adjustment parameters of the new logistic model, were obtained.
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
　このとき、速度定数ｋは、微生物の増殖と温度の関係を表す平方根モデル（数式２）により求めた。 At this time, the rate constant k was determined by a square root model (Formula 2) representing the relationship between the growth of microorganisms and the temperature.
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000002
　以上の方法で得られたパラメーター値を新ロジスティックモデルに当てはめ、Ｅｎｔｅｒｏｂａｃｔｅｒ　ｃｌｏａｃａｅ　Ｂ－８５５の牛乳での温度変動型増殖予測モデルを作成した。
　〔確率論的微生物増殖予測モデルの作成〕 The parameter values obtained by the above method were applied to a new logistic model, and a temperature-variable growth prediction model of Enterobacter cloacae B-855 in milk was prepared.
[Preparation of probabilistic microbial growth prediction model]
　上記で得られた健常菌の確率密度関数、及び損傷菌の確率密度関数、及び得られた温度変動型増殖予測モデルを数学的に連結させ、確率論的微生物増殖予測モデルを作成した。具体的には、任意に設定する初期存在菌数に対し、損傷菌と健常菌のコロニー検出時間の差を求め、その値を温度変動型増殖予測モデルに適用し、任意の温度における任意の時間経過後の増殖菌数を求めることができるようにした。 The probability density function of healthy bacteria obtained above, the probability density function of damaged bacteria, and the obtained temperature fluctuation type growth prediction model were mathematically connected to create a probabilistic microorganism growth prediction model. Specifically, the difference in colony detection time between damaged and healthy bacteria is determined for the initial number of bacteria that are set arbitrarily, and the value is applied to the temperature-variable growth prediction model. It was made possible to determine the number of proliferating bacteria after the lapse.
　〔菌数予測〕
　菌数予測の一例として、牛乳中に最初に存在する菌数を１０ｃｆｕ／Ｌと想定し、保存温度を１０℃に設定し、試行回数１０回のモンテカルロシミュレーションを実施した。結果、保存日数１０日後のＥｎｔｅｒｏｂａｃｔｅｒ　ｃｌｏａｃａｅ　Ｂ－８５５の菌数は７４００００ｃｆｕ／ｍＬと予測された。 [Bacteria count prediction]
As an example of bacterial count prediction, the initial bacterial count in milk was assumed to be 10 cfu / L, the storage temperature was set to 10 ° C., and a Monte Carlo simulation with 10 trials was performed. As a result, the number of Enterobacter cloacae B-855 after 10 days of storage was predicted to be 740000 cfu / mL.
　以上より、５２℃２０分間のストレス損傷を受けたＥｎｔｅｒｏｂａｃｔｅｒ　ｃｌｏａｃａｅ　Ｂ－８５５が牛乳に１０ｃｆｕ／Ｌ汚染した場合の、１０℃１０日後の菌数を予測することができた。 From the above, it was possible to predict the number of bacteria after 10 days at 10 ° C. when Enterobacter cloacae B-855, which received stress damage at 52 ° C. for 20 minutes, was contaminated with 10 cfu / L of milk.
　〔健常菌と損傷菌の確率密度関数の定義〕
　供試菌であるＰｓｅｕｄｏｍｏｎａｓ ｆｌｕｏｒｅｓｃｅｎｃｅ Ｂ－１２５をトリプチケースソイブロス（ＴＳＢ）で賦活した後、滅菌リン酸緩衝生理食塩水で洗浄して菌液を調製した。試験管に同食塩水を入れ、湯浴中で４６℃に昇温したのを確認して、菌液を添加し５分間保持した。健常菌（未加熱菌）と加熱処理菌を標準寒天培地に塗抹して、ＤＭＣＳ　Ｓ－１２（マイクロバイオ社）で１０℃培養を行い、３時間間隔で経時的にコロニー形成を自動計測した。 [Definition of probability density function of healthy and damaged bacteria]
A test bacterium Pseudomonas fluorescence B-125 was activated with trypticase soy broth (TSB) and then washed with sterile phosphate buffered saline to prepare a bacterial solution. The same saline solution was put into a test tube, and it was confirmed that the temperature was raised to 46 ° C. in a hot water bath. Healthy bacteria (unheated bacteria) and heat-treated bacteria were smeared on a standard agar medium, cultured at 10 ° C. in DMCS S-12 (Microbio), and colony formation was automatically measured over time at intervals of 3 hours.
　得られたデータを解析し、単位時間あたりに新たに検出された健常菌及び加熱処理菌（加熱損傷菌）のコロニーのヒストグラムを作成した（図２）。このヒストグラムの確率密度関数を、＠ＲＩＳＫ（Ｐａｌｉｓａｄｅ社）の関数のうちの１つであり、既存のサンプル値を使って連続分布を表現するＲｉｓｋＧｅｎｅｒａｌ関数（各ポイントが値xとその確率の重みを示すpで示された、n個の（ｘ，ｐ）ペアがｘの最小値から最大値の間に定義された確率分布の一般密度関数）として得た。そして、損傷菌の確率密度関数から健常菌の確率密度関数を減算し、誘導期の延長時間を求めた。 The obtained data was analyzed, and a histogram of colonies of healthy bacteria and heat-treated bacteria (heat-damaged bacteria) newly detected per unit time was created (FIG. 2). The probability density function of this histogram is one of the functions of @RISK (Palisade), and is a RiskGeneral function that expresses a continuous distribution using existing sample values (each point has a value x and a weight of its probability) N (x, p) pairs indicated by p are obtained as a general density function of a probability distribution defined between the minimum value and the maximum value of x). Then, the probability density function of healthy bacteria was subtracted from the probability density function of damaged bacteria to obtain the extension time of the induction period.
　〔温度変動型増殖予測モデルの作成〕
　上記と同様の方法で供試菌であるＰｓｅｕｄｏｍｏｎａｓ ｆｌｕｏｒｅｓｃｅｎｃｅ Ｂ－１２５の菌液（健常菌）を調製し、牛乳に系内１００ｃｆｕ／ｍＬになるよう接種した後、５℃、１０℃、１５℃、２０℃、２５℃、３０℃で培養した。経時的に試料を抜き取り、標準寒天培地培養法にて生菌数を測定した。次いで、得られた増殖データ（培養時間と菌数との関係データ）に新ロジスティックモデル（数式１）をフィッティングさせ、新ロジスティックモデルの調整パラメーターであるｍとｎの値を求めた。求めたパラメーター値を新ロジスティックモデルに当てはめ、Ｐｓｅｕｄｏｍｏｎａｓ ｆｌｕｏｒｅｓｃｅｎｃｅ Ｂ－１２５の牛乳での温度変動型増殖予測モデルを作成した。 [Creation of temperature fluctuation type growth prediction model]
After preparing a bacterial solution of Pseudomonas fluorescence B-125 (healthy bacteria) in the same manner as described above and inoculating milk into the system at 100 cfu / mL, 5 ° C, 10 ° C, 15 ° C, The cells were cultured at 20 ° C, 25 ° C and 30 ° C. Samples were withdrawn over time, and the number of viable bacteria was measured by a standard agar culture method. Next, a new logistic model (Formula 1) was fitted to the obtained growth data (relation data between the culture time and the number of bacteria), and values of m and n, which are adjustment parameters of the new logistic model, were obtained. The obtained parameter values were applied to a new logistic model, and a temperature fluctuation type growth prediction model in milk of Pseudomonas fluorescence B-125 was created.
　〔確率論的微生物増殖予測モデルの作成〕
　前記で得られた健常菌の確率密度関数、及び損傷菌の確率密度関数、それに得られた温度変動型増殖予測モデルを、実施例１と同様の手順で数学的に連結させ、確率論的微生物増殖予測モデルを作成した。 [Preparation of probabilistic microbial growth prediction model]
The probability density function of healthy bacteria obtained above, the probability density function of damaged bacteria, and the temperature fluctuation-type growth prediction model obtained thereby are mathematically linked in the same procedure as in Example 1, and a stochastic microorganism. A growth prediction model was created.
　〔菌数予測〕
　菌数予測の一例として、牛乳中に最初に存在する菌数を８ｃｆｕ／Ｌと想定し、保存温度を１０℃に設定し、試行回数８回のモンテカルロシミュレーションを実施した。結果、保存日数６日後のＰｓｅｕｄｏｍｏｎａｓ ｆｌｕｏｒｅｓｃｅｎｃｅ Ｂ－１２５の菌数は１２０００ｃｆｕ／ｍＬと予測された。 [Bacteria count prediction]
As an example of bacterial count prediction, the initial bacterial count in milk was assumed to be 8 cfu / L, the storage temperature was set to 10 ° C., and a Monte Carlo simulation with 8 trials was performed. As a result, the number of Pseudomonas fluorescence B-125 bacteria after 6 days of storage was estimated to be 12000 cfu / mL.
　以上のように４６℃５分間のストレス損傷を受けたＰｓｅｕｄｏｍｏｎａｓ ｆｌｕｏｒｅｓｃｅｎｃｅ Ｂ－１２５が牛乳に８ｃｆｕ／Ｌ汚染した場合の、１０℃６日後の菌数を予測することができた。 As described above, when Pseudomonas fluorescence B-125, which was damaged by stress at 46 ° C. for 5 minutes, was contaminated with 8 cfu / L of milk, the number of bacteria after 10 days at 10 ° C. could be predicted.
　上記の各実施例では、エンテロバクターおよびシュードモナスを用いた予測を行ったが、本発明では、これらの細菌に限定されず、大腸菌やサルモネラ菌、黄色ブドウ球菌といった健康被害や食品の品質劣化を及ぼす可能性のある細菌のみならず、乳酸菌やビフィズス菌などプロバイオティクスに用いられるような細菌にも適用され得る。 In each of the above examples, prediction was performed using Enterobacter and Pseudomonas, but the present invention is not limited to these bacteria, and may cause health damage such as Escherichia coli, Salmonella, Staphylococcus aureus, and food quality degradation. The present invention can be applied not only to bacteria having a characteristic, but also to bacteria used in probiotics such as lactic acid bacteria and bifidobacteria.
　本発明により、外的ストレスにより微生物が不均一に受ける損傷程度を、微生物個々のレベルで定量的に評価でき、同時に、温度変動型の増殖モデルにより食品中での増殖挙動を予測できるため、実際の汚染菌の性質と食品中での挙動を反映した客観的な菌数予測が可能となった。対象となる微生物種、損傷条件で確率論的微生物予測モデルを作成すれば、外的ストレスが付与された微生物を接種した保存試験を実施することなく、任意の温度時間条件での食品中の菌数をシミュレーションにより瞬時に予測できるため、製品の品質設計、賞味期限の設定、検証、生産工程の品質設計、検証、従業員の教育ツール等に利用できる。  According to the present invention, the degree of damage that microorganisms are unevenly affected by external stress can be quantitatively evaluated at the individual microorganism level, and at the same time, the growth behavior in food can be predicted by a temperature fluctuation type growth model. It was possible to objectively predict the number of bacteria that reflected the nature of contaminating bacteria and their behavior in food. If a probabilistic microorganism prediction model is created based on the target microbial species and damage conditions, the bacteria in foods under any temperature and time conditions can be used without carrying out a storage test inoculated with microorganisms to which external stress has been applied. Since the number can be instantaneously predicted by simulation, it can be used for product quality design, setting of expiry date, verification, production process quality design, verification, employee education tools, etc.

Claims (5)

  1. 　飲食品中の微生物数の増殖を予測する方法であって、
    　健常菌に対する損傷菌の誘導期の延長時間を考慮して、増殖を予測する方法。     A method for predicting the growth of the number of microorganisms in a food or drink,
    A method for predicting growth in consideration of the extended time of the induction period of damaged bacteria relative to healthy bacteria.
  2. 　健常菌の増殖の確率密度関数から損傷菌の増殖の確率密度関数を減算して、前記健常菌に対する損傷菌の誘導期の延長時間を求めることを特徴とする、請求項１に記載の方法。 2. The method according to claim 1, wherein a probable density function of the growth of damaged bacteria is subtracted from a probability density function of the growth of healthy bacteria to determine an extension period of the induction period of the damaged bacteria with respect to the healthy bacteria.
  3. 　健常菌及び損傷菌の増殖の確率密度関数を、ディジタル顕微鏡方式細菌検出装置を用いて取得することを特徴とする、請求項２に記載の方法。 The method according to claim 2, wherein a probability density function of growth of healthy bacteria and damaged bacteria is obtained using a digital microscope type bacteria detection apparatus.
  4. 　下記の工程を備える、請求項１～３の何れか一項に記載の方法。
    （１）健常菌及び損傷菌の増殖の確率密度関数、及び健常菌に対する損傷菌の誘導期の延長時間データを取得する工程
    （２）予測対象試料での微生物の増殖データを取得し、そのデータに新ロジスティックモデルをフィッティングさせパラメーター値を算出し、そのパラメーター値を新ロジスティックモデルに当てはめ温度変動型増殖予測モデルを取得する工程
    （３）（１）の確率密度関数と（２）の増殖予測モデルから、確率論的微生物増殖予測モデルを取得する工程
    （４）任意の初期存在菌数、任意の温度、任意の経過時間を（３）のモデルに入力し、確率論的予測方法により、初期存在菌数に対応したそれぞれの増殖菌数を求め、それを合算することで飲食品中の増殖菌数を得る工程     The method according to any one of claims 1 to 3, comprising the following steps.
    (1) Step of obtaining probability density function of growth of healthy bacteria and damaged bacteria and extended time data of induction period of damaged bacteria against healthy bacteria (2) Obtaining growth data of microorganisms in the target sample (3) Probability density function in (1) and (2) growth prediction model in which a new logistic model is fitted to calculate the parameter value and the parameter value is applied to the new logistic model to obtain a temperature fluctuation type growth prediction model (4) Enter an arbitrary initial number of bacteria, arbitrary temperature, and arbitrary elapsed time into the model in (3), and use the probabilistic prediction method to obtain the initial existence. The process of obtaining the number of proliferating bacteria in food and drink by calculating the number of each proliferating bacteria corresponding to the number of bacteria and adding them together
  5. 　確率論的予測方法が、モンテカルロシミュレーション法である、請求項４に記載の方法。 The method according to claim 4, wherein the probabilistic prediction method is a Monte Carlo simulation method.
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