Microorganism analysis device and operating method thereof

Abstract

The present disclosure relates to a microorganism analysis device and an operating method thereof. The microorganism analysis device according to one embodiment of the present disclosure comprises: a communication unit for receiving first data for a plurality of samples obtained in a target environment in which a product is produced through a plurality of steps; and a control unit for generating second data on the type and amount of microorganisms included in the plurality of samples on the basis of the first data, wherein the control unit can determine contaminating microorganisms that contaminate the product on the basis of the second data, calculate a degree of contamination of the product by the contaminating microorganisms with respect to each of the plurality of steps, and determine at least one of a main contamination step and a step in which the contaminating microorganisms are introduced into the product, among the plurality of steps, on the basis of the degree of contamination of the product.

Description

Microbiological analysis device and its operating method

[0001] The present disclosure relates to a microbial analysis device and an operating method thereof, and more particularly, to a microbial analysis device for determining the cause of pollution in an environment and an operating method thereof.

[0002] Recently, as environmental pollution, virus contamination, and other issues have become more prominent, interest in the level of pollution in the surrounding environment has increased.

[0003] Environmental DNA (eDNA) is defined as the DNA of microorganisms collected from various samples and can be used as a biomarker to diagnose the level of environmental contamination. Because each microbial species has a unique DNA sequence, various techniques have been developed to collect samples from environments of interest and measure the types of microorganisms present within those samples.

[0004] In particular, databases that distinguish various microbial species are being developed and shared. Therefore, by extracting microbial DNA sequence information from samples of interest and comparing it with information in the database, it is possible to define the microorganisms within the environment.

[0005] Meanwhile, the sequence reads of the corresponding microbial samples, read and transmitted through NGS (Next Generation Sequencing) equipment, are generated in a standardized format. That is, each sample data is read in a batch-wise, read-by-read state, and each sequence read data is recorded as a FASTQ file. Here, a read can refer to a small DNA fragment produced by the NGS equipment.

[0006] Bioinformatics technologies that process FASTQ files from each sample to generate desired DNA sequence information, as well as microbial classification techniques based on these data, have been proposed in various ways. For example, U.S. Patent No. 11,335,436B2 describes a classification technique capable of simultaneously comparing multiple microorganisms across multiple targets.

[0007] However, in the past, there was a problem in that it was difficult to accurately determine the major contaminated areas in the environment caused by microorganisms, the degree of contamination in each area, the inflow route of microorganisms, etc., by simply detecting the species, quantity, and ratio of microorganisms in the environment from a sample of the environment of interest.

[0008] The present disclosure aims to solve the above-mentioned and other problems.

[0009] Another purpose is to provide a microbial analysis device and its operating method that can accurately select microorganisms that are the cause of contamination among microorganisms in the environment.

[0010] Another purpose is to provide a microbial analysis device and an operating method thereof that can calculate the degree to which a product and / or environment is contaminated by microorganisms that are the cause of contamination.

[0011] Another purpose is to provide a microbial analysis device and its operating method that can determine major contaminated areas in an environment.

[0012] Another purpose is to provide a microbial analysis device and its operating method that can determine the inflow path of microorganisms that are the cause of contamination.

[0013] In order to achieve the above object, according to one embodiment of the present disclosure, a microbial analysis device includes: a communication unit that receives first data on a plurality of samples obtained from a target environment in which a product is produced through a plurality of steps; and a control unit that generates second data on the types and amounts of microorganisms included in the plurality of samples based on the first data, wherein the control unit determines, based on the second data, a contaminating microorganism that contaminates the product, calculates, for each of the plurality of steps, the degree to which the product is contaminated by the contaminating microorganism, and determines, based on the degree to which the product is contaminated, at least one of a main contamination step and a step in which the contaminating microorganism is introduced into the product among the plurality of steps.

[0014] In order to achieve the above object, an operating method of a microbial analysis device according to one embodiment of the present disclosure may include an operation of determining a contaminating microorganism that contaminates a product based on data on the types and amounts of microorganisms included in a plurality of samples obtained from a target environment in which a product is produced through a plurality of steps; an operation of calculating, for each of the plurality of steps, the degree to which the product is contaminated by the contaminating microorganism; and an operation of determining, based on the degree to which the product is contaminated, at least one of a main contamination step and a step in which the contaminating microorganism is introduced into the product among the plurality of steps.

[0015] The effects of the biostimulation device according to the present disclosure are described as follows.

[0016] According to at least one embodiment of the present disclosure, it is possible to accurately select a microorganism that is a cause of contamination among microorganisms in an environment.

[0017] According to at least one embodiment of the present disclosure, the degree to which a product and / or environment is contaminated by a microorganism as a cause of contamination can be calculated.

[0018] According to at least one embodiment of the present disclosure, major contaminated areas within an environment can be determined.

[0019] According to at least one embodiment of the present disclosure, it is possible to determine the inflow path of microorganisms that are the cause of contamination.

[0020] Further scope of the applicability of the present disclosure will become apparent from the detailed description below. However, since various modifications and variations within the spirit and scope of the present disclosure will become apparent to those skilled in the art, it should be understood that the detailed description and specific examples, such as preferred embodiments of the present disclosure, are given by way of example only.

[0021] FIG. 1 is a diagram illustrating a microbial analysis system according to one embodiment of the present disclosure.

[0022] Figure 2 is a block diagram referenced in the description of the configuration of the microbial analysis device of Figure 1.

[0023] FIG. 3A and FIG. 3B are drawings for reference in the description of a DNA sequence of a microorganism according to one embodiment of the present disclosure.

[0024] FIG. 4 is a flowchart of an operation method of a microbial analysis device according to one embodiment of the present disclosure.

[0025] FIGS. 5 to 8 are detailed flowcharts of an operation method of a microbial analysis device according to various embodiments of the present disclosure.

[0026] FIG. 9 is a drawing for reference in explaining the operation of a microbial analysis device according to one embodiment of the present disclosure.

[0027] Hereinafter, the present disclosure will be described in detail with reference to the drawings. In the drawings, portions irrelevant to the description are omitted to clearly and concisely describe the present disclosure, and the same reference numerals are used for identical or extremely similar portions throughout the specification.

[0028] The suffixes "module" and "part" used in the following description are given solely for the convenience of writing this specification and do not impart any particularly significant meaning or role to the components themselves. Therefore, the terms "module" and "part" may be used interchangeably.

[0029] In this application, it should be understood that terms such as “include” or “have” are intended to specify the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0030] Additionally, while terms such as "first" and "second" may be used in this specification to describe various elements, these elements are not limited by these terms. These terms are used only to distinguish one element from another.

[0031] As used herein, the term "Next Generation Sequencing" (NGS) refers to next-generation sequencing, a high-speed method for analyzing genome sequences. NGS can be applied to genome-wide and whole-body analyses to achieve a variety of purposes, including clinical research. Most NGS analyses can simultaneously analyze multiple target samples.

[0032] The term "target sample," as used herein, refers to a sample targeted for NGS analysis. In this case, the target sample may be a biological sample collected from microorganisms obtained from a specific environment, i.e., the target environment. Furthermore, the target sample may be a biological sample collected from microorganisms obtained from raw materials, semi-finished products, or finished products produced in the target environment. The NGS data for each sample may be provided as a pair of files.

[0033] FIG. 1 is a diagram illustrating a microbial analysis system according to one embodiment of the present disclosure.

[0034] Referring to FIG. 1, a microbial analysis system can analyze a microbial sample (310) collected from a target environment (300) through an NGS device (400). When a FASTQ file is generated through NGS analysis, the microbial analysis system can process the FASTQ file to improve accuracy in matching with reference data on microorganisms.

[0035] The microbial sample (310) may include a sample related to a product produced in the target environment (300) (hereinafter, product sample) and / or a sample related to the surrounding environment in which the product is produced (hereinafter, environmental sample). For example, the microbial sample (310) may include a product sample corresponding to a raw material of a product produced in the target environment (300), a product sample corresponding to a semi-finished product for each production step of the product, a product sample corresponding to a finished product, etc. For example, the microbial sample (310) may include an environmental sample corresponding to the air of the surrounding environment for each production step of the product, an environmental sample corresponding to a biofilm detected in washing water used to wash equipment used for each production step of the product, etc.

[0036] Meanwhile, the microbial analysis system may use a sample (hereinafter, referred to as an experimental sample) (not shown) related to the processing of a microbial sample (310). For example, the microbial analysis system may use an experimental sample corresponding to a sterilized cotton swab used in the processing of a microbial sample (310), an experimental sample corresponding to the environment of the laboratory in which the microbial sample (310) is processed, etc.

[0037] In this way, the microbial sample (310) and / or experimental sample can be analyzed through the NGS device (400) and processed through the microbial analysis device (100).

[0038] A FASTQ file is a standardized file format that can be a file that reads the base sequence, i.e., the sequence, for each read of each sample, forward or reverse. A FASTQ file can include a pair of FASTQ files corresponding to the forward and reverse versions.

[0039] The microbial analysis system may include an NGS device (400) that obtains a microbial sample (310) collected from a target environment (300) and performs NGS analysis, a microbial analysis device (100) that receives a FASTQ file from the NGS device (400), processes the file to match it with a specific microorganism, and provides a response method therefor, and / or a user terminal (200).

[0040] The target environment (300) may be a variety of surrounding environments where the user is located, or a variety of surrounding environments of interest to the user. For example, the target environment (300) may be a specific area within the home, particularly an environment vulnerable to microorganisms, such as a kitchen, refrigerator, or sink. For example, the target environment (300) may be a specific environment within a business establishment, such as a display case or kitchen counter in a retail area, such as a convenience store or restaurant.

[0041] The user terminal (200) may be a device capable of wired or wireless communication capable of receiving data from the microbial analysis device (100). For example, the user terminal (200) includes a tablet PC, a PDA (Personal Digital Assistant), a laptop, a cellular phone, a PCS (Personal Communication Service) phone, a hand-held PC, a GSM (Global System for Mobile) phone, a W-CDMA (Wideband CDMA) phone, a CDMA-2000 phone, and a smartphone.

[0042] The user terminal (200) may include a display capable of outputting information about microorganisms provided from the microbial analysis device (100). The user terminal (200) may have an application installed that can receive information about microorganisms provided from the microbial analysis device (100) in various forms.

[0043] For example, the microbial analysis device (100) may be capable of processing data to accumulate and display periodic test results when the user's target environment (300) is infected with a specific microorganism for a long period of time. The microbial analysis device (100) may provide the processed results to the user terminal (200) through an application. The user terminal (200) may determine a response to the microorganism and analyze the results.

[0044] The NGS device (400) can obtain DNA from a collected microbial sample (310). The NGS device (400) can generate a FASTQ file by culturing reads obtained by cutting DNA into a predetermined length and reading bases from both ends of each read.

[0045] Various NGS devices (400) can be applied, and for example, Illumina's NGS equipment can be applied.

[0046] Meanwhile, the microbial analysis device (100) can receive a FASTQ file from the NGS device (400). The microbial analysis device (100) can define the microorganisms in each FASTQ file by matching the FASTQ file with the reference base sequence of the database.

[0047] The microbial analysis device (100) can store the base sequences of each defined microorganism in a database for each category. The microbial analysis device (100) can update a learning model based on the base sequences of each defined microorganism.

[0048] The microbial analysis device (100) can process a set of FASTQ files that are simultaneously cultured and read for DNA receptors of various microorganisms that can be found in a specific environment around the user. This allows for the detection of various microorganisms that can be found in a specific environment without omission.

[0049] The microbial analysis device (100) can perform analysis by filtering only valid files by reflecting the read count and quality score of each location in a set of FASTQ files, rather than performing matching on all read files. This can increase the matching probability and reduce computation.

[0050] Figure 2 is a block diagram referenced in the description of the configuration of the microbial analysis device of Figure 1.

[0051] Referring to FIG. 2, the microbial analysis device (100) may include a communication unit (110) and a control unit (160).

[0052] The communication unit (110) may include an input unit (111) and / or an output unit (113). The communication unit (110) may communicate with the NGS device (400) using wired / wireless communication. The communication unit (110) may communicate with the user terminal (200) using wired / wireless communication. The communication unit (110) may be referred to as a communication interface.

[0053] The communication unit (110) can be implemented in various ways depending on the designated network. Here, the network can be applied with wireless communication technologies such as IEEE 802.11 WLAN, IEEE 802.15 WPAN, UWB, Wi-Fi, Zigbee, Z-wave, Blue-Tooth, etc. One or more communication technologies can be applied to the network.

[0054] The control unit (160) may include at least one processor, and may control the overall operation of the microbial analysis device (100) using the processor included therein. Here, the processor may be a general processor such as a central processing unit (CPU). Of course, the processor may be a dedicated device such as an ASIC or another hardware-based processor.

[0055] The control unit (160) may include a preprocessing unit (120), a normalization module (130), a classifier (140), and / or a processing unit (150).

[0056] The preprocessing unit (120) can process the FASTQ file input through the input unit (111) and provide it in a normalized and matchable state.

[0057] The preprocessing unit (120) can remove meaningless data, leaving only valid data through truncation of the received FASTQ file. For example, the preprocessing unit (120) can generate a single merged lead by pairing a specific ID with each FASTQ file.

[0058] The preprocessing unit (120) can receive multiple sets of FASTQ files. If the number of FASTQ files in each set is different, the merged reads of each set can be provided to the normalization module (130) for normalization, and then matching can be performed according to the modeling of the classifier (140).

[0059] The preprocessing unit (120) can divide multiple sets of FASTQ files into a group of forward read files and a group of reverse read files. At this time, the preprocessing unit (120) can set a common location where truncation can be performed on the read files of each group, and can perform truncation on the read files of the corresponding group at the common location. The common location can be set by considering both the read count and the quality score for the read files of each group.

[0060] When analyzing the diversity of multiple sets of read files, analysis results may be excessive or insufficient depending on the amount of information (sequencing depth) of each microbial community. Therefore, the normalization module (130) can perform normalization to equalize the amount of information.

[0061] The normalization module (130) can perform diversity analysis using an optimal amount of information by setting the read level for each sample to the maximum within the limit that can utilize the maximum amount of information. That is, the normalization module (130) can load a specific diversity table according to the target environment (300) corresponding to each sample, and activate the normalization module (130) according to the loaded diversity table. For example, the microbial analysis device (100) can store different diversity tables for each type of target environment (300), such as for home use, hospital use, retail use, and food production facilities.

[0062] The control unit (160) analyzes previously held data to determine the degree of diversity for each lead, and then stores the diversity relationship with the corresponding lead only for those whose diversity exceeds a predetermined range compared to the diversity saturation value, thereby enabling the data in the diversity table to be databased. Accordingly, each diversity table can be trained to further strengthen its specialized state for the corresponding environment.

[0063] The normalization module (130) can load the filtered final leads based on each specialized diversity table. At this time, the normalization module (130) can read the corresponding diversity table data, and perform normalization by selecting the final lead value having the largest value and the number of filtered leads among the final lead values ​​in the diversity table data being smaller than the first threshold range among the total number of leads.

[0064] The classifier (140) can determine which species and genera of microorganisms are present in the sample by applying a field-specific classifier to the final read and comparing and matching it with a reference sequence database.

[0065] The classifier (140) can separately construct a reference sequence database for each environment. The classifier (140) can perform final read matching based on the reference sequence database for each environment. Similar to diversity analysis, the reference sequence database for each environment can be classified by type of target environment (300), such as home use, hospital use, retail use, and food production facility use, but is not limited thereto.

[0066] The control unit (160) can separately construct a classifier (140) for a specific environment, i.e., a reference sequence database. The control unit (160) can perform a limited comparison of microorganisms specialized for a specific environment by each driving a classification algorithm that matches the reference sequence database for the specific environment.

[0067] For matched final reads, they can be re-saved to each reference sequence database, enabling database updates and classification algorithm training. Using a fixed reference database can result in slow database updates, and using an integrated database with a large number of reference data values ​​complicates computation and significantly reduces matching probabilities. Therefore, building separate reference sequence databases tailored to each environment and applying individual classification algorithms to match these can reduce computation time and improve accuracy. Furthermore, as each algorithm becomes more sophisticated, accuracy can further improve.

[0068] At this time, the specialized reference sequence database for each field may be classified by each environment by selecting and processing the genetic information of individual 16S rRNA genes registered in the US National Center for Biotechnology Information (NCBI), which is publicly available. To this end, the control unit (160) may sample dust for each environment in the corresponding basic database and select markers that can reflect the microbial contamination characteristics of each detailed area. In addition, the control unit (160) may apply weights by counting the occurrence frequency of markers, and may further increase the matching probability by performing matching in the order of highest weights.

[0069] The processing unit (150) can obtain information on the type and quantity of microorganisms matched to the final lead transmitted from the classifier (140). The quantity of a specific microorganism can be expressed as the proportion of a specific microorganism among multiple microorganisms. The processing unit (150) can process information on the type and quantity of microorganisms and provide it to the user terminal (200).

[0070] The processing unit (150) may perform Alpha diversity analysis and / or Beta diversity analysis on the information about the final read transmitted from the classifier (140). Here, Alpha diversity analysis may be provided by individually analyzing the diversity of the final read of each microbial sample (310), and Beta diversity analysis may be provided by analyzing the degree of dissimilarity between the final reads of multiple microbial samples (310). For example, the processing unit (150) may provide the results of performing Alpha diversity analysis and / or Beta diversity analysis in the form of a table and / or graph.

[0071] If there is a history of analyzing microorganisms for the same target environment (300) for the user terminal (200), the processing unit (150) can provide the trend of microbial changes in the target environment (300), response methods, etc. through history analysis.

[0072] The output unit (113) can transmit the result data provided from the processing unit (150) to a designated user terminal (200). For example, the output unit (113) can perform a user alarm if a microorganism having a risk level higher than a predetermined level is included among the detected microorganisms.

[0073] The microbial analysis device (100) may further include a storage unit (not shown) for storing data. The storage unit may store programs for signal processing and control within the control unit (160). For example, the storage unit may store application programs designed for performing various tasks that can be processed by the control unit (160), and may selectively provide some of the stored application programs upon request from the control unit (160).

[0074] The storage unit may include at least one of volatile memory (e.g., DRAM, SRAM, SDRAM, etc.) or non-volatile memory (e.g., flash memory, hard disk drive (HDD), solid-state drive (SSD), etc.). In various embodiments of the present invention, the storage unit and the memory may be used interchangeably.

[0075] According to one embodiment, the storage unit may be configured with a memory card, a library file for microbial analysis, an embedded system board equipped with a signal processing device, etc. For example, a memory card capable of storing output signal data may be inserted into the embedded system board, and the memory card may store a system OS, a driving program, a library file for analysis, etc. In addition, signal processing for analyzing multiple final leads may be calculated through comparative analysis with the library file in the CPU of the embedded system board, and the analyzed results may be stored again in the memory card. In addition, a communication unit (110) may be mounted together with the embedded system board, but is not limited thereto.

[0076] Referring to Figure 3a, microorganisms that can be found in a specific environment largely include bacteria and fungi, and there are hundreds of thousands of species of these.

[0077] The DNA base sequence, i.e., the DNA sequence, of each type of bacteria and fungi can be stored in the database of the microbial analysis device (100). The DNA base sequence can be stored separately according to each environment.

[0078] Referring to Figure 3b, in the case of bacteria, which are bacteria, the V4 or V3~V4 region DNA sequence information of the 16S RNA gene can be utilized to distinguish each species. For example, in the case of V4, the length of the DNA sequence may be approximately 300 bp (base points) or less. For example, the length of the V3~V4 region DNA sequence may be approximately 500 bp or less.

[0079] The maximum base length of a read that can be read by NGS equipment is 600 bp. Reads longer than 600 bp may not be read, may have significantly lower accuracy, and require expensive equipment, resulting in low utilization. A microbial analysis device (100) according to one embodiment of the present disclosure processes a FASTQ file created based on the sequence of a short read, pairs it, merges it into a long file, and then attempts to match it with a sequence in a pre-classified database, thereby improving accuracy.

[0080] At this time, the forward and reverse reads of each ID can be paired and merged after performing truncation to remove some base pairs (bp) from the FASTQ file. The length of the final merged read can be 468 bp or less.

[0081] Truncation can be performed by selecting a common location for each group, including a first group containing multiple forward leads and a second group containing multiple reverse leads within a set, and deleting invalid bp data for multiple leads within each group at that common location. In other words, truncation can be defined as a filtering operation that deletes all bp data after the common location for multiple leads within a group.

[0082] FIG. 4 is a flowchart of an operation method of a microbial analysis device according to one embodiment of the present disclosure.

[0083] Referring to FIG. 4, the microbial analysis device (100) can, in operation S410, obtain a set of FASTQ files for a sample of a target environment (300) from the NGS device (400). For example, a set of FASTQ files is assigned a unique ID for each read, and the FASTQ files for each ID can be transmitted as a pair consisting of a forward read file and a reverse read file.

[0084] The microbial analysis device (100) can perform noise filtering on sequence reads in operation S420. For example, the microbial analysis device (100) can determine a common location for each group included in a set of FASTQ files, i.e., a forward read group and a reverse read group. At this time, the microbial analysis device (100) can filter out the noise portion of each sequence read file by truncating each forward read and reverse read group at the common location.

[0085] The microbial analysis device (100) can perform sequence read pairing on the forward read file and the reverse read file, which are noise-filtered FASTQ files, in operation S430. For example, if two file pairs do not exist for a specific ID, the microbial analysis device (100) can read the forward read file and the reverse read file of the next ID. For example, if two file pairs exist for a specific ID, the microbial analysis device (100) can determine whether an overlapping section exists in the two read files. At this time, the microbial analysis device (100) can determine whether an overlapping section that is complementary to each other from the ends occurs in the DNA base sequences of the two file pairs for the specific ID.

[0086] The microbial analysis device (100) can, in operation S440, merge FASTQ files of a specific ID for which pairing has been completed to generate a single merged read file. For example, if there is an overlapping section that is complementary to each other in the DNA base sequences of two file pairs for a specific ID, the microbial analysis device (100) can generate a merged read by overlapping the overlapping section from the end of the forward read to the end of the reverse read based on the overlapping section.

[0087] The microbial analysis device (100) may, in operation S450, perform normalization for equalizing the amount of information for each set of merged reads. For example, the microbial analysis device (100) may load a specific diversity table with reference to environmental information for each sample and perform normalization based on the loaded diversity table.

[0088] The microbial analysis device (100) can, in operation S460, compare and match the final reads, for which normalization has been completed, with a reference sequence database. For example, the microbial analysis device (100) can determine which species and genera of microorganisms are present in the sample based on a specific diversity table corresponding to environmental information for each sample. Additionally, the microbial analysis device (100) can determine the quantity of each microorganism contained in the sample.

[0089] The microbial analysis device (100) can perform data analysis on microorganisms contained in the sample in operation S470. For example, the microbial analysis device (100) can perform alpha diversity analysis and / or beta diversity analysis on the type, amount, etc. of microorganisms determined based on the final read. For example, the microbial analysis device (100) can calculate the ratio of microorganisms having a risk level higher than a predetermined level among the microorganisms contained in the sample.

[0090] According to one embodiment, the microbial analysis device (100) can perform various data analyses related to microorganisms (hereinafter, contaminating microorganisms) that contaminate products produced in a target environment (300). In this regard, a description will be given with reference to FIGS. 5 to 9.

[0091] Referring to FIG. 5, the microbial analysis device (100) can determine contaminating microorganisms in operation S510. For example, the microbial analysis device (100) can determine contaminating microorganisms based on data on a product sample corresponding to a finished product. This will be described with reference to FIG. 6.

[0092] Referring to FIG. 6, the microbial analysis device (100), in operation S610, can determine a candidate group for contaminating microorganisms (hereinafter, “contamination candidate group”) by comparing the microorganisms contained in a product sample obtained in a step corresponding to a finished product with the microorganisms contained in a sample for a normal finished product (hereinafter, “normal sample”). Here, the product sample obtained in a step corresponding to a finished product may be a product sample in which the degree of product contamination by microorganisms is high due to the large number of microorganisms proliferating.

[0093] According to one embodiment, the microbial analysis device (100) may determine a contaminant candidate group based on the median amount of microorganisms contained in a plurality of product samples obtained at a specific stage, taking into account the possibility that the types and amounts of microorganisms may differ among product samples. Meanwhile, the microbial analysis device (100) may also use the median amount of microorganisms contained in a plurality of normal samples when determining a contaminant candidate group.

[0094] Table 1 below may show microorganisms contained in product samples obtained at specific stages corresponding to finished products and microorganisms contained in normal samples.

[0095] Product SampleNormal SampleA Microorganism 2%15%B Microorganism 0.5%10%C Microorganism 55%0.5%D Microorganism 15%2%E Microorganism 25%25%.........

[0096] The microbial analysis device (100) can determine whether a product sample contains a first microorganism that is included in the product sample but not in the normal sample, based on whether the product sample contains a predetermined amount or more of the first microorganism. For example, if the product sample contains 1% or more of the first microorganism that is not included in the normal sample, the first microorganism can be determined to be a contamination candidate. On the other hand, if the product sample contains less than 1% of the first microorganism, the first microorganism can be excluded from the contamination candidate.

[0097] The microbial analysis device (100) can determine whether a second microorganism included in both a product sample and a normal sample is a contamination candidate based on the difference between the amount included in the product sample and the amount included in the normal sample, the ratio of the amount included in the product sample and the amount included in the normal sample, etc. For example, the microbial analysis device (100) can determine, among the second microorganisms included in both a product sample and a normal sample, a second microorganism for which the amount included in the product sample minus the amount included in the normal sample is equal to or greater than a first reference value set in advance, and the amount included in the product sample divided by the amount included in the normal sample is equal to or greater than a second reference value set in advance, as a contamination candidate. Hereinafter, the value obtained by dividing the amount included in the product sample by the amount included in the normal sample can be calculated as Log2 fold changes (LFC). In addition, the first reference value is set to 10% and the second reference value is set to 1 as an example, and the description will be made. In other words, a microorganism that is included in a certain amount or more in the product sample and is included in a larger amount in the product sample than in the normal sample can be determined as a contamination candidate.

[0098] Based on Table 1, microorganisms A and B can be excluded from the contamination candidate group because they are contained in larger amounts in the normal sample than in the product sample. Microorganism C can be determined as a contamination candidate because the amount contained in the product sample minus the amount contained in the normal sample is 54.5% and the LFC is 6.8. In addition, microorganism D can also be determined as a contamination candidate because the amount contained in the product sample minus the amount contained in the normal sample is 13% and the LFC is 2.9. On the other hand, microorganism E can be excluded from the contamination candidate group because it is contained in the same amount in the product sample and the normal sample.

[0099] The microbial analysis device (100) can determine, in operation S620, whether the microorganisms determined as contamination candidates are microorganisms corresponding to the surrounding environment at the stage of product production. For example, when the temperature of the surrounding environment is room temperature, it can determine whether the contamination candidate includes thermophilic organisms such as Geobacillus and Pyrococcus furiosus. For example, in an environment where salt is not used, it can determine whether the contamination candidate includes halophiles such as Halomonas and Wallemia ichthyophaga.

[0100] The microbial analysis device (100) can determine, in operation S630, whether the microorganisms determined as contamination candidates are microorganisms detected during microorganism processing steps, such as sample collection and sample processing. For example, it can determine whether microorganisms included in an experimental sample corresponding to a sterilized cotton swab used in the microorganism processing step are included in the contamination candidate group. For example, it can determine whether microorganisms included in an experimental sample corresponding to the laboratory environment where microorganism processing is performed are included in the contamination candidate group.

[0101] According to one embodiment, the microbial analysis device (100) can determine whether a microorganism determined to be a contamination candidate group is a microorganism detected during a sample collection, sample processing step, etc., based on data on microorganisms detected during a microbial treatment process. For example, the microbial analysis device (100) can determine whether a microorganism belonging to Stenotrophomonas or Granulicatella, which is included in the data on microorganisms detected during a microbial treatment process, is included in the contamination candidate group.

[0102] The microbial analysis device (100) can, in operation S640, determine as a contaminating microorganism a microorganism that meets certain conditions among the microorganisms included in the contaminating candidate group. For example, among the microorganisms included in the contaminating candidate group, a microorganism that responds to the surrounding environment and is not detected during the microbial treatment process can be determined as a contaminating microorganism.

[0103] The microbial analysis device (100), in operation S650, can exclude microorganisms that do not meet certain conditions from the contaminating microorganisms among the microorganisms included in the contaminating candidate group. For example, in a case where the ambient temperature is room temperature, microorganisms corresponding to thermophilic microorganisms can be excluded from the contaminating microorganisms. For example, in an environment where salt is not used, microorganisms corresponding to halophilic microorganisms can be excluded from the contaminating microorganisms. For example, microorganisms detected from sterilized cotton swabs or in a laboratory environment can be excluded from the contaminating microorganisms. For example, microorganisms included in data on microorganisms detected during a microbial processing process can be excluded from the contaminating microorganisms.

[0104] According to one embodiment, the microbial analysis device (100) can determine a microorganism, even if it is not included in the contamination candidate group, as a contaminant microorganism if the microorganism is included in the product sample, even if the microorganism is not included in the contamination candidate group. For example, the microbial analysis device (100) can determine whether a microorganism included in the product sample is a pathogenic microorganism based on the PATRIC database, data provided by a disease-related central administrative agency, etc. In the present disclosure, the disease-related central administrative agency is the Korea Disease Control and Prevention Agency of the Republic of Korea as an example.

[0105] In one embodiment, if the product being produced is a food product, the microbial analysis device (100) can determine whether a microorganism contained in a product sample is a pathogenic microorganism based on data provided by a central administrative agency regarding food. In the present disclosure, the central administrative agency related to food is the Ministry of Food and Drug Safety of the Republic of Korea, as an example. For example, the microbial analysis device (100) can determine whether a microorganism contained in a product sample is a pathogenic microorganism based on data updated periodically by the Ministry of Food and Drug Safety regarding food poisoning through the food poisoning early warning system.

[0106] According to one embodiment, the microbial analysis device (100) can determine a microorganism as a contaminating microorganism even if the microorganism is not included in the contamination candidate group if the microorganism included in the data provided by the user who requested the analysis of the product is included in the product sample.

[0107] Referring again to FIG. 5, the microbial analysis device (100) can determine the level of risk of contaminating microorganisms (hereinafter, risk level) in operation S520. In the present disclosure, an example will be given where the product being produced is food.

[0108] According to one embodiment, the microbial analysis device (100) can determine the risk level of a contaminating microorganism based on the risk group of the microorganism designated by the Korea Disease Control and Prevention Agency, the biosafety level set in the PATRIC database, data provided by the Ministry of Food and Drug Safety, data provided by the user, etc.

[0109] For example, if the first contaminating microorganism is included in the risk group 4 designated by the Korea Disease Control and Prevention Agency or is designated as biosafety level 4 in the PATRIC database, the risk level of the first contaminating microorganism can be determined as level 4.

[0110] For example, if the second contaminating microorganism is included in the third risk group designated by the Korea Disease Control and Prevention Agency, or is designated as biosafety level 3 in the PATRIC database, or is included in data provided by the user, the risk level of the second contaminating microorganism may be determined as level 3.

[0111] For example, if a third contaminating microorganism is included in the second risk group designated by the Korea Disease Control and Prevention Agency, or is designated as a biosafety level 2 in the PATRIC database, or is included in data provided by the Ministry of Food and Drug Safety, the risk level of the third contaminating microorganism may be determined as level 2.

[0112] For example, the risk level of the remaining contaminating microorganisms whose risk level is not determined as Level 2 to Level 4 may be determined as Level 1.

[0113] The microbial analysis device (100) can calculate the degree to which a product is contaminated by a single contaminating microorganism at each step (hereinafter, product contamination degree) for each contaminating microorganism in operation S530. The microbial analysis device (100) can calculate the product contamination degree for each contaminating microorganism based on the amount of contaminating microorganism contained in the product sample obtained at each step, the risk level of the contaminating microorganism, etc. For example, the product contamination degree corresponding to a specific contaminating microorganism can be calculated as a product of a corresponding to the amount of the specific contaminating microorganism and b corresponding to the risk level of the specific contaminating microorganism.

[0114] The microbial analysis device (100) can determine the major contamination stage that is significantly affected by contaminating microorganisms in operation S540. The microbial analysis device (100) can determine the major contamination stage for each stage of product production. For example, the microbial analysis device (100) can determine whether a specific stage corresponds to a major contamination stage based on the amount of contaminating microorganisms, the risk level of contaminating microorganisms, the degree of product contamination, etc. In this regard, a description will be made with reference to FIG. 7, and FIG. 7 will illustrate one of the production stages of a product as an example.

[0115] Referring to FIG. 7, the microbial analysis device (100) can, in operation S710, calculate the degree to which a product is contaminated by contaminating microorganisms at a specific stage (hereinafter, “overall product contamination level”). For example, the overall product contamination level can be calculated as the sum of product contamination levels calculated for each contaminating microorganism.

[0116] The microbial analysis device (100) can determine, in operation S720, whether a contaminating microorganism exceeding a predetermined risk level is detected at a specific stage. For example, the microbial analysis device (100) can determine whether a product sample obtained at a specific stage contains a contaminating microorganism exceeding a risk level of Level 3.

[0117] The microbial analysis device (100) can determine, in operation S730, whether the overall product contamination level exceeds a preset threshold.

[0118] The microbial analysis device (100) may determine a specific stage as a major contamination stage in operation S740, if a contaminating microorganism exceeding a predetermined risk level is detected at a specific stage and / or if the overall product contamination level exceeds a threshold.

[0119] The microbial analysis device (100) can exclude a specific step from the main contamination step if, in operation S750, no contaminating microorganisms above a predetermined risk level are detected at a specific step and the overall product contamination level is below a threshold.

[0120] Referring again to FIG. 5, the microbial analysis device (100) can determine, in operation S550, the path through which contaminating microorganisms entered the product (hereinafter, "inflow path"). The microbial analysis device (100) can determine the inflow path for each contaminating microorganism. This will be described with reference to FIGS. 8 and 9, with one of the contaminating microorganisms being used as an example.

[0121] Referring to FIG. 8, the microbial analysis device (100) can determine, in operation S810, whether there is a stage among multiple production stages in which the product contamination level by a specific contaminating microorganism is higher than a first threshold.

[0122] In operation S820, if there is a stage in which the product contamination level by a specific contaminating microorganism is higher than a first threshold, the microbial analysis device (100) can determine whether the degree of contamination of the surrounding environment by the specific contaminating microorganism (hereinafter, environmental contamination level) in the stage is higher than a second threshold. For example, the microbial analysis device (100) can calculate the environmental contamination level for each of a plurality of environmental samples acquired for various surrounding environments in the specific stage. At this time, the microbial analysis device (100) can determine whether at least one of the environmental contamination levels calculated for the plurality of surrounding environments in the stage is higher than the second threshold.

[0123] Meanwhile, the calculation of the environmental contamination level corresponding to a specific contaminant microorganism can correspond to the calculation of the product contamination level corresponding to that specific contaminant microorganism. For example, the environmental contamination level can be calculated by multiplying a, which corresponds to the amount of a specific contaminant microorganism, by b, which corresponds to the risk level of that specific contaminant microorganism.

[0124] In one embodiment, the second threshold may be determined based on the product contamination level of the corresponding step. For example, the second threshold may be determined as the product contamination level of the corresponding step multiplied by the first ratio, 0.9.

[0125] The microbial analysis device (100), in operation S830, can determine whether the contamination levels of products in subsequent stages are all higher than a preset third threshold if at least one of the environmental contamination levels of the corresponding stage is higher than a preset second threshold. In other words, it can determine whether the contamination levels of products in the final stage corresponding to the finished product from the corresponding stage onward are all higher than a preset third threshold. In this case, the third threshold may be lower than the first threshold.

[0126] In one embodiment, the third threshold may be determined based on the product contamination level at the corresponding stage. For example, the third threshold may be determined as the product contamination level at the corresponding stage multiplied by the second ratio, 0.95.

[0127] The microbial analysis device (100) can determine, in operation S840, that a specific contaminating microorganism has been introduced into the environment of the corresponding step if the product contamination levels of the corresponding step and subsequent steps are all above the third threshold. In this case, if the environmental contamination level calculated for a specific environment is above the second threshold, it can be determined that a specific contaminating microorganism has been introduced into the specific environment of the corresponding step.

[0128] Meanwhile, the microbial analysis device (100) may determine, in operation S850, that it is impossible to confirm the environment into which a specific contaminating microorganism has been introduced. For example, if the product contamination level is below the first threshold in all production stages, it may not be possible to confirm the environment into which a specific contaminating microorganism has been introduced. For example, if the product contamination level at a specific stage is higher than the first threshold or the environmental contamination level at a specific stage is lower than the second threshold, it may not be possible to confirm the environment into which a specific contaminating microorganism has been introduced. For example, if the product contamination level at a specific stage is higher than the first threshold and the environmental contamination level is higher than the second threshold or the product contamination level at at least one of the stages after the specific stage is lower than the third threshold, it may not be possible to confirm the environment into which a specific contaminating microorganism has been introduced.

[0129] Referring to FIG. 9, the microbial analysis device (100) can calculate product contamination levels corresponding to specific contaminating microorganisms for each of a plurality of production stages. Furthermore, the microbial analysis device (100) can calculate multiple environmental contamination levels corresponding to specific contaminating microorganisms for each of a plurality of production stages. In FIG. 9, the more severe the contamination by specific contaminating microorganisms, i.e., the greater the product contamination level or environmental contamination level, the darker the color can be displayed.

[0130] Among the multiple production stages, the product contamination levels of stages 1 and 2 may be below the first threshold, while the product contamination levels of stages 3 through 5 may exceed the first threshold. Furthermore, among the environmental contamination levels of stages 3 through 5, the first environmental contamination level of stage 3 may exceed the second threshold, while the remaining levels may be below the second threshold. In this case, since the levels from stage 3 through the final stage, stage 5, are above the third threshold, it can be determined that specific contaminating microorganisms were introduced from the first environment of stage 3.

[0131] As described above, according to at least one embodiment of the present disclosure, it is possible to accurately select microorganisms that are the cause of contamination among microorganisms in an environment.

[0132] Additionally, according to at least one embodiment of the present disclosure, it is possible to calculate the degree to which a product and / or environment is contaminated by microorganisms that are the cause of contamination.

[0133] Additionally, according to at least one embodiment of the present disclosure, major contaminated areas within an environment can be determined.

[0134] Additionally, according to at least one embodiment of the present disclosure, it is possible to determine the inflow path of microorganisms that are the cause of contamination.

[0135] Referring to FIGS. 1 to 9, a microbial analysis device (100) according to one aspect of the present disclosure includes a communication unit (110) that receives first data on a plurality of samples obtained from a target environment in which a product is produced through a plurality of steps; and a control unit (160) that generates second data on the types and amounts of microorganisms included in the plurality of samples based on the first data, wherein the control unit (160) determines a contaminating microorganism that contaminates the product based on the second data, calculates the degree to which the product is contaminated by the contaminating microorganism for each of the plurality of steps, and determines at least one of a main contamination step and a step in which the contaminating microorganism is introduced into the product among the plurality of steps based on the degree to which the product is contaminated.

[0136] Additionally, according to one aspect of the present disclosure, the amount of the predetermined microorganism contained in the predetermined sample corresponding to the predetermined step may be a median of the amounts of the predetermined microorganism contained in each of the plurality of samples obtained in the predetermined step.

[0137] In addition, according to one aspect of the present disclosure, if a predetermined microorganism is included in a first sample obtained in a step corresponding to a finished product among the plurality of steps and is not included in a second sample corresponding to a normal product, the control unit (160) may determine whether the predetermined microorganism is included in the first sample in a predetermined amount or more, and if the predetermined microorganism is included in the first sample in a predetermined amount or more, the predetermined microorganism may be determined as a candidate for the contaminating microorganism.

[0138] In addition, according to one aspect of the present disclosure, when a predetermined microorganism is included in both a first sample obtained in a step corresponding to a finished product among the plurality of steps and a second sample corresponding to a normal product, the control unit (160) calculates a first value corresponding to a difference between the amounts of the predetermined microorganism included in each of the first sample and the second sample, calculates a second value corresponding to a ratio between the amounts of the predetermined microorganism included in each of the first sample and the second sample, and when the first value is equal to or greater than a preset first reference value and the second value is equal to or greater than a preset second reference value, the predetermined microorganism may be determined as a candidate group for the contaminating microorganism.

[0139] In addition, according to one aspect of the present disclosure, the control unit (160) can determine, among the microorganisms included in the candidate group for the contaminating microorganisms, a microorganism that responds to the surrounding environment in the plurality of steps and is not detected in the processing process for the plurality of samples as the contaminating microorganism.

[0140] In addition, according to one aspect of the present disclosure, the control unit (160) determines a risk level for each of the contaminating microorganisms, and based on a value obtained by multiplying the amount of each of the contaminating microorganisms by the risk level determined for each of the contaminating microorganisms, the degree to which the product is contaminated can be calculated for each of the contaminating microorganisms.

[0141] Additionally, according to one aspect of the present disclosure, the control unit (160) can determine the risk level for the risk at least based on data updated in relation to food poisoning at regular intervals when the product is related to food.

[0142] In addition, according to one aspect of the present disclosure, the control unit (160) may determine a predetermined stage corresponding to a sample containing the predetermined microorganism as the main contamination stage when the risk level determined for the predetermined microorganism determined as the contaminating microorganism is higher than a preset risk level.

[0143] In addition, according to one aspect of the present disclosure, the control unit (160) may determine the predetermined stage as the main contamination stage when the total sum of the degree to which the product is contaminated by each of the contaminating microorganisms calculated for the predetermined stage is greater than or equal to a preset threshold.

[0144] In addition, according to one aspect of the present disclosure, the control unit (160) determines whether there is at least one first stage among the plurality of stages in which the degree of contamination of the product by the predetermined microorganism determined as the contaminating microorganism is greater than or equal to a first threshold value, and if there is at least one first stage, it determines whether the degree of contamination of the environment by the predetermined microorganism calculated for the predetermined environment of the first stage is greater than or equal to a second threshold value, and if the degree of contamination of the environment is greater than or equal to the second threshold value, it determines whether the degree of contamination of the product calculated for at least one second stage following the first stage is greater than or equal to a third threshold value, and if the degree of contamination of the product calculated for the second stage is greater than or equal to the third threshold value, it can be determined that the predetermined microorganism has been introduced into the predetermined environment of the first stage.

[0145] Additionally, according to one aspect of the present disclosure, the second threshold and the third threshold may be less than the first threshold.

[0146] In addition, according to one aspect of the present disclosure, the first data is a FASTQ file for the plurality of samples received from a Next Generation Sequencing (NGS) device, and the control unit (160) obtains a forward read file and a reverse read file for a predetermined ID from the FASTQ file, detects an overlapping section of the forward read file and the reverse read file to perform pairing, merges the paired forward read file and the reverse read file to generate an integrated read file, and determines the type and amount of the microorganism through matching between the integrated read file and preset data for the DNA base sequence.

[0147] An operating method of a microbial analysis device (100) according to one aspect of the present disclosure may include an operation of determining a contaminating microorganism that contaminates a product based on data on the types and amounts of microorganisms included in a plurality of samples obtained from a target environment in which a product is produced through a plurality of steps; an operation of calculating, for each of the plurality of steps, the degree to which the product is contaminated by the contaminating microorganism; and an operation of determining, based on the degree to which the product is contaminated, at least one of a main contamination step and a step in which the contaminating microorganism is introduced into the product among the plurality of steps.

[0148] The attached drawings are only intended to facilitate understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the attached drawings, and should be understood to include all modifications, equivalents, or substitutes included in the spirit and technical scope of the present disclosure.

[0149] Meanwhile, the operating method of the present disclosure can be implemented as processor-readable code on a processor-readable recording medium. A processor-readable recording medium includes all types of recording devices that store data that can be read by a processor. Examples of processor-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage devices, etc., and also include those implemented in the form of a carrier wave, such as transmission via the Internet. Furthermore, the processor-readable recording medium can be distributed across network-connected computer systems, so that the processor-readable code can be stored and executed in a distributed manner.

[0150] In addition, although the preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above, and various modifications may be made by a person having ordinary skill in the art to which the present invention pertains without departing from the gist of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present disclosure.

Claims

1. A communication unit that receives first data for multiple samples obtained in a target environment where a product is produced through multiple steps; and A control unit is included that generates second data on the types and amounts of microorganisms included in the plurality of samples based on the first data, The above control unit, Based on the second data above, determine the contaminating microorganisms that contaminate the product, For each of the above multiple steps, the degree to which the product is contaminated by the contaminating microorganism is calculated, A microbial analysis device characterized in that, based on the degree of contamination of the product, at least one of the main contamination stage and the stage in which the contaminating microorganisms were introduced into the product is determined among the plurality of stages.

2. In paragraph 1, A microbial analysis device, characterized in that the amount of a predetermined microorganism contained in a predetermined sample corresponding to a predetermined step is a median of the amounts of the predetermined microorganism contained in each of a plurality of samples obtained at the predetermined step.

3. In paragraph 1, The above control unit, if the predetermined microorganism is included in the first sample obtained in the step corresponding to the finished product among the above multiple steps and is not included in the second sample corresponding to the normal product, Determining whether the above-mentioned predetermined microorganism is contained in the above-mentioned first sample in a predetermined amount or more, A microbial separation device characterized in that, if the predetermined microorganism is contained in the first sample in an amount greater than the predetermined amount, the predetermined microorganism is determined as a candidate for the contaminating microorganism.

4. In paragraph 1, The above control unit, if the predetermined microorganism is included in both the first sample obtained in the step corresponding to the finished product among the above multiple steps and the second sample corresponding to the normal product, The above-mentioned predetermined microorganism calculates a first value corresponding to the difference between the amount contained in each of the first sample and the second sample, The above-mentioned predetermined microorganism calculates a second value corresponding to the ratio between the amounts contained in each of the first sample and the second sample, A microbial separation device characterized in that when the first value is equal to or greater than a preset first reference value and the second value is equal to or greater than a preset second reference value, the device determines the predetermined microorganism as a candidate group for the contaminating microorganism.

5. In paragraph 4, The above control unit, A microbial separation device characterized in that, among the microorganisms included in the candidate group for the contaminating microorganisms, microorganisms that respond to the surrounding environment in the plurality of steps and are not detected in the processing of the plurality of samples are determined as the contaminating microorganisms.

6. In paragraph 1, The above control unit, For each of the above contaminating microorganisms, determine the risk level for the risk, A microbial separation device characterized in that the degree to which the product is contaminated is calculated for each contaminating microorganism based on a value obtained by multiplying the amount of each contaminating microorganism by the risk level determined for each contaminating microorganism.

7. In paragraph 6, The above control unit, A microbial separation device characterized in that, if the above product is related to food, the risk level for the above risk is determined at least based on data updated in relation to food poisoning at regular intervals.

8. In paragraph 6, The above control unit, A microbial analysis device characterized in that, when the risk level determined for a predetermined microorganism determined as the above-described contaminating microorganism is higher than a preset risk level, a predetermined stage corresponding to a sample containing the predetermined microorganism is determined as the main contamination stage.

9. In paragraph 6, The above control unit, A microbial analysis device characterized in that when the total amount of contamination of the product by each of the contaminating microorganisms calculated for a given stage is greater than a preset threshold, the given stage is determined as the main contamination stage.

10. In paragraph 6, The above control unit, Among the above multiple steps, it is determined whether there is at least one first step in which the degree of contamination of the product by the predetermined microorganism determined as the contaminating microorganism exceeds a preset first threshold value, If at least one of the above first steps exists, it is determined whether the degree of contamination of the environment by the above-mentioned microorganisms, which is calculated for the above-mentioned first step, is equal to or greater than the preset second threshold, If the degree of contamination of the above environment is greater than or equal to the second threshold, it is determined whether the degree of contamination of the product calculated for at least one second step, which is a step after the first step, is greater than or equal to the preset third threshold. A microbial analysis device characterized in that if the degree of contamination of the product calculated for the second step is all equal to or greater than the third threshold, it is determined that the predetermined microorganism has been introduced into the predetermined environment of the first step.

11. In paragraph 10, A microbial analysis device, characterized in that the second threshold and the third threshold are less than the first threshold.

12. In paragraph 1, The above first data is a FASTQ file for the plurality of samples received from an NGS (Next Generation Sequencing) device, The above control unit, Obtain a forward lead file and a reverse lead file for a given ID from the above FASTQ file, Detecting the overlapping section of the above forward lead file and the above reverse lead file and performing pairing, The above paired forward lead file and reverse lead file are merged to create an integrated lead file, A microbial analysis device characterized in that the type and amount of the microorganism are determined through matching between the integrated lead file and preset data for the DNA base sequence.

13. An operation of determining contaminating microorganisms that contaminate the product based on data on the types and amounts of microorganisms contained in multiple samples obtained from a target environment where the product is produced through multiple steps; For each of the above multiple steps, an operation of calculating the degree to which the product is contaminated by the contaminating microorganism; and An operating method of a microbial analysis device, comprising an operation of determining at least one of a main contamination stage and a stage in which the contaminating microorganism was introduced into the product among the plurality of stages based on the degree of contamination of the product.

Images