JP2018113007A - Information processing apparatus, information processing method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To construct a new production system and to establish a method for promoting six-order industrialization of agriculture by comprehensively profiling soil to be a production medium, a cultivation process, and regional characteristics of an agricultural product and associating the productivity of each production place and variety of the agricultural product with functionality.SOLUTION: An integrated evaluation part 81 integrates and evaluates optimum soil identified by an optimum soil identification part 71, agricultural product information created by an agricultural product information creation part 72, and food metabolome information. An optimum soil selection combination part 91 selects and combines the optimum soil evaluated by the integrated evaluation part 81 in each prescribed agricultural product. An agricultural product information selection combination part 92 selects and combines the agricultural product information evaluated by the integrated evaluation part 81 in each prescribed agricultural product.SELECTED DRAWING: Figure 4

Description

  The present invention relates to an information processing apparatus, an information processing method, and a program.

  Conventionally, there is an information disclosure system that provides soil data, cultivation management data, and quality data of agricultural products in a cultivation field via a communication line such as the Internet (see, for example, Patent Document 1).

  In addition, as the production system for agricultural products, the optimum soil work design for each region, soil type and agricultural product and the soil data of the field where the agricultural product is to be obtained are input and the optimum farm work design is analyzed by the information processing device. There are systems that provide this (see, for example, Patent Document 2).

JP 2004-54379 A JP 2002-345331 A

  However, only by disclosing soil data, cultivation management data, and quality data of the cultivation field of agricultural products, it is not possible to grasp the correlation between the functionality of the components of the soil and agricultural products.

Also, even if agricultural products are produced using the optimum soil data for each agricultural product, what kind of nutrients are contained in each agricultural product's production area / variety, and what components are included It is not possible to ascertain whether or not there is a characteristic for each production area and variety.
Thus, there is a need for a new production system for producing agricultural products having specific nutrients or functions when the correlation between the functionality of soil and agricultural components is unknown.

  The present invention has been made in view of such circumstances, and comprehensively profiles the soil, the cultivation process, and the regional characteristics of agricultural products as production media, and the productivity and functionality of each agricultural product's production area and variety. The purpose of this project is to establish a new production system and promote the sixth industrialization of agriculture.

In order to achieve the above object, an information processing apparatus of one embodiment of the present invention provides:
Sampling means for sampling soil, plants or food;
Extraction means for extracting the components of the soil, plant or food sampled by the sampling means;
Measuring means for measuring the components of the soil, plant or food extracted by the extracting means;
Data extraction processing means for extracting and processing data of the components of the soil, plant or food measured by the measurement means;
Data analysis means for analyzing data of the components of the soil, plant or food extracted and processed by the data extraction processing means;
A metabolome analyzer comprising:
In an information processing apparatus that communicates and receives information,
Soil metabolome acquisition means for acquiring the analysis result of the data analysis means for the soil sampled by the sampling means as soil metabolome information;
Plant metabolome acquisition means for acquiring the analysis result of the data analysis means for the plant sampled by the sampling means as plant metabolome information;
Food metabolome acquisition means for acquiring the analysis result of the data analysis means for the food sampled by the sampling means as food metabolome information;
Integrated evaluation means for generating an integrated evaluation profile based on the soil metabolome information, the plant metabolome information, and the food metabolome information;
Is provided.

  According to the present invention, a new production system can be created by comprehensively profiling regional characteristics of soil, cultivation process, and agricultural products as production media, and associating the productivity and functionality of each production area and variety of agricultural products. It is possible to establish and establish a method to promote the sixth industrialization of agriculture.

It is a figure showing the outline | summary of the integrated profile analysis system which concerns on one Embodiment of the information processing system to which this invention is applied. It is a figure which shows the structural example of the integrated profile analysis system of FIG. It is a block diagram which shows the hardware constitutions of the server 1 among the information processing systems of FIG. It is a functional block diagram which shows the functional structural example for performing integrated profile analysis control among the functional structures of the integrated profile analysis system of FIG. It is a flowchart explaining the flow of extraction of a soil metabolomics and derivatization which the extraction part 42 of the metabolome analyzer 2 performs. It is a figure showing the conditions of GCMS of extraction of a soil metabolomics and derivatization which the extraction part 42 of the metabolome analyzer 2 performs. It is a figure showing GCMS peak picking / alignment which the data extraction and process part 44 of the soil metabolomics information of the metabolome analyzer 2 performs. It is a figure showing an example of the GCMS estimation peak table which the data extraction / processing part 44 of the soil metabolomics information of the metabolome analysis apparatus 2 performs. It is a figure showing the multivariate analysis (grouping and estimation of a contribution component) which the data analysis part 45 of the soil metabolomics information of the metabolome analyzer 2 performs. It is the figure which put different organic substance in each well, and digitized decomposition and decomposition speed. It is a figure which shows the component classification | category by the kind of tea, the production area, and the cultivation method which perform the analysis about the plant metabolomics by the metabolome analyzer 2. FIG. It is a flowchart explaining the flow of extraction of a plant metabolomics and derivatization which the extraction part 42 of the metabolome analyzer 2 performs. It is a figure showing the LCMS conditions of extraction of a plant metabolomics and derivatization which the extraction part 42 of the metabolome analyzer 2 performs. It is a figure showing the analysis result of LCMS which the data extraction and process part 44 of the plant metabolomics information of the metabolome analyzer 2 performed. LCMS analysis results of sencha Yabukita (Nada Ward) and white leaf tea Yabukita (Nagi Ward) TIC and m / z vs. It is the figure which compared RT plot. It is a flowchart which shows the flow of the correlation of the compound information with respect to each peak which the data extraction and process part 44 of the plant metabolomics information of the metabolome analyzer 2 performs. It is a figure showing the comparison by the breed | variety of tea leaves (stem), the production center, and the cultivation method which the data analysis part 45 of the metabolome analyzer 2 performs. It is a figure showing the component comparison of the catechin amino acid of the result of the multivariate analysis (grouping and estimation of a contribution component) by the compound of interest which the data analysis part 45 of the plant metabolomics information of the metabolome analyzer 2 performs. It is a figure showing the compound peak information obtained by GCMS as the data extraction method 1 which the data extraction and process part of a metabolome analyzer performs. It is a figure showing the selected compound peak information among the compound peak information obtained by GCMS which the data extraction and the process part of the soil metabolomics information of a metabolome analysis apparatus perform. It is the figure which extracted and graphed the compound peak strongly detected by the A group with respect to the B group, and the compound peak strongly detected by the B group with respect to the A group. It is a figure showing the compound peak information obtained by LCMS as the data extraction method 2 which the data extraction and process part of a metabolome analyzer performs. It is a figure showing the selected compound peak information among the compound peak information obtained by LCMS as the data extraction method 2 which the data extraction and process part of a metabolome analyzer performs. It is a figure showing the information of the compound peak selected and detected only in A group or B group about 2 groups (for example, A group and B group) to compare. It is the figure which graphed the extracted compound peak. It is the figure which compared green tea and white leaf tea about the component characteristic by the cultivation method of the tea as an example of metabolome analysis. It is the figure which compared the multivariate analysis result of the known compound by GCMS analysis with "Yabukita tea" and "Shiraba tea". It is the figure which compared the multivariate analysis result of the known compound by GCMS analysis for every compound with "Yabukita tea" and "Shiraba tea". It is the figure which compared the multivariate analysis result of the known and unknown compound by LCMS analysis with "Yabukita tea" and "Shiraba tea". It is the figure which compared the multivariate analysis result of the known compound by LCMS analysis for every compound with "Yabukita tea" and "Shiraba tea". It is a figure which shows the known and unknown compound common in white leaf tea. It is a figure which shows the known and unknown compound often found in Yabukita tea. It is a figure which shows the analysis result of the compound of the unidentified peak by LCMS analysis. It is a figure which shows the output result at the time of utilizing correlation network analysis software for the use which compares and analyzes the objects for which direct comparison is difficult.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

FIG. 1 is a diagram showing an outline of an integrated profile analysis system according to an embodiment of an information processing system to which the present invention is applied.
The integrated profile analysis system analyzes three correspondences of soil metabolomics information, plant metabolomics information, and food metabolomics information, and creates, for example, an integrated evaluation profile as an analysis result. This is an output system.
Here, metabolomics is a comprehensive analysis of specific molecules generated by the activity of cells contained in a predetermined sample. Such an analysis result is referred to as “metabolomic information”. Here, metabolomics information in which soil is used as a sample is particularly referred to as “soil metabolomics information”. Metabolomics information in which plants are used as samples is particularly called “plant metabolomics information”. Metabolomics information in which food (food) is used as a sample is particularly called “food metabolomics information”.

  That is, the integrated profile analysis system of the present embodiment comprehensively analyzes specific molecules generated by the activity of soil, plant, and food cells (metabolomics), and produces agricultural products based on the analysis results (metabolomics information). By comprehensively profiling regional characteristics of soil, cultivation process, and agricultural products (creating an integrated evaluation profile), the productivity and functionality of each soil, plant, and food by production area and variety are related. Create a new production system and provide various outputs necessary to promote the sixth industrialization of agriculture.

Specifically, for example, the integrated profile analysis system can specify and output information on soil microorganism clusters, regional varieties of agricultural products, and functional components as optimal production index markers using the integrated evaluation profile.
In addition, for example, the integrated profile analysis system uses the integrated evaluation profile to carry out branding that takes advantage of the locality of agricultural products such as land, water, and weather environment, and functions such as nutrition and health functions. Build a new brand from the search and identification of functional ingredients and health function evaluation, and output it. In this case, the integrated profile analysis system integrates field metabolomics and environmental information based on an all information integration algorithm.
Further, for example, the integrated profile analysis system redefines the relationship between the functional component of the agricultural product and the health function using the integrated evaluation profile, and outputs the redefined content. As a result, it is possible to create a new market and support nursing care and group meals utilizing health functional ingredients for a super-aging society.
Specifically, for example, the integrated profile analysis system uses the integrated evaluation profile to re-edit the nutritional components and functional components of agricultural products, and gives new value by combining the nutritional components and functional components of agricultural products. Curate.

  In order to produce the above-mentioned various effects by outputting as described above, the integrated profile analysis system is based on soil metabolomics information, plant metabolomics information, and food metabolomics information. The integrated evaluation profile is generated by performing the integrated evaluation of each diagnosis result and executing the profiling of each of the soil, the growing crop, and the harvested product.

Soil metabolomics information is information which shows the analysis result of soil organic matter, rhizosphere organic matter, and a failure marker, for example.
Plant metabolomics information is information which shows the analysis result of a nutrient function ingredient, cultivation / variety characteristics, and genome information, for example.
The food metabolomics information is, for example, information indicating an analysis result of a taste marker and a food product.

The soil diagnosis is performed by real-time diagnosis, and the current nutrient state of the soil is diagnosed. In soil diagnosis, the chemical and physical properties of the soil are diagnosed. The soil chemistry refers to nitrogen, phosphoric acid, potash, other trace elements, pH, etc. contained in the soil. Moreover, the physical property of soil means water permeability, water retention, air permeability, aggregate structure, etc. in the soil.
Nutrition diagnosis is performed by crop body nutrient and soil nutrient diagnosis. Specifically, the nutrition diagnosis is performed by metal analysis.
Microbial diversity and activity values are determined by the soil microbial community structure.

  The integrated evaluation based on the soil diagnosis and the nutrition diagnosis is evaluated by integrating all the information from the soil preparation to the sixth industrialization of the harvest based on the results of the soil diagnosis and the nutrition diagnosis.

The optimum soil profile is obtained by profiling the optimum soil for each predetermined agricultural product.
A variety / production area profile is obtained by profiling a variety / production area for each predetermined agricultural product.

In other words, the integrated profile analysis system enables stable production of commodities as commodities and enables production brands within the same variety by focusing on the unique functional ingredients of agricultural products that take advantage of the characteristics of the origin and variety. And
Further, the integrated profile analysis system can analyze a result obtained by adding a metabolomic analysis (each metabolomic information), a microbial diversity activity value, and the like to the result of the real-time diagnosis. As a result, it is widely used to meet the needs of producers, food processors, sellers, and consumers, and in particular, it has become a sixth-order industry that makes use of materials characterized by functional indications of agricultural products and ingredients involved, as well as stabilizing production. Can help.
The integrated profile system as described above can take the following configuration.

In summary, foods are roughly classified into general foods and health functional foods. The insurance functional food is classified into food for specified health use (Tokuho), functional nutrition food, and functional label food.
Functional label foods were introduced by the introduction of a new system. With this new system, it is now possible to display functionality on fresh foods.
As a result, the branding of new agricultural products with geographical indications has become stronger at agricultural production sites.

As described above, an agricultural product that is one of the generated foods can be positioned as a functional material.
Therefore, by introducing the integrated profile analysis system shown in FIG. 1, it is possible to find a new brand value by grasping the agricultural production site from the viewpoint of metabolomic analysis.
Specifically, the following (1) to (3) can be realized.
(1) Activation of agricultural production sites It is possible to comprehensively profile the soil, the cultivation process, and the regional characteristics of agricultural products as production media.
By associating productivity and functionality, it is possible to build a new production system and promote the sixth industrialization.
Specifically, for example, it is possible to specify the optimum production index marker (soil microbial cluster, regional variety, functional component).
(2) Branding of local agricultural products Branding that takes advantage of regional characteristics (land, water, weather environment, etc.) and functionality (nutrition, health functions) becomes possible.
It will be possible to build a new brand from the search and identification of nutritional and functional ingredients of agricultural products and health function evaluation.
Specifically, for example, environmental information can be integrated (field metabolomics, all information integration algorithm).
(3) Creation of new market value A new market can be created by redefining the relationship between functional ingredients and health functions.
It will be possible to create new markets such as nursing care and group meals utilizing health functional ingredients for the super-aging age.
Specifically, for example, curation (reediting of nutritional components and functional components, new value by combination), and agricultural production sites and agricultural branding as seen from metabolomic analysis.
In this way, activation of production sites, branding of local agricultural products, and actions for creating new market value are key. The key concepts are (1) optimal production index marker, (2) integration of environmental information, and (3) curation.

An integrated profile analysis system can be realized based on such a key concept.
In other words, the core of the integrated profile analysis system is to perform integrated analysis of all information from soil preparation to the sixth industrialization of harvested products. That is, integrated profile analysis performs profiling of soil, growing crops, and crops at the production site, geographical characterization (geographical indication), metabolism of nutrient functional components of cultivated crops backed by soil profiles Clarify the characterization of functional ingredients from the product profile and the harvest.

Furthermore, the integrated profile analysis system is easy to expand.
That is, the identification of the functional component of agricultural products is directly linked to clarifying secondary metabolic components at the same time. With secondary metabolic components, it is possible to expand the market by paying attention to components involved in taste fragrance.
New food development is required for the elderly society expected in the near future. By focusing on health functional ingredients and fragrance functionality that are conscious of a healthy life expectancy, the development of foods rich in nutritional components such as nursing foods, medical meals, and school meals, and at the same time improving food functions that take advantage of fragrance functions It can be used for.

FIG. 2 shows a configuration example of the integrated profile analysis system of FIG.
The information processing system shown in FIG. 2 includes a server 1, a metabolome analyzer 2, and user terminals 3-1 to 3-m used by m users (m is an arbitrary integer value equal to or greater than 1). It is a system including The server 1, the metabolome analysis apparatus 2, and each of the user terminals 3-1 to 3-m are connected to each other via a predetermined network N such as the Internet.

The server 1 generates an integrated evaluation profile for each of the user terminals 3-1 to 3-m based on information (various metabolome information) obtained by the metabolome analyzer 2. Then, the server 1 executes various outputs in order to provide various services requested by the user to each of the user terminals 3-1 to 3-m.
The metabolome analyzer 2 generates various metabolome information by comprehensively analyzing all metabolites present in the living body.
Each of the user terminals 3-1 to 3-m is configured by a smartphone or the like operated by each user, and executes various processes.
Hereinafter, when it is not necessary to individually distinguish each of the user terminals 3-1 to 3-m, these are collectively referred to as “user terminal 3”.

  FIG. 3 is a block diagram showing a hardware configuration of the server 1 in the integrated profile analysis system of FIG.

  The server 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an output unit 16, an input unit 17, A storage unit 18, a communication unit 19, and a drive 20.

The CPU 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 18 to the RAM 13.
The RAM 13 appropriately stores data necessary for the CPU 11 to execute various processes.

  The CPU 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output interface 15 is also connected to the bus 14. An output unit 16, an input unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

The input unit 17 is configured with various hardware buttons and the like, and inputs various information according to a user's instruction operation.
The storage unit 18 is configured by a DRAM (Dynamic Random Access Memory) or the like, and stores various data.
The communication unit 19 controls communication with other devices (in the example of FIG. 3, the metabolome analysis device 2 and the user terminal 3) via the network N including the Internet.

  The drive 20 is provided as necessary. A removable medium 31 made of a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is appropriately attached to the drive 20. The program read from the removable medium 31 by the drive 20 is installed in the storage unit 18 as necessary. The removable medium 31 can also store various data stored in the storage unit 18 in the same manner as the storage unit 18.

  Although not shown, the user terminal 3 and the metabolome analysis apparatus 2 have the same configuration as the hardware configuration of the server 1 in FIG.

Control that generates an integrated evaluation profile and analyzes the integrated evaluation profile in cooperation with various hardware and various software of the user terminal 3, the metabolomic analysis device 2, and the server 1 that constitute such an integrated profile analysis system. Processing (hereinafter referred to as “integrated profile analysis control”) can be executed.
In order to execute the integrated profile analysis control, the integrated profile analysis system has a functional configuration as shown in FIG.

  FIG. 4 is a functional block diagram showing a functional configuration example for executing the integrated profile analysis control among the functional configurations of the integrated profile analysis system.

  As shown in FIG. 4, in the metabolome analysis apparatus 2, a sampling unit 41, an extraction unit 42, a measurement unit 43, a data extraction / processing unit 44, and a data analysis unit 45 function.

The sampling unit 41 samples soil, plants, or food.
The extraction unit 42 extracts the components of the soil, plant, or food sampled by the sampling unit 41.
Specifically, the extraction unit 42 performs derivatization of the soil, plant, or food sampled by the sampling unit 41.
In the present embodiment, the sampling unit 41 and the extraction unit 42 are performed by the metabolome analysis apparatus 2, but sampling and extraction may be performed manually or by another apparatus, and the sampling and extraction results may be provided to the measurement unit 43. Good.
The measurement unit 43 measures the components of the soil, plant, or food extracted by the extraction unit 42.
Specifically, the measurement part 43 performs a measurement by LCMS (liquid chromatography) and GCMS (gas chromatography) measurement.
The data extraction / processing unit 44 extracts / processes the data of the soil, plant, or food component measured by the measurement unit 43.
Specifically, the data extraction / processing unit 44 extracts data by peak picking and alignment, and processes the data by annotation and alignment.
Here, peak picking refers to extracting a peak by MS chromatography drawing for each m / z from TIC (total ion chromatogram).
Alignment refers to correcting the deviation of RT and m / z between the same compounds of the picked peak and giving a peak identifier.
Furthermore, annotation refers to estimating the compound name from the RI, MS spectrum, and standard product DB of each unknown peak.
The data analysis unit 45 analyzes the data of the components of the soil, plant, or food extracted / processed by the data extraction / processing unit 44.
Specifically, the data analysis unit 45 performs data analysis by multivariate analysis (grouping and estimation of contributing components).
Here, grouping means visualizing differences between groups by multivariate analysis. Specifically, grouping is performed by performing a PCA plot or the like (principal component analysis) and performing visualization.
In addition, the data analysis unit 45 performs a loading plot or the like (analysis of principal component analysis) to estimate the component contributing to the difference.
The communication unit 51 transmits the soil, plant, or food component data analyzed by the data analysis unit 45 to the communication unit 19 of the server 1.

As shown in FIG. 4, in the CPU 11 of the server 1, the soil metabolome acquisition unit 61, the food metabolome acquisition unit 62, the plant metabolome acquisition unit 63, the optimum soil identification unit 71, and the agricultural product information generation unit 72 The integrated evaluation unit 81, the optimum soil selection combination unit 91, and the agricultural product information selection combination unit 92 function.
The soil metabolome acquisition unit 61 acquires the analysis result of the data analysis unit 45 on the soil sampled by the sampling unit 41 of the metabolome analysis apparatus 2 as soil metabolome information. Soil metabolome information includes soil organic matter, rhizosphere organic matter, failure markers, and the like.
The food metabolome acquisition unit 62 acquires the analysis result of the data analysis unit 45 on the food sampled by the sampling unit 41 of the metabolome analysis apparatus 2 as food metabolome information. The food metabolome information includes taste markers and product components.
The plant metabolome acquisition unit 63 acquires the analysis result of the data analysis unit 45 on the plant sampled by the sampling unit 41 of the metabolome analysis apparatus 2 as plant metabolome information. Plant metabolomic information includes nutrient functional components, cultivation / variety characteristics, genome information, and the like.

The optimum soil identification unit 71 identifies the optimum soil for producing a predetermined agricultural product (for example, a sample source of plant metabolome information) based on the soil metabolome information acquired from the soil metabolome acquisition unit 61.
Specifically, for example, when the soil diagnosis by the real-time diagnosis is performed, the optimum soil specifying unit 71 specifies the chemical and physical properties of the soil based on the result of the soil diagnosis and the soil metabolome information. Thereby, the continuous cropping failure of soil can be suppressed.
The agricultural product information generation unit 72 generates at least one kind of information of a predetermined agricultural product (for example, a sample source of plant metabolome information) based on the plant metabolome information acquired from the plant metabolome acquisition unit 63. Here, the information of at least one kind of predetermined agricultural products includes, for example, varieties and production areas.
Specifically, for example, the agricultural product information generation unit 72 uses the plant metabolome information, so that not only predetermined agricultural product varieties and production areas, but also predetermined agricultural product varieties, crop body nutrient information and soil nutrient diagnosis for each production area. Generate (metal) result information.

The integrated evaluation unit 81 creates an integrated evaluation profile based on the soil metabolome information and optimum soil, plant metabolome information and agricultural product information, and food metabolome information, and performs various comprehensive evaluations using the integrated evaluation profile.
Specifically, for example, the integrated evaluation unit 81 specifies a functional component of soil, plant, or food using the integrated evaluation profile, and performs evaluation by attaching a taste fragrance marker of the food.

The optimum soil selection / combination unit 91 selects and combines the optimum soil evaluated by the integrated evaluation unit 81 for each of a plurality of agricultural products using the integrated evaluation profile.
Specifically, for example, the optimum soil selection / combination unit 91 creates an optimum soil profile for each agricultural product that has been integrated and evaluated, using the integrated evaluation profile.
Moreover, the optimal soil selection combination part 91 memorize | stores the information of the optimal soil for every some agricultural produce in optimal soil selection combination DB101.
The agricultural product information selection combination unit 92 selects and combines the agricultural product information evaluated by the integrated evaluation unit 81 for each of a plurality of agricultural products using the integrated evaluation profile.
Specifically, the agricultural product information selection combination unit 92 uses the integrated evaluation profile to create a variety / production area profile for each agricultural product that has been integrated and evaluated.
Moreover, the agricultural product information selection combination unit 92 stores the agricultural product information for each of the multiple agricultural products in the agricultural product information selection combination DB 102.

In the user terminal 3, the communication unit 111 and the display unit 121 function.
The communication unit 111 communicates with the server 1 and transmits / receives various information. For example, the storage contents of the optimum soil selection combination DB 101 and the agricultural product information selection combination DB 102 are received.
The display unit 121 displays various information such as information received by the communication unit 111 in this way as an image.

In summary, in the integrated profile analysis control of the present embodiment, the following control process is executed.
That is, the server 1 acquires each of soil metabolome information, food metabolome information, and plant metabolome information, analyzes the relationship between these three, performs integrated evaluation, and profiles the result of the integrated evaluation (integrated profile information). Generate). The server 1 generates various types of information using the integrated profile information and transmits it to the user terminal 3.

  Further, the metabolome analysis will be described below with reference to FIGS.

FIG. 5 is a flowchart illustrating the flow of extraction and derivatization of soil metabolomics performed by the extraction unit 42 of the metabolome analysis apparatus 2.
In addition, in sampling performed before extraction, cabbage, lettuce and purple cabbage are produced at Chausuhara and Shimizudai in the west city of Miyazaki Prefecture. Currently, the soil is abandoned farmland, and burdock and carrot in Miyakonojo City, Miyazaki Prefecture.・ Sampling was performed on the soil where Rakkyo was produced.
In addition to soil metabolomics analysis, soil microbial diversity activity value, soil metagenome analysis cluster, soil precise analysis / mineral component characteristics were analyzed.

In step S1, the extraction unit 42 of the metabolome analyzer 2 crushes and extracts a soil sample (200 mg) and MeOH (methanol) 400 μL.
In step S2, the extraction unit 42 adds ultrapure water and IS to MeOH and adjusts to 80% MeOH.
In step S3, the extraction unit 42 incubates the sampled soil at 70 ° C. for 15 minutes.
In step S4, the extraction unit 42 samples the sampled soil at 17500 g for 5 min.
In step S5, the extraction unit 42 separates a part of the sampled soil, converts it to methoxyme, and further trimethylsilylates it.
In step S6, the measurement unit 43 measures the sampled soil by GCMS.
In the extraction, the metabolome analyzer 2 prepared an operation BL sample to improve analysis accuracy, measured in the same manner as the sample, and used for noise peak removal from reagents, containers, and the like.

  FIG. 6 is a diagram illustrating GCMS conditions for extraction and derivatization of soil metabolomics performed by the extraction unit 42 of the metabolomic analysis apparatus 2. The conditions of GCMS are as follows.

As shown in FIG. 6, SHIMADZU QP-2010 Ultra is used for GCMS. For Auto Sampler, SHIMADZU AOC-5000 Plus is used. The column is Agilent DB-5MS 30 m 0.25 mm 1 μm. The temperature of the vaporizing chamber is 280 ° C. The oven is set to 100 ° C. (4 minutes) −Rate 4 ° C./min−320° C. (8 min). The temperature of a connection part shall be 280 degreeC. An ion source shall be 200 degreeC. The EI method is used for the ionization method. For the sample introduction method, a splitless injection method is used. The flow rate is 39 cm / sec (1.1 mL / min). ScanSpeed is 2000 u / sec. Mass Range is m / z = 45-600. The injection volume is 1 μL.
In the GCMS measurement, in order to improve alignment accuracy and the like, an equal mixture of each extracted sample was used as a QC sample and operated in the same manner as the sample.

  FIG. 7 is a diagram illustrating GCMS peak picking / alignment performed by the data extraction / processing unit 44 of the soil metabolomics information of the metabolomic analysis apparatus 2.

The upper graph in FIG. 7 is a graph showing a TIC (total ion chromatogram).
The middle graph of FIG. 7 shows m / z vs. It is a graph which shows RT plot.
The lower left graph in FIG. 7 is a graph showing the MS chromatogram of each m / z.
The graph on the lower right of FIG. 7 is a graph showing an MS spectrum (information specific to each compound).

As illustrated in FIG. 7, the data extraction / processing unit 44 performs peak picking for drawing a MS chromatographic drawing for each m / z from the TIC and extracting a peak.
Further, as shown in FIG. 7, the data extraction / processing unit 44 performs alignment for correcting an RT and m / z shift between the same compounds of the picked peak and adding an identifier to the peak.

  FIG. 8 is a diagram illustrating an example of a GCMS estimation peak table performed by the data extraction / processing unit 44 of the soil metabolomics information of the metabolome analysis apparatus 2.

As shown in FIG. 8, the data extraction / processing unit 44 aligns the aligned data and creates aligned data. Specifically, a peak identifier is assigned to a column in the graph, and alignment data is created for each sample group in a row.
Further, as shown in FIG. 8, the data extraction / processing unit 44 performs annotation for estimating the compound name from the RI, MS spectrum, and standard product DB of each unknown peak. In the present embodiment, the data extraction / processing unit 44 performs estimation of unknown compounds using the known compound 428 component DB.

  FIG. 9 is a diagram illustrating multivariate analysis (grouping and estimation of contributing components) performed by the data analysis unit 45 of the soil metabolomics information of the metabolome analysis apparatus 2.

The data analysis unit 45 performs a PCA plot for visualizing differences between groups by multivariate analysis. For example, as shown on the left side of FIG. 9, the difference can be visualized by grouping for each of the western capital field, the capital city 1 and the capital city 2.
Further, the data analysis unit 45 performs a loading plot for estimating a component that contributes to a difference between groups. For example, as shown on the right side of FIG. 9, the difference between the soil groups of the Saito field, Miyakonojo 1 and Miyakonojo 2 can be estimated as Torehalose and Inositol.

  FIG. 10 is a diagram showing soil microorganism diversity / activity values.

FIG. 10 is a diagram in which different organic substances are put into each well and the presence / absence of decomposition and the decomposition speed are digitized.
As shown in FIG. 10, the color development state of the plate 48 hours after the soil is confirmed, and the soil microorganism diversity / activity value is calculated. In FIG. 10, the soil microbial diversity / activity values of Miyakonojo 1 and Miyakonojo 2 indicate 840,290 (deviation value 51.6). Moreover, in FIG. 10, the soil microorganism diversity and activity value of the Nishito field shows 724 and 731 (deviation value 48.4).
From the soil microbial diversity and activity values shown in FIG. 10, it can be said that there are different types of organic matter that can be decomposed by microorganisms. Moreover, it can be said that various microorganisms exist that various organic substances can be decomposed. Furthermore, it can be said that the fact that the decomposition rate of organic matter is high means that the activity of microorganisms is high.
That is, when these are combined, it can be said that soil with high soil microbial diversity and activity value has the property that it is less likely to cause continuous cropping damage and is less susceptible to disease.

As described above, the following can be derived from a series of metabolomic analysis of soil.
As a result of soil metabolomic analysis, it was found that the characteristics were separated in the healthy area and the disease occurrence area even in the same field by separating the characteristics for each field.
It has also been found that various organic acids, amino acids, fatty acids and the like can be cited as components that contribute to separation.
Furthermore, by clarifying the patterns of various organic acids, amino acids, fatty acids, etc. that vary depending on the fertilizer to be fertilized and the previous crop, it is possible to set production condition markers for stable production.

  Next, an analysis method for plant metabolomics by the metabolome analyzer 2 will be described.

FIG. 11 is a diagram showing component classification according to tea varieties, production areas, and cultivation methods for analysis of plant metabolomics by the metabolome analyzer 2.
As shown in FIG. 11, in performing the metabolome analysis, the metabolome analysis was performed by classification for each tea product classification, variety, and production area.

  FIG. 12 is a flowchart illustrating the flow of extraction and derivatization of plant metabolomics performed by the extraction unit 42 of the metabolome analysis apparatus 2.

In step S11, the extraction unit 42 of the metabolome analyzer 2 freezes and grinds the tea leaf sample (150 mg) after the tea making process, adds cooled 75% MeOH (450 μL), and stirs.
In step S12, the extraction unit 42 performs centrifugation at 15000 rpm, 4 ° C., 10 minutes, and then performs MF at 0.45 μm.
In step S13, the extraction unit 42 performs degreasing by spin column processing (Monotrap C18).
In step S14, the extraction unit 42 performs LCMS measurement (ESI-Positive) after 0.2 μmF.
In the extraction, the metabolome analyzer 2 prepared an operation BL sample to improve analysis accuracy, measured in the same manner as the sample, and used for noise peak removal from reagents, containers, and the like.

  FIG. 13 is a diagram illustrating LCMS conditions for extraction and derivatization of plant metabolomics performed by the extraction unit 42 of the metabolomic analysis apparatus 2. The conditions of LCMS are as follows.

As shown in FIG. 13, Agilent 1200 series is used as a HPLC (high performance liquid chromatography) condition. The column is TSKgel ODS-100V 5 μm 3 × 50 mm. The card column is TSK guardgel ODS-100V 5 μm. The mobile phase is 0.1% FA water / 0.1% FA ACN. The column temperature is 40 ° C. The gradient (B) is 0 min: 3%, 15 min: 97%, 20 min: 97%, 20.1 min: 3%, 25.0 min: 3%. The flow rate is 0.4 mL / min. The injection volume is 5 μL.
Further, as shown in FIG. 13, Thermo LTQ ORBITRAP XL is used as a condition for the MS. The ionization mode is ESI-Positive. The heater temperature is 400 ° C. The sheath gas is 50. The Aux gas is 10. The spray voltage is 3.5 kV. The capillary temperature is 300 ° C. The scan range (m / z) is 100-1500. MS / MS is acquired for each time event at MS1 ionic strength 1-4. The collision energy is 35%.

  FIG. 14 is a diagram illustrating a result of LCMS analysis performed by the data extraction / processing unit 44 of the plant metabolomics information of the metabolomic analysis apparatus 2.

The upper graph in FIG. 14 is a graph showing a TIC (total ion chromatogram).
The middle graph in FIG. 14 shows m / z vs. It is a graph which shows RT plot.
The graph on the lower left of FIG. 14 is a graph showing MS chromatograms for each m / z.
The graph on the lower right side of FIG. 14 is a graph showing an MS spectrum (information specific to each compound).

As shown in FIG. 14, the data extraction / processing unit 44 performs MS-chromatographic drawing for each m / z from TIC (total m / z), and performs peak picking to extract peaks.
Further, as shown in FIG. 14, the data extraction / processing unit 44 performs alignment for correcting an RT and m / z shift between the same compounds of the picked peak and adding an identifier to the peak.

  FIG. 15 shows the TIC and m / z vs. time of Sencha Yabukita (Nada Ward) and Shirahaba Yabukita (Nagi Ward) as a result of LCMS analysis. It is the figure which compared RT plot.

  As shown in FIG. 15, m / z vs. sencha in Sencha Yabukita (Nada Ward) and white leaf tea Yabukita (Nagi Ward). It can be seen that there is a difference in Intensity% of the RT plot.

  FIG. 16 is a flowchart showing a flow of association of compound information with each peak performed by the data extraction / processing unit 44 of the plant metabolomics information of the metabolomic analysis apparatus 2.

In step S21, the data extraction / processing unit 44 acquires peak information obtained as a result of the data processing. Specifically, m / z, RT, and accurate mass are acquired, an expected composition formula is established, and annotation (compound estimation) is performed.
In step S22, the data extraction / processing unit 44 aligns the aligned data and creates aligned data. Specifically, a peak identifier is assigned to a column in the graph, and alignment data is created for each sample group in a row. In step S23, the data analysis unit 45 performs multivariate analysis (data visualization). Do.

  FIG. 17 is a diagram illustrating a comparison of tea leaf (stem) varieties, production areas, and cultivation methods performed by the data analysis unit 45 of the metabolome analysis apparatus 2.

  As shown in FIG. 17, it is possible to grasp which component is contained and to what extent in each kind, between production areas, and the cultivation method.

  FIG. 18 is a diagram illustrating a catechin / amino acid component comparison as a result of multivariate analysis (grouping and estimation of contributing components) by a target compound performed by the data analysis unit 45 of the plant metabolomics information of the metabolome analysis apparatus 2.

The data analysis unit 45 performs a PCA plot for visualizing differences between groups by multivariate analysis. For example, as shown on the left side of FIG. 9, white leaf tea (Kiraka), stem tea (Yabukita), white leaf tea (Yabukita), Sencha (Yabukita), white leaf tea (Golden green), Sencha (Samidori) ) Grouping can be made to visualize the difference in catechin / amino acid content.
Further, the data analysis unit 45 performs a loading plot for estimating a component that contributes to a difference between groups. For example, as shown on the right side of FIG. 9, white leaf tea (Kiraka), stem tea (Yabukita), white leaf tea (Yabukita), Sencha (Yabukita), white leaf tea (Golden green), green tea (Samidori) ) It can be seen that the contributing component is different for each.

As described above, using tea as a sample, we examined what kind of component difference the conditions such as cultivar, production area and cultivation method are involved. Thereby, the following can be derived.
As a result of plant metabolome analysis, even in the same variety of tea, there was a large difference in the composition of catechins and amino acids depending on the production area and cultivation method, suggesting the possibility of being involved in differences in the taste of tea.
It was also found that the composition of the extracted components varied greatly depending on the extraction conditions from fresh tea leaves, and there were differences in product characteristics.

The metabolome analysis of soil and plants has been described above. In the present embodiment, the metabolome analysis can be performed as follows.
By performing metabolomic analysis, the characteristics of soil and plants, for example, the production area, varieties, cultivation, and processing, to grasp the characteristics of branding of agricultural products, selection of varieties, cultivation methods, processing methods, A search for sex components can be performed.
That is, by searching for a functional component and specifying the functional component, the secondary metabolic component of the agricultural product can be clarified. By focusing on the ingredients related to taste, aroma, and health functional ingredients in secondary metabolic components, foods that take advantage of the function of aroma at the same time as the development of foods rich in nutritional components such as nursing care, medical treatment, and school meals It can be used to improve functions.

  Next, compound peak information obtained by GCMS as the data extraction method 1 and LCMS as the data extraction method 2 performed by the data extraction / processing unit 44 of the metabolome analyzer 2 will be described with reference to FIGS. .

  FIG. 19 is a diagram illustrating compound peak information obtained by GCMS as the data extraction method 1 performed by the data extraction / processing unit 44 of the metabolome analysis apparatus 2.

As shown in FIG. 19, the data extraction / processing unit 44 aligns data of compound peak information obtained as a result of analysis by GCMS as the data extraction method 1, and creates aligned data. Specifically, an identifier of peak information is assigned to a column in the graph, and alignment data is created for each sample group in a row.
In addition, as shown in FIG. 19, the data extraction / processing unit 44 performs annotation for estimating the compound name from the RI, MS spectrum, and standard product DB of each unknown peak.

FIG. 20 is a diagram illustrating selected compound peak information among the compound peak information obtained by GCMS performed by the data extraction / processing unit 44 of the soil metabolomics information of the metabolomic analysis apparatus 2.
As shown in FIG. 20, the data extraction / processing unit 44 aligns data of selected compound peak information among compound peak information obtained as a result of analysis by GCMS, and creates aligned data. Specifically, the average intensity of two groups of compound peaks to be compared is used for data extraction. Further, the difference in average intensity was tested (t test), and the compound peak that was 5% or less of the FDR significance level was selected. Furthermore, compound peaks with a retention time of less than 1 minute are excluded due to their low reliability.
Here, “holding time” refers to the time required from the start of sample introduction to elution of the compound.

  Furthermore, as shown in FIG. 20, after calculating the magnification of the group B with respect to the group A for each compound peak for the two groups (for example, the group A and the group B) to be compared, they are rearranged in descending order or ascending order. FIG. 21 shows a graph obtained by extracting and graphing the top 10 compound peaks that are strongly detected in the A group with respect to the B group and the compound peaks that are strongly detected in the B group with respect to the A group. It is.

  In addition, about the extracted compound peak, known compound information can be provided from the compound peak information of FIG.

  FIG. 22 is a diagram showing compound peak information obtained by LCMS as the data extraction method 2 performed by the data extraction / processing unit 44 of the metabolome analysis apparatus 2.

As shown in FIG. 22, the data extraction / processing unit 44 aligns compound peak information data obtained by LCMS as the data extraction method 2 to create aligned data. Specifically, an identifier of peak information is assigned to a column in the graph, and alignment data is created for each sample group in a row.
In addition, compound peak information is a thing at the time of paying attention to an unidentified peak.

FIG. 23 is a diagram illustrating selected compound peak information among the compound peak information obtained by LCMS as the data extraction method 2 performed by the data extraction / processing unit 44 of the metabolome analysis apparatus 2.
As shown in FIG. 23, the data extraction / processing unit 44 aligns data of selected compound peak information among compound peak information obtained as a result of analysis by LCMS, and creates aligned data. Specifically, the average intensity of two groups of compound peaks to be compared is used for data extraction. Further, the difference in average intensity was tested (t test), and the compound peak that was 5% or less of the FDR significance level was selected. Furthermore, compound peaks with a retention time of less than 11 minutes are excluded due to their low reliability.

  Next, FIG. 24 is a diagram showing information of selected compound peaks detected only in the A group or the B group for the two groups to be compared (for example, the A group and the B group).

  Further, in FIG. 24, information on the selected compound peaks was searched from the alignment data shown in FIG. 22, and the unidentified (NA or unknown peak) was extracted. FIG. 25 shows a graph of the extracted compound peaks.

  The compound peak information obtained by GCMS as the data extraction method 1 and LCMS as the data extraction method 2 performed by the data extraction / processing unit 44 of the metabolome analyzer 2 has been described above with reference to FIGS.

Next, an example of metabolomic analysis will be described with reference to FIGS.
The purpose of analysis is to see the difference between cultivars and cultivation.

  FIG. 26 is a diagram comparing green tea and white leaf tea in terms of component characteristics according to the tea cultivation method as an example of metabolomic analysis.

As shown in FIG. 26, white leaf tea is classified into two types: a cultivated white leaf tea in which a normal green tea variety is whitened under strong light-shielding for about two weeks, and a variety white leaf tea that is a white leaf variety in which a new bud is genetically yellowish white. I can be separated. In addition, white leaf tea has a characteristic that it has a higher amino acid content than normal green tea.
For example, the photograph on the left of FIG. 26 shows the relationship between the shading rate and the leaf color of the shoot using the variety “Yabukita”. As shown in the photograph, four light shielding ratios of 0%, 85%, 98%, and 100% are employed. It can be seen from the photograph that the leaf color of the sprout becomes darker green as the light blocking rate is lower, and the leaf color of the sprout becomes whiter as the light blocking rate is higher.
In this metabolomic analysis, the characteristics of green tea and cultivated white leaf tea are grasped. Specifically, metabolome analysis is performed on the sample name “Yabukita”, which is not shaded for the variety “Yabukita”, and the sample name “Shiraba tea”, which is shaded for the variety “Yabukita”. Comprehensive analysis (GCMS analysis, LCMS analysis) is performed. Examples of components detected from the GCMS analysis include amino acids, sugars, organic acids, and nucleic acids. Components detected from LCMS analysis include polyphenols and unidentified components.
In addition, “Yabukita” uses what is cultivated in the same place, and “Shiraba tea” uses what is cultivated in the same place of the previous year.
As an extraction method, a method of performing heat treatment at 85 ° C. for 30 minutes after low temperature extraction is employed.

  FIG. 27 is a diagram comparing the results of multivariate analysis of known compounds by GCMS analysis between “Yabukita tea” and “Shiraba tea”.

As shown in FIG. 27, the comparison was made by statistically processing the component amount of the compound detected by GCMS, and as a result, a difference was found in the component composition between the two groups.
Specifically, referring to the score of the principal component analysis on the left side of FIG. 27, it can be understood that “white leaf tea” has components extending over a wide range in comparison with “Yabukita tea”.

  FIG. 28 is a diagram comparing multivariate analysis results of known compounds by GCMS analysis for each compound of “Yabukita tea” and “Shiraba tea”.

As shown in FIG. 28, the compounds to be compared as analysis results are “Alanine”, “Glutamine”, “Aspartic acid”, and “Ascorbic acid”. Is adopted.
For alanine, comparing the analysis results of "Yabukita tea" and "Shiraba tea"
It can be understood that “white leaf tea” contains more alanine than “Yabukita tea”.
Comparing the analysis results of “Yabukita tea” and “Shiraba tea” for glutamine, “Yabukita tea” does not contain glutamine, whereas “Shiraba tea” contains much glutamine. I can grasp that.
Comparing the analysis results of "Yabukita tea" and "Shiraba tea" for aspartic acid, "Yabukita tea" does not contain aspartic acid, whereas "Shiraba tea" does not contain aspartic acid. It can be understood that a large amount is contained.
Comparing the analysis results of “Yabukita tea” and “Shiraba tea” for ascorbic acid, it can be understood that “Yabukita tea” contains more ascorbic acid than “Shiraba tea”.

  FIG. 29 is a diagram comparing the results of multivariate analysis of known and unknown compounds by LCMS analysis between “Yabukita tea” and “Shiraba tea”.

As shown in FIG. 29, the comparison was made by statistically processing the component amounts of the compounds detected by LCMS. As a result, similar to the GCMS results, there was a difference in the component composition between the two groups. It was.
Specifically, referring to the score of the principal component analysis on the left side of FIG. 29, it can be understood that “white leaf tea” has components extending over a wide range in comparison with “Yabukita tea”. In addition, it can be understood that “Yabukita tea” has components spread over a wide range compared to “white leaf tea”.

  FIG. 30 is a diagram comparing multivariate analysis results of known compounds by LCMS analysis for each compound of “Yabukita tea” and “Shiraba tea”.

As shown in FIG. 30, the compounds to be compared as analysis results are “epigallocatechin (EGC)”, “epicatechin gallate (ECG)”, “epigallocatechin gallate (EGCG)”, “ Epicatechin (EC) "is adopted.
As for epigallocatechin, comparing the analysis results of “Yabukita tea” and “Shiraba tea”, “Shiraba tea” does not contain epigallocatechin, whereas “Yabukita tea” It can be understood that a large amount of gallocatechin is contained.
Comparing the analysis results of "Yabukita tea" and "Shiraba tea" for epicatechin gallate, "Yabukita tea" contains more epicatechin gallate than "Shiraba tea". Can be grasped.
When comparing the analysis results of "Yabukita tea" and "Shiraba tea" for epigallocatechin gallate, "Yabukita tea" contains more epigallocatechin gallate than "Shiraba tea". I can understand.
Comparing the analysis results of “Yabukita tea” and “Shiraba tea” with respect to epicatechin, it can be understood that “Yabukita tea” contains more epicatechin than “Shiraha tea”.

  FIG. 31 is a diagram showing known and unknown compounds common in white leaf tea.

Examples of compounds obtained as a result of using a gas chromatograph (GC) as an analytical instrument include aspartic acid (Asparagine), glutamine (Glutamine), cysteine (Cysteine), and tryptophan.
On the other hand, a compound obtained as a result of using a liquid chromatograph (LC) as an analytical instrument includes oxidized glutathione (Oxidized glutathione). In addition, the compound obtained as a result of using the liquid chromatograph (LC) as an analytical instrument was unidentified (NA or Unknown Peak). 623 and peak no. 197 compounds.
The peak No. M / z of the compound of 623 is 465.257, and peak no. The m / z of the 197 compound is 318.143.

  FIG. 32 is a diagram showing known and unknown compounds common in Yabukita tea.

Examples of the compound obtained as a result of using a gas chromatograph (GC) as an analytical instrument include arabinose.
On the other hand, compounds obtained as a result of using a liquid chromatograph (LC) as an analytical instrument include epigallocatechin (EGC), 5,7,8,2 ′, 4′-pentahydroxyisoflavone, and telephioidin. It is done. Moreover, the compound obtained as a result of using a liquid chromatograph (LC) as an analytical instrument includes diaziphenin and 5,7,8-trihydroxyflavanone 5-glucoside. Furthermore, the compound obtained as a result of using the liquid chromatograph (LC) as an analytical instrument was unidentified (NA or Unknown Peak). 622 and peak no. 530 compounds.
The peak No. M / z of the compound of 622 is 463.838. The m / z of the compound of 530 is 460.090.

  FIG. 33 is a diagram showing an analysis result of an unidentified peak compound by LCMS analysis.

As shown in the graph on the left in FIG. 33, the larger the VIP score of each compound, the greater the difference between the two groups.
In addition, as shown in the graphs on the right in FIG. 622 and peak no. 623 and peak no. 530 compound and peak no. 197 compounds.
Peak No. Comparing the analysis results of “Yabukita tea” and “Shiraba tea” for the compound of 622, “Shiraba tea” “Yabukita-cha” does not contain the compound of 622, but peak No. It can be understood that a large amount of 622 compounds are contained.
Peak No. Comparing the analysis results of “Yabukita tea” and “Shiraba tea” for the compound of 623, “Yabukita tea” “Shiraba tea” does not contain the compound of 623, but peak No. It can be understood that a large amount of 623 compounds are contained.
Peak No. Comparing the analysis results of “Yabukita tea” and “Shiraba tea” for the compound of 530, “Shiraba tea” “Yabukita tea” does not contain the compound of 530, but “No. It can be seen that 530 compounds are contained in a large amount.
Peak No. When comparing the analysis results of “Yabukita tea” and “Shiraba tea” for the compound of 197, “Yabukita tea” “Shiraba tea” does not contain the compound of No. 197. It can be seen that 197 compounds are contained in a large amount.

The integrated profile analysis system according to an embodiment of the information processing system to which the present invention is applied has been described above.
Subsequently, another embodiment accompanying the above-described embodiment will be described.

FIG. 34 is a diagram showing an output result when the correlation network analysis software is used for the purpose of comparing and analyzing objects that are difficult to directly compare.
FIG. 34 is an example of the output result when the metabolome analysis results are compared and analyzed by the correlation network analysis algorithm, and the objects that are difficult to compare directly are indirectly analyzed.

The correlation network analysis algorithm can select a group of highly relevant elements in the correlation matrix data.
Here, the relationship includes not only the similarity between the metabolomic analysis results but also the forward correlation and the inverse correlation.

Specifically, the correlation network analysis algorithm excludes those with a high correlation coefficient by FPO (False-Positive-Out) analysis but low contribution of the module of interest, and correlation by FNI (False-Negative-In) analysis. Add a small number of modules that have a high contribution to the module of interest.
Here, the module contribution is specifically calculated from the network density (statistical index indicating how many lines are connected) and the network specificity (statistical index indicating how specific nodes are). It can be obtained as a harmonic average.

In the FPO analysis, it is possible to extract from all the components that exhibit the same behavior as the element of interest.
In the FNI analysis, selection (screening) of related components linked to the element of interest can be performed.
As a result, the correlation network analysis software can realize the discovery of new functional display components and the like, and shortening of various research and development periods is expected.

The base material S is a symbol representing an object that is a basis for comparative analysis.
The number of objects to be a base is not limited to one, but may be larger, and when it is not necessary to distinguish each of these, they are simply described as “base material S” as a generic name.
In addition, a base material is not necessarily restricted to a material and a material.

The targets T1 and T2 are symbols that represent objects that are targets for comparative analysis.
The number of target objects is not limited to two, and may be larger, and when it is not necessary to distinguish each of these, they are simply described as “target T”.
Further, if there are two or more substrates S, the target T may be one.

The target object is not necessarily limited to an object.
Here, T1 and S and T2 and S are both poorly correlated (or too strong) and may be in a relationship in which direct comparison is difficult, and either of the substrate S and the target T is the substrate S. Or it is not necessary to specify whether it is the target T, and the distinction between the two is merely for convenience.

The reference samples R1 to R6 each have a moderate correlation with each of the target object T and the base material S, thereby representing a target that can indirectly refer to the correlation between the target object T1 or T2 and the base material S. It is.
The number of objects to which the correlation can be indirectly referenced is not limited to six, and may be sufficiently larger than each of the target T and the base material S. If it is not necessary to distinguish each of these, the generic term simply “reference” It is described as “Sample R”.
The reference sample is not necessarily limited to materials.

Here, the line connecting the base material S and the reference samples R1 to R6 and the line connecting the target object T1 or the target object T2 and the reference samples R1 to R6 are solid lines and thick broken lines. , Thin broken line, no line.
And it is desirable for the reference sample R to have a moderate correlation with respect to at least one of the target T and the substrate S without being sufficiently biased.

In the example of FIG. 34, it is shown that the reference samples R1 to R6 have an appropriate correlation without being sufficiently biased with respect to each of the targets T1 and T2.
On the other hand, for the base material S, it is shown that the reference samples R1 to R6 are biased in correlation.

Here, like the reference samples R1 to R3, in the example of FIG. 34, the reference sample R having a strong correlation with the substrate S has a strong correlation with the target T1, and like the reference samples R5 to R6, It is shown that the reference sample R having a weak correlation with the substrate S tends to have a strong correlation with the target T2.
With this, in the example of FIG. 34, the base material S has a strong correlation with the target samples T1 and T2 that could not be determined by direct comparison analysis. Through the comparative analysis, it has been shown that it can be assumed that the target T1 is more correlated than the target T2.

  In this way, similarly, an arbitrary number of base materials S (base target) and an arbitrary number of target objects T (target target) can be correlated even if they are directly compared and analyzed. Even if the presence (or strength difference) cannot be found, the necessary number of appropriate reference samples R (objects that can indirectly refer to the correlation) are selected and incorporated into the elements in the correlation network. It becomes possible to find the existence of a correlation (or a difference in strength) for each relationship between each of S and each of the targets T.

Based on this, for example, metabolome analysis results that differ depending on the tea leaf cultivation location, metabolome analysis results that differ depending on the tea extraction method, and metabolome analysis results of components contained in commercially available tea beverages are compared and analyzed using a correlation network analysis algorithm Then, it is possible to specifically indicate which tea beverages are close to each other.
As a correlation network analysis algorithm that can be adopted in the present embodiment, specifically, “the Confecial algorithm” known as “Kinpei algorithm” is suitable, and “ConfetoGUI” that is a stand-alone tool in which this is implemented, ConfetoGUIplus "can be used.

  As mentioned above, although one embodiment of the present invention and other embodiments attached to the embodiment have been described, the present invention is not limited to the above-described embodiment, and is within a range where the object of the present invention can be achieved. Modifications, improvements and the like are included in the present invention.

Further, for example, the functional configuration of FIG. 4 is merely an example, and is not particularly limited. That is, it is sufficient that the information processing apparatus has a function capable of executing the above-described series of processes as a whole, and what functional block is used to realize this function is not particularly limited to the example of FIG. Further, the location of the functional block is not particularly limited to that in FIG. 4 and may be arbitrary. For example, the functional block of the server 1 may be transferred to the user terminal 3 or the like, and conversely, the functional block of the user terminal 3 may be transferred to the server 1 or the like.
In addition, one functional block may be constituted by hardware alone, software alone, or a combination thereof.

When the processing of each functional block is executed by software, a program constituting the software is installed on a computer or the like from a network or a recording medium.
The computer may be a computer incorporated in dedicated hardware. The computer may be a computer capable of executing various functions by installing various programs, for example, a general-purpose smartphone or personal computer other than a server.

  The recording medium including such a program is provided not only by a removable medium (not shown) distributed separately from the apparatus main body in order to provide the program to the player, but also provided in a state pre-installed in the apparatus main body. Recording medium.

In the present specification, the step of describing the program recorded on the recording medium is not limited to the processing performed in time series along the order, but is not necessarily performed in time series, either in parallel or individually. The process to be executed is also included.
Further, in the present specification, the term “system” means an overall apparatus configured by a plurality of devices, a plurality of means, and the like.

In other words, the information processing apparatus to which the present invention is applied can take various embodiments having the following configurations.
That is, an information processing apparatus to which the present invention is applied
Sampling means for sampling soil, plants or food (for example, sampling unit 41 in FIG. 4);
Extraction means for extracting the components of the soil, plant or food sampled by the sampling means (for example, the extraction unit 42 in FIG. 4);
Measuring means for measuring the components of the soil, plant or food extracted by the extracting means (for example, the measuring unit 43 in FIG. 4);
Data extraction processing means (for example, the data extraction / processing section 44 in FIG. 4) for extracting and processing the data of the components of the soil, plant or food measured by the measurement means;
Data analysis means for analyzing the data of the components of the soil, plant or food extracted and processed by the data extraction processing means (for example, the data analysis unit 45 in FIG. 4);
A metabolome analyzing apparatus (for example, the metabolome analyzing apparatus 2 in FIG. 4),
In an information processing apparatus (for example, server 1 in FIG. 4) that communicates and receives information,
Soil metabolome acquisition means (for example, soil metabolome acquisition unit 61 in FIG. 4) for acquiring the analysis result of the data analysis means on the soil sampled by the sampling means as soil metabolome information;
Plant metabolome acquisition means (for example, the plant metabolome acquisition unit 63 in FIG. 4) for acquiring the analysis result of the data analysis means for the plant sampled by the sampling means as plant metabolome information;
Food metabolome acquisition means (for example, the food metabolome acquisition unit 62 in FIG. 4) for acquiring the analysis result of the data analysis means for the food sampled by the sampling means as food metabolome information;
Based on the soil metabolome information, the plant metabolome information, and the food metabolome information, an integrated evaluation unit that generates an integrated evaluation profile (for example, the integrated evaluation unit 81 in FIG. 4);
Is provided.

  DESCRIPTION OF SYMBOLS 1 ... Server, 2 ... Metabolome analyzer, 3 ... User terminal, 11 ... CPU, 18 ... Memory | storage part, 19 ... Communication part, 41 ... Sampling part, 42. .. Extraction unit, 43... Measurement unit, 44... Data extraction / processing unit, 45 .. Data analysis unit, 51 .. Communication unit, 61 .. Soil metabolome acquisition unit, 62. Food metabolome acquisition unit, 63 ... Plant metabolome acquisition unit, 71 ... Optimal soil identification unit, 72 ... Agricultural product information generation unit, 81 ... Integrated evaluation unit, 91 ... Optimum soil selection combination unit 92 ... Agricultural product information selection combination unit, 101 ... Optimum soil selection combination DB, 102 ... Agricultural product information selection combination DB, 111 ... Communication unit, 121 ... Display unit, S ..Substrate, T1 to T2 ... Target, R1 Itaru R6 ··· reference sample

Claims (1)

Sampling means for sampling soil, plants or food;
Extraction means for extracting the components of the soil, plant or food sampled by the sampling means;
Measuring means for measuring the components of the soil, plant or food extracted by the extracting means;
Data extraction processing means for extracting and processing data of the components of the soil, plant or food measured by the measurement means;
Data analysis means for analyzing data of the components of the soil, plant or food extracted and processed by the data extraction processing means;
A metabolome analyzer comprising:
In an information processing apparatus that communicates and receives information,
Soil metabolome acquisition means for acquiring the analysis result of the data analysis means for the soil sampled by the sampling means as soil metabolome information;
Plant metabolome acquisition means for acquiring the analysis result of the data analysis means for the plant sampled by the sampling means as plant metabolome information;
Food metabolome acquisition means for acquiring the analysis result of the data analysis means for the food sampled by the sampling means as food metabolome information;
Integrated evaluation means for generating an integrated evaluation profile based on the soil metabolome information, the plant metabolome information, and the food metabolome information;
An information processing apparatus comprising: