Methods for analyzing food using metabolomics

Abstract

To provide an analysis method for food product using metabolomics which is capable of reducing time and labor required for building a measurement method.SOLUTION: An analysis method for food product using metabolomics comprises: a measurement step S2 of collecting LC / MS data by performing SIM measurement targeting m / z corresponding within a prescribed time range including a holding time for each of a plurality of predetermined compounds using LC-MS combining LC and single Q-MS with respect to each of a plurality of food product samples; a calculation step S3 of obtaining a quantitative value being at least one of a peak area, height, an area ratio, a height ratio or concentration corresponding to the peak in chromatogram for each compound on the basis of the LC / MS data for each sample; and an analysis step S4 of performing a multivariate analysis based on the quantitative values of the plurality of compounds so as to perform at least one of classification of food product samples according to characteristics, visualization of a degree of similarity or difference for each food product sample, and extraction of compounds that may cause the difference.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a method for analyzing food using metabolomics. [Background technology]

[0002] In recent years, metabolomics, which comprehensively analyzes metabolites in living organisms, has attracted attention. Metabolomics is a technology that comprehensively analyzes dozens or more types of low-molecular-weight metabolites, such as amino acids and organic acids, produced in living organisms, and elucidates differences among multiple sample groups. While metabolomics has primarily developed in fields that deal with living organisms, such as medicine and biochemistry, metabolomics technology has recently been actively introduced into the food industry. The application of metabolomics to food is called food metabolomics, and it is used for a variety of purposes, including food quality appraisal and prediction, improvement of manufacturing and storage processes, evaluation of taste and aroma, and functional performance evaluation.

[0003] Analytical instruments used in metabolomics in the medical and biochemical fields are generally required to detect relatively small amounts of components with high sensitivity and accuracy in the presence of various contaminants at high concentrations. Therefore, for targeted analysis of a wide variety of known components in metabolomics, liquid chromatograph mass spectrometers or gas chromatograph mass spectrometers using a triple quadrupole mass spectrometer as a detector, which offers high component selectivity, sensitivity, and accuracy, are typically used. Hereinafter, liquid chromatograph mass spectrometers and gas chromatograph mass spectrometers using a triple quadrupole mass spectrometer as a detector will sometimes be referred to as LC-MS / MS and GC-MS / MS, respectively.

[0004] Foods contain many types of metabolites, and many of the metabolites related to flavor, quality, functionality, etc. have been elucidated. Therefore, targeted analysis, in which the components to be analyzed are predetermined, is generally performed in food metabolomics as well. As disclosed in Non-Patent Document 1 and elsewhere, LC-MS / MS or GC-MS / MS have generally been used for targeted analysis in food metabolomics. In the example in Non-Patent Document 1, LC-MS / MS was used to simultaneously analyze 97 hydrophilic metabolites. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] "Food Metabolomics: Analysis of Wine Using the LCMSTM-8060NX Triple Quadrupole Mass Spectrometer LC-MS Application News No. C226," [Online], [Retrieved February 10, 2022], Shimadzu Corporation, Internet<URL: https: / / www.an.shimadzu.co.jp / aplnotes / lcms / an_c226.pdf> [Non-patent document 2] "Multi-omics Analysis Package," [Online], [Retrieved February 10, 2022], Shimadzu Corporation, Internet<URL: https: / / www.an.shimadzu.co.jp / lcms / tq-option / multiomics / features.htm> Summary of the Invention [Problem to be solved by the invention]

[0006] While LC-MS / MS and GC-MS / MS enable the analysis of multiple target compounds with high sensitivity and precision, their use requires the creation of a measurement method that includes the multiple reaction monitoring (MRM) transitions (combinations of the mass-to-charge ratio (m / z) values ​​of precursor ions and product ions) and collision energy values ​​for each target compound in advance. Food metabolomics analysis involves a large number of target compounds, and the target compounds vary depending on the type of food being analyzed. Therefore, developing an appropriate measurement method requires a significant amount of time and effort, resulting in a complex analytical workflow. Furthermore, this task requires the involvement of individuals with a certain level of analytical expertise, which limits the number of people available to perform the task.

[0007] As food metabolomics expands from research and development departments to manufacturing and quality control sites, there is a strong demand for simplified analytical workflows so that operators without specialized analytical knowledge can easily perform measurement and analysis tasks. However, due to the above-mentioned circumstances, it is difficult to meet this demand.

[0008] In addition, LC-MS / MS and GC-MS / MS instruments are expensive, which means that introducing the instruments is costly, and the maintenance and management costs of such instruments are also high. Therefore, cost constraints make it difficult to introduce food metabolomics.

[0009] The present invention has been made to solve these problems, and its main purpose is to provide a method for analyzing food using metabolomics that reduces the time and effort required for measurement and analysis work, and enables easier and lower cost understanding and classification of food characteristics, as well as identification of compounds characteristically contained in food. [Means for solving the problem]

[0010] One aspect of the method for analyzing food using metabolomics according to the present invention, which has been made to solve the above problems, is a method for analyzing a food sample using metabolomics, comprising: a measurement step of collecting LC / MS data for each of a plurality of food samples using a liquid chromatograph mass spectrometer that combines a liquid chromatograph and a single quadrupole mass spectrometer, and performing selected ion monitoring measurement for each of a plurality of predetermined compounds in the single quadrupole mass spectrometer, targeting the mass-to-charge ratio corresponding to the compound within a predetermined time range that includes the retention time of the compound; a calculation step of creating a chromatogram for each compound based on the LC / MS data for each food sample collected in the measurement step, and determining a quantitative value, which is at least one of the area, height, area ratio, height ratio, or concentration corresponding to a peak observed in the chromatogram; an analysis step of performing multivariate analysis based on the quantitative values ​​of a plurality of compounds obtained for each of a plurality of food samples, thereby performing at least one of classifying the food samples according to their characteristics, visualizing the degree of similarity or difference between each food sample, or extracting compounds that cause the differences; It has. [Effects of the Invention]

[0011] As mentioned above, the use of triple quadrupole mass spectrometers has become commonplace in metabolomics (metabolome analysis). However, the present inventors conducted a detailed comparison of the analytical results obtained using a triple quadrupole mass spectrometer with those obtained using a single quadrupole mass spectrometer in the metabolome analysis of food. As a result, the present inventors discovered that a single quadrupole mass spectrometer is sufficient for comprehensive analysis of more than several dozen metabolites important in food metabolomics, and that a single quadrupole mass spectrometer can also provide sufficiently high performance in food classification, leading to the completion of the present invention. By using a single quadrupole mass spectrometer, food metabolome analysis can be performed without having to set MRM measurement conditions, which has traditionally been the biggest obstacle to reducing analysis time, thereby significantly reducing analysis time.

[0012] As described above, the above-described embodiment of the food analysis method using metabolomics according to the present invention reduces the time and labor required for measurement and analysis, making it possible to more easily and inexpensively grasp and classify food characteristics and identify compounds characteristically contained in food. Furthermore, the simplified analytical workflow allows operators without specialized analytical knowledge to easily perform measurement and analysis. Furthermore, the cost of introducing an analytical system can be reduced, making analysis easier than ever before. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a schematic block diagram of an embodiment of an analysis system for carrying out a food analysis method according to the present invention; [Figure 2] 2 is a flowchart showing the procedure for analyzing a food sample using the analysis system shown in FIG. 1. [Figure 3] FIG. 1 is a diagram showing an example of a principal component analysis result (score plot) obtained by carrying out the analysis method according to the present invention on an alcoholic beverage. [Figure 4]FIG. 1 is a diagram showing an example of a principal component analysis result (score plot) obtained by carrying out a conventional analysis method on an alcoholic beverage. [Figure 5] FIG. 1 is a diagram showing an example of a principal component analysis result (loading plot) obtained by carrying out the analysis method according to the present invention on an alcoholic beverage. [Figure 6] FIG. 1 is a diagram showing an example of a hierarchical clustering analysis result obtained by carrying out the analysis method according to the present invention on alcoholic beverages. [Figure 7] FIG. 1 shows an example of a metabolite to be measured in the analysis method according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of a method for analyzing food using metabolomics according to the present invention will be described below with reference to the accompanying drawings.

[0015] [System Configuration] FIG. 1 is a schematic block diagram of an embodiment of an analysis system for carrying out a food analysis method according to the present invention. This analysis system includes a liquid chromatograph unit (LC unit) 1 and a mass spectrometry unit (MS unit) 2 as measurement units that perform measurements on food samples, a data processing unit 3 that processes data (LC / MS data) obtained by the MS unit 2, a control unit 4 that controls the operation of the LC unit 1 and the MS unit 2, and an input unit 5 and a display unit 6 that serve as user interfaces.

[0016] The LC section 1 includes a mobile phase container 10 for storing the mobile phase, a liquid delivery pump 11 for sucking and delivering the mobile phase, an injector 12 for injecting a sample into the mobile phase, and a column 13 for temporally separating multiple compounds contained in the sample.

[0017] The MS section 2 is a single quadrupole mass spectrometer, and the interior of the chamber 26 is divided into four chambers: an ionization chamber 261, a first intermediate vacuum chamber 262, a second intermediate vacuum chamber 263, and a high vacuum chamber 264. The ionization chamber 261 has an atmosphere at approximately atmospheric pressure, and the other three chambers are all evacuated to a vacuum by a vacuum pump (not shown), and are configured as a multi-stage differential pumping system in which the degree of vacuum increases in order from the ionization chamber 261 to the high vacuum chamber 264.

[0018] An ion source 20 including an electrospray ionization (ESI) probe 201, a heated gas supply unit 202, and a needle electrode 203 for atmospheric pressure chemical ionization (APCI) is disposed within the ionization chamber 261. The ionization chamber 261 and a first intermediate vacuum chamber 262 communicate with each other through a small-diameter desolvation tube 21, and an ion guide 22 is disposed within the first intermediate vacuum chamber 262. An ion guide 23 is also disposed within the second intermediate vacuum chamber 263, and a quadrupole mass filter 24 and an ion detector 25 are disposed within the high vacuum chamber 264.

[0019] The data processing unit 3 includes, as functional blocks, a data storage unit 30 for storing LC / MS data, etc., a quantitative calculation unit 31 for calculating quantitative values ​​for known metabolites (compounds) contained in a sample, a multivariate analysis processing unit 32 for performing a predetermined multivariate analysis, and a display processing unit 33 for displaying the quantitative results, multivariate analysis results, etc.

[0020] The control unit 4 includes, as functional blocks, an analysis control unit 40 that controls the operation of the LC unit 1 and the MS unit 2, a measurement method memory unit 41 in which measurement methods for food metabolomics are stored, and an input / output control unit 42 that controls the input unit 5 and the display unit 6.

[0021] The measurement method stored in the measurement method storage unit 41 is a method package that includes standard analysis conditions for the LC unit 1 and MS unit 2, as well as information on the m / z values ​​and retention times (retention times under standard analysis conditions) of representative ions for each of the numerous metabolites to be measured in food metabolomics. The standard analysis conditions are determined by the manufacturer of the device, and when performing analysis according to the standard analysis conditions, the user does not need to consider the analysis conditions themselves.

[0022] In food metabolomics, which focuses on food, hydrophilic metabolites related to taste, flavor, odor, quality, and functionality are important. For example, 143 hydrophilic metabolites, such as amino acids, organic acids, and nucleic acids (nucleosides and nucleotides), as listed in Figure 7, can be measured. Morpholinoethanesulfonic acid, marked with an asterisk in Figure 7, is the metabolite used as the internal standard in the actual measurement example described below. Metabolites known (or presumed) not to be present in the food sample being analyzed can be used as the internal standard. Compounds other than these 143 components can also be used as the internal standard. Of course, the compounds to be measured are not limited to those exemplified in Figure 7; some compounds can be removed or other compounds can be added as needed. However, to obtain sufficiently accurate results using the multivariate analysis described below, it is generally preferable to measure approximately 50 or more, preferably 100 or more, hydrophilic metabolites.

[0023] The data processing unit 3 and the control unit 4 are actually personal computers (PCs), and the functions of the above-mentioned blocks can be realized by executing dedicated control and processing software pre-installed on the PC. In this case, the input unit 5 is a pointing device such as a keyboard or a mouse attached to the PC, and the display unit 6 is a display monitor attached to the PC.

[0024] The control and processing software may be a single piece of software, or it may be a software package that aggregates individual software for each function. The computer program that constitutes this software may be provided to the user by being stored on a non-transitory computer-readable recording medium, such as a CD-ROM, DVD-ROM, memory card, or USB memory (dongle). The program may also be provided to the user via data transfer over a communication line such as the Internet. Furthermore, the program may be pre-installed on a computer that is part of the system (strictly speaking, a storage device that is part of the computer) when the user purchases the system.

[0025] [Measurement and analysis procedures] 2 is a flow chart showing the procedure for measuring and analyzing a food sample using the analysis system of this embodiment. The procedure for measuring and analyzing a food sample will be described with reference to FIG.

[0026] The user (operator) sets analytical conditions for measuring multiple food samples (step S1). As described above, when using standard analytical conditions, the analytical conditions included in the food metabolomics measurement method package stored in the measurement method storage unit 41 can be used as is. Therefore, the user does not need to consider analytical conditions, including preliminary experiments, and the user simply instructs the input unit 5 to use a measurement method including standard analytical conditions. When analytical conditions other than the standard analytical conditions are desired, the user can, for example, conduct a preliminary experiment and determine appropriate analytical conditions based on the results and input them into the input unit 5. In this case, although the user needs to consider analytical conditions, the only thing that actually needs to be considered is the m / z value and retention time of the ions corresponding to each compound, so the process is relatively simple and does not require complex consideration.

[0027] Once the analytical conditions have been set, the user prepares multiple food samples to be analyzed and issues an instruction to start the analysis via the input unit 5. Upon receiving this instruction, the analytical control unit 40 controls the LC unit 1 and the MS unit 2 according to the set analytical conditions, and executes the LC / MS analysis (step S2). Note that an internal standard can be added to each food sample, and the quantitative results of each compound can be normalized using the quantitative results of the internal standard.

[0028] That is, in the LC section 1, a liquid delivery pump 11 draws mobile phase from a mobile phase container 10 and delivers it at a constant flow rate to a column 13. An injector 12 injects a predetermined amount of food sample into the mobile phase at a predetermined timing. The injected food sample is carried along with the flow of mobile phase and sent to column 13, and the various components in the food sample are separated over time and eluted as they pass through column 13.

[0029] The eluate coming out of the outlet of the column 13 is passed through the ion source of the MS section 2. 20 The ion source 20 is an ion source capable of simultaneously performing ionization by ESI and ionization by APCI. That is, the eluate that reaches the ESI probe 201 is sprayed into the ionization chamber 261 while being charged at the tip of the probe 201. The sprayed charged droplets collide with gas molecules in the ionization chamber 261 and are broken down into fine particles. As the solvent evaporates, the component molecules in the droplets become gaseous ions. In addition, a high-temperature inert gas is sprayed from a heating gas supply unit 202 toward the spray of charged droplets. This promotes the evaporation of the solvent, particularly in large charged droplets that exist near the outside of the spray, and facilitates the generation of gaseous ions derived from the sample components.

[0030] A needle electrode 203 is disposed ahead of the spray of charged droplets in the direction of travel, and a high DC voltage is applied to the needle electrode 203 from a power supply (not shown). This generates a corona discharge near the tip of the needle electrode 203, which ionizes the buffer gas (inert gas supplied as a nebulizing gas or heating gas, solvent gas evaporated from the droplets, etc.). The buffer ions thus generated react with sample component molecules, generating ions of the sample component molecules.

[0031] In this way, the ion source 20 simultaneously performs ionization by ESI and APCI. While ESI is particularly effective for ionic and highly polar compounds, it often fails to sufficiently ionize compounds with low or medium polarity. In contrast, APCI is effective for compounds with low or medium polarity. Combining these two ionization methods allows for efficient ionization of a wide range of compounds, including those for which sufficient sensitivity cannot be achieved using either ionization method. Furthermore, by spraying heated gas onto the nebulized stream, particularly before reaching the needle electrode 203, to promote solvent vaporization, the ion generation efficiency of both ionization methods can be improved, further enhancing sensitivity. This allows the MS unit 2 to achieve a wide mass range of m / z 2 to 2000.

[0032] Ions derived from sample components generated in the ion source 20 are sent through the desolvation tube 21 to the first intermediate vacuum chamber 262, and then focused by the ion guides 22 and 23 before being sent in turn to the second intermediate vacuum chamber 263 and high vacuum chamber 264. The ions derived from sample components are introduced into the quadrupole mass filter 24 in the high vacuum chamber 264. Under the control of the analysis control unit 40, the quadrupole mass filter 24 is driven to perform SIM measurements targeting the m / z value of the ions derived from each component for a predetermined time range centered on the retention time of each component and ensuring a predetermined time width before and after that time (however, this time width may be different before and after).

[0033] Among the compounds to be measured, some can be detected as positive ions, while others can be detected as negative ions. Therefore, SIM measurements targeting positive ions and SIM measurements targeting negative ions may overlap during the same time period. Even in this case, by using a single quadrupole mass spectrometer as the MS unit 2, which can quickly switch between positive and negative ionization modes, it is possible to ensure sufficient data acquisition time (dwell time) for each SIM measurement while minimizing the sampling interval. This increases the detection sensitivity of each ion and improves the accuracy of the chromatogram waveform, thereby improving quantitation.

[0034] By performing LC / MS analysis on one sample, extracted ion chromatogram (EIC) data for each of the 143 compounds within a predetermined time range around the retention time is obtained, and this data is stored in the data storage unit 30. If an internal standard other than the 143 compounds is added in the preprocessing, extracted ion chromatogram data for the internal standard added in addition to the 143 compounds is also obtained by SIM measurement. Similarly, LC / MS analysis is performed on each of the many food samples to be analyzed, and extracted ion chromatogram data for each compound is stored in the data storage unit 30.

[0035] Each time extracted ion chromatogram data for, for example, one food sample is obtained, the quantitative calculation unit 31 creates an extracted ion chromatogram corresponding to each compound based on the data and performs peak detection. If a significant peak is detected, the area value of that peak is calculated (step S3). Peak height values ​​may be calculated instead of peak area values. To reduce the effects of changes in detection sensitivity over time, for each compound, the peak area or height ratio, which is the ratio to the peak area or height value of the internal standard, may be calculated rather than using the peak area or height values ​​directly, and this peak area or height ratio may be used as the quantitative value. Furthermore, instead of using the peak area or height values, the concentration values ​​calculated by converting the area or height values ​​using a previously prepared calibration curve may be used as the quantitative value.

[0036] The process of step S3 may be performed each time LC / MS data for one sample is obtained, or may be performed collectively after LC / MS data for all samples are obtained.

[0037] Once quantitative values ​​such as peak area ratios for each compound have been determined for all food samples, the multivariate analysis processor 32 uses the quantitative values ​​to perform a predetermined multivariate analysis. Specifically, the multivariate analysis may include, for example, principal component analysis and hierarchical clustering analysis (step S4).

[0038] As is well known, principal component analysis can produce score plots and loading plots. A score plot can locate plots corresponding to multiple food samples in a space formed by multiple principal component axes. Therefore, this score plot visualizes the classification status according to the characteristics of multiple food samples and the degree of similarity or difference between multiple food samples. Furthermore, a loading plot can locate plots corresponding to multiple compounds in a space formed by multiple principal component axes. Therefore, a loading plot visualizes compounds that contribute to the classification of multiple food samples, i.e., compounds that may be the cause of differences between multiple food samples.

[0039] On the other hand, hierarchical clustering analysis can create a dendrogram and a corresponding heat map. The dendrogram visualizes the proximity or distance between multiple food samples and the status of groups of multiple food samples. The heat map visualizes the degree of contribution of compounds corresponding to the proximity or distance between multiple food samples.

[0040] The display processing unit 33 displays the results of the multivariate analysis, such as score plots, loading plots, dendrograms, and heat maps, on the screen of the display unit 6 via the input / output control unit 42 (step 5). This allows the user to obtain information such as how multiple food samples are classified, or which compounds contribute to the classification.

[0041] Conventional analytical systems use triple quadrupole mass spectrometers as the MS section. Therefore, it is usually necessary to consider MRM transitions and appropriate collision energy values ​​for each metabolite in advance, a task that is quite tedious and requires specialized knowledge. In contrast, the analytical system of this embodiment uses a single quadrupole mass spectrometer as the MS section 2. Therefore, even when analytical conditions need to be considered, the task is simple and can be performed by personnel without specialized knowledge. On the other hand, although single quadrupole mass spectrometers have inferior performance, such as component selectivity, compared to triple quadrupole mass spectrometers, they can detect a wide range of metabolites important in the food industry with sufficient sensitivity. Therefore, as shown in the actual measurement examples described below, analytical results that are substantially comparable to those of conventional analytical systems can be obtained.

[0042] [Measurement example] Next, an example of an actual measurement of an alcoholic beverage using the analysis system of the above embodiment will be described. In this example, six types of samples were prepared: Beer 1 (lager beer), Beer 2 (ale beer), Happoshu, New Genre (using soy protein as an ingredient), Non-alcoholic Beer 1 (domestically produced), and Non-alcoholic Beer 2 (overseas produced). However, the number of samples differs depending on the type.

[0043] For sample pretreatment, each sample was diluted 10-fold with ultrapure water. During dilution, morpholinoethanesulfonic acid, an internal standard not originally contained in each sample, was added to each sample to a concentration of 1 μmol / L.

[0044] The device used for the measurements was a Shimadzu Nexera LC unit. TM The XR and MS units 2 are LCMS-2050 manufactured by Shimadzu Corporation (however, this model was not yet on the market at the time of filing this application). The main analytical conditions for each device are as shown in Table 1 below. [Table 1] This device enables simultaneous analysis of 143 hydrophilic metabolites, such as amino acids, organic acids, and nucleic acids, shown in Figure 7, which are important in food analysis.

[0045] A simultaneous analysis of 143 hydrophilic metabolites in each sample revealed a total of 82 components, mainly amino acids, organic acids, and nucleic acid metabolites. More than 70 components were detected in the three types of beer (Beer 1, Beer 2, and Non-alcoholic Beer 2), while only 22 components were detected in Happoshu, revealing differences in the components contained.

[0046] The peak area ratio of each metabolite relative to the internal standard was calculated as the quantitative value of each metabolite, and this peak area ratio was used to perform multivariate analysis, including principal component analysis and hierarchical clustering analysis. For this analysis, a multi-omics analysis package described in Non-Patent Document 2 was used. The score plot resulting from the principal component analysis is shown in Figure 3.

[0047] Figure 3 shows that happoshu and non-alcoholic beer 1 are plotted close together, demonstrating similar trends. The other samples are well separated from one another according to type, and each has its own distinct characteristics. Looking at the first principal component (PC1) axis, the samples can be broadly classified into two groups, on either side of PC1=0: Group A, which includes happoshu, non-alcoholic beer 1, and new genre beer, and Group B, which includes non-alcoholic beer 2, beer 1, and beer 2. Because Group A uses non-beer-based ingredients and Group B uses beer-based ingredients, it is highly likely that the first principal component axis primarily represents differences in ingredients.

[0048] As a comparative example, Figure 4 shows a score plot representing the results of principal component analysis based on data obtained using LC-MS / MS (the mass spectrometer was a Shimadzu LCMS-8045). The number of hydrophilic metabolites detected was 82, the same as in the above-mentioned measurement example. As can be seen by comparing Figures 3 and 4, the separation state of the six types of samples is almost the same. This indicates that almost equivalent metabolomics can be performed even when a single quadrupole mass spectrometer is used instead of a triple quadrupole mass spectrometer.

[0049] Figure 5 is a loading plot for the score plot shown in Figure 3. From Figure 5, it is possible to identify the characteristic metabolites contained in each sample. For example, it was found that Beer 2 contains many amino acid and nucleic acid metabolites, the compound names of which are underlined in Figure 5. In this way, by performing principal component analysis, it is possible to classify each sample according to its characteristics or to identify the components that cause differences.

[0050] Figure 6 shows the results of a hierarchical clustering analysis based on the data obtained in the above-mentioned measurement example. Similar to the results of the principal component analysis, the beers are roughly divided into Group A, which includes new genre beers, non-alcoholic beer 1, and happoshu, and Group B, which includes beer 2, beer 1, and non-alcoholic beer 2. Although non-alcoholic beer 1 and non-alcoholic beer 2 are both non-alcoholic beers, the results of the principal component analysis and hierarchical clustering analysis place them in different categories. Non-alcoholic beer 1 is produced domestically and is produced without fermentation by flavoring the wort. In contrast, non-alcoholic beer 2 is produced overseas and uses raw materials used in beer production, using a fermentation method that suppresses alcohol production. It is likely that these differences in raw materials and production methods affect the differences between the two types of non-alcoholic beer.

[0051] On the other hand, as can be seen from the dendrogram resulting from the hierarchical clustering analysis, Beer 1 and Non-alcoholic Beer 2 are classified into similar categories, which may be due to the fact that they are both made from beer-related ingredients and are produced using the same bottom fermentation method. In this way, by performing hierarchical clustering analysis, it is possible to visualize and present the degree of similarity and difference due to the metabolites contained in each sample.

[0052] In the above embodiment and measurement example, principal component analysis and hierarchical clustering analysis were used as multivariate analysis, but other multivariate analysis techniques, such as factor analysis, correspondence analysis, and multidimensional scaling, can also be used.

[0053] Furthermore, the configurations of the LC unit 1 and MS unit 2 used in the above-described embodiment and measurement examples are merely examples, and the present invention is not necessarily limited to these configurations. That is, the above-described mass range of the MS unit 2, the switching time between positive and negative ionization modes, and other device performances are merely examples, and are not necessarily limitations on the present invention.

[0054] Furthermore, simultaneous execution of ESI ionization and APCI ionization in the ion source 20 of the MS unit 2 is not essential, and if the configuration allows rapid switching between ESI ionization and APCI ionization, a method of switching between ESI ionization and APCI ionization depending on the target compound of SIM measurement can also be adopted. Furthermore, if the ion source of the MS unit 2 is configured to be interchangeable between an ESI ion source and an APCI ion source, it is also possible to perform LC / MS analysis using an ESI ion source and an APCI ion source on the same sample, and then merge the results of both analyses to perform multivariate analysis based on the data obtained.

[0055] Furthermore, the above-described embodiment and modified examples are merely examples of the present invention, and it goes without saying that any appropriate modifications, alterations, additions, etc. made within the spirit of the present invention will also be encompassed within the scope of the claims of the present application.

[0056] [Various aspects] It will be appreciated by those skilled in the art that the exemplary embodiments described above are examples of the following aspects.

[0057] (Item 1) One aspect of the method for analyzing food using metabolomics according to the present invention is a method for analyzing a food sample using metabolomics, comprising: a measurement step of collecting LC / MS data for each of a plurality of food samples using a liquid chromatograph mass spectrometer that combines a liquid chromatograph and a single quadrupole mass spectrometer, and performing SIM measurement on each of a plurality of predetermined compounds in the single quadrupole mass spectrometer, targeting the mass-to-charge ratio corresponding to the compound within a predetermined time range that includes the retention time of the compound; a calculation step of creating a chromatogram for each compound based on the LC / MS data for each food sample collected in the measurement step, and determining a quantitative value, which is at least one of the area, height, area ratio, height ratio, or concentration corresponding to a peak observed in the chromatogram; an analysis step of performing multivariate analysis based on the quantitative values ​​of a plurality of compounds obtained for each of a plurality of food samples, thereby performing at least one of classifying the food samples according to their characteristics, visualizing the degree of similarity or difference between each food sample, or extracting compounds that cause the differences; It has the following characteristics.

[0058] According to the above-described embodiment of the food analysis method using metabolomics described in paragraph 1, the time and labor required for measurement and analysis can be reduced, and it is possible to more easily and at low cost understand and classify food characteristics and identify compounds characteristically contained in food. Furthermore, by simplifying the analysis workflow, operators without specialized analytical knowledge can easily perform measurement and analysis. Furthermore, the cost of introducing an analytical system can be reduced, making analysis easier than before.

[0059] (Item 2) In the method for analyzing food using metabolomics described in Item 1, the measurement step can be configured to perform selective ion monitoring measurements on 100 or more compounds including amino acids, organic acids, and nucleic acid metabolites, including nucleosides and nucleotides.

[0060] The food analysis method using metabolomics described in Section 2 performs simultaneous analysis of more than 100 hydrophilic metabolites that are particularly important in the food industry. These metabolites almost completely cover the metabolites related to food taste (bitterness, sweetness, umami, etc.), flavor, odor, quality, functionality, etc. Therefore, the food analysis method using metabolomics described in Section 2 can accurately classify foods characterized by their components and evaluate the similarities and differences between samples.

[0061] (Item 3) In the method for analyzing food using metabolomics described in item 1 or 2, the measurement step can involve simultaneously performing ionization by ESI and ionization by APCI in the single quadrupole mass spectrometer.

[0062] ESI is an ionization method that is particularly effective for ionic and highly polar compounds, while APCI is particularly effective for compounds with low and medium polarity, and the two methods are highly complementary. Therefore, the food analysis method using metabolomics described in Section 3 can comprehensively ionize a wide range of compounds that cannot be sufficiently ionized using either ionization method. This allows for the detection of many types of metabolites with high sensitivity and improves the accuracy of multivariate analysis.

[0063] (Item 4) In the method for analyzing food using metabolomics described in any one of Items 1 to 3, the measurement step can be capable of detecting ions having a mass-to-charge ratio in the range of m / z 2 to 2000.

[0064] According to the food analysis method using metabolomics described in item 4, metabolites with a wide range of physical properties can be detected comprehensively with high sensitivity, thereby improving the accuracy of multivariate analysis.

[0065] (Item 5) In the method for analyzing food using metabolomics described in any one of items 1 to 4, the measurement step may involve switching between positive ionization mode and negative ionization mode in 10 msec or less.

[0066] Some metabolites are more likely to become positive ions than others, and simultaneous analysis of metabolites requires switching between positive and negative ions targeted in SIM measurements. The metabolomics food analysis method described in Section 5 allows for quick switching between positive and negative ionization modes, lengthening the data acquisition time in SIM measurements and improving sensitivity. Furthermore, the number of SIM measurements that can be performed per unit time can be increased, shortening the sampling time interval (loop time). This increases the accuracy of chromatogram waveforms and improves quantitation.

[0067] (Item 6) In the method for analyzing food using metabolomics according to any one of items 1 to 5, the multivariate analysis may be principal component analysis or hierarchical clustering analysis.

[0068] Principal component analysis can classify and visualize multiple food samples according to their characteristics, and identify the compounds that cause differences between food samples. Hierarchical clustering analysis can also visualize and present the degree of similarity and difference between compounds contained in each food sample. [Explanation of symbols]

[0069] 1...LC section 10...Mobile phase container 11...Liquid transfer pump 12...Injector 13...Column 2...MS section 20...Ion source 201...ESI probe 202...Heating gas supply section 203...Needle electrode 21...Desolvation tube 22, 23...Ion guide 24...Quadrupole mass filter 25...Ion detector 26...Chamber 261...Ionization chamber 262...First intermediate vacuum chamber 263...Second intermediate vacuum chamber 264…High vacuum chamber 3...Data processing section 30...Data storage section 31...Quantitative calculation section 32...Multivariate analysis processing unit 33...Display processing unit 4...Control unit 40...Analysis control unit 41...Measurement method storage section 42... Input / output control unit 5...Input section 6…Display section

Claims

1. 1. A method for analyzing a food sample using metabolomics, comprising: a measurement step of collecting LC / MS data for each of a plurality of food samples using a liquid chromatograph mass spectrometer that combines a liquid chromatograph and a single quadrupole mass spectrometer, and performing selected ion monitoring measurement for each of a plurality of predetermined compounds in the single quadrupole mass spectrometer, targeting the mass-to-charge ratio corresponding to the compound within a predetermined time range that includes the retention time of the compound; a calculation step of creating a chromatogram for each compound based on the LC / MS data for each food sample collected in the measurement step, and determining a quantitative value, which is at least one of the area, height, area ratio, height ratio, or concentration corresponding to a peak observed in the chromatogram; an analysis step of performing multivariate analysis based on the quantitative values ​​of a plurality of compounds obtained for each of a plurality of food samples, thereby performing at least one of classifying the food samples according to their characteristics, visualizing the degree of similarity or difference between each food sample, or extracting compounds that cause the differences; A method for analyzing food using metabolomics.

2. 2. The method for analyzing food using metabolomics according to claim 1, wherein the measuring step comprises selective ion monitoring measurement of 100 or more compounds including amino acids, organic acids, and nucleic acid metabolites.

3. A method for analyzing food using metabolomics as described in claim 1 or 2, wherein in the measurement step, ionization by electrospray ionization and ionization by atmospheric pressure chemical ionization are performed simultaneously in the single quadrupole mass spectrometer.

4. The method for analyzing food using metabolomics according to any one of claims 1 to 3, wherein the measuring step is capable of detecting ions having a mass-to-charge ratio in the range of m / z 2 to 2000.

5. The method for analyzing food using metabolomics according to any one of claims 1 to 4, wherein in the measurement step, the switching time between positive ionization mode and negative ionization mode is 10 msec or less.

6. The method for analyzing food using metabolomics according to any one of claims 1 to 5, wherein the multivariate analysis is principal component analysis or hierarchical clustering analysis.

7. A method for analyzing food using metabolomics described in any of claims 1 to 6, wherein in the analysis step, information is further collected regarding characteristic compounds, which are a portion of the plurality of compounds and are a plurality of compounds that characterize the food sample.

8. The plurality of compounds includes characteristic compounds that are a plurality of compounds that characterize the food sample and non-characteristic compounds that are other compounds, In the calculation step, quantitative values ​​are obtained for all of the plurality of compounds, including the feature compound and the non-feature compound; A method for analyzing food using metabolomics described in any one of claims 1 to 7, wherein in the analysis step, all quantitative values ​​of the multiple compounds, including the characteristic compounds and the non-characteristic compounds, are subjected to multivariate analysis.

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