HK1181115B

HK1181115B - Fp creation method, creation device, and fp

Info

Publication number: HK1181115B
Application number: HK13108442.2A
Authority: HK
Inventors: 森芳和; 野田桂一
Original assignee: 津村股份有限公司
Priority date: 2011-06-01
Filing date: 2012-05-31
Publication date: 2016-11-18

Description

FP creation method, creation device, and FP

Technical Field

The present invention relates to an FP creation method, a creation program, a creation device, and an FP for use in an evaluation target, for example, for performing quality evaluation of a traditional Chinese medicine as a multicomponent drug.

Background

Examples of the evaluation target include natural drugs such as Chinese medicines belonging to a drug composed of a plurality of components (hereinafter, referred to as a multi-component drug). The quantitative and qualitative profile (profile) of these drugs varies depending on factors such as geological factors, ecological factors, collection time, collection place, collection time, and growth time of the raw crude drug used.

Therefore, certain standards are defined for the quality of these multicomponent drugs and the like to secure safety and effectiveness thereof, and quality evaluations are performed by national regulatory agencies, chemical organization groups, manufacturers, and the like based on the standards.

However, the criteria for determining the quality of a multicomponent drug are generally set by selecting one or more components that are characteristic of the multicomponent drug and setting the selected component or components according to the content thereof.

For example, in non-patent document 1, when the identification of an effective component in a multicomponent drug is impossible, a plurality of components having physical properties such as quantitative analysis availability, high solubility in water, no decomposition in hot water, and no chemical reaction with other components are selected, and the content of these components obtained by chemical analysis is used as a criterion for evaluation.

Further, it is also known to apply chromatography to a multicomponent drug, obtain an ultraviolet-visible absorption spectrum for each retention time, and set a criterion for evaluation from part of the component information.

For example, in patent document 1, a part of peaks in HPLC chromatogram data (hereinafter, referred to as a chromatogram) is selected and a multi-component drug is evaluated by barcoding.

However, in these methods, the evaluation target is limited to "the content of the specific component" or "the chromatogram peak of the specific component", and only a part of the components contained in the multicomponent drug is the evaluation target. Therefore, since a multicomponent drug contains many components other than those to be evaluated, the method for evaluating a multicomponent drug is not accurate enough.

In order to accurately evaluate the quality of a multicomponent drug, it is necessary to evaluate peak information that spans all peak information or all peak information that is close to trivial information with several% being removed.

However, it is difficult to efficiently correspond a plurality of peaks with high accuracy, which hinders highly accurate and efficient evaluation of a multicomponent drug.

Further, even in the case of a multicomponent drug of the same product name, the crude drug as the raw material is a natural product, and therefore, the components may slightly differ from each other. Therefore, even for the same quality of medicines, the content ratios of the constituent components may be different or the components present in a certain medicine may not be present in other medicines (hereinafter, referred to as an error between medicines). Further, there are also factors such as the peak intensity of the chromatogram and the time for dissolution of the peak, which do not have strict reproducibility (hereinafter referred to as analysis error). Accordingly, peaks derived from the same component cannot be correlated with respect to all peaks or peaks close to all peaks between multicomponent drugs (hereinafter referred to as peak assignment), and therefore, highly accurate and efficient evaluation is hindered.

Documents of the prior art

Patent document 1: japanese laid-open patent publication No. 2002-214215

Non-patent document 1: journal pharmaceutical affairs vol.28, No.3, 67-71(1986)

Disclosure of Invention

Problems to be solved by the invention

The conventional evaluation method has a problem that the quality of an evaluation target is evaluated with high accuracy and efficiency.

Means for solving the problems

The present invention is an FP production method for an evaluation object including an FP production process for producing an FP including a peak detected from a chromatogram of the evaluation object and a retention time thereof, in order to contribute to improvement of evaluation accuracy and efficiency, the FP production method including: the FP creation step creates a three-dimensional chromatogram (hereinafter referred to as a 3D chromatogram) using the retention time, the detection wavelength, and the signal intensity as data, and creates the FP using the peak detected from the 3D chromatogram at a specific wavelength, the retention time, and the UV spectrum of the peak.

Further, the FP preparation program is an FP preparation program for realizing an evaluation target for preparing an FP preparation function of FP composed of a peak detected from a 3D chromatogram of the evaluation target and a retention time thereof on a computer, the FP preparation program comprising: the FP creation function creates a 3D chromatogram using the retention time, the detection wavelength, and the peak as data, and creates the FP using the peak detected from the 3D chromatogram at a specific wavelength, the retention time, and the UV spectrum of the peak.

Further, the FP preparation apparatus is an FP preparation apparatus for an evaluation object including an FP preparation unit for preparing an FP composed of a peak detected from a 3D chromatogram of the evaluation object and a holding time thereof, and is characterized in that: the FP creation unit creates a 3D chromatogram using the retention time, the detection wavelength, and the peak as data, and creates the FP using the peak detected from the 3D chromatogram at a specific wavelength, the retention time, and the UV spectrum of the peak.

Further, the FP produced by the FP production method is characterized in that: the FP is composed of a peak detected from the 3D chromatogram at a specific wavelength, a retention time thereof, and a UV spectrum of the peak.

ADVANTAGEOUS EFFECTS OF INVENTION

The FP creation method of the present invention is constituted as described above, and can create data (hereinafter referred to as fingerprint data: FP) including a maximum value or an area value (hereinafter referred to as a peak) of a signal intensity (height) of a peak detected at a specific wavelength from three-dimensional chromatogram data (hereinafter referred to as a 3D chromatogram) of an evaluation object, a time of appearance (hereinafter referred to as a retention time) of the peak, and an ultraviolet-visible absorption spectrum (hereinafter referred to as a UV spectrum) of the peak

The FP is composed of three-dimensional information (peak, retention time, and UV spectrum) as in the 3D chromatogram.

Therefore, FP is data directly receiving information specific to the drug. Nevertheless, since the data size can be greatly reduced, the amount of information to be processed can be greatly reduced compared to 3D tomograms, and the processing speed can be increased.

As a result, the respective peaks of the target FP can be accurately and efficiently assigned to the respective peaks of the reference FP, which contributes to further improvement of the accuracy and efficiency of the evaluation.

The FP production program according to the present invention is configured as described above, and therefore, each function can be realized on a computer, and an FP of three-dimensional information can be produced, which contributes to further improvement of the accuracy and efficiency of evaluation.

The FP production device of the present invention is configured as described above, and therefore, it is possible to produce an FP of three-dimensional information by operating each part, and further improve the accuracy and efficiency of evaluation.

The FP of the invention is formed by the above structure, thereby being beneficial to further improving the evaluation accuracy and efficiency.

Drawings

FIG. 1 is a block diagram of an apparatus for evaluating a multicomponent drug (example 1);

FIG. 2 is a block diagram showing the evaluation sequence of a multicomponent drug (example 1);

FIG. 3 is an explanatory diagram of FPs created from three-dimensional chromatogram data (hereinafter referred to as 3D chromatogram) (example 1);

FIG. 4 shows FP per drug, (A) is a graph showing drug A, (B) is a graph showing drug B, and (C) is a graph showing drug C (example 1);

FIG. 5 is a graph showing the holding times of the object FP and the reference FP (example 1);

fig. 6 is a diagram showing a retention time appearance pattern of the object FP (example 1);

FIG. 7 is a diagram showing a retention time appearance pattern of a reference FP (example 1);

fig. 8 is a diagram showing the coincidence number of the holding time appearance distances of the object FP and the reference FP (embodiment 1);

fig. 9 is a diagram showing the degree of coincidence of retention time appearance patterns of the object FP and the reference FP (embodiment 1);

fig. 10 is a diagram showing a home object peak of the object FP (example 1);

FIG. 11 is a peak pattern diagram formed by 3 peaks including a peak of a belonging object (example 1);

FIG. 12 is a peak pattern diagram formed by 5 peaks including a peak of a belonging object (example 1);

fig. 13 is a diagram showing the allowable width of a peak of an object of interest (example 1);

fig. 14 is a diagram showing the attribution candidate peak of the reference FP with respect to the attribution target peak (example 1);

fig. 15 is a peak pattern diagram (example 1) formed by 3 peaks of the belonging target peak and the belonging candidate peak;

fig. 16 is a peak pattern diagram (example 1) formed by 3 peaks of the belonging target peak and the other belonging candidate peaks;

fig. 17 is a peak pattern diagram (example 1) formed by 3 peaks of the belonging target peak and the other belonging candidate peaks;

fig. 18 is a peak pattern diagram (example 1) formed by 3 peaks of the belonging peak and the other belonging candidate peaks;

fig. 19 is a peak pattern diagram (example 1) formed by 5 peaks of the belonging target peak and the belonging candidate peak;

fig. 20 is a peak pattern diagram (example 1) formed by 5 peaks of the belonging target peak and the other belonging candidate peaks;

fig. 21 is a peak pattern diagram (example 1) formed by 5 peaks of the belonging target peak and the other belonging candidate peaks;

fig. 22 is a peak pattern diagram (example 1) formed by 5 peaks of the belonging target peak and the other belonging candidate peaks;

fig. 23 is a diagram showing candidate peaks formed by peak patterns of the belonging target peak and the belonging candidate peak (example 1);

fig. 24 is a diagram showing the total number of peak patterns of the belonging peak when 4 peak pattern formation candidate peaks are present (example 1);

fig. 25 is a diagram showing the total number of peak patterns belonging to candidate peaks when 4 peak pattern formation candidate peaks are present (example 1);

fig. 26 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 27 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 28 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 29 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 30 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 31 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 32 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 33 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 34 is an explanatory diagram (example 1) of a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 35 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 36 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 37 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 38 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 39 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 40 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 41 is an explanatory diagram (example 1) of a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 42 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 43 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 44 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 45 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 46 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 47 is an explanatory diagram illustrating a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak. (example 1)

Fig. 48 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 49 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 50 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 51 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 52 is an explanatory diagram (example 1) showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak;

fig. 53 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 54 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 55 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 56 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 57 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 58 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 59 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 60 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 61 is an explanatory diagram showing a network comparison of the peak pattern of the belonging candidate peak with respect to the peak pattern of the belonging target peak (example 1);

fig. 62 is a diagram showing a method of calculating the degree of matching of peak patterns formed by 3 peaks of the belonging target peak and the belonging candidate peak (example 1);

fig. 63 is a diagram showing a method of calculating the degree of matching of peak patterns formed by 3 peaks of the belonging target peak and the belonging candidate peak (example 1);

fig. 64 is a diagram showing a method of calculating the degree of matching of peak patterns formed by 5 peaks of the belonging target peak and the belonging candidate peak (example 1);

fig. 65 is a diagram showing UV spectra of the belonging target peak and the belonging candidate peak (example 1);

fig. 66 is an explanatory diagram showing the coincidence degree of the UV spectra of the belonging target peak and the belonging candidate peak (example 1);

FIG. 67 is an explanatory view showing the coincidence degree of the belonging candidate peaks obtained by comparing the peak pattern with the UV spectrum (example 1);

fig. 68 is an explanatory diagram showing that the object FP belongs to the reference group FP (embodiment 1);

fig. 69 is a diagram showing a condition that a subject FP belongs to a reference group FP (embodiment 1);

fig. 70 is a diagram showing various objects FP and their evaluation values (MD values) (example 1);

fig. 71 is a diagram showing various objects FP and their evaluation values (MD values) (example 1);

fig. 72 is a diagram showing various objects FP and their evaluation values (MD values) (example 1);

fig. 73 is a diagram showing various objects FP and their evaluation values (MD values) (example 1);

fig. 74 is a diagram showing various objects FP and their evaluation values (MD values) (example 1);

FIG. 75 is a process view showing an evaluation method of a multicomponent drug (example 1);

FIG. 76 is a flowchart showing the evaluation of a multicomponent drug (example 1);

FIG. 77 is a flow chart of data processing in the FP creation function of a single wavelength (example 1);

FIG. 78 is a flow chart of data processing in the FP creation function for a plurality of wavelengths (example 1);

FIG. 79 is a flow chart of data processing in FP creation function for a plurality of wavelengths (example 1);

fig. 80 is a data processing flow chart in peak attribution processing 1 (selection of reference FP);

fig. 81 is a data processing flowchart in the peak assignment process 2 (calculation of an assignment score) (example 1);

fig. 82 is a flowchart of data processing in peak assignment processing 3 (specific to a peak) (example 1);

fig. 83 is a data processing flow chart in the peak attribution process 4 (attribution to the reference group FP) (embodiment 1);

fig. 84 is a data processing flow chart in peak attribution processing 4 (attribution to reference group FP) (embodiment 1);

fig. 85 is a flowchart of the coincidence degree calculation process of the retention time appearance pattern in the peak assignment process 1 (selection of reference FP) (embodiment 1);

fig. 86 is a flowchart of the process of calculating the degree of matching of UV spectrum in the peak assignment process 2 (calculation of the assignment score) (example 1);

fig. 87 is a flowchart of a matching degree calculation process of peak patterns in the peak assignment process 2 (calculation of an assignment score) (example 1);

FIG. 88 is a flowchart for creating a reference FP feature file (example 1);

fig. 89 is a flowchart showing details of "reference FP attribution result merging processing (creation of a reference FP correspondence table") (embodiment 1);

fig. 90 is a flowchart showing details of "reference FP attribution result merging processing (creation of a reference FP correspondence table") (embodiment 1);

FIG. 91 is a flowchart showing details of "peak feature value processing (creation of reference group FP)" (embodiment 1);

FIG. 92 is a graph showing an example of data of a 3D chromatogram (example 1);

FIG. 93 is a graph showing an example of data of peak information (example 1);

FIG. 94 is a graph showing an example of data of FP (example 1);

fig. 95 is a graph (example 1) showing the case of the assignment score calculation result (determination result) of the target FP with respect to the reference FP;

FIG. 96 is a graph showing a comparison process of peaks corresponding to a subject FP and a reference FP (example 1);

FIG. 97 is a graph showing the comparison result file case (example 1);

FIG. 98 is a graph showing an example of data of the reference group FP (example 1);

FIG. 99 is a graph showing an example of a FP peak eigenvalue profile of the object (example 1);

fig. 100 is a flowchart showing details of a modification of subroutine 2 applied in place of fig. 86 (example 1); and

fig. 101 is a graph showing a calculation example of the moving average and the moving inclination (example 1).

Detailed Description

The purpose of contributing to improvement of the accuracy and efficiency of evaluation can be achieved by FP created from three-dimensional information (peak, retention time, and UV spectrum).

Example 1

Example 1 of the present invention is a method, program, device and FP for creating an FP of a multicomponent drug, for example, which is an object to be evaluated such as a multicomponent substance.

The multicomponent drug is defined as a drug containing a plurality of effective chemical components, but is not limited thereto, and includes crude drugs, combinations of crude drugs, extracts of these, traditional Chinese medicines, and the like. The formulation is not particularly limited, and may include, for example, a liquid, an extract, a capsule, a granule, a pill, a suspension, an emulsion, a powder, an alcoholic preparation, a lozenge, an extract decoction, a tincture, a tablet, an aromatic water preparation, a fluid extract, and the like, which are prescribed in the general formulation of the pharmaceutical preparation modified from the 15 th ministry of medicine. The multicomponent material may contain a material other than the drug.

Specific examples of the Chinese herbs are described in the "attention on use" of the prescription 148 of Chinese medicinal preparation for medical use, which is commonly revised and entered through the introduction of the general prescription of Chinese (1978).

In the evaluation of a multicomponent drug, in order to evaluate whether or not an evaluation target drug is equivalent to a plurality of drugs evaluated as normal products, first, information unique to the drug is extracted from three-dimensional chromatogram data (hereinafter, referred to as a 3D chromatogram) of the evaluation target drug to create a target FP.

Next, each peak of the target FP is assigned to peak correspondence data (hereinafter, referred to as a reference group FP) of all reference FPs created by performing peak assignment processing on all reference FPs, and a peak feature value is obtained.

Next, the identity of the peak of the target FP (hereinafter referred to as the peak belonging to the target FP) belonging to the peak of the reference group FP is evaluated by the MT method. Finally, the obtained evaluation value (hereinafter referred to as MD value) is compared with a preset determination value (upper limit value of MD value), and it is determined whether or not the evaluation target drug is equivalent to a normal drug.

The 3D chromatogram is HPLC chromatogram data (hereinafter referred to as a chromatogram) of a multicomponent drug, which is a multicomponent substance to be evaluated, and includes a UV spectrum.

FP is fingerprint data composed of a maximum value or an area value (hereinafter referred to as a peak) of the signal intensity (height) of a peak detected at a specific wavelength and an appearance time (hereinafter referred to as a retention time) of the peak.

The object FP is a plurality of peaks at a specific detection wavelength, retention time thereof, and UV spectrum extracted from 3D chromatogram data, which is three-dimensional chromatogram data of a chinese medicine to be evaluated.

The reference FP is a FP of a traditional Chinese medicine which is a multicomponent drug which is a multicomponent substance determined as a normal product, corresponding to the target FP.

Evaluation device for multicomponent drug

Fig. 1 is a block diagram of an apparatus for evaluating a multi-component drug, fig. 2 is a block diagram showing a procedure for evaluating a multi-component drug, fig. 3 is an explanatory view of an FP created from a 3D chromatogram, fig. 4(a) is a drug a, fig. 4(B) is a drug B, and fig. 4(C) is a FP of a drug C.

As shown in fig. 1 and 2, a multicomponent drug evaluation device 1 as a pattern evaluation device includes: an FP creation section 3, a reference FP selection section 5, a peak pattern creation section 7, a peak assignment section 9, and an evaluation section 11 as pattern acquisition sections. The multi-component medicine evaluation device 1 is constituted by a computer, and includes a CPU, ROM, RAM, and the like, although not shown.

In the present embodiment, the FP production section 3, the reference FP selection section 5, the peak pattern production section 8, the peak assignment section 9, and the evaluation section 11 are constituted by one computer. However, the FP producing section 3, the reference FP selecting section 5, the peak pattern producing section 7, the peak assigning section 9, and the evaluating section 11 may be constituted by respective computers, or the FP producing section 3, the reference FP selecting section 5, the peak pattern producing section 7, the peak assigning section 9, and the evaluating section 11 may be constituted by respective computers.

The FP preparation unit 3 is provided in an FP preparation device configured as a part of the evaluation device 1 for a multicomponent drug, and, for example, as a chromatogram of a chinese medicine 13 (see fig. 2), a functional unit for extracting a target FP17 (hereinafter, abbreviated as "FP 17") at a specific detection wavelength, a retention time thereof, and a UV spectrum from a 3D chromatogram 15 as three-dimensional chromatogram data is prepared as shown in fig. 3 by an FP preparation program installed in a computer. The FP production program can be produced by using an FP production program recording medium on which the FP production program is recorded and reading the FP production program by the FP production unit 3 configured by a computer.

The FP17 is composed of three-dimensional information (peak, retention time, and UV spectrum) as in the 3D chromatogram 15.

Therefore, FP17 is data obtained by receiving information unique to the medicine as it is. However, since the data volume is compressed to about 1/70, the amount of information to be processed can be significantly reduced and the processing speed can be increased as compared with the 3D tomogram 15.

The 3D chromatogram 15 is the result of applying High Performance Liquid Chromatography (HPLC) to the chinese medicine 13. The 3D chromatogram 15 represents the moving speed of each component, and shows the moving distance at a specific time, or shows the appearance of the column (column) end in time series on a graph. In HPLC, the appearance time of a peak is called retention time (retentime) because the detector response is plotted against the time axis.

As the detector, there is no particular limitation, and an absorbance detector (absorbance detector) using optical properties may be used, and the peak is a signal intensity obtained in a three-dimensional state as a detection wavelength corresponding to Ultraviolet (UV). As a detector utilizing optical properties, a transmittance detector (transmittincedetector) may also be used.

The detection wavelength is not limited, but is preferably a plurality of wavelengths selected from the range of 150nm to 900nm, particularly preferably from 200nm to 400nm in the ultraviolet-visible light absorption region, and more preferably from 200nm to 300 nm.

The 3D chromatogram 15 has at least the number (lot number) of the Chinese medicine, the retention time, the detection wavelength, and the peak as data.

The 3D chromatogram 15 can be obtained by a commercially available apparatus, for example, Agilent1100 system. The chromatography is not limited to HPLC, and various methods can be used.

As shown in fig. 2 and 3, the 3D chromatogram 15 is displayed with the x-axis as the retention time, the y-axis as the detection wavelength, and the z-axis as the signal intensity.

FP17 has at least the number of the Chinese medicine (lot number), the retention time, the peak of a specific wavelength and UV spectrum as data.

As shown in fig. 2 and 3, FP17 is displayed in a quadratic form with the x-axis as the holding time and the y-axis as the peak of a specific detection wavelength, and is data having UV spectrum information similar to UV spectrum 25 shown as peak 1 for each peak as shown in fig. 3. The specific detection wavelength for creating FP17 is not particularly limited, and can be variously selected. However, it is important for the relay information that all peaks in the FP17 contain the 3D tomogram. Therefore, in example 1, the detection wavelength is set to 203nm including all peaks in the 3D chromatogram.

On the other hand, all peaks may not be contained in a single wavelength. In this case, a plurality of detection wavelengths are used, and as will be described later, an FP is formed which combines a plurality of wavelengths and includes all peaks.

In embodiment 1, the peak is set to the maximum value of the signal intensity (peak height), but an area value may be used as the peak. In addition, the FP may be a two-dimensional information in which the x-axis is the retention time and the y-axis is the peak of a specific detection wavelength without containing the UV spectrum. In this case, the FP may be prepared from a 2D chromatogram having the number of the Chinese medicine (batch number) and the retention time as the data.

Fig. 4(a) is the FP of agent a, fig. 4(B) is the FP of agent B, and fig. 4(C) is the FP of agent C.

The reference FP selection unit 5 is a functional unit that selects a reference FP used in the peak pattern creation unit 7 from among the plurality of reference FPs. The reference FP selection unit 5 selects an FP of the multi-component material suitable for peak assignment of the target FP from the plurality of reference FPs. That is, in order to assign the peak of each peak of the target FP with high accuracy, as shown in fig. 5 to 9, the matching degree of the retention time appearance pattern of the peak is calculated between the target FP and the reference FP, and the reference FP having the smallest matching degree is selected from all the reference FPs. As will be described in detail later. As shown in fig. 10 to 12, the peak pattern creating unit 7 is a functional unit that creates, as a peak pattern of the peak to be assigned, a peak pattern made up of n +1 peaks including n peaks present at least one of before and after the peak in the time axis direction, for the peak to be assigned (hereinafter referred to as the peak to be assigned) in the target FP 33. n is a natural number. As will be described in detail later.

Fig. 11 shows a peak pattern composed of a total of 3 peaks including 2 peaks present at least one of front and rear in the time axis direction, and fig. 12 shows a peak pattern composed of a total of 5 peaks including 4 peaks present at least one of front and rear in the time axis direction.

As shown in fig. 13 to 22 (described later), the peak pattern creating unit 7 is a functional unit that creates a peak pattern including a total of n +1 peaks including n peaks present at least one of before and after the time axis direction as a peak pattern of the belonging candidate peak, with respect to all peaks (hereinafter referred to as belonging candidate peaks) within a range (allowable width) in which a difference from the retention time of the belonging peak is set in the reference FP 55. Fig. 15 to 18 (described later) show peak patterns each including a total of 3 peaks including 2 peaks present at least one of front and rear in the time axis direction. Fig. 19 to 22 (described later) show peak patterns each including a total of 5 peaks including 4 peaks existing at least one of front and rear in the time axis direction.

The allowable width is not limited, but is preferably 0.5 to 2 minutes from the viewpoint of accuracy and efficiency. In example 1, the score was 1.

In addition, in the peak pattern creating unit 7, even when the number of peaks of the object FP33 and the reference FP55 is different (that is, there is a peak that is not present in any one of them), it is possible to flexibly cope with this. Therefore, as shown in fig. 23 to 61 (described later), among the belonging target peak and the belonging candidate peak (both of them), a peak constituting a peak pattern (hereinafter, referred to as a peak pattern constituting peak) is changed to create a peak pattern comprehensively. Fig. 23 to 61 show peak patterns composed of a total of 3 peaks including 2 peaks present at least one of front and rear in the time axis direction.

The peak assigning unit 9 is a functional unit that compares the peak patterns of the object FP and the reference FP and specifies the corresponding peak. In the embodiment, the degree of coincidence between the peak pattern of the belonging peak and the peak pattern of the belonging candidate peak and the degree of coincidence between the UV spectra are calculated, and the corresponding peak is identified. The following specific constitution.

The functional unit calculates the matching degree of the belonging candidate peaks obtained by combining the matching degrees of the two peaks, and belongs each peak of the object FP33 to each peak of the reference FP55 based on the matching degree.

Further, the functional section finally assigns the peaks of the target FP to the peaks of the reference group FP as shown in fig. 68 and 69 (described later) based on the assignment result between the target FP33 and the reference FP 55.

In the peak assigning unit 9, the degree of coincidence of the peak patterns is calculated from the difference between the corresponding peak and holding time between the peak patterns of the assignment target peak and the assignment candidate peak, as shown in fig. 62 to 64 (described later). As shown in fig. 65 and 66 (described later), the degree of matching between the UV spectra is calculated from the difference between the absorbance at each wavelength of the UV spectrum 107 of the assignment target peak 45 and the absorbance at each wavelength of the UV spectrum 111 of the assignment candidate peak 67. Further, as shown in fig. 67 (described later), the degree of coincidence between the two is multiplied to calculate the degree of coincidence of the candidate peaks 67.

The evaluation unit 11 is a functional unit that compares and evaluates the peak specified and assigned by the peak assignment unit 9 with the peaks of the plurality of reference FP. In the embodiment, the functional units are equivalent parts between the target FP belonging peak 21 and the reference FP19 evaluated by the MT method.

The MT method is a commonly known calculation method in quality engineering. For example, it is described in journal of the national institute of quality engineering, published by the Japanese standards Association (2000), pages 136 and 138, the application of quality engineering lecture, technical development of chemical and pharmaceutical biology, edited by the Japanese standards Association (1999), pages 454 and 456, quality engineering 11(5), 78-84(2003), and the entry MT system (2008).

In addition, general commercially available MT method program software can be used. Commercially available MT method program software includes: ATMTS by Angley (Inc.), TM-ANOVA by Japan standards Association (Profibus), MT method for windows by Ohken (Inc.), and the like.

The evaluation unit 11 assigns an axis of variable of the MT method to one of the lot number, the holding time, and the UV detection wavelength of the chinese medicine of the subject FP17, and sets the peak as a characteristic value of the MT method.

The assignment of the variable axis is not particularly limited, but it is preferable that the holding time is assigned to a so-called item axis of the MT method, the number of the multicomponent drug is assigned to a so-called number row axis, and the peak is assigned to a so-called eigenvalue of the MT method.

Here, the item axis and the number row axis are defined as follows. That is, in the MT method, the mean value mj and the standard deviation σ j are obtained for the data set (dataset) Xij, and the correlation coefficient r between i and j is obtained from the normalized value Xij ═ of (Xij-mj)/σ j of Xij, thereby obtaining the unit space or mahalanobis distance (mahalanobis distance). In this case, the item axis and the number row axis are defined as "the average value mj and the standard deviation σ j are obtained by changing the value of the number row axis for each value of the item axis".

The MT method is used to obtain the reference point and the unit quantity (hereinafter, simply referred to as "unit space") from the data and the feature value to which the axis is assigned. Here, the reference point, the unit amount, and the unit space are defined according to the description of the MT method.

The MD value is obtained by the MT method as a value indicating a degree of difference from the unit space of the drug to be evaluated. Here, the MD value is defined in the same manner as described in the literature of the MT method, and is obtained by the method described in the literature.

Using the MD values obtained in this manner, the degree of difference between the drug to be evaluated and the plurality of drugs evaluated as normal can be determined.

For example, by performing the attribution processing as described above for each object FP in FIGS. 70 to 74, MD values (MD values: 0.25, 2.99, etc.) can be obtained by the MT method described above.

When the MD value is evaluated with respect to the MD value of a normal product, the MD value is similarly determined for a plurality of medicines evaluated as normal products. The MD value of the normal product is set as a threshold value, and the MD value of the evaluation target drug is plotted as shown by the evaluation result 23 of the evaluation unit 11 in fig. 2, whereby the normal product or the abnormal product can be determined. In the evaluation result 23 of the evaluation unit 11 in fig. 2, for example, the MD value is 10 or less, and is set as a normal product.

The evaluation unit 11 may be adapted to compare and evaluate the equivalence of the target FP belonging peak 21 and the reference group 19, and may be adapted to a pattern recognition method other than the MT method.

Action principle of wave crest pattern processing

Fig. 5 to 67 are diagrams for explaining the operation principle of the reference FP selecting section 5, the peak pattern creating section 7, the peak assigning section 9, and the evaluating section 11.

Fig. 5 to 9 are diagrams for explaining the degree of coincidence between the retention time appearance patterns of the target FP and the reference FP in the reference FP selection unit 5. Fig. 5 is a diagram showing the holding times of the object FP and the reference FP, fig. 6 is a diagram showing the appearance pattern of the holding times of the object FP, and fig. 7 is a diagram showing the appearance pattern of the holding times of the reference FP. Fig. 8 is a diagram showing the number of coincidence between the retention time appearance distances of the object FP and the reference FP, and fig. 9 is a diagram showing the degree of coincidence between the retention time appearance patterns of the object FP and the reference FP.

Fig. 5 shows the holding times of the object FP33 and the reference FP 55. Fig. 6 and 7 show retention time appearance patterns in which the distances between all retention times are calculated from the retention times of the object FP33 and the reference FP55, and the distances are collected in a table format. In fig. 8, the number of coincidence between the appearance distances of the retention times is calculated from these appearance patterns, and the number of coincidence between the appearance distances of the retention times, which is obtained by integrating these numbers into a table, is displayed. In fig. 9, the degree of coincidence of the retention time appearance patterns is calculated based on the number of coincidences, and the degree of coincidence of the retention time appearance patterns is displayed in a table format. Fig. 10 to 12 are diagrams illustrating a peak pattern created by the belonging peak and the peripheral peaks of the pattern creating unit 7. Fig. 10 is a diagram showing the belonging target peak of the target FP, fig. 11 is an explanatory diagram of a peak pattern made up of 3 peaks including 2 peripheral peaks, and fig. 12 is an explanatory diagram of a peak pattern made up of 5 peaks including 4 peripheral peaks.

Fig. 13 and 14 are explanatory diagrams of the relationship between the belonging peak and the belonging candidate peak of the peak pattern creating unit 7, fig. 13 is a diagram showing the allowable width of the belonging peak, and fig. 14 is a diagram showing the belonging candidate peak of the reference FP with respect to the belonging peak.

Fig. 15 to 18 are examples of peak maps of the belonging target peak and the belonging candidate peak created by the 3 peaks of the peak pattern creating unit 7. Fig. 15 is a peak pattern diagram formed of 3 peaks of the belonging peak and the belonging candidate peak, fig. 16 is a peak pattern diagram formed of 3 peaks of the belonging peak and the other belonging candidate peaks, fig. 17 is a peak pattern diagram formed of 3 peaks of the belonging peak and the other belonging candidate peaks, and fig. 18 is a peak pattern diagram formed of 3 peaks of the belonging peak and the other belonging candidate peaks.

Fig. 19 to 22 are peak pattern diagrams of the belonging target peak and the belonging candidate peaks created by the 5 peaks of the peak pattern creation unit 7.

Fig. 23 to 61 are diagrams for describing the principle of the comparative comprehensive comparison by comprehensively creating peak patterns of the assignment target peak and the assignment candidate peak of the peak pattern creation unit 7.

Fig. 62 and 63 are diagrams for explaining a method of calculating the matching degree of the peak patterns created by the 3 peaks of the peak assigning section 9.

Fig. 64 is a diagram for explaining a method of calculating the matching degree of the peak patterns created by the 5 peaks of the peak assigning section 9.

Fig. 65 is a diagram showing UV spectra 107 and 111 of the target peak 45 and the candidate peak 67 belonging to the peak belonging part 9.

Fig. 66 is an explanatory diagram of the degree of coincidence between the UV spectrum 107 of the target peak 45 belonging to the peak belonging section 9 and the UV spectrum 111 of the candidate peak 67 belonging thereto.

Fig. 67 is an explanatory diagram of the degree of coincidence of candidate peaks assigned by the degree of coincidence of the peak patterns of the peak assignment unit 9, the target peak 45, and the candidate peak 67, and the degree of coincidence of the UV spectrum.

Fig. 68 is a diagram illustrating attribution of the target FP17 of the peak attribution unit 9 to the reference group FP19 for each peak.

Fig. 69 is an explanatory diagram of the target FP peak feature value 21 showing a state in which each peak of the target FP17 of the peak assigning unit 9 belongs to the reference group FP 19.

Fig. 70 to 74 are diagrams showing various objects FP of the evaluation unit 11 and their evaluation values (MD values).

Selection of reference FP

The function of the reference FP selection unit 5 will be described further with reference to fig. 5 to 9.

Fig. 5 is a diagram showing the holding times of the object FP and the reference FP, fig. 6 is a diagram showing the appearance pattern of the holding times of the object FP, and fig. 7 is a diagram showing the appearance pattern of the holding times of the reference FP. Fig. 8 is a diagram showing the number of coincidence between the retention time appearance distances of the object FP and the reference FP, and fig. 9 is a diagram showing the degree of coincidence between the retention time appearance patterns of the object FP and the reference FP.

Fig. 5 shows the holding times of the object FP33 and the reference FP 55. Fig. 6 and 7 show retention time appearance patterns in which the distances of all retention times are calculated from the retention times of the object FP33 and the reference FP55, and the distances are collected in a table format. In fig. 8, the number of coincidence in the appearance distance of the retention time is calculated from these appearance patterns, and the number of coincidence in the appearance distance of the retention time is displayed by integrating these numbers into a table. In fig. 9, the degree of coincidence of the retention time appearance patterns is calculated based on the coincidence number, and the degree of coincidence of the retention time appearance patterns is displayed by integrating the degrees of coincidence in a table format.

In the peak attribution processing of the object FP33, each peak of the object FP33 is attributed with a reference FP that is as similar as possible to the FP pattern of the object FP 33. Selecting a reference FP similar to this object FP33 from a plurality of reference FPs is important in attributing high accuracy.

Here, as a method of objectively and easily evaluating the FP pattern similarity with the object FP33, the similarity of the FP pattern is evaluated by maintaining the degree of coincidence of the appearance pattern with time.

For example, the holding times of the object FP33 and the reference FP55 are as shown in fig. 5, and the patterns of the holding times of the object FP33 and the reference FP55 appear as shown in fig. 6 and 7. In fig. 6 and 7, the object FP33 and the reference FP55 on the upper layer are patterned in a table format in which the value of each cell is constituted by the pitch of the retention time as shown in the lower layer chart.

In fig. 6, the retention time of each peak (35, 37, 39, 41, 43, 45, 47, 49, 51, 53) of the object FP33 is (10.2), (10.5), (10.8), (11.1), (11.6), (12.1), (12.8), (13.1), (13.6), (14.0).

Therefore, the interval of the holding time between the peak 35 and the peak 37 is (10.5) - (10.2) ═ 0.3. Similarly, (0.6) between the peak 35 and the peak 39, and (0.3) between the peak 37 and the peak 39, and so on. Hereinafter, similarly, the object FP appearing pattern 79 which becomes the lower graph of fig. 6 appears.

In fig. 7, the retention time of each peak (57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77) of the reference FP55 is (10.1), (10.4), (10.7), (11.1), (11.7), (12.3), (12.7), (13.1), (13.6), (14.1), and (14.4).

Thus, likewise, the pitch of the holding time is the reference FP appearance pattern 81 of the lower graph of fig. 7.

As shown in fig. 6 and 7, the peaks that have been patterned are cyclically compared to find the number of matches. For example, the values of the cells of the target FP appearance pattern 79 in the lower graph in fig. 6 and the values of the cells of the reference FP appearance pattern 81 in the lower graph in fig. 7 are compared to obtain the matching number 83 as shown in fig. 8.

That is, the pitches of all the holding times of the holding time appearance patterns of the object FP33 and the reference FP55 are cyclically compared in line units, and the number of distances matching each other is calculated within a set range.

For example, when comparing 1 line of the object and reference FP retention time appearance patterns 79 and 81 in fig. 6 and 7, the number of coincidence is 7. These 7 coincidence numbers are written in line 1 of the object and reference FP hold time appearance pattern of fig. 8. Similarly, in the other rows in fig. 6 and 7, the number of matches can be obtained by cyclically comparing the number of matches until the 1 row to the 9 rows of the target FP holding time appearance pattern and the 1 row to the 10 rows of the reference FP holding time appearance pattern.

The results are shown in fig. 8. In fig. 8, the numerical value of 7 at the left end surrounded by a circle is the comparison result of the line 1 of the object and reference FP holding time appearance pattern, and the numerical value of the adjacent 7 is the comparison result of the line 1 of the object FP holding time appearance pattern and the line 2 of the reference FP holding time appearance pattern. The range is not limited, but is preferably 0.05 to 0.2 minutes. Example 1 was set to 0.1 min.

Let RP be the degree of coincidence of the occurrence pattern of the hold times, and the hold time of the f-th line of the object FP33Degree of coincidence (RP) of appearance pattern with retention time appearance pattern of g-th row of reference FP55_fg) Using Tanimoto coefficients, to

RP_fgCalculated as {1- (m/(a + b-m)) } × (a-m + 1).

In the formula, a denotes the number of peaks of the object FP33 (the number of peaks of the object FP), b denotes the number of peaks of the reference FP55 (the number of peaks of the reference FP), and m denotes the number of occurrences of coincidence (see fig. 8). The coincidence degree (RP) of the appearance pattern at each retention time is calculated from the above equation based on the coincidence number 83 in fig. 8 (see coincidence degree 85 in fig. 9).

The minimum value (RP _ min) of these RPs is set as the degree of coincidence between the retention time appearance pattern of the object FP33 and the reference FP 55. In fig. 9, (0.50) represents the degree of coincidence of the object FP33 with respect to the reference FP.

The matching degrees are calculated for all reference FPs, the reference FP with the smallest matching degree is selected, and the peak assignment of the target FP is performed for the reference FP.

The reference FP selecting unit 5 may pattern the object FP33 and the reference FP55 at a peak height ratio.

The peaks patterned with the peak height ratio are cyclically compared, and the number of height ratio matches is calculated within a set range. By this calculation, the matching number can be obtained in the same manner as in fig. 8.

In addition, when patterning is performed with a peak height ratio, there are cases where there are a plurality of identical values in 1 line, and it is necessary to count these not repeatedly.

The matching degree is obtained by setting the Tanimoto coefficient to "the matching number of height ratios/(the matching number of target FP peaks + the reference FP peaks-height ratios)" and making (1-Tanimoto coefficient) approach zero.

Further, the (1-Tanimoto coefficient) is weighted (the number of matching peaks of the target FP wave-height ratio +1) to obtain "(1-Tanimoto coefficient) × (the number of peaks of the target FP wave-the number of matching peaks of the appearance distance or the height ratio + 1"), and the reference FP which makes the peaks (35, 37, · · · · · · · ·) of the target FP33 more uniform can be selected by the weighting.

Feature valuation of peak patterns

The functions of the peak pattern creating section 7 and the peak assigning section 9 will be described with reference to fig. 10 to 69.

As shown in fig. 10, when the belonging target peak 45 is assigned to any peak of the reference FP55, it becomes a problem as to which peak should be assigned. Even when such peak assignment is performed only on any one of the peak, the retention time, and the UV spectrum, since any one of these three kinds of information contains errors due to the aforementioned errors between the medicines and the analysis errors, the peak assignment by the individual information cannot be obtained with sufficient accuracy.

As shown in fig. 13 and 14, an allowable width of the variation in the holding time is set between the peak to be assigned 45 and each peak of the reference FP55, and the assignment target is determined by integrating all the information of the peak of the reference FP55 and the peak assignment formed by the information of both the UV spectrum information existing within the allowable width.

However, even if the peak assignment using three types of information is performed, since the UV spectra of similar components are almost the same in terms of the characteristics of the UV spectrum, when the assignment candidate peak contains a plurality of similar components, only the peak information is assigned, and sufficient accuracy cannot be obtained. Therefore, in order to perform peak assignment with higher accuracy, information needs to be added to these three kinds of information.

Here, a peak pattern including information of the peripheral peaks shown in fig. 11 and 12 is created, and peaks are assigned by comparing the peak patterns.

When a peak pattern including peripheral peaks is created, peripheral information is added to the three types of information so far, and peak assignment can be performed using the four types of information, thereby achieving higher assignment accuracy.

As a result, a large number of peaks can be simultaneously assigned with high accuracy and efficiency by a single assignment process.

Further, by making the data used for peak assignment four kinds of information including peripheral information, the restriction conditions (peak definition and the like) set at the time of existing peak assignment are not necessary.

In fig. 11, a peak pattern 87 is created which includes peaks 43 and 47 on both sides in the time axis direction with respect to the belonging peak 45.

In fig. 12, a peak pattern 97 is created which includes peaks 41, 43, 47, and 49 on both sides in the time axis direction with respect to the belonging peak 45.

In fig. 13 and 14, an allowable width of the variation in the holding time is set between the belonging peak 45 and each peak of the reference FP55, and the peak of the reference FP55 existing within the allowable width is made as a candidate peak (hereinafter referred to as an belonging candidate peak) corresponding to the belonging peak 45.

In fig. 15, a peak pattern 89 including peaks 63 and 67 located on both the front and rear sides in the time axis direction with respect to the belonging candidate peak 65 is created as a peak pattern compared with the peak pattern 87 of the belonging target peak 45.

In fig. 16 to 18, peak patterns 91, 93, and 95 including peaks existing on both the front and rear sides in the time axis direction are created as peak patterns compared with the peak pattern 87 of the belonging target peak 45 with respect to the other belonging candidate peaks 67, 69, and 71.

When comparing the peak patterns with higher accuracy, it is necessary to create a peak pattern in which the number of peripheral peaks is increased in both the target FP and the reference FP, as shown in fig. 19 to 22.

For example, when a comparison of peak patterns formed by a total of 5 peaks including 4 peaks in the periphery is made, higher attribution accuracy can be obtained.

In fig. 19, a peak pattern 99 including peaks 61, 63, 67, and 69 present on both sides in the time axis direction is created for the belonging candidate peak 65 as a peak pattern to be compared with the peak pattern 97 of the belonging target peak 45.

In fig. 20 to 22, peak patterns 101, 103, and 105 including peaks existing on both the front and rear sides in the time axis direction are created as peak patterns compared with the peak pattern 97 of the belonging target peak 45 with respect to the other belonging candidate peaks 67, 69, and 71.

When attribution using this peak pattern is performed with higher accuracy, it is necessary to cope with a case where the number of peaks of the target FP and the reference FP is different (that is, there is a case where there is no peak). Therefore, as shown in fig. 23 to 25, it is necessary to create a peak pattern in which peak pattern constituent peaks change comprehensively, in both the belonging target peak and the belonging candidate peak.

Specifically, a peak as a candidate of a peak pattern constituting peak (hereinafter, referred to as a peak pattern constituting candidate peak) is set in advance from among the peripheral peaks of the target peak belonging to the target FP, and this peak pattern constituting candidate peak is sequentially made as a peak pattern constituting peak to make a peak pattern. Similarly, a peak pattern constituting candidate peak is set for the belonging candidate peak of the reference FP, and the peak pattern constituting candidate peaks are sequentially made as peak pattern constituting peaks to make a peak pattern.

For example, as shown in fig. 23, 4 neighboring peaks (41, 43, 47, 49) in the time axis direction are set as peak pattern configuration candidate peaks of the belonging target peak 45, 4 neighboring peaks (61, 63, 67, 69) in the time axis direction are set as peak pattern configuration candidate peaks of the belonging candidate peak 65, and the peak pattern configuration peaks are set to 2 arbitrary peaks, respectively. At this time, as shown in fig. 24 and 25, a peak pattern of 4C2 (6) is created for each of the belonging target peak 45 and the belonging candidate peak 65.

When 10 peak pattern formation candidate peaks are set and arbitrary 2 peak pattern formation peaks are set, peak patterns of 10C2(═ 45) patterns can be created in the target peak and the candidate peak to be assigned, respectively. When the peak pattern constituting peaks are set to any 4, peak patterns of 10C4(═ 210) patterns can be created for each of the belonging target peaks and the belonging candidate peaks.

The function of the peak assigning unit 9 will be described further with reference to fig. 26 to 67.

The peak assigning unit 9 calculates the degree of matching of the peak pattern (hereinafter referred to as P _ Sim) between all the peak patterns of the assignment target peak and the assignment candidate peaks created by the peak pattern creating unit 7, based on the difference between the corresponding peak and the holding time. The peak assigning unit 9 sets the minimum value of P _ Sim (hereinafter referred to as P _ Sim _ min) as the degree of matching between the peak patterns of the assignment target peak and the assignment candidate peak.

For example, as shown in fig. 26 to 61, the peak pattern formation candidate peaks are set to 4 in the front and rear peripheries in the time axis direction and the peak pattern formation peaks are set to 2 arbitrarily in each of the belonging target peak 45 and the belonging candidate peak 65. In this setting, a peak pattern of a 4C2(═ 6) pattern is created for each of the belonging target peak and the belonging candidate peak. Therefore, the P _ Sim of the belonging peak 45 and the belonging candidate peak 65 is calculated as shown by 6 pattern × 6 pattern (seed) (═ 36), and the minimum value of the P _ Sim, P _ Sim _ min, is set as the degree of coincidence between the belonging peak 45 and the belonging candidate peak 65.

In each of the belonging target peak 45 and the belonging candidate peak 65, 10 peak pattern constituting candidate peaks are provided in the front and rear peripheries in the time axis direction, 2 peak pattern constituting peaks are provided arbitrarily, and a peak pattern of a 10C2 (45) pattern is formed in each of the belonging target peak and the belonging candidate peak. Therefore, the P _ Sim _ min of the belonging peak 45 and the belonging candidate peak 65 is calculated as shown in 45 pattern × 45 pattern (kind) (═ 2025), and the minimum value of the P _ Sim _ min is set as the matching degree between the belonging peak 45 and the belonging candidate peak 65. When 4 arbitrary peak pattern constituting peaks are set, peak patterns of 10C4 (210) patterns are created for each of the belonging target peak and the belonging candidate peak. Therefore, P _ Sim _ min, which is the minimum value of P _ Sim, of the assignment target peak 45 and the assignment candidate peak 65 is calculated as shown by a 210 pattern × 210 pattern (44100), and is set as the matching degree between the assignment target peak 45 and the assignment candidate peak 65.

This P _ Sim is calculated similarly for all the candidate peaks to be assigned to the peak 45.

Fig. 62 and 63 illustrate a method for calculating the degree of matching of peak patterns by comparing peak patterns composed of 3 peaks. In this case, the peak pattern 87 of the belonging target peak 45 and the peak pattern 91 of the belonging candidate peak 67 are taken as examples.

In the peak pattern 87 of the belonging target peak 45, the peak data and the holding time of the belonging target peak 45 are p1 and r1, the peak data and the holding time of the peak pattern constituting peak 43 are dn1 and cn1, and the peak data and the holding time of the peak pattern constituting peak 47 are dn2 and cn 2.

In the peak pattern 91 of the belonging candidate peak 67, the peak data and the holding time of the belonging candidate peak 67 are p2 and r2, the peak data and the holding time of the peak pattern constituting peak 65 are fn1 and en1, and the peak data and the holding time of the peak pattern constituting peak 69 are fn2 and en 2.

When the peak pattern matching degree is P _ Sim, the matching degree of the peak pattern composed of 3 peaks of the belonging candidate peak 67 and the belonging target peak 45 (P _ Sim (45-67)) is defined as P _ Sim

P_Sim(45-67)＝(|p1-p2|+1)×(|(r1-(r2+d)|+1)

+(|dn1-fn1|+1)×(|(cn1-r1)-(en1-r2)|+1)

And + (| dn2-fn2| +1) × (| (cn2-r1) - (en2-r2) | + 1).

In the formula, d is a value for correcting the variation in the holding time.

Fig. 64 illustrates a method for calculating the degree of matching of peak patterns by comparing peak patterns consisting of 5 peaks. In this case, the peak pattern 97 of the belonging target peak 45 and the peak pattern 101 of the belonging candidate peak 67 are taken as examples.

In the peak pattern 97 of the belonging target peak 45, the peak data and the holding time of the belonging target peak 45 are p1 and r1, and the peak data and the holding time of the peak pattern constituting peaks 41, 43, 47, 49 are dn1 and cn1, dn2 and cn2, dn3 and cn3, dn4 and cn4, respectively.

In the peak pattern 101 of the belonging candidate peak 67, the peak data and the holding time of the belonging candidate peak 67 are p2 and r2, and the peak data and the holding time of the peak pattern constituting peaks 63, 65, 69, 71 are fn1 and en1, fn2 and en2, fn3 and en3, fn4 and en4, respectively.

The degree of coincidence (P _ Sim (45-67)) of the peak pattern formed by the 5 peaks of the belonging target peak 45 and the belonging candidate peak 67,

P_Sim(45-67)＝(|p1-p2|+1)×(|(r1-(r2+d)|+1)

+(|dn1-fn1|+1)×(|(cn1-r1)-(en1-r2)|+1)

+(|dn2-fn2|+1)×(|(cn2-r1)-(en2-r2)|+1)

+(|dn3-fn3|+1)×(|(cn3-r1)-(en3-r2)|+1)

and + (| dn4-fn4| +1) × (| (cn4-r1) - (en4-r2) | + 1).

In the formula, d is a value for correcting the variation of the holding time.

In the peak assigning unit 9, as shown in fig. 65 and 66, the matching degree of the UV spectrum is calculated between the assignment target peak and the assignment candidate peak.

FIG. 65 is a diagram showing UV spectrums (107 and 111) of the subject peak 45 and the candidate peak 67, and the coincidence degree (UV _ Sim (45-67)) between these two UV spectrums is shown in FIG. 66

UV _ Sim (45-67) was calculated as RMSD (107vs 111).

RMSD is the root mean square deviation, defined as the square root of the sum of the corresponding 2-point distances (dis) squared separately. That is to say, the first and second electrodes,

the RMSD was calculated as √ Σ dis2/n }.

n is the number dis.

Here, the waveform of the UV spectrum includes a maximum wavelength and a minimum wavelength, and the degree of coincidence may be calculated by comparing the maximum wavelength and the minimum wavelength or any one of them. However, although the maximum wavelength and the minimum wavelength are the same in a compound having no absorption property or a compound having similar absorption property, the waveforms as a whole may be greatly different, and there is a possibility that the degree of coincidence of the waveforms cannot be calculated in comparison of the maximum wavelength and the minimum wavelength.

In contrast, when RMSD is used with a waveform of a UV spectrum, the waveform of the UV spectrum can be compared with the entire waveform, so that the degree of coincidence can be calculated more accurately, and the waveform can be identified accurately with a compound having no absorption characteristics or a compound having similar absorption characteristics.

The matching degree of the UV spectrum is calculated similarly in accordance with the method of assigning all candidate peaks of the assignment target peak 45.

Further, the peak assigning unit 9 calculates the matching degree of the candidate peaks to be assigned by combining the matching degrees of the both as shown in fig. 67.

As shown in fig. 67, the coincidence degree of the belonging candidate peaks (SCORE (45-67)) is calculated by multiplying the coincidence degrees of the peak patterns and the UV spectrum. The score showing the degree of coincidence of the peak patterns 45, 67 is P _ Sim _ min (45-67), and the score showing the degree of coincidence of the corresponding UV spectra 107, 111 is UV _ Sim (45-67). At this time, the coincidence degree SCORE (45-67) of the candidate peaks is assigned

SCORE (45-67) ═ P _ Sim _ min (45-67) × UV _ Sim (45-67) was calculated.

The degree of matching of the belonging candidate peaks is calculated in the same manner as in the method of attributing all the candidate peaks of the target peak 45.

Then, the SCORE is compared between the all-attribution candidate peaks, and the attribution candidate peak with the smallest SCORE is determined as the attribution peak of the attribution target peak 45.

The peak attribution unit 9 determines the peak to be attributed of the attribution target peak by combining two viewpoints, and thus can accurately attribute the peak.

In addition, the peak assigning unit 9 assigns each peak of the object FP17 to the reference group FP19 as shown in fig. 68, based on the assignment result of the object FP to the reference FP.

Each peak of the object FP17 is assigned to the reference FP constituting the reference group FP by the foregoing belonging process. According to the attribution result, the peak is finally attributed to the reference group FP 19.

The reference group FP19 is created by assigning all of the plurality of reference FPs rated as normal products in the above-described manner, and each peak is represented by the mean value (black dot) ± standard deviation (vertical line) of the assigned peak.

Fig. 69 is a result of attributing the object FP17 to the reference group FP19, which is a final result of the attribution processing of the object FP 17.

Value of MD

As a result, the MD values (MD values: 0.25, 2.99, etc.) can be obtained by the MT method as described above (see FIGS. 70 to 74).

Method for evaluating multicomponent drug

Fig. 75 is a process diagram showing the method of evaluating a multicomponent drug substance according to example 1 of the present invention.

As shown in fig. 75, the method for evaluating a multicomponent drug as a pattern evaluation method includes an FP creation process 113 as a pattern acquisition process, a reference FP selection process 115 as a reference pattern selection process, a peak pattern creation process 117, a peak assignment process 119, and an evaluation process 121. In the present embodiment, the FP production process 113, the reference FP selection process 115, the peak pattern production process 117, the peak assignment process 119, and the evaluation process 121 are performed by the multi-component medicine evaluation device 1, the FP production process 113 is performed by the function of the FP production unit 3, and similarly, the reference FP selection process 115, the peak pattern production process 117, the peak assignment process 119, and the evaluation process 121 are performed by the functions of the reference FP selection unit 5, the peak pattern specification unit 7, the peak assignment unit 9, and the evaluation unit 11.

The FP creation process 113 is provided as an FP creation method, and creates a chromatogram as a 3D chromatogram using the retention time, the detection wavelength, and the peak as data, and creates FP17 using the peak detected at a specific wavelength from the 3D chromatogram 15, the retention time thereof, and the UV spectrum of the peak.

Evaluation procedure for multicomponent drug

Fig. 76 to 91 are flowcharts of a multi-component drug evaluation program, fig. 92 is a graph showing a data example of a 3D chromatogram, fig. 93 is a graph showing a data example of peak information, fig. 94 is a graph showing an example of FP data, fig. 95 is a graph showing a case of a determination result file created in step S3, fig. 96 is a graph showing two examples of intermediate files (belonging candidate peak score table, belonging candidate peak number table) created in a process of specifying a peak corresponding to a target FP and a reference FP, fig. 97 is a graph showing a comparison result file as a result of specifying a peak corresponding to the target FP and the reference FP, fig. 98 is a graph showing an example of reference group FP data, and fig. 99 is a graph showing a case of peak data characteristic value of the target FP as data of the target FP.

Fig. 76 is a flowchart showing the overall procedure of the process for evaluating a drug to be evaluated, which is initiated by system startup and implemented on a computer: the FP creation function of the FP creation unit 3, the reference FP selection function of the reference FP selection unit 5, the peak pattern creation function of the peak pattern creation unit 7, the peak assignment function of the peak assignment unit 9, and the evaluation function of the evaluation unit 11.

The FP creation function is realized by step S1, the reference FP selection function is realized by step S2, the peak pattern creation function is realized by step S3, the peak assignment function is realized by steps S3 to S5, and the evaluation function is realized by steps S6 and S7.

In step S1, "FP creation processing" is executed using the 3D tomogram and peak information at the specific detection wavelength as input data.

The 3D chromatogram is data obtained by analyzing the evaluation target drug by HPLC, and is data composed of three-dimensional information of retention time, detection wavelength, and peak (signal intensity), as shown in data example 123 of the 3D chromatogram in fig. 92. The peak information is obtained by processing chromatogram data at a specific wavelength obtained by the HPLC analysis with an HPLC data analysis tool (for example, ChemStation), and is data composed of a maximum value and an area value of all peaks detected as peaks, a retention time at that time point, and the like, as shown in a peak information example 125 of fig. 93.

In step S1, the FP production unit 3 (fig. 1) of the computer functions to produce the object FP17 (fig. 2) from the 3D tomogram and the peak information, and outputs the data as an archive. As shown in the FP data example 127 in fig. 94, the object FP17 is data composed of a retention time, a peak height, and a UV spectrum for each peak height.

In step S2, "target FP attribution processing 1" is executed with the target FP and all the reference FPs output in step S1 as inputs.

In step S2, the reference FP selection unit 5 of the computer functions to calculate the degree of matching between the retention time appearance pattern of the target FP17 and all the reference FPs, and selects a reference FP suitable for attribution of the target FP 17.

The reference FP is an FP created from the 3D chromatogram of the drug evaluated as a normal product and the peak information by the same processing as in step S1. In addition, the normal product is defined as a drug with confirmed safety and effectiveness, and belongs to a plurality of drugs with different product batches. The reference FP is also data configured in the same manner as the FP data example 127 in fig. 94.

In step S3, the "subject FP attribution processing 2" is executed with the subject FP17 and the reference FP selected in step S2 as inputs.

In step S3, the peak pattern creating unit 7 (fig. 1) and the peak assigning unit 9 (fig. 1) of the computer function. With this function, peak patterns are made in a comprehensive manner as shown in fig. 23 to 61 for all peaks of the object FP17 and the reference FP selected in step S2, and then the degree of coincidence between these peak patterns is calculated (P _ Sim in fig. 63 or 64). Further, the matching degree of the UV spectrum is calculated between the peaks of the object FP and the reference FP (UV _ Sim in fig. 66). Further, the matching degree of the belonging candidate peak is calculated from these two matching degrees (SCORE of fig. 67). The calculation result is outputted to a file (determination result file).

Step S4 executes "target FP attribution processing 3" with the determination result file output at step S3 as input.

In step S4, the peak assignment unit 7 of the computer functions to identify the peak of the reference FP corresponding to each peak of the target FP according to the matching degree (SCORE) of the candidate peaks between the target FP17 and the reference FP, and outputs the result to the file (comparison result file).

Step S5 receives the comparison result file output in step S4 and the reference group FP, and executes the "target FP assignment process 4".

The reference group FP is peak correspondence data between all reference FPs created by the same processing as in steps S2 to S4.

In step S5, the peak assignment unit 7 of the computer functions to assign the peaks of the target FP17 to the peaks of the reference group FP based on the comparison result file of the target FP17 as shown in fig. 68 and 69, and outputs the result to the file (peak data characteristic value file).

Step S6 executes "FP evaluation processing" with the peak data feature value file output in step S5 and the reference group FP as input.

In step S6, the evaluation unit 11 of the computer functions to evaluate the equivalence between the peak data feature value data output in step S5 and the reference group FP by the MT method, and outputs the evaluation result as an MD value (fig. 70 to 74).

Step S7 takes the MD value output in step S6 as an input value, and performs "determination of pass and fail".

In step S7, the computer evaluation unit 11 functions to compare the MD value output in step S6 with a preset threshold value (upper limit value of MD value) and determine pass or fail (fig. 2, table 23).

S1: FP creation process (using only a single wavelength)

Fig. 77 is a flowchart of the peak information of a single wavelength in the "FP creation process" in step S1 of fig. 76.

FIG. 77 is a detail of a procedure for creating an FP as an evaluation target at a single wavelength, for example, 203 nm. In this processing, from the 3D chromatogram and the peak information at a detection wavelength of 203nm, the FP consisting of the retention time and peak of the peak detected at 203nm and the UV spectrum of these peaks is created.

In step S101, a process of "reading peak information" is performed. In this processing, the peak information is read, and the process proceeds to step S102 as the first of two data necessary for the creation of an FP.

In step S102, a process of sequentially acquiring the holding time (R1) of the peak and the corresponding peak data (P1) is performed. In this process, the peak holding time (R1) and the peak data (P1)1 are sequentially obtained from the peak information, and the process proceeds to step S103.

In step S103, a process of "reading 3D tomogram" is performed. In this process, the 3D tomogram is read, and the process proceeds to step S104 as the second of the two data necessary for the creation of an FP.

In step S104, a process of sequentially acquiring the peak holding time (R2) and the corresponding UV spectrum (U1) is performed. In this process, the retention time (R2) and the UV spectrum (U1) are acquired from the 3D chromatogram at each "sampling rate/2 (samplinglate/2)" in the HPLC analysis, and the process proceeds to step S105.

In step S105, "| R1-R2| <? "judgment processing. In this processing, it is determined whether or not R1 and R2 read in steps S102 and S104 correspond to a threshold range. If so (yes), it is determined that the two holding times are the same, the UV spectrum at the peak of the holding time R1 is U1, and the process proceeds to step S106. If there is no correspondence (no), it is determined that the two retention times are different, and the UV spectrum of the peak having the retention time R1 is not U1, and the process proceeds to step S104 to compare with the next data of the 3D chromatogram. Further, the threshold value in this judgment process is the "sampling rate" of the 3D tomogram.

In step S106, a process of "normalizing U1 with a maximum value of 1" is executed. In this process, the U1 of the UV spectrum determined to be R1 in step S105 is normalized to have a maximum value of 1, and the process proceeds to step S107.

In step S107, a process of "outputting R1 and P1 and normalized U1 (object FP)" is performed. In this processing, R1 and P1 obtained from the peak information and U1 normalized in step S106 are output to the subject FP, and the process proceeds to step S108.

In step S108, "processing of all peaks is completed? "judgment processing. In this processing, it is determined whether or not all peaks in the peak information have been processed, and if the processing of all peaks has not been completed (no), the process proceeds to step S102 to process the unprocessed peaks. The processing in steps S102 to S108 is repeated until the processing of all peaks is completed, and once the processing of all peaks is completed (yes), the FP creation processing is completed.

S1: FP creation process (using a plurality of wavelengths)

Fig. 78 and 79 are flowcharts in the case of replacing the peak information of the single wavelength with peak information of a plurality of wavelengths in the FP creation process of step S1 in fig. 76. For example, the FP is formed by selecting a plurality of (n) wavelengths in the detection wavelength axis direction including 203 nm.

In this FP creation process, when all peaks detected from the 3D chromatogram cannot be covered at a single wavelength as shown in fig. 77, an FP covering all peaks of the 3D chromatogram is created using peak information of a plurality of wavelengths.

In addition, fig. 78 and 79 are details of a procedure of creating n FPs for each wavelength in the FP creation process using only the single wavelength, and then creating FPs formed of a plurality of wavelengths from these FPs.

In step S110, a process of "create FP for each wavelength" is executed. In this process, FP creation processing using only the single wavelength is performed for each wavelength, n FPs are created, and the process proceeds to step S111.

In step S111, a process of "tabulating FPs by the number of peaks (descending order)" is performed. In this processing, n FPs are tabulated in order of the number of peaks from large to small, and the process proceeds to step S112.

In step S112, 1 is substituted into n to initialize a counter for sequentially processing n FPs (n ← 1), and the process proceeds to step S113.

In step S113, the process of "reading the nth FP of the list" is performed. In this process, the nth FP of the list is read, and the process moves to step S114.

In step S114, "acquire all holding times (X)" is executed. In this processing, all the holding time information of the FP read in step S113 is acquired, and the process proceeds to step S115.

In step S115, a process of "update of n (n ← n + 1)" is executed. In this processing, since the processing is moved to the next FP, n +1 is substituted into n as an update of n, and the process moves to step S116.

In step S116, the process of "reading the nth FP of the list" is performed. In this processing, the nth FP of the list is read, and the process moves to step S117.

In step S117, a process of "acquiring all holding times (Y)" is executed. In this processing, all the holding time information of the FP read in step S116 is acquired, and the process proceeds to step S118.

In step S118, a process of "merging (Z) X and Y without repetition" is performed. In this process, the retention time information X obtained in step S114 and the retention time information Y obtained in step S117 are combined without repetition, stored in Z, and the process proceeds to step S119.

In step S119, the process of "update of X (X ← Z)" is executed. In this process, Z stored in step S118 is substituted into X, and the process proceeds to step S120 as an update of X.

In step S120, "complete FP processing is performed? "judgment processing. In this processing, it is determined whether all the n FPs created in step S110 have been processed, and if so, the process proceeds to step S121. If there is an unprocessed FP (no), the process proceeds to step S115 to execute the processes of steps S115 to S120 on the unprocessed FP. The processing in steps S115 to S120 is repeated until the processing of all FPs is completed.

In step S121, 1 is substituted into n to initialize a counter for sequentially processing n FPs again (n ← 1), and the process proceeds to step S122.

In step S122, the process of "reading the nth FP of the list" is performed. In this processing, the nth FP of the list is read, and then, it moves to step S123.

In step S123, a process of "sequentially acquiring the holding time (R1), the peak data (P1), and the UV spectrum (U1) of each peak" is performed. In this process, the holding time (R1), the peak data (P1), and the UV spectrum (U1) are sequentially acquired from the FP, 1 peak read in step S122, and then the process proceeds to step S124.

In step S124, a process of "obtaining the holding time (R2) in order of X" is executed. In this processing, X stored without being repeated from the holding time of all FPs is acquired one by one for 1 holding time (R2), and the process proceeds to step S125.

In step S125, "R1 ═ R2? "judgment processing. In this processing, it is determined whether or not R1 acquired in S123 is equal to R2 acquired in S124, and the process proceeds to step S127. If not equal (no), the process proceeds to step S126.

In step S126, "complete holding time comparison of X is completed? "judgment processing. In this processing, it is determined whether or not comparison with the total holding time of X is completed for R1 acquired in step S123. When the processing is completed (yes), it is determined that the peak of the holding time R1 has been processed, and the process proceeds to step S123 to move the processing to the next peak. When not completed (no), the process proceeds to step S124 to move to the next hold time of X.

In step S127, a process of "add (n-1) × analysis time (T) (R1 ← R1+ (n-1) × T) on R1" is performed. In this process, the holding time of the peak of the 1 st FP present in the list with the largest number of peaks is maintained as it is, the holding time of the peak of the 2 nd FP not present in the list of the 1 st FP is obtained by adding the analysis time (T) to R1, and the holding time of the peak of the n nd FP present in the list without the list of the 1 st to n-1 st FPs is obtained by adding (n-1) × T to R1, and the process proceeds to step S128.

In step S128, a process of "output R1, P1, and U1 (object FP)" is performed. In this processing, R1 processed in step S127 and P1 and U1 acquired in step S123 are output to the subject FP, and the process proceeds to step S129.

In step S129, a process of "delete R2 from X" is performed. In this process, since the process of the retention time R1(═ R2) is completed in steps S127 and S128, the retention time (R2) for which the process is completed is deleted from X, and the process proceeds to step S130.

In step S130, "complete peak processing? "judgment processing. In this processing, it is determined whether or not the processing has been completed for all peaks of the n-th FP in the list, and when the processing has been completed (yes), the FP creation processing of the n-th FP in the list is completed, and the process proceeds to step S131. If there is an unprocessed peak (no), the process proceeds to step S123 to process the unprocessed peak. The processing in steps S123 to S130 is repeated until all the peak processing is completed.

In step S131, a process of "n update (n ← n + 1)" is executed. In this processing, the processing moves to the next FP, substitutes n +1 for n as an update of n, and moves to step S132.

In step S132, "complete FP processing? "judgment processing. In this processing, it is determined whether all the n FPs created in step S110 have completed processing, and if so (yes), the FP creation processing is completed. If there is an unprocessed FP (no), the process proceeds to step S122 to execute the processes of steps S122 to S132 on the unprocessed FP. The processing in steps S122 to S132 is repeated until the processing of all FPs is completed.

S2: object FP attribution processing 1

Fig. 80 is a flowchart showing the detailed configuration of the "target FP attribution processing 1" of step S2 of fig. 76. This processing is a preprocessing for attribution, and a reference FP suitable for attribution of the target FP17 is selected from among a plurality of reference FPs serving as normal products.

In step S201, the process of "reading the object FP" is executed. In this processing, the FP of the home object is read, and the process proceeds to step S202.

In step S202, a process of "acquiring all holding times (R1)" is executed. In this processing, all the holding time information of the object FP read in S201 is acquired, and the process proceeds to step S203.

In step S203, a process of "listing the file names of all the reference FPs" is executed. In this processing, the file names of all the reference FPs are listed in advance so that all the reference FPs can be processed in sequence thereafter, and the process proceeds to step S204.

In step S204, 1 is substituted into n as an initial value of a counter for sequentially processing all the references FP (n ← 1), and the process proceeds to step S205.

In step S205, "read list nth reference FP (reference FP)_n) "is processed. In this processing, the nth FP in the file name list of all the reference FPs listed in step S203 is read, and the process proceeds to step S206.

In step S206, a process of "acquiring all holding times (R2)" is executed. In this processing, all pieces of holding time information of the reference FP read in step S205 are acquired, and the process proceeds to step S207.

In step S207, "calculating the coincidence degree of the retention time occurrence patterns of R1 and R2 (RP)_nMin) ". In this processing, RP is calculated from the holding time of the target FP acquired in step S202 and the holding time of the reference FP acquired in S206_nMin, the process proceeds to step S208. Further, RP_nThe detailed calculation flow of _minis additionally described by subroutine 1 of fig. 85.

In step S208, "RP" is executed_nPreservation of _min (RP)_allMin) ". In this process, the RP calculated in S207 is used_nStoring in RP_allMin, the process proceeds to step S209.

In step S209, the process of "update of n (n ← n + 1)" is executed. In this processing, in order to move the processing to the next FP, n +1 is substituted into n as an update of n, and the flow moves to step S210.

In step S210, "complete all reference FP processing? "judgment processing. In this processing, it is determined whether all the reference FPs have been processed, and if so, the process proceeds to step S211. If an unprocessed reference FP is present (no), the processing of S205 to S210 is executed on the unprocessed FP, and the process proceeds to step S205. The processing in steps S205 to S210 is repeated until the processing of all the reference FPs is completed.

In step S211, a slave RP is executed_allAnd (4) selecting the reference FP' with the minimum consistency in min. In this process, the calculated RPs are used for all reference FPs_lMin comparison RP_nMin, the reference FP having the smallest degree of coincidence with the retention time appearance pattern of the target FP is selected, and the target FP attribution process 1 is completed.

S3: object FP attribution processing 2

Fig. 81 is a flowchart of the detailed case of the "subject FP attribution processing 2" of step S3 of fig. 76. This processing is a main attribution processing, and the degree of agreement (SCORE) between the target FP17 and the reference FP selected in step S2 is calculated from the degree of agreement between the peak pattern and the UV spectrum.

In step S301, a process of "reading the object FP" is executed. In this processing, the FP of the home object is read, and the process proceeds to step S302.

In step S302, a process of "sequentially acquiring the holding time (R1) and the peak data (P1) of the belonging object peak and the UV spectrum (U1)" is performed. In this processing, the peaks of the target FP read in step S301 are sequentially set as the belonging target peak, R1, P1, and U1 are acquired, and the process proceeds to step S303.

In step S303, the process of "reading reference FP" is executed. In this processing, the reference FP selected in the "target FP belonging process 1" in fig. 80 is read, and the process proceeds to step S304.

In step S304, a process of sequentially acquiring the retention time (R2) of the peak of the reference FP, the peak data (P2), and the UV spectrum (U2) is performed. In this processing, R2, P2, and U2 are obtained in order of 1 peak to 1 peak from the reference FP read in step S303, and the process proceeds to step S305.

In step S305, "| R1- (R2+ d) | < threshold? "judgment processing. In this processing, it is determined whether R1 and R2 read in steps S302 and S304 correspond to within the range of the threshold value. In response to this, it is determined that the peak at the holding time R2 is the belonging candidate peak of the peaks at the holding time R1, and the process proceeds to step S306 to calculate the degree of matching (SCORE) of the belonging candidate peaks. If the correspondence is not made (no), it is determined that the peak cannot be the belonging candidate peak because the difference between the holding time R2 and the holding time R1 is too large, and the process proceeds to step S309. In this determination process, d is a value for correcting the holding time of the peaks of the target FP and the reference FP, and the initial value is set to 0. The threshold is an allowable width for determining whether or not the retention time of the belonging candidate peak should be set.

In step S306, a process of "calculating the coincidence degree of UV spectrum (UV _ Sim)" is performed. In this process, UV _ Sim is calculated from U1 of the belonging peak acquired in step S302 and U2 of the belonging candidate peak acquired in step S304, and the process proceeds to step S307. The detailed calculation flow of UV _ Sim is described in subroutine 2 of fig. 86.

In step S307, a process of "calculating the matching degree of peak patterns (P _ Sim _ min)" is executed. In this processing, peak patterns are collectively created for the R1 and P1 of the belonging target peak acquired in step S302 and the R2 and P2 of the belonging candidate peaks acquired in step S304. P _ Sim _ min of these peak patterns is calculated, and the process proceeds to step S308. The detailed calculation flow of P _ Sim _ min is described in subroutine 3 of fig. 87.

In step S308, a process of "calculating the matching degree of the belonging candidate peak (SCORE)" is executed. In this process, from UV _ Sim calculated in step S306 and P _ Sim _ min calculated in step S307, SCORE of the candidate peak to be assigned and the candidate peak to be assigned are calculated so as to be equal to UV _ Sim × P _ Sim _ min, and the process proceeds to step S310.

In step S309, processing of "substitute 888888 into SCORE (SCORE ← 888888)" is executed. In this processing, SCORE of a peak of the belonging candidate peak that does not match the belonging target peak is set to 888888, and the process proceeds to step S310.

In step S310, a process of "save of SCORE (SCORE _ all)" is executed. In this processing, the SCORE obtained in step S308 or S309 is saved in SCORE _ all, and the process proceeds to step S311.

In step S311, "processing of reference all peaks is completed? "judgment processing. In this processing, it is determined whether all peaks of the reference FP have been processed, and if so (yes), the process proceeds to step S312. If an unprocessed peak exists (no), the process proceeds to step S304 to step S311 to execute the process of step S304 to step S311 on the unprocessed peak. The processing of steps S304 to S311 is repeated until the processing of all peaks is completed.

In step S312, a process of "outputting SCORE _ all to the determination result file and initializing (setting to null) SCORE _ all" is executed. In this process, after the SCORE _ all is output to the determination result file, the SCORE _ all is initialized (set to null), and the process proceeds to step S313.

In step S313, "is the processing for all peaks of the subject completed? "judgment processing. In this processing, it is determined whether all peaks of the target FP have been processed, and when processed (yes), the target FP assignment processing 2 is completed. If an unprocessed peak is present (no), the process proceeds to step S302 to execute the processes of steps S302 to S313 on the unprocessed peak. The processing in steps S302 to S313 is repeated until the processing of all peaks is completed.

The output determination result file case 129 is shown in fig. 95.

S4: object FP attribution processing 3

Fig. 82 is a flowchart showing the details of the "subject FP belonging process 3" of step S4 of fig. 76. This processing is an attribution post-processing for specifying a peak of the reference FP corresponding to each peak of the target FP from the attribution candidate peak coincidence degree (SCORE) calculated as described above.

In step S401, a process of "reading the determination result file" is performed. In this processing, the determination result file created in "target FP assigning processing 2" in fig. 81 is read, and the process proceeds to step S402.

In step S402, a process of "creating an attribution candidate peak SCORE table with data satisfying the condition of" SCORE < threshold "is executed. In this process, the assignment candidate SCORE table 131 of fig. 96 (upper part of the figure) is created based on SCORE of the determination result file, and the process proceeds to step S403. The attribution candidate peak SCORE table is a table in which only SCOREs smaller than a threshold value are arranged in ascending order from SCOREs calculated for all peaks of the target FP for each peak of the reference FP. The smaller the SCORE value, the higher the probability that the peak should be assigned. The threshold is an upper limit value of SCORE for determining whether or not the SCORE should be set as the belonging candidate.

In step S403, a process of "creating an attribution candidate peak number table" is executed. In this processing, the belonging candidate peak number table 133 of fig. 96 (lower part of the figure) is created based on the belonging candidate peak score table, and the process proceeds to step S404. The attribution candidate peak number table is a table in which each score of the attribution candidate peak score table is replaced with a peak number of the object FP corresponding to the score. Thus, the table is a table in which the peak numbers of the corresponding target FP are arranged in order for the peaks of the reference FP.

In step S404, a process of "obtaining the peak number of the target FP to be attributed" is executed. In this processing, the peak number of the highest target FP is obtained for each peak of the reference FP from the belonging candidate peak number table created in step S403, and the process proceeds to step S405.

In step S405, "the acquired peak numbers are arranged in descending order (not repeated)? "judgment processing. In this processing, it is determined whether or not the peak numbers of the object FP acquired in step S404 are arranged in descending order without being repeated. In the arrangement (yes), it is determined that the peak of the object FP corresponding to each peak of the reference FP can be identified, and the process proceeds to step S408. If the alignment is not performed (no), the process proceeds to step S406 to re-evaluate the peak of the target FP to which the peak of the reference FP having the problem belongs.

In step S406, a process of "compare SCORE between problematic peaks and update the belonging candidate peak number table" is executed. In this process, SCORE corresponding to the peak number of the problematic object FP is compared with the belonging candidate SCORE table, and the belonging candidate peak number table is updated by replacing the peak number having a large SCORE with the peak number having the 2 nd position, and the process proceeds to step S407.

In step S407, a process of "updating the belonging candidate peak score table" is executed. In this processing, the belonging candidate peak score table is updated in accordance with the update content of the belonging candidate peak number table at S406, and the process proceeds to step S404. The processing of steps S404 to S407 is repeatedly performed until there is no problem with the peak number of the object FP (there is a repetition, not arranged in descending order).

In step S408, a process of "save attribution result (TEMP)" is performed. In this processing, the peak numbers, the holding times, and the peaks of all the peaks of the reference FP and the peaks corresponding to these peaks are stored in TEMP as peak data of the specific target FP, and the process proceeds to step S409.

In step S409, "have all peaks of the subject FP included in TEMP? "judgment processing. In this processing, it is determined whether or not peak data of all peaks of the target FP is included in the TEMP stored in step S408. If all the peaks are included (yes), it is determined that the processing is completed on all the peaks of the target FP, and the process proceeds to step S412. If there is an unincorporated peak (no), the process proceeds to step S410 to add peak data of the unincorporated peak to the TEMP.

In step S410, a process of "correcting the holding time of the peak of the target FP not including the TEMP" is executed. In this process, the retention time of the peak of the subject FP not including the TEMP (the peak of the subject FP to be corrected) is:

correction value k1+ (k2-k1) (t0-t1)/(t2-t1)

k 1: the holding time of a peak having a shorter holding time out of the peaks on the two reference FP sides belonging to the vicinity of the peak of the object FP to be corrected,

k 2: the holding time of the peak having a longer holding time out of the peaks on the two reference FP sides belonging to the vicinity of the peak of the object FP to be corrected,

t 0: the holding time of the peak of the object FP that has to be corrected,

t 1: the holding time of a peak having a shorter holding time out of the peaks on the two object FP sides belonging to the vicinity of the peak of the object FP to be corrected,

t 2: the holding time of the peak having a longer holding time out of the peaks on the two object FP sides belonging to the vicinity of the peak of the object FP to be corrected,

the holding time to the reference FP is corrected in this manner. Subsequently, the process proceeds to step S411.

In step S411, a process of "adding the corrected holding time and the peak data of the peak to the TEMP, and updating the TEMP" is executed. In this processing, the holding time of the peak of the target FP not including the TEMP corrected in step S410 is compared with the holding time of the reference FP in the TEMP, the corrected holding time of the peak of the target FP not including the TEMP and the peak data are added to an appropriate position in the TEMP to update the TEMP, and the process proceeds to step S409. The processing of steps S409 to S411 is repeated until all peaks of the object FP are added.

In step S412, a process of outputting TEMP to the comparison result file is performed. In this processing, the TEMP output specifying the correspondence relationship between all the peaks of the reference FP and all the peaks of the target FP is used as a comparison result file, and the target FP attribution processing 3 is completed.

The comparison result file 135 outputted as described above is shown in fig. 97.

S5: object FP attribution processing 4

Fig. 83 and 84 are flowcharts showing the details of the "object FP attribution processing 4" in step S5 of fig. 76. This processing is final processing of attribution, and each peak of the target FP is attributed to a peak of the reference group FP based on the comparison result file created in step S4 in fig. 76. The reference group FP is an FP specifying a correspondence relationship of peaks among all the reference FPs as described above, and is data composed of a reference group FP peak number, a reference group holding time, and a peak height as shown in reference group FP data example 137 in fig. 98. As shown in the reference group FP19 of fig. 2, each peak can be displayed as a mean value (black dot) ± standard deviation (vertical line).

In step S501, a process of "reading the comparison result file" is performed. In this process, the comparison result file output in step S412 in fig. 82 is read, and the process proceeds to step S502.

In step S502, the process of "reading reference group FP" is executed. In this processing, the reference group FP of the last belonging object ( owner) of each peak of the object FP is read, and the process proceeds to step S503.

In step S503, a process of "merge and save target FP and reference group FP" (TEMP) "is executed. In this process, the two files are merged based on the peak data of the reference FP existing together in the comparison result file and the reference group FP, the result is saved as TEMP, and the process proceeds to step S504.

In step S504, a process of "correcting the holding time of the peak of the target FP having no peak corresponding to the reference FP" is executed. In this processing, the holding times of all peaks of the target FP having no peak corresponding to the reference FP in the comparison result file are corrected to the holding time of TEMP stored in step S503, and the process proceeds to step S505. The correction of the holding time is performed in the same manner as in step S410 of the "target FP assignment process 3" in step S4.

In step S505, a process of sequentially acquiring the corrected retention times (R1, R3) and the corresponding peak data (P1) is performed. In this processing, the holding times R1 and R3 corrected in step S504 and the peak data of the corresponding peak P1 are sequentially acquired, and the process proceeds to step S506.

In step S506, a process of sequentially acquiring the holding time (R2) of the belonging candidate peak of the target FP and the corresponding peak data (P2) by TEMP is performed. In this processing, from the TEMP stored in step S503, the retention time in which the peak of the target FP is not assigned is R2, and the corresponding peak data is P2, in order, and the process proceeds to step S507.

In step S507, "| R1-R2| < threshold 1? "judgment processing. In this processing, it is determined whether or not the difference between R1 and R2 acquired in steps S505 and S506 is smaller than a threshold value 1. If it is small (yes), it is determined that there is a possibility that the peak having the holding time R1 of the target FP corresponds to the peak having the holding time R2 of the reference FP, and the process proceeds to step S508. When the difference between R1 and R2 is equal to or greater than the threshold value 1 (no), it is determined that there is no possibility of correspondence, and the process proceeds to step S512.

In step S508, a process of "obtaining UV spectra (U1, U2) corresponding to R1, R2" is performed. In this processing, the UV spectrum corresponding to the peak of R1 and R2 is acquired from each FP at the holding time determined in step S507 to be likely to correspond, and the process proceeds to step S509.

In step S509, a process of "calculating the coincidence degree of UV spectrum (UV _ Sim)" is performed. In this process, UV _ Sim is calculated from the UV spectra U1 and U2 acquired in step S508 in the same manner as in step S306 of the "target FP assignment process 2" in step S3, and the process proceeds to step S510. The detailed calculation flow of UV _ Sim is also described in subroutine 2 of fig. 86.

In step S510, "UV _ Sim < threshold 2? "judgment processing. In this processing, it is determined whether or not UV _ Sim calculated in step S509 is smaller than the threshold value 2. If it is small (yes), it is determined that the peak of U1 of the UV spectrum corresponds to the peak of U2, and the process proceeds to step S511. If UV _ Sim is equal to or larger than threshold 2 (no), it is determined that the correspondence is not established, and the process proceeds to step S507.

In step S511, the process of "R3 ← R2, threshold 2 ← UV _ Sim" is executed. In this process, after updating R3 (i.e., R1) determined in step S510 as the corresponding holding time to R2 as the holding time of the corresponding object (object to the handle), the threshold value 2 is updated to the value of UV _ Sim, and the process proceeds to step S507.

In step S512, "the holding times of all the belonging candidate peaks are compared and completed? "judgment processing. In this processing, it is determined whether or not comparison of R1 with the holding times of all the belonging candidate peaks is completed, and when the comparison is completed (yes), the process proceeds to step S513. When not completed (no), the process proceeds to step S507.

In step S513, a process of "save R1, R3, and P1 and threshold 2(TEMP 2)" is performed. In this process, R3, which is determined to correspond to the holding time (R1) at step S510, which is updated to the holding time (R2) of the corresponding object (object) (toward hand), the corresponding peak (P1) and the current threshold 2 are stored (TEMP2), and the process proceeds to step S507.

In step S514, "the holding times of all the non-corresponding peaks are compared to be completed? "judgment processing. In this process, it is determined whether or not the comparison between the holding times of all the non-corresponding peaks and the holding times of the belonging candidate peaks is completed. When the processing is completed (yes), it is determined that all the non-corresponding peak assignment processing is completed, and the process proceeds to step S516. If not (no), it is determined that an unprocessed non-corresponding peak remains, and the process proceeds to step S515.

In step S515, the process of "threshold 2 ← initial value" is executed. In this processing, the threshold value 2 updated to UV _ Sim in S511 is restored to the initial value, and the process proceeds to step S505.

In step S516, "there is a peak of the same value of R3 at TEMP 2? "judgment processing. In this process, it is determined whether a plurality of non-corresponding peaks belongs to the same peak in the TEMP. When a non-corresponding peak belonging to the same peak exists (yes), it moves to step S517. If not (no), the process proceeds to step S518.

In step S517, a process of comparing the threshold 2 of R3, which is the same peak, and restoring R3 of the peak having a larger value to the original value (R1) is performed. In this processing, the R3 value in the comparison TEMP2 is equal to the threshold value 2 of the peak, and the R3 value of the peak having a larger value is restored to the original value (i.e., R1), and the process proceeds to step S518.

In step S518, a process of "adding a peak of TEMP2 (a peak whose only the holding time of TEMP matches R3)" to TEMP "is executed. In this process, only the peak whose TEMP hold time coincides with R3 is added to the peak corresponding to R3 in TEMP, and the process proceeds to step S519. The peak where R3 does not match the holding time of TEMP is not added because the peak as the belonging object ( relative) does not exist in the reference group FP.

In step S519, a process of "outputting a peak (peak feature value profile) of the object FP in TEMP" is performed. In this processing, the peak data of the target FP belonging to the reference group FP137 is output as a peak data feature value file, and the target FP attribution processing 4 is completed.

Fig. 99 shows an example of the profile 139 of the peak data feature value outputted in the above-described manner.

Subroutine 1

Fig. 85 is a flowchart showing details of "subroutine 1" of the "reference FP selecting process" of fig. 80. This processing calculates the degree of coincidence of the retention time appearance patterns between FPs (e.g., the object FP and the reference FP).

In step S1001, the process of "x ← R1, y ← R2" is executed. In this processing, R1 and R2 obtained in steps S202 and S206 of fig. 80 are substituted for x and y, respectively, and the process proceeds to step S1002.

In step S1002, a process of "acquiring the number of data (a, b) of x, y" is executed. In this process, the data numbers x and y are acquired as a and b, respectively, and the process proceeds to step S1003.

In step S1003, 1 is substituted into i as an initial value of a counter for sequentially calling the retention time of x (i ← 1), and the process proceeds to step S1004.

In step S1004, a process of "obtaining the total distance (f) from the xi-th holding time" is executed. In this process, the interval between the xi-th holding time and the total holding time thereafter is acquired as f, and the process proceeds to step S1005.

In step S1005, 1 is substituted into j as an initial value of a counter for sequentially calling the retention time of y (j ← 1), and the process proceeds to step S1006.

In step S1006, a process of "obtaining the full distance (g) from the yj-th holding time" is executed. In this process, the interval between the yj-th holding time and the subsequent total holding time is taken as g, and the process proceeds to step S1007.

In step S1007, a process of "acquiring the number of data (m) satisfying the condition that the pitch | < threshold value" of the holding times of the pitch-g of the holding times of | f "is executed. In this processing, the pitches f and g of the holding times obtained in steps S1004 and S1006 are cyclically compared, and the number of data satisfying the condition that "| f pitch of each holding time-pitch of g | < threshold" is obtained as m, and the process proceeds to step S1008.

In step S1008, "calculating the coincidence degree of the hold time appearance patterns of f and g (RP)_fg) "is processed. In this processing, RP is selected from a, b acquired in step S1002 and m acquired in step S1007_fgTo be provided with

RP_fgCalculated as (1- (m/(a + b-m))) × (a-m +1), and the process proceeds to step S1009.

In step S1009, "save RP is performed_fg(RP _ all) ". In this processing, the matching degree calculated in step S1008 is stored in RP _ all, and the process proceeds to step S1010.

In step S1010, the process of "update of j (j ← j + 1)" is executed. In this process, in order to shift the process of y to the next holding time, j +1 is substituted into j as an update of j, and the process shifts to step S1011.

In step S1011, "complete processing for all holding times at y? "judgment processing. In this process, it is determined whether the process of the holding time of all y is completed. If the process is completed (yes), it is determined that the process for the entire holding time of y is completed, and the process proceeds to step S1012. In the case of incomplete (no), it is determined that an unprocessed holding time remains in y, and the process proceeds to step S1006. That is, the processing of steps S1006 to S1011 is repeated until all the holding times of y are processed.

In step S1012, a process of "update of i (i ← i + 1)" is executed. In this process, in order to shift the process of x to the next holding time, i +1 is substituted into i as an update of i, and the process shifts to step S1013.

In step S1013, "complete all holding time processing at x? "judgment processing. In this process, it is determined whether the process of all the holding times of x is completed. When the process is completed (yes), it is determined that the process for all the holding times of x is completed, and the process proceeds to step S1014. If not (no), it is determined that the unprocessed retention time remains in x, and the process proceeds to step S1004. That is, the processing of steps S1004 to S1013 is repeated until all the holding times of x are processed.

In step S1014, a process of "obtaining the minimum value (RP _ min) from RP _ all" is executed. In this process, the minimum value of RP _ all of the RPs storing all combinations of retention time appearance patterns of the target FP and the reference FP is acquired as RP _ min, and this RP _ min is transmitted to S207 in fig. 80, and the consistency degree calculation process of the retention time appearance patterns is completed.

Subroutine 2

Fig. 86 is a flowchart showing the details of "subroutine 2" in "subject FP belonging process 2" of fig. 81. This process calculates the degree of coincidence of the UV spectrum.

In step S2001, the processes "x ← U1, y ← U2, and z ← 0" are executed. In this process, x and y are substituted into the UV spectra U1 and U2 obtained in steps S302 and S304 of fig. 81, respectively, and 0 is further substituted as an initial value of the sum of squares of the distances between the UV spectra (z), and the process proceeds to step S2002.

In step S2002, a process of "acquiring x data number (a)" is executed. In this process, the number of data x is acquired as a, and the process proceeds to step S2003.

In step S2003, the process of "i ← 1" is executed. In this process, 1 is substituted into i as an initial value for sequentially calling the absorbance of each detection wavelength constituting the UV spectra U1 and U2 from x and y, and the process proceeds to step S2004.

In step S2004, the process of "acquiring the xi-th data (b)" is executed. In this process, the ith absorbance data of x substituted into the UV spectrum U1 is acquired as b, and the process proceeds to step S2005.

In step S2005, "get ith data (c)" is executed. In this process, the ith absorbance data of y substituted into the UV spectrum U2 is acquired as c, and the process proceeds to step S2006.

In step S2006, "the sum of squares (z) of the UV spectral pitch (d) and the UV spectral pitch is calculated" is executed. In this process, the sum of the squares of the UV spectral separation d and the UV spectral separation z is calculated

d＝b-c

z＝z+d²The method (4) is calculated, and the process proceeds to step S2007.

In step S2007, a process of "update of i (i ← i + 1)" is executed. In this process, i +1 is substituted into i as an update of i, and the process proceeds to step S2008.

In step S2008, "complete all data processing at x? "judgment processing. In this process, it is determined whether or not the processing of all the data x and y is completed. When the processing is completed (yes), it is determined that the processing of all the data x and y is completed, and the process proceeds to step S2009. If not (no), it is determined that unprocessed data remains in x and y, and the process proceeds to step S2004. That is, the processing in steps S2004 to S2008 is repeated until all the absorbance data of x and y are processed.

In step S2009, a process of "calculating the coincidence degree of the UV spectra of x and y (UV _ Sim)" is performed. In this process, UV _ Sim is calculated from the data number a of the sum of squares z and x of the aforementioned UV spectral spacings and

UV _ Sim is calculated as √ (z/a), and this UV _ Sim is transferred to step S306 in fig. 81, and the process of calculating the degree of coincidence of the UV spectrum is completed.

Subroutine 3

Fig. 87 is a flowchart showing the details of the "subroutine 3" of the "subject FP belonging process 2" of fig. 81. This process calculates the degree of conformity of the peak pattern.

In step S3001, a process of "setting the number of peak pattern configuration candidates (m) and the number of peak pattern configuration peaks (n)" is performed. In this process, the number of peak pattern formation candidates (m) and the number of peak pattern formation peaks (n) are set as settings for comprehensively forming the peak patterns, and the process proceeds to step S3002.

In step S3002, the process of "x ← object FP name, R1 ← R1, P1 ← P1, y ← reference FP name, R2 ← R2, P2 ← P2" is executed. In this processing, the file names of the target FP and the reference FP necessary for the processing, and the holding time and the peak data acquired in steps S302 and S304 of fig. 81 are substituted into x, r1, p1, y, r2, and p2, respectively, and the process proceeds to step S3003.

In step S3003, a process of "acquiring all holding times (a) of x" is executed. In this process, the file (object FP) with the name x substituted in S3002 is read, and the entire retention time of the file is acquired as a, and the process proceeds to step S3004.

In step S3004, a process of "acquiring all the holding times (b) of y" is executed. In this processing, the file (reference FP) with the name y substituted in step S3002 is read, and the total retention time of the file is acquired as b, and the process proceeds to step S3005.

In step S3005, a process of "obtaining m holding times (cm) and peak data (dm) of the candidate peaks m by the peak pattern of r 1" is performed. In this processing, m holding times of the r1 peak pattern configuration candidate peaks to which the holding times of the belonging target peaks belong are obtained as cm and peak data is obtained as dm, respectively, by a, and the process proceeds to step S3006. The m peak pattern formation candidate peaks are m peaks r1 close to the retention time.

In step S3006, a process of "obtaining the retention time (em) and the peak data (fm) of m candidate peaks m from the peak pattern of r 2" is executed. In this processing, the holding times of m candidate peaks m by the peak pattern r2 to which the holding times of the belonging candidate peaks belong are respectively obtained as em, and the peak data is obtained as fm, and the process proceeds to step S3007. The m peak pattern formation candidate peaks are m peaks r2 close to the retention time.

In step S3007, a process of "arranging cm, dm in order of holding time (ascending order)" is performed. In this processing, cm and dm acquired in step S3005 are replaced in order of increasing retention time, and the process proceeds to step S3008.

In step S3008, a process of "arranging em, fm in order of retention time (ascending order)" is executed. In this processing, em and fm acquired in step S3006 are replaced in an order of increasing retention time, and the process proceeds to step S3009.

In step S3009, a process of "sequentially acquiring the holding time (cn) and the peak data (dn) of n peaks constituting the peak pattern from cm and dm" is performed. In this processing, m cm and dm of candidate peaks are constituted by peak patterns, the holding times of n peaks constituting the peak patterns are sequentially acquired as cn, and the peak data is acquired as dn, and the process proceeds to step S3010.

In step S3010, a process of "sequentially obtaining the holding time (en) and the peak data (fn) of the n peaks constituting the peak pattern by em and fm" is performed. In this processing, em and fm of m candidate peaks are formed from the peak patterns, the holding time of n candidate peaks formed from the peak patterns is sequentially acquired as en, and the peak data is acquired as fn, and the process proceeds to step S3011.

In step S3011, a process of "calculating the degree of coincidence of peak patterns (P _ Sim)" is performed. In this processing, when the n-th and dn-th peaks are constituted by r1 and P1 of the peak to be assigned and the peak pattern thereof obtained so far, and the n-th and fn-th peaks are constituted by r2 and P2 of the peak to be assigned and the peak pattern thereof, and the coincidence degree (P _ Sim) of the peak patterns is assumed to be n-4 as shown in fig. 64

P_Sim＝(|p1-p2|+1)×(|(r1-(r2+d)|+1)

+(|dn1-fn1|+1)×(|(cn1-r1)-(en1-r2)|+1)

+(|dn2-fn2|+1)×(|(cn2-r1)-(en2-r2)|+1)

+(|dn3-fn3|+1)×(|(cn3-r1)-(en3-r2)|+1)

And (c) calculating (+ (| dn4-fn4| +1) × (| (cn4-r1) - (en4-r2) | +1), and moving to step S3012.

In step S3012, a process of "save P _ Sim (P _ Sim _ all)" is executed. In this processing, the P _ Sim calculated in step S3011 is sequentially stored in P _ Sim _ all, and the process proceeds to step S3013.

In step S3013, "do all combinations of m fetch n pieces in em? "judgment processing. In this processing, it is determined whether or not processing is completed in all combinations of m peak pattern configuration candidate peaks from the peak pattern configuration candidate peaks belonging to the candidate peaks, and n peak pattern configuration peaks. When the determination is completed (yes), it is determined that the calculation of the matching degree between the generation of the net peak pattern and the corresponding pattern is completed in the belonging candidate peaks, and the process proceeds to step S3014. If not, it is determined that the combination of m pieces of the n pieces of the extracted combination is not completed, and the process proceeds to step S3010. That is, the processing in steps S3010 to S3013 is repeated until the processing is completed in all combinations where n pieces are taken out from m pieces.

In step S3014, "do all combinations of m fetch n pieces in cm complete? "judgment processing. In this process, m candidate peaks are formed from the peak pattern of the belonging peak, and it is determined whether or not the process is completed in all combinations of n extracted peak pattern forming peaks. When the calculation is completed (yes), it is determined that the calculation of the matching degree between the generation of the netted peak pattern and the pattern is completed in the target peak, and the process proceeds to step S3015. If not, it is determined that the combination of m pieces of the n pieces of the extracted combination is not completed, and the process proceeds to step S3009. That is, the processing of steps S3009 to S3014 is repeated until the processing is completed in all combinations where n pieces are taken out from m pieces.

In step S3015, a process of "obtaining the minimum value (P _ Sim _ min) from P _ Sim _ all" is executed. In this process, the minimum value of P _ Sim _ all stored in step S3012 is acquired as P _ Sim _ min, and this P _ Sim _ min is transferred to step S307 in fig. 81, and the peak pattern matching degree calculation process is completed.

Generation of reference group FP

A reference FP characteristic value file for comparing the target FP characteristic value data with the reference FP characteristic value data is created as shown in fig. 88 to 91.

FIG. 88 is a flowchart for creating a reference FP feature merge file, implementing the following functions on a computer: an FP creation function of the reference FP creation unit, a reference FP peak assignment function of the reference FP peak assignment unit, a reference FP assignment result combination function of the reference FP assignment result combination unit, and a reference FP peak feature value creation function of the reference FP peak feature value creation unit.

The reference FP creation function is realized in step S10001. The reference FP peak attribution function is realized by steps S10002, S10003, and S10004. The reference FP attribution result merging function is realized by step S10005. The function of creating the reference FP peak feature value is realized in step S10006.

Steps S10001 to S10004 correspond to steps S1 to S4 in the creation of the object FP feature value merged file in fig. 76.

Step S10001 executes "FP creation processing" using the 3D chromatogram and the peak information of the specific detection wavelength as input data.

Each of the plurality of evaluation standard drugs (standard chinese medicines) serving as an evaluation standard has a 3D chromatogram and peak data.

In step S10001, the FP production unit of the computer functions to produce a reference FP from the 3D tomogram and the peak information in the same manner as the object FP17 (fig. 2), and outputs data of the reference FP as a file.

Step S10002 receives all the reference FPs output in step S10001, and executes "reference FP assigning process 1".

In step S10002, the reference FP peak assignment unit of the computer functions to select a combination from all the reference FPs and moves to step S10003 in order to calculate the assignment score in order for the selected combination with respect to all the reference FPs.

Step S10003 executes "reference FP assigning process 2" with the combination of the selected reference FPs as input.

In step S10003, peak patterns are made in a comprehensive manner as shown in fig. 23 to 61 for all peaks of the combination of reference FPs selected in step S2, and then the matching degree of these peak patterns is calculated (P _ Sim in fig. 63 or 64). Further, the degree of coincidence of the UV spectrum (UV _ Sim in fig. 66) is calculated between the peaks of the selected combination of reference FPs. Then, the matching degree of the belonging candidate peaks is calculated from the matching degrees of the two peaks (SCORE in fig. 67). The calculation result is outputted as a determination result file (see determination result file case 129 of fig. 95).

Step S10004 receives the determination result file output in step S10003, and executes "reference FP assignment process 3".

In step S10004, a peak corresponding to the selected combination of reference FPs is identified according to the matching degree (SCORE) of the belonging candidate peaks among the selected combinations of reference FPs. The result is output as reference FP attribute data for each reference FP.

Step S10005 receives all the reference FP attribute data outputted in step S10004, and executes the "reference FP attribute result merge process".

In step S10005, the reference FP attribution result merging unit of the computer functions to merge all the reference FP attribution data to create a reference FP correspondence table by referring to the peak correspondence relationship of each reference FP specified in the reference FP peak attribution unit, and the process proceeds to step S10006. In step S10006, the reference FP peak feature value creation unit of the computer functions as a peak feature value (reference group FP) based on all the reference FPs based on the reference FP correspondence table created by the reference FP attribution result merging unit. The processing of the reference FP peak feature value creation unit calculates statistics (maximum value, minimum value, median value, average value, etc.) with respect to each peak (row) of the reference FP correspondence table, and selects a peak (row) based on the information. The selected peak (column) is output as a reference group FP (see reference group FP example 137 in fig. 98).

S10005: creation of reference FP mapping Table

Fig. 89 and 90 are flowcharts showing details of the "reference FP assignment result merging process (creation of the reference FP correspondence table)" in step S10005.

In step S10101, a process of "reading belonging order 1 st belonging data as merged data" is performed. In this processing, reference FP attribute data which is subjected to attribute processing in item 1 in step S10004 and has a correspondence relationship of a peak specified is read as merged data, and the process proceeds to step S10102.

In step S10102, "sequentially read the 2 nd and later items of attribution data" processing is performed. In this processing, first, reference FP attribute data which is subjected to attribute processing in item 2 in step S10004 and has a correspondence relationship of a peak specified is read as merged data, and the process proceeds to step S10103.

In step S10103, a process of "merging the merged data and the belonging data with the common peak data" is performed. In this processing, the two files are merged based on the peak data of the reference FP in which the merged data and the belonging data coexist, the merged data is updated with the result, and the process proceeds to step S10104.

In step S10104, "add all peaks in the attribution data to the merged data? "judgment processing. In this processing, it is determined whether or not all peaks of the belonging data have been added to the merged data. If the addition is completed (yes), the process proceeds to step S10105. If there is an unadditized peak (missing peak) (no), the process proceeds to step S10107 to add the missing peak to the merged data. The missing peak addition processing (steps S10107 to S10120) to the combined data is similar to the processing performed in steps S504 to S517 of S5 (target FP assignment processing 4).

In step S10121, a process of "adding data of TEMP2 to the merged data (all holding times and peaks)" is executed. In this process, the entire holding time (R3) and the peak (P1) of the TEMP2 are added to the supposed position of the merged data, and the process proceeds to step S10122.

In step S10122, the process of "threshold 2 ← initial value, and deletion of all data in TEMP 2" is executed. In this process, the threshold value 2 updated to UV _ Sim is restored to the initial value, all the data is deleted from the TEMP2 containing the data such as the retention time of all the missing peaks and the peaks, and the process returns to step S10104.

In step S10105 advanced by step S10104, "processing of all attribution data is completed? "judgment processing. In this processing, it is determined whether or not the processing of all the reference data is completed. When the processing is completed (yes), the process proceeds to step S10106 to output the reference FP correspondence table of the merging result of all the belonging data. If all the processes are not completed (no), the process returns to step S10102, and the remaining attribute data are sequentially processed.

In step S10106, "output merged data (reference FP corresponding table)" processing is performed. In this processing, the result of merging all the attribution data is output as the reference FP table, and the reference FP table creation processing is completed.

S10006: peak feature value processing

Fig. 91 is a flowchart showing details of the "peak feature value processing (creation of reference group FP)" in step S10006 in fig. 88.

In step S10201, a process of "reading a reference FP correspondence table" is performed. In this processing, the reference FP correspondence table created in S10005 is read, and the process proceeds to step S10202.

In step S10202, a process of "calculating a statistic for each peak (column)" is performed. In this process, statistics (maximum value, minimum value, median, average value, dispersion, standard deviation, number of occurrences, and presence rate) are calculated for each peak (column) of the reference FP correspondence table, and the process proceeds to step S10203.

In step S10203, a process of "referring to the calculated statistic amount and selecting a peak (column)" is performed. In this process, a peak is selected with reference to the statistic calculated in step S10102, and the process proceeds to step S10204.

In step S10204, a process of "outputting the selected peak (column) (reference group FP)" is performed. In this processing, the result of selecting the peak (column) is output as the reference group FP based on the statistic, and the reference group FP is created.

Fig. 98 shows a reference FP correspondence table example 137 output as described above.

Effect of example 1

The embodiment 1 of the present invention includes: an FP creation step 113 of creating an object FP17 composed of a peak detected from the 3D chromatogram 15 of the multicomponent drug to be evaluated at a specific wavelength, for example, 203nm, a retention time of the peak, and a UV spectrum of the peak; a reference FP selection process 115 of selecting a reference FP suitable for peak attribution of the object FP17 from the plurality of reference FPs; a peak pattern creating step 117 of creating a peak pattern including, for example, 3 peaks including 2 peaks existing at least one of front and rear in the time axis direction for the peaks of each of the target FP and the selected reference FP; a peak attribution process 119 for comparing the peak pattern with the UV spectrum of the peak and specifying the corresponding peak; and an evaluation step 121 for comparing and evaluating the peak of the attribute with the peaks of the plurality of reference FPs by, for example, MT method.

By processing the 3D chromatogram 15 of the multicomponent drug to be evaluated in these 5 steps (113, 115, 117, 119, and 121), the accuracy and efficiency of quality evaluation of the drug to be evaluated can be further improved.

The object FP17 created in the object FP creation process 113 is composed of three-dimensional information (peak, retention time, and UV spectrum) in the same manner as the 3D chromatogram 15. Therefore, the data inherits the information specific to the medicine as it is. Nevertheless, since the data capacity is compressed to about 1/70, the amount of information to be processed can be greatly reduced compared to 3D tomograms 15, and the processing speed can be increased.

In the target FP production process 113, an FP is produced in which a plurality of FPs having different detection wavelengths are synthesized. Thus, even in a multicomponent drug in which components of all components cannot be detected at one wavelength are combined, quality evaluation of all components can be performed by synthesizing FPs of a plurality of detection wavelengths.

In the target FP creation process 113, an FP including all peaks detected from the 3D tomogram is created. Therefore, it is suitable for quality evaluation of a Chinese medicinal preparation containing multiple components.

In the reference FP selection process 115, the retention time appearance pattern between the reference FP suitable for attribution of the target FP and the FP is compared, and the reference FP having a good pattern consistency is selected. Thus, in the peak assignment step 119, since assignment processing can be performed between FPs having similar patterns, assignment with high accuracy can be performed.

In the peak pattern creating step 117, a plurality of neighboring peaks are used for each of the belonging target peak and the belonging candidate peak, and a peak pattern is created comprehensively. Thus, even if the overall pattern of the target FP and the reference FP are slightly different, the peak assignment process 119 can assign the peak with high accuracy.

In the peak assignment step 119, the peak to be assigned is specified by adding the matching degree of the UV spectrum between the peak to be assigned and the candidate peak to be assigned, in addition to the matching degree of the peak pattern created in the peak pattern creation step 117. Therefore, highly accurate attribution can be performed.

In the peak attribution process 119, all peaks of the object FP are simultaneously attributed to the peaks of the reference FP. Therefore, efficient attribution processing can be performed.

In the evaluation process 121, FP formed of multi-components belonging to the multi-order metadata are collected as MD values into one dimension by the MT method, and a plurality of evaluation target batches are simply compared and evaluated. Therefore, it is suitable for evaluating a multicomponent drug composed of a plurality of components.

The evaluation program of the multi-component medicament of the embodiment of the invention enables the computer to realize each function, and can further improve the accuracy and efficiency of evaluation.

The multi-component medicament evaluation device provided by the embodiment of the invention enables the parts 3, 5, 7, 9 and 11 to play a role, and further improves the evaluation accuracy and efficiency.

Variation of the calculation of the degree of coincidence of peak patterns (P _ Sim)

The calculation of the degree of coincidence (P _ Sim) of the peak patterns in fig. 63, 64, and 87 is applied to the case where FP is made with the peak height as in the above-described embodiment, and is performed based on the difference in the peak heights to be compared.

On the other hand, the peak detected from the chromatogram to be evaluated in the evaluation method, the evaluation program, and the evaluation apparatus of the present invention may include any of the case where the peak indicates the maximum value of the signal intensity (height) and the case where the area value of the signal intensity (peak area) is expressed by the height as described above.

That is, the FP is also produced by expressing the area value by the height, and thus the FP is expressed similarly to the case of producing the peak height in the above-described embodiment. Therefore, the same evaluation can be made by using the same processing of the above embodiment in the case where FP is created by the peak height.

However, when FP is made by the peak area, the difference in the peak value of the comparison object becomes large, so calculation based on the ratio is preferable in view of easy processing.

Hereinafter, the matching degree (P _ Sim) of the peak pattern calculated from the ratio will be described by taking a case where n is2 and n is 4 as an example.

When n is2

P_Sim＝(p1/p2^#1)×(|(r1-(r2+d)|+1)

+(dn1/fn1^#1)×(|(cn1-r1)-(en1-r2)|+1)

+(dn2/fn2^#1)×(|(cn2-r1)-(en2-r2)|+1)

When n is 4

P_Sim＝(p1/p2^#1)×(|(r1-(r2+d)|+1)

+(dn1/fn1^#1)×(|(cn1-r1)-(en1-r2)|+1)

+(dn2/fn2^#1)×(|(cn2-r1)-(en2-r2)|+1)

+(dn3/fn3^#1)×(|(cn3-r1)-(en3-r2)|+1)

+(dn4/fn4^#1)×(|(cn4-r1)-(en4-r2)|+1)

In this case, the amount of the solvent to be used,^#1the ratio (large value/small value) of the two values of the comparison object is displayed.

In addition, the degree of matching of the peak pattern (P _ Sim) can be calculated by a ratio when the peak height is used as FP, and the degree of matching of the peak pattern (P _ Sim) can be obtained by a difference between the peak height and the peak area value when the peak area is used as FP.

Variation of subroutine 2

Fig. 100 is a flowchart showing a modification of the subroutine 2 applied in place of fig. 86, and shows details of a modification of the subroutine 2 of the target FP assigning process 2 of fig. 81. By the processing of this modification, the degree of coincidence of the UV spectrum was calculated.

In this modification of the subroutine 2, the processing of adding the inclination information (DNS) of the moving average of the UV pattern to the RMSD of the subroutine 2 in fig. 86 can be performed. The DNS is expressed by the following equation, and the moving inclination of the moving average of the UV pattern is defined as the number of disagreements in the inclination sign (+/-) when comparing two patterns. That is, DNS is a value for evaluating the coincidence of the positions of the maximum and minimum values of the UV pattern.

By adding this DNS information to the RMSD, the degree of coincidence of the waveforms in the UV spectrum can be calculated more accurately.

The subroutine 2 of the modification of fig. 100 is substantially the same as the subroutine 2 of fig. 86 up to steps S2001 to S2008. However, in step S2001, the initial setting of the section 1 ← w1 and the section 2 ← w2 is added, and a section for calculation of a moving average or a moving inclination, which will be described later, can be used.

In the subroutine 2 of the present modification, steps S2010 to S2013 are added to add the DNS, and the matching degree of adding the DNS can be calculated in step S2009A.

In step S2010, "add DNS? The "determination process proceeds to step S2011 when it is determined that DNS is added (yes), and proceeds to step S2009A when it is determined that DNS is not added (no). Whether or not DNS is added depends on, for example, initial settings. For example, when FP is made with peak area, DNS is added, and when FP is made with peak height, DNS is not added.

However, even in the case of the above embodiment in which the FP is made by the peak height, the UV pattern matching degree can be calculated by the process of adding the DNS, and in the case of the FP made by the peak area, the UV pattern matching degree can be calculated by the process of the above embodiment in which the DNS is not added.

In step S2011, the process of "calculating the moving average of x and y in the section 1(w 1)" is executed to determine the moving average of the section 1(w 1). The section 1(w1) is a section of the wavelength of the UV data, and in the initial setting in step S2001, when w1 is 3, the section 1(3) is obtained, and the average of the UV intensities of the 3 wavelengths is obtained. Specifically, the table of fig. 101 will be described later.

In step S2012, the process of "calculating the movement inclinations of x and y in the section 2(w 2)" is executed to determine the movement inclination of the section 2(w 2). The section 2(w2) is a section of the moving average obtained in step S2011, and if w2 is 3 in the initial setting in step S2001, it is the section 2(3), and the inclination (±) covering 3 moving averages is obtained from the moving average calculated in step S2011. Specifically, the table of fig. 101 will be described later.

In step S2013, a process of "calculating the number of Disagreements (DNS) in the signs of the movement inclinations x and y" is executed, and the number of coincidences (±) in the inclination is calculated from the movement inclination calculated in step S2012. Moving the plus of the tilt, in fig. 66, represents the right shoulder up tilt, moving the tilt-represents the right shoulder down tilt.

When the process proceeds from step S2013 to step S2009A, the process of step S2009A calculates the degree of matching by adding the DNS.

In step S2009A, a process of "calculating the coincidence degree of the UV spectra of x and y (UV _ Sim)" is performed, and in the calculation of the coincidence degree added to DNS, the number a of UV _ Sim data obtained by summing the squares of the UV spectral intervals z and x is set to be equal to √ (z/a) × 1.1 by UV _ Sim, based on DNS^DNSThe UV _ Sim is calculated and moved to the step of FIG. 81And S306, finishing the consistency calculation processing of the UV spectrum.

The processing from step S2010 to step S2009A is the same as that in step S2009 in fig. 86.

Fig. 101 is a graph showing an example of calculation of the moving average and the moving inclination.

The upper layer of fig. 101 shows an example of UV data, the middle layer shows an example of calculation of moving average, and the lower layer shows an example of calculation of moving tilt. In the UV data, the UV intensities are represented by a1 to a7, instead of the specific values. For example, the UV intensity at 220nm is a1, the UV intensity at 221nm is a2, etc. The example of calculation of the moving average and the example of calculation of the moving inclination also use the UV intensities a 1-a 7 instead of the specific values.

The moving average takes section 1(w1 ═ 3) as an example, and in step S2012 (fig. 100), m1, m2, · · ·, is calculated as values calculated for sections (a1, a2, a3), sections (a2, a3, a4), ·. The movement tilt is also exemplified by the interval 2(3), and in step S2013 (fig. 100), S1, · · · · · · is calculated as a value calculated by the intervals (m1, m2, m3), the intervals (m2, m3, m4), · · · S. For example, the difference m3-m1 is the moving inclination, and (. + -. sup. -.) is extracted.

In this way, when creating an FP using the peak area, the UV pattern matching degree can be calculated by adding DNS in the assignment process for the reference group FP and the result merging process for reference FP assignment. By this calculation, even if the corresponding 2-point distance (dis) shown in fig. 66 is larger than the FP created with the peak height, it is easy to handle, and the UV pattern matching degree can be accurately calculated.

Others

The embodiment of the invention is suitable for evaluating the traditional Chinese medicine serving as a multi-component medicament, but can also be suitable for evaluating other multi-component substances.

The FP of the above embodiment is directed to all peaks on the 3D chromatogram, but it is also possible to remove detailed data, and to remove, for example, peaks having a peak area of less than 5% on the 3D chromatogram to create an FP.

The FP of the above example was produced based on the peak height to obtain the evaluation of fig. 70 to 74, and similarly when the FP was produced based on the peak area, the MD value was obtained by the MT method in the same procedure as in the above example produced based on the peak height, and the evaluation was obtained in the same manner as in fig. 70 to 74.

Description of the symbols

1 evaluation device for multicomponent drug

3FP creation part (FP creation device)

13 Chinese medicine (evaluation object)

153D chromatogram

17 subject FP

19 reference group FP

21 object FP belonging to reference group FP

UV Spectroscopy of the peaks contained in 25 object FP

27FP of agent A

29 FP of agent B

31 FP of agent C

33 subject FP (retention time 10.0-14.5 minutes)

35. 37, 39, 41, 43, 45, 47, 49, 51, 53 peaks in the subject FP (retention time 10.0-14.5 min)

55 Standard FP (holding time 10.0-14.5 minutes)

107 UV spectrum of subject peak

UV spectrum of 111 belonging candidate peaks

113FP creation Process

1233D chromatogram data example

125 example of wave information data

127FP data example

Claims

1. An FP creation method including an FP creation process for creating FPs, each FP including a peak detected from a chromatogram to be evaluated and a retention time thereof, the FP creation method comprising:

in the FP creation step, a 3D chromatogram having a retention time, a detection wavelength, and a peak as data is used as the chromatogram, and peaks and retention times, which are the appearance times of peaks and the peaks, which are obtained by using a maximum value of signal intensity or an area value of signal intensity expressed in height, detected from the 3D chromatogram at a specific wavelength, are displayed two-dimensionally, thereby creating the FP having UV spectrum information for each peak.

2. The FP creation method according to claim 1, wherein the evaluation target is a multicomponent drug.

3. The method for producing FP according to claim 2, wherein the multi-component drug is any of crude drugs, a combination of crude drugs, an extract of crude drugs or a combination of crude drugs, or a Chinese medicine.

4. The FP production method of any one of claims 1 to 3, wherein in the FP production process, FPs of different specific wavelengths are synthesized to produce FPs.

5. The FP creation method according to any one of claims 1 to 3, wherein all peaks of the 3D chromatogram are extracted to create the FPs in the FP creation process.

6. An FP creation device to be evaluated, the FP creation device including an FP creation unit to create an FP, the FP including a peak detected from a chromatogram of the evaluation object and a retention time thereof, the FP creation device comprising:

the FP creation unit displays a 3D chromatogram having a retention time, a detection wavelength, and a peak as data as the chromatogram, and displays a peak representing a maximum value of a signal intensity or an area value of the signal intensity in height, which is detected from the 3D chromatogram at a specific wavelength, and a retention time, which is an appearance time, two-dimensionally, thereby creating the FP having UV spectrum information for each peak.

7. The FP creation apparatus according to claim 6, wherein the evaluation target is a multicomponent drug.

8. The FP creation device according to claim 7, wherein the multi-component drug is any one of a crude drug, a combination of crude drugs, an extract of a crude drug or a combination of crude drugs, and a Chinese medicine.

9. The FP creation device according to any one of claims 6 to 8, wherein the FP creation section creates FPs by synthesizing a plurality of FPs having different specific wavelengths.

10. The FP creation device according to any one of claims 6 to 8, wherein said FP creation section extracts all peaks of said 3D tomogram to create said FP.

11. An FP produced by the FP production method according to any one of claims 1 to 5, characterized in that:

the FP is created using a peak detected from the 3D chromatogram at a specific wavelength, a retention time thereof, and a UV spectrum of the peak.