US20130204539A1 - Feature value preparing method, feature value preparing program, and feature value preparing device for pattern or fp - Google Patents

Feature value preparing method, feature value preparing program, and feature value preparing device for pattern or fp Download PDF

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US20130204539A1
US20130204539A1 US13/806,725 US201213806725A US2013204539A1 US 20130204539 A1 US20130204539 A1 US 20130204539A1 US 201213806725 A US201213806725 A US 201213806725A US 2013204539 A1 US2013204539 A1 US 2013204539A1
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peak
target
peaks
feature value
preparing
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Shoichi Teshima
Yoshikazu Mori
Keiichi Noda
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Tsumura and Co
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Tsumura and Co
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/20—Identification of molecular entities, parts thereof or of chemical compositions
    • G06F19/70—
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/74—Optical detectors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G01N30/8624—Detection of slopes or peaks; baseline correction
    • G01N30/8631—Peaks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
    • G01N30/8686—Fingerprinting, e.g. without prior knowledge of the sample components
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/15—Medicinal preparations ; Physical properties thereof, e.g. dissolubility
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N2030/022—Column chromatography characterised by the kind of separation mechanism
    • G01N2030/027—Liquid chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8886—Analysis of industrial production processes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information

Definitions

  • the present invention relates to a feature value preparing method for a pattern, a feature value preparing method for a FP of a multicomponent material used for evaluating the quality of the multicomponent material, for example, a kampo medicine that is a multicomponent drug, a feature value preparing program, and a feature value preparing device.
  • multicomponent materials for example, there are natural product-originated drugs such as kampo medicines that are drugs (hereinafter, referred to as multicomponent drugs) that are composed of multiple components.
  • the quantitative and qualitative profiles of such drugs change due to a geological factor, an ecological factor, collecting season, a collecting area, a collecting aetas, weather during the growing period, and the like of raw material crude drugs.
  • predetermined criteria are regulated as qualities for securing the safety and the effectiveness thereof, and national supervising agencies, chemical organizations, manufacturing companies, and the like perform quality evaluations based on the criteria.
  • the determination criteria on the quality and the like of a multicomponent drug are set based on the content and the like of one or several distinctive components selected from components in the multicomponent drug.
  • Non-Patent Literature 1 in a case where effective components of a multicomponent drug are not identified, it selects a plurality of components that have physical properties such as a quantitatively analyzability, high water-solubility, a undegradability in hot water, and non-chemical reactability with other components and uses the contents of the components acquired through chemical analysis as evaluation criteria.
  • evaluation targets are limited to a “contents of a specific component” or a “chromatogram peaks of specific components”, and thus only some components contained in a multicomponent drug are set as the evaluation targets. Accordingly, since a multicomponent drug includes many components other than components that are evaluation targets, such methods are insufficient as a method of evaluating a multicomponent drug in terms of accuracy.
  • crude drugs are natural products, and therefore, multicomponent drugs even which have the same product name may have slightly different components.
  • content ratios of components thereof may be different from each other or a component present in one drug may not be present in the other drug (hereinafter, referred to as an inter-drug error).
  • an analysis error there is also a factor that peak intensity or peak elution time in a chromatogram has no precise repeatability (hereinafter, referred to as an analysis error). Accordingly, all peaks or almost all peaks may not be associated with peaks that are originated from the same components between the multicomponent drugs (hereinafter, referred to as peak assignment), thereby interfering with an efficient evaluation with high accuracy.
  • a problem to be solved is that there is a limit on an efficient evaluation of the quality and the like of a multicomponent material with high accuracy with use of an existing evaluation method.
  • the present invention provides a feature value preparing method for a pattern, comprising a pattern area segmentation feature value preparing step of segmenting a pattern whose peaks change in a time series into a plurality of areas and preparing pattern area segmentation feature values based on an existence rate or existence amount of peaks existing in each area.
  • the present invention provides a feature value preparing method for a FP, comprising a FP area segmentation feature value preparing step of segmenting a FP composed of peaks and retention time points thereof detected from a chromatogram of a multicomponent material into a plurality of areas and preparing pattern area segmentation feature values based on an existence rate or existence amount of peaks existing in each area.
  • the present invention provides a feature value preparing program for a pattern that causes a computer to execute a function, the function comprising a pattern area segmentation feature value preparing function of segmenting a pattern whose peaks change in a time series into a plurality of areas and preparing pattern area segmentation feature values based on an existence rate or existence amount of peaks existing in each area.
  • the present invention provides a feature value preparing program for a FP that causes a computer to execute a function, the function comprising a FP area segmentation feature value preparing function of segmenting a FP composed of peaks and retention time points thereof detected from a chromatogram of a multicomponent material into a plurality of areas and preparing pattern area segmentation feature values based on an existence rate or existence amount of peaks existing in each area.
  • the present invention provides a feature value preparing device for a pattern, comprising a pattern area segmentation feature value preparing part of segmenting a pattern whose peaks change in a time series into a plurality of areas and preparing pattern area segmentation feature values based on an existence rate or existence amount of peaks existing in each area.
  • the present invention provides a feature value preparing device for a FP, comprising a FP area segmentation feature value preparing part of segmenting a FP composed of peaks and retention time points thereof detected from a chromatogram of a multicomponent material into a plurality of areas and preparing pattern area segmentation feature values based on an existence rate or existence amount of peaks existing in each area.
  • the feature value preparing method of a pattern or FP according to the present invention has the above-described configuration, so that the feature values of the pattern or the FP can be simply acquired through the area segmentation. Accordingly, feature values can be prepared while capturing, for example, fine peaks.
  • the feature value preparing program for a pattern or FP according to the present invention has the above-described configuration, so that it causes a computer to execute each function, thereby simply acquiring the feature values of a pattern or FP.
  • the feature value preparing device for a pattern or FP according to the present invention has the above-described configuration, so that it operates each function, thereby simply acquiring the feature values of a pattern or FP.
  • FIG. 1 It is a block diagram of an evaluating apparatus for a multicomponent drug (Embodiment 1).
  • FIG. 2 It is a block diagram illustrating procedures of evaluating a multicomponent drug (Embodiment 1).
  • FIG. 3 It is an explanatory diagram of a FP that is prepared from three-dimensional chromatogram data (hereinafter, referred to as a 3D chromatogram) (Embodiment 1).
  • FIG. 4 It is a graph illustrating FPs of respective drugs in which (A) is a drug A, (B) is a drug B, and (C) is a drug C (Embodiment 1).
  • FIG. 5 It is a diagram illustrating retention time points of a target FP and a reference FP (Embodiment 1).
  • FIG. 6 It is a diagram illustrating a retention time appearance pattern of the target FP (Embodiment 1).
  • FIG. 7 It is a diagram illustrating a retention time appearance pattern of the reference FP (Embodiment 1).
  • FIG. 8 It is a table illustrating the numbers of matches in a retention time appearance distance between the target FP and the reference FP (Embodiment 1).
  • FIG. 9 It is a table illustrating the degrees of matching between the retention time appearance patterns of the target FP and the reference FP (Embodiment 1).
  • FIG. 10 It is diagram illustrating an assignment target peak of the target FP (Embodiment 1).
  • FIG. 11 It is a peak pattern diagram according to three peaks including the assignment target peak (Embodiment 1).
  • FIG. 12 It is a peak pattern diagram according to five peaks including the assignment target peak (Embodiment 1).
  • FIG. 13 It is a diagram illustrating an allowable range for the assignment target peak (Embodiment 1).
  • FIG. 14 It is a diagram illustrating assignment candidate peaks of the reference FP for the assignment target peak (Embodiment 1).
  • FIG. 15 It is a peak pattern diagram according to three peaks of assignment candidate peaks for the assignment target peak (Embodiment 1).
  • FIG. 16 It is a peak pattern diagram according to three peaks of another assignment candidate peaks for the assignment target peak (Embodiment 1).
  • FIG. 17 It is a peak pattern diagram according to three peaks of another assignment candidate peaks for the assignment target peak (Embodiment 1).
  • FIG. 18 It is a peak pattern diagram according to three peaks of another assignment candidate peaks for the assignment target peak (Embodiment 1).
  • FIG. 19 It is a peak pattern diagram according to five peaks of assignment candidate peaks for the assignment target peak (Embodiment 1).
  • FIG. 20 It is a peak pattern diagram according to five peaks of another assignment candidate peaks for the assignment target peak (Embodiment 1).
  • FIG. 21 It is a peak pattern diagram according to five peaks of another assignment candidate peaks for the assignment target peak (Embodiment 1).
  • FIG. 22 It is a peak pattern diagram according to five peaks of another assignment candidate peaks for the assignment target peak (Embodiment 1).
  • FIG. 23 It is a diagram illustrating peak pattern configuring candidate peaks for the assignment target peak and an assignment candidate peak (Embodiment 1).
  • FIG. 24 It is a diagram illustrating the number of all the peak patterns for the assignment target peak in a case that four peak pattern configuring candidate peaks are set (Embodiment 1).
  • FIG. 25 It is a diagram illustrating the number of all the peak patterns for an assignment candidate peak in a case that four peak pattern configuring candidate peaks are set (Embodiment 1).
  • FIG. 26 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for an assignment candidate peak (Embodiment 1).
  • FIG. 27 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 28 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 29 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 30 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 31 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 32 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 33 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 34 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 35 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 36 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 37 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 38 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 39 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 40 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 41 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 42 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 43 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 44 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 45 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 46 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 47 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 48 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 49 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 50 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 51 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 52 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 53 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 54 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 55 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 56 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 57 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 58 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 59 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 60 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 61 It is an explanatory diagram illustrating comprehensive comparison of peak patterns for the assignment target peak with respect to peak patterns for the assignment candidate peak (Embodiment 1).
  • FIG. 62 It is a diagram illustrating a calculating method of the degree of matching between peak patterns of the assignment target peak and an assignment candidate peak according to three peaks (Embodiment 1).
  • FIG. 63 It is a diagram illustrating a calculating method of the degree of matching between peak patterns of the assignment target peak and the assignment candidate peak according to three peaks (Embodiment 1).
  • FIG. 64 It is a diagram illustrating a calculating method of the degree of matching between peak patterns of the assignment target peak and the assignment candidate peak according to five peaks (Embodiment 1).
  • FIG. 65 It is a diagram illustrating UV spectra of an assignment target peak and an assignment candidate peak (Embodiment 1).
  • FIG. 66 It is an explanatory diagram illustrating the degree of matching between the UV spectra of the assignment target peak and the assignment candidate peak (Embodiment 1).
  • FIG. 67 It is an explanatory diagram illustrating the degree of matching of the assignment candidate peak by comparison of both the peak patterns and the UV spectra together (Embodiment 1).
  • FIG. 68 It is an explanatory diagram illustrating assignment of the target FP to a reference group FP (Embodiment 1).
  • FIG. 69 It is a diagram illustrating a state in which the target FP is assigned to the reference group FP (Embodiment 1).
  • FIG. 70 It is a diagram illustrating quantification according to area segmentation (Embodiment 1).
  • FIG. 71 It is a diagram illustrating a relation with variations in retention time points and the like (Embodiment 1).
  • FIG. 72 It is an explanatory diagram illustrating a case where quantification is carried out with changing positions of areas (Embodiment 1).
  • FIG. 73 It is a table illustrating data of FP type-2 (Embodiment 1).
  • FIG. 74 It is an explanatory diagram illustrating patterns of the FP type-2 (Embodiment 1).
  • FIG. 75 It is an explanatory diagram illustrating quantification of feature values for each area through area segmentation with use of vertical and horizontal segmenting lines (Embodiment 1).
  • FIG. 76 It is an explanatory diagram illustrating the setting of a vertical segmenting line (1st) (Embodiment 1).
  • FIG. 77 It is an explanatory diagram illustrating the setting of a horizontal segmenting line (1st) (Embodiment 1).
  • FIG. 78 It is an explanatory diagram illustrating the area segmentation with use of the vertical and horizontal lines (Embodiment 1).
  • FIG. 79 It is an explanatory diagram illustrating the number of the areas that are quantified as feature values (Embodiment 1).
  • FIG. 80 It is an explanatory diagram illustrating specifying area 1 (Embodiment 1).
  • FIG. 81 It is a table illustrating heights of all the peaks and a sum thereof (Embodiment 1).
  • FIG. 82 It is an explanatory diagram illustrating a sum of peak heights in the area 1 (Embodiment 1).
  • FIG. 83 It is a table illustrating feature values of all the areas (Embodiment 1).
  • FIG. 84 It is a table illustrating a feature value of each area that is formed by sequentially changing a position of the vertical 1st (Embodiment 1).
  • FIG. 85 It is a table illustrating a feature value of each area that is formed by sequentially changing a position of the horizontal 1st (Embodiment 1).
  • FIG. 86 It is a table illustrating feature values in one way in which the positions of the vertical and horizontal segmenting lines are not changed (Embodiment 1).
  • FIG. 87 It is a diagram illustrating various target FPs and evaluation values (MD values) thereof (Embodiment 1).
  • FIG. 88 It is a diagram illustrating various target FPs and evaluation values (MD values) thereof (Embodiment 1).
  • FIG. 89 It is a diagram illustrating various target FPs and evaluation values (MD values) thereof (Embodiment 1).
  • FIG. 90 It is a diagram illustrating various target FPs and evaluation values (MD values) thereof (Embodiment 1).
  • FIG. 91 It is a diagram illustrating various target FPs and evaluation values (MD values) thereof (Embodiment 1).
  • FIG. 92 It is a process chart illustrating an evaluating method for a multicomponent drug (Embodiment 1).
  • FIG. 93 It is a quality evaluating flow chart for a multicomponent drug (Embodiment 1).
  • FIG. 94 It is a quality evaluating flow chart for a multicomponent drug (Embodiment 1).
  • FIG. 95 It is a data processing flowchart in a FP preparing function according to a single wavelength (Embodiment 1).
  • FIG. 96 It is a data processing flowchart in a FP preparing function according to a plurality of wavelengths (Embodiment 1).
  • FIG. 97 It is a data processing flowchart in the FP preparing function according to the plurality of wavelengths (Embodiment 1).
  • FIG. 98 It is a data processing flowchart of a peak assigning process 1 (selection of a reference FP) (Embodiment 1).
  • FIG. 99 It is a data processing flowchart of a peak assigning process 2 (calculation of an assignment score) (Embodiment 1).
  • FIG. 100 It is a data processing flowchart of a peak assigning process 3 (specifying a corresponding peak) (Embodiment 1).
  • FIG. 101 It is a data processing flowchart of a peak assigning process 4 (assignment to a reference group FP) (Embodiment 1).
  • FIG. 102 It is a data processing flowchart of the peak assigning process 4 (assignment to the reference group FP) (Embodiment 1).
  • FIG. 103 It is a flowchart of a process of calculating the degree of matching between retention time appearance patterns in the peak assigning process 1 (selection of the reference FP) (Embodiment 1).
  • FIG. 104 It is a flowchart of a process of calculating the degree of matching between UV spectra in the peak assigning process 2 (calculation of an assignment score) (Embodiment 1).
  • FIG. 105 It is a flowchart of a process of calculating the degree of matching between peak patterns in the peak assigning process 2 (calculation of an assignment score) (Embodiment 1).
  • FIG. 106 It is a flowchart illustrating details of the “preparation of FP_type-2” (Embodiment 1).
  • FIG. 107 It is a flowchart illustrating details of a “feature value quantification process of the target FP_type-2 as feature values through area segmentation” (Embodiment 1).
  • FIG. 108 It is a flowchart illustrating details of “integration of peak feature values of a target FP and area segmentation feature values” (Embodiment 1).
  • FIG. 109 It is a flowchart for preparing a reference FP feature value integrated file (Embodiment 1).
  • FIG. 110 It is a flowchart for preparing the reference FP feature value integrated file (Embodiment 1).
  • FIG. 111 It is a flowchart illustrating details of a “reference FP assigning result integrating process (preparation of a FP correspondence table)” (Embodiment 1).
  • FIG. 12 It is a flowchart illustrating details of the “reference FP assigning result integrating process (preparation of a reference FP correspondence table)” (Embodiment 1).
  • FIG. 113 It is a flowchart illustrating details of a “peak-feature value quantification process (preparation of a reference group FP)” (Embodiment 1).
  • FIG. 114 It is a flowchart illustrating details of a “process of preparing reference FP_type-2” (Embodiment 1).
  • FIG. 115 It is a flowchart illustrating a “feature value quantification process of a reference FP as feature values through area segmentation” in detail (Embodiment 1).
  • FIG. 116 It is a flowchart according to the feature value integrating process of a reference FP (Embodiment 1).
  • FIG. 117 It is table illustrating a data example of a 3D chromatogram (Embodiment 1).
  • FIG. 118 It is a table illustrating a data example of peak information (Embodiment 1).
  • FIG. 119 It is a table illustrating a data example of a FP (Embodiment 1).
  • FIG. 120 It is a table illustrating an assignment score calculation result example (determination result file) of a target FP to a reference FP (Embodiment 1).
  • FIG. 121 It is a table illustrating a process of collating corresponding peaks between a target FP and a reference FP (Embodiment 1).
  • FIG. 122 It is a table illustrating a result example (collation result file) specifying corresponding peaks between a target FP and a reference FP (Embodiment 1).
  • FIG. 123 It is a table illustrating a data example of a reference group FP (Embodiment 1).
  • FIG. 124 It is a table illustrating a reference FP peak feature value file example (Embodiment 1).
  • FIG. 125 It is a table illustrating a data example of a target and reference FP type-2 (Embodiment 1).
  • FIG. 126 It is a table illustrating a target FP area segmentation feature value file example (Embodiment 1).
  • FIG. 127 It is a table illustrating a target FP integrated feature value file an example (Embodiment 1).
  • FIG. 128 It is a table illustrating a reference type-2 group FP example (Embodiment 1).
  • FIG. 129 It is a table illustrating a reference group integrated data example (Embodiment 1).
  • FIG. 130 It is a flowchart illustrating a modified example of Subroutine 2 that is applied instead of FIG. 104 (Embodiment 1).
  • FIG. 131 It is a table illustrating a calculation example of moving averages and moving inclinations (Embodiment 1).
  • An object of contributing to the improvement of the accuracy and the efficiency of an evaluation is realized by segmenting a FP composed of peaks and retention time points thereof detected from a chromatogram of a multicomponent material into a plurality of areas and preparing FP area segmentation feature values based on an existence rate or existence amount of peaks existing in each area.
  • Embodiment 1 of the present invention there are provided an evaluating method and evaluating program for a multicomponent material, for example, a multicomponent drug, and a feature value preparing method, preparing program, and preparing device for a FP as a pattern.
  • a multicomponent drug is defined as a drug that contains a plurality of effective chemical components.
  • the multicomponent drug include a crude drug, a combination of crude drugs, an extract thereof, and a kampo medicine, but are not limited thereto.
  • the dosage form is not particularly limited, and, examples include a liquid medicine, an extract, a capsule, a granule, a pill, suspension emulsion, a powder, a spiritus, a tablet, an infusion-decoction, a tincture, a troche, aromatic water, a fluid extract, which are specified in “general rule for preparations” of “The Japanese Pharmacopoeia”, Fifteenth Edition.
  • the multicomponent material materials other than a drug are also included.
  • a target FP is prepared by extracting information unique to the drug from a three-dimensional chromatogram data (hereinafter, referred to as a 3D chromatogram) of the evaluation target drug.
  • each peak of the target FP is assigned to peak correspondence data (hereinafter, referred to as a reference group FP) of all reference FPs, which is prepared by performing a peak assigning process to all the reference FPs, whereby peak feature values are acquired.
  • a reference group FP peak correspondence data
  • FP type-2 is prepared by remaining peaks with the exclusion of assigned peaks from the target FP, and area segmentation feature values are acquired by performing area segmentation of the FP type-2.
  • target FP integrated feature values are acquired.
  • an acquired evaluation value hereinafter, referred to as a MD value
  • a preset determination value an upper limit value of the MD value
  • FIG. 1 is a block diagram of an evaluating apparatus for a multicomponent drug
  • FIG. 2 is a block diagram illustrating a procedure of evaluating a multicomponent drug
  • FIG. 3 is an explanatory diagram of a FP that is prepared based on a 3D chromatogram
  • FIG. 4(A) is a FP of a drug A
  • (B) is a FP of a drug B
  • (C) is a FP of a drug C.
  • the evaluating apparatus for a multicomponent drug 1 includes a FP preparing part 3 , a target FP peak assigning part 5 , a target FP peak feature value preparing part 7 , a target FP type-2 preparing part 9 , a target FP area segmentation feature value preparing part 11 , a target FP feature value integrating part 13 , a reference FP peak assigning part 15 , a reference FP assigning result integrating part 17 , a reference FP peak feature value preparing part 19 , a reference FP type-2 preparing part 21 , a reference FP area segmentation feature value preparing part 23 , a reference FP feature value integrating part 25 , and an evaluating part 27 .
  • the evaluating apparatus 1 for a multicomponent drug includes a feature value preparing device for a FP being a pattern.
  • the FP preparing part 3 includes a target FP preparing part 29 and a reference FP preparing part 31 .
  • the target FP peak assigning part 5 includes a reference FP selecting part 33 , a peak pattern preparing part 35 , and a peak assigning part 37 .
  • the evaluating apparatus 1 for a multicomponent drug is configured by a computer and, although not illustrated in the drawings, includes a CPU, a ROM, a RAM, and the like.
  • the evaluating apparatus 1 for a multicomponent drug can acquire feature values of a FP by implementing a feature value preparing program for a FP a feature value preparing program for a pattern that is installed in the computer.
  • the acquisition of the feature values of a FP may be realized by using a feature value preparing program recording medium for a FP that stores the feature value preparing program and by reading out it with the evaluating apparatus 1 configured by the computer for a multicomponent drug.
  • the parts of the evaluating apparatus for a multicomponent drug 1 may be configured by discrete computers, and, for example, the target FP peak assigning part 5 , the target FP peak feature value preparing part 7 , the target FP type-2 preparing part 9 , the target FP area segmentation feature value preparing part 11 , the target FP feature value integrating part 13 , and the evaluating part 27 may be configured by a single computer, and the reference FP preparing part 31 , the reference FP peak assigning part 15 , the reference FP assigning result integrating part 17 , the reference FP peak feature value preparing part 19 , the reference FP type-2 preparing part 21 , the reference FP area segmentation feature value preparing part 23 , and the reference FP feature value integrating part 25 are configured by another computer.
  • the reference FP integrated feature values are prepared by the another computer and are input to the evaluating part 27 of the evaluating apparatus 1 .
  • the target FP integrated feature values are prepared by the target FP preparing part 29 , the target FP peak assigning part 5 , the target FP peak feature value preparing part 7 , the target FP type-2 preparing part 9 , the target FP area segmentation feature value preparing part 11 , and the target FP feature value integrating part 13 .
  • the reference FP integrated feature values are prepared by the reference FP preparing part 31 , the reference FP peak assigning part 15 , the reference FP assigning result integrating part 17 , the reference FP peak feature value preparing part 19 , the reference FP type-2 preparing part 21 , the reference FP area segmentation feature value preparing part 23 , and the reference FP feature value integrating part 25 . These are compared and evaluated so as to evaluate the equivalency between the target FP 43 and the reference group FP 45 .
  • the target FP preparing part 29 of the FP preparing part 3 configures a target pattern acquiring part that acquires a target pattern of an evaluation target whose peaks change in a time series. More specifically, the target FP preparing part 29 , for example, as illustrated in FIGS. 2 and 3 , is a functional part that prepares a target FP 43 (hereinafter, it may be simply referred to as an “FP 43 ”) as a target pattern by extracting a plurality of peaks at a specific detection wavelength, retention time points thereof, and UV spectra from a 3D chromatogram 41 that is a three-dimensional chromatogram data as a chromatogram of a kampo medicine 39 .
  • a target FP 43 hereinafter, it may be simply referred to as an “FP 43 ”
  • the FP 43 similarly to the 3D chromatogram 41 , is configured by three-dimensional information (peaks, retention time points, and UV spectra).
  • the FP 43 therefore, is data that directly succeed to information unique to the drug. In spite of that, the data volume is compressed at the ratio of about 1/70, and therefore, the amount of information to be processed is much smaller than that of the 3D chromatogram 41 , thereby increasing processing speed.
  • the 3D chromatogram 41 is a result of applying high performance liquid chromatography (HPLC) to a kampo medicine 39 .
  • HPLC high performance liquid chromatography
  • a movement speed of each component appears to represent as a movement distance during specific time, or an appearance in a time series from a column end is represented in a chart.
  • detector responses are plotted with respect to the time axis, and appearance time points of peaks are called retention time points.
  • the detector is not particularly limited, an absorbance detector employing an optical characteristic is used as the detector.
  • a peak is three-dimensionally acquired as a signal strength according to a detection wavelength of ultraviolet (UV).
  • a transmittance detector may be used as a detector employing an optical characteristic.
  • the detection wavelengths are not particularly limited, and are a plurality of wavelengths selected preferably from a range of 150 nm to 900 nm, selected more preferably from a range of 200 nm to 400 nm corresponding to a UV-visible absorption range, and selected further more preferably from a range of 200 nm to 300 nm.
  • the 3D chromatogram 41 at least includes a number (lot number), retention time points, detection wavelengths, and peaks of a kampo medicine as data.
  • the 3D chromatogram 41 can be also acquired by using a device commercially-available devices.
  • a commercially-available device there is an “Agilent 1100 system” or the like.
  • the chromatograph is not limited to the HPLC, and any other type of chromatography may be employed.
  • the x-axis represents the retention time point
  • the y-axis represents the detection wavelength
  • the z-axis represents signal strength
  • the FP 43 at least includes a number (lot number), retention time points, peaks at a specific wavelength, and UV spectra of a kampo medicine as data.
  • the FP 43 is two-dimensionally represented with the x-axis representing the retention time points and the y-axis representing the peaks for the specific detection wavelength as illustrated in FIGS. 2 and 3 .
  • the FP 43 is data that includes UV spectrum information for each peak that is similar to the UV spectrum 42 represented with respect to one peak as illustrated in FIG. 3 .
  • the specific detection wavelength for which the FP 43 is prepared is not particularly limited and may be selected in various manners. However, it is important for the FP 43 to include all the peaks of the 3D chromatogram in order to succeed to the information. Accordingly, in Embodiment 1, the detection wavelength is set to 203 nm that includes all the peaks of the 3D chromatogram.
  • a plurality of detection wavelengths are set to prepare a FP that includes all the peaks by combining the plurality of wavelengths as described later.
  • the peak is set as the maximum value of the signal strength (peak height)
  • the area value may be used as the peak.
  • a FP may not include UV spectra, so that the FP is set as two-dimensional display information in which the x-axis represents the retention time points and the y-axis represents the peaks for a specific detection wavelength.
  • the FP can be prepared from a 2D chromatogram as a chromatogram that includes a number (lot number) and retention time points of a kampo medicine as data.
  • FIG. 4A is a FP 55 of Drug A
  • FIG. 4B is a FP 57 of Drug B
  • FIG. 4C is a FP 59 of Drug C.
  • the target FP peak assigning part 5 is a functional part that compares peaks of a target FP and peaks of a reference FP that corresponds to the target FP of a multicomponent material and is an evaluation criteria to specify corresponding peaks.
  • the target FP peak assigning part 5 comprises a reference FP selecting part 33 , a peak pattern preparing part 35 , and a peak assigning part 37 .
  • the reference FP selecting part 33 is a functional part that selects a FP of a multicomponent material that is appropriate to the assignment of the peaks to the target FP from among a plurality of reference FPs.
  • the peak pattern preparing part 35 is a functional part that, as illustrated in FIGS. 10 to 12 (to be described later), prepares a peak pattern configured by a total of n+1 peaks including n peaks that are present at least on one of sides located in front and in the rear in the direction of the time axis for a peak (hereinafter, referred to as an assignment target peak) of the target FP 61 that is a target to be assigned, as a peak pattern of an assignment target peak.
  • an assignment target peak a peak of the target FP 61 that is a target to be assigned
  • n is a natural number.
  • FIG. 11 illustrates a peak pattern configured by a total of three peaks that include two peaks being present at least on one of sides located in front and in the rear in the time axis direction
  • FIG. 12 illustrates a peak pattern configured by a total of five peaks that include four peaks being present at least on one of sides located in front and in the rear in the time axis direction.
  • the peak pattern preparing part 35 is a functional part that, as illustrated in FIGS. 13 to 22 (to be described later), prepares peak patterns each configured by a total of n+1 peaks including n peaks that are present at least on one of sides located in front and in the rear in the time axis direction for all the peaks (hereinafter, referred to as assignment candidate peaks) each having a difference from the retention time point of the assignment target peak within a set range (allowable range) in the reference FP 83 , as the peak patterns of the assignment candidate peaks.
  • FIGS. 15 to 18 illustrate peak patterns each configured by a total of three peaks including two peaks that are located at least on one of sides located in front and in the rear in the time axis direction.
  • FIGS. 19 to 22 illustrate peak patterns each configured by a total of five peaks including four peaks that are located at least on one of sides located in front and in the rear in the time axis direction.
  • the allowable range is not particularly limited, but is preferably in the range of 0.5 minutes to two minutes with the object of the accuracy and efficiency. In Embodiment 1, the allowable range is set to one minute.
  • the peak pattern preparing part 35 is configured to be able to flexibly respond to even a case where there is a difference between the numbers of the peaks of the target FP 61 and the reference FP 83 (in other words, there are one or more peaks that are not present on one side).
  • peak patterns are comprehensively prepared by changing peaks configuring the peak patterns (hereinafter, referred to as peak pattern configuring peaks) for both assignment target peaks and assignment candidate peaks.
  • FIGS. 23 to 61 illustrate cases where the peak pattern is configured by a total of three peaks including two peaks that are located at least on one of sides located in front and in the rear in the time axis direction.
  • the peak assigning part 37 is a functional part that compares the peak patterns of the respective target FP and reference FP to specify corresponding peaks.
  • the corresponding peaks are specified by calculating the degree of matching between the peak pattern of the assignment target peak and the peak patterns of the assignment candidate peaks and the degree of matching between the UV spectra.
  • the peak assigning part 37 is a functional part that calculates the degrees of matching for the assignment candidate peaks by integrating the two kinds of the degrees of matching to assign each peak of the target FP 61 to each peak of the reference FP 83 based on the calculated degrees of matching.
  • the peak assigning part 37 calculates the degree of matching between peak patterns, as illustrated in FIGS. 62 to 64 (to be described later) based on differences in corresponding peaks and retention time points between the peak patterns of the assignment target peak and the assignment candidate peak.
  • the degree of matching between the UV spectra is calculated based on a difference between the absorbance of the UV spectrum 135 of the assignment target peak 73 and the absorbance of the UV spectrum 139 of the assignment candidate peak 95 for each wavelength as illustrated in FIGS. 65 and 66 (to be described later). Further, as illustrated in FIG. 67 (to be described later), the degree of matching of the assignment candidate peak 95 is calculated by multiplying these two kinds of the degrees of matching together.
  • the target FP peak feature value preparing part 7 is a functional part that prepares target FP peak feature values that are quantified as feature values through comparisons and evaluations of peaks specified by the target FP peak assigning part 5 so as to be assigned and peaks of the reference group FP 45 that are plural reference FPs.
  • the plurality of reference FPs are prepared in correspondence with a plurality of kampo medicines that are multicomponent materials as evaluation criteria, and the plurality of kampo medicines are reputed as normal products.
  • the target FP peak feature value preparing part 7 is a functional part that, based on the assigning result of the target FP 61 and the reference FP 83 , finally assigns the peaks of the target FP 43 to the peaks of the reference group FP 45 to prepare target FP peak feature values 47 that are quantified as feature values as illustrated in FIGS. 2 , 68 , and 69 (to be described later).
  • the target FP type-2 preparing part 9 prepares a pattern as a target pattern type-2 that is composed of remaining peaks with the exclusion of the peaks that are quantified as feature values from the target pattern.
  • the target FP type-2 preparing part 9 is a functional part that prepares a FP as a target pattern type-2 that is a target FP type-2 ( 49 ) illustrated in FIG. 2 composed of remaining peaks with the exclusion of peaks 47 specified by the target FP peak feature value preparing part 7 from the original target FP 43 and of the retention time points thereof.
  • This target FP type 2 ( 49 ) is set as a FP by collecting peaks that are not quantified as feature values by the target FP peak feature value preparing part 7 . By quantifying the target FP type-2 ( 49 ) as feature values to be added to the evaluation, it performs more accurate evaluation.
  • the target FP area segmentation feature value preparing part 11 configures a FP area segmentation feature value preparing part that segments a target pattern type-2 into a plurality of areas and prepares target pattern area segmentation feature values based on an existence rate of peaks existing in each area, and is a functional part that segments the target FP type-2 ( 49 ) into a plurality of areas and prepares target FP area segmentation feature values as the target pattern area segmentation feature values based on the existence rate of peaks existing in each area.
  • the target FP area segmentation feature value preparing part 11 may use an existence amount instead of the existence rate.
  • the existence rate is a value acquired by dividing an existence amount of the peak heights in each area by a sum of all the peak heights (in other words, an existence amount of a total peak height). Accordingly, it may be configured to prepare area segmentation feature values with use of the existence amount of the peak heights in each area as itself.
  • This target FP area segmentation feature value preparing part 11 segments the target FP type-2 ( 49 ) into lattice-shaped areas with a plurality of vertical segmenting lines parallel to a signal strength axis and a plurality of horizontal segmenting lines parallel to the time axis as illustrated in FIG. 70 (to be described later) to prepare the target FP area segmentation feature values 51 illustrated in FIG. 2 .
  • the target FP feature value integrating part 13 is a functional part that prepares target FP integrated feature values by integrating the target FP peak feature values 47 prepared by the target FP peak feature value preparing part 7 and the target FP area segmentation feature values 51 prepared by the target FP area segmentation feature value preparing step 11 .
  • the reference FP preparing part 31 of the FP preparing part 3 is a functional part that, similarly to the target FP preparing part 29 , prepares a plurality of reference FPs.
  • the reference FP preparing part 31 prepares a reference FP for each reference kampo medicine by extracting a plurality of peaks at a specific detection wavelength, retention time points thereof, and UV spectra from each 3D chromatogram that is three-dimensional chromatogram data of a plurality of kampo medicines (reference kampo medicines) that are determined as normal products.
  • the reference FP peak assigning part 15 similarly to the target peak assigning part 5 , is a functional part that specifies peaks to be assigned through pattern recognition. However, the reference FP peak assigning part 15 , for all the reference FPs, specifies peaks by calculating assignment scores for a selected combination in a selected order.
  • the reference FP assigning result integrating part 17 is a functional part that prepares a reference peak correspondence table (to be described later) by integrating peaks that are specified and assigned by the reference peak assigning part 15 .
  • the reference FP peak feature value preparing part 19 is a functional part that prepares reference FP peak feature values by quantifying the plurality of reference FPs as feature values based on the reference peak correspondence table prepared by the reference FP assigning result integrating part 17 .
  • the reference FP type-2 preparing part 21 functions similar to the target FP type-2 preparing part 9 and is a functional part that prepares a FP as a reference FP type-2 that is composed of remaining peaks with the exclusion of the peaks that are quantified as feature values from the plurality of reference FPs and of retention time points thereof.
  • the reference FP area segmentation feature value preparing part 23 functions similar to the target FP area segmentation feature value preparing part 11 and is a functional part as a FP area segmentation feature value preparing part that segments the reference FP type-2 into a plurality of areas and prepares reference FP area segmentation feature values based on an existence rate of peaks existing in each area.
  • the reference FP area segmentation feature value preparing part 23 changes a position of each segmented area to prepare reference FP area segmentation feature values before and after the change. In other words, by changing and setting a position of each of the vertical and horizontal segmenting lines so as to move parallel within a set range, the position of each area is changed.
  • the reference FP feature value integrating part 25 functions similar to the target FP feature value integrating part 13 and is a functional part that prepares reference FP integrated feature values by integrating the reference FP peak feature values and the reference FP area segmentation feature values.
  • the evaluating part 27 compares and evaluates the target pattern integrated feature values and the reference pattern integrated feature values that correspond to the target pattern integrated feature values and are based on a plurality of reference patterns being evaluation criteria.
  • the evaluating part 27 is a functional part that compares and evaluates the target FP integrated feature values as the target pattern integrated feature values and the reference FP integrated feature values as the reference pattern integrated feature values.
  • the equivalency between the target FP integrated feature values and the reference FP integrated feature values is evaluated using MT method.
  • MT method represents a calculation technique that is generally known in quality engineering.
  • MT method is described in pp 136 to 138 of “Mathematics for Quality Engineering” published by Japanese Standards Association (2000); pp 454 to 456 of Quality Engineering of Application Course of “Technical Developments in Chemistry, Pharmacy and Biology” published by Japanese Standards Association (1999); pp 78 to 84 of Quality Engineering 11(5) (2003); and “Introduction to MT System” (2008).
  • MT method program software that is commercially available in the market can be used.
  • MT method program software there are “ATMTS” provided by Angle Try Associates, “TM-ANOVA” provided by Japanese Standards Association, an “MT method for Windows” provided by OHKEN Co., Ltd, and the like.
  • the evaluating part 27 assigns a variable axis according to MT method to one of the lot number and the retention time point of a kamnpo medicine or the UV detection wavelength of the target FP 43 and sets the peaks as feature values according to MT method.
  • the retention time point is assigned to a so-called category-axis according to MT method
  • the number of a multicomponent-based drug is assigned to a so-called number row axis
  • the peak is assigned to a so-called feature value according to MT method.
  • the category axis and the number row axis are defined as below.
  • an average value m j and a standard deviation ⁇ j are acquired for a data set X ij
  • a unit space or a Mahalanobis distance is acquired.
  • the category axis and the number row axis are defined such that “the average value m j and the standard deviation ⁇ j are acquired for each value of the category axis by changing the value of the number row axis.”
  • a reference point and an unit quantity (hereinafter, it may be abbreviated as an “unit space”) are acquired using MT method.
  • the reference point, the unit quantity, and the unit space are defined in accordance with the description of MT method presented in the above-described literatures.
  • a MD value is acquired as a value that represents the degree of a difference between a drug to be evaluated and the unit space.
  • the MD value is defined in the same way as the description of MT method presented in the literatures, and the MD value is acquired with the method described in the literatures.
  • the drug to be evaluated can be evaluated by determining the degree of a difference from a plurality of drugs defined as normal products.
  • a MD value (MD values: 0.26, 2.20, and the like) can be acquired in accordance with MT method.
  • MD values are similarly acquired for a plurality of drugs defined as normal products.
  • a threshold value is set from the MD values of these normal products, the MD value of the evaluation target drug is plotted as an evaluation result 53 of the evaluating part 27 illustrated in FIG. 2 to determine a normal product or an abnormal product.
  • a MD value of 10 or less is determined as a normal product.
  • the evaluating part 27 it is sufficient for the evaluating part 27 to be able to compare and evaluate the equivalency between the target FP integrated feature values and the reference FP integrated feature values, and therefore, a pattern recognition technique other than MT method or the like can be used.
  • FIGS. 5 to 69 illustrate an operating principle of the reference FP selecting part 33 , the peak pattern preparing part 35 , the peak assigning part 37 , and the target FP peak feature value preparing part 7 .
  • FIGS. 5 to 9 are diagrams each illustrating the degree of matching between the retention time appearance patterns of the target FP and the reference FP according to the reference FP selecting part 33 .
  • FIG. 5 is a diagram illustrating retention time points of the target FP and the reference FP
  • FIG. 6 is a diagram illustrating the retention time appearance patterns of the target FP
  • FIG. 7 is a diagram illustrating the retention time appearance patterns of the reference FP.
  • FIG. 8 is a diagram illustrating the numbers of matches in the retention time appearance distance between the target FP and the reference FP
  • FIG. 9 is a diagram illustrating the degrees of matching in the retention time appearance pattern between the target FP and the reference FP.
  • FIG. 5 shows the retention time points of the target FP 61 and the reference FP 83 .
  • FIGS. 6 and 7 show the retention time appearance patterns in which all of inter-retention time point distances calculated based on the retention time points of the target FP 61 and the reference FP 83 are arranged in a table form.
  • FIG. 8 shows the numbers of matches between the retention time appearance distances calculated based on the appearance patterns and arranged in a table form.
  • FIG. 9 shows the degrees of matching between the retention time appearance patterns calculated based on the number of matches and arranged in a table form.
  • FIGS. 10 to 12 are diagrams explaining a peak pattern that is prepared with use of an assignment target peak and peripheral peaks thereof by the peak pattern preparing part 35 .
  • FIG. 10 is a diagram illustrating an assignment target peak of the target FP.
  • FIG. 11 is diagram illustrating a peak pattern prepared with use of three peaks including two peripheral peaks, and
  • FIG. 12 is a diagram illustrating a peak pattern prepared with use of five peaks including four peripheral peaks.
  • FIGS. 13 and 14 explain a relation between the assignment target peak and assignment candidate peaks according to the peak pattern preparing part 35
  • FIG. 13 is a diagram illustrating an allowable range of the assignment target peak
  • FIG. 14 is a diagram illustrating assignment candidate peaks of the reference FP for the assignment target peak.
  • FIGS. 15 to 18 are peak pattern examples of the assignment target peak and assignment candidate peaks that are prepared by three peaks according to the peak pattern preparing part 35 .
  • FIG. 15 is a peak pattern diagram according to three peaks of the assignment target peak and assignment candidate peaks
  • FIG. 16 is a peak pattern diagram according to three peaks of another assignment candidate peaks for the assignment target peak
  • FIG. 17 is a peak pattern diagram according to three peaks of another assignment candidate peaks for the assignment target peak
  • FIG. 18 is a peak pattern diagram according to three peaks of another assignment candidate peaks for the assignment target peak.
  • FIGS. 19 to 22 are peak pattern diagrams of an assignment target peak and assignment candidate peaks that are prepared with use of five peaks according to the peak pattern preparing part 35 .
  • FIGS. 23 to 61 are diagrams explaining the principle of comprehensive comparison in which peak patterns of the assignment target peak and assignment candidate peak according to the peak pattern preparing part 35 are comprehensively prepared and compared with each other.
  • FIGS. 62 and 63 are diagrams explaining a calculating method of the degree of matching between peak patterns prepared with use of three peaks according to the peak assigning part 37 .
  • FIG. 64 is a diagram explaining a calculating method of the degree of matching between peak patterns prepared with use of five peaks according to the peak assigning part 37 .
  • FIG. 65 is a diagram illustrating UV spectra 135 and 139 of an assignment target peak 73 and an assignment candidate peak 95 according to the peak assigning part 37 .
  • FIG. 66 is a diagram explaining the degree of matching between the UV spectrum 135 of the assignment target peak 73 and the UV spectrum 139 of the assignment candidate peak 95 according to the peak assigning part 37 .
  • FIG. 67 is a diagram explaining the degree of matching between assignment candidate peaks that is calculated based on the degree of matching between peak patterns of the assignment target peak 73 and the assignment candidate peak 95 and the degree of matching between UV spectra according to the peak assigning part 37 .
  • FIG. 68 is a diagram explaining the assignment of each peak of the target FP 43 to the reference group FP 45 according to the peak assigning part 37 .
  • FIG. 69 is a diagram explaining a target FP peak feature value 47 that represents a state in which each peak of the target FP 43 is assigned to the reference group FP 45 according to the peak assigning part 37 .
  • FIG. 5 is the diagram illustrating retention time points of the target FP and the reference FP
  • FIG. 6 is the diagram illustrating the retention time appearance patterns of the target FP
  • FIG. 7 is the diagram illustrating the retention time appearance patterns of the reference FP
  • FIG. 8 is the diagram illustrating the numbers of matches in the retention time appearance distance between the target FP and the reference FP
  • FIG. 9 is the diagram illustrating the degrees of matching in the retention time appearance pattern between the target FP and the reference FP.
  • FIG. 5 shows the retention time points of the target FP 61 and the reference FP 83 .
  • FIGS. 6 and 7 show the retention time appearance patterns in which all of inter-retention time point distances calculated based on the respective retention time points of the target FP 61 and the reference FP 83 are arranged in a table form.
  • FIG. 8 shows the numbers of matches between the retention time appearance distances calculated based on the appearance patterns and arranged in a table form.
  • FIG. 9 shows the degrees of matching between the retention time appearance patterns calculated based on the number of matches and arranged in a table form.
  • the peaks of the target FP 61 are assigned to a reference FP whose FP pattern is closest to the target FP 61 as much as possible. Selecting this reference FP that is closest to the target FP 61 from among a plurality of reference FPs is an important point for performing assignment with high accuracy.
  • the similarity of the FP pattern is evaluated based on the degree of matching in the retention time appearance pattern.
  • retention time appearance patterns of the target FP 61 and the reference FP 83 are as illustrated in FIGS. 6 and 7 .
  • FIGS. 6 and 7 for the target FP 61 and the reference FP 83 illustrated on the upper side, as tables illustrated on the lower side, patterns are prepared in the form of tables in which the value of each cell is configured by an inter-retention time point distance.
  • the retention time points of peaks ( 63 , 65 , 67 , 69 , 71 , 73 , 75 , 77 , 79 , and 81 ) of the target FP 61 are (10.2), (10.5), (10.8), (11.1), (11.6), (12.1), (12.8), (13.1), (13.6), and (14.0).
  • an inter-retention time point distance between the peaks 63 and 67 is (0.6), an inter-retention time point distance between the peaks 65 and 67 is (0.3), etc.
  • the followings are similarly acquired and a target FP appearance pattern is formed into a table on the lower side of FIG. 6 .
  • the retention time points of the peaks ( 85 , 87 , 89 , 91 , 93 , 95 , 97 , 99 , 101 , 103 , and 105 ) of the reference FP 83 are (10.1), (10.4), (10.7), (11.1), (11.7), (12.3), (12.7), (13.1), (13.6), (14.1), and (14.4).
  • inter-retention time point distances form a reference FP appearance pattern into a table on the lower side of FIG. 7 .
  • the individual peaks patterned as illustrated in FIGS. 6 and 7 are compared in a round-robin so as to acquire the number of matches.
  • the value of each cell of the target FP appearance pattern represented in the table illustrated on the lower side of FIG. 6 is compared with the value of each cell of the reference FP appearance pattern represented in the table on the lower side of FIG. 7 , thereby acquiring the number of matches as illustrated in FIG. 8 .
  • all the inter-retention time point distances of the retention time appearance patterns of the target FP 61 and the reference FP 83 are sequentially compared with each other in units of rows in a round-robin, thereby calculating the number of the distances that match within a set range.
  • the number of matches is seven. This number of matches of seven is written into the first row of the target and reference FP retention time appearance pattern illustrated in FIG. 8 .
  • the first to ninth rows of the target FP retention time appearance pattern are compared with the first to tenth rows of the reference FP retention time appearance pattern in a round-robin, thereby acquiring the numbers of matches, respectively.
  • a leftmost circled number of 7 is a result of the comparison between the first rows of the target and reference FP retention time appearance patterns, and a number of 7 represented next thereto is a result of the comparison between the first row of the target FP retention time appearance pattern and the second row of the reference FP retention time appearance pattern.
  • the set range is not particularly limited, but is preferably in the rage of 0.05 minutes to 0.2 minutes. In Embodiment 1, the set range is 0.1 minutes.
  • a degree (RP fg ) of matching between a retention time appearance pattern of the f-th row of the target FP 61 and the retention time appearance pattern of the g-th row of the reference FP 83 is calculated using Tanimoto coefficient as:
  • RP fg ⁇ 1 ⁇ ( m /( a+b ⁇ m )) ⁇ ( a ⁇ m+ 1) using a Tanimoto coefficient.
  • “a” is the number of peaks of the target FP 61 (the number of target FP peaks)
  • “b” is the number of peaks of the reference FP 83 (the number of reference FP peaks)
  • “m” is the number of matches in the retention time appearance patterns (the number of matches in an appearance distance) (see FIG. 8 ).
  • the degree (RP) of matching between retention time appearance patterns is calculated using the above-described equation based on the number of matches in FIG. 8 (see FIG. 9 ).
  • RP_min that is the minimum value of these RPs is set as the degree of matching between the retention time appearance patterns of the target FP 61 and the reference FP 83 .
  • (0.50) is the degree of matching of the target FP 61 with respect to the reference FP.
  • the degrees of matching are calculated for all the reference FPs, and a reference FP having the smallest degree of matching is selected, and the peaks of the target FP are assigned to the reference FP.
  • the reference FP selecting part 5 may pattern the target FP 61 and the reference FP 83 at peak height ratios.
  • the peaks patterned with use of the peak height ratios are compared in a round-robin, to calculate the number of matches in the height ratio within a set range. By performing such a calculation, similarly to the case of FIG. 8 , the number of matches can be acquired.
  • the peaks are patterned at the peak height ratios, there is a case where a plurality of similar values are present in one row, and thus these values are required not to be counted a plurality of times.
  • the degree of matching can be acquired by setting the Tanimoto coefficient as “the number of matches in the height ratio/(the number of target FP peaks+the number of reference FP peaks ⁇ the number of matches in the height ratio)” and approaching (1 ⁇ Tanimoto coefficient) to zero.
  • (1 ⁇ Tanimoto coefficient) is weighted by (the number of target FP peaks ⁇ the number of matches in the appearance patterns or height ratio+1) to be “(1 ⁇ Tanimoto coefficient) ⁇ (the number of target FP peaks ⁇ the number of matches in the appearance pattern or the height ratio+1”, whereby a reference FP that matches more peaks ( 63 , 65 , . . . ) of the target FP 61 in accordance with the weighting can be selected.
  • the assignment target peak 73 When the assignment target peak 73 is assigned to one of peaks of the reference FP 83 as illustrated in FIG. 10 , it works out to that the peak should be assigned to which one of the peaks. If this peak assignment is carried out based on only information of the peak retention time or UV spectra, sufficient accuracy cannot be acquired by the peak assignment based on the single kind of information. This is because all the three kinds of information include errors due to the inter-drug error and the analysis error.
  • an assignment destination is determined by synthesizing all the information to improve the accuracy compared to the peak assignment according to the single kind of information.
  • peak patterns including information of peripheral peaks as illustrated in FIGS. 11 and 12 are prepared, and the peak assignment is performed based on the comparison of the peak patterns.
  • the peak pattern includes the peripheral peaks
  • the peripheral information is added to the prior three kinds of information. Accordingly, the peak assignment can be performed based on four kinds of information, whereby higher assignment accuracy can be secured.
  • a peak pattern 115 that includes peaks 71 and 75 being present on both sides in the time axis direction is prepared for the assignment target peak 73 .
  • a peak pattern 125 including peaks 69 , 71 , 75 , and 77 that are present on both sides in the time axis direction is prepared for the assignment target peak 73 .
  • an allowable range of the deviation between the retention time points of the respective peaks of the assignment target peak 73 and the reference FP 83 is set, and peaks of the reference FP 83 that are present within the allowable range are set as candidate peaks (hereinafter, referred to as assignment candidate peaks) that correspond to the assignment target peak 73 .
  • a peak pattern 117 that includes peaks 91 and 95 being present on both sides located in front and in the rear in the time axis direction is prepared for an assignment candidate peak 93 .
  • peak patterns 119 , 121 , and 123 that include peaks being present on both sides located in front and in the rear in the time axis direction are prepared for another assignment candidate peaks 95 , 97 , and 99 , respectively.
  • a peak pattern 127 that includes peaks 89 , 91 , 95 , and 97 being present on both sides in the time axis direction is prepared for the assignment candidate peak 93 .
  • peak patterns to be compared with a peak pattern 125 of the assignment target peak 73 peak patterns 129 , 131 , and 133 that include peaks being present on both sides located in front and in the rear in the time axis direction are prepared as peak patterns for another assignment candidate peaks 95 , 97 , and 99 , respectively.
  • peaks being candidates for the peak pattern configuring peak are set from among peripheral peaks of the assignment target peak of the target FP in advance. Peak patterns are prepared by setting the peak pattern configuring candidate peaks as the peak pattern configuring peak in turns. Also for the assignment candidate peaks of the reference FP, similarly, peak pattern configuring candidate peaks are set to prepare peak patterns are by setting the peak pattern configuring candidate peaks as the peak pattern configuring peak in turn.
  • peaks 69 , 71 , 75 , and 77 located on the periphery in the time axis direction are set as the peak pattern configuring candidate peaks for the assignment target peak 73
  • four peaks 89 , 91 , 95 , and 97 located on the periphery in the time axis direction are set as the peak pattern configuring candidate peaks for the assignment candidate peak 93
  • the peak pattern configuring peaks are set to arbitrary two peaks.
  • the peak assigning part 37 calculates the degree of matching between peak patterns (hereinafter, referred to as P_Sim) based on differences in corresponding peaks and retention time points over all the peak patterns for the assignment target peak and the assignment candidate peaks prepared by the peak pattern preparing part 35 .
  • the peak assigning part 37 sets the minimum value of the P_Sim (hereinafter, referred to as P_Sim_min) as the degree of matching between peak patterns of the assignment target peak and the assignment candidate peak.
  • the P_Sim is similarly calculated for all the assignment candidate peaks of the assignment target peak 73 .
  • a calculating method of the degree of matching between peak patterns for comparing peak patterns each configured by three peaks will be described with reference to FIGS. 62 and 63 .
  • the peak pattern 115 of the assignment target peak 73 and the peak pattern 119 of the assignment candidate pattern 95 will be described as an example.
  • a peak and a retention time point of the assignment target peak 73 are assumed to be p 1 and r 1
  • a peak and a retention time point of a peak pattern configuring peak 71 are assumed to be dn 1 and cn 1
  • a peak and a retention time point of the peak pattern configuring peak 75 are assumed to be dn 2 and cn 2 .
  • a peak and a retention time point of the assignment candidate peak 95 are assumed to be p 2 and r 2
  • a peak and a retention time point of the peak pattern configuring peak 93 are assumed to be fn 1 and en 1
  • a peak and a retention time point of a peak pattern configuring peak 97 are assumed to be fn 2 and en 2 .
  • P_Sim ⁇ ( 73 - 95 ) ( ⁇ p ⁇ ⁇ 1 - p ⁇ ⁇ 2 ⁇ + 1 ) ⁇ ( ⁇ ( r ⁇ ⁇ 1 - ( r ⁇ ⁇ 2 + d ) ⁇ + 1 ) + ( ⁇ dn ⁇ ⁇ 1 - fn ⁇ ⁇ 1 ⁇ + 1 ) ⁇ ( ⁇ ( cn ⁇ ⁇ 1 - r ⁇ ⁇ 1 ) - ( en ⁇ ⁇ 1 - r ⁇ ⁇ 2 ) ⁇ + 1 ) + ( ⁇ dn ⁇ ⁇ 2 - fn ⁇ ⁇ 2 ⁇ + 1 ) ⁇ ( ⁇ ( cn ⁇ ⁇ 2 - r ⁇ ⁇ 1 ) - ( en ⁇ ⁇ 2 - r ⁇ ⁇ 2 ) ⁇ + 1 ) .
  • d represented in the equation is a value used for correcting the deviation of the retention time point.
  • the calculating method of the degree of matching between peak patterns used for comparing the peak patterns each configured by five peaks will be described with reference to FIG. 64 .
  • the peak pattern 125 of the assignment target peak 73 and the peak pattern 129 of the assignment candidate peak 95 will be described as an example.
  • a peak and a retention time point of the assignment target peak 73 are assumed to be p 1 and r 1
  • peaks and retention time points of peak pattern configuring peaks 69 , 71 , 75 , and 77 are assumed to be dn 1 and cn 1 , dn 2 and cn 2 , dn 3 and cn 3 , and dn 4 and cn 4 .
  • a peak and a retention time point of the assignment candidate peak 95 are assumed to be p 2 and r 2
  • peaks and retention time points of peak pattern configuring peaks 91 , 93 , 97 , and 99 are assumed to be fn 1 and en 1 , fn 2 and en 2 , fn 3 and en 3 , and fn 4 and en 4 .
  • the degree of matching between peak patterns (P_Sim( 73 - 95 )), each composed of five peaks, of the assignment target peak 73 and the assignment candidate peak 95 is calculated as:
  • P_Sim ⁇ ( 73 - 95 ) ( ⁇ p ⁇ ⁇ 1 - p ⁇ ⁇ 2 ⁇ + 1 ) ⁇ ( ⁇ ( r ⁇ ⁇ 1 - ( r ⁇ ⁇ 2 + d ) ⁇ + 1 ) + ( ⁇ d ⁇ ⁇ n ⁇ ⁇ 1 - fn ⁇ ⁇ 1 ⁇ + 1 ) ⁇ ( ⁇ ( cn ⁇ ⁇ 1 - r ⁇ ⁇ 1 ) - ( en ⁇ ⁇ 1 - r ⁇ ⁇ 2 ) ⁇ + 1 ) + ( ⁇ dn ⁇ ⁇ 2 - fn ⁇ ⁇ 2 ⁇ + 1 ) ⁇ ( ⁇ ( cn ⁇ ⁇ 2 - r ⁇ ⁇ 1 ) - ( en ⁇ ⁇ 2 - r ⁇ ⁇ 2 ) ⁇ + 1 ) + ( ⁇ dn ⁇ ⁇ 2 - f
  • d represented in the equation is a value used for correcting the deviation of the retention time point.
  • the peak assigning part 37 calculates the degree of matching between the UV spectra of the assignment target peak and the assignment candidate peak as illustrated in FIGS. 67 and 68 .
  • FIG. 65 is the diagram illustrating UV spectra ( 135 and 139 ) of the assignment target peak 73 and the assignment candidate peak 95 , and, as illustrated in FIG. 66 , the degree of matching between these two UV spectra (U VSim( 73 - 95 )) is calculated as:
  • UV_Sim(73-95) RMSD(135 vs 139).
  • the RMSD is defined as a mean square deviation and is defined as the square root of arithmetic average of a value that is a square of a distance between two corresponding points (dis). In other words, RMSD is calculated as ⁇ dis 2 /n ⁇ .
  • n is the number of dis.
  • the waveform of the UV spectrum has a maximum wavelength and a minimum wavelength
  • the degree of matching also can be calculated by comparing either the maximum wavelengths or the minimum wavelengths.
  • compounds having no absorbance property, compounds having similar absorbance properties or the like they may quite differs from each other in the waveforms as a whole while having the same maximum and minimum wavelengths. Accordingly, there is a risk that the degree of matching in the waveform may not be calculated by comparing either the maximum wavelengths or the minimum wavelengths.
  • the degree of matching between the waveforms of the UV spectra can be calculated with accuracy, whereby even compounds having no absorbance property or compounds having similar absorbance properties can be identified with accuracy.
  • the degree of matching between the UV spectra is calculated similarly for all the assignment candidate peaks of the assignment target peak 73 .
  • the peak assigning part 37 calculates the degree of matching of an assignment candidate peak that is acquired by integrating the above-described two degrees of matching as illustrated in FIG. 67 .
  • the degree (SCORE( 73 - 95 )) of matching of the assignment candidate peaks is calculated by multiplying the degree of matching between the peak patterns by the degree of matching between the UV spectra. It is assumed that a score representing the degree of matching between peak patterns 73 and 95 is P_Sim_min( 73 - 95 ), and a score representing the degree of matching between the corresponding UV waveform data 135 and 139 is UV_Sim( 73 - 95 ). At this time, the degree SCORE( 73 - 95 ) of matching of the assignment candidate peaks is calculated as:
  • the degree of matching of the assignment candidate peak is similarly calculated for all the assignment candidate peaks of the assignment target peak 73 .
  • the peak assigning part 37 determines the peaks to which the assignment target peaks should be assigned by integrating two viewpoints, it can realize peak assignment with accuracy.
  • the target FP peak feature value preparing part 7 assigns each peak of the target FP 43 to the reference group FP 45 based on the result of the assignment of the target FP to the reference FP as illustrated in FIG. 68 .
  • Each peak of the target FP 43 is assigned to the reference FP that configures the reference group FP 45 through the above-described assignment process. Based on the result of the assignment, finally, the peaks are assigned to the reference group FP 45 .
  • the reference group FP 45 is prepared by performing an assignment process like the above for the plurality of reference FPs determined as normal products, and each peak is represented by an average value (black point) of assigned peaks ⁇ standard deviation (vertical segmenting line).
  • FIG. 69 shows the result of assigning the target FP 43 to the reference group FP 45 , and this result represents the target FP peak feature values 47 of the target FP 43 .
  • FIGS. 70 to 86 illustrate an operating principle of preparing FP area segmentation feature values.
  • FIG. 70 is a diagram illustrating quantification according to area segmentation
  • FIG. 71 is a diagram illustrating the relation with variations in retention time points and the like
  • FIG. 72 is an explanatory diagram illustrating a case where the quantification is carried out with changing positions of the areas
  • FIG. 73 is a table illustrating data of FP type-2
  • FIG. 74 is an explanatory diagram illustrating the patterns of FP type-2
  • FIG. 75 is an explanatory diagram illustrating the quantification of feature values for each area through the area segmentation with use of vertical and horizontal segmenting lines
  • FIG. 76 is an explanatory diagram illustrating the setting of a vertical segmenting line (1st)
  • FIG. 77 is an explanatory diagram illustrating the setting of a horizontal segmenting line (1st)
  • FIG. 78 is an explanatory diagram illustrating the area segmentation with use of the vertical and horizontal segmenting lines
  • FIG. 79 is an explanatory diagram illustrating the number of areas that are quantified as feature values
  • FIG. 80 is an explanatory diagram illustrating specifying area 1
  • FIG. 81 is a table illustrating heights of all the peaks and a sum thereof
  • FIG. 82 is an explanatory diagram illustrating a sum of peak heights in area 1
  • FIG. 83 is a table illustrating feature values of all the areas according to the first one pattern
  • FIG. 84 is a table illustrating a feature value of each area that is formed by sequentially changing a position of the vertical 1st
  • FIG. 85 is a table illustrating a feature value of each area that is formed by sequentially changing a position of the horizontal 1st
  • FIG. 86 is a table illustrating feature values in one way in which the positions of the vertical and horizontal segmenting lines are not changed.
  • the target FP area segmentation feature value preparing part 11 or the reference FP area segmentation feature value preparing part 23 prepares target FP area segmentation feature values or reference FP area segmentation feature values based on an existence rate of peaks existing in each area acquired by segmenting the target FP type-2 or the reference FP type-2 as described above.
  • the area segmentation is performed as illustrated in FIG. 70 .
  • the FP 55 of Drug A is segmented.
  • a plurality of lattices 145 that are a plurality of areas are prepared by segmenting the FP with use of a plurality of vertical segmenting lines 141 that are parallel to the signal strength axis and a plurality of horizontal lines 143 that are parallel to the time axis.
  • the plurality of horizontal segmenting lines 143 are set at geometric sequence ratio intervals in a direction in which the signal strength increases. Due to this setting, the area segmentation for a portion in which peaks are densely aggregated is finely performed, thereby more accurately grasping the existence rate of the peaks.
  • the plurality of horizontal segmenting lines 143 may be set at equal difference intervals while increasing the number of the plurality of horizontal segmenting lines 143 or the like.
  • each lattice 145 it is quantified at the ratio of peak heights that exist so as to be set as the feature value.
  • the retention time points or the peak heights change like FPs 55 A and 55 B due to a slight variation of the analysis condition or the like. There is a risk that the value of each lattice 145 may markedly change due to such a variation.
  • each lattice 145 is changed (shifted) and quantification is performed before and after the change. Due to this operation, it is possible to accurately prepare the reference FP area segmentation feature values.
  • the position of each lattice 145 is changed by setting so as to move each of the vertical and horizontal segmenting lines 141 and 143 parallel in a set range.
  • FIG. 73 illustrates data d 202 , d 207 , and d 208 of the reference FP type-2 as an example. These data are configured only by information of retention time points (RT) and peak heights (Height). These data correspond to the reference FP type-2 that is composed by remaining peaks with the exclusion of the peaks quantified as feature values in the reference FP type-2 preparing part 21 from the plurality of reference FPs and of retention time points thereof. The UV spectra of all the peaks are excluded.
  • RT retention time points
  • Height peak heights
  • the patterns of data d 202 , data d 207 , and data d 208 of the reference FP type-2 are as illustrated in FIG. 74 .
  • These FP patterns are segmented by the vertical and horizontal segmenting lines 141 and 143 , to quantify each area as a feature value.
  • a retention time point (RT), an amplitude, and a pitch of the 1st are designated.
  • a plurality of positions of the vertical 1st are set under the following conditions.
  • a height, an amplitude, and a pitch of the 1st are designated.
  • a plurality of positions of the horizontal 1st are set under the following conditions.
  • the 2nd and subsequent sample lines are sequentially set to perform the area segmentation.
  • the 2nd and subsequent segmenting lines are sequentially set based on combinations of 100 ways, to segment the areas.
  • the 2nd and subsequent vertical lines are set at designated intervals (equal differences) until vertical segmenting lines of a designated number are acquired.
  • the 2nd and subsequent horizontal segmenting lines are set at a designated intervals (equal ratio) until horizontal segmenting lines of a designated number are acquired.
  • Horizontal segmenting lines 0.5, 1.5, 3.5, 7.5, 15.5, and 31.5 are set.
  • the FP is quantified as a feature value.
  • Each area is quantified as a feature value with use of the following equation.
  • the feature value of area 1 is calculated as:
  • the feature values of all the areas according to a first pattern are calculated.
  • the calculation result is represented in FIG. 83 .
  • Each area set by sequentially changing the position of the 1st vertical segmenting line is quantified as a feature value by the above-described method.
  • the result is represented in FIG. 84 .
  • Each area is formed by changing the vertical 1st in one way whenever the position of the 1st horizontal segmenting line is quantified as feature vale. The result is represented in FIG. 85 .
  • the past process is performed for all the reference data. For example, in a case where there are three reference data of d 202 , d 207 and d 208 , it is formed into:
  • FIGS. 87 to 91 are the diagrams representing various target FPs and evaluation values (MD values) thereof according to the evaluating part 27 as described above.
  • the evaluating part 27 can acquire MI) values (MD values: 0.26, 2.20, and the like) by the above-described MT method.
  • FIG. 92 is a process chart illustrating an evaluating method for a multicomponent drug as an evaluating method for a pattern according to Embodiment 1 of the present invention.
  • the evaluating method for a multicomponent drug includes: a FP preparing step 148 ; a target FP peak assigning step 149 ; a target FP peak feature value preparing step 151 ; a target FP type-2 preparing step 153 ; a target FP area segmentation feature value preparing step 155 ; a target FP feature value integrating step 157 ; a reference FP peak assigning step 159 ; a reference FP assigning result integrating step 161 ; a reference FP peak feature value preparing step 163 ; a reference FP type-2 preparing step 165 ; a reference FP area segmentation feature value preparing step 167 ; a reference FP feature value integrating step 169 ; and an evaluating step 171 .
  • the FP preparing step 148 includes a target FP preparing step 173 and a reference FP preparing step 175 .
  • the target FP peak assigning step 149 includes a reference FP selecting step 177 , a peak pattern preparing step 179 , and a peak assigning step 181 .
  • the evaluating apparatus 1 for a multicomponent drug carries out the FP preparing step 148 , the target FP peak assigning step 149 , the target FP peak feature value preparing step 151 , the target FP type-2 preparing step 153 , the target FP area segmentation feature value preparing step 155 , the target FP feature value integrating step 157 , the reference FP peak assigning step 159 , the reference FP assigning result integrating step 161 , the reference FP peak feature value preparing step 163 , the reference FP type-2 preparing step 165 , the reference FP area segmentation feature value preparing step 167 , the reference FP feature value integrating step 169 , and the evaluating step 171 .
  • the FP preparing step 148 is perfbrmed by the function of the FP preparing part 3 illustrated in FIG. 1 .
  • the target FP peak assigning step 149 , the target FP peak feature value preparing step 151 , the target FP type-2 preparing step 153 , the target FP area segmentation feature value preparing step 155 , the target FP feature value integrating step 157 , the reference FP peak assigning step 159 , the reference FP assigning result integrating step 161 , the reference FP peak feature value preparing step 163 , the reference FP type-2 preparing step 165 , the reference FP area segmentation feature value preparing step 167 , the reference FP feature value integrating step 169 , and the evaluating step 171 are performed by using the respective functions of the target FP peak assigning part 5 , the target FP peak feature value preparing part 7 , the target FP type-2 preparing part 9 , the target FP area segmentation feature value preparing part 11 , the target FP
  • the processes may be performed as respective functions of discrete computers.
  • the target FP preparing step 173 , the target FP peak assigning step 149 , the target FP peak feature value preparing step 151 , the target FP type-2 preparing step 153 , the target FP area segmentation feature value preparing step 155 , the target FP feature value integrating step 157 , and the evaluating step 171 may be performed as functions of one computer, and the reference FP preparing step 175 , the reference FP peak assigning step 159 , the reference FP assigning result integrating step 161 , the reference FP peak feature value preparing step 163 , the reference FP type-2 preparing step 165 , the reference FP area segmentation feature value preparing step 167 , and the reference FP feature value integrating step 169 may be performed as functions of another computer.
  • reference FP integrated feature values are prepared by another computer and are supplied to the evaluating step 171 .
  • the FP tyep-2 preparing step 153 prepares the target FP type-2 as a pattern whose peaks change in a time series.
  • the target FP area segmentation feature value preparing step 155 configures a FP area segmentation feature value preparing step as a target pattern area segmentation feature value preparing step segmenting the target pattern type-2 into a plurality of areas and preparing target pattern area segmentation feature values based on an existence rate or existence amount of peaks existing in each area.
  • FIGS. 93 to 108 are flowcharts according to an evaluating program for a multicomponent drug
  • FIGS. 109 to 116 are flowcharts according to preparation of reference data
  • FIG. 117 is a table illustrating a data example of a 3D chromatogram.
  • FIG. 118 is a table illustrating a data example of peak information
  • FIG. 119 is a table illustrating a data example of a FP
  • FIG. 120 is a table illustrating an assignment score calculation result example (determination result file) of the target FP to the reference FP
  • FIG. 121 is a table illustrating two intermediate file examples (an assignment candidate peak score table and an assignment candidate peak number table) prepared in a collating process of corresponding peaks between the target FP and the reference FP
  • FIG. 122 is a table illustrating a collation result file example that is a result of specifying corresponding peaks between the target FP and the reference FP
  • FIG. 123 is a table illustrating a data example of a reference group FP
  • FIG. 124 is a table illustrating a file example of peak feature value data of the target FP that are assigned to the reference group FP
  • FIG. 125 is a table illustrating a data example of the target and reference FP type-2
  • FIG. 126 is a table illustrating a target FP area segmentation feature value file example
  • FIG. 127 is a table illustrating a target FP feature value integrated file example
  • FIG. 128 is a table illustrating a reference type-2 group FP example
  • FIG. 129 is a table illustrating a reference group integrated data example.
  • FIGS. 93 and 94 are flowcharts illustrating steps of the whole processes performed for evaluating an evaluation target drug. It is started in accordance with system activation to cause a computer to execute the FP preparing function of the FP preparing part 3 , the target FP peak assigning function of the target FP peak assigning part 5 , the target FP peak feature value preparing function of the target FP peak feature value preparing part 7 , the target FP type-2 preparing function of the target FP type-2 preparing part 9 , the target FP area segmentation feature value preparing function of the target FP area segmentation feature value preparing part 11 , the target FP feature value integrating function of the target FP feature value integrating part 13 , the reference FP peak assigning function of the reference FP peak assigning part 15 , the reference FP assigning result integrating function of the reference FP assigning result integrating part 17 , the reference FP peak feature value preparing function of the reference FP peak feature value preparing part 19 , the reference FP type-2 preparing
  • the FP preparing function is realized in Step S 1 .
  • the target FP peak assigning function is realized in Steps S 2 , S 3 , and S 4 .
  • the target FP peak feature value preparing function is realized in Step S 5 .
  • the target FP type-2 preparing function is realized in Step S 6 .
  • the target FP area segmentation feature value preparing function is realized in Step S 7 .
  • the target FP feature value integrating function is realized in Step S 8 .
  • the evaluation function is realized in Steps S 9 and S 10 .
  • Step S 1 the “FP preparing process” is performed with a 3D chromatogram and peak information at a specific detection wavelength as input data.
  • the 3D chromatogram is data that is acquired by analyzing an evaluation target drug through HPLC and it is configured as three-dimensional information including a retention time points, detection wavelengths, and peaks (signal strength) as represented as a data example 183 of the 3D chromatogram in FIG. 117 .
  • the peak information is data that is acquired by processing chromatogram data at a specific wavelength, which is acquired through the same HPLC analysis, with a HPLC data analyzing tool (for example, a “ChemStation” or the like).
  • the peak information is data configured by the maximum values and area values of all the peaks detected as peaks and retention time points at those time point.
  • Step S 1 the target FP preparing part 29 ( FIG. 1 ) of the computer functions to prepare the target FP 43 ( FIG. 2 ) base on the 31 ) chromatogram and the peak information and output the data as a file.
  • the target FP 43 like a data example 187 of a FP in FIG. 19 , is data configured by retention time points, peak heights, and UV spectra for respective peak heights.
  • Step S 2 the “target FP assigning process 1 ” is performed with input of the target FP and all the reference FPs output in Step S 1 .
  • Step S 2 the reference FP selecting part 33 of the computer functions to calculate the degree of matching in the retention time appearance pattern between the target FP 43 and all the reference FPs, to select a reference FP that is appropriate to the assignment of the target FP 43 .
  • the reference FPs are FPs that are prepared by the same process as that of Step S 1 based on the 3D chromatogram and peak information of drugs determined as normal products.
  • the normal product is defined as a drug (reference kampo medicine) of which the safety and the effectiveness are checked, and a plurality of drugs with different product lots correspond thereto.
  • the reference FP is data configured similarly to the FP data example 187 illustrated in FIG. 119 .
  • Step S 3 the “target FP assigning process 2 ” is performed according to the target FP 43 and the reference FP selected in Step S 2 as inputs.
  • Step S 3 the peak pattern preparing part 35 ( FIG. 1 ) and the peak assigning part 37 ( FIG. 1 ) of the computer function.
  • peak patterns are comprehensively prepared for all the peaks of the target FP 43 and the reference FP selected in Step S 2 as illustrated in FIGS. 23 to 61 , to calculate the degree of matching between the peak patterns (P_Sim illustrated in FIG. 63 or 64 ).
  • the degree of matching in the UV spectrum (UV_Sim illustrated in FIG. 66 ) between the target FP and the reference FP is calculated.
  • the degree of matching of the assignment candidate peaks (SCORE illustrated in FIG. 67 ) is calculated based on these two kinds of the degrees of matching.
  • the calculation result is output to a file similar to the determination result file example 189 in FIG. 120 .
  • Step S 4 the “target FP assigning process 3 ” is performed according to the determination result file 189 output in Step S 3 as an input.
  • Step S 4 the peak assigning part 37 of the computer functions to specify peaks of the reference FP that correspond to the respective peaks of the target FP between the target FP 43 and the reference FP based on the degree of matching of the assignment candidate peaks (SCORE).
  • the result is output to a collation result file that is similar to a collation result file example 195 in FIG. 122 .
  • Step S 5 the “target FP assigning process 4 ” is performed according to the collation result file output in Step S 4 and the reference group FP 197 as inputs.
  • the reference group FP 197 is peak correspondence data over all the reference FPs prepared from the all reference FPs in the same process as that of Steps S 2 to S 4 .
  • Step S 5 the target FP peak feature value preparing part 7 of the computer functions to assign each peak of the target FP 43 to peaks of the reference group FP 197 based on the collation result file of the target FP 43 as illustrated in FIGS. 68 and 69 .
  • the result is output to a file that is similar to a file example 199 of peak data feature values in FIG. 124 .
  • Step S 6 a process of “preparing the FP_type-2” is performed with the peak data feature value file output in Step S 5 and the target FP as inputs.
  • Step S 6 the target FP type-2 preparing part 9 of the computer functions to prepare a FP as a target FP type-2 ( 49 ) that is composed of remaining peaks with the exclusion of the peaks 47 that are specified by the target FP peak feature value preparing part 7 from the original target FP 43 and of retention time points thereof.
  • the result is output to the FP type-2 file (a FP type-2 file example 201 in FIG. 125 ).
  • Step S 7 a “feature value quantification of the target FP-type 2 through area segmentation” is performed.
  • the target FP area segmentation feature value preparing part 11 of the computer functions to prepare target FP area segmentation feature values through the area segmentation illustrated in FIG. 70 .
  • the result is output to the target FP area segmentation feature value file (a target FP area segmentation feature value file example 203 in FIG. 126 ).
  • Step S 8 a process of “integrating the peak data feature values and the area segmentation feature values” is performed.
  • the target FP feature value integrating part 13 of the computer functions to prepare target FP integrated feature values by integrating the target FP peak feature values 47 prepared by the target FP peak feature value preparing part 7 and the target FP area segmentation feature values 51 prepared by the target FP area segmentation feature value preparing part 11 .
  • the result is output to a target FP feature value integrated file (a target FP feature value integrated file example 205 in FIG. 127 ).
  • Step S 9 the evaluating part 27 of the computer functions to evaluate the equivalency between the target FP integrated feature values output in Step S 8 and the reference FP integrated feature values using MT method and output the evaluation result as MD values as illustrated in FIGS. 87 to 91 ( FIGS. 87 to 91 ).
  • Step S 10 a “determination of a success or not” is performed according to the MD value output in Step S 9 as an input.
  • Step S 10 the evaluating part 27 of the computer functions to compare the MD value output in Step S 9 and a threshold value (the upper limit of the MD value) set in advance so as to make a decision to pass or fail (the evaluation result 53 in FIG. 2 ).
  • a threshold value the upper limit of the MD value
  • FIG. 95 is a flowchart in a case where single-wavelength peak information of the “FP preparing process” in Step S 1 illustrated in FIG. 93 is used.
  • FIG. 95 shows details of the step of preparing the evaluation target FP for a single wavelength, for example, 203 nm.
  • a FP is prepared to comprise retention time points, peaks and UV spectra detected at the detection wavelength of 203 nm.
  • Step S 101 a process of “reading peak information” is performed.
  • peak information is read out as the first one of two kinds of data that are necessary for preparing a FP, and the procedure proceeds to Step S 102 .
  • Step S 102 a process of “sequentially acquiring a retention time point (R 1 ) of a peak and peak data (P 1 ) corresponding thereto” is performed.
  • retention time points (R 1 ) and peak data pieces (P 1 ) of the peaks are sequentially acquired from the peak information one by one, and the procedure proceeds to Step S 103 .
  • Step S 103 a process of “reading a 3D chromatogram” is performed.
  • a 3D chromatogram is read as the second one of the two kinds of data necessary for preparing the FP, and the procedure proceeds to Step S 104 .
  • Step S 104 a process of “sequentially acquiring a retention time point (R 2 ) of a peak and a UV spectrum (U 1 ) corresponding thereto” is performed.
  • retention time points (R 2 ) and UV spectra (U 1 ) are acquired from the 3D chromatogram at each period that is a half of a sampling rate at the time of analyzing the HPLC, and the procedure proceeds to Step S 105 .
  • Step S 105 a process of determining “
  • the threshold value used in this determination process is the “sampling rate/2” of the 3D chromatogram.
  • Step S 106 a process of “normalizing the UV spectrum U 1 with the maximum value of “1”” is performed.
  • the UV spectrum U 1 determined as the UV spectrum of the retention time point R 1 in Step S 105 is normalized with the maximum value of “1,” and the procedure proceeds to Step S 107 .
  • Step S 107 a process of “outputting R 1 , the peak P 1 as well as the normalized U 1 (target FP)” is performed.
  • the R 1 and P 1 acquired from the peak information and the U 1 normalized in S 106 are output to the target FP, and the procedure proceeds to Step S 108 .
  • Step S 108 a determining process “Has the process for all the peaks been completed?” is performed. In this process, it is determined whether or not all the peaks included in the peak information have been processed. If the process has not been completed for all the peaks (NO), the procedure proceeds to Step S 102 in order to process one or more peaks that have not been processed. The process of Steps S 102 to S 108 is repeated until the process for all the peaks is completed. If the process for all the peaks has been completed (YES), the FP preparing process is finished.
  • FIGS. 96 and 97 are flowcharts of a case where peak information at a plurality of wavelengths are used instead of the peak information at the single wavelength in the “FP preparing process” of Step S 1 illustrated in FIG. 93 .
  • this is a case where a plurality of (n) wavelengths are selected in the direction of the detection wavelength axis including 203 nm to prepare a FP.
  • This FP preparing process is for preparing a FP that covers all the peaks of the 3D chromatogram with use of peak information of a plurality of wavelengths in a case where all the peaks detected in the 3D chromatogram cannot be covered for the single wavelength as illustrated in FIG. 95 .
  • FIGS. 96 and 97 illustrate details of the step in which n FPs are prepared at respective wavelengths by performing the above-described FP preparing process by means of only a single wavelength, and, based on the FPs, a FP according to the plurality of wavelengths is prepared.
  • Step S 110 a process of “preparing a FP for each wavelength” is performed.
  • the above-described FP preparing process using only the single wavelength is performed for each wavelength so as to prepare n FPs, and the procedure proceeds to Step S 111 .
  • Step S 111 a process of “listing the FPs according to the number of peaks (descending order)” is performed. In this process, the n FPs are listed in the descending order of the number of peaks, and the procedure proceeds to Step S 112 .
  • Step S 112 as initialization of a counter for sequentially processing n FPs, one is substituted into n (n ⁇ 1), and the procedure proceeds to Step S 113 .
  • Step S 113 a process of “reading the n-th FP in the list” is performed.
  • the n-th FP in the list is read, and the procedure proceeds to Step S 114 .
  • Step S 114 a process of “acquiring all the retention time points (X)” is performed. In this process, all the retention time point information of the FPs read in S 113 is acquired, and the procedure proceeds to Step S 115 .
  • Step S 115 a process of “updating n (n ⁇ n+1)” is performed.
  • “n+1” is substituted into “n” as the update of “n” in order to advance the process to the next FP, and the procedure proceeds to Step S 116 .
  • Step S 116 a process of “reading the n-th FP in the list” is performed.
  • the n-th FP in the list is read, and the procedure proceeds to Step S 117 .
  • Step S 117 a process of “acquiring all the retention time points (Y)” is performed.
  • the retention time point information of all the FPs read in S 116 is acquired, and the procedure proceeds to Step S 118 .
  • Step S 118 a process of “integrating X and Y without duplication (Z)” is performed.
  • the retention time point information X acquired in S 114 and retention time point information Y acquired in Step S 117 are integrated without duplication, thereafter, the integrated information is stored in Z, and then, the procedure proceeds to Step S 119 .
  • Step S 119 a process of “updating X (X ⁇ Z)” is performed.
  • Z stored in Step S 118 is substituted for X, and the procedure proceeds to Step S 120 .
  • Step S 120 a determining process “Have all the FPs been processed?” is performed. In this process, it is determined whether or not all the n FPs prepared in Step S 110 have been processed. If processed (YES), the procedure proceeds to Step S 121 . If there are one or more FPs that have not been processed (NO), the procedure proceeds to Step S 115 in order to perform the process of Steps S 115 to S 120 for the FPs that have not been processed. Until the process for all the FPs is completed, the process of Steps S 115 to S 120 is repeated.
  • Step S 121 as the initialization of the counter for sequentially processing n FPs, “1” is substituted in n (n ⁇ 1), and the procedure proceeds to Step S 122 .
  • Step S 122 a process of “reading the n-th FP in the list” is performed.
  • the n-th FP in the list is read, and the procedure proceeds to Step S 123 .
  • Step S 123 a process of “sequentially acquiring a retention time point (R 1 ), peak data (P 1 ), and a UV spectrum (U 1 ) of each peak” is performed.
  • the retention time points (R 1 ), the peak data pieces (P 1 ), and the UV spectra (U 1 ) of the peaks are sequentially acquired from the FP read in Step S 122 peak by peak, and the procedure proceeds to Step S 124 .
  • Step S 124 a process of “sequentially acquiring the retention time points (R 2 ) from X” is performed.
  • retention time points (R 2 ) are sequentially acquired from X in which the retention time points of all the FPs are stored without duplication one by one, and the procedure proceeds to Step S 125 .
  • Step S 126 a determining process “Has the comparison of all the retention time points of X been completed?” is performed. In this process, it is determined whether or not the comparison of R 1 acquired in S 123 with all the retention time points of X has been completed. If completed (YES), it is determined that the peak at the retention time point of R 1 has been processed, and the procedure proceeds to Step S 123 in order to move the process to the next peak. If not completed (NO), the procedure proceeds to Step S 124 in order to advance the process to the next retention time point of X.
  • Step S 127 a process of “adding (n ⁇ 1) ⁇ analysis time (T) to R 1 (R 1 ⁇ R 1 +(n ⁇ 1) ⁇ T)” is performed.
  • the retention time points are unchanged, for the retention time points of peaks that are not present in the first FP in the list but are present in the second FP in the list, an analysis time (T) is added to R 1 , and, for the retention time points of peaks that are not present in the first to (n ⁇ 1)-th FP in the list but are present in the n-th FP in the list, (n ⁇ 1) ⁇ T is added to R 1 .
  • the procedure proceeds to Step S 128 .
  • Step S 128 a process of outputting “R 1 , P 1 , and U 1 (target FP)” is performed.
  • R 1 processed in Step S 127 , P 1 and U 1 acquired in Step S 123 are output to the target FP, and the procedure proceeds to Step S 129 .
  • Step S 129 a process of “removing R 2 from X” is performed.
  • Step S 130 a determining process “Have all peak processes been completed?” is performed. In this process, it is determined whether or not the process has been completed for all the peaks of the n-th FP in the list. If completed (YES), the FP preparing process for the n-th FP in the list is finished to proceed to Step S 131 . If not completed (NO), the procedure proceeds to Step S 123 in order to process any peak that has not been completed. Until the process for all the peaks is finished, the process of Steps S 123 to S 130 is repeated.
  • Step S 131 a process of “updating n (n ⁇ n+1)” is performed.
  • “n+1” is substituted into “n” as the update of “n”, and the procedure proceeds to Step S 132 .
  • Step S 132 a determining process “Have all FP processes been completed?” is performed. In this process, it is determined whether or not all the n FPs prepared in Step S 110 have been processed. If processed (YES), the FP preparing process is finished. If there are one or more FPs that have not been processed (NO), the procedure proceeds to Step S 122 in order to perform the process of Steps S 122 to S 132 for the FPs that have not been processed. Until the process of all the FPs is completed, the process of Steps S 122 to S 132 is repeated.
  • FIG. 98 is a flowchart illustrating details of the “target FP assigning process 1 ” of Step S 2 illustrated in FIG. 93 .
  • This process is a preprocess of the assigning process and selects a reference FP that is appropriate to the assignment of the target FP 43 from among a plurality of reference FPs regarded as normal products.
  • Step S 201 a process of “reading a target FP” is performed.
  • a FP that is an assignment target is read, and the procedure proceeds to Step S 202 .
  • Step S 202 a process of “acquiring all the retention time points (R 1 )” is performed.
  • all the retention time point information of the target FP that is read in S 201 is acquired, and the procedure proceeds to Step S 203 .
  • Step S 203 a process of “listing file names of all the reference FPs” is performed.
  • the file names of all the reference FPs are listed in advance in order to sequentially process all the reference FPs later, and the procedure proceeds to Step S 204 .
  • Step S 204 “1” is substituted into “n” (n ⁇ 1) as an initial value of a counter used for sequentially processing all the reference FPs, and the procedure proceeds to Step S 205 .
  • Step S 205 a process of “reading the n-th reference FP (reference FP n ) in the list” is performed.
  • the n-th FP of the file name list of all the reference FPs listed in Step S 203 is read, and the procedure proceeds to Step S 206 .
  • Step S 206 a process of “acquiring all the retention time points (R 2 )” is performed.
  • Step S 207 all the retention time point information of the reference FP that are read in Step S 205 is acquired, and the procedure proceeds to Step S 207 .
  • Step S 207 a process of “calculating the degree of matching between retention time appearance patterns of R 1 and R 2 (RP n — min)” is performed.
  • RP n — min is calculated based on the retention time point of the target FP that is acquired in Step S 202 and the retention time point of the reference FP that is acquired in Step S 206 , and the procedure proceeds to Step S 208 .
  • a detailed calculation flow of RP n — min will be described with reference to “Subroutine 1 ” of FIG. 103 separately.
  • Step S 208 a process of “storing RP n — min (RP all — min)” is performed.
  • RP n — min calculated in Step S 207 is stored in RP all — min, and the procedure proceeds to Step S 209 .
  • Step S 209 a process of “updating n (n ⁇ n+1)” is performed.
  • “n+1” is substituted for “n” as the update of “n”, and the procedure proceeds to Step S 210 .
  • Step S 210 a determining process “Have all reference FP processes been completed?” is performed. In this process, it is determined whether or not all the reference FPs have been processed. If processed (YES), the procedure proceeds to Step S 211 . If there are one or more reference FPs that have not been processed (NO), the procedure proceeds to Step S 205 in order to perform the process of Steps S 205 to S 210 for the FPs that have not been processed. Until the process of all the reference FPs is completed, the process of Steps S 205 to S 210 is repeated.
  • Step S 211 a process of “selecting a reference FP demonstrating the minimum degree of matching from RP all — min” is performed.
  • RP 1 — min to RPn_min calculated for all the reference FPs are compared with each other, to select a reference FP demonstrating the minimum degree of matching with respect to the retention time appearance pattern of the target FP, and the target FP assigning process 1 is finished.
  • FIG. 99 is a flowchart illustrating details of the “target FP assigning process 2 ” of Step S 3 illustrated in FIG. 93 .
  • This process is a main process of the assigning process and calculates the degree (SCORE) of matching for each assignment candidate peak based on the degree of matching between peak patterns and the UV spectra of the target FP 43 and the reference FP selected in Step S 2 .
  • Step S 301 a process of “reading a target FP” is performed.
  • a FP that is an assignment target is read, and the procedure proceeds to Step S 302 .
  • Step S 302 a process of “sequentially acquiring a retention time point (R 1 ), peak data (P 1 ), and a UV spectrum (U 1 ) of an assignment target peak” is performed.
  • the peaks of the target FP read in Step S 301 are sequentially set as the assignment target peak to acquire R 1 , P 1 , and U 1 , and the procedure proceeds to Step S 303 .
  • Step S 303 a process of “reading the reference FP” is performed.
  • the reference FP that is selected in the “Target FP Assigning Process 1 ” in FIG. 98 is read, and the procedure proceeds to Step S 304 .
  • Step S 304 a process of “sequentially acquiring a retention time point (R 2 ), peak data (P 2 ), and a UV spectrum (U 2 ) of the peak of the reference FP” is performed.
  • R 2 , P 2 , and U 2 are acquired from the reference FP read in Step S 303 for each peak, and the procedure proceeds to Step S 305 .
  • Step S 305 a determining process “
  • Step S 309 “d” used in this determination process is a value for correcting the retention time points of the peaks of the target FP and the reference FP, and the initial value is set to zero. A difference between the retention time points of peaks is acquired whenever being assigned during the progress of the process to update “d” with the value.
  • the threshold value is an allowable range of the retention time points used for determining whether to be set as an assignment candidate peak.
  • Step S 306 a process of “calculating the degree of matching between UV spectra (UV_Sim)” is performed.
  • UV_Sim is calculated based on U 1 of the assignment target peak acquired in Step S 302 and U 2 of the assignment candidate peak acquired in S 304 , and the procedure proceeds to Step S 307 .
  • a detailed calculation flow of UV_Sim will be described with reference to “Subroutine 2 ” in FIG. 86 separately.
  • Step S 307 a process of “calculating the degree of matching between peak patterns (P_Sim_min)” is performed.
  • peak patterns are comprehensively prepared for these peaks.
  • P_Sim_min of these peak patterns is calculated, and the procedure proceeds to Step S 308 .
  • a detailed calculation flow of P_Sim_min will be described with reference to “Subroutine 3 ” in FIG. 87 separately.
  • Step S 308 a process of “calculating the degree of matching for the assignment candidate peaks (SCORE)” is performed.
  • SCORE of the assignment target peak and the assignment candidate peak is calculated as:
  • Step S 310 the procedure proceeds to Step S 310 .
  • Step S 309 a process of “substituting 888888 into SCORE (SCORE ⁇ 888888)” is performed.
  • SCORE of a peak of an assignment target peak that does not correspond to an assignment candidate peak is set to “888888,” and the procedure proceeds to Step S 310 .
  • Step S 310 a process of “storing SCORE (SCORE_all)” is performed.
  • SCORE acquired in Step S 308 or S 309 is stored in SCORE_all, and the procedure proceeds to Step S 311 .
  • Step S 311 a determining process “Has the process of all reference peaks been completed?” is performed. In this process, it is determined whether or not all the peaks of the reference FP have been processed. If processed (YES), the procedure proceeds to Step S 312 . If there are one or more peaks that have not been processed (NO), the procedure proceeds to Step S 304 in order to perform the process of S 304 to S 311 for the peaks that have not been processed. Until the process for all the peaks is completed, the process of Steps S 304 to S 311 is repeated.
  • Step S 312 a process of “outputting the SCORE_all to a determination result file to initialize (vacate) the SCORE_all” is performed.
  • the SCORE_all is output to the determination result file, thereafter, the SCORE_all is initialized (vacated), and then, the procedure proceeds to Step S 313 .
  • Step S 313 a determining process “Has the process of all target peaks been completed?” is performed. In this process, it is determined whether all the peaks of the target FP have been processed. If processed (YES), the target FP assigning process 2 is finished. If there are one or more peaks that have not been processed (NO), the procedure proceeds to Step S 302 in order to perform the process of Steps S 302 to S 313 for the unprocessed peaks. Until the process of all the peaks is completed, the process of S 302 to S 313 is repeated.
  • FIG. 120 illustrates an output determination result file example.
  • FIG. 100 is a flowchart illustrating the “target FP assigning process 3 ” of Step S 4 in FIG. 93 .
  • This process is a post-process of the assignment and specifies the peak of the reference FP corresponding to each peak of the target FP based on the degree of matching between assignment candidate peaks (SCORE) calculated as described above.
  • Step S 401 a process of “reading a determination result file” is performed.
  • the determination result file prepared by the “target FP assigning process 2 ” illustrated in FIG. 81 is read, and the procedure proceeds to Step S 402 .
  • Step S 402 a process of “preparing an assignment candidate peak score table with data satisfying the condition of “SCORE ⁇ Threshold value” is performed.
  • an assignment candidate score table (the assignment candidate score table 191 of an upper diagram in FIG. 121 ) is prepared based on SCORE of the determination result file, and the procedure proceeds to Step S 403 .
  • This assignment candidate peak score table is a table in which only SCOREs less than the threshold value in the SCORE calculated for the all peaks of the target FP are aligned in an ascending order for each peak of the reference FP. The smaller the value of SCORE is, the higher the possibility for peak to be assigned is.
  • the threshold value is an upper limit value of the SCORE to determine whether to set as an assignment candidate.
  • Step S 403 a process of “preparing an assignment candidate peak number table” is performed.
  • an assignment candidate peak number table (the assignment candidate peak number table 193 of a lower diagram in FIG. 121 ) is prepared based on the assignment candidate peak score table, and the procedure proceeds to Step S 404 .
  • This assignment candidate peak number table is a table that is acquired by substituting each score included in the assignment candidate peak score table into a peak number of the target FP corresponding to the score. Accordingly, this table is a table that sequentially aligns the peak numbers of the target FP to be associated for each peak of the reference FP.
  • Step S 404 a process of “acquiring the peak numbers of the target FP to be assigned” is performed.
  • a peak number of the target FP that is located at the highest position is acquired for each peak of the reference FP from the assignment candidate peak number table prepared in Step S 403 , and the procedure proceeds to Step S 405 .
  • Step S 405 a determining process “Are the acquired peak numbers aligned in a descending order (without duplication)?” is performed. In this process, it is determined whether or not the peak numbers of the target FP acquired in Step S 404 are aligned in the descending order without duplication. If aligned (YES), it is determined that the peaks of the target FP corresponding to respective peaks of the reference FP can be settled, and the procedure proceeds to Step S 408 . If not aligned (NO), in order to reconsider one or more problematic peaks of the target FP to be assigned to peaks of the reference FP, the procedure proceeds to Step S 406 .
  • Step S 406 a process of “comparing SCOREs of problematic peaks to update the assignment candidate peak number table” is performed.
  • SCOREs corresponding to the peak numbers of the target FP that have the problem are compared with use of the assignment candidate score table, and the assignment candidate peak number table is updated in which a peak number having a larger SCORE is substituted into a peak number located in the second, and the procedure proceeds to Step S 407 .
  • Step S 407 a process of “updating the assignment candidate peak store table” is performed.
  • the assignment candidate peak score table is updated, and the procedure proceeds to Step S 404 .
  • the process of Steps S 404 to S 407 is repeated.
  • Step S 408 a process of “storing an assignment result (TEMP)” is performed.
  • the peak numbers of all the peaks, the retention time points, and the peaks of the reference FP and peak data of the target FP that is specified as the peaks corresponding to these peak of the reference FP are stored in TEMP, and the procedure proceeds to Step S 409 .
  • Step S 409 a determining process “Are all the peaks of the target FP included in TEMP?” is performed. In this process, it is determined whether the peak data of all the peaks of the target FP is included in TEMP stored in Step S 408 . If all included (YES), it is determined that the process for all the peaks of the target FP has been completed, and the procedure proceeds to Step S 412 . If there is any excluded peak (NO), in order to add to peak data of the excluded peak to TEMP, the procedure proceeds to Step S 410 .
  • Step S 410 a process of “correcting the retention time point of the peak of the target FP that is not included in TEMP” is performed.
  • k 1 it is a retention time point of a peak having a shorter retention time point of two reference FP-side peaks that are assigned in the vicinity of a peak of a target FP for which correction is necessary;
  • k 2 it is a retention time point of a peak having a larger retention time point of two reference FP-side peaks that are assigned in the vicinity of the peak of the target FP for which correction is necessary;
  • t 0 it is a retention time point of the peak of the target FP for which correction is necessary;
  • t 1 it is a retention time point of a peak having a shorter retention time point of two target FP-side peaks that are assigned in the vicinity of the peak of the target FP for which correction is necessary;
  • t 2 it is a retention time point of a peak having a longer retention time point of two target FP-side peaks that are assigned in the vicinity of the peak of the target FP for which correction is necessary.
  • Step S 411 the procedure proceeds to Step S 411 .
  • Step S 411 a process of “adding the corrected retention time point and the peak data thereof to TEMP, and updating TEMP” is performed.
  • the retention time point of the peak of the target FP corrected in S 410 and not included in TEMP is compared with the retention time points of the reference FP in TEMP, to add the corrected retention time point and peak data of the peak of the target FP that is not included in TEMP to a valid position in TEMP and update TEMP, and it proceeds to Step S 409 .
  • the process of Steps S 409 to S 411 is repeated.
  • Step S 412 a process of “outputting TEMP to a collation result file” is performed.
  • TEMP that specifies the correspondence relation between all the peaks of the reference FP and the all the peaks of the target FP is output as a collation result file, and the target FP assigning process 3 is finished.
  • FIGS. 101 and 102 are flowcharts that illustrate details of the “target FP assigning process 4 ” of Step S 5 illustrated in FIG. 93 .
  • This process is a final process of the assignment and assigns the peaks of the target FP to the respective peaks of the reference group FP (the reference group FP data example 197 in FIG. 123 ) based on the collation result file (the collation result file example 195 in FIG. 122 ) prepared in Step S 4 of FIG. 93 .
  • the reference group FP 197 is a FP that specifies the correspondence relation of peaks among all the reference FPs as described above.
  • the reference group FP data 197 is data that is configured by reference group FP peak numbers, reference group retention time points, and peak heights. As illustrated in the reference group FP 45 in FIG. 2 , each peak can be denoted by an average value (black point) ⁇ standard deviation (vertical line).
  • Step S 501 a process of “reading a collation result file” is performed.
  • the collation result file output in Step S 412 illustrated in FIG. 100 is read, and the procedure proceeds to Step S 502 .
  • Step S 502 a process of “reading the reference group FP” is performed.
  • the reference group FP 197 that is a final assignment opponent of each peak of the target FP is read, and the procedure proceeds to Step S 503 .
  • Step S 503 a process of “integrating and storing the target FP and the reference group FP (TEMP)” is performed.
  • this process two files are integrated based on the peak data of the reference FP that is commonly present in the collation result file and the reference group FP 197 to store the result as TEMP, and the procedure proceeds to Step S 504 .
  • Step S 504 a process of “correcting the retention time points of all the peaks of the target FP that do not correspond to any peaks in the reference FP” is performed.
  • the retention time points of all the peaks of the target FP that do not correspond to any peaks in the reference FP in the collation result file are corrected to the retention time points of TEMP stored in Step S 503 , and the procedure proceeds to Step S 505 .
  • the correction for the retention time point is performed using the same method as that of Step S 410 of the “Target FP Assigning Process 3 ” of Step S 4 described above.
  • Step S 505 a process of “sequentially acquiring the peak data (P 1 ) corresponding to the corrected retention time point (R 1 and R 3 )” is performed.
  • peak data pieces of peaks corresponding to retention time points corrected in Step S 504 as R 1 and R 3 are sequentially acquired as P 1 , and the procedure proceeds to Step S 506 .
  • Step S 506 a process of “sequentially acquiring the peak data (P 2 ) of the target FP corresponding to the retention time point (R 2 ) of assignment candidate peak from TEMP” is performed.
  • peak data pieces are sequentially acquired as P 2 corresponding to the retention time points R 2 at which no peak of the target FP are assigned from TEMP stored in Step S 503 , and the procedure proceeds to Step S 507 .
  • Step S 507 a determining process “
  • Step S 508 a process of “acquiring UV spectra (U 1 , U 2 ) corresponding to the retention time points R 1 and R 2 ” is performed.
  • the UV spectra corresponding to the peaks of the retention time points of R 1 and R 2 that are determined to have the possibility of the correspondence in Step S 507 are acquired from respective FPs, and the procedure proceeds to Step S 509 .
  • Step S 509 a process of “calculating the degree of matching between the UV spectra (UV_Sim)” is performed.
  • the UV_Sim is calculated using the same method as that of Step S 306 of the “Target FP Assigning Process 2 ” of Step S 3 based on the UV spectra U 1 and U 2 acquired in Step S 508 , and the procedure proceeds to Step S 510 .
  • a detailed calculation flow of the UV_Sim will be described with reference to “Subroutine 2 ” illustrated in FIG. 104 separately.
  • Step S 510 a determining process “UV_Sim ⁇ threshold value 2 ?” is performed. In this process, it is determined whether the UV_Sim calculated in Step S 509 is less than the threshold value 2 . If it is less than the threshold value 2 (YES), it is determined that the peak of the UV spectrum U 1 corresponds to the peak of U 2 , and the procedure proceeds to Step S 511 . If the UV_Sim is the threshold value 2 or more (NO), it is determined that there is no correspondence, and the procedure proceeds to Step S 507 .
  • Step S 511 a process of “R 3 ⁇ R 2 , and threshold value 2 ⁇ UV_Sim” is performed.
  • the retention time point R 3 that is, R 1
  • the threshold value 2 is updated with the value of UV_Sim, and the procedure proceeds to Step S 507 .
  • Step S 512 a determining process “Have the retention time points of all the assignment candidate peaks been compared?” is performed. In this process, it is determined whether comparisons of R 1 with the retention time points of all the assignment candidate peaks have been compared. If completed (YES), the procedure proceeds to Step S 513 . If not completed (NO), the procedure proceeds to Step S 507 .
  • Step S 513 a process of “storing R 1 , R 3 and P 1 as well as the threshold value 2 (TEMP 2 )” is performed.
  • the retention time point (R 1 ) determined to have correspondence in Step S 510 and a peak (P 1 ) corresponding to R 3 updated to the retention time point (R 2 ) of the corresponding opponent are stored as well as the threshold value 2 (TEMP 2 ) at this time, and the procedure proceeds to Step S 507 .
  • Step S 514 a determining process “Have the retention time points of all non-corresponding peaks been compared?” is performed. In this process, it is determined whether or not comparisons with the retention time points of the assignment candidate peaks have been completed in the retention time points of all non-corresponding peaks. If completed (YES), it is determined that the assignment process of all the non-corresponding peaks has been completed, and the procedure proceeds to Step S 516 . If not completed (NO), it is determined that one or more non-corresponding peaks that have not been processed remain, and the procedure proceeds to Step S 515 .
  • Step S 515 a process of “threshold value 2 ⁇ initial value” is performed.
  • the threshold value 2 that is updated to UV_Sim in Step S 511 is returned to the initial value, and the procedure proceeds to Step S 505 .
  • Step S 516 a determining process “Are there peaks having the same value of R 3 present in TEMP 2 ?” is performed. In this process, it is determined whether or not a plurality of non-corresponding peaks are assigned to the same peak in TEMP. If there are non-corresponding peaks assigned to the same peak (YES), the procedure proceeds to Step S 517 . If such non-corresponding peak is not present (NO), the procedure proceeds to Step S 518 .
  • Step S 517 a process of “comparing the threshold values 2 of the peaks having the same values of R 3 and returning R 3 of the peak having a larger threshold value to its original value (R 1 )” is performed.
  • the threshold values 2 of the peaks having the same value of R 3 in TEMP 2 are compared with each other, to return the value of R 3 of the peak having a larger threshold value to its original value (in other words, R 1 ), and the procedure proceeds to Step S 518 .
  • Step S 518 a process of “adding a peak of TEMP 2 to TEMP (only a peak of whose R 3 coincides with the retention time point of TEMP)” is performed.
  • every peak of which R 3 coincides with the retention time point of TEMP is added to TEMP, and the procedure proceeds to Step S 519 .
  • Every peak of which R 3 does not coincide with the retention time point of TEMP is not added, because there is no peak to be an assignment opponent in the reference group FP.
  • Step S 519 a process of “outputting the peaks of the target FP included in T′EMP (peak feature value file)” is performed.
  • the peak data of the target FP assigned to the reference group FP 197 is output as a peak data feature value file, to finish the target FP assigning process 4 .
  • FIG. 124 illustrates an example of the peak data feature value file 199 output as described above.
  • FIG. 103 is a flowchart illustrating details of the “Subroutine 1 ” of the “reference FP selecting process” illustrated in FIG. 98 . This process calculates the degree of matching between retention time appearance patterns of FPs (for example, a target FP and a reference FP).
  • Step S 1001 a process of “x ⁇ R 1 and y ⁇ R 2 ” is performed.
  • R 1 and R 2 acquired in Steps S 202 and S 206 illustrated in FIG. 98 are respectively substituted into “x” and “y”, and the procedure proceeds to Step S 1002 .
  • Step S 1002 a process of “acquiring the numbers of data sets “x” and “y” (a, b)” is performed.
  • the numbers of data pieces “x” and “y” are acquired as “a” and “b,” respectively, and the procedure proceeds to Steps S 1003 .
  • Step S 1003 as an initial value of a counter used tbr sequentially invoking the retention time points of “x”, “1” is substituted into “i” (i ⁇ 1), and the procedure proceeds to Step S 1004 .
  • Step S 1004 a process of “acquiring entire distance from the xi-th retention time point (f)” is performed. In this process, all distances, from the xi-th retention time point, of retention time points after the xi-th retention time point are acquired as “f”, and the procedure proceeds to Step S 1005 .
  • Step S 1005 as an initial value of a counter for sequentially invoking the retention time points of “y,” “1” is substituted into “j” (j ⁇ 1), and the procedure proceeds to Step S 1006 .
  • Step S 1006 a process of “acquiring all distances from the yj-th retention time point (g)” is performed.
  • all distances, from the yj-th retention time point, of retention time points after the yj-th retention time point are acquired as “g,” and the procedure proceeds to Step S 1007 .
  • Step S 1007 a process of “acquiring the number of data sets satisfying a condition of “
  • inter-retention time point distances “f” and “g” acquired in Steps S 1004 and S 1006 are compared with each other in a round-robin, the number of data pieces satisfying the condition of “
  • Step S 1008 a process of “calculating the degree of matching between the retention time appearance patterns of ‘f’ and “g” (RP fg )” is performed.
  • RP fg is calculated based on “a” and “b” acquired in Step S 1002 and “m” acquired in Step S 1007 as:
  • Step S 1009 the procedure proceeds to Step S 1009 .
  • Step S 1009 a process of “storing RP fg (RP_all)” is performed.
  • the degree of matching calculated in Step S 1008 is stored in RP_all, and the procedure proceeds to Step S 1010 .
  • Step S 1010 a process of “updating “j” (j ⁇ j+1)” is performed.
  • “j+1” is substituted into “j” as the update of “j”, and the procedure proceeds to Step S 1011 .
  • Step S 1011 a determining process “Has the process been completed at all the retention time points of “y”?” is performed. In this process, it is determined whether or not the process for all the retention time points of “y” has been completed. If completed (YES), it is determined that the process for all the retention time points of “y” has been completed, and the procedure proceeds to Step S 1012 . If not completed (NO), it is determined that one or more retention time points that have not been processed remain in “y,” to proceed to Step S 1006 . In other words, the process of Steps S 1006 to S 1011 is repeated until all the retention time point of “y” is processed.
  • Step S 1012 a process of “updating “i” (i ⁇ i+1)” is performed.
  • Step S 1013 a process of “updating “i” (i ⁇ i+1)” is performed.
  • the update of “i” for advancing the process of “x” to the next retention time point “i+1” is substituted into “i,” and the procedure proceeds to Step S 1013 .
  • Step S 1013 a determining process “Has the process been completed at all the retention time points of “x”?” is performed. In this process, it is determined whether or not the process for all the retention time point of “x” has been completed. If completed (YES), it is determined that the process for all the retention time points of “x” has been completed, to proceed to Step S 1014 . If not completed (NO), it is determined that one or more retention time points that have not been processed remain in “x”, to proceed to Step S 1004 . In other words, the process of Steps S 1004 to S 1013 is repeated until all the retention time points of “x” are processed.
  • Step S 1014 a process of “acquiring a minimum value from RP_all (RP_min)” is performed.
  • the minimum value in RP_all in which RPs for all the combinations of the retention time appearance patterns of the target FP and the reference FP are stored is acquired as RP_min, and RP_min is input to Step S 207 of FIG. 98 to finish the process of calculating the degree of matching between the retention time appearance patterns.
  • FIG. 104 is a flowchart illustrating details of the “Subroutine 2 ” of the “target FP assigning process 2 ” of FIG. 99 . In this process, the degree of matching between UV spectra is calculated.
  • Step S 2001 a process of “x ⁇ U 1 , y ⁇ U 2 , z ⁇ 0” is performed.
  • the UV spectra U 1 and U 2 acquired in Steps S 302 and S 304 of FIG. 99 are respectively substituted into “x” and “y”, and furthermore, “0” is substituted as an initial value of sum (z) of squares of a distance of the UV spectra, and the procedure proceeds to Step S 2002 .
  • Step S 2002 a process of “acquiring the number of data pieces of “x” (a)” is performed.
  • the number of data pieces of “x” is acquired as “a” and the procedure proceeds to Step S 2003 .
  • Step S 2003 as an initial value used for sequentially invoking absorbance at each detection wavelength configuring the UV spectrum U 1 from “x,” “1” is substituted into “i,” and the procedure proceeds to Step S 2004 .
  • Step S 2004 a process of “acquiring xi-th data (b)” is performed.
  • the i-th absorbance data of “x” into which the UV spectrum “U 1 ” is substituted is acquired as “b,” and the procedure proceeds to Step S 2005 .
  • Step S 2005 a process of “acquiring yi-th data (c)” is performed.
  • the i-th absorbance data of “y” into which the U V spectrum “U 2 ” is substituted is acquired as “c,” and the procedure proceeds to Step S 2006 .
  • Step S 2006 a process of “calculating an inter-UV spectrum distance (d) and a sum (z) of squares of the inter-UV spectrum distances” is performed.
  • the inter-UV spectrum distance “d” and the sum “z” of squares of the inter-UV spectrum distances are calculated as:
  • Step S 2007 the procedure proceeds to Step S 2007 .
  • Step S 2007 a process of “updating “i” (i ⁇ i+1)” is performed. In this process, as the update of “i”, “i+1” is substituted into “1,” and the procedure proceeds to Step S 2008 .
  • Step S 2008 a determining process “Have the process of all data of “x” been completed ?” is performed. In this process, it is determined whether the process for all data of “x” and “y” have been completed. If completed (YES), it is determined that the process for all data of “x” and “y” has been completed, and the procedure proceeds to Step S 2009 . If not completed (NO), it is determined that there are one or more data pieces of “x” and “y” that have not been processed, and the procedure proceeds to Step S 2004 . In other words, the process of Steps S 2004 to S 2008 is repeated until all the absorbance data of “x” and “y” is processed.
  • Step S 2009 a process of “calculating the degree of matching between the UV spectra of “x” and “y” (UV_Sim)” is performed.
  • the UV_Sim is calculated based on the sum “z” of squares of the inter-UV spectrum distances and the number “a” of data sets of “x” as follows:
  • UV _Sim ⁇ ( z/a ).
  • UV_Sim is input to Step S 306 in FIG. 99 , to finish the process of calculating the degree of matching between UV spectra.
  • FIG. 105 is a flowchart illustrating details of the “Subroutine 3 ” of the “target FP assigning process 2 ” of FIG. 99 . In this process, the degrees of matching between peak patterns are calculated.
  • Step S 3001 a process of “setting the number (m) of peak pattern configuring candidates and the number (n) of peak pattern configuring peaks” is performed.
  • this process as setting for comprehensively preparing peak patterns, the number (m) of peak pattern configuring candidates and the number (n) of peak pattern configuring peaks are set, and the procedure proceeds to Step S 3002 .
  • Step S 3002 a process of “x ⁇ target FP name, r 1 ⁇ R 1 , p 1 ⁇ P, y ⁇ reference FP name, r 2 ⁇ R 2 , and p 2 ⁇ P 2 ” is performed.
  • the file names of the target FP and the reference FP that are necessary for the process, and the retention time points and the peak data acquired in Steps S 302 and S 304 of FIG. 99 are substituted into “x,” “r 1 ,” and “p 1 ,” and “y,” “r 2 ,” and “p 2 ,” and the procedure proceeds to Step S 3003 .
  • Step S 3003 a process of “acquiring all retention time points of “x” (a)” is performed.
  • a file (target FP) having a name substituted into “x” in Step S 3002 is read, all the retention time points of the file are acquired as “a,” and the procedure proceeds to Step S 3004 .
  • Step S 3004 a process of “acquiring all retention time points of “y” (b)” is performed.
  • a file (reference FP) having a name substituted into “y” in Step S 3002 is read, all the retention time points of the file are acquired as “b,” and the procedure proceeds to Step S 3005 .
  • Step S 3005 a process of “acquiring the retention time points (cm) and peak data (dm) of m peak pattern configuring candidate peaks of “r 1 ” from “a”” is performed.
  • retention time points of m peak pattern configuring candidate peaks of “r 1 ” that are retention time points of the assignment target peaks are acquired as “cm” and “dm” from “a,” and the procedure proceeds to Step S 3006 .
  • m peak pattern configuring candidate peaks are m peaks with retention time points close to “r 1 .”
  • Step S 3006 a process of “acquiring the retention time points (em) and peak data (fm) of m peak pattern configuring candidate peaks of “r 2 ” from “b”” is performed.
  • retention time points of m peak pattern configuring candidate peaks of “r 2 ” that are the retention time points of the assignment target peaks are acquired as “em” and the peak data thereof as “fm” from “b,” and the procedure proceeds to Step S 3007 .
  • m peak pattern configuring candidate peaks are m peaks with retention time points close to “r 2 .”
  • Step S 3007 a process of “aligning “cm” and “dm” in the retention time order (ascending order)” is performed.
  • “cm” and “dm” acquired in Step S 3005 are rearranged so as to be in the ascending order of the retention time, and the procedure proceeds to Step S 3008 .
  • Step S 3008 a process of “aligning “em” and “fm” in the retention time order (ascending order)” is performed.
  • “em” and “fm” acquired in Step S 3006 are rearranged so as to be in the ascending order of the retention time, and the procedure proceeds to Step S 3009 .
  • Step S 3009 a process of “sequentially acquiring retention time points (cn) and peak data (dn) of n peak pattern configuring peaks from “cm” and “dm” is performed.
  • the retention time points of n peak pattern configuring peaks are sequentially acquired as “cn” and the peak data thereof as “dn” from “cm” and “dm” of m peak pattern configuring candidate peaks, and the procedure proceeds to Step S 3010 .
  • Step S 3010 a process of “sequentially acquiring retention time points (en) and peak data (fn) of n peak pattern configuring peaks from “em” and “fm”” is performed.
  • retention time points of n peak pattern configuring peaks are sequentially acquired as “en” and the peak data thereof as “fn” from “em” and “fm” of m peak pattern configuring candidate peaks, and the procedure proceeds to Step S 3011 .
  • Step S 3011 a process of “calculating the degree of matching between peak patterns (P_Sim)” is performed.
  • P_Sim ( ⁇ p ⁇ ⁇ 1 - p ⁇ ⁇ 2 ⁇ + 1 ) ⁇ ( ⁇ ( r ⁇ ⁇ 1 - ( r ⁇ ⁇ 2 + d ) ⁇ + 1 ) + ( ⁇ dn ⁇ ⁇ 1 - fn ⁇ ⁇ 1 ⁇ + 1 ) ⁇ ( ⁇ ( cn ⁇ ⁇ 1 - r ⁇ ⁇ 1 ) - ( en ⁇ ⁇ 1 - r ⁇ ⁇ 2 ) ⁇ + 1 ) + ( ⁇ dn ⁇ ⁇ 2 - fn ⁇ ⁇ 2 ⁇ + 1 ) ⁇ ( ⁇ ( cn ⁇ ⁇ 2 - r ⁇ ⁇ 1 ) - ( en ⁇ ⁇ 2 - r ⁇ ⁇ 2 ) ⁇ + 1 ) + ( ⁇ dn ⁇ ⁇ 3 - fn ⁇ ⁇ 3 ⁇ + 1 ) ⁇ (
  • Step S 3012 the procedure proceeds to Step S 3012 .
  • Step S 3012 a process of “storing P_Sim (P_Sim_all)” is performed.
  • P_Sim calculated in Step S 3011 is sequentially stored in P_Sim_all, and the procedure proceeds to Step S 3013 .
  • Step S 3013 a determining process “Have all the combinations to take out n pieces from m pieces included in “em” been completed?” is performed. In this process, it is determined whether or not the process has been completed for all the combinations to take out n peaks pattern configuration peaks out from m peak pattern configuring candidate peaks. If completed (YES), it is determined that the preparation of comprehensive peak patterns and the calculation of the degrees of matching for the patterns have been completed for the assignment candidate peaks, to proceed to Step S 3014 . If not completed (NO), it is determined that one or more combinations to take out n pieces out from m pieces have not been completed, to proceed to Step S 3010 . In other words, the process of Steps S 3010 to S 3013 is repeated until the process is completed for all the combinations acquired by taking n pieces out from m pieces.
  • Step S 3014 a determining process “Have all the combinations to take out m pieces from n pieces included in “cm” been completed?” is performed. In this process, it is determined whether or not the process has been completed for all the combinations to take out n peak pattern configuring peaks from m peak pattern configuring candidate peaks of the assignment target peaks. If completed (YES), it is determined that the preparation of comprehensive peak patterns and the calculation of the degrees of matching for the patterns have been completed for the assignment candidate peak, to proceed to Step S 3015 . If not completed (NO), it is determined that one or more combinations to take out n pieces from m pieces have not been completed, to proceed to Step S 3009 . In other words, the process of Steps S 3009 to S 3014 is repeated until the process is completed for all the combinations to take n pieces out from m pieces.
  • Step S 3015 a process of “acquiring a minimum value from P_Sim_all (P_Sim_min)” is performed.
  • the minimum value of the P_Sim-all stored in S 3012 is acquired as P_Sim_min, and the P_Sim_min is input to Step S 307 of FIG. 99 to finish the process of calculating the degree of matching between peak patterns.
  • FIG. 106 is a flowchart illustrating details of the “preparation of FP_type-2” of Step S 6 in FIG. 93 .
  • Step S 601 a process of “reading a target FP” is performed.
  • a file of the target FP 43 (a data example 187 of the FP in FIG. 119 ) is read, and the procedure proceeds to Step S 602 .
  • Step S 602 a process of “reading a peak data feature value file” is performed.
  • the peak data feature value file (a file example 199 of the peak data feature values in FIG. 124 ) is read, and the procedure proceeds to Step S 603 .
  • the peak data feature value file example includes the peak information of the target FP 43 assigned to the peaks of the reference group FP 45 by the target FP peak feature value preparing part 7 .
  • Step S 603 a process of “comparing the target FP with the peak data feature value file with each other” is performed.
  • the file of the target FP 43 is compared with the peak data feature value file. Through this comparison, remaining peaks of the target FP 43 that have not assigned to the peaks of the reference group FP 45 are specified to proceed to Step S 604 .
  • Step S 604 a process of “outputting retention time points and peak data of peaks that are present only in the target FP” is performed.
  • the retention time points and the peak data of the remaining peaks of the target FP 43 are output to a data file (a data example 201 of the reference and target FP type-2 in FIG. 125 ) of the target FP type-2.
  • FIG. 107 is a flowchart illustrating details of the “process of quantifying the target FP_type-2 as feature values through area segmentation” of Step S 7 in FIG. 94 .
  • Step S 701 a process of “setting area segmentation conditions of a FP space” is performed.
  • this process in order to segment the area of the target FP type-2, one position for each of the 1st vertical and horizontal lines (segmenting lines) is set. Due to this setting, for example as illustrated in FIGS. 76 and 77 , the vertical and horizontal segmenting lines (1st) are set as segmenting lines to a FP space. However, in the case of the target FP type-2, amplitude is not related because there is no change in a position of an area. After the vertical and horizontal lines (1st) are set in Step S 701 , the procedure proceeds to Step S 702 .
  • Step S 702 a process of “preparing an area segmentation pattern in the FP space” is performed.
  • positions of 2nd and subsequent segmenting lines are set according to all the combinations of the 1st vertical and horizontal segmenting lines, thereby preparing a segmentation pattern (one). Due to this process, for example as illustrated in FIG. 78 , the area segmentation for the FP space is performed by the vertical and horizontal segmenting lines. After the area segmentation is performed, the procedure proceeds to Step S 703 .
  • Step S 703 a process of “reading a file of the target FP_type-2” is performed. Through this process, the file of the target FP type-2 is read, and the procedure proceeds to Step S 704 .
  • Step S 704 a process of “calculating total peak data of the entire FP space” is performed. This process, for example, a sum of heights of all the peaks that are present in respective lattices 145 segmented as illustrated in FIG. 79 is calculated ( FIG. 81 ), and the procedure proceeds to Step S 705 .
  • Step S 705 a process of “segmenting the FP space by the segmentation pattern” is performed.
  • the area of the target FP type-2 read in Step S 703 is segmented according to the area segmentation pattern set in Step S 702 as illustrated in FIG. 79 , and the procedure proceeds to Step S 706 .
  • Step S 706 a process of “calculating an existence rate of peak data within a segmented area” is performed.
  • the calculation result is as illustrated in FIG. 86 .
  • the procedure proceeds to Step S 707 .
  • Step S 707 a process of “outputting the existence rate of each area as a feature value” is performed.
  • This process outputs a FP area segmentation feature value file (a target FP area segmentation feature value file example 203 in one way illustrated in FIG. 126 ) in one way.
  • FIG. 108 is a flowchart illustrating detail of the “integration of peak data feature values and area segmentation feature values” of Step S 8 illustrated in FIG. 94 .
  • Step S 801 a process of “reading the peak data feature value file” is performed. Through this process, a file similar to the file example 199 of the peak data feature values that is illustrated in FIG. 124 is read, and the procedure proceeds to Step S 802 .
  • Step S 802 a process of “reading the area segmentation feature value file” is performed. Through this process, the target FP area segmentation feature value file 203 illustrated in FIG. 126 is read, and the procedure proceeds to Step S 803 .
  • Step S 803 a process of “integrating two sets of feature value data as data of a horizontal one row” is performed.
  • the file of the peak data feature values (the file example 199 of the peak data feature values illustrated in FIG. 124 ) and the target FP area segmentation feature value file (the target FP area segmentation feature value file example 203 illustrated in FIG. 126 ) are integrated as the target FP feature value integrated file (a target FP feature value integrated file example 205 in FIG. 127 ) of one row, and the procedure proceeds to Step S 804 .
  • Step S 804 a process of “outputting the integrated data” is performed. This process outputs the target FP feature value integrated file 205 illustrated in FIG. 127 .
  • a reference FP feature value integrated file for comparing the target FP feature value integrated data with the reference FP feature value integrated data is prepared as illustrated in FIGS. 109 to 116 .
  • FIGS. 109 and 110 are flowcharts for preparing the reference FP feature value integrated file, to cause the computer to execute the FP preparing function of the reference FP preparing part 31 , the reference FP peak assigning function of the reference FP peak assigning part 15 , the reference FP assigning result integrating function of the reference FP assigning result integrating part 17 , the reference FP peak feature value preparing function of the reference FP peak feature value preparing part 19 , the reference FP type-2 preparing function of the reference FP type-2 preparing part 21 , the reference FP area segmentation feature value preparing function of the reference FP area segmentation feature value preparing part 23 , and the reference FP feature value integrating function of the reference FP feature value integrating part 25 .
  • the reference FP preparing function is realized in Step S 10001 .
  • the reference FP peak assigning function is realized in Steps S 10002 , S 10003 , and S 10004 .
  • the reference FP assigning result integrating function is realized in Step S 10005 .
  • the reference FP peak feature value preparing function is realized in Step S 10006 .
  • the reference FP type-2 preparing function is realized in Step S 10007 .
  • the reference FP area segmentation feature value preparing function is realized in Step S 10008 .
  • the reference FP feature value integrating function is realized in Step S 10009 .
  • Steps S 10001 to S 10004 correspond to Steps S 1 to S 4 relating to the preparation of the target FP feature value integrated file of FIGS. 93 and 94
  • Steps S 1007 to S 10009 correspond to Steps S 6 to S 8 of the same.
  • Step S 10001 the “FP preparing process” is performed using a 3D chromatogram and peak information at a specific detection wavelength as inputs.
  • Both the 3D chromatogram and the peak data are included for each one of a plurality of evaluation reference drugs (reference kampo medicines) that are evaluation criteria.
  • Step S 10001 the reference FP preparing part 31 ( FIG. 1 ) of the FP preparing part 3 of the computer functions to prepare a reference FP in the same way as the target FP 43 ( FIG. 2 ) based on the 3D chromatogram and the peak information, and data of the reference FP is output as a file.
  • Step S 10002 the “reference FP assigning process 1 ” is performed using all reference FPs output in Step S 10001 as inputs.
  • Step S 10002 the reference FP peak assigning part 15 of the computer functions, for all the reference FPs, selects combinations from among all the reference FPs in order to calculate assignment scores for the selected combinations in the selected order, and the procedure proceeds to Step S 10003 .
  • Step S 10003 the “reference FP assigning process 2 ” is performed according to the selected combinations of the reference FPs as an input.
  • Step S 10003 for all the peaks of the combinations of the reference FPs that are selected in Step S 2 , peak patterns are comprehensively prepared as illustrated in FIGS. 23 to 61 . Then, the degree of matching between the peak patterns (P_Sim illustrated in FIG. 63 or 64 ) is calculated. In addition, the degrees of matching between UV spectra (UV_Sim illustrated in FIG. 66 ) of the peaks of the selected combinations of the reference FPs are calculated. Furthermore, the degrees of matching of the assignment candidate peaks (SCORE illustrated in FIG. 67 ) are calculated based on these two degrees of matching. The calculation result is output as a determination result file (the determination result file example 189 in FIG. 120 ).
  • Step S 10004 the “reference FP assigning process 3 ” is performed according to the determination result file output in Step S 10003 as an input.
  • Step S 10004 between the reference FPs in the selected combinations, peaks of the reference FPs in the selected combinations, which correspond to each other, are specified based on the degree of matching between the assignment candidate peaks (SCORE). The result is output as the reference FP assigning data for each reference FP.
  • Step S 10005 the “reference FP assigning result integrating process” is performed according to all the reference FP assigning data output in Step S 10004 as an input.
  • Step S 10005 the reference FP assigning result integrating part 17 of the computer functions to prepare a reference FP correspondence table by integrating all the FP assigning data with reference to the peak correspondence relation of the individual reference FP specified by the reference FP peak assigning part 15 , and proceeds to Step S 10006 .
  • the reference FP peak feature value preparing part 19 of the computer functions to prepare a peak feature value (reference group FP) according to all the reference FPs based on the reference FP correspondence table that is prepared by the reference FP assigning result integrating part 17 .
  • statistic values a maximum value, a minimum value, a medium value, an average value, and the like
  • the selected peak (column) is output as the reference group FP (the reference group FP example 197 illustrated in FIG. 123 ).
  • Step S 10007 a process of “preparation of the FP_type-2” is performed according to the reference group FP output in Step S 10006 and all the reference FPs as inputs.
  • Step S 10007 the reference FP type-2 preparing part 21 of the computer functions similar to the target FP type-2 preparing part 9 and, in the same way as Step S 6 illustrated in FIG. 93 , prepares each FP as a reference FP type-2 (the FP type-2 file example 201 in FIG. 125 ) composed of remaining peaks with the exclusion of the peaks quantified as the feature values from each of a plurality of reference FPs and of the retention time points thereof:
  • Step S 10008 a process of “feature value quantification of the reference FP_type-2” is performed.
  • the reference FP area segmentation feature value preparing part 23 of the computer functions to prepare the reference FP area segmentation feature values through the area segmentation illustrated in FIGS. 73 to 85 .
  • the result is output as a reference type-2 group FP (a reference type-2 group FP example 207 in FIG. 128 ).
  • Step S 10009 a process of “reference data preparing process” is performed.
  • the reference FP feature value integrating part 25 of the computer functions to prepare the feature value data of all the reference FPs by integrating the reference group FP prepared by the reference FP peak feature value preparing part 19 and the reference type-2 group FP prepared by the reference FP area segmentation feature value preparing part 23 .
  • the result is output as reference group integrated data (a reference group integrated data example 209 in FIG. 129 ).
  • FIGS. 111 and 112 are flowcharts that illustrate details of the “reference FP assigning result integrating process (preparation of the reference FP correspondence table)” of Step S 10005 in FIG. 110 .
  • Step S 10101 a process of “reading the 1st assignment data in the assignment order as integrated data” is performed.
  • the reference FP assigning data in which the assignment process is performed first to specify the correspondence relation of the peaks in Step S 10004 , is read as the integrated data. Then, the procedure proceeds to Step S 10102 .
  • Step S 10102 a process of “sequentially reading 2nd and subsequent data” is performed.
  • the reference FP assigning data in which the assignment process is secondarily performed to specify the correspondence relation of the peaks in Step S 10004 , is read as integrated data. Then, the procedure proceeds to Step S 10103 .
  • Step S 10103 a process of “integrating the integrated data and the assignment data as common peak data” is performed.
  • the two files are integrated based on the peak data of the reference FP commonly-existing in the integrated data and the assignment data, the integrated data is updated as a result thereof, and the procedure proceeds to Step S 10104 .
  • Step S 10104 a determining process “Have all the peaks included in the assignment data been added to the integrated data?” is performed. In this process, it is determined whether or not all the peaks in the assignment data have been added to the integrated data. If added (YES), the procedure proceeds to Step S 10105 . If there are one or more peaks (lacking peaks) that have not been added (NO), in order to add the lacking peaks to the integrated data, the procedure proceeds to Step S 10107 . In addition, in the process (S 10107 to S 10120 ) of adding the lacking peaks to the integrated data, the same process as that of Steps S 504 to S 517 in S 5 (target FP assigning process 4 ) is performed.
  • Step S 10121 a process of “adding data of TEMP 2 to the integrated data (all the retention time points and peaks)” is performed. In this process, all the retention time points (R 3 ) and the peaks (P 1 ) in TEMP 2 are added to corresponding positions in the integrated data, and the procedure proceeds to Step S 10122 .
  • Step S 10122 a process of “setting threshold value 2 ⁇ initial value, and deleting all the data in TEMP 2 ” is performed.
  • the threshold value 2 updated to UV_Sim is returned to the original value, all the data is removed from TEMP 2 storing data such as retention time points and peaks of all the lacking peaks and the like, and the process is returned to Step S 10104 .
  • Step S 10105 a determining process “Has the process of all the assignment data been completed?” is performed. In this process, it is determined whether or not the process for all reference data has been completed. If completed (YES), in order to output the reference FP correspondence table that is the integration result of all the assignment data, the procedure proceeds to Step S 10106 . If not completed (NO), the procedure is returned to Step S 10102 to sequentially process the remaining assignment data.
  • Step S 10106 a process of “outputting the integrated data (reference FP correspondence table)” is performed.
  • the result integrating all the assignment data is output as the reference FP correspondence table, to finish the process of preparing the reference FP correspondence table.
  • FIG. 113 is a flowchart illustrating details of the “peak feature value quantification process of peak feature values (preparation of a reference group FP)” of Step S 10006 illustrated in FIG. 109 .
  • Step S 10201 a process of “reading the reference FP correspondence table” is performed.
  • the reference FP correspondence table prepared in Step S 10005 is read to proceed to Step S 10202 .
  • Step S 10202 a process of “calculating statistic values for each peak (column)” is performed.
  • the statistic values a maximum value, a minimum value, a medium value, an average value, a variance, a standard deviation, an existence number, and an existence ratio
  • the procedure proceeds to Step S 10203 .
  • Step S 10203 a process of “selecting a peak (column) with reference to the calculated statistic values” is performed.
  • a peak is selected with reference to the statistic values calculated in Step S 10102 , to proceed to Step S 10204 .
  • Step S 10204 a process of “outputting the selected peak (column) (reference group FP)” is performed.
  • the selecting result of the peak (column) according to the statistic values is output as the reference group FP, to finish the process of preparing a reference group FP.
  • FIG. 123 illustrates a reference FP correspondence table example 197 output as described above.
  • FIG. 114 is a flowchart illustrating details of the “reference FP editing process (preparation of a reference FP_type-2)” of Step S 10007 in FIG. 110 .
  • Step S 10301 a process of “sequentially reading the reference FPs” is performed.
  • a file (a data example 187 of a FP in FIG. 119 ) of a plurality of reference FPs is read, and the procedure proceeds to Step S 10302 .
  • Step S 10302 a process of “reading the reference group FP” is performed.
  • a data file (the reference group FP example 197 in FIG. 123 ) of the reference group FP is read, and the procedure proceeds to Step S 10303 .
  • Step S 10303 a process of “extracting peak data feature values of the reference FP from the reference group FP” is performed.
  • peak data feature values that are processed to be assigned to the reference FP are extracted from the file of the reference group FP 45 , and the procedure proceeds to Step S 10304 .
  • Step S 10304 a process of “comparing the reference FP with the extracted peak data feature value file” is performed, the reference FP is compared with the peak data feature value file, and the procedure proceeds to Step S 10305 .
  • Step S 10305 a process of “outputting the retention time points and the peak data of peaks that are present only in the reference FP” is performed, the peaks of the peak data feature value file are excluded from the reference FP, and the procedure proceeds to Step S 10306 .
  • Step S 10306 a determining process “Has process completed for all the reference FPs?” is performed. In this process, if the process has been completed for all the reference FPs (YES), Step S 10007 is terminated. If the process has not been completed for all the reference FPs (NO), Steps of S 10301 to S 10305 are repeated. Accordingly, the plurality of reference FPs are sequentially processed, and the file (the data example 201 of the target and the reference FP type-2 illustrated in FIG. 125 ) of the reference FP type-2 is prepared from each reference FP with the exclusion of the peaks of the peak data feature value file.
  • the file the data example 201 of the target and the reference FP type-2 illustrated in FIG. 125
  • FIG. 115 is a flowchart illustrating details of the “feature value quantification process of the reference FP_type-2 through area segmentation” of Step S 10008 in FIG. 110 .
  • Step S 10401 a process of “setting area segmentation conditions of the FP space” is performed.
  • a plurality of the positions of the 1st vertical and horizontal lines are set. Due to this setting, for example as illustrated in FIGS. 76 and 77 , a plurality of vertical and horizontal segmenting lines (1st) 141 and 143 are set as segmenting lines in the FP space.
  • the procedure proceeds to Step S 10402 .
  • Step S 10402 a process of “setting an area segmentation pattern in the FP space” is performed.
  • positions of 2nd and subsequent segmenting lines are set according to all the combinations of all the combinations of the 1st vertical and horizontal segmenting lines, thereby preparing (m ⁇ n) segmentation patterns.
  • a plurality of patterns of the area segmentation according to the vertical and horizontal segmenting lines 141 and 143 are set to the FP space.
  • the procedure proceeds to Step S 10403 .
  • Step S 10403 a process of “sequentially reading the file of the reference FP_type-2” is performed. Through this process, the file of the reference FP type-2 is read to proceed to Step S 10404 .
  • Step S 10404 a process of “calculating total peak data of the entire FP space” is performed.
  • a sum of heights of peaks that are present in all the respective lattices 145 segmented as illustrated in FIG. 79 is calculated ( FIG. 81 ), and the procedure proceeds to Step S 10405 .
  • Step S 10405 a process of “sequentially segmenting the FP space by the segmentation patterns” is performed.
  • the area of the FP space is sequentially segmented according to a plurality of area segmentation patterns set in Step S 10402 , and the procedure proceeds to Step S 10406 .
  • Step S 10406 a process of “calculating an existence rate of peak data within the segmented area” is performed.
  • the calculation result for example, is as illustrated in FIGS. 83 to 85 .
  • the procedure proceeds to Step S 10408 .
  • Step S 10408 a process of “completing the segmentation to all the segmentation patterns” is performed. In this process, it is determined whether or not the feature value process is completed for all the plural area segmentation patterns set in Step S 10402 . If the feature value process is completed (YES), the procedure proceeds to Step S 10409 . If the feature value process has not been completed (NO), the procedure proceeds to Step S 10405 . Steps of S 10405 to S 10408 are repeated until the feature value process for all the area segmentation pattern is completed.
  • Step S 10409 a determining process “Has the process been completed for all the reference FP_type-2?” is performed. In this process, it is determined whether or not the feature value process has been completed for a plurality of all the reference FP type-2 prepared for each of the plurality of reference FPs. If all the reference FP type-2's are completed (YES), Step S 10008 is terminated. If all the reference FP type-2's have not been completed (NO), the procedure proceeds to Step S 10403 . Steps S 10403 to S 10409 are repeated until the feature value process for the reference FP type-2 is completed.
  • FIG. 128 shows a reference type-2 group FIP example 207 .
  • FIG. 116 is a flowchart illustrating details of the “reference data preparing process” of Strep S 10009 in FIG. 110 .
  • Step S 10501 a process of “reading an area segmentation feature value file” is performed.
  • a reference FP area segmentation feature value file (a reference type-2 group FP example 207 in FIG. 128 ) is read, and the procedure proceeds to Step S 10502 .
  • Step S 10502 a process of “calculating the number of segmentation patterns at the time of segmenting the area” is performed. Through this process, the number of the segmentation patterns for the area segmentation is calculated. The number of the segmentation patterns is calculated, as described with reference to FIGS. 70 to 80 , for example, in 100 ways. After the calculation, the procedure proceeds to Step S 10503 .
  • Step S 10503 a process of “reading the reference group FP” is performed, the reference group FP is read, and the procedure proceeds to Step S 10504 .
  • Step S 10504 a process of “preparing files (reference group FP 2 ) acquiered by replicating each row of the reference group FP as many as the number of segmentation patterns” is performed.
  • the row of the reference group FP is replicated in correspondence with the number of the segmentation patterns, thereby preparing the reference group FP- 2 .
  • the reference group FP file example 197 in FIG. 123 is replicated so as to be in correspondence with the peak data feature value (reference group FP 2 ) of the reference group integrated data example 209 in FIG. 129 .
  • the procedure proceeds to Step S 10505 .
  • Step S 10505 a process of “integrating the reference group FP- 2 and the area segmentation feature value file at each row” is performed.
  • the data of the reference group FP- 2 replicated in Step S 10504 and the data of the area segmentation feature value file are integrated at each row, and the procedure proceeds to Step S 10506 .
  • Step S 10506 a process of “outputting integrated data” is performed.
  • the reference FP feature value integrated file (the reference group integrated data example 209 in FIG. 129 ) according to the integration result is output.
  • the evaluating method for a multicomponent material according to Embodiment 1 of the present invention includes the FP preparing step 148 , the target FP peak assigning step 149 , the target FP peak feature value preparing step 151 , the target FP type-2 preparing step 153 , the target FP area segmentation feature value preparing step 155 , the target FP feature value integrating step 157 , the reference FP peak assigning step 159 , the reference FP assigning result integrating step 161 , the reference FP peak feature value preparing step 163 , the reference FP type-2 preparing step 165 , the reference FP area segmentation feature value preparing step 167 , the reference FP feature value integrating step 169 , and the evaluating step 171 .
  • the FP preparing step 148 includes the target FP preparing step 173 and the reference FP preparing step 175 .
  • the target FP peak assigning step 149 includes the reference FP selecting step 177 , the peak pattern preparing step 179 , and the peak assigning step 181 .
  • the 3D chromatogram 41 of a multicomponent drug that is an evaluation target is processed with these seven processes ( 178 , 149 , 151 , 153 , 155 , 157 , and 171 ), thereby improving the accuracy and the efficiency of the evaluation of the quality of the evaluation target drug.
  • the target FP peak feature values that are quantified as feature values are prepared based on the target FP 43 and a plurality of reference FPs, the target FP type-2 is prepared as remaining peaks of the target FP 43 that are excluded from the feature value quantification, the target FP type-2 is segmented into a plurality of areas, the target FP area segmentation feature values are prepared based on an existence rate of peaks that are present in each area, the target FP integrated feature values are prepared by integrating the target FP peak feature values and the target FP area segmentation feature values, and the target FP integrated feature values and the reference FP integrated feature values that correspond to the target FP integrated feature values and are based on the plurality of reference FPs of multicomponent materials being evaluation criteria are compared and evaluated. Accordingly, the peaks for the target peak that are not included in the target FP peak feature values can be additionally evaluated, thereby certainly improving the accuracy of the evaluation of the quality of the evaluation target drug.
  • the target FP 43 prepared by the target FP preparing step 173 is configured as three-dimensional information (peaks, retention time points, and UV spectra) similar to the 3D chromatogram 41 . Accordingly, the target FP 43 is data that directly succeed to the information that is peculiar to the drug. In spite of that, the volume of data is compressed at the ratio of about 1/70, compared to the 3D chromatogram 41 , the amount of information to be processed can be greatly reduced to increase the processing speed.
  • the target FP preparing step 173 prepares a FP by composing a plurality of FPs at different detection wavelengths. Accordingly, for even a multicomponent drug acquired by combining components all of which cannot be detected using one wavelength, a quality evaluation covering all the components can be performed by composing FPs at a plurality of detection wavelengths.
  • the target FP preparing step 173 prepares a FP that includes all the peaks detected in the 3D chromatogram. Accordingly, the target FP preparing step is suite for an evaluation of the quality of a kampo medicine that is a multicomponent drug.
  • the reference FP selecting step 177 compares retention time appearance patterns of FPs with each other, to select a reference FP having a high degree of matching between patterns as a reference FP that is appropriate to the assignment of the target FP. Accordingly, in the peak assigning step 181 , the assignment process can be performed between FPs having similar patterns, whereby assignment with high accuracy can be performed.
  • the peak pattern preparing step 179 comprehensively prepares peak patterns with use of a plurality of peripheral peaks for each of the assignment target peak and the assignment candidate peak. Accordingly, even if there is a difference between the whole patterns of the target FP and the reference FP more or less, assignment can be performed through the peak assigning step 181 with high accuracy.
  • the degree of matching between UV spectra of the assignment target peak and the assignment candidate peak is used for specifying the peak to be assigned. Accordingly, the assignment can be performed with high accuracy.
  • the peak assigning step 181 assigns all the peaks of the target FP to the peaks of the reference FP all together. Accordingly, the assignment process can be performed with high efficiency.
  • the evaluating step 171 collects a FP that is composed by multiple components as multi-dimensional data as a MD value in one dimension by MT method, to easily compare and evaluate a plurality of evaluation target lots. Accordingly, it is suited for evaluating a multicomponent based drug that is composed of multiple components.
  • the target FP area segmentation feature value preparing step 155 performs the segmentation of the areas with a plurality of vertical segmenting lines 141 that are parallel to the signal strength axis and a plurality of horizontal segmenting lines 143 that are parallel to the time axis.
  • the plurality of horizontal segmenting lines 143 are set at geometric sequence ratio intervals in a direction in which the signal strength increases.
  • the area can be finely segmented in a portion having a high peak density, thereby efficiently performing the calculation of the peak existence rate through the area segmentation.
  • the evaluating method for a multicomponent material further includes the reference FP preparing step 175 , the reference FP peak assigning step 159 , the reference FP assigning result integrating step 161 , the reference FP peak feature value preparing step 163 , the reference FP type-2 preparing step 165 , the reference FP area segmentation feature value preparing step 167 , and the reference FP feature value integrating step 169 .
  • the reference FP integrated feature values are prepared by integrating the reference FP peak feature values and the reference FP area segmentation feature values and can be compared with the target FP integrated feature values in the evaluating step 171 , thereby improving the accuracy and the efficiency of the quality evaluation of an evaluation target drug.
  • the reference FP area segmentation feature value preparing step 167 changes the position of each area and prepares the reference FP area segmentation feature values before and after the change.
  • the reference FP area segmentation feature value preparing step 167 performs the segmentation of the areas with the plurality of vertical segmenting lines 141 that are parallel to the signal strength axis and the plurality of horizontal segmenting lines 143 that are parallel to the time axis.
  • the plurality of horizontal segmenting lines 143 are set at geometric sequence ratio intervals in a direction in which the signal strength increases.
  • the area can be finely segmented in a portion having a high peak density, thereby efficiently performing the calculation of the peak existence rate through the area segmentation.
  • the reference FP area segmentation feature value preparing step 167 changes and sets each of the vertical and horizontal segmenting lines 141 and 143 so as to be moved parallel within a set range, thereby changing the position of each area 145 .
  • the evaluating program for a multicomponent drug causes the computer to execute each function to improve the accuracy and the efficiency of the evaluation.
  • the evaluating apparatus for a multicomponent drug operates the parts 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 , 19 , 21 , 23 , 25 , and 27 to improve the accuracy and efficiency of the evaluation.
  • the calculation of the degree of matching between peak patterns is performed based on a difference between peak heights of comparison targets in the above-described embodiment in which the FPs are prepared with use of peak heights.
  • a peak represents a maximum value of a signal strength (height) as described above or a case where a peak represents an area value (peak area) of a signal strength in a form of a height.
  • the FP Even in the case where the FP is prepared with use of peak areas, the area values are represented in a form of height to prepare the FP. Accordingly, the FP has the same representation as that of the case where the FP is prepared with use of the peak heights as in the above-described embodiment. Therefore, similar to the case where the FP is prepared with the peak heights, the FP can be evaluated by the process of the above-described embodiment.
  • P_Sim ( p ⁇ ⁇ 1 / p ⁇ ⁇ 2 # ⁇ ⁇ 1 ) ⁇ ( ⁇ ( r ⁇ ⁇ 1 - ( r ⁇ ⁇ 2 + d ) ⁇ + 1 ) + ( dn ⁇ ⁇ 1 / fn ⁇ ⁇ 1 #1 ) ⁇ ( ⁇ ( cn ⁇ ⁇ 1 - r ⁇ ⁇ 1 ) - ( en ⁇ ⁇ 1 - r ⁇ ⁇ 2 ) ⁇ + 1 ) + ( dn ⁇ ⁇ 2 / fn ⁇ ⁇ 2 # ⁇ ⁇ 1 ) ⁇ ( ⁇ ( cn ⁇ ⁇ 2 - r ⁇ ⁇ 1 ) - ( en ⁇ ⁇ 2 - r ⁇ ⁇ 2 ) ⁇ + 1 ) + ( dn ⁇ ⁇ 3 / fn ⁇ ⁇ 3 #1 ) ⁇ ( ⁇ ( cn ⁇ ⁇
  • #1 represents a ratio (larger value/smaller value) of two comparison target values.
  • the degree of matching between peak patterns (P_Sim) can be calculated based on a ratio, and, also in the case where the FP is prepared by means of the peak areas, similarly to the case of a difference between the peak heights, the degree of matching between peak patterns (P_Sim) can be acquired based on a difference between peak area values.
  • FIG. 130 is a modified example of “Subroutine 2 ” that is applied instead of that illustrated in FIG. 104 and is a flowchart illustrating details of the modified example of “Subroutine 2 ” in the “target FP assigning process 2 ” illustrated in FIG. 99 .
  • the degree of matching between UIV spectra is calculated by the process according to this modified example.
  • a process of adding inclination information of the moving average of a UV pattern (DNS) to the RMSD of Subroutine 2 in FIG. 104 can be performed.
  • the DNS is represented in an equation to be described later and is defined as the number of mismatches of inclination codes (+/ ⁇ ) when the moving inclination of the moving average values in the UV pattern are compared between two patterns.
  • the DNS is a value that represents an evaluation of the matching state of the positions of the maximum and minimum values of the UV patterns.
  • the degree of matching between waveforms of UV spectra can be calculated more accurately.
  • Steps S 2001 to S 2008 are almost the same as those of Subroutine 2 in FIG. 104 .
  • initial setting of “Interval 1 ⁇ w 1 and Interval 2 ⁇ w 2 ) is additionally performed, to be used for calculating the moving average and the moving inclination to be described later.
  • Steps S 2010 to S 2013 are added so as to add the DNS, so that it enables Step S 2009 A to calculate the degree of matching to which the DNS is added.
  • Step S 2010 a determining process “Is the DNS added?” is performed If the DNS is determined to be added (YES), the procedure proceeds to Step S 2011 . If the DNS is determined not to be added (NO), the procedure proceeds to Step S 2009 A. For example, whether the DNS is added or not is based on the initial setting. The determination whether the DNS is added or not is based on, for example, an initial setting. For example, if the FP is prepared by means of peak areas, the DNS is set to be added; and if the FP is prepared by means of peak heights, the DNS is set to be not added.
  • the degree of matching between UV patterns can be calculated through a process to which the DNS is added; and also in the case where the FP is prepared by means of peak areas, the degree of matching between UV patterns can be calculated through the process of the above-described embodiment to which the DNS is not added.
  • Step S 2011 a process of “calculating the moving averages of “x” and “y” in interval 1 (w 1 )” is performed, to find the moving averages for interval 1 (w 1 ).
  • Interval 1 (w 1 ) is an interval relating to the wavelength of the UV data.
  • interval 1 ( 3 ) is set and the average of the UV intensities of three wavelengths is acquired. More specifically, description will be made later with reference to a table represented in FIG. 131 .
  • Step S 2012 the process of “calculating the moving inclinations of “x” and “y” in interval 2 (w 2 )” is performed to find the moving inclinations in interval 2 (w 2 ).
  • Step S 2013 a process of “calculating the number of mismatches between the codes of the moving inclinations of “x” and “y” (DNS)” is performed, to calculate the number of matches in the inclinations of ( ⁇ ) based on the moving inclinations calculated in Step S 2012 .
  • the moving inclination of (+) represents rising to the right in FIG. 66
  • the moving inclination of ( ⁇ ) represents falling to the right.
  • Step S 2013 to Step S 2009 A the degree of matching to which the DNS is added is calculated in the process of Step S 2009 A.
  • Step S 2009 A a process of “calculating the degree of matching between UV spectra of “x” and “y” (UV_Sim)” is performed.
  • the UV_Sim is calculated based on the sum “z” of squares of inter-UV spectrum distance, the number “a” of data of “x” and the DNS as:
  • UV_Sim ⁇ ( z/a ) ⁇ 1.1 DNS .
  • This UV_Sim is input to Step S 306 in FIG. 81 , to finish the process of calculating the degree of matching between UV spectra.
  • Step S 2010 proceeds from Step S 2010 to Step S 2009 A is the same as that of Step S 2009 in FIG. 86 .
  • FIG. 131 is a table illustrating a calculating example of moving averages and moving inclinations.
  • the upper row represents an example of UV data
  • the intermediate row represents an example of calculation of moving averages
  • the lower row represents an example of calculation of moving inclinations.
  • the UV intensity is represented as a 1 to a 7 instead of specific numeric values.
  • the UV intensity of 220 nm is a 1
  • the UV intensity of 221 nm is a 2
  • the like is also used instead of specific numeric values.
  • the moving averages are calculated as m 1 , m 2 . . . as respective values calculated for an interval (a 1 , a 2 , a 3 ), an interval (a 2 , a 3 , a 4 ) . . . in Step S 2012 (see FIG. 130 ).
  • the moving inclinations are calculated as s 1 . . . as respective values calculated for an interval (m 1 , m 2 , m 3 ), an interval (m 2 , m 3 , m 4 ) . . . in Step S 2013 (see FIG. 130 ).
  • a difference m 3 ⁇ m 1 between the moving averages is the moving inclination, and ( ⁇ ) thereof are extracted.
  • the degree of matching between UV patterns can be calculated through the process to which the DNS is added. With this calculation, even if a distance (dis) between two corresponding points illustrated in FIG. 66 is larger relative to the FP prepared by means of peak heights, the handling thereof can be easily performed, thereby calculating the degree of matching between UV patterns with high accuracy.
  • the FP is prepared with use of peak areas, it may be applied such that the signal strength axis is set as an area value axis, and the signal strength is set as an area value.
  • area segmentation feature values are prepared for the target FP type 2 or the reference FP type 2
  • area segmentation feature values may be prepared for the target FP or the reference FP.
  • the present invention is widely applicable to what includes a target pattern area segmentation feature value preparing step of segmenting a pattern whose peak change in a time series into a plurality of areas to prepare pattern area segmentation feature values based on the existence rate or the existence amount of peaks existing in each area.
  • the FP may be prepared with the exclusion of fine data such as peaks each having a peak area corresponding to 5% or less on the 3D chromatogram.
  • the FP is prepared based on the peak heights, and evaluations represented in FIGS. 87 to 91 are acquired.
  • the evaluations can be acquired as those of FIGS. 87 to 91 .
  • the chromatogram is not limited to the 3D chromatogram, and a FP that is composed of peaks and retention time points, in which the UV spectrum is not included, may be used. In such a case, the process can be performmed similarly to the above-described embodiment with the exception of the degree of matching between UV spectra.

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