JP2017015557A - Signal detection method, calibration curve generation method, quantitative method, signal detection device, and measurement device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a new technology for highly accurately detecting a signal related to a trace amount of target component contained in a measurement signal.SOLUTION: A signal detection method includes steps of: acquiring a measurement signal (M) including a first signal that is a signal of a target component and a second signal that is a signal of an interfering component by a measurement signal acquisition part 20; and taking an inner product value (M-I) of the measurement signal and a unit signal (I) of the first signal by a density measurement part 320.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a signal detection method, a calibration curve creation method, a quantification method, a signal detection device, and a measurement device.

  Various techniques are known as techniques for analyzing a predetermined component signal included in a measurement signal (signal from a living body). In particular, independent component analysis (also called independent component analysis) is known as one of representative techniques.

For example, in Patent Document 1, independent component analysis is performed on an observation signal that is a measurement signal (signal from a living body), and the calculated independent component is used as a basic function, and the observation signal is expressed as a linear sum of basic functions. A technique for analyzing the concentration of a target component contained in an observation signal is disclosed.
In Patent Document 2, independent component analysis is performed on observation data which is a measurement signal (signal from a living body), a mixing coefficient for a target component included in the observation data is obtained, and the content of the target component in the original observation data is obtained. And a technique for obtaining a calibration curve from the mixing coefficient.

JP 2007-44104 A JP 2013-36973 A

  Ideally, a signal related to an independent component is a signal of a unique component, and thus is not influenced by other components and is “independent” with respect to other components. However, in reality, each independent component extracted from the mixed component by the independent component analysis may not be completely “independent”. In such a case, for example, even if an independent component analysis is performed on the measurement target to detect the concentration of a slight trace component of 1% or less such as 0.01% or less contained in the measurement target, It was difficult to accurately detect the concentration of.

  The present invention has been devised in view of the above-described circumstances, and an object of the present invention is to detect a signal related to a trace component contained in a measurement signal (for example, a signal from a living body) with high accuracy. It is to propose a new technology.

  Application Example 1 A signal detection method according to this application example includes a step of obtaining a measurement signal (M) including a first signal and a second signal different from the first signal, the measurement signal, And taking the inner product value (M · I) of the first signal with the unit signal (I).

  According to the signal detection method of this application example, the measurement signal (M) including the first signal and the second signal is acquired, and the inner product value (I) of the measurement signal (M) and the unit signal (I) of the first signal is obtained. M · I), the magnitude (scalar amount) of the first signal included in the measurement signal can be obtained with high accuracy.

  Application Example 2 In the signal detection method according to the application example described above, it is preferable that the unit signal of the first signal is orthogonalized to the second signal by orthogonal calculation.

  According to the earnest study by the inventors of the present application, the vector representing the first signal is orthogonal to the vector representing the second signal, and the first signal and the second signal may constitute an orthogonal vector space. found. According to the signal detection method of this application example, since the unit signal (I) of the first signal is orthogonalized to the second signal by the orthogonal calculation, the component related to the second signal is hardly included. Since the inner product value (M · I) of the unit signal (I) of the first signal and the measurement signal (M) is taken, the second signal is removed from the measurement signal including the first signal and the second signal. The magnitude of one signal can be obtained with high accuracy.

Application Example 3 In the signal detection method according to the application example described above, a second sample signal obtained by measuring a sample that includes the component related to the second signal and does not include the component related to the first signal in the orthogonal calculation. It is preferable to include using the second feature signal (P _k ) obtained by performing multivariate analysis processing on (Q).

According to the signal detection method of this application example, the second sample signal (Q) obtained by measuring the sample that includes the component related to the second signal and does not include the component related to the first signal is subjected to the multivariate analysis process. A second feature signal (P _k : second sample feature signal) that is a feature amount of a component related to the signal is extracted. Since the orthogonal calculation is performed using the obtained second feature signal, the second signal is effectively removed from the measurement signal obtained by measuring the sample including the component related to the first signal and the component related to the second signal. be able to.

Application Example 4 In the signal detection method according to the application example, the orthogonal calculation is performed on the first signal in a space (E−P · P ⁺ ) orthogonal to the space spanned by the second feature signal. A projection operation for projecting the measurement signal (R _g ) obtained from a reference sample whose physical quantity is known may be included. The unit signal (I) of the first signal may be a unit signal of the signal (S _g ) obtained as a result of this projection calculation. In other words, the unit signal (I) of the first signal may be obtained by Expression (1).

According to the signal detection method of this application example, the physical quantity related to the first signal is present in the space (E · P · P ⁺ ) orthogonal to the space spanned by the second feature signal (P _k : second sample feature signal). Projection calculation is performed to project a measurement signal (R _g ) obtained from a known reference sample. Thereby, the second signal can be removed from the measurement signal including the first signal and the second signal, and the first signal (S _g ) of the reference sample can be detected with high accuracy. Since the unit signal (I) of the first signal is generated from the first signal (S _g ) of the reference sample, the first signal included in the measurement signal (scalar amount) is measured with respect to a sample whose size is unknown. The magnitude of the first signal can be obtained with high accuracy.

Application Example 5 In the signal detection method according to the application example described above, the orthogonal calculation is performed on the measurement signal (R _g ) obtained from a reference sample whose physical quantity related to the first signal is known. The Gram-Schmidt orthogonalization method using the second feature signal (P _k ) may be applied. The unit signal (I) of the first signal may be a unit signal of the signal (S _g ) obtained as a result of this orthogonalization method. In other words, the unit signal (I) of the first signal may be obtained by Expression (2). In Equation (2), the intermediate vector (W _k ) is formed using the second feature signal (P _k ).

According to the signal detection method of this application example, the second feature signal (P _k : second sample feature signal) is compared with the measurement signal (R _g ) obtained from the reference sample whose physical quantity related to the first signal is known. Apply the Gram-Schmidt orthogonalization method using. Thereby, the second signal can be removed from the measurement signal including the first signal and the second signal, and the first signal (S _g ) of the reference sample can be detected with high accuracy. Since the unit signal (I) of the first signal is generated from the first signal (S _g ) of the reference sample, the first signal included in the measurement signal (scalar amount) is measured with respect to a sample whose size is unknown. The magnitude of the first signal can be obtained with high accuracy.

  Application Example 6 In the signal detection method according to the application example described above, the ratio of the first signal to the measurement signal may be 1% or less.

  According to the signal detection method of this application example, even when the component related to the first signal is a minute amount and the first signal is included in the measurement signal at a ratio of 1% or less, the first component of the minor component in the measurement signal is detected. One signal can be detected with high accuracy.

Application Example 7 A calibration curve creation method according to this application example includes a component related to a first signal and a component related to a second signal different from the first signal, and the physical quantity related to the first signal is A calibration curve for calculating the inner product value of the measurement signal (R _g ) obtained from a known reference sample and the unit signal of the first signal, and indicating the relationship between the physical quantity related to the first signal and the inner product value It is characterized by creating.

According to the calibration curve creation method of this application example, the inner product value detects the first signal with high accuracy from the measurement signal (R _g ), and uses this to create the calibration curve. A calibration curve having a strong correlation between the physical quantity of the component and the inner product value can be created.

  Application Example 8 A quantification method according to this application example includes a step of quantifying a physical quantity with reference to the inner product value obtained by the signal detection method of the application example.

  According to the quantification method of this application example, the inner product value detects the first signal from the measurement signal with high accuracy, and uses this to quantify the physical quantity. The physical quantity (for example, concentration) of the component related to the first signal in the sample including the component can be accurately quantified.

  [Application Example 9] A quantification method according to the application example, wherein, in the quantifying step, refer to the inner product value and the calibration curve obtained by the calibration curve creation method described in the application example. Is preferred.

  According to the quantification method of this application example, the first signal is obtained by referring to the inner product value of the measurement signal and the unit signal of the first signal and the calibration curve indicating the relationship between the physical quantity and the inner product value related to the first signal. The physical quantity of the component related to the first signal in the sample including the component related to and the component related to the second signal can be quantified with higher accuracy.

  Application Example 10 In the determination method according to the application example, the physical quantity may be a glucose concentration in blood.

  According to the quantification method of this application example, the physical quantity of glucose (component related to the first signal) contained in a minute amount with respect to water (component related to the second signal) contained in blood at a high rate is quantified with high accuracy. Can be

  Application Example 11 The signal detection apparatus according to this application example obtains a measurement signal obtained by measuring a measurement object including a component related to the first signal and a component related to the second signal different from the first signal. And a storage unit that stores the unit signal of the first signal, and an arithmetic processing unit that takes an inner product value of the measurement signal and the unit signal of the first signal.

  According to the configuration of this application example, the acquisition unit acquires a measurement signal including the first signal and the second signal, and the arithmetic processing unit acquires the measurement signal and the unit signal of the first signal stored in the storage unit. Since the inner product value is taken, the magnitude (scalar amount) of the first signal included in the measurement signal can be obtained with high accuracy.

  Application Example 12 A measurement apparatus according to this application example acquires a measurement signal obtained by measuring a measurement object including a component related to a first signal and a component related to a second signal different from the first signal. An acquisition unit, a storage unit that stores a unit signal of the first signal, an arithmetic processing unit that takes an inner product value of the measurement signal and the unit signal of the first signal, and quantifies a physical quantity using the inner product value And.

  According to the configuration of this application example, the acquisition unit acquires a measurement signal including the first signal and the second signal, and the arithmetic processing unit acquires the measurement signal and the unit signal of the first signal stored in the storage unit. Since the inner product value is taken and the physical quantity is quantified using the inner product value, the physical quantity of the component related to the first signal in the sample including the component related to the first signal and the component related to the second signal can be accurately measured. Can do.

The figure explaining the concept of this embodiment. The block diagram explaining the structure of the signal detection apparatus which concerns on this embodiment. The flowchart which shows the flow of the disturbance component feature-value extraction process which concerns on 1st Embodiment. The figure which shows the data obtained by the disturbance component feature-value extraction process which concerns on 1st Embodiment. The flowchart which shows the flow of the calibration curve creation process which concerns on 1st Embodiment. The figure which shows the data obtained by the calibration curve preparation process which concerns on 1st Embodiment. The figure which shows an example of the calibration curve created by the calibration curve creation process which concerns on 1st Embodiment. The figure explaining the orthogonalization reference vector obtained by the projection calculation which concerns on 1st Embodiment. The flowchart which shows the flow of the density | concentration measurement process which concerns on 1st Embodiment. The figure which shows the data obtained by the calibration curve preparation process which concerns on 2nd Embodiment. The block diagram explaining the structure of the measuring device which concerns on the modification 1. FIG. The figure which shows the comparison data at the time of performing an independent component analysis.

  DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, embodiments of the invention will be described with reference to the drawings. First, the principle of this embodiment will be described, and then several embodiments will be described as specific examples of this embodiment.

〔principle〕
In the present invention, the physical quantity to be measured is considered as a vector represented by a linear sum of various physical quantities. That is, it is considered that a measurement signal (for example, a signal from a living body) obtained by measuring a measurement object includes two or more physical quantity components, and the measurement signal is expressed by a linear sum of signals of the respective physical quantity components. Further, in the present invention, the measurement signal is expressed as a linear sum of the target signal that is the first signal and the interference signal that is the second signal, and the first signal is considered to be orthogonal to the second signal.

  According to the earnest study by the inventor of the present application, the first signal and the second signal should be independent from each other. Therefore, when the signal obtained by orthogonalizing the measurement signal to the second signal is the first signal. It is reasonable to think. Therefore, if the measurement signal is orthogonalized with respect to the second signal, a signal corresponding to the first signal should be extracted with high accuracy.

  The measurement object of the present application may be an electric signal, an audio signal, an electromagnetic wave signal, and the like. When measuring a specific signal component included in these signals, a substance such as a gas or a liquid is used as a measurement object. The present invention can be applied when measuring the concentration and mass of a specific component contained in a measurement object. In the following embodiments, the present invention is described using the concentration as an example of the physical quantity component to be measured. However, in the following embodiment, the physical quantity component is not limited to the concentration, but includes all fluctuation parameters (concentration / temperature).・ Atmospheric pressure etc.).

  Further, in the present invention, since the measurement signal is considered to be represented by the linear sum of the signals of the respective physical quantity components, if the signal of each physical quantity component is represented by a vector, a vector representing the target physical quantity component (a vector representing the first signal) The vector space in which the vector representing the physical quantity component of the obstruction (vector representing the second signal) is orthogonal can be defined, and the measurement signal vector can be defined on this vector space. Note that the number of dimensions of the vector space is the number of independent physical quantity components included in the measurement signal.

FIG. 1 is a diagram for explaining the concept of the present invention. FIG. 1 shows a simplified vector space, a vector representing a measurement signal (referred to as a measurement vector M), and the like. In the example of FIG. 1, a measurement vector M obtained from a measurement target is expressed as a linear sum of a first signal that is a target signal with one minute component and a second signal that is an interference signal with two high-ratio components. ing. The first signal is a signal representing a target physical quantity included in the measurement signal, and is represented by a first vector M _{0 in} the example of FIG. On the other hand, the second signal is a signal representing the interference signal of the measurement signal, and is represented by a vector sum of the first interference vector μ ₁ P ₁ and the second interference vector μ ₂ P _{2 in} the example of FIG. Yes.

The first signal (first vector M ₀ ) is orthogonalized with respect to the second signal (vector sum of the first disturbance vector μ ₁ P ₁ and the second disturbance vector μ ₂ P ₂ ). As described above, in the present invention, the measurement vector M representing the measurement signal includes the first signal (first vector M ₀ ) representing the target physical quantity and the second signal (first disturbance vector μ ₁ P ₁ or The first signal and the second signal are considered to be orthogonal to each other, expressed as a linear sum of the total interference vector such as the second interference vector μ ₂ P ₂ .

In FIG. 1, since the case where there are three independent components included in the measurement signal is illustrated, the vector space in which the measurement vector M is defined is represented as a three-dimensional space. Specifically, in the example of FIG. 1, the first disturbance vector μ ₁ P ₁ representing the first disturbance component with the first high ratio component and the second disturbance vector μ ₂ P representing the second disturbance component with the second high ratio component. A linear sum of ₂ and the first vector M ₀ representing the target component as a trace component represents the measurement vector M.

In the example of FIG. 1, the first disturbance vector μ ₁ P ₁ and the second disturbance vector μ ₂ P ₂ are drawn orthogonally, but it is not necessary that the individual interference vectors are orthogonal to each other. . The first signal (first vector M ₀ ) with respect to the entire disturbance vector as the second signal (in the example of FIG. 1, the vector sum of the first disturbance vector μ ₁ P ₁ and the second disturbance vector μ ₂ P ₂ ). Need only be orthogonalized.

For example, even if the first disturbance vector μ ₁ P ₁ and the second disturbance vector μ ₂ P _{2 form an} oblique coordinate system, the space in which the entire disturbance vector extends (in the example of FIG. 1, the first disturbance vector μ ₁ It is only necessary that the first signal (first vector M ₀ ) is orthogonalized with respect to a plane determined by P ₁ and the second disturbance vector μ ₂ P ₂ . In the present invention, a vector orthogonal to the space spanned by the entire disturbance vector is considered as the first vector M ₀ .

In the following embodiments, a trace component is set as a “target component”, and an object is to accurately detect a signal related to the trace component from a measurement signal (signal from a living body). Accordingly, it is an object to detect the first vector M ₀ of the trace component in the measurement vector M representing the measurement signal (signal from the living body). On the other hand, the high proportion component is called a “interference component” because it can be said to be a component that inhibits detection of a signal related to a trace component from a measurement signal (signal from a living body).

  By the way, independent component analysis is known as a signal processing technique for analyzing how much each component is contained. Problems may arise when trying to measure the amount (which may be a ratio or concentration) of a specific component contained in a measurement object using independent component analysis. Specifically, when the proportion of the specific component in the measurement object is extremely small compared to the proportion of the other components, the content of the trace component (may be the proportion or concentration) in the independent component analysis. It is a problem that it is difficult to determine accurately.

  Independent component analysis is a technique for estimating the number of contained components and the amount thereof by a statistical method using random variables. Therefore, in the case where the amount of one independent component in the measurement signal (signal from the living body) is a minute component such that it is 1% or less, it may be difficult to accurately measure the minute component. is there.

FIG. 1 is a diagram for explaining the principle of the present invention. With reference to FIG. 1, the problem of quantification by independent component analysis found by the present inventor will be described. In the case where one trace component is completely independent of two high-ratio components, the trace component of the vector sum of the first disturbance vector μ ₁ P ₁ and the second disturbance vector μ ₂ P ₂ The first vector M ₀ is orthogonal. That is, each target component amount can be accurately measured regardless of its size.

  However, in the actual independent component analysis, it is not possible to completely separate one minute target component from the disturbing component (two high proportion components). The inventor of the present application has found that a state including an error is regarded as independent. This is because the independent component analysis is performed by a statistical method using random variables.

In the independent component analysis, the fact that the target component is not strictly separated from the interfering component is considered to appear in the orthogonality between the first signal as the target component and the second signal as the interfering component. There is no problem. For example, a signal obtained directly by independent component analysis using the example of FIG. 1 will be described. Independent of the normal of the plane determined by the first disturbance vector μ ₁ P ₁ and the second disturbance vector μ ₂ P _2. This means that the target signal obtained directly by component analysis is tilted away from the normal.

  Even if the deviation of the orthogonality of the target signal (the inclination of the target signal with respect to the normal of the disturbing component) is a slight error of about 1/100, the target component is insignificant. It cannot be ignored. If the first signal that is the target component is completely orthogonal to the second signal that is the interference component, the amount of the target component can be strictly measured regardless of the magnitude.

  However, since the target signal obtained directly by independent component analysis is not completely orthogonal to the interfering component, the size (amount) of the high percentage component slightly contained affects the size (amount) of the minor component. Will give. In contrast, according to the present invention, since the component orthogonal to the disturbing component in the measurement signal is considered as the first signal, the influence of the disturbing component can be significantly reduced as compared with the prior art.

  After all, in quantification by independent component analysis, even if there is a slight error in the content of the extracted high proportion component, the error will affect the content of the trace component, so there is a big change for the trace component It becomes. Therefore, it can be said that quantification by independent component analysis alone is unsuitable for detection of minute amounts as a method for determining the amount (or ratio or concentration) of the minor components.

  The high proportion component is a component whose component amount (which may be a proportion or concentration) can be determined with high accuracy by independent component analysis, and a proportion of the measurement signal is, for example, 3% or more. .

In order to solve the above problems, in this embodiment, a signal of a target component which is a trace component is detected by using orthogonalization (also referred to as “orthogonal calculation” in this embodiment) which is one method of signal processing. . Specifically, the measurement vector M is orthogonal to the vector (vector sum of the first disturbance vector μ ₁ P ₁ and the second disturbance vector μ ₂ P ₂ ) representing the second signal of the disturbance component which is a high proportion component. By doing so, the first signal (first vector M ₀ ) of the target component which is a trace component is detected.

Here, since the second signal of the disturbing component is a high proportion component signal sufficiently included in the measurement signal (signal from the living body), the independent component analysis works effectively. Therefore, by preparing a sample that contains the interference component of the measurement target and does not include the target component, and analyzing the measurement signal of this sample by independent component analysis, the interference component that is an independent component of the interference component contained in the sample A feature amount (the first disturbance unit vector P ₁ and the second disturbance unit vector P _{2 in} FIG. 1) can be obtained.

[Signal detection device]
Next, a configuration example of a signal detection apparatus to which the present invention is applied will be described. FIG. 2 is a block diagram illustrating the configuration of the signal detection apparatus according to the present embodiment. Since the signal detection device 1 according to the present embodiment includes the functions of the signal detection device, the calibration curve creation device, and the measurement device, it can also be referred to as a calibration curve creation device or a measurement device. Although the signal detection device 1 is described as being configured separately from the absorbance measurement device 6, the signal detection device 1 may be configured to include the absorbance measurement device 6.

  The signal detection device 1 is a kind of computer system including a processing unit 10, a storage unit 50, an operation unit 70, a display unit 80, and a communication unit 90. The processing unit 10 is, for example, a microprocessor such as a CPU (Central Processing Unit) or a GPU (Graphic Processor Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), an IC (Integrated Circuit) memory, or the like. Realized by electronic components. Then, the processing unit 10 performs data input / output control with each functional unit, and performs various types of operations based on a predetermined program and data, an operation input signal from the operation unit 70, a measurement result of the absorbance measuring device 6, and the like. An arithmetic process is executed to calculate the concentration of the target component contained in the measurement object.

  The processing unit 10 includes a measurement signal acquisition unit 20 as an acquisition unit and an arithmetic processing unit 30. The measurement signal acquisition unit 20 controls the absorbance measurement device 6 by performing predetermined communication with the absorbance measurement device 6 and acquires a result measured by the absorbance measurement device 6 as a measurement signal. The measurement signal may be an analog signal. In this case, the measurement signal acquisition unit 20 converts the measurement signal into measurement signal data that is a digital signal. The absorbance measurement device 6 is a device that measures the absorbance spectrum in which various light having different wavelengths are incident on the measurement object, the transmitted light that has passed through the measurement object is received, and the absorbance for each light wavelength is expressed. It is. That is, the measurement signal is expressed as an absorbance spectrum.

  The measurement object of the absorbance measuring device 6 includes a disturbing component sample that is a sample of a disturbing component that does not include the target component, a concentration known sample that is a sample whose concentration of the target component is known or determined by a separate measurement, The concentration of the component is unknown, and there are three types, that is, a concentration measurement object to be measured. The measured absorbance spectra are stored in the storage unit 50 as the interference component sample measurement signal data 531, the known concentration sample measurement signal data 532, and the concentration measurement target measurement signal data 533 by the measurement signal acquisition unit 20, respectively.

  The arithmetic processing unit (signal processing unit) 30 is a processing unit that performs various digital signal processing on the measurement signal acquired by the measurement signal acquisition unit 20, and can be said to be a kind of signal processing unit. The arithmetic processing unit 30 includes a calibration curve creation unit 310 and a concentration measurement unit 320.

  The calibration curve creation unit 310 is a functional unit that executes a calibration curve creation process (see FIG. 5) in accordance with the calibration curve creation program 510 stored in the storage unit 50, and calculates the concentration of the target component contained in the concentration measurement object. Create a calibration curve for The calibration curve creation unit 310 includes an interference component feature amount extraction unit 312, a component analysis unit 314, and a first target component signal detection unit 316.

  The interference component feature amount extraction unit 312 is a functional unit that executes the interference component feature amount extraction processing according to the interference component feature amount extraction program 512, which is a subroutine program of the calibration curve creation program 510. The component analysis unit 314 is a functional unit that performs component analysis processing (multivariate analysis processing) of interference components on the measurement signal. The first target component signal detection unit 316 performs a target component signal detection process for detecting a target component signal from a sample having a known concentration in accordance with a target component signal detection program 514 that is a subroutine program of the calibration curve creation program 510. Part.

The concentration measurement unit 320 is a functional unit that executes concentration measurement processing according to the concentration measurement program 520. Specifically, the concentration measurement unit 320 uses the calibration curve created by the calibration curve creation unit 310 to measure the concentration of the target component contained in the concentration measurement object. The concentration measurement unit 320 includes a second target component signal detection unit 322. The second target component signal detection unit 322 follows the target component signal detection program 522 which is a subroutine program of the concentration measurement program 520, that is, the signal of the target component included in the concentration measurement object, that is, the first signal (first vector M _0). ) To detect the target component signal.

  Note that the measurement signal acquisition unit 20 and the arithmetic processing unit 30 may be configured as an electronic circuit that performs signal processing, not as a software function unit realized by executing a program as described above. Moreover, although the 1st target component signal detection part 316 and the 2nd target component signal detection part 322 were demonstrated as a separate function part, it is good also as designing as a shared function part.

  The storage unit 50 is realized by a storage medium such as an IC memory, a hard disk, or an optical disk, and stores various programs and various data such as calculation process data of the processing unit 10. The connection between the processing unit 10 and the storage unit 50 is not limited to a connection by an internal bus circuit in the apparatus, but may be realized by a communication line such as a LAN (Local Area Network) or the Internet. In that case, the storage unit 50 may be realized by an external storage device different from the signal detection device 1.

  The storage unit 50 stores a calibration curve creation program 510 and a concentration measurement program 520. The calibration curve creation program 510 includes, as subroutine programs, an interference component feature amount extraction program 512 for executing interference component feature amount extraction processing, and a target component signal detection program 514 for creating a calibration curve. The concentration measurement program 520 includes a target component signal detection program 522 for measuring the concentration of the concentration measurement object as a subroutine program.

  In addition, the storage unit 50 calculates disturbance component sample measurement signal data 531, concentration known sample measurement signal data 532, and concentration measurement, which are calculated when the interference component feature amount extraction processing, calibration curve creation processing, and concentration measurement processing are executed. The object measurement signal data 533, the disturbing component feature amount data 541, the target component feature amount data 543, and the calibration curve data 545 are stored. In addition to these, the storage unit 50 can appropriately store temporary data calculated when each process is executed.

  The operation unit 70 receives various operation inputs from the user and outputs an operation input signal corresponding to the operation input to the processing unit 10. The operation unit 70 can be realized by, for example, a button switch, a lever switch, a dial switch, a track pad, a mouse, a keyboard, a touch panel, or the like.

  The display unit 80 displays a calculation result displayed by the processing unit 10, a guidance display indicating an operation procedure, and the like. The display unit 80 can be realized by, for example, a liquid crystal display or a touch panel.

  The communication unit 90 realizes a communication function for connecting the signal detection device 1 to an external device and exchanging data with each other. The communication form may be wired or wireless. Moreover, it is good also as a structure connectable to an internet line or a public communication network.

[Signal detection method, calibration curve creation method, and quantitative method]
<First Embodiment>
Next, a signal detection method, a calibration curve creation method, and a quantification method according to the first embodiment will be described. The signal detection method, calibration curve creation method, and quantification method according to the first embodiment include interference component feature quantity extraction processing, calibration curve creation processing, and concentration measurement processing.

  First, the disturbance component feature amount extraction processing according to the first embodiment will be described. FIG. 3 is a flowchart showing the flow of the disturbance component feature amount extraction process according to the first embodiment. FIG. 4 is a diagram illustrating data obtained by the interference component feature amount extraction processing according to the first embodiment. Specifically, FIG. 4A is a diagram illustrating an example of an absorbance spectrum acquired from an interference component sample, and FIG. 4B is a diagram illustrating an example of an interference unit vector spectrum acquired by an independent component analysis process. .

  In the first embodiment, a method for acquiring a first signal will be described by taking, as an example, calculation of a glucose concentration contained in an aqueous glucose solution whose concentration is unknown by the signal detection device 1. The glucose aqueous solution to be measured contains glucose as a target component (target physical quantity) at a concentration of 1% or less, and water as a disturbing component (interference physical quantity) at a concentration of 90% or more, which is 3% or more. Therefore, the target component glucose is a trace component and the interfering component water is a high proportion component.

  The disturbing component feature amount extraction processing is to extract the feature amount of the disturbing component from the measurement signal (second sample signal) of the disturbing component sample that includes the disturbing component related to the second signal but does not include the target component related to the first signal. It is processing. In the present embodiment, the interfering component sample is a component other than glucose that is the target component, that is, water that is a high proportion component. The interference component feature amount extraction processing is realized by executing an interference component feature amount extraction program 512 which is a subroutine program included in the calibration curve creation program 510 shown in FIG.

  In step S01 shown in FIG. 3, a plurality of disturbing component samples including the disturbing component related to the second signal and not including the target component related to the first signal are prepared. Since water, which is an interfering component sample, changes its spectral data (or feature ratio) according to the temperature, multiple interfering water samples (β, β is an integer of 2 or more) with different temperatures are used. Prepare as.

  Subsequently, in step S02, each measurement signal (second sample signal) of β water having different temperatures is acquired. Here, an absorbance spectrum is acquired as a measurement signal of water which is a disturbing component sample. The absorbance spectrum of the interference component sample is acquired from the absorbance measurement device 6 via the measurement signal acquisition unit 20 shown in FIG. 2 and stored in the storage unit 50 as interference component sample measurement signal data 531. This is repeated until the measurement of β interference component samples is completed (step S03: NO to step S02).

  When measurement has been completed for all (β) interference component samples (step S03: YES), as a result, data on the absorbance spectrum of water, which is the interference component, is obtained from the interference component sample. FIG. 4A shows an absorbance spectrum obtained from the interference component sample (water). In FIG. 4A, the horizontal axis is a measurement point corresponding to the wavelength of light (i: 1 to α, α is an integer of 2 or more), and the vertical axis is the intensity of the absorbance spectrum.

  Here, as an example, the level number (j: 1 to β) of the interference component sample (water) was set to 11 at intervals of 1 ° C. from 30 ° C. to 40 ° C. That is, 11 samples up to 40 ° C. where j = β = 11 were measured, such that j = 1 was 30 ° C. and j = 2 was 31 ° C. Here, the measurement points (i: 1 to α) were set to 90 points with a wavelength of 800 nm to 1245 nm at intervals of 5 nm. That is, for each of the β samples, measurement was performed at 90 points up to 1245 nm where i = α = 90, such that i = 1 was a wavelength of 800 nm and i = 2 was a wavelength of 805 nm.

Subsequently, in step S04 shown in FIG. 3, a second sample signal (second sample vector Q _j ) is formed based on the absorbance spectrum data acquired from the interference component sample (water). The second sample signal (second sample vector Q _j ) corresponds to the measurement signal (measurement vector M) in FIG. 1 and is a measurement signal of an interference component with a known concentration. The second sample vector Q _j is expressed by a column vector of α rows and 1 column and β is formed as the number of levels as shown in Equation (3) by measurement points i (1 ≦ i ≦ α). The

Specifically, for example, the element Q _1j in the first row of the second sample vector Q _{j at} the j-th level is the absorbance at a wavelength of 800 nm with i = 1 at the j-th water temperature. Further, for example, elements Q _.alpha.j of alpha-th row in the second sample vector Q _j (in the present example, the wavelength 1245nm at alpha = 90) j th of a wavelength of at i = alpha to the water temperature the absorbance of the. In this way, αβ pieces of measurement data are expressed by β column vectors of α rows and one column. The second sample vector Q _j formed from the measured spectrum data is stored in the storage unit 50 as the second sample signal (interference component sample measurement signal data 531).

Subsequently, in step S05, component analysis processing (multivariate analysis processing) is performed on the second sample signal (second sample vector Q _j ) acquired in step S04 by the component analysis unit 314 shown in FIG. The disturbing component feature amount shown in S06 is acquired. As multivariate analysis processing, various analysis processing such as independent component analysis processing and principal component analysis processing can be used. Among these, the independent component analysis processing is suitable for detecting a signal with a high proportion with high accuracy because the obtained interference vector is highly orthogonal and excellent in reducing errors.

By performing an independent component analysis process on the second sample signal (second sample vector Q _j ) in step S05, the disturbance component feature quantity (interference unit vector P _k ) that is the second sample feature signal (second feature signal). ) Is obtained (step S06). The disturbing unit vector P _k (k = 1 to γ) is a column vector of α rows and 1 column, and γ is the number of independent components formed from the second sample vector Q _j . Here, since there were three independent components, γ = 3.

FIG. 4B shows the disturbance unit vector P _k acquired in step S06. In FIG. 4B, the horizontal axis represents each element (i: 1 to α) of the interference unit vector P _k set at 90 points at intervals of 5 nm from 800 nm to 1245 nm corresponding to the wavelength of light. The axis is the intensity of the absorbance spectrum. As described above, since γ = 3, the first disturbance unit vector (first disturbance component feature amount) P ₁ and the second disturbance unit vector (second disturbance unit) are included as three independent components included in the interference component water. Component feature amount) P ₂ and third disturbance unit vector (third disturbance component feature amount) P ₃ are extracted. The γ interference unit vectors P _k are stored as interference component feature amount data 541 in the storage unit 50 shown in FIG.

The second sample vector Q _j is represented by a linear sum of the disturbing unit vectors P _k as shown in Equation (4). μ _kj is a coefficient. For example, when the water temperature is 30 ° C. (j = 1), the first sample vector Q _{1 at} the first level is expressed by the first disturbance unit vector P ₁ and the second disturbance unit vector P ₂ as shown in Equation (5). And the third interference unit vector P ₃ .

  This is the end of the disturbing component feature amount extraction process shown in FIG.

  Next, the calibration curve creation process according to the first embodiment will be described. FIG. 5 is a flowchart showing the flow of the calibration curve creation process according to the first embodiment. FIG. 6 is a diagram illustrating data obtained by the calibration curve creation process according to the first embodiment. Specifically, FIG. 6A is a diagram illustrating an example of an absorbance spectrum acquired from a sample having a known concentration, and FIG. 6B is a diagram illustrating an example of a spectrum of a target component feature amount. FIG. 7 is a diagram illustrating an example of a calibration curve created by the calibration curve creation process according to the first embodiment. FIG. 8 is a diagram for explaining the concept of the orthogonalization reference vector obtained by the projection calculation according to the first embodiment. FIG. 12 is a diagram showing comparison data when quantification is performed only by independent component analysis.

  The calibration curve creation process is a process for creating a calibration curve for measuring the concentration of the target component. Therefore, it is necessary to prepare a calibration curve in advance before executing a concentration measurement process described later. Further, before executing the calibration curve creation process, it is necessary that the interference component feature amount is acquired in advance.

  Therefore, first, in step S11 shown in FIG. 5, when the disturbing component feature quantity is not stored as the disturbing component feature quantity data 541 (step S11: NO), the disturbing component feature quantity extraction process in step S12 is executed. The disturbing component feature amount extraction processing shown in FIG. 3 corresponds to step S12. If the interference component feature quantity is acquired and stored as the interference component feature quantity data 541 in step S11 (step S11: YES), a target component signal detection process for detecting a target component signal is performed (step S13 to step S13). S17).

  In step S13, a reference sample having a known physical quantity of the target component related to the first signal is prepared. In the example of this embodiment, the target component is glucose, and the physical quantity of the target component is the glucose concentration in the aqueous solution. Therefore, the reference sample is a sample with a known glucose concentration. Specifically, plural (δ, δ is an integer of 2 or more) aqueous solutions having known concentrations of glucose as a target component and different from each other are prepared as samples with known concentrations (measurement objects). Since water, which is an interfering component, changes in spectrum data (or feature ratio) according to temperature, in addition to samples with different concentrations, prepare multiple samples with different temperatures as well. Is preferred.

  Since the target component is a trace component with a concentration of 1% or less, any sample with a known concentration has a glucose concentration of 1% or less. This is because the range of glucose concentration to be measured in the living body is about 50 mg / dl to 600 mg / dl. The specific gravity of blood is almost the same as that of water and can be considered as 1 g / cc. 1 dl (1 deciliter) is 100 g and the glucose concentration is 1000 mg / dl or less, so the glucose concentration is 1% or less.

  Subsequently, in step S14, respective measurement signals of δ glucose aqueous solutions having different concentrations which are samples having known concentrations are acquired. Here, an absorbance spectrum is acquired as a measurement signal of a sample having a known concentration, as in the case of the interference component sample. The absorbance spectrum of the sample with a known concentration is acquired from the absorbance measurement device 6 via the measurement signal acquisition unit 20 illustrated in FIG. 2 and is stored in the storage unit 50 as the known concentration sample measurement signal data 532. This is repeated until the measurement of δ known samples is completed (step S15: NO to step S14).

  When measurement is completed for all (δ) samples with known concentrations (step S15: YES), as a result, data on the absorbance spectrum of the aqueous glucose solution that is the sample with known concentrations is obtained. FIG. 6A shows an absorbance spectrum obtained from a sample having a known concentration (glucose aqueous solution). In Fig.6 (a), a horizontal axis is a measurement point (i: 1- (alpha)) corresponding to the wavelength of light, and a vertical axis | shaft is a light absorbency.

  Here, the number of levels δ of the aqueous glucose solution was set to 28 at intervals of 25 mg / dl from 25 mg / dl to 700 mg / dl. That is, 28 samples up to 700 mg / dl where g = δ = 28 were measured such that g = 1 was a concentration of 25 mg / dl and g = 2 was a concentration of 50 mg / dl. In FIG. 6A, the absorbance spectra of 28 glucose aqueous solutions having different concentrations are drawn in an overlapping manner. The measurement points (i: 1 to α) are set to 90 points at intervals of 5 nm from 800 nm to 1245 nm.

Subsequently, in step S16 shown in FIG. 5, the reference vector R _g (g: 1 to δ) of the target component is acquired based on the absorbance spectrum data acquired from the sample with known concentration (glucose aqueous solution). The reference vector R _g is obtained for each of δ = 28 samples with known concentrations. The reference vector R _g is composed of δ column vectors of α rows and 1 column, as shown in Expression (6), based on the measurement point i (1 ≦ i ≦ α) and the number of levels g (1 ≦ g ≦ δ). expressed. The acquired reference vector R _g is stored as the known concentration sample measurement signal data 532 in the storage unit 50 shown in FIG.

Subsequently, in step S17, orthogonal processing (orthogonal calculation) is performed in which the measurement signal (that is, the reference vector R _g ) of the sample having a known concentration is orthogonal to the water signal that is the interference component. In the first embodiment, projection calculation is used as orthogonal calculation. As shown in FIG. 8, a measurement signal (reference vector R _g ) of a sample having a known concentration is orthogonalized with respect to the entire γ interference unit vectors as an orthogonal reference vector S _g of the target component, and this orthogonal The size of the normalization reference vector S _g (the absolute value of the orthogonalization reference vector S _g ) corresponds to the density. In the previous example, the interference unit vector P _k is γ = 3. However, in FIG. 8, the interference unit vector P _k includes the interference unit vector P ₁ and the interference unit vector for easy understanding of the concept. Only _two with P2 are drawn.

In the first embodiment, a measurement signal (reference vector R _g ) obtained from a sample having a known concentration is orthogonal to the space spanned by the second sample feature signal (second feature signal, disturbance unit vector P _k ) (E− A projection operation for projecting to (P · P ⁺ ) is performed to obtain an orthogonal reference vector S _g of the target component. The orthogonalization reference vector S _g of the target component is obtained by Expression (7).

In Equation (7), E is a unit matrix of α rows and α columns and is expressed by Equation (8). Note that δ _ij is a delta function.

In Equation (7), P is an interference matrix of α rows and γ columns, and is a space spanned by γ interference unit vectors P _k as represented by Equation (9).

In Equation (7), P ⁺ is a pseudo inverse matrix of the interference matrix P, and is obtained by Equation (10).

In Equation (10), P ^T is a transposed matrix of the interference matrix P, and is obtained by Equation (11). The transposed matrix P ^T is a matrix of γ rows and α columns.

By performing the projection operation shown in Equation (7), the reference vector R _g is projected onto the orthogonal subspace spanned by the second sample feature signal (second feature signal, disturbance unit vector P _k ), and is orthogonalized. A reference vector S _g is obtained. The orthogonal reference vector S _g is obtained for each of δ = 28 samples with known concentrations. Since the orthogonalization reference vector S _g is orthogonal to the interference unit vector P _k , the interference component is hardly included.

The orthogonalization reference vector S _g is obtained from the measurement point i (1 ≦ i ≦ α) and the level number g (1 ≦ g ≦ δ), as shown in Equation (12), δ α rows and 1 column Expressed as a vector. The acquired orthogonal reference vector S _g is stored as the known concentration sample measurement signal data 532 in the storage unit 50 shown in FIG.

  The target component signal detection process (steps S13 to S17) is executed by the first target component signal detection unit 316 shown in FIG. 2 according to the target component signal detection program 514 which is a subroutine program of the calibration curve creation program 510. .

Subsequently, in step S18 shown in FIG. 5, the orthogonal processing (projection operation) to the orthogonal reference vectors S _g of the target component obtained by component analysis by component analyzing unit 314 shown in FIG. 2 (multivariate analysis )I do. As the multivariate analysis process, various analysis processes such as an independent component analysis process and a principal component analysis process can be used. In this embodiment, the independent component analysis process is performed. By performing component analysis with respect to orthogonal reference vectors S _g in step S18, as shown in Equation (13), the objective component feature amounts (object unit vector I is a unit signal of the first signal) are obtained (Step S19).

The target unit vector I is a space spanned by the entire disturbance unit vector P _k (in the example of FIG. 1, the first disturbance unit vector P ₁ and the _first disturbance unit vector P _k) even when the respective disturbance unit vectors P _k are not orthogonal to each other. 2 (a plane determined by the interference unit vector P ₂ ). Since there is one target component (glucose), there is also one target component feature amount extracted by the component analysis process. The target unit vector I is represented by a column vector of α rows and 1 column, as shown in Equation (14), corresponding to the measurement point i (1 ≦ i ≦ α).

  FIG. 6B shows an example of the spectrum of the target component feature amount (target unit vector I) obtained in step S19. In FIG. 6B, the horizontal axis corresponds to measurement points (i: 1 to α) corresponding to the wavelength of light, and the vertical axis is the spectral intensity. The acquired target unit vector I is stored as target component feature amount data 543 in the storage unit 50 shown in FIG.

Subsequently, in step S20 shown in FIG. 5, the calibration curve creation unit 310 shown in FIG. 2 performs inner product calculation that takes the inner product of the orthogonal reference vector _Sg and the target unit vector I as shown in the equation (15). Do. The orthogonal reference vector S _g is represented by a column vector of α rows and 1 column as shown in the equation (12), and a row vector of 1 row α column that is the transposed matrix and the equation (14). An inner product with the target unit vector I represented by a column vector of α rows and 1 column is calculated. By this inner product calculation, the size of the orthogonalization reference vector S _g is determined as a scalar quantity.

  Each level corresponding to the concentration of the aqueous glucose solution (g is an integer of 1 to δ, δ = 28 in the present example) is calculated as shown in Equation (15). The inner product value of is obtained. For example, the inner product value in the case of g = 1 where the concentration of the sample with known concentration (glucose aqueous solution) is 25 mg / dl is calculated as in Expression (16).

In the inner product calculation in step S20, the inner product of the measurement signal (reference vector R _g ) of the sample of known concentration and the target unit vector I may be taken instead of the orthogonalized reference vector S _g . Since the target unit vector I is orthogonalized with respect to the space spanned by the entire disturbance unit vector P _k by orthogonal calculation, the target unit vector I contains almost no component relating to the second signal (interference signal). Therefore, even taking the inner product value between the object unit vector I and the reference vector R _g, it can be determined the magnitude of the first signal with high accuracy.

  Subsequently, in step S21 shown in FIG. 5, a calibration curve showing the relationship between the physical quantity of the target component (in this example, a known glucose concentration) and the inner product value obtained in step S20 is converted into a calibration curve creation unit shown in FIG. Created in step 310. The calibration curve created in step S21 is stored as calibration curve data 545 in the storage unit 50 shown in FIG. FIG. 7 shows an example of a calibration curve created in step S21.

In FIG. 7, the horizontal axis represents the glucose concentration in Table 1, and the vertical axis represents the inner product value in Table 1. Each point indicated by a rhombus, a square, a triangle, a circle, etc. indicates the obtained calibration curve data, and approximate straight lines for these calibration curve data are also drawn. Fitted line obtained by the least square method, the equations of the obtained approximate straight line and the contribution ratio R ² is described in FIG. The contribution rate R ² is the square of the correlation coefficient R.

As shown in FIG. 7, the calibration curve data is on the approximate line, and the intercept of the mathematical expression of the approximate line passes through the origin in the error range, and the contribution ratio R ² = 1. Thus, it can be seen that the calibration curve data obtained in the present embodiment shows a very strong positive correlation between the concentration and the inner product value, regardless of the temperature of the sample with known concentration. This indicates that the method for quantifying the physical quantity of the target component according to the present embodiment is extremely accurate. Therefore, the calibration curve obtained in step S21 is also highly accurate, and by using the calibration curve of this embodiment, the target physical quantity can be quantified with high accuracy even for a measurement target whose physical quantity of the target component is unknown. become.

As a comparative example, FIG. 12A shows the result when the absorbance spectrum of a sample with a known concentration is quantified using an independent component analysis technique. In the example shown in FIG. 12A, four components J _{1 to} J ₄ are extracted, and the spectrum of each component is calculated. Of J _{1 to} J ₄ , the spectrum of the component J ₂ showing a waveform close to the target component glucose is assumed to be the target component feature amount data, and the component J ₂ and the reference vector R _g are used to create a calibration curve. FIG. 12 (b) is a diagram in which the inner product value is calculated and plotted. As shown in FIG. 12B, in the case of quantification using the method of independent component analysis, a straight line passing through the plot cannot be drawn, and a calibration curve cannot be created. This indicates that a small amount of the target component cannot be separated independently in the independent component analysis.

At the same time, as described in the present embodiment, an operation for orthogonalizing the reference vector R _g to the second feature signal (orthogonal subspace spanned by the entire disturbance unit vector P _k ) is performed, and the orthogonality of the target component is obtained. seeking criterion vector S _g, determined the desired unit vector I from the orthogonalization reference vector S _g, how to take an inner product calculation of the reference vector R _g and purpose unit vector I have very good determination of trace components Tells the story.

  Next, the concentration measurement process according to the first embodiment will be described. FIG. 9 is a flowchart showing the flow of the concentration measurement process according to the first embodiment. The concentration measurement process is a process for measuring the concentration of a target component which is a trace component from a measurement target sample whose concentration is unknown. In the concentration measurement process, a calibration curve for measuring the concentration of the target component is referred to. Therefore, before executing the concentration measurement process, it is necessary to previously create a calibration curve by executing the above-described calibration curve creation process.

  Steps S31 to S33 shown in FIG. 9 are steps for performing target component signal detection processing for detecting a target component signal from a measurement target sample whose concentration is unknown. First, in step S31, a measurement target sample with an unknown concentration is prepared. In the present embodiment, an aqueous solution with an unknown concentration of glucose as a target component is prepared as a measurement target sample.

  Subsequently, in step S32, a measurement signal of the measurement target sample (glucose aqueous solution whose concentration is unknown) is acquired. As the measurement signal, the absorbance spectrum of the sample to be measured is acquired as in the case of the sample having a known concentration. The measurement target sample contains glucose as a target component and water as a disturbing component. Therefore, the measurement signal includes a target component signal (first signal) and an interference component signal (second signal). The absorbance spectrum of the measurement target sample is acquired from the absorbance measurement device 6 via the measurement signal acquisition unit 20 illustrated in FIG. 2, and is stored in the storage unit 50 as the concentration measurement target measurement signal data 533.

  Subsequently, in step S33, the second target component signal detection unit 322 shown in FIG. 2 acquires the measurement vector M based on the absorbance spectrum data acquired from the measurement target sample (glucose aqueous solution whose concentration is unknown). The measurement vector M is represented by a column vector of α rows and 1 column as shown in Equation (17) by the measurement point i (1 ≦ i ≦ α).

Subsequently, in step S36, the concentration measurement unit 320 shown in FIG. 2 uses the measurement vector M acquired in step S33 and the target component feature amount data 543 in the storage unit 50 shown in FIG. To calculate the inner product with the target unit vector I stored as. By this inner product calculation, as shown in FIG. 1, the absolute value of the first vector M ₀ (that is, the vector indicating the target physical quantity component of the measurement vector M) is obtained as the scalar quantity m ₀ .

Here, since the target unit vector I which is a unit signal of the first signal (target signal) is orthogonalized with respect to the space spanned by the entire interference unit vector P _k by orthogonal calculation, the second signal (interference) Signal)). Since the inner product value m ₀ of the target unit vector I and the measurement vector M is taken, the magnitude of the first signal is obtained by removing the second signal from the measurement signal (measurement vector M) including the first signal and the second signal. Can be obtained with high accuracy.

Next, in step S37, the concentration measurement unit 320 shown in FIG. 2 uses the calibration curve data 545 stored in the storage unit 50 (the calibration shown in FIG. 7) as the concentration corresponding to the inner product value m ₀ obtained in step S36. The glucose concentration of the sample to be measured is determined (step S38). More specifically, in the calibration curve shown in FIG. 7, the value on the horizontal axis when the inner product value calculated in step S36 is taken as the value on the vertical axis is the glucose concentration to be obtained. Thereby, the density | concentration of glucose which is the target component contained in a measurement object sample can be measured.

As described above, according to the signal detection apparatus 1 according to the first embodiment, the signal detection method, the calibration curve creation method, and the quantification method, the second sample signal (the second sample signal) measured from the sample that does not include the target component. 2 sample vectors Q _j ) are subjected to independent component analysis processing to obtain a second feature signal (interference unit vector P _k ). Then, the target component feature quantity (target unit vector I) and the measurement signal (measurement vector M) orthogonalized by the projection operation with respect to the space orthogonal to the space spanned by the second feature signal (interference unit vector P _k ). since taking the inner product value m ₀ with it can be determined accurately the magnitude of the first signal and the first signal by removing the second signal from the measurement signal and a second signal. In addition, a calibration curve can be accurately created using detection of the target component feature amount. This makes it possible to accurately measure the concentration of the target component contained as a trace component in the measurement object.

Second Embodiment
Next, a second embodiment will be described. In the second embodiment, the configuration of the signal detection device 1 is the same as that of the first embodiment, and the signal detection method, the calibration curve creation method, and the quantification method are the same as the orthogonal calculation in the calibration curve creation process and the concentration measurement process. The second embodiment is almost the same as the first embodiment except that the Schmidt orthogonalization method is used. Here, the difference between the orthogonal calculation method according to the second embodiment and the first embodiment will be described.

In the calibration curve preparation process of the second embodiment, the quadrature processing of step S17 shown in FIG. 5 performs projection operation for projecting the orthogonal subspace spanned the reference vector R _g known concentration sample by interfering unit vector P _k Instead, the Gram-Schmidt orthogonalization method using the interference unit vector P _k is applied to the reference vector R _{g of the} sample of known concentration.

In the second embodiment, the interference component feature amount (interference unit vector P _k shown in FIG. 4B) extracted by the interference component feature amount extraction process is sequentially orthogonalized to form the intermediate vector W _k . k (k = 1 to γ) is the number of disturbing unit vectors as in the first embodiment. A first intermediate vector W ₁ obtained from the first disturbance unit vector P ₁ by the Gram-Schmidt orthogonalization method is expressed by Equation (19).

Then, a second intermediate vector W ₂ corresponding to the first intermediate vector W ₁ to the second interference unit vector P ₂ is orthogonal, first the first intermediate vector W ₁ and the second intermediate vector W ₂ The third intermediate vector W ₃ corresponding to the three disturbance unit vectors P ₃ is orthogonalized in order, for example. Accordingly, the intermediate vectors W _k are orthogonal to each other. An intermediate vector W _t (t = 2 to γ) corresponding to the disturbance unit vector P _t is expressed by Equation (20).

When the case where there are three independent components (γ = 3) is illustrated, the second intermediate vector W ₂ is expressed by the following equation (21) and the third intermediate vector W ₃ is expressed by the following equation (22). Is done.

The orthogonalization of the Gram-Schmidt orthogonalization reference vector S _g obtained in step S17 shown in FIG. 5 is expressed by equation (23). The orthogonal reference vector S _g is orthogonal to each intermediate vector W _k and, of course, is orthogonalized to the linear sum of each intermediate vector W _k .

Thereafter, similarly to the first embodiment, by performing the component analysis processing to orthogonal reference vectors S _g in step S18 shown in FIG. 5 (multivariate analysis), target component feature amount (object unit vector I ) Is obtained (step S19). Even when the disturbing unit vectors P _k are not orthogonal to each other, the intermediate vectors W _k are orthogonal to each other, and the target unit vector I is a space spanned by the entire disturbing unit vector P _k (that is, the intermediate unit Is orthogonal to the space spanned by the entire vector W _k .

  FIG. 10 is a diagram illustrating data obtained by the calibration curve creation process according to the second embodiment. FIG. 10A shows a spectrum of the target component feature quantity (target unit vector I) obtained in step S19 of the second embodiment. In FIG. 10A, the horizontal axis represents measurement points (i: 1 to α) corresponding to the wavelength of light, and the vertical axis represents spectral intensity. As shown in FIG. 10A, a spectrum similar to the spectrum obtained in the first embodiment shown in FIG. 6B was also obtained in the second embodiment.

Subsequently, in step S20 shown in FIG. 5, inner product calculation is performed to take the inner product of the orthogonalization reference vector _Sg and the target unit vector I. Then, a calibration curve is created based on the inner product value obtained by the inner product calculation (step S21).

FIG. 10B shows the calibration curve created in step S21 of the second embodiment. FIG. 10B shows the mathematical expression of the approximate line obtained by the least square method and the contribution ratio R ² for the calibration curve data. As shown in FIG. 10B, the calibration curve data is on the approximate line, and the intercept of the mathematical expression of the approximate line passes through the origin in the error range, and the contribution ratio R ² = 1. Therefore, it can be seen that, in the second embodiment, a calibration curve with extremely high accuracy was obtained as in the first embodiment.

  Thereafter, similarly to the first embodiment, the concentration of glucose, which is the target component contained in the measurement target sample, can be measured by executing steps S36 to S38 shown in FIG.

As described above, also in the second embodiment, the second feature signal (interference unit vector P _k ) is used for the measurement signal (reference vector R _g ) obtained from the reference sample whose physical quantity of the target component is known. Since the inner product value m ₀ of the objective component feature quantity (target unit vector I) and the measurement signal (measurement vector M) orthogonalized by applying the Gram-Schmidt orthogonalization method used is taken, the first signal and the first signal The magnitude | size of a 1st signal can be calculated | required accurately by removing a 2nd signal from the measurement signal containing 2 signals. This makes it possible to accurately measure the concentration of the target component contained as a trace component in the measurement object.

<Third Embodiment>
Next, a third embodiment will be described. In the third embodiment, the configuration of the signal detection device, the signal detection method, the calibration curve creation method, and the quantification method are the same as those in the first embodiment and the second embodiment, but their uses are different.

  That is, in the third embodiment, human body fluid is used as a measurement object, and the concentration of a specific trace component in the body fluid is measured. The body fluid can be, for example, blood, lymph, tissue fluid, sweat, urine and the like. The target component (trace component) to be measured for concentration can be blood sugar, cholesterol, or neutral fat when the body fluid is blood, and uric acid or sugar when the body fluid is urine.

  Also in the third embodiment, the disturbing component contained in the measurement object can be water. Thus, the interfering component sample is water. In addition, the samples with known concentrations need to be a plurality of samples having different concentrations of target components to be measured for concentration. Therefore, for example, body fluids collected at various times, places, and physical conditions in daily life are used as samples having known concentrations.

  Since water, which is a high proportion component contained in bodily fluids, has a characteristic that the spectral data (or the composition ratio of the characteristic amount) changes depending on the temperature, a plurality of samples with known concentrations in which the temperature of the collected bodily fluids is changed are further prepared. It is preferable. For example, if the body fluid that is the measurement target is blood and the target component is blood glucose, blood is collected before and after meals, before and after exercise, before and after going to bed, and the blood glucose level is measured with another measuring device. The sample with a known concentration can be obtained.

  In the third embodiment, the sample with known concentration is the body fluid actually collected. However, a sample simulating the body fluid may be created and used.

  The above-described embodiments merely show one aspect of the present invention, and can be arbitrarily modified and applied within the scope of the present invention. As modifications, for example, the following can be considered.

<Modification 1>
In the above-described embodiment, the signal detection device 1 includes the functions of the signal detection device, the calibration curve creation device, and the measurement device. However, the present invention is not limited to the above-described embodiment. If the interference component feature quantity extraction process and the calibration curve creation process are performed separately, the functions of the signal detection apparatus and the calibration curve creation apparatus are omitted from the signal detection apparatus 1 of the above-described embodiment, and the measurement apparatus specialized in the concentration measurement process Can also be provided. FIG. 11 is a block diagram illustrating the configuration of the measurement apparatus according to the first modification.

  As illustrated in FIG. 11, the measurement device 2 includes a processing unit 10A, a storage unit 50A, an operation unit 70, a display unit 80, and a communication unit 90. The processing unit 10A includes a measurement signal acquisition unit 20 and an arithmetic processing unit 30A. The arithmetic processing unit 30A includes a concentration measurement unit 320, and the calibration curve creation unit 310 is omitted from the arithmetic processing unit 30 of the above-described embodiment. The storage unit 50A stores a concentration measurement program 520 and omits a calibration curve creation program 510. Further, the storage unit 50A stores target component feature amount data 543 (target unit vector I) and calibration curve data 545 acquired in advance, and a concentration measurement object measurement signal calculated when the concentration measurement process is executed. Data 533 is stored.

  The measurement signal acquisition unit 20 executes Step S32 and Step S33 shown in FIG. The density measuring unit 320 executes step S36 shown in FIG. 9 using the target component feature quantity data 543, and then executes steps S37 and S38 shown in FIG. 9 using the calibration curve data 545 to determine the density. measure. The measured concentration is displayed on the display unit 80 or transferred to another electronic device (for example, a large-scale storage device such as a smartphone or a server) via the communication unit 90.

  According to the configuration of the measurement device 2 shown in the first modification, when the measurement object and the target component and the interference component included in the measurement object can be specified, the target component included in the measurement object at a lower cost. An apparatus capable of measuring the concentration of can be provided.

<Modification 2>
An example to which the present invention can be applied is not limited to the above-described embodiment. For example, an embodiment for measuring the concentration or amount of impurities, which are trace components that can be contained in an active pharmaceutical ingredient, an embodiment for detecting a frequency signal with a small amplitude that can be contained in radio waves, and an interference such as geomagnetism The present invention can also be applied to an embodiment for detecting a human magnetocardiogram, which is a trace component, in an environment with a component magnetism, an embodiment for detecting a minute abnormal amplitude signal buried in a blood pulse wave signal, and the like. Further, when a defective pixel is detected as a display inspection apparatus, the present invention can also be applied to a method for detecting a defective pixel signal from the display (interference component) of the entire screen. Furthermore, the present invention can be applied to an algorithm for detecting a fingerprint of a specific person from among many fingerprints.

<Modification 3>
In the above-described embodiment, as an example of the orthogonal calculation in the orthogonal processing in step S17, the projection calculation is used in the first embodiment, and the Gram-Schmidt orthogonalization method is used in the second embodiment. The orthogonal operation may be realized using another orthogonal method such as a conversion method.

<Modification 4>
In the above-described embodiment, independent component analysis is used for the component analysis processing in steps S05 and S18. However, the component analysis processing is not limited to independent component analysis as long as it is multivariate analysis. For example, it may be principal component analysis or Fourier transform. As described in detail in the first embodiment, since the target component is orthogonalized with respect to the entire disturbance component, there is no need for the individual interference vectors to be orthogonal to each other. However, since the interference vectors obtained by the independent component analysis are highly orthogonal to each other, the independent component analysis is most excellent in reducing errors.

  DESCRIPTION OF SYMBOLS 1 ... Signal detection apparatus, 2 ... Measuring apparatus, 6 ... Absorbance measuring apparatus, 10, 10A ... Processing part, 20 ... Measurement signal acquisition part (acquisition part), 30, 30A ... Arithmetic processing part (signal processing part), 310 ... Calibration curve creation unit, 320 ... concentration measurement unit, 50, 50A ... storage unit, 510 ... calibration curve creation program, 520 ... concentration measurement program, 531 ... interference component sample measurement signal data, 532 ... concentration known sample measurement signal data, 533 ... concentration measurement object measurement signal data, 541 ... disturbing component feature quantity data, 543 ... target component feature quantity data, 545 ... calibration curve data, 70 ... operation section, 80 ... display section, 90 ... communication section.

Claims (12)

Obtaining a measurement signal including a first signal and a second signal different from the first signal;
Taking an inner product value of the measurement signal and the unit signal of the first signal;
A signal detection method comprising:   The signal detection method according to claim 1, wherein the unit signal of the first signal is orthogonalized to the second signal by orthogonal calculation.   In the orthogonal calculation, a second feature signal obtained by performing multivariate analysis processing on a second sample signal obtained by measuring a sample including the component related to the second signal but not including the component related to the first signal is used. The signal detection method according to claim 2, further comprising:   The orthogonal calculation includes a projection calculation for projecting the measurement signal obtained from a reference sample whose physical quantity related to the first signal is known in a space orthogonal to the space spanned by the second feature signal. The signal detection method according to claim 3, wherein:   The orthogonal calculation applies a Gram-Schmidt orthogonalization method using the second feature signal to the measurement signal obtained from a reference sample whose physical quantity related to the first signal is known. The signal detection method according to claim 3.   The signal detection method according to claim 1, wherein a ratio of the first signal to the measurement signal is 1% or less.   A measurement signal obtained from a reference sample including a component related to the first signal and a component related to a second signal different from the first signal, and a physical quantity related to the first signal is known; and the first signal A calibration curve creating method, comprising: calculating an inner product value of the unit signal and generating a calibration curve indicating a relationship between the physical quantity related to the first signal and the inner product value.   A quantification method comprising a step of quantifying a physical quantity with reference to the inner product value obtained by the signal detection method according to claim 1.   The quantification method according to claim 8, wherein in the quantifying step, the inner product value and the calibration curve obtained by the calibration curve creation method according to claim 7 are referred to.   The method according to claim 8 or 9, wherein the physical quantity is a glucose concentration in blood. An acquisition unit that acquires a measurement signal obtained by measuring a measurement object including a component related to a first signal and a component related to a second signal different from the first signal;
A storage unit for storing a unit signal of the first signal;
An arithmetic processing unit that takes an inner product value of the measurement signal and the unit signal of the first signal;
A signal detection apparatus comprising: An acquisition unit that acquires a measurement signal obtained by measuring a measurement object including a component related to a first signal and a component related to a second signal different from the first signal;
A storage unit for storing a unit signal of the first signal;
An arithmetic processing unit that takes an inner product value of the measurement signal and the unit signal of the first signal, and quantifies a physical quantity using the inner product value;
A measuring device comprising:

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