CALIBRATION CURVE GENERATION METHOD, CALIBRATION CURVE GENERATION DEVICE, TARGET COMPONENT CALIBRATION METHOD, TARGET COMPONENT CALIBRATION DEVICE, ELECTRONIC DEVICE, GLUCOSE CONCENTRATION CALIBRATION METHOD, AND GLUCOSE CONCENTRATION CALIBRATION DEVICE

PRIORITY INFORMATION

The present invention claims priority to Japanese Patent Application No. 2014-207163 filed Oct. 8, 2014, which is incorporated herein by reference in its entirety.

BACKGROUND

1. Technical Field

The present invention relates to a technique of generating a calibration curve used for deriving the content of a target component related to a subject from observation data of the subject and a technique of acquiring the content of the target component related to the subject.

2. Related Art

In the related art, the use of independent component analysis for generating a calibration curve of a target component and calibration of a target component using the calibration curve has been suggested. For example, in Japanese Patent No. JP-A-2013-160574, the independent component analysis related to observation data of a plurality of samples whose components are known is performed at the time of generating a calibration curve and a simple regression calibration curve is determined from a relationship between the mixing ratio of acquired independent components and the amount of known target components. In addition, at the time of calibration of the target component, calibration of target components is performed using the independent components and the calibration curve acquired at the time of generating a calibration curve with respect to the observation data related to a target sample whose component amount is unknown. In addition, a technique of removing a fluctuation in the observation data by applying project on null space (PNS) or standard normal variate transformation (SNV) and performing calibration with high precision as first pre-processing of the observation data is described.

Despite these advances, however, there is problem with the known methods and systems because fluctuation cannot be completely removed and the precision cannot be sufficiently obtained in some cases in the pre-processing of the related art.

SUMMARY

An advantage of some aspects of the invention is to solve at least a part of the problems described above, and the invention can be implemented as the following aspects or application examples.

(1) A first aspect of the invention provides a calibration curve generation method of generating a calibration curve used for deriving a content of a target component of a subject from observation data of the subject. The calibration curve generation method includes acquiring an independent component matrix including independent components of each sample, by performing first pre-processing that includes normalizing the observation data, second pre-processing that includes whitening, and independent component analysis processing in order. Further, the same noise is added to the observation data related to the plurality of samples in the first pre-processing.

According to this method, by adding the same noise to the observation data related to the plurality of samples, it is possible to reduce influence of the fluctuation in the observation data compared to a case where noise is not added thereto and to improve the calibration precision.

In one embodiment, the calibration curve generating method includes (a) acquiring, by a computer, the observation data related to the plurality of samples of the subject; (b) acquiring, by the computer, the content of the target component related to each of the samples; (c) estimating, by the computer, a plurality of independent components when the observation data for each of the samples is divided into the plurality of independent components and acquiring a mixing coefficient corresponding to the target component for each of the samples based on the plurality of independent components; and (d) acquiring, by the computer, a regression formula of the calibration curve based on the content of the target component of the plurality of samples and the mixing coefficient for each of the samples. The (c) estimating of the plurality of independent components includes (i) acquiring, by the computer, an independent component matrix that includes the independent components of each of the samples; (ii) acquiring, by the computer, an estimated mixed matrix indicating a set of vectors that define a ratio of independent component elements for each of the independent components in each of the samples from the independent component matrix; (iii) acquiring, by the computer, a correlation with respect to the content of the target component of the plurality of samples for each of the vectors included in the estimated mixed matrix and selecting the vector determined that the correlation is the maximum as a mixing coefficient corresponding to the target component. The independent component matrix is acquired by the computer performing first pre-processing that includes normalizing the observation data, second pre-processing that includes whitening, and independent component analysis processing in order, and the computer adds the same noise to the observation data related to the plurality of samples in the first pre-processing.

According to this calibration curve generation method, a calibration curve for deriving the amount of target components included in the subject from the observation data of the subject is generated from the observation data acquired from each of the samples and the content of the target component related to the plurality of samples of the subject. For this reason, when the calibration curve is used, the content of the target component can be acquired with high precision even if the observation data of the subject is one piece of data. Accordingly, when the calibration curve is generated in advance according to the calibration curve generation method, only one piece of observation data related to the subject needs to be acquired during the calibration. As a result, the amount of target components can be acquired with high precision from one piece of observation data which is a measured value. In addition, since an estimated mixed matrix is acquired and then a vector highly correlative with the content of the target components of samples is drawn out from the estimated mixed matrix, a mixing coefficient with high estimation precision can be obtained. Moreover, the calibration precision can be improved by adding the same noise to the observation data related to the plurality of samples in the first pre-processing. Further, the computer described in the processes (a) to (d) may be the one same computer or a plurality of computers different from one another. That is, each of the processes may be performed through data communication of the plurality of computers. Similarly, when each of the processes is performed by a computer in the present specification, the computer may be the one same computer or a plurality of computers different from one another.

(2) A second aspect of the invention provides a calibration curve generation device which generates a calibration curve used for deriving a content of a target component of a subject from observation data of the subject. The calibration curve generation device includes an independent component matrix calculation unit that acquires an independent component matrix having independent components of each sample. The independent component matrix calculation unit acquires the independent component matrix by performing first pre-processing that includes normalizing the observation data, second pre-processing that includes whitening, and independent component analysis processing in order. Further, the independent component matrix calculation unit adds the same noise to the observation data related to the plurality of samples in the first pre-processing.

According to this calibration curve generation device, by adding the same noise to the observation data related to the plurality of samples, it is possible to reduce influence of the fluctuation in the observation data compared to a case where noise is not added thereto and to improve the calibration precision.

In one embodiment, the calibration curve generation device includes a sample observation data acquisition unit that acquires the observation data related to a plurality of samples of the subject, a sample target component amount acquisition unit that acquires the content of the target component related to each of the samples, a mixing coefficient estimation unit that estimates a plurality of independent components when the observation data for each of the samples is divided into the plurality of independent components and acquires a mixing coefficient corresponding to the target component for each of the samples based on the plurality of independent components, and a regression formula calculation unit that acquires a regression formula of the calibration curve based on the content of the target component of the plurality of samples and the mixing coefficient for each of the samples. The mixing coefficient estimation unit includes an independent component matrix calculation unit that acquires an independent component matrix including each of the independent components of each of the samples, an estimated mixed matrix calculation unit that acquires an estimated mixed matrix indicating a set of vectors that define a ratio of independent component elements for each of the independent components in each of the samples from the independent component matrix, and a mixing coefficient selection unit that acquires a correlation with respect to the content of the target component of the plurality of samples for each of the vectors included in the estimated mixed matrix and selecting the vector determined that the correlation is the maximum as a mixing coefficient corresponding to the target component. The independent component matrix calculation unit acquires the independent component matrix by performing first pre-processing that includes normalizing the observation data, second pre-processing that includes whitening, and independent component analysis processing in order. Further, the independent component matrix calculation unit adds the same noise to the observation data related to the plurality of samples in the first pre-processing.

According to this calibration curve generation device, a calibration curve for deriving the amount of target components included in the subject from the observation data of the subject is generated from the observation data acquired from each of the samples and the content of the target component related to the plurality of samples of the subject. For this reason, when the calibration curve is used, the content of the target component can be acquired with high precision even if the observation data of the subject is one piece of data. Accordingly, when the calibration curve is generated in advance according to the calibration curve generation method, only one piece of observation data related to the subject needs to be acquired during the calibration. As a result, the amount of target components can be acquired with high precision from one piece of observation data which is a measured value. In addition, since an estimated mixed matrix is acquired and then a vector highly correlative with the content of the target components of samples is drawn out from the estimated mixed matrix, a mixing coefficient with high estimation precision can be obtained. Moreover, the calibration precision can be improved by adding the same noise to the observation data related to the plurality of samples in the first pre-processing.

(3) A third aspect of the invention provides a target component calibration method of acquiring a content of a target component related to a subject. The target component calibration method includes acquiring a mixing coefficient with respect to the target component related to the subject based on observation data and calibration data related to the subject, first pre-processing that includes normalizing the observation data and second pre-processing that includes whitening are performed in order in the acquiring of the mixing coefficient, and noise is added to the observation data related to the subject in the first pre-processing.

According to this target component calibration method, by adding the noise to the observation data related to the subject, it is possible to reduce influence of the fluctuation in the observation data compared to a case where noise is not added thereto and to improve the calibration precision.

In one embodiment, a target component calibration method includes (a) acquiring, by a computer, observation data related to the subject; (b) acquiring, by the computer, calibration data including at least an independent component corresponding to the target component; (c) acquiring, by the computer, a mixing coefficient corresponding to the target component related to the subject based on the observation data and the calibration data related to the subject; and (d) calculating, by the computer, the content of the target component based on a constant of a regression formula indicating a relationship between the mixing coefficient and the content corresponding to the target component, prepared in advance, and the mixing coefficient acquired in (c) the acquiring of the mixing coefficient. The computer performs first pre-processing that includes normalizing the observation data and second pre-processing that includes whitening in this order in (c) the acquiring of the mixing coefficient, and the computer adds noise to the observation data related to the subject in the first pre-processing.

According to this target component calibration method, the content of the target component related to the subject can be acquired with high precision only by obtaining one piece of observation data related to the subject. Further, by adding noise to the observation data related to the subject, the calibration precision can be improved.

(4) A fourth aspect of the invention provides a target component calibration device which acquires a content of a target component related to a subject. The target component calibration device includes a mixing coefficient calculation unit that acquires a mixing coefficient with respect to the target component related to the subject. The mixing coefficient calculation unit performs first pre-processing that includes normalizing observation data and second pre-processing that includes whitening in order and adds noise to the observation data related to the subject in the first pre-processing.

According to this target component calibration device, by adding the noise to the observation data related to the subject, it is possible to reduce influence of the fluctuation in the observation data compared to a case where noise is not added thereto and to improve the calibration precision.

In one embodiment, a target component calibration device includes a subject observation data acquisition unit that acquires the observation data related to the subject, a calibration data acquisition unit that acquires calibration data including at least an independent component corresponding to the target component, a mixing coefficient calculation unit that acquires a mixing coefficient corresponding to the target component related to the subject based on the observation data and the calibration data related to the subject, and a target component amount calculation unit that calculates the content of the target component based on a constant of a regression formula indicating a relationship between the mixing coefficient and the content corresponding to the target component, prepared in advance, and the mixing coefficient acquired by the mixing coefficient calculation unit. The mixing coefficient calculation unit performs first pre-processing that includes normalizing the observation data and second pre-processing that includes whitening in order and adds noise to the observation data related to the subject in the first pre-processing.

According to this target component calibration device, the content of the target component related to the subject can be acquired with high precision only by obtaining one piece of observation data related to the subject. Further, by adding noise to the observation data related to the subject, the calibration precision can be improved.

In the method or device, the process using the project on null space and the normalization may be performed in order after the noise is added in the first pre-processing. According to this configuration, the influence of various fluctuations in the observation data can be effectively reduced and the calibration precision can be improved by both effects of the process of adding the noise and the process using the project on null space.

Alternatively, in the method or device, the addition of the noise and the normalization may be performed in order after the process using the project on null space is performed in the first pre-processing. According to this configuration, the influence of various fluctuations in the observation data can be effectively reduced and the calibration precision can be improved by both effects of the process of adding the noise and the process using the project on null space.

The invention can be implemented using various aspects other than the above-described aspects. For example, the invention can be implemented using an electronic device including the above-described device, a computer program implementing functions of respective units of the above-described device, and a recording medium which stores a computer program and is not temporary (non-transitory storage medium).

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be described with reference to the accompanying drawings, wherein like numbers reference like elements.

FIGS. 1A through 1F are an explanatory diagram showing an outline of a calibration curve generation process using independent component analysis;

FIGS. 2A through 2D are an explanatory diagram showing an outline of a calibration process of a target component;

FIGS. 3A through 3C are an explanatory diagram showing an outline of the independent component analysis using noise addition;

FIG. 4 is an explanatory diagram showing the results obtained by comparing calibration precision when noise is added with calibration precision when noise is not added;

FIG. 5 is a flowchart showing the calibration curve generation process;

FIG. 6 is an explanatory diagram showing a computer used for the calibration curve generation process;

FIG. 7 is a functional block diagram of a device to be used for the calibration curve generation process;

FIG. 8 is a functional block diagram showing an example of an internal configuration of an independent component matrix calculation unit;

FIG. 9 is an explanatory diagram schematically showing a measurement dataset DS1;

FIG. 10 is a flowchart showing a mixing coefficient estimation process;

FIG. 11 is an explanatory diagram describing an estimated mixed matrix ̂A;

FIG. 12 is a flowchart showing a calculation process of a regression formula;

FIG. 13 is a functional block diagram of a device to be used in the calibration process of the target component; and

FIG. 14 is a flowchart showing the calibration process of the target component.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, embodiments of the invention will be described in the following order.

A. Outline of calibration curve generation process and calibration process:

B. Calibration method using noise addition:

C. Calibration curve generation method:

D. Calibration method of target component:

E. Various algorithms and influence thereof:

F. Modification Examples:

In the present embodiment, the following abbreviations are used.

ICA: independent component analysis
SNV: standard normal variate transformation
PNS: project on null space
PCA: principal component analysis
FA: factor analysis

A. OUTLINE OF CALIBRATION CURVE GENERATION PROCESS AND CALIBRATION PROCESS

FIG. 1 is an explanatory diagram showing the outline of a calibration curve generation process using independent component analysis. FIG. 1(A) shows an example of observation data (also referred to as “measurement data”) related to a plurality of samples. The observation data relates to spectral absorbance and, for example, can be obtained through spectrometry of samples including a plurality of chemical components such as glucose. As a plurality of samples used in the calibration curve generation process, samples in which the content of target components (for example, glucose) is known are used. Alternatively, the content of the target components included in the plurality of samples may be measured by an analysis device.

When a calibration curve is generated, first, fluctuation or noise included in observation data is reduced by performing pre-processing on the observation data (FIG. 1(B)). As the pre-processing, for example, first pre-processing is performed that includes normalizing the observation data and second pre-processing is performed that includes whitening. In the first pre-processing, in order to reduce the influence of various fluctuation factors in the observation data (such as the state of a sample, a change in the measurement environment, and the like), it is preferable to perform projection on null space. Next, by performing independent component analysis processing on observation data after the pre-processing is finished, a plurality of independent components IC1, IC2, . . . , and the like (FIG. 1(C)) can be obtained. These independent components IC1, IC2, . . . , and the like are data corresponding to respective material components included in each sample and are statistically independent from each other. The observation data of each sample can be reproduced as a linear combination of these independent components IC1, IC2, . . . , and the like. In FIG. 1(C), only two independent components IC1 and IC2 are shown, but the number of independent components is appropriately set to an arbitrary number of 2 or more. Further, in the description of embodiments, the term “target component” indicates a material or a chemical component included in a sample and the term “independent component” indicates data having a data length which is the same as that of observation data of the sample.

Next, as shown in (D) to (F) in FIG. 1, the inner product between the observation data after the pre-processing is performed and an independent component (for example, IC1) is calculated. The observation data of FIG. 1(D) is the same as the data of FIG. 1(B). When the inner product between one piece of observation data and one independent component IC1 is acquired, one inner product value in regard to the observation data is obtained. Therefore, when the inner product between a plurality of pieces of observation data and the same independent component IC1 is calculated, a plurality of inner product values are obtained with respect to the same independent component IC1 in regard to the plurality of samples. FIG. 1(F) is a diagram in which an inner product value P in regard to the plurality of samples is set as the horizontal axis and a known content C of a target component included in the plurality of samples is set as the vertical axis and then plotted. If the independent component IC1 used in the inner product is an independent component corresponding to a target component, as shown in FIG. 1(F), the inner product value P is strongly correlated with the content C of the target component of each sample. Here, an independent component showing the strongest correlation among the plurality of independent components IC1, IC2, . . . , and the like obtained in FIG. 1(C) can be selected as an independent component corresponding to a target component. In the example of FIG. 1, the independent component IC1 is the independent component corresponding to a target component (for example, glucose) of calibration. The calibration curve is expressed as a straight line given by a simple regression formula “C=uP+v” of the plot in FIG. 1(F). Moreover, since the inner product value P is a value proportional to the content of the independent component IC1 in each sample, the inner product value P is also referred to as a “mixing coefficient.”

FIG. 2 is an explanatory diagram showing the outline of a calibration process of the target component using a calibration curve. The calibration process is performed using the independent component IC1 (FIG. 1(E)) of the target component obtained by the calibration curve generation process shown in (A) to (F) in FIG. 1 and the calibration curve (FIG. 1(F)). In the calibration process, first, observation data of a sample in which the content of a target component is unknown is obtained (FIG. 2(A)). Next, the pre-processing is performed on the observation data (FIG. 2(B)). It is preferable that the pre-processing is performed in the same manner as the pre-processing used at the time of generating a calibration curve. Further, the inner product value P in regard to the observation data is calculated by acquiring the inner product between the observation data after the pre-processing and the independent component IC1 (FIG. 2(B)). When the inner product value P is applied to the calibration curve, the content C of a target component can be determined. Moreover, the calibration curve generation process of FIG. 1 and the calibration process of FIG. 2 will be described greater detail below.

The simple regression formula of a calibration curve is represented by the following expression.

C=u·P+v (1)

Here, C represents the content of a target component, P represents the inner product (mixing coefficient), and u and v represent a constant.

In the present embodiment, as described below, the influence due to various fluctuations in measurement data (observation data) is reduced and the calibration precision is improved by adding noise to the measurement data in at least one of the pre-processing of FIG. 1 and the pre-processing of FIG. 2. As is well-known, the independent component analysis is a technique of performing analysis using statistical characteristics of measurement data. The inventor of the present application found that the influence due to the fluctuations in the measurement data due to the measurement conditions or the like can be reduced by adding noise to the measurement data. However, in the calibration curve generation process, when random noise is simply added to each of a plurality of pieces of the measurement data, unnecessary information is added and thus original information of the measurement data may be decreased. For this reason, it is devised that the statistical characteristics of measurement data can be adjusted without affecting the information of the measurement data and thus the calibration precision is improved by adding one same noise to all pieces of the measurement data. In addition, since a process is performed based on independency of data in the independent component analysis, the process can be performed without being influenced by noise addition as long as information of the statistics is stored even when the shape of the measurement data is changed due to the noise addition. Therefore, influence of the fluctuation in measurement data can be reduced by noise addition and the calibration precision can be improved.

B. CALIBRATION METHOD USING NOISE ADDITION

FIG. 3 is an explanatory diagram showing the outline of independent component analysis using noise addition. FIG. 3 (A) shows an example of measurement data (observation data) related to a plurality of samples. Here, glucose aqueous solutions having different concentrations are used as samples and the infrared absorption spectrum is measured, thereby obtaining measurement data. A plurality of pieces of measurement data overlap each other in FIG. 3(A) and a part thereof is shown by being expanded or magnified in FIG. 3(A).

FIG. 3(B) shows an example of the noise to be added to the plurality of pieces of measurement data. The noise has a data length (number of segments in the wavelength bandwidth) which is the same as that of the measurement data. The noise may be noise according to normal distribution or noise according to non-normal distribution. In the example of FIG. 3(B), a normal random number generated by a computer is used as noise.

In the calibration curve generation process shown in FIG. 1, the same noise is added to the plurality of pieces of measurement data, another pre-processing and independent component analysis are performed on measurement data after noise addition, and then a regression formula of the calibration curve is generated. The same noise is referred to as “fixed noise.”

For example, the noise addition is performed as follows. A noise vector Z according to normal distribution with an average μ (=0), a distribution σ2 (predetermined set value), and a data length N is generated using a computer program having a function of generating random numbers or a function. Here, N represents the data length (number of segments in the wavelength bandwidth) of measurement data. The term “noise vector Z” indicates one-dimensional data having N number of elements. Further, the value of the distribution σ2 of the noise vector Z may be experimentally adjusted along with the kind of sample or the waveform of measurement data. It is expected that the calibration precision is further improved through the adjustment.

Next, a fixed noise matrix Nz is generated using the noise vector Z.

N
_z
=Z×1^T (2)

Here, 1T represents a column vector in which n elements are all 1.0 and n represents the number of pieces of measurement data (that is, the number of samples). Accordingly, the fixed noise matrix Nz is a matrix with n rows and N columns, in which row vectors are all the same noise vector Z.

Next, a measurement data matrix X′ with noise is generated by adding the fixed noise matrix Nz to a measurement data matrix X.

X′=X+N
_z (3)

Here, the measurement data matrix X is a matrix with n rows and N columns in which each of n pieces of measurement data is set as a row vector. In the calibration curve generation procedures described below, the measurement data matrix X or the measurement data matrix X′ with noise is referred to as “spectral data X.” Another pre-processing or independent component analysis described in (A) to (C) in FIG. 1 is performed on the measurement data matrix X′ with noise.

FIG. 3(C) shows an example of a plurality of independent components obtained by following the procedures of (A) to (C) in FIG. 1 after the fixed noise in FIG. 3(B) is commonly added to a plurality of pieces of measurement data of FIG. 3(A). Here, three independent components of glucose, water, and noise are obtained. The wavelength bandwidth of the measurement data is set to the range of 1100 nm to 1250 nm. Further, the added fixed noise is a normal random number having an average μ of 0 and a distribution σ2 of 0.05. As the first pre-processing, a process according to project on null space (PNS) described below and standard normal variate transformation (SNV) are performed after noise addition. In addition, whitening, described further below, is performed as the second pre-processing. Further, independent component analysis (ICA) is performed on the measurement data on which the first pre-processing and the second pre-processing are performed. Three independent components shown in FIG. 3(C) are obtained by the processing procedures.

In the calibration process shown in (A) to (D) in FIG. 2, noise is added to the measurement data of the subject, another pre-processing and the inner product process (also referred to as a “mixing coefficient calculation process”) are performed on the measurement data after noise addition, and calibration is performed on the target component. The noise used in the calibration process has a data length N which is the same as that of the measurement data. The noise may be noise according to normal distribution or noise according to non-normal distribution. Further, as the noise used in the calibration process, noise which is different from the noise used in the calibration curve generation process may be used, but it is preferable to use fixed noise which is the same as the noise used in the calibration curve generation process. However, in both of the calibration curve generation process and the calibration process, when noise generated according to the same noise generation method is used (for example, a normal random number in which an average value and a distribution are respectively set as a predetermined value), calibration results close to the results obtained when the same noise is used can be obtained. In this case, noise addition may be omitted in the calibration process.

FIG. 4 is an explanatory diagram showing the results obtained by comparing calibration precision when noise is added with calibration precision when noise is not added. Here, three sample groups SG1 to SG3 are used. Each of the three sample groups SG1 to SG3 include a sample of approximately thirty glucose aqueous solutions having concentrations different from one another. At the time of calibration, the fixed noise shown in FIG. 3(B) is added to the measurement data in both of the calibration curve generation process and the calibration process. FIG. 4 shows the results obtained by comparing calibration precision when noise is added with the results of the calibration precision when noise is not added in each of the sample groups. Here, the following two values are used as index values indicating calibration precision.

SEP: prediction standard deviation between measured value (adjusted value) and calibration value of glucose concentration
R2: square of correlation coefficient R between measured value (adjusted value) and calibration value of glucose concentration

In the first sample group SG1, the calibration precision SEP is 32.75 mg/dL in a case where noise is not added, but improved to 21.13 mg/dL in a case where noise is added. Further, the correlation coefficient R2 is improved to 0.9814 from 0.9576. In the second sample group SG2, the calibration precision SEP is 22.01 mg/dL in a case where noise is not added, but improved to 14.14 mg/dL in a case where noise is added. Further, the correlation coefficient R2 is improved to 0.9916 from 0.9799. In the third sample group SG3, the calibration precision SEP is 22.13 mg/dL in a case where noise is not added, but improved to 11.33 mg/dL in a case where noise is added. Further, the correlation coefficient R2 is improved to 0.9946 from 0.9798.

In this manner, it can be confirmed that the calibration precision is improved by adding noise to the measurement data in the pre-processing.

C. CALIBRATION CURVE GENERATION METHOD

FIG. 5 is a flowchart showing the calibration curve generation method as an example of the embodiment of the invention. The calibration curve generation method is configured of five steps comprising processes 1 to 5. The respective processes 1 to 5 are performed in order. The respective processes 1 to 5 will be described in this order.

Process 1

The process 1 is a preparation process and performed by an operator. The operator prepares the same kind of a plurality of samples (for example, a glucose aqueous solution or a human body) in which the contents of target components are different from one other. In the example, n (n represents an integer of 2 or greater) samples are used.

Process 2

The process 2 is a process of measuring the spectrum and performed by the operator using a spectrometer. The operator measures the spectrum of spectral reflectance related to each of the samples by imaging each of the plurality of samples prepared in the process 1 using the spectrometer. The spectrometer is a known device in which light from an object to be measured passes through a spectroscope, the spectrum output from the spectroscope is received by an imaging surface of an imaging element, and thus the spectrum is measured. The spectrum of the spectral reflectance and the spectrum of the absorbance satisfy the relationship represented by the following expression.

[Absorbance]=−log₁₀[Reflectance] (4)

The spectrum of the measured spectral reflectance is transformed to the absorbance spectrum using the expression (4). The reason for conversion of the spectrum of the spectral reflectance to the absorbance spectrum is that a linear combination needs to be established in a mixed signal to be analyzed by the independent component analysis described below. In this instance, the linear combination related to the absorbance is established according to a Lambert-Beer's law. Therefore, in the process 2, the absorbance spectrum may be measured in place of the spectral reflectance spectrum. As the measurement results, absorbance distribution data showing characteristics with respect to the wavelength of the object to be measured is output. The absorbance distribution data is referred to as spectrum data.

Moreover, the spectral reflectance spectrum or the absorbance spectrum may be estimated from other measurement values instead of measuring these spectra using a spectroscope. For example, a sample is measured using a multi-band camera and the spectral reflectance or the absorbance spectrum may be estimated from an obtained multi-band image. As such an estimation method, a method described in JP-A-2001-99710 can be used.

Process 3

The process 3 is a process of measuring the content of target components and is performed by the operator. The operator performs chemical analysis on each of the plurality of samples prepared in the process 1 and measures the content of the target components (for example, the amount of glucose) in regard to each of the samples. In a case where the content of the target components in the samples prepared in the process 1 is known, the process 3 can be omitted.

Process 4

The process 4 is a process of estimating a mixing coefficient and is typically performed using a computer. FIG. 6 is an explanatory diagram showing a computer 100 and peripheral devices thereof used in the process 4 and the process 5 described below. The computer 100 is electrically connected to a spectrometer 200.

The computer 100 is a known device including a CPU 10 that performs various processes or control by executing computer programs, a memory 20 (storage unit) which stores a location of data, a hard disk drive 30 that stores computer programs and data, an input I/F 50, and an output I/F 60.

FIG. 7 is a functional block diagram of a device used in the processes 4 and 5. A device 400 includes a sample observation data acquisition unit 410, a sample target component amount acquisition unit 420, a mixing coefficient estimation unit 430, and a regression formula calculation unit 440. The mixing coefficient estimation unit 430 includes an independent component matrix calculation unit 432, an estimated mixed matrix calculation unit 434, and a mixing coefficient selection unit 436. Moreover, the sample observation data acquisition unit 410 and the sample target component amount acquisition unit 420 are implemented by, for example, the CPU 10 of FIG. 6 in cooperation with the input I/F 50 and the memory 20. The mixing coefficient estimation unit 430, the independent component matrix calculation unit 432, the estimated mixed matrix calculation unit 434, and the mixing coefficient selection unit 436 are implemented by, for example, the CPU 10 of FIG. 6 in cooperation with the memory 20. Further, the regression formula calculation unit 440 is implemented by, for example, the CPU 10 of FIG. 6 in cooperation with the memory 20. Moreover, these respective units can be implemented by other specific devices or a hardware circuit other than the computer shown in FIG. 6.

FIG. 8 is a functional block diagram showing an example of the internal configuration of the independent component matrix calculation unit 432. The independent component matrix calculation unit 432 includes a first pre-processing unit 450, a second pre-processing unit 460, and an independent component analysis processing unit 470. These three processing units 450, 460, and 470 acquire an independent component matrix (described below) by performing data processing (the absorbance spectrum in the present embodiment) in this order. The processing contents of these respective units will be described below.

The spectrometer 200 shown in FIG. 6 is used in the process 2. The computer 100 acquires the absorbance spectrum obtained from the spectral distribution measured by the spectrometer 200 in the process 2 as spectrum data through the input I/F 50 (corresponding to the sample observation data acquisition unit 410 in FIG. 7). In addition, the computer 100 acquires the content of the target components measured in the process 3 through an operation of a keyboard performed by the operator (corresponding to the sample target component amount acquisition unit 420 in FIG. 7).

As a result of acquisition of the spectrum data and the content of target components described above, a dataset (hereinafter, referred to as a “measurement dataset”) DS1 including the spectrum data and the content of target components is stored in the hard disk drive 30 of the computer 100.

FIG. 9 is an explanatory diagram schematically showing the observation dataset DS1 stored in the hard disk drive 30. As shown in the figure, the observation dataset DS1 has a data structure including the sample numbers B1, B2, . . . , Bn for identifying the plurality of samples prepared in the process 1, the target component contents C1, C2, . . . , Cn related to each sample, and spectrum data X1, X2, . . . , Xn related to each sample. In the measurement dataset DS1, the target component contents C1, C2, . . . , Cn and the spectrum data X1, X2, . . . , Xn are associated with the sample numbers B1, B2, . . . , Bn so as to understand which sample the content and the data relate to.

The CPU 10 performs a process of estimating a mixing coefficient, which is the operation of the process 4, by loading a predetermined program stored in the hard disk drive 30 in the memory 20 and executing the program. Here, the predetermined program can be downloaded using a network such as the Internet. In the process 4, the CPU 10 functions as the mixing coefficient estimation unit 430 of FIG. 7.

FIG. 10 is a flowchart showing the mixing coefficient estimation process performed by the CPU 10. When the process is started, the CPU 10 initially performs independent component analysis (Step S110).

The independent component analysis (ICA) is one of multi-dimensional signal analysis methods and is a technique of observing a mixed signal in which independent signals overlap each other under several different conditions and separating independent original signals based on the observation. When the independent component analysis is used, the spectrum of the independent component can be estimated from the spectrum data (observation data) obtained in the process 2 by grasping the spectrum data obtained in the process 2 as data mixed with m independent components (unknown) including target components.

In the present embodiment, the independent component analysis is performed by the three processing units 450, 460, and 470 shown in FIG. 8 by performing the processes in this order. The first pre-processing unit 450 includes a noise addition processing unit 451 that performs a noise addition process described in FIG. 3, standard normal variate transformation (SNV) 452, and project on null space (PNS) 454, and pre-processing using a part or all of these can be performed. The SNV 452 is a process of obtaining normalized data in which the average value is 0 and the standard deviation is 1 by subtracting the average value of data to be processed and dividing by the standard deviation. The PNS 454 is a process for removing base line fluctuation included in the data to be processed. In the spectrum measurement, variation referred to as the base line fluctuation, for example, fluctuation in the average values of data for each piece of measurement data is generated between data due to various factors. Therefore, before the independent component analysis processing is performed, it is preferable to remove the factors of fluctuation. The PNS can be used as pre-processing capable of removing arbitrary base line fluctuation. Further, the PNS is described in, for example, “Extracting Chemical Information from Spectral Data with Multiplicative Light Scattering Effects by Optical Path-length Estimation and Correction” written by Zeng-Ping Chen, Julian Morris, and Elaine Martin, 2006.

In addition, in a case where the SNV 452 is performed on the spectrum data obtained in the process 2 of FIG. 5, it is not necessary to perform a process using the PNS 454. Meanwhile, in a case where a process is performed using the PNS 454, it is preferable to perform any normalization process (for example, the SNV 452) thereafter. The order of processes performed by the three processing units 451, 452, and 454 included in the first pre-processing unit 450 is arbitrary. For example, first, the noise addition process is performed, processing using the PNS is performed, and then the SNV may be performed with respect to the measurement data before the first pre-processing is performed. Alternatively, first, the process using the PNS is performed, the noise addition process is performed, and then the SNV may be performed. Alternatively, first, the process using the PNS is performed, the SNV is performed, and then the noise addition process may be performed. In these all cases, the influence of various fluctuations included in the observation data can be effectively reduced, and the calibration precision can be improved because of the effects of both of the noise addition and the PNS. The process order of the three processing units 451, 452, and 454 can be appropriately and experimentally selected according to the kind of measurement data (that is, the kind of sample). Further, in regard to a sample containing glucose, it can be confirmed that the calibration precision is greatly improved by firstly performing the noise addition process, performing the process using the PNS, and then performing the SNV as described in FIGS. 3A to 4.

Moreover, as the first pre-processing, a process other than the noise addition, the SNV, and the PNS may be performed. Preferably, any normalization process is performed in the first pre-processing, but the normalization process may be omitted. Hereinafter, the first pre-processing unit 450 is referred to as the “normalization processing unit.” The contents of these two processes 452 and 454 will be described below. Further, in a case where the data to be processed provided for the independent component matrix calculation unit 432 is normalized data, the first pre-processing may be omitted.

In the second pre-processing unit 460, pre-processing using any one of principal component analysis (PCA) 462 and factor analysis (FA) 464 can be performed. In addition, as the second pre-processing, processes other than PCA or FA may be performed. Hereinafter, the second pre-processing unit 460 is referred to as the “whitening processing unit.” In a general technique of the ICA, dimension compression of data to be processed or decorrelation is performed as the second pre-processing. Since a transformation matrix to be acquired by the ICA is restricted to an orthogonal transformation matrix by the second pre-processing, the calculation amount of the ICA can be reduced. Such second pre-processing is referred to as “whitening,” and the PCA is used in many cases. However, in a case where random noise is included in the data to be processed, an error may be generated in the results of the PCA due to the influence of the random noise. Here, in order to reduce the influence of the random noise, it is preferable to perform whitening using the FA having robustness with respect to noise in place of the PCA. The second pre-processing unit 460 of FIG. 8 can perform whitening by selecting any one of the PCA and the FA. The contents of the two processes 462 and 464 will be described below. Further, the whitening process may be omitted.

The independent component analysis processing unit (ICA processing unit) 470 estimates the spectrum of independent components by performing the ICA with respect to the spectrum data to which the first pre-processing and the second pre-processing are applied. The ICA processing unit 470 can perform analysis using any one of a first processing 472 using a kurtosis as an independence index and a second process 474 using β divergence as the independence index. As the index for separating independent components from each other, the ICA uses higher order statistics representing independence of separated data as the independence index. The kurtosis is a typical independence index. However, in a case where an outlier such as spike noise is present in the data to be processed, the statistics including the outlier are calculated as the independence index. For this reason, an error is generated between original statistics related to the data to be processed and calculated statistics and this causes degradation of separation precision. Here, in order to reduce the influence from the outlier in the data to be processed, it is preferable to use an independence index which is unlikely to be affected by the outlier. It is possible to use the β divergence as the independence index having such characteristics. The contents of the kurtosis and the β divergence will be described below. Further, as the independence index of the ICA, an index other than the kurtosis and the β divergence may be used.

The contents of a typical process of the independent component analysis will be described below. It is assumed that a spectrum S (hereinafter, also simply referred to as an “unknown component”) of m unknown components (sources) is provided as a vector of the expression (5) below and n pieces of spectrum data X obtained in the process 2 is provided as a vector in the expression (6) below. Further, respective elements (S1, S2, . . . , Sm) included in the expression (5) are vectors (spectra). That is, the element S1 is represented by the expression (7). Elements (X1, X2, . . . , Xn) included in the expression (6) are vectors and, for example, the element Xj is represented by the expression (8). The suffix j of the element Xj represents the number of wavelength bandwidths measuring a spectrum. Moreover, an element number m of the spectrum S of an unknown component represents an integer of 1 or greater and is empirically or experimentally determined in advance according to the kind of sample.

S=[S₁, S₂, . . . , S_n]^T (5)

X=[X₁, S₂, . . . , X_n]^T (6)

S₁={S₁₁, S₁₂, . . . , S₁₁} (7)

X₁={X₁₁, X₁₂, . . . , S₁₁} (8)

The respective unknown components are statistically independent. The unknown component S and the spectrum data X satisfy a relationship of the following expression.

X=A·S (9)

In the expression (9), A represents a mixed matrix and can be represented by the expression (10) below. Further, the character “A” here needs to be written in bold as shown in the expression (10), but a normal character is used here because of the restriction of characters used in the specification. Hereinafter, similarly, other bold characters indicating matrixes are written in normal characters.

$\begin{matrix} A = (\begin{matrix} a_{i 1} & \dots & a_{1 m} \\ ⋮ & ⋱ & ⋮ \\ a_{n 1} & \dots & a_{mn} \end{matrix}) & (10) \end{matrix}$

A mixing coefficient aij included in the mixed matrix A indicates a degree of contribution of an unknown component Sj (j=1 to m) to spectrum data Xi (i=1 to n) which is observation data.

In a case where the mixed matrix A is known, a least square solution of the unknown component S can be simply acquired as A+·X using a pseudo inverse matrix A+ of A. However, in the present embodiment, since the mixed matrix A is unknown, the unknown component S and the mixed matrix A need to be estimated from only the observation data X. That is, as represented by the expression (11) below, a matrix (hereinafter, referred to as an “independent component matrix”) Y indicating a spectrum of independent components is calculated using a separation matrix W of m×n from only the observation data X. As an algorithm acquiring the separation matrix W in the expression (11) below, various algorithms such as Infomax, fast independent component analysis (FastICA), and joint approximate diagonalization of eigenmatrices (JADE) can be employed.

Y=W·X (11)

The independent component matrix Y corresponds to an estimated value of the unknown component S. Accordingly, the expression (12) below can be obtained and the expression (13) below can be obtained by transforming the expression (12).

X=Â·Y (12)

Â=X·Y
⁺ (13)

In the expression, ̂A represents an estimated mixed matrix of A and Y+ indicates a pseudo inverse matrix of Y.

The estimated mixed matrix ̂A (this notation is made because of the restriction of characters used in the specification and actually means the character with a symbol on the left side of the expression (13), and the same applies to other characters) obtained by the expression (13) can be represented by the following expression.

$\begin{matrix} \hat{A} = (\begin{matrix} {\hat{a}}_{i 1} & \dots & {\hat{a}}_{1 m} \\ ⋮ & ⋱ & ⋮ \\ {\hat{a}}_{n 1} & \dots & {\hat{a}}_{mn} \end{matrix}) & (14) \end{matrix}$

In Step S110 of FIG. 10, the CPU 10 performs a process of acquiring the separation matrix W described above. Specifically, the spectrum data X for each of the samples obtained in the process 2 and stored in the hard disk drive 30 in advance is set as an input, and the separation matrix W is acquired using any algorithm from among Infomax, FastICA, and JADE described above based on the input. Further, as shown in FIG. 8 described above, it is preferable that the first pre-processing unit 450 performs a normalization process and the second pre-processing unit 460 performs a whitening process as the pre-processing of the independent component analysis.

After the process of Step S110 is finished, the CPU 10 performs a process of calculating the independent component matrix Y based on the separation matrix W and the spectrum data X for each sample obtained in the process 2 and stored in the hard disk drive 30 in advance (Step S120). The calculation process is a process of performing calculation according to the expression (11) above. In the processes of Steps S110 and 120, the CPU 10 functions as the independent component matrix calculation unit 432 of FIG. 7.

Next, the CPU 10 performs a process of calculating the estimated mixed matrix ̂A based on the spectrum data X for each sample stored in the hard disk drive 30 in advance and the independent component matrix Y calculated in Step S120 (Step S130). The calculation process is a process of performing calculation according to the expression (13) above.

FIG. 11 is an explanatory diagram for describing the estimated mixed matrix ̂A. In a table TB, each of the sample numbers B1, B2, . . . , Bn is arranged in the vertical direction and each of the elements Y1, Y2, . . . , Ym of the independent component matrix Y (hereinafter, referred to as an “independent component element”) is arranged in the horizontal direction. Elements in the table TB determined from the sample number B1 (i=1 to n) and the independent component element Yj (j=1 to m) are the same as the elements ̂aij (see the expression (14)) of the estimated mixed matrix ̂A. From the table TB, it is understood that the elements ̂aij of the estimated mixed matrix ̂A represent the ratios of respective independent component elements Y1, Y2, . . . , Ym of samples. A target component order k shown in FIG. 11 will be described below. In the process of Step S130, the CPU 10 functions as the estimated mixed matrix calculation unit 434 of FIG. 7.

The estimated mixed matrix ̂A is obtained by the processes up to Step S130. That is, an element (estimated mixing coefficient) ̂aij of the estimated mixed matrix ̂A is obtained. That is, the estimated mixing coefficient ̂aij corresponds to the inner product value P calculated in (D) to (F) in FIG. 1 in the example of (A) to (F) in FIG. 1. Subsequently, the process proceeds to Step S140.

In Step S140, the CPU 10 acquires a correlation (degree of similarity) between target component contents C1, C2, . . . , Cn measured by the process 3 and components of respective columns included in the estimated mixed matrix ̂A (hereinafter, referred to as a mixing coefficient vector ̂α) calculated in Step S130. Specifically, a correlation between the target component content C (C1, C2, . . . , Cn) and the mixing coefficient vector ̂α1 in the first column (̂a11, ̂a21, . . . , ̂an1) is acquired, a correlation between the target component content C (C1, C2, . . . , Cn) and the mixing coefficient vector ̂α2 in the second column (̂a12, ̂a22, . . . , ̂an2) is acquired, and then a correlation with respect to the target component content C related to each column is sequentially acquired.

As the index indicating the degree of the correlation, a correlation coefficient R following the expression below can be used. The correlation coefficient R is referred to as Pearson's product-moment correlation coefficient.

$\begin{matrix} R = \frac{\sum_{i = 1}^{n} (C_{i} - \overline{C}) ({\hat{a}}_{k} - {\overline{\hat{α}}}_{k})}{\sqrt{\sum_{i = 1}^{n} {(C_{i} - \overline{C})}^{2}} \sqrt{\sum_{i = 1}^{n} {({\hat{a}}_{k} - {\overline{\hat{α}}}_{k})}^{2}}} & (15) \end{matrix}$

−C and −̂α_keach independently represent a target component amount and an average value of elements of a vector ̂α_k.

As a result of Step S140 in FIG. 10, correlation coefficients Rj (j=1, 2, . . . , m) for each independent component (independent component spectrum) Yj are obtained. Subsequently, the CPU 10 specifies a coefficient with the maximum correlation, that is, a coefficient whose correlation is close to 1 from among the correlation coefficients Rj obtained in Step S140. Next, a column vector ̂α obtained by the maximum correlation coefficient R is selected from the estimated mixed matrix ̂A (Step S150).

The selection in Step S150 is to select one column from among a plurality of columns when considered with the table TB of FIG. 11. Elements of the selected column are mixing coefficients of independent components corresponding to target components. As a result of the selection, mixing coefficient vectors ̂αk (̂α1k, ̂α2k, . . . , ̂αnk) are obtained. Here, k represents an integer of 1 to m. Further, the value of k may be temporarily stored in the memory 20 as a target component order indicating that which number of the independent component corresponds to the target component. Elements ̂αk, ̂α2k, . . . , ̂αnk included in the mixing coefficient vectors ̂αk correspond to “the mixing coefficient corresponding to the target component.” In addition, in the example of FIG. 11, the “target component order k=2” indicates “mixing coefficient vectors ̂α2=(̂α12, ̂α22, . . . , ̂αn2)” corresponding to the independent component Y. Further, in the present specification, the term “order” means a “value indicating the position in a matrix.” In the processes of Step S140 and S150, the CPU 10 functions as the estimated coefficient selection unit 436 of FIG. 7. After the process of Step S150 is performed, the CPU finishes the process of calculating the mixing coefficient. As a result, the process 4 is completed and then the process proceeds to the process 5.

Process 5

The process 5 is a process of calculating a regression formula and performed using the computer 100 in the same manner as in the process 4. In the process 5, the computer 100 performs the process of calculating a regression formula of a calibration curve. Moreover, the process 5 may be performed after the data up to the process 4 is moved to another computer or a device.

FIG. 12 is a flowchart showing the process of calculating a regression formula performed by the CPU 10 of the computer 100. When the process is started, the CPU 10 calculates a regression formula based on the target component contents C (C1, C2, . . . , Cn) measured in the process 3 and the mixing coefficient vectors ̂αk (̂α1k, ̂α2k, . . . , ̂αnk) selected in Step S150 (Step S210). The regression formula can be represented by the expression (16) below. In Step S210, constants u and v in the expression (16) are acquired.

C=u·P+v (16)

Here, C represents a target component content, P represents an inner product between measurement data and an independent component, and u and v represent an integer.

After the process in Step S210 is performed, the CPU 10 stores the constants u and v of the regression formula acquired in Step S210 and an independent component Yk corresponding to a target component order k (FIG. 11) determined in Step S150 in the hard disk drive 30 as a dataset DS2 for calibration (Step S220). Next, the CPU 10 exits to “return” and the process of calculating the regression formula is temporarily finished. As a result, the regression formula of a calibration curve can be acquired and the calibration curve generation method shown in FIG. 5 is finished. In the processes of Steps S210 and S220, the CPU 10 functions as the regression formula calculation unit 440 of FIG. 7.

D. CALIBRATION METHOD OF TARGET COMPONENT

The calibration method of a target component will be described below. The subject is set to be configured of the same components as those of a sample used when a calibration curve is generated. Specifically, the calibration method of a target component is performed using a computer. Moreover, the computer here may be the computer 100 used when a calibration curve is generated or another computer.

FIG. 13 is a functional block diagram of a device used when calibration is performed on a target component. A device 500 includes a subject observation data acquisition unit 510, a calibration data acquisition unit 520, a mixing coefficient calculation unit 530, a target component amount calculation unit 540, and a non-volatile storage device 550. The mixing coefficient calculation unit 530 includes a pre-processing unit 532. The pre-processing unit 532 has functions of both of the first pre-processing unit 450 and the second pre-processing unit 460 of FIG. 8. Since the mixing coefficient calculation unit 530 has a function of performing an inner product operation described in (A) to (C) in FIG. 2, the mixing coefficient calculation unit can be referred to as an “inner product operation unit.” The subject observation data acquisition unit 510 is implemented by, for example, the CPU 10 of FIG. 6 being in cooperation with the input I/F 50 and the memory 20. The calibration data acquisition unit 520 is implemented by, for example, the CPU 10 of FIG. 6 being in cooperation with the memory 20 and the hard disk drive 30. The mixing coefficient calculation unit 530 and the target component amount calculation unit 540 are implemented by, for example, the CPU 10 of FIG. 6 being in cooperation with the memory 20. The calibration dataset DS2 (independent components and constants u and v of a regression formula) is stored in the non-volatile storage device 550. Further, the device in FIG. 13 may be mounted as another device or an electronic device which is different from the computer of FIG. 6. In this case, it is preferable that the device of FIG. 13 or the electronic device including the device includes a spectrometer.

FIG. 14 is a flowchart showing the target component calibration process performed by the CPU 10 of the computer 100. The target component calibration process is implemented by the CPU 10 loading a predetermined program, which is stored in the hard disk drive 30, in the memory 20 and executing the program. First, the CPU 10 performs a process of imaging the subject using a spectrometer (Step S310). The imaging of the subject in Step S310 can be performed in the same manner as in the process 2 and, as a result, an absorbance spectrum Xp of the subject is obtained. It is preferable that the spectrometer used in the calibration process is the same kind of device as that used for generation of a calibration curve for the purpose of suppressing errors. In order to further suppress errors, it is more preferable that the spectrometer used in the calibration process is the same as that used for generation of a calibration curve. In addition, similar to the process 2 of FIG. 5, instead of measuring the spectral reflectance spectrum or the absorbance spectrum with a spectroscope, these spectra may be estimated from other measurement values. The spectrum Xp of the absorbance of a subject obtained when the subject is imaged once is represented by a vector as in the expression below.

X_p={X_p1, X_p2, . . . , X_p3} (17)

In the process of Step S310, the CPU 10 functions as the subject observation data acquisition unit 510 of FIG. 13. Next, the CPU 10 acquires the calibration dataset DS2 from the hard disk drive 30 (the non-volatile storage device 550 of FIG. 13) and stores the acquired calibration dataset DS2 in the memory 20 (Step S320). In the process of Step S320, the CPU 10 functions as the calibration data acquisition unit 520 of FIG. 13.

After the process of Step S320 is performed, pre-processing is performed with respect to the observation data (absorbance spectrum Xp) of the subject obtained in Step S310 (Step S330). As the pre-processing, it is preferable to perform the same processing as that (that is, the normalization process performed by the first pre-processing unit 450 and the whitening process performed by the second pre-processing unit 460) used in the process 4 (more specifically, Step S110 of FIG. 10) of FIG. 5 at the time when a calibration curve is generated.

Thereafter, the CPU 10 acquires the inner product value P between the independent component included in the calibration dataset DS2 and the pre-processed spectrum (pre-processed observation data) obtained in Step S330 (Step S340). The process of Step S340 corresponds to the process of (B) and (C) in FIG. 2 described above. Further, the inner product value P corresponds to the mixing coefficient calculated in Step S130 of FIG. 10 at the time when a calibration curve is generated. Therefore, the inner product value P is also referred to as a “mixing coefficient.”

In the processes of Steps S330 and S340, the CPU 10 functions as the mixing coefficient calculation unit 530 of FIG. 13.

Next, the CPU 10 acquires the content C of the target component by reading the constants u and v of the regression formula included in the calibration dataset DS2 from the hard disk drive 30 (the non-volatile storage device 550 in FIG. 13) and substituting the constants u and v and the inner product value P obtained in Step S340 for the right side of the expression (16) above (Step S350). At this time, the constants u and v may be adjusted if necessary. The content C can be acquired as the unit volume or the mass of the target component per unit mass (for example, per 1 dL or per 100 g) of the subject. In the process of Step S350, the CUP 10 functions as the target component amount calculation unit 540 of FIG. 13. Next, the CPU 10 exits to “return” and finishes the target component calibration process.

Further, in the present embodiment, the content C acquired in Step S350 is set as the content of the target component of the subject, but, alternatively, the content C acquired in step S350 is corrected by a normalization coefficient used for normalization in Step S330 and then the corrected value may be used as the content to be acquired. Specifically, an absolute value (gram) of the content may be acquired by multiplying the content C by a standard deviation. According to the configuration, depending on the kind of target component, it is possible to make the content C have improved precision.

According to the above-described calibration method, the content of the target component can be acquired with high precision from one spectrum which is a measured value of the subject.

E. VARIOUS ALGORITHMS AND INFLUENCE

Hereinafter, various algorithms used in the first pre-processing unit 450, the second pre-processing unit 460, and the independent component analysis processing unit 470 shown in FIG. 8 will be sequentially described.

E-1. First Pre-Processing (Normalization Process Using SNV and PNS):

As the first pre-processing performed by the first pre-processing unit 450, the noise addition process, standard normal variate transformation (SNV), and project on null space (PNS) can be used. The contents and the effects of the noise addition process have been described in FIGS. 3 and 4.

The SNV is given by the expression below.

$\begin{matrix} z = \frac{x - x_{are}}{σ} & (18) \end{matrix}$

Here, z represents processed data, x represents data to be processed (in the present example, the absorbance spectrum), xave represents an average value of data x to be processed, and σ represents a standard deviation of data x to be processed. As a result of the standard normal variate transformation, normalized data z in which the average value is 0 and the standard deviation is 1 is obtained.

When the PNS is performed, it is possible to decrease the base line fluctuation included in the data to be processed. In measurement of the data to be processed (the absorbance spectrum in the present embodiment), variation referred to as the base line fluctuation, for example, fluctuation in the average values of data for each piece of measurement data is generated between data due to various factors. Therefore, before the independent component analysis (ICA) is performed, it is preferable to remove the factors of fluctuation. The PNS can be used as pre-processing capable of decreasing the base line fluctuation in the data to be processed. Particularly, in regard to measurement data of an absorbance spectrum including an infrared region and a reflected light spectrum, since such base line fluctuation is large, application of the PNS is advantageous. Hereinafter, a principle in which the base line fluctuation included in data obtained by measurement (also simply referred to as “measurement data x”) is removed by the PNS will be described. Further, as a typical example, a case where the measurement data is an absorbance spectrum including an infrared region or is a reflected light spectrum will be described. In this case, in regard to other kinds of measurement data (for example, audio data or the like), the PNS can be applied in the same manner as described above.

In an ideal system, the measurement data x (data x to be processed) is represented by the expression below using m (m is an integer of 2 or greater) independent components si (i=1 to m) and respective mixing ratios ci.

$\begin{matrix} \begin{matrix} x = \sum_{i - 1}^{m} c_{i} s_{i} \\ = A \cdot s \end{matrix} & (19) \end{matrix}$

Here, A represents a matrix (mixed matrix) formed by a mixing ratio ci.

In the independent component analysis (ICA), the process is performed on the assumption of this model. However, various fluctuation factors (the state of a sample or a change in the measurement environment) are present in actual measurement data. For this reason, a model inconsideration of the fluctuation factors, a model expressing the measurement data x using the expression below can be considered.

$\begin{matrix} x = b \sum_{i = 1}^{m} c_{i} s_{i} + aE + b_{1} f_{1} (λ) + b_{2} f_{2} (λ) + \dots b_{g} f_{g} (λ) + ɛ & (20) \end{matrix}$

Here, the parameter b represents the amount of fluctuation in an amplitude direction of a spectrum, the parameter a represents the amount of constant base line fluctuation E (also referred to as “fluctuation in the average value”), the parameters b1, . . . , bg represent the amount of g (g is an integer of 1 or greater) fluctuations f1 (λ) to fg(λ) depending on the wavelength, and ε represents a fluctuation component other than those described above. Further, the constant base line fluctuation E is given by “E={1, 1, 1, . . . , 1}T (T on the right side represents transposition) and is a constant vector whose data length is equivalent to a data length N (the number of segments in the wavelength bandwidth) of the measurement data x. As a variable λ indicating a wavelength, N integers from 1 to N are used. That is, the variable λ corresponds to an ordinal number of the data length N (N is an integer of 2 or greater) of the measurement data x. At this time, fluctuations f1(λ), . . . , fg(λ) depending on the wavelength are given by “f1(λ)={f1(1), f1(2), . . . , f1(N)}T, . . . , fg(λ)={fg(1), fg(2), . . . , fg(N)}T.” Since these fluctuations are error factors in the ICA or calibration, it is desirable to remove the fluctuations in advance.

As the function f(λ), it is preferable to use one variable function in which the function f(λ) value monotonically increases according to an increase of λ in the range in which the value of λ is 1 to N. In the project on null space, the fluctuation included in the measurement data can be further reduced when a function other than an exponential function λα of λ in which an exponent α is an integer is used.

As a method of determining the functional types of a preferable function f(λ) and the number g thereof, experimental trial and errors may be employed or existing parameter estimation algorithms (for example, an expectation maximization method (EM) algorithm) may be used.

In the PNS, when a space formed of the above-described respective base line fluctuation component E and f1(λ) to fg(λ) is considered and the measurement data x is projected to a space (null space) without having these fluctuation components, data with reduced base line fluctuation component E and f1(λ) to fg(λ) can be obtained. As a specific operation, data z processed by the PNS is calculated by the expression below.

$\begin{matrix} z = (1 - {PP}^{+}) x = b \sum_{i = 1}^{m} c_{i} k_{i} + ɛ^{*} P = {1, f_{1} (λ), f_{2} (λ) \dots f_{g} (λ)} & (21) \end{matrix}$

Here, P+ represents a pseudo inverse matrix of P. ki is obtained by projecting a constituent component si of the expression (20) to a null space without having fluctuation components. Further, ε* is obtained by projecting a fluctuation component ε of the expression (20) to a null space.

Moreover, when normalization (for example, the SNV) is performed after the process of the PNS, it is possible to remove the influence of a fluctuation amount b in the amplitude direction of the spectrum in the expression (20).

When the ICA is performed on data pre-processed by the PNS, the obtained independent component becomes an estimated value of a component ki of the expression (21) and becomes a value different from the true constituent component si. However, since the mixing ratio ci is not changed from the original value in the expression (20), the mixing ratio ci does not affect the calibration process ((A) to (D) in FIG. 2 and FIG. 14) using the mixing ratio ci. In this manner, when the PNS is performed as the pre-processing of the ICA, since the true constituent component si cannot be obtained by the ICA, an idea in which the PNS is applied to the pre-processing of the ICA is not normally generated. Meanwhile, in the present embodiment, since the calibration process is not affected even when the PNS is performed as the pre-processing of the ICA, when the PNS is performed as the pre-processing, it is possible to perform calibration with high precision.

Further, the details of the PNS are described in “Extracting Chemical Information from Spectral Data with Multiplicative Light Scattering Effects by Optical Path-length Estimation and Correction” written by Zeng-Ping Chen, Julian Morris, and Elaine Martin, 2006.

E-2. Second Pre-Processing (Whitening Process Using PCA/FA)

As the second pre-processing performed by the second pre-processing unit 460, the principal component analysis (PCA) and the factor analysis (FA) can be used.

In a general technique of the ICA, dimension compression of data to be processed or decorrelation is performed as the pre-processing. Since a transformation matrix to be acquired by the ICA is restricted to an orthogonal transformation matrix by the pre-processing, the calculation amount of the ICA can be reduced. Such pre-processing is referred to as “whitening,” and the PCA is used in many cases. The whitening using the PCA is described in Chapter 6 of “Independent Component Analysis” written by Aapo Hyvarinen, Juha Karhumen, and Erkki Oja, published by John Wiley & Sons, Inc., 2001 (“Independent Component Analysis,” in February 2005, published by publishing department of Tokyo Denkki University).

However, in the PCA, in a case where random noise is included in the data to be processed, an error may be generated in the measurement results due to the influence of the random noise. Here, in order to reduce the influence of the random noise, it is preferable to perform whitening using the factor analysis (FA) having robustness with respect to noise in place of the PCA. Hereinafter, the principle of whitening using the FA will be described.

As described above, generally in the ICA, a linear mixed model (the expression (19) above) which represents the data x to be processed as a linear sum of the constituent components si is assumed and the mixing ratio ci and the constituent components si are acquired. However, in actual data, random noise other than the constituent components si is added in many cases. Here, as a model in consideration with the random noise, a model expressing the measurement data x using the expression below can be considered.

x=A·s+ρ (22)

Here, ρ represents the random noise.

In addition, whitening in consideration with the noise mixed model is performed and then an estimate of the mixed matrix A and the independent component si can be obtained by performing the ICA.

In the FA of the present embodiment, it is assumed that the independent component si and the random noise ρ respectively follow normal distribution N(0, Im) and N(0, Σ). Moreover, as is generally known, the first parameter x1in normal distribution N (x1, x2) represents an expected value and the second parameter x2 therein represents a standard deviation. At this time, since the data x to be processed becomes a linear sum of variables following the normal distribution, the data x to be processed also follows the normal distribution. Here, when a covariance matrix of the data x to be processed is set as V[x], the normal distribution followed by the data x to be processed can be implemented as N(0, V[x]). At this time a likelihood function related to the covariance matrix V[x] of the data x to be processed can be calculated by the following procedures.

First, when it is assumed that the independent components si are orthogonal to each other, the covariance matrix V[x] of the data x to be processed is calculated by the expression below.

V[x]=E|xx
^T
|=AA
^T+Σ (23)

Here, Σ represents a covariance matrix of noise ρ.

In this manner, the covariance matrix V[x] can be represented by the mixed matrix A and the covariance matrix Σ of noise. At this time, a log-likelihood function L (A, Σ) is given by the expression below.

L(A, Σ)=−n/2{tr((AA^T+Σ)⁻¹C)+log(det(AA¹+Σ))+m log 2π} (24)

Here, n represents the number of pieces of data x, m represents the number of independent components, the operator tr represents a trace of a matrix (sum of diagonal components), and the operator det represents a determinant. In addition, C represents a sample covariance matrix acquired by sample calculation from the data x and is calculated by the expression below.

$\begin{matrix} C = \frac{1}{n} \sum_{i = 1}^{n} x_{i} x_{i}^{T} & (25) \end{matrix}$

The covariance matrix Σ of the mixed matrix A and the noise can be acquired by the maximum-likelihood method using the log-likelihood function L (A, Σ ) of the expression (24). A matrix which is hardly affected by the random noise ρ of the expression (22) is obtained as the mixed matrix A. This is the basic principle of the FA. Further, as the algorithm of the FA, there are various algorithms using an algorithm other than the maximum likelihood method. In the present embodiment, various kinds of FAs can be used.

The estimated value obtained by the FA is only a value of AAT and the influence of the random noise is reduced and the data can be decorrelated in a case where a mixed matrix A suitable for the value of AAT is determined, but a plurality of constituent components si cannot be uniquely determined because the degree of freedom of rotation remains. Meanwhile, the ICA is a process of reducing the degree of freedom of rotation of the plurality of constituent components si such that the plurality of constituent components si are orthogonal to each other. In the present embodiment, the values of the mixed matrix A acquired by the FA are used as a whitened matrix and arbitrary properties with respect to the remaining rotation are specified by the ICA. In this manner, after a robust whitening process is performed on the random noise, independent constituent components si which are orthogonal to each other can be determined by performing the ICA. In addition, as a result of such process, the calibration precision in regard to the constituent components si can be improved by reducing the influence of the random noise.

E-3. ICA (Kurtosis and β Divergence as Independence Index):

In the independent component analysis (ICA), as the index for separating independent components from each other, higher order statistics representing independence of separated data are used as the independence index. The kurtosis is a typical independence index. The ICA using the kurtosis as the independence index is described in Chapter 8 of “Independent Component Analysis” written by Aapo Hyvarinen, Juha Karhumen, and Erkki Oja, published by John Wiley & Sons, Inc., 2001 (“Independent Component Analysis,” in February 2005, published by publishing department of Tokyo Denkki University).

However, in a case where an outlier such as spike noise is present in the data to be processed, the statistics including the outlier are calculated as the independence index. For this reason, an error is generated between original statistics related to the data to be processed and calculated statistics and this causes degradation of separation precision. Here, it is preferable to use an independence index which is unlikely to be affected by the outlier in the data to be processed. It is possible to use the β divergence as the independence index having such characteristics. Hereinafter, the principle of β divergence as the independence index of the ICA will be described.

As described above, generally in the ICA, a linear mixed model (the expression (19) above) which represents the data x to be processed as a linear sum of the constituent components si is assumed and the mixing ratio ci and the constituent components si are acquired. An estimated value y of the constituent component s acquired by the ICA is represented by “y=W·y” using a separation matrix W. It is desired that the separation matrix W is an inverse matrix of the mixed matrix A.

Here, a log-likelihood function L (̂W) of an estimated value ̂W of the separation matrix W can be represented by the expression below.

$\begin{matrix} L (\hat{W}) = \frac{1}{N} \sum_{i = 1}^{N} l (x (t), \hat{W}) & (26) \end{matrix}$

Here, an element of an integration symbol Σ is a log likelihood in each data point x(t). The log-likelihood function L (̂W) can be used as an independence index in the ICA. The technique of β divergence is a method of transforming the log-likelihood function L (̂W) with respect to an outliner such as spike noise in data for the purpose of suppressing the influence of the outliner by acting an appropriate function on the log-likelihood function L (̂W).

In a case of using the β divergence as the independence index, first, the log-likelihood function L (̂W) is transformed by the expression below using a function Φβ selected in advance.

$\begin{matrix} L_{Φ} (\hat{W}) = \frac{1}{N} \sum_{i = 1}^{N} Φ_{β} (l (x (t), \hat{W})) & (27) \end{matrix}$

In addition, the function LΦ (̂W) is considered as a new likelihood function.

As the function Φβ for reducing the influence of an outliner such as spike noise, a function in which the value of the function Φβ is exponentially attenuated when the value of the log likelihood (value in parentheses of the function Φβ) becomes smaller is considered. As such a function Φβ, the following function can be used.

$\begin{matrix} Φ_{β} (z) = \frac{1}{β} {\exp (β z) - 1} & (28) \end{matrix}$

In this function, the function value with respect to each data point z (log likelihood in the expression (27) above) becomes smaller when the value of β becomes larger. The value of β can be empirically determined and can be set as, for example, approximately 0.1. Further, the function Φβ is not particularly limited to the function of the expression (28) and another function in which the function value with respect to each data point z becomes smaller when the value of β becomes larger can be used.

When such β divergence is used as the independence index, the influence of an outliner such as spike noise can be appropriately suppressed. When the likelihood function LΦ (̂W) as in the expression (27) is considered, a pseudo distance between probability distribution to be minimized in correspondence with the maximization of likelihood is the β divergence. When the ICA using such β divergence as the independence index is performed, the influence of an outliner such as spike noise is reduced and the calibration precision related to the constituent component si can be improved.

In addition, the ICA using the β divergence is described in “Robust Blind Source Separation by β-Divergence” written by Minami Mihoko and Shinto Eguchi, 2002.

F. MODIFICATION EXAMPLES

The invention is not limited to examples or modification examples and can be implemented with various aspects within the range not departing from the scope of the invention, and the following modifications are possible.

Modification Example 1

In the embodiment, the element number m of the spectrum S of an unknown component is empirically and experimentally determined in advance, but the element number m of the spectrum S of an unknown component may be determined based on the information criterion known as the minimum description length (MDL) or the akaike information criteria (AIC). In a case of using the MDL or the like, the element number m of the spectrum S of an unknown component can be automatically determined by an operation from observation data of samples. In addition, for example, the MDL is described in “Independent component analysis for noisy data? MEG data analysis, 2000.”

Modification Example 2

In the embodiment, the subject as a target of the calibration process is configured of the sample components as those of a sample used when the calibration curve is generated, but unknown components other than the components which are the same as those of the sample used when the calibration curve is generated may be included in the subject. This is because the inner product between independent components is set as 0 and the inner product of independent components corresponding to the unknown component is considered as 0, the influence of the unknown component in a case of acquiring a mixing coefficient using the inner product can be ignored.

Modification Example 3

The computer used in the embodiment may be configured as a dedicated device. For example, the device shown in FIG. 7 or 13 may be implemented only by a hardware circuit. Alternatively, a part of the function of the device shown in FIG. 7 or 13 is implemented by the hardware circuit and another part may be implemented by software.

Modification Example 4

In the embodiment, an input of a spectrum of the spectral reflectance related to a sample or a subject is performed by inputting the spectrum measured by a spectrometer, but the invention is not limited thereto. For example, a spectrum is estimated from a plurality of band images whose wavelength bandwidths are different from each other and the spectrum may be input. The band images can be obtained by imaging a sample or a subject using a multi-band camera including a filter capable of changing a transmission wavelength bandwidth.

Further, elements other than the elements described in the aspects from among the constituent elements in each of the examples and modification examples described above are additional elements and can be appropriately omitted.

CALIBRATION CURVE GENERATION METHOD, CALIBRATION CURVE GENERATION DEVICE, TARGET COMPONENT CALIBRATION METHOD, TARGET COMPONENT CALIBRATION DEVICE, ELECTRONIC DEVICE, GLUCOSE CONCENTRATION CALIBRATION METHOD, AND GLUCOSE CONCENTRATION CALIBRATION DEVICE

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