US20060063983A1 - Non-invasive blood component value measuring instrument and method - Google Patents

Non-invasive blood component value measuring instrument and method Download PDF

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US20060063983A1
US20060063983A1 US10/508,833 US50883305A US2006063983A1 US 20060063983 A1 US20060063983 A1 US 20060063983A1 US 50883305 A US50883305 A US 50883305A US 2006063983 A1 US2006063983 A1 US 2006063983A1
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light
blood
spectrum
blood glucose
glucose concentration
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Ken-ichi Yamakoshi
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TYT Inst of Tech Corp
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TYT Inst of Tech Corp
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Publication of US20060063983A1 publication Critical patent/US20060063983A1/en
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Assigned to TYT INSTITUTE OF TECHNOLOGY CORPORATION reassignment TYT INSTITUTE OF TECHNOLOGY CORPORATION CORRECTED COVER SHEET CORRECTING INVENTOR'S NAMES ON REEL/FRAME 018032/0878 Assignors: YAMAKOSHI, KEN-ICHI
Priority to US11/819,324 priority Critical patent/US8275433B2/en
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/14—Devices for taking samples of blood ; Measuring characteristics of blood in vivo, e.g. gas concentration within the blood, pH-value of blood
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14546—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring analytes not otherwise provided for, e.g. ions, cytochromes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/49—Blood
    • G01N33/491—Blood by separating the blood components
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/122—Kinetic analysis; determining reaction rate
    • G01N2201/1228—Reading time being controlled, e.g. by microprocessor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/123—Conversion circuit
    • G01N2201/1232—Log representation, e.g. for low transmittance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods

Definitions

  • the present invention relates to an instrument and a method for measuring blood biochemical constituent including blood glucose concentration and, more particularly, to a non-invasive blood constituent measuring instrument and a method for measuring blood glucose concentration without sampling blood from a living body.
  • this method is to measure glucose in blood serum using the PLS method (partial least squares analysis) that is one of chemometrics by measuring infrared spectrum with lights in two wavelength ranges of 1325 ⁇ 1800 nm and 2035 ⁇ 2375 nm applied to glucose sample melted in blood serum.
  • PLS method partial least squares analysis
  • the glucose absorption band overlaps the absorption ranges of other biological tissues in living bodies such as bones, veins, muscles and it is difficult to separate the ranges and the accurate measurement is not feasible and is therefore not put in practical use.
  • an objective of the present invention is to provide a non-invasive blood glucose measuring instrument and a measuring method by solving the above-mentioned problems to allow the blood glucose concentration measurement with a simple way as well as with high accuracy.
  • a non-invasive blood constituent measuring instrument includes a light source to irradiate a light having plural wavelengths to a living body; a light detector to detect the light transmitted through a living body or reflected therefrom; an instantaneous spectrum analyzer to analyze spectrum of light transmitted through or reflected on the living body at different times when the output signal of the light receiver is supplied; a spectrum subtraction generator to generate spectrum subtraction from light spectrum at the different times measured by the spectrum analyzer; and a blood constituent predictor into which output data of the spectrum subtraction is input and blood constituent is output.
  • a blood constituent predictor is provided with a multi-regression analyzing model using plural spectrum data of whole blood constituent of which is known as an explanatory variable and using the blood constituent as an objective variable, wherein being input the spectrum subtraction data obtained from the blood of which blood constituent is known as the explanatory variable, the multi-regression analyzing model computes the object variable and outputs this objective variable as a blood constituent.
  • the non-invasive blood glucose concentration measuring instrument is composed of a light source to irradiate a light containing plural wavelengths; a light detector to detect the light transmitted through a living body or reflected therefrom; an instantaneous spectrum analyzer to which the output signal of the light receiver is supplied and which analyzes spectrum of the light transmitted through the living body or reflected therefrom at different times; a spectrum subtraction generator to generate spectrum subtraction from the spectrum of the light measured by the spectrum analyzer at the different times; and a blood glucose concentration predictor into which the output data of the spectrum subtraction generator is input and which outputs the blood glucose concentration.
  • the blood glucose concentration predictor is constructed with a multi-regression analyzing model into which spectrum subtraction data of plural whole blood samples of known blood constituent is input as the explanatory variable and in which the blood glucose concentration is computed as an objective variable and output as blood glucose concentration.
  • a non-invasive blood constituent measuring method includes the steps of irradiating a light containing plural wavelengths to a living body; detecting light transmitted through or reflected from the living body and converting it into an electric signal; analyzing spectrum of the light transmitted through the living body or reflected therefrom at different times using the converted electric signal; generating spectrum subtraction from the spectrum of the light at the different times; and predicting corresponding blood constituents from the spectrum subtraction.
  • the blood constituent predicting step further includes the steps of preparing a multi-regression analyzing model, into which spectrum data of plural whole blood samples having known blood constituent is input as an explanatory variable and blood constituent is output as an objective variable, inputting the spectrum subtraction data obtained from blood of which blood constituent is not known as an explanatory variable, and outputting the blood constituent as an objective variable.
  • FIG. 1 is a block diagram according to the embodiment of the present invention.
  • FIG. 2 is a flowchart showing a construction method of an analytical prediction model used in the blood concentration prediction instrument shown in FIG. 1 ;
  • FIG. 3 is a diagram showing an arterial pulsatile volume waveform in a living body
  • FIG. 4 is a waveform diagram showing examples of spectrums output from an instantaneous spectrum analyzer in FIG. 1 ;
  • FIG. 5 is a diagram for explaining the operation of a blood glucose prediction instrument shown in FIG. 1 ;
  • FIG. 6 is a diagram showing properties of the light passed through a living body for explaining the principle of the present invention.
  • FIG. 7 is a diagram showing another embodiment of the non-invasive blood glucose concentration measuring instrument according to the present invention.
  • FIG. 1 is a block diagram showing a non-invasive blood glucose concentration measuring instrument of the present invention.
  • the light source 11 to emit a light having a near infrared wavelength range of, for example, 800 ⁇ 2400 nm wavelength has been installed in a non-invasive blood glucose concentration measuring instrument.
  • the light emitted from the light source 11 is irradiated to a living body 13 such as a fingertip, an ear lobule, etc. through an active spectroscope 12 .
  • the active spectroscope 12 separates light emitted from the light source 11 sequentially over its whole wavelength range at an interval of, for example, 3 nm and sequentially outputs about 530 number of lights having a different wavelength.
  • the scanning of the wavelength by the active spectroscope 12 in the above-mentioned wavelength range is executed repeatedly about 20 times or more in one cycle time of the arterial volume waveform in the living body 13 .
  • the active spectroscope 12 transmits the lights in a near infrared range sequentially at an interval of about 50 ms or less and irradiates them to the living body 13 .
  • the light passed through the living body 13 is detected by a light detector arranged at the opposite side of the light source 11 and is converted into an electric signal.
  • An output signal of the light detector 14 is supplied to an instantaneous spectrum analyzer 15 , wherein an absorption spectrum obtained as an output of the light detector 14 for each wavelength of the light source 11 is produced. That is, the output from a sensor 16 that detects an intensity of the light incident to the living body 13 from the light source 11 , that is, an intensity of the incident light I ⁇ o with each wavelength ( ⁇ ) is supplied with the output signal of the light detector 14 to the spectrum analyzer 15 .
  • the absorption spectrum data obtained by the spectrum analyzer 15 is stored in a spectrum data memory 17 .
  • the spectrum data memory 17 stores and maintains output data for several seconds of the spectrum analyzer 15 sequentially on the first-in first-out basis.
  • the subtraction data ( ⁇ OD ⁇ ti ) include only information of arterial blood without the other biological tissue components such as skin, bones, muscles etc, as also described later.
  • the spectrum analyzer 15 , the spectrum data memory 17 and the subtraction processor 18 are operated in sync with the 20 times scanning per second of the active analyzer 12 .
  • the synchronization between these units is made by a timing device 19 to supply a synchronizing signal to them.
  • the spectrum subtraction data ( ⁇ OD ⁇ ti ) produced by the subtraction processor 18 is stored in a spectrum subtraction memory 20 .
  • the spectrum subtraction memory 20 also stores the output data of the subtraction processor 18 for several seconds sequentially on the first-in first-out basis.
  • the spectrum subtraction data read out of this spectrum subtraction memory 20 is input into a blood glucose predictor 21 .
  • the blood glucose predictor 21 is a device to predict blood glucose concentration through the multi-regression analysis using the PLS (Partial Least Squares Regression) method that is one of multi-regression analyses from input spectrum subtraction data. That is, the blood glucose predictor 21 is constructed as a software model to compute the blood glucose concentration according to the PLS method using whole blood samples that have many known blood glucose concentrations.
  • PLS Partial Least Squares Regression
  • FIG. 2 is a flow chart showing a method for constructing the blood glucose predictor 21 as the software model shown in FIG. 1 .
  • Known blood glucose concentration samples 31 are the whole blood samples filled in plural quartz photo-cells whose glucose concentrations are known and are slightly-different from each other. These samples 31 were taken directly from, for example, seven healthy adult males and were made the plural whole blood samples having different albumin or hematocrit concentrations from other blood samples by 18 mg/dl like 36, 54, . . . 486 mg/dl in the glucose concentration range 30 ⁇ 450 mg.
  • a PLS regression analysis prediction model 34 is determined by data X consisting of these absorption spectrum 32 , together with corresponding known n number of blood glucose concentrations (yn) 33 . That is, data X consisting of the absorption spectrum 32 is an absorbance for different m (about 530 waves) number of the spectroscopic waveforms. Expressing these absorbance with x 1 , x 2 , . . . , x m , the known n number of blood glucose concentrations y 1 , y 2 , . .
  • a coefficient of this determinant is determined using the PLS method by substituting the absorption spectrum data using the above-mentioned sample solution into the determinant.
  • a blood glucose prediction model formula is thus obtained.
  • the PLS method is a technique to consider the correlation of potential variables T PLS as explanatory variables and to utilize data contained in X as many as possible.
  • T Potential variable
  • P of the determinant 2 and regression coefficient q of potential variable T are determined by inputting blood glucose y 1 , y 2 , . . . y n of n known blood glucose samples into a regression analytical computer application software (for example, Trade Name: MATLAB) according to the PLS method available in the market.
  • the regression analysis prediction model blood glucose computing model
  • a new T is computed based on P that is determined when a model is prepared, when new absorbance of respective spectroscopic wavelengths x 1 , x 2 , . . . , x m obtained from blood of which blood glucose concentration is unknown are input as data.
  • the light emitted from the light source 11 is spectroscopically scanned over the wavelength range by the active spectroscope 12 at a rate of, for example, 20 times per second and is irradiated to the living body 13 .
  • the light transmitted through the living body 13 is detected by the light detector 14 and each absorption spectrum is measured by the spectrum analyzer 15 at an intervals of 40 ⁇ 50 ms.
  • the spectrum data thus measured is stored in the spectrum memory 20 until the next spectrum measuring time.
  • FIG. 3 shows the arterial pulsatile volume waveform in the living body 13 , the horizontal axis shows time and the vertical axis shows arterial blood volume change (pulsatile volume waveform). Time t 1 , t 2 , . . .
  • FIG. 3 shows the time when the scanning of the wavelength starts by the active spectroscope 12 , where n is 20 in this case.
  • the spectrum subtraction processor 18 shown in FIG. 1 produces a spectrum subtraction from absorption spectrums at two any optional times, for example, a time t 1 and a peak time t m in the arterial pulsatile volume waveform selected from the times t 1 , t 2 , . . . , t n .
  • FIG. 5 is a diagram for explaining an operation of the blood glucose predictor 21 shown in FIG. 1 .
  • One example of the above-mentioned spectrum subtraction is shown in FIG. 5 ( a ).
  • the horizontal axis in FIG. 5 shows the wavelength ( ⁇ ) and the vertical axis shows a difference in the absorbance ( ⁇ OD ⁇ ti ).
  • the curved line indicating the spectrum subtraction is a plotted difference in the absorbance at respective wavelengths of absorption spectrum, for example, at t 3 and t 6 in this case.
  • Graphes (S 1 ), (S 2 ), . . . , (Sm) in FIG. 5 show absorption spectrums of m number of whole blood samples of known blood glucose concentration.
  • Spectrum subtraction data shown in FIG. 5 ( a ) are input to the blood glucose concentration predictor 21 . Further, a PLS regression analytical model is incorporated in the blood glucose concentration predictor 21 .
  • the PLS regression analytical model is a numerical expression showing the relation between absorption spectrums of m number of whole blood samples (S 1 ), (S 2 ), . . . , (Sm) shown in FIG. 5 each having known blood glucose concentration and the known blood glucose concentrations corresponding to the samples.
  • the blood glucose concentration predictor 21 compares the spectrum subtraction given from the spectrum subtraction memory 20 as input data with each of the absorption spectrums of the sample solutions and outputs the blood glucose concentration of the sample solution having the most similar absorption spectrum as a predicted blood glucose concentration.
  • FIG. 6 is a schematic diagram showing the relation of the intensity of incident light I ⁇ o , the intensities of transmitted lights I ⁇ 1 , I ⁇ 2 and absorption amount in the living body 13 at the wavelength ⁇ .
  • the arterial blood volume waveform P as shown in FIG. 3 is also shown in FIG. 6 .
  • FIG. 6 is a schematic diagram showing the relation of the intensity of incident light I ⁇ o , the intensities of transmitted lights I ⁇ 1 , I ⁇ 2 and absorption amount in the living body 13 at the wavelength ⁇ .
  • the arterial blood volume waveform P as shown in FIG. 3 is also shown in FIG. 6 .
  • the absorption light spectrum in the spectrum analyzer 15 or the spectrum data memory l shown in FIG. 1 contains the absorption light element in the venous blood and biological tissues excluding blood
  • the spectrum subtraction ( ⁇ OD ⁇ ) generated in the spectrum subtraction processor 18 becomes the light absorption spectrum depending on the light absorption element of arterial blood absorption element only.
  • the measured result easy to look by inputting the result into the blood glucose concentration predictor 21 by executing the time series average of these spectrum subtractions or by smoothing successively computed blood glucose concentrations through the statistical procedure such as the time average or moving average by the blood glucose concentration predictor 21 .
  • the transmitted light spectrum from the living body 13 is measured but the reflected light from the living body 13 may be measured other than the transmitted light.
  • FIG. 7 is a partial explanatory diagram showing this embodiment, in which the same composed elements as those in FIG. 1 are assigned with the same reference numerals and the detailed explanation thereof will be omitted.
  • the light detector 14 is arranged at the same side as the light source 11 to the living body 13 as illustrated and detects the reflected light from the living body 13 .
  • the spectrum analyzer 15 shown in FIG. 1 it is possible to measure blood glucose concentration likewise the embodiment described above.
  • the light from the light source 11 is separated by the active spectroscope 12 and then irradiated to the living body 13 .
  • the transmitted light or reflected light may be separated for spectrum analysis after the light from the light source 11 is irradiated to the living body 13 .
  • the light can be separated by an array of plural light detectors each having a sensitivity only for specific wavelengths ( ⁇ ).
  • a model applied with the PLS method is used as the blood glucose concentration predictor 21 .
  • a model according to the principal constituents regression shown in Formula 3, which is one of multi-regression analyses may be used.
  • a multi-regression analysis blood glucose concentration computing model is constructed by corresponding a known blood glucose concentration of the whole blood sample 31 to an objective variable y, applying spectrum data of the whole blood sample 31 to an explanatory variable x and deciding a multi-regression analysis blood glucose concentration computing model.
  • spectrum subtraction data of an unknown blood glucose concentration is input into the blood glucose concentration predictor 21 in which this principal constituents score regression coefficient b is set, a blood glucose concentration predict value ya is computed and output.
  • the sample 31 having a known blood glucose concentration is filled in plural quartz cells and absorption spectrum data X 1 , X 2 , . . . Xm are developed with a spectroscopic analyzer comprising the light source 11 , the spectroscope 12 , the light receiver 14 and the spectrum analyzer 15 .
  • a spectroscopic analyzer comprising the light source 11 , the spectroscope 12 , the light receiver 14 and the spectrum analyzer 15 .
  • spectrum subtraction data obtained with units ranging from the light source 11 to the spectrum subtraction memory shown in FIG. 1 using plural living bodies of which blood glucose concentrations are known can be used.
  • the measurement of blood glucose concentration is shown.
  • concentration of another material having absorption characteristics and scatter reflection characteristics existing in the arterial blood it is possible to predict and compute the concentration of that material existing in the arterial blood similarly. That is, it is possible to predict and compute the concentration by measuring spectrum of wavelength band corresponding to the absorption characteristics or the reflecting characteristics of the material and deciding the regression coefficient of the multi-regression analyzing model using the PLS method or the PCR method referring to a concentration of a sample of that is the standard of that material using the same system and procedures shown in the above embodiment.
  • the non-invasive blood constituent measuring instrument and the method according to the embodiment of the present invention it is possible to measure blood constituents in a living body by irradiating near infrared light to a finger tip, etc. quickly and highly precisely without feeling pain and burden involved in the blood drawing.
  • spectrum subtraction using the arterial blood beat is used as described above.
  • the spectrum subtraction analysis may be made by generating the venous blood volume change in the biological tissues using such a method as the venous occlusion method, for example.
  • the adverse effect of other biological tissue constituents is eliminated and blood constituent can be measured at a highly precise and sensitive level.

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US20060063987A1 (en) * 2004-08-25 2006-03-23 Matsushita Electric Works, Ltd. Method of preparing calibration curve for quantitative analysis of in-vivo component, and quantitative analyzer using the calibration curve
US20090079964A1 (en) * 2007-09-13 2009-03-26 Zhi Xu Optical device components
US20090105565A1 (en) * 2007-10-04 2009-04-23 Zhi Xu Optical device components
US20090247843A1 (en) * 2008-03-25 2009-10-01 The Curators Of The University Of Missouri Method and System for Non-Invasive Blood Glucose Detection Utilizing Spectral Data of One or More Components Other Than Glucose
US20100331636A1 (en) * 2007-03-23 2010-12-30 Enverdis Gmbh Method for the continuous non-invasive determination of the concentration of blood constituents
US7961305B2 (en) 2007-10-23 2011-06-14 The Curators Of The University Of Missouri Optical device components
US8340738B2 (en) 2008-05-22 2012-12-25 The Curators Of The University Of Missouri Method and system for non-invasive optical blood glucose detection utilizing spectral data analysis
US8552359B2 (en) 2009-04-01 2013-10-08 The Curators of the Univesity of Missouri Optical spectroscopy device for non-invasive blood glucose detection and associated method of use
US20160139045A1 (en) * 2014-09-29 2016-05-19 Zyomed Corp. Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing
US9554738B1 (en) 2016-03-30 2017-01-31 Zyomed Corp. Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing
CN107941741A (zh) * 2017-10-23 2018-04-20 苏州市中心血站 利用近红外光谱检测血浆灭活袋中亚甲蓝释放量的方法
US20210161432A1 (en) * 2016-07-25 2021-06-03 Samsung Electronics Co., Ltd. Apparatus and method for estimating biological substance, apparatus for acquiring unit spectrum, and wearable device
US11089981B2 (en) 2018-07-23 2021-08-17 Samsung Electronics Co., Ltd. Methods and systems for performing universal calibration to non-invasively determine blood glucose concentration
US11596331B2 (en) 2018-10-04 2023-03-07 Samsung Electronics Co., Ltd. Apparatus and method for estimating analyte concentration
US11678818B2 (en) 2017-12-15 2023-06-20 Boe Technology Group Co., Ltd. Blood glucose detection device and method of determining blood glucose level
US11759129B2 (en) 2019-07-31 2023-09-19 Tsinghua University Noninvasive glucometer and blood glucose detection method
WO2023224176A1 (en) * 2022-05-19 2023-11-23 Samsung Electronics Co., Ltd. Device and method for measuring blood constituents

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JP4563075B2 (ja) * 2004-05-14 2010-10-13 日本分光株式会社 血糖値測定装置
NZ552046A (en) * 2004-06-01 2010-05-28 Prophagia Inc Index and method of use of adapted food compositions for dysphagic persons
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