Title of Invention

AN APPARATUS FOR MEASURING THE GLUCOSE CONCENTRATION IN A TISSUE SAMPLE

Abstract THERE IS DISCLOSED AN APPARATUS (10) FOR MEASURING THE GLUCOSE CONCENTRATION IN A TISSUE SAMPLE COMPRISING: A SPECTROSCOPIC SENSOR (20) FOR COLLECSTING AN ANALOG SPECTROSCOPIC SIGNAL FROM SAID SAMPLE, AN ANALOG-TO-DIGITAL CONVERTER (21) COUPLEDTO AN OUTPUT OF SAID SPECTGROSCOPIC SENSOR FOR CONVERTING SAID SPECSTROSCOPIC SIGNAL COLLECTED BY SAID SPECTROSCOPIC SENSOR TO A DIGITAL SIGNAL; AN ELECTRONIC FILTER COUPLED TO SAID ANALOG TO DIGITAL CONVERTER FOR RECEIVING SAID DIGITAL SIGNAL THEREFROM, AND A DISPLAY (26) FOR RECEIVING OUTPUT SIGNAL FROM SAID ELECTRONIC FILTER AND FOR GENERATING A HUMAN PERCEPTIBLE REPRESENTATION THEREOF.
Full Text The present invention relates in general to an apparatus for measuring the glucose concentration
in a tissue sample and more particularly, to an apparatus for determining the concentration of a target
analyte in an aqueous sample by using multi spectral analysis.
Data analysis during spectroscopic analysis refers to the process of finding optimum
wavelengths and generating accurate calibrations to relate a given set of spectroscopic data to reference
laboratory values for the composition of a set of samples such that it is possible to analyze, i.e. predict
the values of future samples of unknown composition. Calibration of spectroscopic instruments that are
used to perform spectroscopic measurements is typically accomplished by application of multiple
regression of the absorbance at some number of wavelengths against the reference laboratory values,
i.e. mathematically determining the best possible fit of a straight line to a set of data (see for example.
H. Mark, Principles and Practice of spectroscopic calibration, John Wiley & Sons, Inc. (1991))
An error free calibration, i.e. a sample for which Beer"s law applies is one in which the
constituent of interest and which the only constituent in the sample is dissolved in a completely non
absorbing solvent and has only a signal absorbance band. In this case the concentration of the
constituent is known exactly over a board range for the set of calibration samples; and the spectrometer
has no noise, nonlinerity, or other fault. In such an idealized case, the height of the absorbance peak is
strictly proportional to the concentration of the constituent. Thus it is possible to calibrate a system
using only two samples because two points determine the line and the slope of the line and intercept of
data are readily determined using known mathematical formulae.
Unfortunately, the ideal case does not prevail in the real world. For example,
spectroscopic measurements are subject to such phenomena as skew in the data, which is
caused by physical changes in the instrument, sample, or experiment. For example,
interfering and/or dominating constituents in the sample other than the constituent of interest
can affect the data. Temperature, medium, pathlength, and scattering effects must also be
considered.
Near-infrared (near-IR) absorbance spectra of liquid samples contain a large amount
of information about the various organic constituents of the sample. Specifically, the
vibrational, rotational, and stretching energy associated with organic molecular structures
(e.g. carbon-hydrogen, oxygen-hydrogen, and nitrogen-hydrogen chemical bonds)
produce perturbations in the hear-IR region which can be detected and related to the
concentration of various organic constituents present in the sample. However, in complex
sample matrices, near-IR spectra also contain an appreciable amount of interference, due in
part to similarities of structure amongst analytes, relative levels of analyte concentration,
interfering relationships between analytes, and the magnitude of electronic and chemical
noise inherent in a particular system. Such interference reduces the efficiency and precision
of measurements obtained using near-IR spectrometry to determine the concentration of
liquid sample analyses.
For example, temperature is a critical parameter for near-IR spectroscopic analysis
of aqueous based samples. Major water absorption bands are centered at approximately
3800, 5200, and 6900 nm, but the exact positions of these bands are temperature sensitive.
These bands shift to higher frequencies at higher temperatures. Changes in temperature
also alter the extent of water hydrogen bonding to other chemical species, which causes
significant shifts in band positions. The large water content of most clinical samples, e.g.
when determining glucose concentration in an aqueous solution, necessitates precise control
of the sample temperature.
With regard to temperature, K. Hazen, M. Arnold, G. Small, Temperature-
Insensitive Near-Infrared Spectroscopic Measurement of Glucose in Aqueous Solutions,
Applied Spectroscopy, Vol. 48, No. 4, pp. 477-483 (1994) disclose the use of a digital
Fourier filter that is combined with partial least squares (PLS) regression to generate a
calibration model for glucose that is insensitive to sample temperature. The calibration
model is initially created using spectra collected over the 5000 to 4000 nm spectral range
with samples maintained at 37°C. The model is evaluated by judging the ability to
determine glucose concentrations from a set of prediction spectra. Absorption spectra in the
prediction set are obtained by ratioing single-beam spectra collected from solutions at
temperatures ranging from 32°C to 41°C to reference spectra collected at 37°C. The
temperature sensitivity of the underlying water absorption bands creates large baseline
variations in the prediction spectra that are effectively eliminated by the Fourier filtering
step.
See, also, G. Small, M. Arnold, L. Marquardt, Strategies for Coupling Digital
Filtering with Partial Least-Squares Regression: Application to Determination of Glucose in
Plasma by Fourier Transform Near-Infrared Spectroscopy, Analytical Chemistry, Vol. 65,
No. 22, pp. 3279-3289 (1993) (Gaussian-shaped bandpass digital filters are implemented
by use of Fourier filtering techniques and employed to preprocess spectra to remove
variations due to the background absorbance of the [bovine] plasma matrix. PLS
regression is used with the filtered spectra to compute calibration models for glucose); M.
Arnold, G. Small, Determination of Physiological Levels of Glucose in an Aqueous Matrix
with Digitally Filtered Fourier Transform Near-Infrared Spectra, Analytical Chemistry,
Vol. 62, No. 14, pp. 1457-1464 (1990) (and G. Small, M. Arnold, Method and Apparatus
for Non-Invasive Detection of Physiological Chemicals, Particularly Glucose, U.S. Patent
No. 5,459,317 (17 October 1995)) (...A digital Fourier filter... removes both high-
frequency noise and low-frequency base-line variations from the spectra. Numerical
optimization procedures are used to identify the best location and width of a Gaussian-
shaped frequency response function for this Fourier filter. A dynamic area calculation,
coupled with a simple linear base-line correction, provides an integrated area from the
processed spectra that is linearly related to glucose concentration...); and K. Hazen,
Glucose Determination in Biological Matrices Using Near-Infrared Spectroscopy, Ph.D.
Thesis, Univ. of Iowa (August 1995) (glucose determinations in water, serum, blood, and
the body are performed using near-IR spectroscopy, multivariate analysis is used to
correlate minor spectral variations with analyte concentrations.
A number of near-IR devices and methods have been described that may be used in
connection with the foregoing techniques to provide noninvasive blood analyte
determinations:
U.S. Patent No. 5,360,004 to Purdy et al. describes a method and apparatus for the
determination of blood analyte concentrations, wherein a body portion is irradiated with
radiation containing two or more distinct bands of continuous-wavelength incident
radiation. Purdy et al. emphasize filtration techniques to specifically block radiation at the
two peaks in the near-IR absorption spectrum for water, occurring at about 1440 and 1935
nm. Such selective blocking is carried out in order to avoid a heating effect that may be due
to the absorption of radiation by water in the body part being irradiated.
By contrast, U.S. Patent No. 5,267,152 to Yang et al. describes noninvasive
devices and techniques for measuring blood glucose concentration using only the portion of
the IR spectrum which contains the near-IR water absorption peaks {e.g. the water
transmission window, which includes those wavelengths between 1300 and 1900 nm),
where water absorbance reaches a minimum at 1600 nm. Optically controlled light is
directed to a tissue source and then collected by an integrating sphere. The collected light is
analyzed and blood glucose concentration calculated using a stored reference calibration
curve.
U.S. Patent No. 5,606,164 to Price et al. describes a method and apparatus for
measuring the concentration of an analyte present in a biological fluid. near-IR radiation is
applied to calibration samples to produce calibration data. Unknown sample data is
analyzed using data pretreatment followed by projection into the calibration model space
with prediction of analyte concentration using the calibration model.
Devices have also been described for use in determination of analyte concentrations
in complex samples, for example:
U.S. Patent No. 5,242,602 to Richardson et al. describes methods for analyzing
aqueous systems to detect multiple components. The methods involve determination of the
absorbance or emission spectrum of the components over the range of 200 to 2500 nm, and
application of chemometrics algorithms to extract segments of the spectral data obtained to
quantify multiple performance indicators.
U.S. Patent No. 5,252,829 to Nygaard et al. describes a method and apparatus for
measuring the concentration of urea in a milk sample using an infrared attenuation
measuring technique. Multivariate techniques are carried out to determine spectral
contributions of known components using partial least squares algorithms, principal
component regression, multiple linear regression or artificial neural network learning.
Calibration is carried out by accounting for the component contributions that block the
analyte signal of interest. Thus, Nygaard et al. describe a technique of measuring multiple
analyte infrared attenuations and compensating for the influence of background analyses to
obtain a more accurate measurement.
U.S. Patent No. 4,975,581 to Robinson et al. describes a method and apparatus for
determining analyte concentration in a biological sample based on a comparison of infrared
energy absorption (i.e. differences in absorption at several wavelengths) between a known
analyte concentration and a sample. The comparison is performed using partial least squares
analysis or other multivariate techniques.
U.S. Patent No. 4,882,492 to Schlager describes a method and apparatus for
noninvasive determination of blood analyte concentrations. Modulated IR radiation is
directed against a tissue sample (e.g. an ear lobe) and either passed through the tissue or
impinged on a skin surface where it is spectrally modified by a target analyte (glucose).
The spectrally modified radiation is then split, wherein one portion is directed through a
negative correlation cell and another through a reference cell. Intensity of the radiation
passing through the cells are compared to determine analyte concentration in the sample.
U.S. Patent No. 4,306,152 to Ross et al. describes an optical fluid analyzer
designed to minimize the effect of background absorption (i.e. the overall or base level
optical absorbance of the fluid sample) on the accuracy of measurement in a turbid sample
or in a liquid sample which is otherwise difficult to analyze. The apparatus measures an
optical signal at the characteristic optical absorption of a sample component of interest and
another signal at a wavelength selected to approximate background absorption, and then
subtracts to reduce the background component of the analyte dependent signal.
U.S. Patent No. 4,893,253 to Lodder describes a method for analyzing intact
capsules and tablets by using near-infrared reflectance spectroscopy. The method detects
adulterants in capsules by obtaining spectra for a training set of unadulterated samples,
representing each spectrum as a point in a hyperspace, creating a number of training set
replicates and a bootstrap replicate distribution, calculating the center of the bootstrap
replicate distribution, obtaining a spectrum for an adulterated sample, transforming the
spectrum into a point in hyperspace, and identifying the adulterated sample as abnormal
based on a relationship between the adulterated sampl"s hyperspatial point and the bootstrap
replication distribution.
See, also, R. Rosenthal, L. Paynter, L. Mackie, Non-Invasive Measurement of
Blood Glucose, U.S. Patent No. 5,028,787 (2 July 1991) (A near-infrared quantitative
analysis instrument and method non-invasively measures blood glucose by analyzing near-
infrared energy following interactance with venous or arterial blood, or transmission
through a blood containing body part.).
The accuracy of information obtained using the above described methods and
devices is limited by the spectral interference caused by background, i.e. non-analyte,
sample constituents that also have absorption spectra in the near-IR range. Appreciable
levels of background noise represent an inherent system limitation particularly when very
little analyte is present. In light of this limitation, attempts have been maderto improve
signal-to-noise ratios, e.g. by avoiding water absorption peaks to enable the use of
increased radiation intensity, by reducing the amount of spectral information to be analyzed,
or by using subtraction or compensation techniques based on an approximation of
background absorption. As discussed above, these techniques have focused primarily
upon examining all constituents of a spectra simultaneously. Although such techniques
have provided some improvement, there remains a need to provide a method and apparatus
for performing a more precise determination of the concentration of analytes, for example in
a liquid matrix, i.e. where an accurate representation of each and every sample component
is obtained during analysis.
SUMMARY OF THE INVENTION
The invention provides one or more basis sets that are applied to a spectroscopic
signal during analysis to produce an accurate spectral representation from which analyte
concentration may be accurately determined. The presently preferred embodiment of the
invention is applicable for the determination of such analytes as glucose in serum, as
determined using non-invasive techniques. For example, in the basis sets, near-IR
absorbance features over the 1100 to 2500 nm spectral region are provided for water,
albumin protein, globulin protein, triacetin, cholesterol, BUN, and glucose. In addition,
sample temperature effects are also included, along with instrument noise levels.
A basis set includes all interfering components found in a sample, such as serum.
These components can include, for example, water, temperature/hydrogen bonding effects,
albumin globulin protein, triglycerides, cholesterol, urea, and all organic components. The
basis set also includes electrolytes, such as Na+, K+ and Cl-
The basis set does not include those components that do not interfere, such as
anything that in terms of concentration is loss than the background signal or noise level.
With regard to an analyte, such as glucose, it is necessary to define those components of a
sample that have a larger interference than that of glucose. Instead of considering only the
analytes that are mentioned above, which are all in blood or serum, a basis set may be
generated, for example, that produces a transform for the red blood cells that interfere or
scatter the light; and also for skin effects.
Once the spectra of each of these components is known, it is then necessary to
determine how the components interact, e.g. taking serum data, extracting each of the
components, and then comparing the spectra for the individual components with that of the
components in solution.
Thus, once a basis set is generated for glucose in the presence of water, it is
determined that water interferes with glucose, and it is determined how to remove the
water, then a basis set for a next component can be generated, such as for temperature
effect. In the example of non-invasive glucose concentration determination, the invention
sequentially adds basis sets for other components, e.g. globulin, protein, triglycerides,
urea, or cholesterol, in the presence of water, to build up to a serum matrix. Once basis
sets are generated for serum, it is then possible to generate basis sets for red blood cells,
muscle layers, skin layers, fat layers, even the whole body.
The present invention relates to an apparatus for measuring the glucose concentration
in a tissue sample, comprising the steps of:
a spectroscopic sensor for collecting an analog spectroscopic signal from said sample,
said spectrographic signal comprising substantially all relevant spectral components of said
sample;
an analog-to-digital converter coupled to an output of said spectroscopic sensor for
converting said spectroscopic signal collected by said spectroscopic sensor to a digital signal;
an electronic filter coupled to said analog to digital converter for receiving said digital
signal therefrom, said electronic filter substantially removing at least one spectral component
of said spectrographic signal that comprises a relevant interfering component of said sample,
wherein an interfering component comprises any of a chemical, physical, and environmental
phenomenon that results in a signal at least one wavelength where glucose absorbs, said
electronic filter outputting a signal that is representative of said glucose concentration in said
sample; and
a display for receiving said output signal from said electronic filter and for generating
a human perceptible representation thereof.
It is significant to note that the basis set approach herein thus characterizes each
component in a sample, as well as all other possible interference and, after producing an
accurate representation of each component at each frequency of interest, subtracts each
interferant from the spectra produced at the frequency of interest. In this way, all
interferants may be identified within the context of all other relevant sample components,
and thence removed from the spectra, leaving substantially only the signal produced by the
analyte of interest.
The various basis sets may be also combined mathematically to generate a set of
transforms that may be stored in a look-up table for use during analysis. In this way, a fast
real time determination of analyte concentration may be made using relatively simple, low
power computer hardware, e.g. a low power embedded controller.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 is a flow diagram showing the generation of a basis set according to the
invention;
Fig. 2 is a block schematic diagram of an instrument that incorporates one or more
basis sets according to the invention;
Fig. 3 is a block schematic diagram of an instrument that implements an algorithm
which incorporates one or more basis sets according to the invention;
Fig. 4 is a plot of water absorbance vs. wavelength;
Fig. 5 a is a plot of water absorbance for a varying temperature vs. wavelength;
Fig. 5b is a plot showing temperature effect;
Fig. 6a is a plot of water absorbance vs. wavelength showing absorbance when a
1500 nm long pass filter is used;
Fig. 6b is a plot of absorbance vs. wavelength showing protein(aq) - water
absorbance;
Fig. 7 is a plot of absorbance vs. wavelength showing albumin(aq) - buffer
(pathlength corrected);
Fig. 8 is a plot of absorbance vs. wavelength showing globulin(aq) - buffer;
Fig. 9 is a plot of absorbance vs. wavelength showing albumin(aq) - buffer
(pathlength corrected);
Fig. 10 is a plot showing pathlength corrections required for albumin, globulin, and
triactetin;
Fig. 11 is a plot of absorbance vs. wavelength showing triacetin(aq) - buffer
(pathlength corrected);
Fig. 12a is a plot of absorbance vs. wavelength showing urea- buffer;
Fig. 12 b is a plot of absorbance vs. wavelength showing urea - buffer (baseline
corrected);
Fig. 13 is a plot of absorbance vs. wavelength showing glucose - buffer;
Fig. 14 is a plot of absorbance vs. wavelength for solid samples;
Fig. 15 is a plot of normalized absorbance vs. wavelength for solid samples;
Fig. 16 is a second plot of normalized absorbance vs. wavelength for solid samples;
Fig. 17 is a plot of standard error (mg/dL) vs. number of PLS factors for
glucose(aq);
Fig. 18a is another view of the fourth plot of standard error (mg/dL) vs. resolution
(nm) for glucose(aq);
Fig. 18b is another view of the fourth plot of standard error (mg/dL) vs. resolution
(nm) for glucosc(aq) showing an expanded y-axis;
Fig. 19 is another view of the eighth plot of standard error (mg/dL) vs. resolution
(nm) for glucose(aq);
Fig. 20 is another view of the eighth plot of standard error (mg/dL) vs. resolution
(nm) for glucose(aq) showing averaged points of six PLS factors;
Fig. 21 is a plot of standard error (mg/dL) vs. resolution (nm) for glucose in serum;
Fig. 22 is a second plot of standard error (mg/dL) vs. resolution (nm) for glucose in
serum;
Fig. 23 is a third plot of standard error (mg/dL) vs. resolution (nm) for glucose in
serum;
Fig. 24 is a fourth plot of standard error (mg/dL) vs. resolution (nm) for glucose in
serum;
Fig. 25 is a fifth plot of standard error (mg/dL) vs. resolution (nm) for glucose in
serum;
Fig. 26 is a sixth plot of standard error (mg/dL) vs. resolution (nm) for glucose in
serum;
Fig. 27 is a seventh plot of standard error (mg/dL) vs. resolution (nm) for glucose
in serum;
Fig. 28 is an eighth plot of standard error (mg/dL) vs. resolution (nm) for glucose
in serum;
Fig. 29 is a ninth plot of standard error (mg/dL) vs. resolution (nm) for glucose in
serum;
Fig. 30 is a tenth plot of standard error (mg/dL) vs. resolution (nm) for glucose in
serum;
Fig. 31 is another view of the eighth plot of standard error (mg/dL) vs. resolution
(nm) for glucose in serum;
Fig. 32 is a plot of absorbance vs. wavelength showing raw absorbance for a
sample containing water, albumin, globulin, and triacetin;
Fig. 33 is a plot of absorbance vs. wavelength for a sample containing albumin,
globulin, and triacetin and from which water is subtracted and temperature and pathlength
arc corrected;
Fig. 34 is a plot of absorbance vs. wavelength showing linearity for albumin
spectra where temperature and pathlength are corrected;
Fig. 35 is a plot of absorbance vs. wavelength for a sample containing globulin and
triacetin and from which water and albumin are subtracted;
Fig. 36 is a plot of absorbance vs. wavelength showing linearity for globulin
spectra where temperature and pathlength are corrected; and
Fig. 37 is a plot of absorbance vs. wavelength for a sample containing triacetin and
from which water, albumin, and globulin are subtracted.
DETAILED DESCRIPTION OF THE INVENTION
The following discussion describes what the basis set is, how it is collected, the
instrument that is required, the data,collection parameters, the data analysis as far as such
factors as temperature and path length are concerned, and what are considered to be
additional basis sets.
The simplest example of a basis set is a basis set that includes all interfering
components in a sample, such as serum. These components can include, for example,
water, temperature/hydrogen bonding effects, albumin, and globulin protein, triglycerides,
cholesterol, urea, all organic components, and Na+, K+, and Cl. To a lesser degree, the
basis set may include additional electrolytes.
- The basis set does not include those components that do not interfere, such as
anything that in terms of concentration is less than the background signal or noise. With
regard to an analyse, such as glucose, it is necessary to define those components of a
sample that have a larger interference than that of glucose. Instead of considering only the
analytes that are mentioned above, which are all in blood or serum, a basis set may be
generated, for example, that produces a transform for the red blood cells that interfere or
scatter the light; and also for skin effects.
Once the spectra of all these components is known, it is then necessary to determine
how each of these components interact, e.g. taking serum data, extracting each of the
components, and then comparing the spectra for the individual components with that of the
components in solution.
Once a basis set is generated for glucose in the presence of water, it is determined
that water interferes with glucose, and it is determined how to remove the water, then a
basis set for a next component can be generated, such as for temperature effect. In the
example of non-invasive glucose concentration determination, the invention sequentially
adds basis sets for other components, e.g. globulin, protein, triglycerides, urea, or
cholesterol, in the presence of water, to build up to a serum matrix. Once basis sets are
generated for serum, it is then possible to generate basis sets for red blood cells, muscle
layers, skin layers, fat layers, even the whole body.
It is significant to note that the basis set approach herein thus characterizes each
component in a sample, as well as all other possible interferants, and subtracts each
interferant from the spectra produced at the frequency of interest. In this way, all
interferants are identified within the context of the sample and systematically from the
spectra, leaving substantially only the signal produced by the analyte of interest.
The various basis sets may be combined mathematically to generate a set of
transforms that may be stored in a look-up table for use during analysis. In this way, a
fast, real time determination of analyte concentration may be made using relatively simple,
low power computer hardware, e.g. a low power embedded controller.
Once it is determined which components are present in the sample, it is necessary to
determine the best method of collecting spectra for these components. However, it is also
necessary to define instrument specifications, such as signal to noise ratio, resolution, and
wavelength reproducibility, before the spectra can be collected. These instrument
considerations are discussed in detail below.
The procedure for generating basis sets is iterative. In some embodiments of the
invention it is necessary to consider such factors as scatter correction, refractive index
correction, depth of penetration into the tissue, total optical path length, and temperature.
Once the basis sets are generated, they may be applied to a spectroscopic input signal. The
signal thus processed by the basis sets is then preprocessed using standard chemometric
techniques, such as smoothing and second derivative analysis.
Another approach to processing after application of the basis sets is that of
deconvolution. If deconvolution is used, then it is necessary to perform temperature
correction after the data collection and scatter correction. This approach uses the basis sets
to identify and isolate various components of the sample in an iterative fashion. Thereafter,
multivariate analysis may be applied, which may include partial least squares analysis or
principal components analysis. Such processing is a matter of choice and is well known in
the art. For example, in a glucose concentration C for which there is a spectra of
interferograms n x m, where m is the number of interferograms and n is the number of
interferogram points, a data reduction is performed, in which:
C = bo + b1P1+...bnPn; (1)
where Pi are PLS factor scores derived from the interferogram points and concentration
values; and bj provides the regression coefficients.
Unique to the invention, various transforms such as deconvolution are performed
with reference to the basis set. Pre-processing also relies on the basis set in the invention.
For example, in the case of a Fourier filter in which certain frequencies pass through the
filter, it is necessary to know what frequency the filter passed. In this way, it is possible to
determine if the analyte is passed by the filter. The basis set is referenced to identify the
analyte concentration at various frequencies, such that the Fourier filter only need be
applied at those frequencies of interest, and not across a broad range of absorbance
frequencies (as is practiced in the prior art). Thus, the basis set may loosely be thought of
in this application as a filter for the filter.
Various molecular relations may be considered to be basis sets in themselves, such
as carbon-hydrogen, oxygen-hydrogen, and nitrogen-hydrogen bonding. In such cases,
there are more absorbance bands than can be accounted for by these fundamental
components. This means that there are related effects for those portions of molecular
structures to which these components are bonded. Thus, even though these molecules or
pieces of molecules may be found in common among different constituents, it is possible to
assign them to a constituent and then discard them during deconvolution because of the
signature across the spectra of a particular constituent.
Fig. 1 is a flow diagram showing the generation of a basis set according to the
invention. The first step of the process involves identifying relevant interfering
components of the sample at the same frequency as that of the analyte (100). This step and
subsequent steps may be performed using known spectroscopic and chemometric
techniques, as is discussed in greater detail below. Once the interfering components are
identified, the relevant interfering components are then all identified at other frequencies to
quantify absorbance at these other frequencies (102)."-The interfering components, once
quantified, are then removed at the frequency of the analyte (104). Each iteration of the
foregoing steps may be described as a separate basis set. Thus, the invention produces a
plurality of basis sets for an analyte.
Fig. 2 is a block schematic diagram of an instrument that incorporates one or more
basis sets according to the invention. In operation, a device 10, collects spectra 20 using
standard or modified (see below) spectroscopic devices. The spectra are provided to the
input port/buffer 21 of a system that includes a processor 22. The input port/buffer may
include an analog-to-digital conversion function, such that spectral data collected by the
spectroscopic device are converted to digital data. The processor operates upon such digital
input data in accordance with various transforms stored in one or more look-up tables
(LUTs). The LUTs contain transforms that incorporate the various basis sets. The
transform process performed by the processor uses the basis sets to identify and remove
substantially all interfering constituents from the spectral signal produced by the
spectroscopic device. Once processing with regard to the basis sets is completed, the
digital signal contains substantially only the analyte information. This information is then
further processed in accordance with known spectroscopic calibration and chemometric
techniques and provided to an output port/buffer 24. The output information may then be
observed on a display 26, in any desired format, to provide an accurate indication of analyte
r
concentration within the sample.
As also discussed both above and below, the basis sets generated in accordance
with the invention herein may be stored in a lookup table or they may be mixed in with the
other transform information. In producing such look-up tables, the basis sets first exist as
matrix raw data collected during the iterative process of generating the several basis sets. In
view of the several basis sets generated in the preferred embodiment of thejnvention, there
may be different matrices in the look-up tables, or there may be a single matrix that
generates a transform which is representative of all of the basis sets and that is applied
directly to the raw data. Thus, one embodiment of the invention takes each of the
components and builds them into a complex matrix that comprises an algorithm for
identifying and removing interferants. In this way, the invention provides a system that
accurately represents how the components appear within the spectra of interest when such
components are all combined. It is this ability of the invention to identify each relevant
component of a sample individually within the context of each other component that allows
the ultimate determination of look-up table entries for an analyte of interest.
Although possible, the presently preferred embodiment of the invention does not
provide spectra of glucose that has been corrected for all interferants at all concentration
levels in a lookup table. Rather, there are a series of spectra of the analyte at certain
different physiological concentrations of interest. For example, in the case of glucose,
there are look-up basis set values for hypo- and hyperglycemia concentrations. Thus, the
invention does not need to represent all of the information in all of the basis sets in the look-up
tables. Rather, it is only necessary to represent information over the whole range of
glucose that occurs in the body. The approach is taken for albumin protein, and other
sampje components. As discussed above, a single equation may be written for all of the
spectral information in this matrix, or one or more look-up tables may be provided. In any
event, this approach of storing only useful spectral information in the look-up tables
reduces the memory and processing power requirements of the instrument.
As discussed more fully below, the basis sets are first generated and, thereafter,
incorporated into an instrument for use during analysis. To determine those values that are
to be put into the look-up tables it is necessary to go through any number of basis sets. As
discussed above, it is necessary to identify the major interfering components that affect the
analyte in the sample and generate basis sets for each and every one of these components.
Fig. 3 is a block schematic diagram of an instrument that implements an algorithm
which incorporates one or more basis sets according to the invention. Fig. 3 provides a
detailed overview of the software/firmware component 30 of the device discussed in
connection with Fig. 2. It should be appreciated that the invention herein is readily applied
to any spectroscopic system. Thus, the system described in connection with Figs. 2 and 3
is provided only as an example of a presently preferred embodiment of the invention and
. not by way of limitation.
During processing within the instrument, the digitized spectral information is first
applied to the basis sets 31. As discussed above (and in greater detail below), the basis sets
are reduced to transforms that remove interfering constituents and/or components (chemical
and/or physical) from the spectral information. The basis sets may be applied before or in
connection with a physical model 32 that corrects for such interfering physical factors as
scattering, pathlength, and/or temperature.
After the spectral information is applied to the basis sets and (optionally) the
physical model, the signal thus produced is deconvolved 33 to correct the signal to a
reference. The signal is next preprocessed 34 and digitally filtered 35. Preprocessing may
employ such techniques as Kubelka-Munk transformation, mean centering, normalization,
baseline correction, scatter correction, and interference correction, although it is presently
preferred that the basis sets be used to resolve such issues as scatter correction, baseline
correction, and interference correction. Correction techniques that may be applied, for
example to scattering, can include multiplicative scatter correction, standard normal variate
correction, and extended multiplicative signal correction. The digital filtering function may
be accomplished by such techniques as Gaussian filtering, low and high bandpass filters,
and Lorentzian filtering.
Spectral wavelengths for the analyte are selected 36 and a multivariate analysis,
such as higher order partial least squares (PLS) is performed 37. Such analysis techniques
may include principal component regression, partial least squares, rotated principal
components, or correlation principal components analysis.
The preferred embodiment of the invention provides a plurality of basis sets that are
used to quantify an analyte in a liquid sample. For purpose of illustration and example, the
invention is now described in connection with glucose quantification in noninvasive
spectra.
Data Collection. The first step of the process involves identifying major interfering
chemical analytes and structures in the body. These factors include, inter alia, water
percentage present in the sample, temperature/hydrogen bonding effects on water, albumin
protein, globulin protein, triglycerides, cholesterol, urea, glucose, lactate, ethanol, also
Na+, K+, C1-, and other electrolytes, glycosylated hemoglobin, skin, keratin, fibrinogen,
and red blood cells. One advantage of the invention is the basis set may be generated in
such way that it includes spectra for all interfering components.
Noninterfering components include, for example, components of lower molar
absorptivity concentration, such as low dosage drugs and medications.
In the presently preferred embodiment of the invention, data collection
instrumentation should take into account the following:
• Signal is defined for each analyte by first determining the delta absorption from top
of absorbance band to base, and then by defining the slope of change in absorbance
versus concentration for samples spanning the physiological concentration at all"
frequencies.
• Noise is defined as root mean square (rms) noise of analyte absorbance in the band
of interest.
• Signal to Noise is defined as (slope X concentration) / noise. This value must be
greater than one for a minimum specified concentration to be analyzed.
• Resolution in the presently preferred embodiment of the invention requires a
minimum of seven points per peak.
Another factor to be considered is wavelength reproducibility. In the invention, a
modified NIRS 5000 spectrometer is used to achieve the above criteria.
The data collection parameters for the basis set include the following:
Pathlengths due to. absorbance of water, which is the primary interferant. It
is necessary to select different pathlengths for each spectral window for an
optimal basis set.
In the presently preferred embodiment of the invention, these pathlengths are:
• 0 to 2 mm for combination band region;
• 5 to 10 mm for first overtone region; and
• 10 mm or greater for second overtone region.
While not necessary, it is possible to generate a basis set over the entire frequency
range in a single data collection to compare information in different regions. In the
preferred embodiment of the invention, this dictates a 1 mm pathlength. Optimal signal to
noise levels are obtained separately. It is also necessary to provide continuous spectra to
identify and model parameters, such as change in refractive index as a function of
frequency.
For applications that involve the use of diffuse reflection, pathlength considerations
are not taken into account because light penetration is proportional to the inverse of water
absorbance, as defined by the system, based upon molecules interacting with specific
concentrations at specific refractive indices.
In some embodiments of the invention it is desirable to use optical filters. Because
water behaves as natural short pass filter, it is advantageous to use long pass cutoff filters
in conjunction with water bands to form a bandpass filter (although a system that provides
sufficient resolution, i.e. a sufficient number of analog-to-digital (A/D) bit, may not require
a filter). In the presently preferred embodiment of the invention, any filter in the midst of
the H2O absorbance band may be used, e.g. the following filters may be used:
• 1950 nm long pass filter for the combination band;
• 1450 nm long pass filter for the first overtone; and
• 1100 nm long pass filter for the second overtone.
The number of averaged scans for each spectra must be determined, where noise
decreases with an increasing number of scans. In the presently preferred embodiment of
the invention, noise vs. number of averaged scans is set to 64 averaged scans.
Replicate spectra. Experiments were conducted in which four replicates were
collected due to the temperature coefficient of the spectrometer. The following results were
obtained (which were due to the spectrometer used to make the measurements ~ these
results are not indicative of a general phenomenon):
• First replicate - outlier due to temperature;
• Second replicate - small outlier characteristics; and
• Fourth replicate - acceptable for further analysis.
For purpose of experiments conducted in connection with the invention, the
following additional parameters were defined:
• Ionic strength is 0.1 M to match that of the body;
• pH 7.35 phosphate buffer to approximate that of the body;"
• Temperature maintained at 38.0 ± 0.2°C to match that of the body;
• Components: ACS reagent grade chemicals used as standards.
Data Analysis. Data analysis must take into account temperature variations. In the
presently preferred embodiment of the invention, temperature variations of 0.1 °C are
observed to severely obscure the analyte absorbance bands (even concentrated albumin).
Laboratory and instrument temperatures are impossible to control to 0.01°C for daily use.
This effect is amplified in regions of high water absorbance and large changes of water
absorbance due to temperature.
Data analysis must take into account pathlength. This consideration is similar to
differential measurements taken in dual beam spectrometers, where one beam is focused
through the sample and a second beam is focused through a pathlength corresponding to the
pathlength interference in the sample.
It is desirable to control pathlength to 0.0001 mm. For a 1 mm cell with buffer
present there is a 1 mm pathlength of water. When an analyte is present, the pathlength of
water is reduced due to displacement. The displacement is linearly proportional to the
concentration of the analyte present. While various components of the sample may be
rotated out if their concentration is unknown, such processing is unnecessary upon using
the invention because such concentrations are known.
Temperature and pathlength correction algorithm. The following discussion
provides an exemplary temperature and pathlength correction algorithm in accordance with
the invention.
1. Response function: residual (sample - buffer) about zero for regions where
the analyte does not absorb.
2. Residual as function of spectral range is inversely weighted by the spectral
noise of the sample.
3. Thousands of buffers collected at roughly 38°C are compared with the
sample to match temperature. By using thousands of buffers, a good temperature match
can be found.
4. For each buffer tested, incremental pathlengths of water are tested to match
pathlength of buffer in the sample. For example, to get a pathlength of .99 mm, the buffer
being tested as a possible background is multiplied by 0.9900. For example, albumin
protein from .95 mm to1 mm at 0.0005 mm steps is tested with each buffer.
The following is a Matlab temperature/pathlength correction program for selected
parameters that must be optimized for each analyte, such as pathlength, and regions for the
response function:
temppath.M
% PROBLEM: Basis Set
% spectra require background subtraction of temperature and pathlength.
% This program corrects the temperature by searching for a buffer
% collected at the same temperature as the sample and match the amount
% of buffer present in the sample.
clear
% enter wavelength region
wavelength = 1100:2:2498;
% load sample spectra
load albl2_1 txt
sample = albi2_1;
[o p] = size (sample);
% load buffer spectra
% usually use all buffers collected to date
load albbuff.txt
buff=albbuff;
[m n] = size (buff);
% Code minimizes residual over user set regions
% These regions can not have absorbance from the analyte
% they are fine tuned iteratively.
% in this case - three regions are used in the response function.
b_lst_pt = find(wavelength>= 1640 & wavelength b_2nd_pt = find(wavelength>= 2077 & wavelength b_3rd_pt = find(wavelength>= 1640 & wavelength s_lst_pt = b_1st_pt;
s_2nd_pt = b_2nd_pt;
s_3rd_pt = b_3rd_pt;
% initialize to large residual
pathlength = 0;
for aaa = 1 : p
best_min(aaa) = 1000000;
end
% pathlength optimization
% determine pathlength matching water in sample
% (water absorbance * pathlength)
for j = 0.95 : 0.0005 : 0.997 %.98 j=manual_pathlength
pathlength = pathlength + 1;
% temperature optimization
% for each pathlength, test every buffer for temperature match
for temp = 1:n
avg_b_1st_pt = mean(buff(b_lst_pt,temp))*j;
avg_b_2nd_pt = mean(buff(b_2nd_pt,temp))*j;
avg_b_3rd_pt = mean(buff(b_3rd_pt,temp))*j,
% repeat for every sample and replicate
for sample_num = 1 : p
avg_s_lst_pt = mean(sample(s_1st_pt,sample_num));
avg_s_2nd_pt = mean(sample(s_2nd_pt,sample_num))";
avg_s_3rd_pt = mean(sample(s_3rd_pt,sample_num));
diff_1st_pt = abs( avg_s_1st_pt - avg_b_1st_pt);
diff_2nd_pt = abs(avg_s_2nd_pt - avg_b_2nd_pt);
diff_3rd_pt = abs( avg_s_3rd_pt - avg_b_3rd_pt);
% store results of each loop
results(sample_num) = diff_1st_pt + diff_2nd_pt + diff_3rd_pt;
% usually add in weighting function as inverse of noise for each region here
% if response function for given sample is best - record parameters
if results(sample_num) best_min(sample_num) = results(sample_num);
best_pathlength(sample_num) = j;
best temp(sample num) = temp;
end % end if
end % end sample
end % end temperature
end % end pathlength
% dump best parameters to screen for interpretation
best_min
best_pathlength
best_temp
% plot temperature and pathlength corrected spectra
hold off
clg
hold on
v = [1500 2500-0.01 0.07];
axis(v);
for sample_num = l:p
best_sample_corr(:,sample_num) = sample(:,sample_num) - buff(
:,best_temp(sample_num)) * best_pathlength(sample_num);
plot (wavelength,best_sample_corr(:,sample_num));
intensity(sample_num) = best_sample_corr(481,sample_num);
end
The resulting Spectra are clean and baseline resolved. The spectra are selected to
cover physiological concentrations for each analyte.
The following example illustrates the generation of a first basis set "Basis Set I."
Example - Basis Set I
near-IR absorbance features over the 1500 to 2500 nm spectral region are provided for
water, albumin protein, globulin protein, triacetin, cholesterol, urea, and glucose with a 1
mm pathlength. In addition, sample temperature effects are included along with instrument
noise levels.
Experimental: Spectra of the major constituents of serum were collected over
their respective physiological ranges. Sample preparation consisted of dissolving dried,
reagent grade solid samples in a 0.1 M phosphate buffer adjusted to pH 7.35. All speetra
were collected on a NIRS 5000 in transmission mode, with a 1 mm path length infrasil
quartz cell, with a 120 second equilibration period, at 38.0°C, with 64 averaged scans,
done in quadruplicate. A single instrument was used for all data acquisition.
Results and Discussion: Spectra are analyzed in order of decreasing absorbance
changes in the two spectral windows from 2050.to 2350 nm"and 1550 to 1850 am. The
first replicate is discarded in all cases due to a consistent variation in temperature caused by
the instability of the NIRS 5000 spectrometer and photons heating the sample (data not
included). The sample is in equilibrium by the second sample replicate.
Water Spectra: The near-IR is dominated by three large water absorbance bands
centered at 2500, 1950, and 1450 nm as presented in Fig. 4. The high absorbance limits
analysis done in aqueous solution in the near-IR to three spectral regions. The region from
23,50 to 2050 nm is referred to herein as the combination band region; the region from 1850
to 1550 nm is referred to herein as the first overtone spectral region; and the region from
1400 to 1100 nm is referred to as the second overtone spectral region.
The NIRS spectrometer sets the gain and hence the dynamic range of the detector
based upon the spectral region with the most light intensity reaching the detector. This is
the second overtone spectral region for aqueous samples. However, the combination band
region has the largest absorbance, followed by the first overtone region, and then the
second overtone region. Due to the low absorbance of water in the 1300 nm region versus
the 2200 nm region, a relatively small dynamic range is left for the 2200 nm region where
glucose bands are the largest. Therefore, a 1500 nm long pass filter was employed which
forces the NIRS system to set the gain based upon the first overtone spectral region.
Hence, in the initial basis set no spectral information is provided for the second overtone
region, optimum signal to noise levels are provided for the first overtone spectral region,
and slightly degraded signal to noise levels are obtained for the combination bands. Among
many modifications made to the NIRS system is an order sorter which allows a different
gain setting for each of the three spectral regions during a single scan for the next basis data
set.
Temperature Effects on Water Spectra: All three water absorbance bands in the
near-IR shift to higher frequency with increasing temperature. Buffer spectra collected
from 38.2 to 43.0°C are presented in Fig. 5A. The instrument should be modified to collect
lower temperature spectra. A slight broadening of the lines can be observed on each of the
water absorbance band shoulders. Subtracting a spectrum of water collected at 38.2°C
from spectra of water collected at higher temperatures reveals the magnitude and direction
of the shift. Negative absorbance bands that increase with temperature are observed at
2000 and 1480 nm. As the water bands shift to higher temperature, there is less water
absorbance in these regions, so that subtracting out a water absorbance band from a lower
temperature results in too much background being subtracted. Positive absorbance bands
that correlate with increasing temperature are observed at 2300, 1890, and 1400 nm. With
increasing temperature, the water absorbance increasingly moves into these spectral
regions. Subtracting out the 38.2°C water spectrum does not subtract out enough in these
regions.
The large water absorbance, coupled with the temperature shift, greatly hinders
near-IR analysis. Fig. 5B reveals that in the subtraction, no useful information is obtained
where the raw absorbance is greater than 3.0 ± 0.1, indicating the limit of the dynamic
range of the NIRS system. Therefore, the regions above 2460 nm and from 2010 to 1890
nm result in no analytically useful information and may be discarded for data collected with
a 1 mm pathlength. Information in these spectral regions may be obtained by adjusting the
pathlength. Due to the water absorption, the width of the regions that need to be discarded
increases as the pathlength analyzed increases. In addition, the temperature effects are seen
to span the entire combination band region and first overtone spectral region. As will be
shown, these changes in baseline are roughly equal in magnitude to the highly absorbing
protein and much greater in magnitude than all other spectral analytes examined.
Albumin Protein: After water and temperature effects, serum spectra are primarily
composed of absorption from albumin protein which has a physiological range of 2.6 to
7.9 g/dL. Albumin protein absorbance bands are difficult to see in the presence of water,
as shown in Fig. 6A. Subtracting out a buffer spectrum results in protein absorbance peaks
at 2285, 2170, 1730, and 1690 nm, as shown in Fig. 6B. Large negative absorption bands
also appear in the resulting spectra where water absorbs. These bands are not primarily due
to variation in temperature as a derivative of the water band would appear as seen in Fig.
5B. The negative bands are due to displacement of water by albumin and scattering.
A program, such as the MATLAB program described above, is used to determine
the best buffer in terms of temperature and best calculated pathlength to be used as a
background spectrum for subtraction. In Fig. 6B, the buffer and albumin in buffer spectra
both had the same 1 mm fixed pathlength. Because albumin is present in the 1 to 12 g/dL
range in this example and water is 100 g/dL, the albumin occupies a significant volume of
the cell and less water is present per unit volume. A program was written that multiplies the
water spectrum by a percentage that can be sequentially varied over a wide range. The
optimum calculated pathlength for each albumin in buffer spectrum was determined by
minimizing the sum of the absolute value of the residuals in locations where albumin does
not absorb and temperature effects are at a minimum (2085 to 2077 and 1655 to 1640 nm).
The residual in the overtone region was weighted twice as much to compensate for the
higher noise in the combination band region. To further minimize temperature effects, all
buffer spectra collected were run through this optimization to find the best buffer in terms
of temperature matching with the sample. Each albumin in buffer spectrum was run
through this algorithm independently.
The results of subtracting the best buffer at the adjusted pathlength for each albumin
spectrum are presented in Fig. 7. Additional albumin absorbance bands are now visible at
2060 nm and 2335 nm. Expansion of the graph about the 2060 nm absorbance bands
reveals increasing absorbance for each increase in albumin concentration. The albumin
band centered at 2170 nm is more symmetrical than the one seen in Fig. 6B. The two
peaks in the first overtone spectral region have a better baseline correction and now increase
in absorption linearly with increasing concentration. However, negative absorbance bands
are still evident where water absorbs at 2020 and 1870 nm. The region between 2000 and
1900 nm is an artifact of the mathematical correction over a region where the absorbance is
greater than 3 and the system does not respond. In addition, there is a large difference
between 0 absorbance and the 1 g/dL albumin spectrum. This difference should be equal to
the difference in absorbance from 1 to 2 g/dL. This offset can be reduced if the
combination band region and the overtone region are treated individually. It should be
pointed out that no baseline correction, smoothing, or scatter correction has been employed
at this point.
Globulin Protein: Physiological concentrations of globulin (0.7 to 8.1 g/dL) are
less absorbing in the near-IR than albumin. Straight subtraction of the phosphate buffer
allows the same peaks to be observed that are seen in albumin protein, as shown in Fig. 8.
The temperature and pathlength correction algorithm discussed above was run with exactly
the same parameters as for albumin and the same additional extra peaks were found, as
shown on Fig. 9. Overlaying the albumin and globulin spectra reveals that the globulin
absorbance band centered at 2170 nm is slightly broader than that of albumin protein.
The calculated pathlengths required for the background subtraction from each of the
spectra are presented in Fig. 10. For albumin, the correction is linear with increasing
concentration, but has a y-axis intercept of 0.996 mm. This is consistent with the poor
baseline observed in Fig. 7. The corrections for globulin are also linear, but greater
corrections are required per mg/dL analyte. This is consistent with the scattering tendencies
of globulin. The y-axis intercept is 1.00, consistent with the excellent background
subtraction.
Triglycerides: Triacetin is used to simulate triglycerides. The physiological range
of triacetin is 50 to 450 mg/dL. The temperature and pathlength correction algorithm is
again employed, but different regions are used in determining the minimum residual (2420
to 2440, 2080 to 2090, and 1575 to 1635, weighted 1:5:20). Six triacetin absorbance
bands result centered at 2320, 2250, 2130, 1760, 1715, and 1675 nm, as shown on Fig.
11. The resulting pathlengths required for correction are linear with concentration, but
much smaller deviations from 1 mm result due to the lower concentration of triacetin versus
protein in serum, as shown on Fig. 10. The signal levels of the smaller triacetin
absorbance bands approach the noise level of the spectrometer.
Urea: Twelve urea in buffer spectra were collected. Due to the small physiological
concentration of urea (6 to 123 mg/dL), the algorithm used to optimize the background by
changing the effective pathlength of the buffer subtracted fails because no significant Figs.
of water are displaced. No temperature matching algorithm is employed, but buffer spectra
collected with each sample are used. A straight background subtraction followed by a two
point baseline correction (2094 to 2106 and 2320 to 2332 nm) was performed and is
presented in Fig. 12. A single absorbance band is present centered at 2190 nm. No
overtone peak is present. This is consistent with this absorption being related to N-H,
whereas all of the other analyses have O-H fundamental vibrations. Only four spectra are
presented due to large baseline drifts that obscure the linearity of the additional spectra.
Higher concentration samples can be run to obtain a higher S/N and cleaner spectra,
although the same resulting basis set is obtained.
Glucose: A complete glucose in buffer study was performed over the combination
and first overtone spectral region of which a subset is presented here. Glucose was
examined from 30 to 600 mg/dL (also from 0 to 5000 mg/dL) to cover the physiological as
well as hypoglycemic and hyperglycemic levels of glucose. A straight subtraction of buffer
from glucose in buffer shows absorbance bands centered at 2326, 2272, 1800, 2150,
1730, and 1590 nm, as shown in Fig. 13.
Conclusions: Consistent with theory, for all analytes, the combination band
spectral region yields larger absorbance than the first overtone spectral region. However,
longer pathlengths quickly degrade the signal to noise level in the combination band region
due to the large water absorbance, whereas the spectral quality in the first overtone spectral
region should increase with small millimeter increases in pathlength. The absorbance bands
in the region of glucose absorbance in decreasing order of absorbance are water,
temperature effects, albumin protein, globulin protein, cholesterol, triglycerides, urea, and
glucose. While every analyte analyzed absorbs more than glucose and over the same
general spectral region, every analyte has a distinct absorbance signature. In principle, the
serum spectra or the noninvasive spectra, can be deconvoluted.
* * *
The invention contemplates the generation of additional basis sets, such that
substantially all interfering components are identified and factored into the spectroscopic
analysis.
The following example illustrates the generation of a second basis set "Basis Set
II."
Example - Basis Set II
A study was rerun on dried, crushed, and pressed solid samples to give absorbance
spectra with no water. A second basis set was collected based upon spectra of solid or neat
components of human serum. The resulting absorbance spectra show the combination,
first, and second overtone absorbance bands. In addition, for a given component the
relative absorbance between regions may be compared. Combined, another method of
wavelength selection is made available.
Experimental: Pure component spectra of the liquid form of water (pH 7.35, 0.1
M phosphate buffer 38.0 ± 0.2°C), triacetin, and lactic acid were collected. Albumin,
globulin, cholesterol, urea, and glucose exist as a solid in their pure state. . For these
analytes, each was individually ground with a mortal and pestle to a fine powder in the
absence of potassium bromide. The powder was then compressed into a transparent pellet
in a specially designed press that fits into the NIRS 5000 transmission module. Four
replicates of each component were then obtained in the transmission mode. The pathlength
of each analyte was not controlled.
Results and Discussion: The raw absorbance spectra for water, albumin,
globulin, cholesterol, triacetin, urea, glucose, and lactic acid are presented in Fig. 14.
Because the pathlength of each pellet was not controlled, the relative absorbance between
components can not be compared. The relative absorbance between frequencies for a given
analyte can be compared. The large baseline offsets are due to the thickness of the sample
and resulting total light throughput. This plot is included to show the total absorbance of
each analyte relative to the dynamic range of the NIRS 5000.
For each component in Fig. 14, the minimum absorbance was subtracted out and
the resulting spectra was normalized to 1 absorbance unit, as shown in Fig. 15. The
resulting full scale plots make it easier to compare absorbance as a function of frequency
and differences between components. For all three spectral regions, i.e. combination (2050
to 2350 nm), first (1550 to 1850 nm), and second overtone (1100 to 1400 nm), the
absorbance bands are observed to be distinct. In principle, each component can be
deconvoluted. It should be noted that when interacting with water, these absorbance bands
may shift and broaden. Comparing with the aqueous absorbance from Basis Set I (above)
reveals the absorbance bands of the neat or solid water (140), albumin (141), globulin
(142), and triacetin (143) to be in the same location with the same widths. Both urea (144)
and glucose (145) reveal additional resolution of peaks that have broadened and merged in
the presence of water.
Several key spectral signatures emerge from this Example. First, the combination
band region contains absorbance for each of the individual analytes. These absorbance are
in every case more intense than those in the first and second overtone spectral regions.
Cholesterol (146) absorbance drops off rapidly in this region as does triacetin. Neither
interfere significantly with the glucose absorbance band centered at 2150 nm. The only
interference is from water, albumin, and globulin which are shown in the Example -
Linearity Study (below) to be removable by simple subtraction.
In the first overtone spectral region every component has an absorbance band except
urea with its N-H bonds. Here the intensities of the absorbance bands range from 15% to
50% that of the corresponding combination band absorbance. It should be recognized that
these values are for a fixed pathlength and can be adjusted based upon total pathlength.
The second overtone spectral region has absorbance bands for every component
examined, but the relative absorbance are the smallest, as shown on Fig. 16. The glucose
band (145) seen here is very difficult to see in the presence of water (140).
Conclusions: Each of the three regions contains information about every analyte
with the exception of urea in the first overtone spectral region. The absorbance bands are
highly overlapped and are generally less intense at higher frequencies. The absorbance
bands are all distinct.
* * *
The following example illustrates the generation of a third basis set "Basis Set III."
Example - Basis Set III
The first basis set was repeated with no edge filter present to allow comparison of
all spectral ranges. The first Example above used a 1500 nm long pass filter to force the
NIRS spectrometer to gain range on the 1700 nm spectral region. This Example could be
repeated with increased optical pathlengths to yield higher signal to noise levels in the first
and second overtone spectral regions.
The following example illustrates the generation of a fourth basis set "Basis Set
IV."
Example - Basis Set IV
It is necessary to measure interactions of molecules in solution. In this Example, a
serum data set is collected.
Data Sets: The first data set consists of spectra of glucose dissolved in a 0.1 M
phosphate buffer adjusted to pH 7.35. Reagent grade glucose was weighed and diluted to a
known volume with the 0.1 M phosphate buffer. Spectra were collected in the
transmission mode with a 1 mm quartz cell using the NIRS 5000 spectrometer over the
range of 1100 to 2500 nm with readings taken every 2 nm. A 1500 nm long pass filter was
placed before the sample to force the NIRS spectrometer to set the gain on the peak signal at
1600 nm. Before and after every sample, 7 spectra of the 0.1 M phosphate buffer were
collected. A total of 64 glucose (aq) samples were collected with 7 sequential replicates of
each sample. The glucose samples covered a dynamic range of approximately 20 to 600
mg/dL. All samples were maintained at 38.0 ± 0.2°C.
The second data set consists of serum samples prepared by Western States Plasma.
Each serum sample was analyzed using a standard SMAC analysis yielding concentrations
for calcium, ionized calcium (calculated), phosphorus, glucose, uric acid, urea nitrogen
(BUN), creatinine, creatinine/BUN ratio, total protein, albumin, globulin, A/G ratio, total
bilirubin, ALT, ALP, LD (LDH), AST, GGT, sodium, potassium, chloride, carbon
dioxide, triglycerides, and cholesterol. To extend the dynamic range and level the
concentration distribution, reagent grade urea and glucose were quantitatively added to the
serum samples. The NIRS 5000 spectrometer was used in the fashion described above
with the same wavelength region, pathlength, temperature control, and long pass filter. A
0.1 M phosphate buffer adjusted to pH 7.35 was run before and after each serum sample."
A total of 196 serum samples were collected with 4 sequential replicates of each sample.
The glucose analyte covered a dynamic range of approximately 20 to 600 mg/dL.
Experimental: Glucose is determined in each data set using PLS regression
analysis. The data sets are broken up into calibration and prediction keeping all replicate
spectra together. A data point was originally collected every 2 nm from 1100 to 2500 nm.
Additional data sets are formed from this data set by keeping every other point, every 3 rd
point, every 4th point, ..., to every 32nd point. PLS calibration models and predictions are
then determined using 1 to 10 PLS factors.
Results and Discussion: For each resolution, the resulting standard error of
calibration (SEC) and standard error of prediction (SEP) is determined for 1 to 10 PLS
factors, as shown on Fig. 17. Here, selection of the optimum number of factors needs to
be achieved. As different ranges need to be compared, differences in the number of PLS
factors employed can lead to erroneous conclusions. Statistical approaches to determining
the optimum number of factors have failed. Because" the SEP does not increase as the
system is over-modeled, and further because the SEC and SEP yield similar results with 10
factors, it was decided for the purposes of this Example only to compare standard errors
from range to range using the results obtained with ten PLS factors.
Ten spectral ranges are analyzed in both the glucose in water and glucose in serum
data sets. These are summarized in Table 1 below. Ranges 1 to 3 and 5 to 7 correspond to
the full width at zero height of the six glucose absorbance bands isolated in the near-IR.
Ranges 4 and 8 splice together regions 1 to 3 and 5 to 7, respectively. Ranges 9 and 10
expand regions 4 and 8 into regions of increasing water absorbance, increasing noise, and
no additional glucose information.
Clearly, the wider spectral region that incorporates more glucose information (and water
and temperature) results in a lower standard error at any resolution than any of the three
individual glucose absorbance bands.
The nominal resolution of the NIRS spectrometer is 10 nm for the standard 0.040"
exit slit used in this Example. Still, the standard error is observed to increase slightly as the
resolution degrades from 2 to 10 nm. This is due to the manner in which the data sets were
created from the original 2 nm resolution data set. For instance, in the 6 nm resolution data
set generated, every third spectral point is kept. This means that two-thirds of the data are
discarded. The discarded data has glucose, water, and temperature signal. In addition, by
keeping these extra points, the effective noise is decreased by signal averaging. In as much
as the true resolution of the NIRS 5000 is 10 nm, 100% of the slope observed on the SE
vs. resolution graph is due to this systematic error. In addition, the same slope is observed
from 10 to 32 nm resolution.
The original data set with points every two manometers was again broken down
into data sets with resolution ranging from 2 to 32 nm at 2 nm intervals. This time, the data
was averaged instead of just discarding extra points. For example, at 6 nm resolution
points at 1100, 1102, and 1104 nm were averaged to a single point. The next point
averaged the data points at 1106, 1108, and 1110 nm. The PLS analysis was then repeated
and the standard errors with the tenth factor determined, as shown in Fig. 18. The increase
in standard error observed with degrading resolution is observed to range from 5 to 10
mg/dL standard error as opposed to 5 to 25 mg/dL standard error from 2 to 32 nm
resolution. Clearly, the failure to average the data points results in an increase of the slope
of standard error versus resolution. While the standard error roughly doubles from 2 to 32
nm resolution, the data indicates that for a glucose in water solution, the acceptable
resolution may be 32 nm or more. This makes chemical sense in as much as the narrowest
absorbance band in this Example is 54 nm wide. In must be pointed out that there are no
spectral interferences in this Example. Therefore, the actual acceptable resolution can only
degrade from this resolution.
2°C for the first overtone region, for data sets generated at 2 to 32 nm resolution
using averaged data, the increase in standard error with degrading resolution is greatly
reduced, as shown on Fig. 19. In addition, for this spectral region, less than ten points are
retained at resolutions greater than 16 nm. The PLS algorithm used only operates on as
many factors as there are data points. If queried for standard errors with additional factors,
the standard error for the number of factors equal to the number of points available is "
generated. Because the standard errors continue to decrease with an increasing number of
factors in this Example (see Fig. 17), the comparison of standard errors for various
resolutions using ten PLS factors is not valid. A direct comparison of standard errors at
degrading resolution for the 1587 to 1754 nm spectral region with six PLS factors is
presented in Fig. 20. The increased standard error observed with degrading resolution is
now not observed with resolutions under 15 nm. This is a true comparison of standard
errors for this spectral region. The results in Fig. 18 for the 2078 to 2366 nm spectral
region are still valid due to its large range which contains ten or more points up to 30 nm
resolution.
Glucose in Serum: The SEC and SEP plots versus resolution for glucose in the
serum study for the ten different spectral regions are provided in Figs. 21 to 30. The
results are generally the same as for glucose in water.
The combination band region is analyzed first. Range 1 with the largest glucose
absorbance band yields the lowest standard errors for a region isolating a single glucose
absorbance band, as shown on Fig. 21. Ranges 2 and 3 yield larger standard errors and
have smaller glucose absorbance bands with a decreased signal to noise level, as shown on
Figs. 22 and 23. Analysis of ranges 2 and 3 at degraded resolutions is limited by the
number of data points present in each range. Range 4 which couples the first three regions
demonstrates the lowest standard errors, as shown on Fig. 24. Again, the averaging of
points reduces the increase in standard error with degrading resolution. The increase in
standard error from 35 to 50 mg/dL observed as resolution degrades from 2 to 30 nm is
entirely due to the loss of information in extracting rather than averaging data points. While
the standard errors are higher than in the glucose in water Example, this Example
demonstrates that even in the presence of all of the spectral interferences, except skin and
blood cells, the resolution is essentially not an effect until after a resolution of 30 nm. This
is the same result as for glucose in water. The number of PLS factors incorporated is not
an issue due to the fact that 10 points are present even at 30 nm resolution. Range 9
incorporates all of range 4 and extends past where glucose absorbs at both higher and lower
frequencies, as shown in Fig. 29. No resolution effect on standard error is observed from
2 to 32 nm.
The effects of resolution in the first overtone spectral region are more difficult to
interpret due to decreased signal to noise and the narrower spectral ranges chosen. Range 5
has the largest glucose absorbance band in the overtone spectral window and results in the
lowest standard errors. Ranges 6 and 7 were shown to have very poor signal to noise
levels for glucose in water (not presented). The standard errors are essentially mean
centered prediction values, as shown on Figs. 26 and 27. The effect is worsened at
degrading resolution due to the number of points in each spectral range. Range 8 reveals
real glucose predictions, as shown on Fig. 28. This range was reanalyzed with the
averaged rather than the selected data, see Fig. 31. Using ten PLS factors, the increasing
standard error with degrading resolution observed is virtually identical to the nonaveraged
data due to the number of points present in the data. This is shown by comparing the
standard errors with only six PLS factors (6 points present at 30 nm resolution). No
resolution effect is observed until a resolution of 20 nm. Range 10 which expands to
higher and lower frequencies from range 8 has 10 data points present at 30 nm resolution
and shows no resolution effect until 20 nm, as shown in Fig. 30.
Conclusions: The glucose in water data set has sufficient signal to noise to
determine glucose with the specifications required. The rise in standard error for the
narrow glucose absorbance bands with degrading resolution is not real. It is partially the
result of selecting the points rather than averaging the points to generate new data sets. In
addition, the new data sets did not contain enough data points to compare analysis of 2 nm
resolution data and 32 nm resolution data with ten PLS factors. Resolution effects may be
addressed by using fewer PLS factors in this comparison or by using larger spectral
ranges. For both methods, the resolution effects are minimal to 30 nm in the combination
band region and 15 nm in the first overtone spectral window.
Because it is preferred to get the highest signal to noise ratio possible from the
instrument, it is acceptable to have 30 nm resolution. That is, by having less (but,
nonetheless, acceptable) resolution, e.g. by having 30 nm resolution instead of 10 nm
resolution, the instrument captures more signal relative to noise. Thus, even though the
resolution is coarser, more information is contained in signal generated by the instrument.
As a result, the resolution selected in the preferred embodiment of the invention provides a
more accurate picture of the spectra, even though the instrument has coarser resolution.
This is because there is a higher signal to noise ratio at the resolution required. In contrast,
if extra resolution were available in the instrument, but there was a lower signal to noise
ratio, less information would be available for processing by the basis sets.
In the Example, the glucose in serum data sets resulted in roughly three times the
standard error as in the glucose data set. Again, analysis is limited to either large spectral
windows or to comparisons with fewer PLS factors for narrower ranges. In the
combination band spectral region, the increase in standard error observed with degrading
resolution is minimal to 30 nm resolution. In the first overtone spectral window, the slope
to standard error versus resolution is minimal to 20 nm resolution. These results are
virtually identical to those generated in the glucose in water study. The effects of the
proteins, triglycerides, cholesterol, urea, salts, and minor organic constituents is observed
not to effect the required resolution.
* * *
Example - Basis Set V
It is necessary to measure effect of scattering of whole blood cells. This basis set is
generated as follows:
• Collect blood data set in transmission and as diffuse reflectance.
• Repeat component extraction.
• Couple in scatter correction
• Deconvolve (see deconvolution discussion below.).
Example - Basis Set VI
It is necessary to measure the effect of skin. Animal studies are performed and all
prior analysis techniques are repeated. Noninvasive studies can be viewed as extensions of
the basis set.
* * *
Uses of Basis Sets.
Chemical and physical knowledge of a system arc required for such factors as:
• Intelligent wavelength selection, e.g. knowledge of the location and degree of
interferences of each analyte.
• Interpretation of noise levels as a function of region.
• Interpretation of signal levels for each analytes as a function of wavelength.
• Selection of optimal signal to noise regions for each analyte.
Resolution specifications for an instrument implementation of the invention are set
forth above. The number of analog-to-digital (A/D) bits required to provide appropriate
instrument resolution can be calculated from noninvasive spectra and glucose intensities
(absorbance). For this determination, it is necessary to know the maximum intensity of the
whole system and the intensity of glucose at the required standard error. If the maximum
intensity of the sample is 10 to the negative absorbants unit, it is only necessary calculate
the intensity of the body scan, including all absorbants. To determine the intensity of
glucose, the required standard error is 9 mg/dL. The intensity of glucose and water, and
the intensity of water is used (as described above) to calculate the intensity of the glucose
and water minus the intensity of water. This results in a value for the intensity of glucose.
Once the intensity of glucose is determined, it is then necessary to determine the change in
intensity of glucose, e.g. by drawing in a base line to the peak, and plotting the change in
intensity of glucose versus glucose concentration. This provides a best fit of, the data that
can be fitted to a line to calculate the change in intensity at 9 mg/dL. Once this value is
obtained, the ratio of this value to maximum intensity of the glucose is readily calculated.
This ratio defines the number of bits that are required in the system for analog-to-digital
conversion. For example, if the ratio is 50,000, then a 16 bit A/D is required because
sufficient quantization must be provided to avoid aliasing problems. Thus, the basis set is
useful in defining instrument parameters.
Interpretation of multivariate results. Multivariate results are difficult to validate.
Standard errors must correlate with basis set information. If noisy regions are added, the
signal to noise ratio decreases. It is therefore necessary to correlate standard errors with the
signal to noise ratio.
With regard to the removal of second, third, ... order light in a grating based
spectrometer, a long pass filter is required. The basis set dictates the specifications of the
filter.
With regard to the removal of scatter, such determinations are based upon refractive
index change. In the preferred embodiment of the invention, the basis sets remove scatter
and temperature effects. This step is repeated for additional analytes, and the reduced
spectra are further processed using multivariate approaches.
Deconvolution of noninvasive spectra. The partial deconvolution reduces the rank
of first temperature and water, then proteins, then organic constituents. The resulting
spectra can then be fed into the multivariate approaches. However, the reduced dynamic
range of signals forces PLS to lock in on smaller analytes, such as urea and glucose,
instead of water and temperature.
There are a limited number of interferences for glucose in the near-IR. The major
interferences have convenient breaks in concentration. The largest concentrations / effects
are temperature and water. Processing should remove the Refractive index, which is on the
order of 100 g/dL.
Large concentration gaps exists between water and the proteins. Iterative
deconvolution can be used to take advantage of this fact.
Albumin and globulin proteins are on the order of 1 to 7 g/dL. These interferants
are easily identified and removed by spectral subtraction or rotation.
Example - Linearity
Introduction: The basis set is used to determine the location and intensity of each
of the major species interfering with glucose. It also demonstrates that for a given
component, the absorbance increases linearly with,increasing concentration. In this
Example, it is shown that the absorbance of multiple components is the sum of the
individual components, as assumed by Beer"s law. This is critical to the herein described
approach of using spectral subtraction of chemical information to enhance the signal to
noise level of glucose.
. Experimental: Spectra were collected in quadruplicate with a NIRS 5000
spectrometer configured in the transmission mode with a 1 mm pathlength quartz sample
cell. All samples are reagent grade and were prepared in a 0.1 M phosphate buffer at pH
7.35 and spectra were collected at 38.0 ± 0.2°C.
Six single analyte solutions were prepared: 4000 & 8000 mg/dL albumin, 2000 &
4000 mg/dL globulin, and 200 & 400 mg/dL triacetin. Eight additional samples were
prepared consisting of all possible permutations and combinations of the above six sample
concentrations. For example, one sample consisted of 8000 mg/dL albumin, 2000 mg/dL
globulin, and 200 mg/dL triacetin.
Results and Discussion: Three spectra of water, 8000 mg/dL albumin, 2000
mg/dL globulin and 200 mg/dL triacetin appear primarily as water absorbance bands, as
shown on Fig. 32. Subtraction of the water with the same algorithm used in the basis data
set that attempts to match pathlength and temperature effects (discussed above) was
employed to minimize the residual about zero absorbance over the spectral ranges 1640 to
1655 nm and 2077 to 2085 nm, as shown in Fig. 33. Results of incomplete temperature
and pathlength subtraction dominate in the regions surrounding 1890 to 2010 nm where no
signal results due to large water absorbance. The resulting spectra show the six dominant
protein absorbance bands centered at 1690, 1730, 2060, 2170, 2285, and 2335 nm.
Spectra of the single analyte albumin samples are shown in Fig. 34. The 8000 •
mg/dL albumin peaks are nearly exactly double the 4000 mg/dL albumin peaks indicating
that Beer"s law is holding. The average of the 8000 mg/dL albumin spectra was subtracted
from the spectra in Fig. 33 to yield the spectra shown in Fig. 35. Overlaid with this are the
2000 mg/dL globulin spectra. Clearly, the basic shape of the globulin spectra is discernible
after subtraction of the 100,000 mg/dL (100 g/dL) water and the 8000 mg/dL albumin. The
difference is the sum of the 200 mg/dL triacetin and baseline drift.
Spectra of the single analyte globulin samples are shown in Fig. 36. The 4000
mg/dL globulin peaks (260, 261, 262) are nearly exactly double the 2000 mg/dL globulin
peaks (263, 264). Again, the average of the 2000 mg/dL globulin spectra is subtracted
from the spectra shown in Fig. 33 to yield the spectra in Fig. 37. Overlaid with this are the
standard 200 mg/dL triacetin spectra. Once again, the 200 mg/dL triacetin peaks centered at
1675, 1715, 1760, 2130, 2250, and 2320 mg/dL can be seen after the subtraction of
100,000 mg/dL water, 8000 mg/dL albumin, and the 2000 mg/dL globulin. Unknown
concentrations may be subtracted by rotation.
Conclusions: For a relatively simple mixture, subtraction of the high
concentration water, albumin, and globulin results in spectra of triacetin. Clearly, small
errors in temperature and pathlength correction propagate into large errors of baseline for
the lower concentration analytes. It is also possible that the error in subtraction may be due
to scattering. To correct for this, a standard multiple scatter correction algorithm may be
used. Clearly, straight subtraction can yield spectra that visually appear to yield higher
signal to noise for the lower concentration analytes.
NOTE: the only two species in serum that have higher near-IR absorption than
glucose that were not included in this Example are cholesterol and urea.
* * *
In applying the invention, direct spectral subtraction is replaced with iterative
subtraction, based upon regions of minimal or defined absorbance of remaining analytes.
In another, equally preferred embodiment of the invention, another concentration gap may
taken advantage of for purposes of isolating the analyte vis-a-vis interferants. Two
presently preferred approaches includes:
• Analyze with multivariate techniques because the dynamic range of interferences
and glucose is the same; and
• Further removal of triglycerides, cholesterol, urea by deconvolution/ subtraction.
One approach to generating basis sets is iterative. For example, within a sample,
after subtracting water, a determination of albumin and globulin is made. Once albumin
and globulin are determined, and there is knowledge of water concentration, the albumin
and globulin may be again removed, only this time more accurately. This iterative process
proceeds to some predetermined limit of precision, and then triglycerides and cholesterol
are integrated into the analysis.
Although the invention is described herein with reference to the preferred
embodiment, one skilled in the art will readily appreciate that other applications may be
substituted for those set forth herein without departing from the spirit and scope of the
present invention. Accordingly, the invention should only be limited by the Claims
included below.
We claim:
1. An apparatus for measuring the glucose concentration in a tissue sample, comprising
the steps of:
a spectroscopic sensor for collecting an analog spectroscopic signal from said sample,
said spectrographic signal comprising substantially all relevant spectral components of said
sample;
an analog-to-digital converter coupled to an output of said spectroscopic sensor for
converting said spectroscopic signal collected by said spectroscopic sensor to a digital signal;
an electronic filter coupled to said analog to digital converter for receiving said digital
signal therefrom, said electronic filter substantially removing at least one spectral component
of said spectrographic signal that comprises a relevant interfering component of said sample,
wherein an interfering component comprises any of a chemical, physical, and environmental
phenomenon that results in a signal at least one wavelength where glucose absorbs, said
electronic filter outputting a signal that is representative of said glucose concentration in said
sample; and
a display for receiving said output signal from said electronic filter and for generating
a human perceptible representation thereof.
2. The apparatus of Claim 1, wherein said filter removes spectral components related to
substantially all of said relevant interfering components at other frequencies to quantify
absorbance of said interfering components at said other frequencies.
3. The apparatus of claim 1, wherein said filter does not filter components that do not
interfere with detection of glucose.
4. The apparatus of Claim 2, wherein relevant interferants include:
water, temperature and/or hydrogen effects, bonding effects, albumin, globulin,
protein, triglycerides, cholesterol, urea, scatter correction, refractive index
correction, depth of penetration, and organic, body, and physical components.
5. The apparatus of Claim 1, wherein said filter is configured by determining
in advance how each of said interfering components interact.
6. The apparatus of Claim 6, wherein said filter is configured by extracting in
advance each of said interfering components.
7. The apparatus of Claim 7, wherein said filter is configured by comparing
spectra for each of said interfering components with that of each of said
interfering components in solution in advance.
8. The apparatus of Claim 1, wherein said filter is configured by
characterizing each of said interfering components and subtracting each of said
interfering components from spectra produced at a frequency of interest in
advance.
9. The apparatus of Claim 1, wherein said filter includes a plurality of filters.
10. The apparatus of Claim 1, wherein said filter includes:
filters for spectra at different physiological glucose concentrations of interest.
11. The apparatus of Claim 1, wherein said at least one filter includes a filter
that corrects for interfering physical factors that include any of scattering,
pathlength, and temperature.
12. The apparatus of claim 1, wherein said filter is configured to apply one or more
corrections to said digital spectrum to produce an accurate representation of glucose
concentration.
13. The apparatus of claim 1, wherein said spectrum is non-invasively measured.
14. An apparatus for measuring the glucose concentration in a tissue sample, substantially
as herein described with reference to the accompanying drawings.
There is disclosed an apparatus (10) for measuring the glucose concentration in a tissue
sample comprising: a spectroscopic sensor (20) for collecting an analog spectroscopic signal from
said sample, an analog-to-digital converter (21) coupled to an output of said spectroscopic sensor for
converting said spectroscopic signal collected by said spectroscopic sensor to a digital signal;
an electronic filter coupled to said analog to digital converter for receiving said digital signal
therefrom, and a display (26) for receiving out put signal from said electronic filter and for
generating a human perceptible representation thereof.

Documents:

1385-CAL-1998-CORRESPONDENCE.pdf

1385-CAL-1998-FORM 27.pdf

1385-cal-1998-granted-abstract.pdf

1385-cal-1998-granted-assignment.pdf

1385-cal-1998-granted-claims.pdf

1385-cal-1998-granted-correspondence.pdf

1385-cal-1998-granted-description (complete).pdf

1385-cal-1998-granted-drawings.pdf

1385-cal-1998-granted-form 1.pdf

1385-cal-1998-granted-form 2.pdf

1385-cal-1998-granted-form 3.pdf

1385-cal-1998-granted-form 5.pdf

1385-cal-1998-granted-letter patent.pdf

1385-cal-1998-granted-pa.pdf

1385-cal-1998-granted-specification.pdf

1385-cal-1998-granted-translated copy of priority document.pdf


Patent Number 212301
Indian Patent Application Number 1385/CAL/1998
PG Journal Number 48/2007
Publication Date 30-Nov-2007
Grant Date 28-Nov-2007
Date of Filing 04-Aug-1998
Name of Patentee SENSYS MEDICAL INC.
Applicant Address 7470 WEST CHANDLER BLVD., CHANDLER, AZ 85226
Inventors:
# Inventor's Name Inventor's Address
1 STEPHEN F. MALIN 16228 S. 4TH STREET, PHOENIX, ARIZONA 85044
2 KEVIN H. HAZEN 13220 S. 48TH STREET, PHOENIX, ARIZONA 85044
PCT International Classification Number G01N 21/27
PCT International Application Number N/A
PCT International Filing date
PCT Conventions:
# PCT Application Number Date of Convention Priority Country
1 08/911,588 1997-08-14 U.S.A.