U.S. patent application number 10/592218 was filed with the patent office on 2008-11-06 for method and apparatus for implementing threshold based correction functions for biosensors.
This patent application is currently assigned to BAYER HEALTHCARE LLC. Invention is credited to George A. Mecklenburg.
Application Number | 20080274447 10/592218 |
Document ID | / |
Family ID | 34967238 |
Filed Date | 2008-11-06 |
United States Patent
Application |
20080274447 |
Kind Code |
A1 |
Mecklenburg; George A. |
November 6, 2008 |
Method and Apparatus for Implementing Threshold Based Correction
Functions for Biosensors
Abstract
A biosensor system, method and apparatus are provided for
implementing threshold based correction functions for biosensors. A
primary measurement of an analyte value is obtained. A secondary
measurement of a secondary effect is obtained and is compared with
a threshold value. A correction function is identified responsive
to the compared values. The correction function is applied to the
primary measurement of the analyte value to provide a corrected
analyte value. The correction method uses correction curves that
are provided to correct for an interference effect. The correction
curves can be linear or non-linear. The correction method provides
different correction functions above and below the threshold value.
The correction functions may be dependent or independent of the
primary measurement that is being corrected. The correction
functions may be either linear or nonlinear.
Inventors: |
Mecklenburg; George A.;
(Elkhart, IN) |
Correspondence
Address: |
NIXON PEABODY LLP
161 N. CLARK STREET, 48TH FLOOR
CHICAGO
IL
60601
US
|
Assignee: |
BAYER HEALTHCARE LLC
Elkhart
IN
|
Family ID: |
34967238 |
Appl. No.: |
10/592218 |
Filed: |
March 31, 2005 |
PCT Filed: |
March 31, 2005 |
PCT NO: |
PCT/US2005/011077 |
371 Date: |
July 11, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60557907 |
Mar 31, 2004 |
|
|
|
60609570 |
Sep 13, 2004 |
|
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|
Current U.S.
Class: |
435/4 |
Current CPC
Class: |
C12Q 1/006 20130101;
G01N 27/3274 20130101 |
Class at
Publication: |
435/4 |
International
Class: |
C12Q 1/00 20060101
C12Q001/00 |
Claims
1. A method for implementing threshold based correction functions
for a biosensor comprising the steps of: applying a sample to the
biosensor and obtaining a primary measurement of an analyte value;
obtaining a secondary measurement of a secondary effect; comparing
said secondary measurement of the secondary effect with a threshold
value; responsive to said compared values, identifying a correction
function; and applying said identified correction function to said
primary measurement to provide a corrected analyte value.
2. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the step responsive
to said compared values, of identifying a correction function
includes the steps of identifying said secondary measurement of the
secondary effect less than or equal to said threshold value,
identifying a first coefficient A, said first coefficient A to
control magnitude of said correction function.
3. A method for implementing threshold based correction functions
for a biosensor as recited in claim 2 further includes the steps of
calculating said correction function represented by
C.sub.n=F*T+A*(T.sub.c-T)+H, where T represents said secondary
measurement of the secondary effect, T.sub.c represents said
threshold value; and F, H are predefined coefficients.
4. A method for implementing threshold based correction functions
for a biosensor as recited in claim 3 wherein the step of applying
said identified correction function to said primary measurement to
provide a corrected analyte value further includes the steps of
calculating said corrected analyte value represented by Gc=Gn/Cn,
where Gn represent said primary measurement of said analyte
value.
5. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the step responsive
to said compared values, of identifying a correction function
includes the steps of identifying said secondary measurement of the
secondary effect greater than said threshold value, identifying a
coefficient I, said coefficient I being used to control magnitude
of said correction function.
6. A method for implementing threshold based correction functions
for a biosensor as recited in claim 5 further includes the steps of
calculating said correction function represented by
C.sub.n=F*T+I*(T-T.sub.c)+H, where T represents said secondary
measurement of the secondary effect, T.sub.c represents said
threshold value; and F, H are predefined coefficients.
7. A method for implementing threshold based correction functions
for a biosensor as recited in claim 6 wherein the step of applying
said identified correction function to said primary measurement to
provide a corrected analyte value further includes the steps of
calculating said corrected analyte value represented by Gc=Gn/Cn,
where Gn represent said primary measurement of said analyte
value.
8. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the step of
responsive to said compared values, identifying a correction
function includes the steps of storing predefined correction
curves; said predefined correction curves being provided to correct
for an interference effect.
9. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the step of
responsive to said compared values, identifying a correction
function includes the steps responsive to said secondary
measurement of the secondary effect being less than or equal to
said threshold value of identifying a first coefficient A and
identifying said correction function responsive to said identified
first coefficient A.
10. A method for implementing threshold based correction functions
for a biosensor as recited in claim 9 wherein the step of
responsive to said compared values, identifying a correction
function includes the steps responsive to said secondary
measurement of the secondary effect being greater than said
threshold value of identifying a second coefficient I and
identifying said correction function responsive to said identified
second coefficient I.
11. A method for implementing threshold based correction functions
for a biosensor as recited in claim 10 wherein the steps of
identifying said first coefficient A and identifying a second
coefficient I include the steps of providing stored correction
curves; said correction curves representing characteristics of said
secondary measurement of the secondary effect.
12. A method for implementing threshold based correction functions
for a biosensor as recited in claim 10 wherein the steps of
identifying said correction function responsive to said identified
second coefficient A and identifying said correction function
responsive to said identified second coefficient I includes the
steps of identifying a linear function for said correction
function.
13. A method for implementing threshold based correction functions
for a biosensor as recited in claim 10 wherein the steps of
identifying said correction function responsive to said identified
second coefficient A and identifying said correction function
responsive to said identified second coefficient I includes the
steps of identifying a nonlinear function for said correction
function.
14. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the steps of
identifying said correction function includes the steps of
identifying said correction function using at least one coefficient
value; said at least one coefficient value being dependent upon
said primary measurement of said analyte value.
15. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the steps of
identifying said correction function includes the steps of
identifying said correction function using at least one coefficient
value; said at least one coefficient value being a predefined value
independent of said primary measurement of said analyte value.
16. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the step of obtaining
a secondary measurement of a secondary effect include the step of
obtaining a temperature measurement.
17. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the analyte is
glucose and wherein step of obtaining a secondary measurement of a
secondary effect include the step of obtaining a hemoglobin
measurement.
18. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the analyte is
glucose and wherein step of obtaining a secondary measurement of a
secondary effect include the step of obtaining a measurement
indicating a concentration of hematocrit.
19. A method for implementing threshold based correction functions
for a biosensor as recited in claim 1 wherein the analyte is
glucose and wherein step of obtaining a secondary measurement of a
secondary effect include the step of obtaining a measurement
indicating a concentration of hematocrit and obtaining a
temperature measurement.
20. Apparatus for implementing threshold based correction functions
comprising: a biosensor for receiving a sample; a processor coupled
to said biosensor; said processor responsive to said biosensor for
receiving the sample for obtaining a primary measurement of an
analyte value; said processor for obtaining a secondary measurement
of a secondary effect; said processor for comparing said secondary
measurement of the secondary effect with a threshold value; said
processor responsive to said compared values, for identifying a
correction function; and said processor for applying said
identified correction function to said primary measurement to
provide a corrected analyte value
21. Apparatus for implementing threshold based correction functions
as recited in claim 20 includes stored correction curves used by
said processor for identifying said correction function; said
correction curves representing characteristics of said secondary
measurement of the secondary effect.
22. Apparatus for implementing threshold based correction functions
as recited in claim 20 wherein said processor is responsive to
identifying said secondary measurement of the secondary effect less
than or equal to said threshold value, for identifying a first
coefficient A, said first coefficient A being used to control
magnitude of said correction function.
23. Apparatus for implementing threshold based correction functions
as recited in claim 22 wherein said processor is responsive to
identifying said secondary measurement of the secondary effect
greater than said threshold value, for identifying a second
coefficient I, said second coefficient I being used to control
magnitude of said correction function.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to Application Nos.
60/557,907, filed Mar. 31, 2004 and 60/609,570, filed Sep. 13,
2004, which are incorporated by reference in its entirety.
FIELD OF THE INVENTION
[0002] The present invention relates generally to biosensors, and
more particularly, relates to a method and apparatus for
implementing threshold based correction functions for
biosensors.
DESCRIPTION OF THE RELATED ART
[0003] The quantitative determination of analytes in body fluids is
of great importance in the diagnoses and maintenance of certain
physiological abnormalities. For example lactate, cholesterol and
bilirubin should be monitored in certain individuals. In
particular, the determination of glucose in body fluids is of great
importance to diabetic individuals who must frequently check the
level of glucose in their body fluids as a means of regulating the
glucose intake in their diets. While the remainder of the
disclosure herein will be directed towards the determination of
glucose, it is to be understood that the procedure and apparatus of
this invention can be used for the determination of other analytes
upon selection of the appropriate enzyme. The ideal diagnostic
device for the detection of glucose in fluids must be simple, so as
not to require a high degree of technical skill on the part of the
technician administering the test. In many cases, these tests are
administered by the patient which lends further emphasis to the
need for a test which is easy to carry out. Additionally, such a
device should be based upon elements which are sufficiently stable
to meet situations of prolonged storage.
[0004] Methods for determining analyte concentration in fluids can
be based on the electrochemical reaction between an enzyme and the
analyte specific to the enzyme and a mediator which maintains the
enzyme in its initial oxidation state. Suitable redox enzymes
include oxidases, dehydrogenases, catalase and peroxidase. For
example, in the case where glucose is the analyte, the reaction
with glucose oxidase and oxygen is represented by equation (A).
##STR00001##
[0005] In a calorimetric assay, the released hydrogen peroxide, in
the presence of a peroxidase, causes a color change in a redox
indicator which color change is proportional to the level of
glucose in the test fluid. While calorimetric tests can be made
semi-quantitative by the use of color charts for comparison of the
color change of the redox indicator with the color change obtained
using test fluids of known glucose concentration, and can be
rendered more highly quantitative by reading the result with a
spectrophotometric instrument, the results are generally not as
accurate nor are they obtained as quickly as those obtained using
an electrochemical biosensor. As used herein, the term biosensor
system refer to an analytical device that responds selectively to
analytes in an appropriate sample and converts their concentration
into an electrical signal via a combination of a biological
recognition signal and a physico-chemical transducer.
H.sub.2O.sub.2-->O.sub.2+2H.sup.++2e.sup.- (B)
The electron flow is then converted to the electrical signal which
directly correlates to the glucose concentration.
[0006] In the initial step of the reaction represented by equation
(A), glucose present in the test sample converts the oxidized
flavin adenine dinucleotide (FAD) center of the enzyme into its
reduced form, (FADH.sub.2). Because these redox centers are
essentially electrically insulated within the enzyme molecule,
direct electron transfer to the surface of a conventional electrode
does not occur to any measurable degree in the absence of an
unacceptably high overvoltage. An improvement to this system
involves the use of a nonphysiological redox coupling between the
electrode and the enzyme to shuttle electrons between the
(FADH.sub.2) and the electrode. This is represented by the
following scheme in which the redox coupler, typically referred to
as a mediator, is represented by M:
Glucose+GO(FAD)->gluconolactone+GO(FADH.sub.2)
GO(FADH.sub.2)+2M.sub.OX->GO(FAD)+2M.sub.red+2H.sup.+
2M.sub.red->2M.sub.OX+2e.sup.- (at the electrode)
[0007] In this scheme, GO(FAD) represents the oxidized form of
glucose oxidase and GO(FADH.sub.2) indicates its reduced form. The
mediating species M.sub.red shuttles electrons from the reduced
enzyme to the electrode thereby oxidizing the enzyme causing its
regeneration in situ which, of course, is desirable for reasons of
economy. The main purpose for using a mediator is to reduce the
working potential of the sensor. An ideal mediator would be
re-oxidized at the electrode at a low potential under which
impurity in the chemical layer and interfering substances in the
sample would not be oxidized thereby minimizing interference.
[0008] Many compounds are useful as mediators due to their ability
to accept electrons from the reduced enzyme and transfer them to
the electrode. Among the mediators known to be useful as electron
transfer agents in analytical determinations are the substituted
benzo- and naphthoquinones disclosed in U.S. Pat. No. 4,746,607;
the N-oxides, nitroso compounds, hydroxylamines and oxines
specifically disclosed in EP 0 354 441; the flavins, phenazines,
phenothiazines, indophenols, substituted 1,4-benzoquinones and
indamins disclosed in EP 0 330 517 and the
phenazinium/phenoxazinium salts described in U.S. Pat. No.
3,791,988. A comprehensive review of electrochemical mediators of
biological redox systems can be found in Analytica Clinica Acta.
140 (1982), Pp 1-18.
[0009] Among the more venerable mediators is hexacyanoferrate, also
known as ferricyanide, which is discussed by Schlapfer et al in
Clinica Chimica Acta., 57 (1974), Pp. 283-289. In U.S. Pat. No.
4,929,545 there is disclosed the use of a soluble ferricyanide
compound in combination with a soluble ferric compound in a
composition for enzymatically determining an analyte in a sample.
Substituting the iron salt of ferricyanide for oxygen in equation
(A) provides:
##STR00002##
since the ferricyanide is reduced to ferrocyanide by its acceptance
of electrons from the glucose oxidase enzyme.
[0010] Another way of expressing this reaction is by use of the
following equation (C):
Glucose+GO.sub.X(OX)->Gluconalactone+GO.sub.X(red)
GO.sub.X(red)+2Fe(CN.sub.3).sup.3-.sub.6->GO.sub.X(OX)+2Fe(CN).sup.4--
+2e.sup.- (C)
The electrons released are directly equivalent to the amount of
glucose in the test fluid and can be related thereto by measurement
of the current which is produced through the fluid upon the
application of a potential thereto. Oxidation of the ferrocyanide
at the anode renews the cycle.
[0011] U.S. Pat. No. 6,391,645 to Huang et al., issued May 21, 2002
and assigned to the present assignee, discloses a method and
apparatus for correcting ambient temperature effect in biosensors.
An ambient temperature value is measured. A sample is applied to
the biosensors, then a current generated in the test sample is
measured. An observed analyte concentration value is calculated
from the current through a standard response curve. The observed
analyte concentration is then modified utilizing the measured
ambient temperature value to thereby increase the accuracy of the
analyte determination. The analyte concentration value can be
calculated by solving the following equation:
G2=(G1-(T.sub.2.sup.231
24.sup.2)*I2-(T.sub.2-24)*I1)/((T.sub.2.sup.2-24.sup.2)*S2+(T.sub.2-24)*S-
1+1)
where G1 is said observed analyte concentration value, T.sub.2 is
said measured ambient temperature value and I1, I2, S1, and S2 are
predetermined parameters.
[0012] While the method and apparatus disclosed by U.S. Pat. No.
6,391,645 provided improvements in the accuracy of the analyte
determination, a need exists for an improved correction mechanism
and that can be applied to any system that measures an analyte
concentration.
[0013] As used in the following specification and claims, the term
biosensor means an electrochemical sensor strip or sensor element
of an analytical device or biosensor system that responds
selectively to an analyte in an appropriate sample and converts
their concentration into an electrical signal. The biosensor
generates an electrical signal directly, facilitating a simple
instrument design. Also, a biosensor offers the advantage of low
material cost since a thin layer of chemicals is deposited on the
electrodes and little material is wasted.
[0014] The term sample is defined as a composition containing an
unknown amount of the analyte of interest. Typically, a sample for
electrochemical analysis is in liquid form, and preferably the
sample is an aqueous mixture. A sample may be a biological sample,
such as blood, urine or saliva. A sample may be a derivative of a
biological sample, such as an extract, a dilution, a filtrate, or a
reconstituted precipitate.
[0015] The term analyte is defined as a substance in a sample, the
presence or amount of which is to be determined. An analyte
interacts with the oxidoreductase enzyme present during the
analysis, and can be a substrate for the oxidoreductase, a
coenzyme, or another substance that affects the interaction between
the oxidoreductase and its substrate.
SUMMARY OF THE INVENTION
[0016] Important aspects of the present invention are to provide a
new and improved biosensor system for determining the presence or
amount of a substance in a sample including a method and apparatus
for implementing threshold based correction functions for
biosensors.
[0017] In brief, a method and apparatus are provided for
implementing threshold based correction functions for biosensors. A
sample is applied to the biosensor and a primary measurement of an
analyte value is obtained. A secondary measurement of a secondary
effect is obtained and is compared with a threshold value. A
correction function is identified responsive to the compared
values. The correction function is applied to the primary
measurement of the analyte value to provide a corrected analyte
value.
[0018] In accordance with features of the invention, the correction
method uses correction curves that are provided to correct for an
interference effect. The correction curves can be linear or
non-linear. The correction method provides different correction
functions above and below the threshold value. The correction
functions may be dependent or independent of the primary
measurement that is being corrected. The correction functions may
be either linear or nonlinear.
[0019] In accordance with features of the invention, the secondary
measurement of a secondary effect includes a plurality of effects
that are use separately or together in combination to identify the
correction function. For example, the secondary effects include
temperature, Hemoglobin, and the concentration of hematocrit of a
blood sample that are identified and used to minimize the
interference of the secondary effects on the accuracy of the
reported results.
BRIEF DESCRIPTION OF THE DRAWING
[0020] The present invention together with the above and other
objects and advantages may best be understood from the following
detailed description of the preferred embodiments of the invention
illustrated in the drawings, wherein:
[0021] FIG. 1 is a block diagram representation of biosensor system
in accordance with the present invention;
[0022] FIG. 2 is a flow chart illustrating exemplary logical steps
performed in accordance with the present invention of the method
for implementing threshold based correction of secondary effects,
such as correcting ambient temperature effect, in the biosensor
system of FIG. 1; and
[0023] FIGS. 3 and 4 are graphs of exemplary stored correction
curves illustrating corrections characteristics in accordance with
the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0024] Having reference now to the drawings, in FIG. 1 there is
shown a block diagram representation of biosensor system designated
as a whole by the reference character 100 and arranged in
accordance with principles of the present invention. Biosensor
system 100 includes a microprocessor 102 together with an
associated memory 104 for storing program and user data and
correction curves for implementing threshold based correction of
secondary effects in accordance with the present invention. A meter
function 106 coupled to a biosensor 108 is operatively controlled
by the microprocessor 102 for recording test values, such as blood
glucose test values. An ON/OFF input at a line 110 responsive to
the user ON/OFF input operation is coupled to the microprocessor
102 for performing the blood test sequence mode of biosensor system
100. A system features input at a line 112 responsive to a user
input operation is coupled to the microprocessor 102 for
selectively performing the system features mode of biosensor 100. A
thermistor 114 provides a temperature signal input indicated at a
line 116 is coupled to the microprocessor 102 for detecting
interfering effects, for example, the temperature information for
the sensor 108 in accordance with the invention. A signal input
indicated at a line 120 is coupled to the microprocessor 102 for a
second measure of interfering substances, for example, Hemoglobin,
optionally provided by the meter function 106.
[0025] A display 130 is coupled to the microprocessor 102 for
displaying information to the user including test results. A
battery monitor function 132 is coupled to the microprocessor 102
for detecting a low or dead battery condition. An alarm function
134 is coupled to the microprocessor 102 for detecting predefined
system conditions and for generating alarm indications for the user
of biosensor system 100. A data port or communications interface
136 is provided for coupling data to and from a connected computer
(not shown). Microprocessor 102 contains suitable programming to
perform the methods of the invention as illustrated in FIG. 2.
[0026] Biosensor system 100 is shown in simplified form sufficient
for understanding the present invention. The illustrated biosensor
system 100 is not intended to imply architectural or functional
limitations. The present invention can be used with various
hardware implementations and systems.
[0027] In accordance with the invention, biosensor system 100
performs a correction method of the preferred embodiment, for
example, to reduce the temperature bias having a general form as
shown in the following TABLE 1 and as illustrated and described
with respect to FIG. 2. This invention provides an algorithmic
correction method that advantageously improves the accuracy of
diagnostic chemistry tests by correcting for secondary effects,
such as interfering substances or temperature effects.
[0028] It should be understood that the present invention can be
applied to any system, electrochemical or optical, that measures an
analyte concentration as a primary measurement and then uses a
second measure of interfering substances, for example, Hemoglobin,
or interfering effects for example, temperature, to compensate for
the secondary effect and improve the accuracy of the reported
result.
[0029] It is also desirable to minimize the interference from
hematocrit or volume fraction of erythrocytes on the accuracy of
the reported results. The conductivity or impedance of whole blood
is dependent on the concentration of hematocrit. Meter function 120
can be used to measure the resistance of the sample fluid at signal
input line 120 and the measured value advantageously used to
correct for the effect of hematocrit on the reported result. For
example, the measured resistance advantageously is used to estimate
the concentration of hematocrit of a blood sample and then to
correct the measurement for hematocrit effect for determining the
concentration of a substance of interest in blood. This invention
provides an algorithmic correction method that advantageously
improves the accuracy of diagnostic chemistry tests by correcting
for secondary effects including interference from hematocrit and
temperature effects.
[0030] In accordance with the invention, the algorithmic correction
method uses correction curves, for example, as illustrated and
described with respect to FIGS. 3 and 4, that can be tailored to
correct for any well-defined interference effect. The correction
curves can be linear or non-linear. The algorithmic correction
method has characteristics that can be modified by changing only
the equation coefficients as follows. First, different correction
functions can be provided above and below a threshold. Second, the
correction functions may be dependent or independent of the primary
measurement that is being corrected. Third, functions used for
correction may be either linear or nonlinear.
TABLE-US-00001 TABLE 1 General Correction Algorithm Form Step 1.
Obtain primary measurement (G.sub.n). Step 2. Obtain secondary
measurement used to correct G.sub.n(T) Step3A If T .ltoreq. T.sub.c
then: 1. A = f(G.sub.n) 2. C.sub.n = F * T + A * (T.sub.c - T) + H
Step 3B If T > T.sub.c then: 3. I = f.sub.2(G.sub.N) 4. C.sub.n
= F*T + I*(T - T.sub.c) + H 5. G.sub.c = (G.sub.N/C.sub.n)
Where:
[0031] G.sub.n=Uncorrected measurement of analyte concentration;
T=Secondary measurement used to correct primary measurement;
T.sub.C=Decision point or threshold, secondary measurements greater
of less than threshold advantageously can use different correction
functions; G.sub.C=Final corrected result; and A, I, F, H, are
coefficients that control magnitude of correction lines or define
correction curves.
[0032] Referring now to FIG. 2, there are shown exemplary logical
steps performed in accordance with the present invention of the
method for implementing threshold based correction of secondary
effects, such as correcting ambient temperature effect, in the
biosensor system 100. A strip is inserted as indicated in a block
200 and then waiting for a sample to be applied is performed as
indicated in a block 202. A primary measurement Gn is obtained as
indicated in a block 204. Then a secondary measurement T to be used
for correction Gn(T) is obtained as indicated in a block 206. The
secondary measurement T is compared with the threshold value Tc as
indicated in a decision block 208. If the secondary measurement T
is less than or equal to the threshold value Tc, then a coefficient
A to control magnitude of the correction is identified as indicated
in a block 210, where A=f(Gn). Then a correction Cn is calculated
as indicated in a block 210, where C.sub.n=F*T+A*(Tc-T)+H.
Otherwise If the secondary measurement T is greater than the
threshold value Tc, then a coefficient I to control magnitude of
the correction is identified as indicated in a block 214, where
I=f2(Gn). Then a correction Cn is calculated as indicated in a
block 216, where C.sub.n=F*T+I*(T-T.sub.C)+H. A final corrected
result Gc is calculated as indicated in a block 218, where Gc=Gn/Cn
to complete the correction algorithm as indicated in a block
220.
[0033] Referring now to FIGS. 3 and 4, there are shown respective
first and second examples generally designated by reference
characters 300 and 400 illustrating exemplary theoretical lines of
correction. In FIGS. 3 and 4, a percentage (%) correction is
illustrated relative to a vertical axis and a secondary measurement
T is illustrated relative to a horizontal axis. A threshold value
Tc is indicated by a line labeled Tc.
[0034] FIG. 3 illustrates isometric correction lines at different
primary measurement concentrations Gn where the correction is
dependent on the primary measurement concentrations Gn. As shown in
the example 300 in FIG. 3, the magnitude of the correction Cn
changes with analyte concentration Gn when the secondary
measurement T is above or below the threshold Tc. FIG. 4
illustrates isometric correction lines at different primary
measurement concentrations Gn where the correction is dependent on
the primary measurement concentrations Gn above the threshold value
Tc and is constant and independent of the primary measurement
concentrations Gn below and equal to the threshold value Tc.
[0035] While the present invention has been described with
reference to the details of the embodiments of the invention shown
in the drawing, these details are not intended to limit the scope
of the invention as claimed in the appended claims.
* * * * *