U.S. patent application number 12/192050 was filed with the patent office on 2008-12-25 for breath-based sensors for non-invasive molecular detection.
This patent application is currently assigned to University of Iowa Research Foundation. Invention is credited to Luke M. Haverhals, Johna Leddy.
Application Number | 20080314116 12/192050 |
Document ID | / |
Family ID | 36588675 |
Filed Date | 2008-12-25 |
United States Patent
Application |
20080314116 |
Kind Code |
A1 |
Leddy; Johna ; et
al. |
December 25, 2008 |
BREATH-BASED SENSORS FOR NON-INVASIVE MOLECULAR DETECTION
Abstract
A method of diagnosing the health of an individual by collecting
a breath sample from the individual and measuring the amount of
each of a plurality of analytes in the sample. The amount of each
analytes is measured by fitting a time response curve of a
sample-evaluation fuel cell in which the fuel cell sample electrode
is contacted with the sample with the analysis based on a function
of standard time response curves for an equivalent fuel cell
configuration obtained separately for each of the analytes on a
fuel cell with equivalent construction as sample-evaluation fuel
cell. Each of the plurality of analytes is generally indicative of
an aspect of the individual's health. Suitable analytes include,
for example, inorganic compounds as well as compositions that
exhibit negative reduction reactions at least for a portion of the
time response curve. In particular, acetone exhibits a negative
potential/current peak when it is an analyte in a fuel cell in an
sample electrode with a counter electrode exposed to oxygen, which
may or may not be introduced in the form of air. Various forms of
analysis to estimate acetone concentrations in the breath can be
used.
Inventors: |
Leddy; Johna; (Iowa City,
IA) ; Haverhals; Luke M.; (Coralville, IA) |
Correspondence
Address: |
FOLEY AND LARDNER LLP;SUITE 500
3000 K STREET NW
WASHINGTON
DC
20007
US
|
Assignee: |
University of Iowa Research
Foundation
|
Family ID: |
36588675 |
Appl. No.: |
12/192050 |
Filed: |
August 14, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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11305799 |
Dec 16, 2005 |
7421882 |
|
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12192050 |
|
|
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60636951 |
Dec 17, 2004 |
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Current U.S.
Class: |
73/23.3 |
Current CPC
Class: |
G01N 33/497 20130101;
H01M 8/1023 20130101; Y10S 436/90 20130101; H01M 2250/00 20130101;
Y02E 60/50 20130101; H01M 8/04753 20130101; H01M 8/1039 20130101;
G01N 33/4972 20130101 |
Class at
Publication: |
73/23.3 |
International
Class: |
G01N 33/497 20060101
G01N033/497 |
Claims
1-8. (canceled)
9. A method for diagnosing the health of an individual, the method
comprising measuring the amount of a plurality of analytes in a
breath sample from an individual by fitting a time response curve
of a sample-evaluation fuel cell with at least one of the analytes
having a negative potential peak in the time response curve
indicating a reduction process, wherein a sample electrode of the
fuel cell is exposed to a breath sample and the counter electrode
of the fuel cell is exposed to a selected reactant.
10. The method of claim 9 wherein an analyte having a negative
potential peak in the time response curve is acetone.
11. The method of claim 9 wherein the measuring is performed with a
portable breathalyzer comprising the sample-evaluation fuel cell
and a microprocessor.
12. The method of claim 9 wherein the selected reactant comprises
O.sub.2.
13-26. (canceled)
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a division of U.S. application Ser. No.
11/305,799 filed Dec. 16, 2005, which claims priority to U.S.
provisional application 60/636,951 filed on Dec. 17, 2004 to Leddy
et al., both incorporated herein by reference in their
entireties.
FIELD OF THE INVENTION
[0002] The present invention relates to approaches for measuring
and differentiating volatile organic and/or inorganic compositions
in vapor samples. More particularly, the present invention relates
to using fuel cells as sensors for measuring the amount of various
diagnostic analytes, which can be characteristic of a disease
state, in an individual's breath.
BACKGROUND OF THE INVENTION
[0003] Acetone found in a human's blood, urine, and breath can be a
marker for various biological processes, the most notable of which
is diabetic ketoacidosis associated with insulin insufficiency.
Also, acetone can also be an indicator of poor regulation of a
ketogenic diet that is used to control refractory epileptic
seizures.
[0004] Conventional monitoring devices for diabetic ketoacidosis
and regulating ketogenic diets often rely on invasive sample
collection, such as blood tests. The American Diabetes Association
recommends that diabetics monitor their glucose levels several
times a day. However, because of the invasive nature of
conventional monitoring devices, many diabetics with type 1
("insulin dependent") diabetes monitor glucose levels only once a
day and most diabetics with type 2 ("insulin resistant") diabetes
no not monitor glucose levels daily.
[0005] Ketone generation in the body is known to be associated with
certain conditions. Referring to FIG. 1, insulin facilitates the
transport of glucose into the cell to generate energy. Diabetes
generally occurs when either the amount of insulin is insufficient
(type 1 diabetes) or the insulin is not effective (type 2
diabetes). As a result, the blood glucose level can rise and the
cells become glucose-starved. Ketogenesis in the mitochondria then
converts triglycerides (fatty acids) to acetoacetate (AcAc) and
energy. The AcAc interconverts with 3-hydroxybutyrate (3HB) and
also undergoes spontaneous decarboxylation to form acetone
(Me.sub.2O). Together these three products (AcAc, 3HB, and
Me.sub.2O) are known as ketone bodies, which can partition across
the cell wall and into the blood.
[0006] Of the three ketone bodies, only acetone is sufficiently
volatile to partition into the alveolar air, while AcAc and 3HB
remain in the blood. The partition coefficient, K for Me.sub.2O at
the blood/air interface is between 208 and 597, a factor even more
favorable that that of ethanol. The ethanol partition coefficient
is used in determining the blood alcohol content or blood alcohol
concentration (BAC) of an individual. The acetone that partitions
in to the alveolar air generates the sweet smell characteristic of
diabetic ketoacidosis, which is sometimes referred to as "acetone
breath."
[0007] Diabetic ketoacidosis occurs as the fatty acids are consumed
and the concentration of ketone bodies rises. For normal subjects,
the concentration ratio of 3HB to AcAc is about 1:1 and the total
concentration of ketone bodies is below 0.5 mM. Under diabetic
ketoacidotic conditions, the ratio of 3HB to AcAc increases to
about 3:1, or even as high as 10:1, and the concentration of the
ketone bodies drastically increases. Concentrations for the ketone
bodies are listed in Table 1 for human subjects who are healthy
individuals, treated diabetics, and ketoacidotic diabetics.
TABLE-US-00001 TABLE 1 Plasma Concentrations of Ketone Bodies in
Plasma (mM). Ketoacidotic Ketone Body Normal Subject Treated
Diabetic Diabetic Acetone (Me.sub.2O) 0.015 .+-. 0.005 1.69 .+-.
0.78 3.26 .+-. 0.79 Acetoacetate (AcAc) 0.114 .+-. 0.029 0.306 .+-.
0.05 2.84 .+-. 0.40 3-Hydroxy-butyrate 0.160 .+-. 0.050 0.810 .+-.
0.171 8.23 .+-. 1.48 (3KB) pH -- -- 7.29 .+-. 0.01
[0008] As can be seen in Table 1, the concentration of acetone in a
ketoacidotic diabetic is approximately two times greater than that
of a treated diabetic, and the concentration of acetone is roughly
a hundred times greater in a treated diabetic than a normal
subject. Also, the concentrations of AcAc and 3HB in a ketoacidotic
diabetic are roughly 25 times higher and 50 times higher than that
of a normal subject, respectively.
[0009] The ketone body composition illustrates that acetone
measurement can be an effective marker for the onset of
ketoacidosis and the ketoacidotic state. Ketoacidosis can be
followed by the 3HB concentration as it tracks with the total
ketone load. Breath acetone correlates with plasma 3HB over a
clinically relevant range. Thus, by tracking acetone on the breath,
3HB can be reliably measured and the onset of ketoacidosis can be
tracked.
[0010] Portable sensors have been developed for measuring alcohol
on a human's breath. Breathalyzers determine the BAC by measuring
the ethanol concentration in alveolar air that is exhaled from deep
within the lungs. Because there is an equilibrium of ethanol
between the blood and alveolar air, the ethanol concentration in
the breath is generally proportional to the ethanol concentration
in the blood.
SUMMARY OF THE INVENTION
[0011] In all of the following, the potential/current time response
curve is discussed with respect to a sample analyte being exposed
to a fuel cell sample electrode and a selected reactant exposed to
the counter electrode. In principle, the electronics for making the
measurement can be connected with either polarity, although only
one connection results in the conventional response curve. To
simplify the discussion, the following is based on a conventional
connection for measuring the response curve with a positive
displacement corresponding to oxidation at the sample electrode and
reduction at the counter electrode. If the alternative connection
is made, all of the values can be reversed in sign to use the
analysis below, although alternatively the sign can be changed and
notations correspondingly flipped in the following analyses.
[0012] In a first aspect, the invention pertains to a method for
the estimation of acetone concentration in a person's breath.
Generally, the method comprises fitting a time response curve of a
sample-evaluation fuel cell in which a sample electrode of the fuel
cell is exposed to a breath sample and a counter electrode of the
fuel cell is exposed to O.sub.2. The fitting can be performed
through the de-convolution of the sample time response curve in
comparison of the sample time response curve with the time response
curve of standard aqueous acetone solutions. This method can be
adapted for the evaluation of diabetic ketoacidosis of an
individual by estimating the concentration of acetone in a person's
blood stream from an estimate of acetone concentration within the
breath sample.
[0013] In further aspects, the invention pertains to a method for
diagnosing the health of an individual in which the method
comprises measuring the amount of a plurality of analytes in a
breath sample from an individual. The measurements are obtained
through fitting a time response curve of a sample-evaluation fuel
cell. In some embodiments, at least one of the analytes has a
negative potential peak in the time response curve indicating a
reduction process. A sample electrode of the fuel cell is exposed
to a breath sample, and the counter electrode of the fuel cell is
exposed to a selected reactant. The selected reactant can comprise
O.sub.2, which may or may not be delivered as air.
[0014] In other aspects, the invention pertains to a method for
diagnosing the health of an individual in which the method
comprises measuring the amount of a plurality of analytes in a
breath sample from an individual. The measurements are obtained
through fitting a time response curve of a sample-evaluation fuel
cell. In some embodiments, at least one of the analytes is an
inorganic compound. A sample electrode of the fuel cell is exposed
to a breath sample, and the counter electrode of the fuel cell is
exposed to a selected reactant. The selected reactant can comprise
O.sub.2, which may or may not be delivered as air.
[0015] Moreover, the invention pertains to a system comprising a
flow apparatus, a fuel cell and an analyzer. The flow apparatus is
configured to operably receive a breath sample from an individual.
The fuel cell comprises a sample electrode operably coupled to the
flow apparatus and a counter electrode exposed to O.sub.2. The
sample transported within the flow system operably contacts the
sample electrode. The analyzer receives a signal related to the
potential of the fuel cell and evaluates the amount of acetone from
a time response of the fuel cell signal as a function of time based
on a standard time response of the fuel cell with acetone.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 is a schematic representation of fatty acid
metabolism.
[0017] FIG. 2 is a schematic representation of a system for
evaluation of amounts of volatile organic compositions in a vapor
sample as described herein.
[0018] FIG. 3 is a plot of fuel cell response as a function of time
for three fuel cells using a standard ethanol sample to compare the
results of different fuel cells assembled into the apparatus for
evaluating vapor samples.
[0019] FIG. 4 is a plot of fuel cell response as a function of time
for breath samples taken from a person after taking a drag on a
cigarette, after taking one clean breath after a drag on a
cigarette, and after two clean breaths following a drag on a
cigarette.
[0020] FIG. 5 is a plot of fuel cell response for the breath of two
non-smokers and one smoker with at least 24 hours since having
smoked their last cigarette.
[0021] FIG. 6 is a plot of fuel cell as a function of time after an
unknown sample from a smoker with ethanol on their breath, plotted
along with a fit to ethanol and smoke from standard curves and the
resulting curve fit to the unknown sample.
[0022] FIG. 7 is a plot of the absolute value of the unknown sample
response minus the normalized recombination fit for the data in
FIG. 6.
[0023] FIG. 8 is a plot of fuel cell as a function of time after a
second unknown sample from a smoker with ethanol on their breath,
plotted along with a fit to ethanol and smoke from standard curves
and the resulting curve fit to the second unknown sample.
[0024] FIG. 9 is a plot of the absolute value of the second unknown
sample response minus the normalized recombination fit for the data
in FIG. 8.
[0025] FIG. 10 is a plot of fuel cell response as a function of
time for a "clean" breath sample from a healthy individual who had
eaten roughly two hours prior to providing the breath samples.
[0026] FIG. 11 is a plot of fuel cell response as a function of
time for a fuel cell using a 27 mM standard acetone sample.
[0027] FIG. 12 is a plot of fuel cell response as a function of
time for a fuel cell using a 140 mM standard acetone sample.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0028] A vapor analysis system comprising a breath-based sensor can
be used for noninvasive detection of acetone and other analytes in
a human's breath. The analysis of these analytes in the breath can
be used for diagnostic purposes and/or for the evaluation of a
particular condition to which the person is known to be
susceptible. For example, acetone is a marker for various
biological processes, the most notable of which is diabetic
ketoacidosis that is associated with insulin insufficiency and poor
regulation of the ketogenic diet used to control refractory
epileptic seizures. The breath-based sensor, which can be portable,
according to the various embodiments provides a highly selective,
non-invasive, and rapid measurement device having the accuracy,
precision, and detection limit appropriate to the assay of acetone
in the physiologically relevant range for these conditions. Other
compounds characteristic of various disease states can also be
measured by the breath-based sensor as described herein.
Representative compounds include, for example, carbonyl sulfide
(COS) as a marker for lung transplant rejection, ammonia (NH.sub.3)
in end stage renal failure, carbon disulfide (CS.sub.2) as a marker
for coronary artery disease, alkanes and benzene derivatives in the
breath of lung cancer patients, and nitric oxide (NO) as a marker
for asthma. Thus, the techniques herein extend to volatile
inorganic analytes in a person's breath. Also, the sample
evaluations are shown to extend to analytes, such as acetone, that
undergo a reduction reaction at the sample electrode, at least over
a portion of the time response curve, against a counter electrode
exposed to a suitable reactant, such as oxygen, i.e., O.sub.2.
[0029] The signature of the time response curve for a fuel cell
signal, e.g., voltage/potential or current, upon exposure with
volatile organic compositions from a breath sample can be used to
evaluate the relative concentrations of a combination of
compositions in the sample. Corresponding systems can be designed
to collect a vapor sample that contains volatile compositions and
direct the sample to the sample electrode of a fuel cell. The fuel
cell response as a function of time is a signature for a particular
compound. For samples that have a plurality of volatile organic
compositions, the fuel cell response is for practical purposes a
linear or nonlinear combination of the response of the fuel cell
for the particular compositions appropriately weighed for the
relative amounts. Therefore, an analysis can be performed to
de-convolute the time response curve to obtain values for the
relative concentrations. Average standard curves can be used for
the performance of the de-convolution. For samples such as acetone
with a distinctive negative reduction peak in the time response
curve, fitting of the area, the depth of the peak or the general
time response curve shape can be used to estimate concentrations of
acetone or other reducing analytes. In some embodiments, the vapor
sample is a breath sample from a person, or other patient, such as
a pet or farm animal for medical evaluation.
[0030] Generally, a volatile organic composition evaluation system
comprises a vapor/gas sampling component, a flow apparatus, a fuel
cell and an analysis instrument. Vapor refers generally to gas(es)
and/or vapor of a volatile composition(s) at a particular vapor
pressure. The sampling component comprises an appropriate
collection system suitable for the particular application of the
system. For a breath analyzer, the sampling component can comprise
a mouthpiece and associated conduits to connect the flow with the
flow apparatus. The flow apparatus directs the gas sample to a fuel
cell. The fuel cell comprises a sample electrode operably connected
to the flow apparatus in which the fuel cell generates a voltage or
current in response to a broad range of volatile organic and/or
inorganic compositions reacting at the sample electrode. The
counter electrode can be exposed to a selected reactant. In some
embodiments, the counter electrode can be exposed to air, although
an alternative oxygen (O.sub.2) source or other chemical supply can
be used to supply reactant to the counter electrode. The analysis
instrument measures the fuel cell output signal, e.g., voltage or
current, as a function of time from the fuel cell and evaluates the
composition of the vapor in response to the fuel cell performance
as a function of time.
[0031] The sample electrode can function as an anode for the
oxidation of the analyte while the electrode exposed to atmospheric
oxygen functions as a cathode to reduce the molecular oxygen
(O.sub.2). However, alternatively, the sample electrode can
function as a cathode with the reduction of the analyte and with
the corresponding oxidation taking place at the electrode exposed
to air or other reactant. In addition, the respective electrodes
can function partially as both anodes and cathodes either
simultaneously or sequentially with the passage of time as
different analytes react within the analyzer. For example, as
discussed below, acetone is reduced in the analyzer at least
initially although the resulting reduced compound may be
subsequently oxidized. Also, one analyte such as acetone may be
reduced while another analyte, such as ethanol, may be
oxidized.
[0032] Fuel cells of particular interest are proton exchange
membrane fuel cells, also known as PEM fuel cells. Polymer
electrolyte membrane fuel cells are one type of proton exchange
membrane fuel cells. Proton exchange membrane fuel cells have a
separator or electrolyte between the anode and cathode that
provides for transport of protons across the separator. Generally,
the separator/electrolyte is hydrated to perform its function as
electrolyte. The separator can be a polymer film. PEM fuel cells
operate at lower temperatures than most other fuel cell types with
operating temperatures generally less than about 100.degree. C. and
can be operated at temperatures down to freezing.
[0033] Other types of fuel cells may also be appropriate, such as
phosphoric acid fuel cells, molten carbonate fuel cells and solid
oxide fuel cells. Phosphoric acid fuel cells use phosphoric acid as
the electrolyte. These fuel cells generally operate at about
150.degree. C. to about 220.degree. C. The electrolyte for molten
carbonate fuel cells is molten carbonate salts, as their name
implies. To achieve sufficient ion mobility through the carbonate
salts, these fuel cells operate at temperatures on the order of
650.degree. C. The electrolyte for solid oxide fuel cells is a
ceramic oxide material that can transport O.sub.2 ions at
temperatures from 600.degree. C. to about 1000.degree. C.
Phosphoric acid fuel cells, molten carbonate fuel cells and solid
oxide fuel cells are described, respectively, in U.S. Pat. No.
5,302,471 to Ito et al., entitled "Compact Phosphoric Acid Fuel
Cell System And Operating Method Thereof," U.S. Pat. No. 5,595,832
to Tomimatsu et al., entitled "Molten Carbonate Fuel Cell," and
U.S. Pat. No. 5,595,833 to Gardner et al., entitled "Solid Oxide
Fuel Cell Stack," all of which are hereby incorporated by reference
herein. Since PEM fuel cells are desired due to their operating
temperatures and other desirable characteristics, the following
discussion focuses on these embodiments, although other fuel cell
types can be substituted based on the disclosure herein.
[0034] The fuel cell should have an appropriate response for a
range of organic and/or inorganic analytes. While commercial fuel
cells for ethanol detection may not be well suited for the present
applications, they may provide acceptable performance for the
present applications. PEM fuel cells generally have catalyst
materials in contact with both sides of the electrolyte/separator.
One side forms the electrode exposed to oxygen in the atmosphere.
The other side of the electrolyte/separator forms the analyte
electrode where the analyte reacts. Protons, or other available
ions, typically flow from the anode to the cathode as mediated by
the electrolyte.
[0035] In some embodiments, fuel cells with magnetic composites can
be particularly desirable due to their improved transport of
paramagnetic materials, such as oxygen to the appropriate electrode
and enhance electrolysis. Fuel cells with magnetic materials
incorporated into the fuel cell are described further, for example,
in U.S. Pat. No. 6,479,176 to Leddy et al, entitled "Gradient
Interface Magnetic Composites And Methods Therefor," and U.S. Pat.
No. 5,928,804 to Leddy et al., entitled "Fuel Cells Incorporating
Magnetic Composites Having Distinct Flux Properties," both of which
are incorporated herein by reference.
[0036] For power production, fuel cells are generally formed into
stacks with a series of fuel cells connected in series to generate
an additive voltage from the cells. Bipolar plates or other
suitable current collector with flow channels separate adjacent
cells. However, in general, for the present application, a single
fuel cell of modest size is suitable that generates a reasonable
voltage or current for the particular supply of volatile analytes.
Voltage is not substantially dependent on electrode area, although
small changes in internal resistance may relate to electrode area.
Using a single fuel cell, the structure of the systems can be much
simpler in comparison with a fuel cell stack, especially with
respect to the flow of analyte and counter electrode reactant,
generally, oxygen from air. However, the sensor can use a plurality
of fuel cells, such as two fuel cells or more than two fuel cells,
connected either in parallel of in series to obtain desired
responsiveness of the sensor.
[0037] The analysis is based on a unique time dependent signature
of the different volatile organic and/or inorganic compositions
with respect to the time dependent response of a fuel cell
operating using the volatile organic composition as a reactant. To
de-convolute the time dependent response curve, standard curves are
generated for selected volatile reactant compounds or particular
mixtures thereof, which are thought or known to be in a breath
sample for analysis. The particular mixtures can be analyzed
together as a particular composition. For example, tobacco smoke,
such as cigarette smoke, has a mixture of volatile organic
compounds that are relatively fixed with respect to relative
amounts such that the mixture can be considered a separate
composition that is analyzed together for the purposes of the
de-convolution. The standard curves can be based on averages from a
plurality of runs to improve the precision and accuracy of the
standard curve. Then, the sample curve can be de-convoluted with
the standard curves. The de-convolution can be based on a linear or
non-linear combination at a plurality of time points.
[0038] The methodologies described herein can be used in a variety
of applications, such as breathalyzers, vehicle interlocks, medical
diagnostics, screening of large populations and environmental
evaluations. Fuel cells are already used commercially for
breathalyzers for the detection of ethanol to determine if the
values are within legal limits. Portable devices can be used by law
enforcement officials for testing drivers suspected of driving
under the influence of alcohol. Similarly, other devices have been
connected to vehicles, especially automobiles, for the evaluation
of the sobriety of a potential driver and disabling the vehicle as
appropriate. These devices can benefit from the improved analytical
systems and methodologies described herein since more accurate
readings can be obtained if a variety of sources of volatile
organic compositions can be distinguished. Present commercial fuel
cell breathalizers generally are not suitable alone for evidentiary
purposes.
[0039] Furthermore, volatile compositions, e.g., organic solvents,
are often environmental pollutants that result from a wide range of
human activities. The ability to efficiently identify pollutants in
a particular gas sample can greatly facilitate the evaluation of a
potential environmental pollutant. Similar to the evaluation of
environmental pollutants, analyses can be performed in industrial
settings to evaluate release of pollutants and/or to evaluate
exposure levels to individuals to determine if they are within
acceptable levels. These industrial limits may be evaluated in view
of specific regulations, such as regulations from the U.S.
Occupational Health and Safety Administration (OSHA) or the U.S.
Environmental Protection Agency (EPA).
[0040] In other embodiments, measurements from the systems
described herein can assist with medical evaluations since the
presence of certain compositions in the breath can be indicative of
certain illnesses or conditions. For example, the level of acetone
in a person's breath can be used to evaluate the presence of a
diabetic condition or similarly to evaluate the maintenance of the
person's diabetic control. As seen below, acetone has a distinctive
signature in the fuel cell response curve that can be used to
evaluate acetone concentration. In other embodiments, the analyzer
can be used to evaluate the health of a person, who may or may not
have identifiable symptoms. For example, the analysis can be
performed as part of a well patient visit for the early detection
of conditions such as diabetes. Alternatively, the analysis can be
performed as part of a diagnosis procedure on a patient with
symptoms that have not yet been definitively connected with a
particular disease. For the measurement of unknown analytes for
medical diagnosis, the fuel cell response curve is generally
deconvoluted with respect to a range of potential analytes found in
a person's breath. For samples thought to include acetone, the
distinctive negative peak can be used for evaluating the
concentration of acetone, and several specific algorithms are
described below.
[0041] Once estimates of the concentration of medically related
analytes are determined within a person's breath, these breath
concentrations can be correlated with serum concentrations using
either known relationships or relationships that can be determined
through measurements on individuals within known medical
conditions. Using the estimates of serum concentrations of various
analytes, this information can be incorporated as additional data
that can be used for diagnostic purposes along with other tests and
examinations. Generally, a medical professional would be involved
in the evaluation of the collective test information for arriving
at the ultimate diagnosis.
Vapor Analysis System
[0042] A vapor analysis system generally comprises a vapor/gas
sampling component, a flow apparatus, a fuel cell and an analysis
instrument. The sampling component can be designed based on the
source of the particular sample. In some embodiments of interest,
the vapor sampling component can be a breath collection component.
The flow apparatus provides for controlled flow of the vapor sample
to the fuel cell. The analysis instrument collects the time
dependent response of the fuel cell following interaction with the
vapor sample and the de-convolution of the time dependent response
of the fuel cell to obtain the relative amounts of the samples.
While the fuel cell can be optimized for certain analytes such as
ethanol, general fuel cells can be used that are responsive to
organic compositions and/or inorganic compositions generally. Thus,
a fuel cell sensor may or may not be sensitive to a particular
analyte depending on the particular objective of the device.
[0043] Referring to FIG. 2, a schematic diagram is depicted for a
vapor analysis system described herein. A vapor analysis system 100
can comprise sampling component 102, flow apparatus 104, fuel cell
106 and analysis instrument 108. Sampling component 102 facilitates
introducing a vapor sample into flow apparatus 104. In general,
sampling component 102 can be any mechanical or passive structure
that facilitates collection and introduction of desired vapors into
the flow apparatus and/or the fuel cell of a vapor analysis system.
Sampling component 102 can comprise appropriate combinations of one
or more tubes, mouthpieces, or the like.
[0044] In some embodiments, flow apparatus 104 can comprise inlet
flow line 110, which provides a fluid flow pathway for vapor
samples from sampling component 102 to the sample electrode of fuel
cell 106. Flow apparatus 104 can also comprise outlet flow line
112, which provides a vapor flow pathway for vapor samples and/or
fuel cell by-products from the sample electrode of fuel cell 106
to, for example, exhaust 114. In some embodiments, flow apparatus
104 and/or fuel cell 106 can comprise one or more pumps to
facilitate moving vapor samples into and out of the sample
electrode of fuel cell 106. Vapor analysis system 100 can be
configured to function in a variety of devices such as, for
example, breathalyzers, ignition interlock systems, medical
diagnostic devices, broad population screening, and environmental
and industrial sensors or monitors.
[0045] As described below, the various components of vapor analysis
system 100 can be adjusted and designed to suit the intended
application of the device. In embodiments where the vapor analysis
system is designed to be incorporated into a breathalyzer, sampling
component 102 can comprise stem having a tube fitting adapted to
removably engage a sample tube or mouthpiece. In some embodiments,
the stem can be formed integrally with the housing of the
breathalyzer. Breathalyzers having a stem and a tube fitting are
described in U.S. Pat. No. 4,487,055 to Wolf, entitled "Breath
Alcohol Testing Device," which is hereby incorporated by reference
herein. In embodiments where the vapor analysis system is designed
to be incorporated into an ignition interlock, sampling component
102 can comprise a mouthpiece that extends from the interior of the
housing to the exterior of the housing. Sampling components for
ignition interlocks are described in, for example, U.S. Pat. No.
5,426,415 to Prachar et al., entitled "Breath Analyzer For Use In
Automobile Ignition Locking Systems," which is hereby incorporated
by reference herein. In other embodiments, the vapor analysis
system can be incorporated in medical diagnostic devices or an
environmental sensor or detector. Suitable breath sampling
components for a medical examination are described, for example, in
U.S. Pat. No. 5,081,871 to Glazer, entitled "Breath Sampler,"
incorporated herein by reference. Suitable environmental sampling
systems are described for example in U.S. Pat. No. 5,753,185 to
Mathews et al., entitled "Vehicle Emissions Testing System,"
incorporated herein by reference.
[0046] The vapor analysis devices 100 of the present disclosure can
comprise a flow apparatus 104 that provides desired fluid flow
within vapor analysis device. In general, flow apparatus 104 can
regulate and provide fluid flow to and from fuel cell 106 during
analysis of a sample. Flow apparatus can comprise appropriate
combinations of flow lines or pipes and one or more pumps to
facilitate desired fluid flow within vapor analysis system 100. The
pump and/or other flow control elements can be connected to a
microcomputer, which can control the function of the pump, and thus
the introduction of vapor samples into fuel cell 106.
[0047] A flow apparatus suitable for use in ignition interlock
systems is disclosed in, for example, U.S. Pat. No. 5,426,415 to
Prachar et al., entitled "Breath Analyzer For Use In Automobile
Ignition Locking Systems," which is hereby incorporated by
reference herein. In this system, a diaphragm pump is used to
divert a portion of a breath sample through a fuel cell sample
electrode while exhausting flow from the sample electrode. Flow
structures suitable for use in a breathalyzers are described in,
for example, U.S. Pat. No. 4,487,055 to Wolf, entitled "Breath
Alcohol Testing device," and in U.S. Pat. No. 5,291,898 to Wolf,
entitled "Breath Alcohol Device," both of which are incorporated
herein by reference. In these systems, a diaphragm draws breath
into and from a chamber adjacent a fuel cell sample electrode to
control exposure of the fuel cell sample electrode to the
breath.
[0048] As described above, flow apparatus 104 can be connected to
one or more fuel cells 106 to facilitate analysis of a vapor
sample. Fuel cell 106 can be any fuel cell that can produce a
response to desired compositions. Suitable fuel cells include, for
example, PEM fuel cells, phosphoric acid fuel cells, molten
carbonate fuel cells and solid oxide fuel cells, as noted above. In
some embodiments, fuel cell 106 can be a PEM fuel cell comprising a
proton exchange membrane as the electrolyte, such as Nafion.RTM.,
with catalyst particles in contact with the electrolyte forming the
sample electrode and counter electrode. A current collector
contacts the electrolyte particles to complete the electrodes. A
particular embodiment is described further with respect to the
Examples below. In some breathalyzer fuel cells, the separator is
formed from sintered or pressed polymer balls, such as
polyvinylchloride, to form pores with about 1 to about 25 micron
diameters extending through the membrane. A layer of catalyst mixed
with conductive carbon and binder is applied to each side of the
membrane to form the sample electrode and the counter electrode.
The porous framework can be filled with sulfuric acid, phosphoric
acid or a mixture thereof to complete the circuit, although other
electrolytes can be effectively used.
[0049] Suitable analysis instruments include, for example, windows
based computers, person digital assistants, and dedicated computer
processors, i.e., microprocessor, integrated into a portable
analysis apparatus, in which portable digital assistant technology
can be incorporated into the apparatus.
Acetone Detection
[0050] As will be discussed below, the time response to acetone
produces a signature negative curvature when using a fuel cell
sensor. This time response indicates that the acetone is reduced at
the fuel cell cathode, although the reduction product may be
subsequently oxidized at the same electrode. After an initial rise,
the signal from an aqueous acetone solution rapidly decreases and
then increases to a maximum followed by slow decay. This
distinctive short time negative peak from acetone reduction can
provide a signal that enables ready estimation of acetone
concentrations in alveolar air and discriminate against possible
interferents on the breath, such as ethanol and cigarette smoke.
The distinctive wave shape for acetone is observable at suitably
lower concentrations of acetone of biological relevance. Thus, a
breath-based fuel cell sensor enables quantifying breath acetone
that is associated with diabetic ketoacidosis and effective
management of a ketogenic diet and serves as a screening tool for
the diabetic state.
[0051] Due to the presence of the negative peak, several approaches
can be used to estimate the amount of acetone in a person's breath
sample. However, the acetone signal with the negative peak is
generally on the same order of magnitude as a "clean" breath signal
(breath with no detectable acetone concentration). In contrast, the
response of the fuel cell sensor to ethanol and smoke is much
stronger than fuel cell response to "clean" breath (or acetone
spiked breath) so that a "clean" breath signal generally can be
ignored for an ethanol evaluation. In other words, any signal due
to "clean" breath does not need to be subtracted from the
representative ethanol or smoke signals, and a "clean" breath
signal does not need to be subtracted from actual breath samples to
evaluate significant ethanol contributions. On the other hand, to
determine the presence and amount of acetone on a person's breath,
a subject's "clean" breath generally may be taken into
consideration in some analysis approaches.
[0052] Three different algorithms to estimate the acetone
concentration in a person breath are discussed. In the following
section, an approach for evaluating a person's breath for a
plurality of medically related analytes is discussed. The
concentration of acetone in the person's breath provides
information on the corresponding blood sugar level(s) and
metabolic/diabetic state of the subject. The potential or current
is a function of time, and these functions can be considered
vectors from a calculational perspective with the discrete time
points selected as described below in the context of more general
algorithms for multiple analytes.
[0053] In a first approach, the fuel cell sensor signal is taken as
a linear combination of an acetone signal and a "clean" breath
signal. To build a calibration set, a signal vector can be used
that has a known amount of acetone (V.sub.acetone, which is an
acetone concentration slightly above the highest level one would
ever expect to find on a subject's breath). A signal that is known
to not carry acetone (V.sub.clean) is subtracted from
V.sub.acetone. "Clean" samples can be collected by bubbling clean,
compressed air or a healthy person's breath through a water bath at
37.degree. C. (body temperature). Linear combinations Of
V.sub.acetone and V.sub.clean are taken to match a real subject's
breath (V.sub.sample) and an appropriate calibration table based on
the original concentration of acetone used to collect V.sub.acetone
is used to determine the acetone concentration. To evaluate the
amount of acetone in an individual's breath, the following
equations can be used with a non-linear fit using the vectors:
C.sub.1.times.V.sub.acetone+C.sub.2.times.V.sub.clean=V.sub.lin.
Combo, (1)
Absolute Value of .SIGMA.(V.sub.lin.
Combo-V.sub.sample).sub.i=Gross Error, (2)
where the minimum Gross Error (GE) gives the best fit result. The
summation for calculating the Gross Error involves a summation over
different time points. To do the minimization, the values of
C.sub.1 and C.sub.2 can be obtained by minimization for each
concentration dependent standard vector V.sub.acetone(C.sub.a)
where C.sub.a is a particular acetone concentration for evaluating
the standard potential or current response curve. Then, for each
value of the standard concentration, the gross error can be
evaluated. The sample concentration can be estimated as the
concentration of the standard response curve that leads to the
lowest gross error.
[0054] Since the most distinctive portion of the acetone response
curve is at relatively short times, the calculation can be weighted
using a fixed weight vector, V.sub.weight. For example, the weight
vector can have a step function that cuts off the time at a cut
off, such as 35 seconds. As another example, V.sub.weight can be
two from 5 to 15 seconds and one elsewhere. Then the gross error
can be generalized to the following:
Absolute Value of .SIGMA.(V.sub.weight(V.sub.lin.
Combo-V.sub.sample).sub.i)=Gross Error, (3)
which involves a vector dot product and i indicates a particular
time point.
[0055] By developing the table of linear combinations of
V.sub.acetone and V.sub.clean ahead of time, a very simple
instruction set can be used that would execute quickly on even the
very low cost hardware presently available. If the negative acetone
spike is not linear with the concentration of acetone the subject's
breath is compared with a table of vectors; each vector
representing a particular acetone concentration. These vectors can
be determined experimentally using the bubbling apparatus described
in the Examples below.
[0056] In a second approach, the magnitude, i.e., depth, of the
negative peak is used directly to estimate the acetone
concentration. The negative peak is superimposed on a positive
slope from "clean" breath and possibly an oxidation response
related to the oxidation of the acetone reduction product. The
negative peak depth can be estimated from the position of the
negative peak subtracted from an estimate of the positive going
contribution. The positive going contribution can be estimated with
a line connecting the local maxima on either side of the negative
peak.
[0057] In principle, the first derivative curve can be used to
locate the local extrema, but this can be complicated from noise in
the plot that masks the desired local extrema by generating a large
number of local maxima and minima. A variety of numerical
approaches can be used to identify the desired extrema, which may
or may not involve the input regarding windows on the expected
location of the extrema. In a representative approach, the end
points of the acetone peak are located with a stepwise approach to
identify the local extrema, i.e., a local maximum or a local
minimum. The extrema, involving two local maximum with a local
minimum between them, can be located as time progresses. In
particular, the first maxima V.sub.M1 can be picked as the highest
value from an initial time before a significant dip. The negative
peak or dip can be identified as a drop of at least about 1% in
magnitude from the first maximum. The lowest point V.sub.min in the
dip can be identified when the curve increases by at least about 1%
in magnitude from the previous minimum. Also, the second maximum
can be identified by a subsequent drop of 1% in magnitude from the
previous maximum.
[0058] More specifically, in one embodiment, starting from an
initial reading, a tentative maximum potential/current reading and
its corresponding time value are stored as one progresses in time
with a new maximum replacing a previous tenetative maximum until a
present potential/current reading has dropped at least 1% from the
previous maximum reading. When the 1% drop is identified, the
tentative maximum potential/current reading is then saved with its
time as V.sub.M1, t.sub.M1, respectively. Then, a going forward
tentative minimum and its corresponding time are stored with a new
minimum replacing a previous tentative minimum until the
potential/current has gone back up by at least about 1% from the
previous tentative minimum value. When this point is reached, the
tentative minimum potential/current value is stored along with its
time as V.sub.min, t.sub.min respectively. Then, a forward going
tentative maximum value and corresponding time are stored with a
new maximum value replacing a previous tentative maximum value
until the current potential/current value has fallen by at least 1%
from the previous tentative maximum value. When this point is
reaches, the tentative maximum value and corresponding time are
stored as V.sub.M2, t.sub.M2, respectively.
[0059] Once these values are obtained, a line is fit through
(V.sub.M1, t.sub.M1)-(V.sub.M2, t.sub.M2) by solving linear
equations for m and b:
V.sub.M1=m.times.t.sub.M1+b,
V=m.times.t.sub.M2+b.
[0060] Then, the depth D of the negative peak is evaluated as
D=V.sub.min-(m.times.t.sub.min+b). To obtain the concentration from
this value for D, a set of standard values D.sub.s can be evaluated
for D using standard acetone solutions at 37.degree. C.,
D.sub.s(C.sub.i), where C.sub.i are a set of standard
concentrations. Either the value of C.sub.i corresponding to
D.sub.s(C.sub.i), which is the closest value of D.sub.s to the
sample value of D, can be selected as the estimate of the acetone
concentration in the person, or a linear or nonlinear extrapolation
can be performed between the two closest values of D.sub.s to get a
more precise value of acetone concentration if a higher precision
is desired.
[0061] In a third approach, the area of the negative spike can be
evaluated to estimate the acetone calculation. While slightly more
involved than the negative peak depth measurement, the area based
approach on average should be less sensitive to noise so that it
may be slightly more accurate than the peak depth approach. To
estimate the negative peak area, the values of t.sub.M1 and
t.sub.M2 can be used to fix the end points of the peak. The
difference is evaluated between the line V.sub.1=mt+b, with m and b
determined as described above and V.sub.i where this is a
particular potential/current reading along the negative peak. The
difference between the sample value V.sub.i and linear V.sub.1,
i.e., (Vi-V.sub.1), is integrated between the two time values,
t.sub.M1 and t.sub.M2. The area is the integral of this difference,
and the integration can be performed with any standard numerical
integration routine, such as the trapezoid rule integration or
others known in the art.
[0062] The evaluated area can be compared with standard areas
evaluated with standard aqueous acetone solutions. Again, the
concentration can be estimated by finding the concentration C.sub.i
corresponding with the closest standard area A.sub.i to the sample
area A. If greater precision is desired, the standard area values
can be linearly interpolated to obtain a more accurate estimate of
the sample concentration.
Analysis Algorithm
[0063] The time dependent response of the fuel cell is dependent on
the chemical composition of the sample introduced into a vapor
sampling system of the fuel cell sensor, such as those described
herein. Thus, if a sample comprises a plurality of volatile organic
and/or inorganic compositions that can react at the fuel cell
electrode, the time dependent response curve of the fuel cell
reflects the overall composition of the vapor sample. The
de-convolution of the time dependent response curve can be used
then to obtain the amounts of the different volatile organic
compositions in the vapor sample. The de-convolution can be based
on a linear combination or a nonlinear combination of the
independent response curves. The de-convolution of the vapor sample
is based on standard curves for the individual compositions, which
may be normalized.
[0064] The use of a fuel cell signal to de-convolute ethanol
contributions from cigarette smoke contributions is described
further in published U.S. Patent application 2005/0214169A to Leddy
et al., entitled "Multicomponent Analysis of Volatile Organic
Compositions in Vapor Samples," incorporated herein by reference.
Here, the analysis is generalized to provide for medical diagnosis
assisted with an analysis of a breath sample. Acetone is a
significant composition for a medical evaluation since it is
indicative of diabetic individuals and their maintenance of their
condition. As noted above, acetone has a characteristic negative
peak at relatively short times indicating a reduction reaction.
[0065] The procedure for medical evaluation generally involves a
significant component related to the selection of analytes for the
de-convolution of the potential/current measurements. To be
applicable for a large number of individuals, it is useful to
include cigarette smoke and ethanol since these compositions may be
on the breath of individuals being evaluated for medical conditions
and since these compositions generally yield relatively strong
signals. Other analytes of interest include, for example, CO.sub.2,
COS, NH.sub.3, CS.sub.2, alkanes, benzene derivatives and NO. In
general, the analytes for these measurements may not yield signals
as strong as an ethanol signal of an intoxicated person or
cigarette smoke for a person who has smoked shortly before the
measurement. For these samples, the background "clean" breath
signal may be relevant. "Clean" samples can be collected by
bubbling clean, compressed air or a healthy person's breath through
a water bath at 37.degree. C. (body temperature). Then, the clean
breath measurements can be included as one of the analytes within
the de-convolution of the sample measurement curve. The
de-convolution should work even if an analyte has a
negative/reduction peak in its response curve, such as acetone.
[0066] To perform the analysis, each curve can be converted to a
vector by the selection of a specific number of time points. The
dimension of each vector, i.e., the number of time points used, can
be selected to obtain a desired degree of fitting. The number of
time points is selected to yield a desired accuracy of the
de-convolution. All of the collected time points can be used in the
analysis such that the hardware response time sets the spacing of
the time points, although a subset of the time points can be used
as desired. Generally, the data are collected until the signal has
significantly decayed from its peak value, and in some embodiments
the signal is monitored until it has decayed 60 percent from its
peak value, in further embodiments 75 percent from its peak value
and in additional embodiments 85 percent from its peak value. A
person of ordinary skill in the art will recognize that additional
ranges for the time cut-off within the explicit ranges are
contemplated and are within the present disclosure.
[0067] Generally, the degree of fitting does not significantly
increase after a certain number of time points are selected. The
number of time points may be fixed by the timing of the data
collection system and the response time for the analog-to-digital
conversion. In general, the time points do not necessarily have to
be equally spaced, although certain spacings may be convenient for
certain types of numerical analysis. The resulting vector can be
written as V with elements v.sub.n for the nth time point
recorded.
[0068] Each standard vector can normalized to a normalized vector
NV for the later de-convolution. Specifically, the normalization is
performed according to:
The Nth Normalized element in
NV=Nv.sub.n=(v.sub.n-v.sub.small)/(v.sub.large-v.sub.small),
(1)
where v.sub.small is the smallest element in V, v.sub.large is the
largest element in V. Equation (1) ensures that the largest value
in NV is 1 and the smallest value is 0. Other normalizations can be
used to standardize the peak value, if desired, although the
normalization in Eq. (1) has been found in the examples below to
yield good results.
[0069] A number of normalized curves from known samples can be
averaged to get a standard curve V.sub.average for a particular
analyte, such as acetone, ethanol, cigarette smoke, COS, CS.sub.2,
NO, clean breath or any other volatile organic and/or inorganic
composition. Depending on the magnitude of the other signals in the
sensor, it may be useful to include "clean" breath as one of the
analytes. The standard vector for analyte "a" from an average of i
sample runs can be written as:
Nth element of the standard vector for analyte a=V.sub.average,
a=v.sub.na=(Nv.sub.na1+Nv.sub.na2+ . . . + NV.sub.nai)/i, (2)
where Nv.sub.nai is the nth normalized element at t.sub.n for the
i-th sample of analyte a. In general, slight variations between
vectors NV.sub.1 to NV.sub.i distort the values of V.sub.average,
so that the largest value in V.sub.average is not necessarily equal
to 1, and the smallest value in V.sub.average is not necessarily
equal to 0. Thus, the average response curve itself can be
normalized based on the formula in Eq. 1 to obtain a normalized
average or standard curve for a particular analyte,
NV.sub.average.
[0070] A linear combination of the vectors NV.sub.average,A,
NV.sub.average,B, NV.sub.average,C, etc. can be used to form a
vector V that approximates a sample vector. In principle, any
number of standard vectors for analytes A, B, C, D, . . . can be
used. In some embodiments, there are 2 analytes, such as acetone
and cigarette smoke, in other embodiments, 3 analytes, in further
embodiments 4, in additional embodiments 50 or more, and any number
in between. The instrument can use alternative algorithms based on
input from the operator. For example, an algorithm can be selected
that includes de-convolution involving cigarette smoke if the
subject is a smoker or ethanol if the person had a drink in the
previous 6 hours or other time threshold. A selection among a few
different algorithms can provide improved sensitivity for other
analytes, and the instrument generally can be simply programmed to
achieve this selection among algorithms.
[0071] For two analytes, the equation is as follows:
V.sub.Lin.
Combo.=A.times.NV.sub.average,A+(1-A).times.NV.sub.average, B,
where 0<=A<=1 (3).
[0072] For three analytes A, B and C, this equation becomes:
V.sub.Lin.
Combo.=A.times.NV.sub.average,A+B.times.NV.sub.average,B+(1-A-B).times.NV-
.sub.average,C, (4)
where 0<=A<=1 and 0<=B<=1. Equations for other numbers
of analytes can be written based on these examples. In general,
there are N-1 unknowns for N analytes. Thus, as long as there are
at least N-1 time points, the linear combination (or nonlinear
combination) can be fit, although having additional time points
presumably leads to a better fit through an over determination of
the linear fit.
[0073] The new linear combination vector can be normalized
according to Eq. 1. Similarly, the sample vector can also be
normalized to yield a vector NV.sub.unknown. Because the two
vectors, NV.sub.Lin.Combo. and NV.sub.unknown are normalized to the
same range, the proportions of the signal due to the two analytes,
ethanol and cigarette smoke in the example below, the proportions
of the two analytes can be evaluated regardless of the absolute
magnitude of the response. The calculation at some point involves
scaling the linear combination curve to the actual measurement to
obtain the absolute quantities of the analyte. This scaling back to
the total values can be performed before or after the fitting.
[0074] The best fit for the unknowns can be determined using
established mathematical techniques. Thus, for Eq.3, A.sub.Best Fit
is determined, and similarly, for Eq. 4, A.sub.Best Fit and
B.sub.Best Fit can be determined. For example, the unknown
parameters can be obtained by iteration. The sum of the differences
between the elements in NV.sub.Lin. Combo. and NV.sub.Unknown can
be called the "Gross Error" and is defined by the following
equation:
Gross Error=.SIGMA.|Nv.sub.n,unknown-Nv.sub.n,Lin. Combo.| (5).
[0075] Equation (5) results in a fit that weights all time points
equally. Other expressions for the gross error can be used, if
desired. This fitting to reduce the gross error to obtain the best
fit can use standard approaches to automate the process. An initial
value can be estimated for the parameters based on known
information about the sample. Standard methods for performing the
fit are known, such as the Downhill Simplex Method and the
Conjugate Gradient Method. These are described further, for
example, in Numerical Recipes: The Art of Scientific Computing, W.
H. Press et al., (Cambridge University Press, 1986), incorporated
herein by reference.
[0076] Once the value of A.sub.BestFit is known, it can be used to
obtain BrAC or other concentrations for other analytes besides
ethanol. Similarly, if additional unknown parameters are calculated
for other analytes, these can be used to obtain useful
concentration information. For a particular parameter, the
concentration data can be obtained from the following
calculation:
BrAC or other Concentration
value=A.sub.BestFit.times.C.sub.Calibration.times.(V.sub.large,LinCombo-v-
.sub.small,LinCombo).times.(v.sub.large,Unknown-v.sub.small,Unknown)
(6).
Similar integration based calibrations to obtain areas of peaks or
other areas of the time response curve are also possible to obtain
concentrations.
[0077] C.sub.calibration can be obtained from a calculation of the
responses of a fuel cell to a known, pure analyte sample. The
concentration value for the vapor sample is then divided by the
response to yield C.sub.Calibration. The last part of Eq. (6),
i.e.,
(v.sub.large,LinCombo-v.sub.small,LinCombo).times.(v.sub.large,Unknown-v.-
sub.small,Unknown), uses the largest and smallest values in vectors
V.sub.Unknown and V.sub.LinCombo and is a scalar ratio between the
range of the response to the unknown sample and the range of the
linear combination fit. Since the linear combination of the
normalized analytes responses are used, this scalar ratio can be
used to find the actual response curve for that analyte.
[0078] Equations (3) and (4) above are directed to linear
combinations of the fuel cell response for the different analytes.
However, there may be circumstances in which the analytes interact
in the anode such that the response of the fuel cell may be
non-linear with respect to the presence of the different analytes.
For example, Eq. (3) can be generalized to:
V.sub.Nlin.
Combo=A.times.NV.sub.average,A+(1-A).times.NV.sub.average,B+C.times.(NV.s-
ub.average,A.times.NV.sub.average,B). (7)
[0079] The parameters for the nonlinear fit can be established by
obtaining the smallest value of the Gross Error in a similar
fashion as the linear parameters were established. The parameters A
and (1-A) can similarly be used to evaluate concentrations of the
analytes as described above.
EXAMPLES
[0080] In performing the measurements herein, the apparatus
incorporated a design with a fuel cell as described below. The fuel
cell is placed within the apparatus to provide regulated breath
flow to the anode of the fuel cell. The fuel cell output voltage
was converted to a digital signal with an A/D converter for
analysis by a computer. The analysis was performed through an
iteration using 0.001 increments in the parameters over the range
of possible values. This approach is straightforward to implement
with computationally limited processors.
[0081] The fuel cells used in the test devices are essentially
described in U.S. Pat. No. 5,928,804 to Leddy et al., entitled,
"Fuel Cells Incorporating Magnetic Composites Having Distinct Flux
Properties," which is hereby incorporated by reference, except that
the fuel cells did not contain magnetic composites. These fuel
cells are proton exchange membrane fuel cells with Nafion.RTM.
perfluoronated, sulfonic acid polymer used as the
electrolyte/separator. The ionomer Nafion.RTM. has superior ionic
conductivity. Platinum coated carbon black particles (20 weight
percent platinum) were used as the catalysts. The catalyst
particles are formed by mixing Pt from Alfa Aesar with carbon black
(XC-72 from E-Tek) and mixing vigorously with a drill. The fuel
cells were prepared with catalytic ink preparation and application
procedures, Nafion.RTM. membrane pretreatments, and hot press
lamination techniques. Specifically, a catalytic ink is mixed from
platinum, carbon black, water, ethanol and isopropyl alcohol. This
combination is mixed thoroughly. This solution is applied to carbon
cloth or carbon paper (Toray paper) by painting with a brush or
spraying with an air brush. Suitable carbon paper or carbon cloth
are available from Aldrich Chemical or E-Tek. Solubilized
Nafion.RTM. (Ion Power or Aldrich) is then sprayed over the dry ink
that has already been supplied to the electrode(s). The two counter
electrodes are formed equivalently. A Nafion.RTM. membrane (Aldrich
or Ion Power) is sandwiched between the two electrodes and hot
pressed at about 130 degrees C. under about 0.1 metric tons per
square centimeter. The membrane electrode assembly (MEA) is allowed
to cool while under pressure. Once cooled to about 50 degrees C.,
the MEA can be removed from the press for use.
[0082] The catalyst/separator interface has the bulk of the
catalyst sites available to volatile reactive compositions in the
vapor sample, in comparison with commercial breathalyzer fuel
cells. The fuel cells were circular with diameters of about 1.5
centimeters and are mounted in cartridges for easy exchange within
the testing apparatus. Due to their construction and corresponding
high general sensitivity, the fuel cells have unique time response
curves with respect to volatile compositions of interest. In
particular, commercial breathalyzer fuel cells tend to be much more
sensitive to ethanol than to other types of volatile organics.
[0083] The test apparatus provided for the introduction of various
breath samples under specified conditions into the fuel cell. Data
were collected at 10 Hz, i.e., 10 points per second for 1 to 100
seconds for a total of 990 time points. For the examples below
directed to the separation of measurements of ethanol and cigarette
smoke, the cut off for the time was not significant after 30
seconds. Some testing was performed with standard solutions, while
other testing was performed with actual human breath samples. To
produce a breath-based test sample, human subjects blew into the
device for ten seconds. The first six seconds of the sample were
allowed to bypass the fuel cell. The last four seconds of a run
come from deeper within the subject's lungs. Thus, last four
seconds of the breath is sampled for flow into the fuel cell. A
pump with a valve system controls the flow to the fuel cell. A
computer records the fuel cell voltage multiplied by 10,000 as a
function of time. The fuel cell temperature was 25.degree. C. for
all samples. To control the conditions for the test, the tests were
performing a in a controlled environment chamber.
Example 1
Evaluation of Reproducibility
[0084] This example is directed to the evaluation of
reproducibility of the time response curves for different fuel
cells assembled as described herein.
[0085] Three fuel cell test devices were constructed as described
above. "Breath" samples were produced and introduced into each of
the fuel cell test devices. The "breath" samples were generated
using a Toxitest Breath Alcohol Simulator containing a 0.05 BAC
standard solution. The solution was formed with a mixture of
ethanol and water to simulate a blood sample and heated to
37.degree. C. to simulate body temperature. Breath was bubbled
through the solution to simulate a breath sample. A known quantity
of the "breath" sample was introduced into the fuel cell of each
fuel cell test device.
[0086] As depicted in FIG. 3, the shape of the amplified potential
v. time curve is similar for all three fuel cells. However, the
magnitude of the three curves are different, with fuel cell 1
having the largest magnitude, followed by fuel cell 2 and fuel cell
3, respectively. The magnitude of each curve is different because
of the inexact nature of catalyst application during construction.
In other words, one particular fuel cell may have more catalyst
than another fuel cell, which appears to affect the magnitude of
the response curve but not the general shape of the response curve.
Since the shapes of the curves are the same, the usefulness of the
fuel cells for the detection of different volatile organic
compositions should not depend on the magnitude of the available
catalyst and differences in catalyst loading is at least in part
corrected by the normalization.
Example 2
Cigarette Smoke Detection
[0087] This example is directed to detection of cigarette smoke in
breath as a function of time from inhaling smoke from the
cigarette.
[0088] Breath samples containing cigarette smoke from a single
individual were introduced into a fuel cell test device as
described above. The shape of the amplified potential v. time curve
for each breath sample was similar, however, the magnitude of the
curve decreased over time as the number of "clean" breaths after
inhalation of the cigarette increased. In other words, the
magnitude of the response to cigarette smoke is dependent on time.
Referring to FIG. 4, the curve with the largest magnitude was from
a breath sample taken after no clean breaths, while the curve with
the smallest magnitude was from a breath sample taken after two
clean breaths after a drag. The shape of the amplified potential v.
time curve has been found to be consistent for at least one hour
after a cigarette has been inhaled. Additionally, the shape of the
curve has been consistent for different brands of cigarettes. Thus,
the approaches described herein can be generally effective for the
detection of cigarette smoke on human breath.
Example 3
"Clean" Breath Samples
[0089] This example is directed to the comparison of the breath of
a non-smoker with the breath of a smoker after a 24 hour period
without smoking.
[0090] Clean breath samples were introduced from different
individuals into a fuel cell test device as described above. Two of
the individuals who provided breath samples were non-smokers, while
the other individual was a smoker who had not inhaled a cigarette
for about 24 hours prior to giving the breath sample. As depicted
in the FIG. 5, the shape of the amplified potential v. time curve
for all three individuals is similar. Thus, significant voltage, or
response, is not detected by individuals who have "clean"
breath.
Example 4
Distinguishing Ethanol and Cigarette Smoke
[0091] This example demonstrates the ability to distinguish ethanol
and cigarette smoke on the breath of a subject.
[0092] Breath samples from regular smokers who had been consuming
alcohol were introduced into a fuel cell test device as described
above. As depicted in the figures below, the total amplified
potential v. time curve can be approximated as a linear combination
of the separate responses to cigarette smoke and ethanol
components. based on the analysis using Equations (3) and (5)
above. The linear combination fit was conducted for data taken
between 1 and 100 seconds, using a data sampling rate of 10 Hz.
[0093] Results from a first sample are depicted in FIG. 6. The
curve was de-convoluted to obtain the contributions from the
cigarette smoke and the ethanol, with the ethanol contribution
having a larger magnitude and reaching a peak maximum at a
significantly later time. A curve is also plotted of the ethanol
and cigarette smoke linearly recombined. The linearly recombined
curve is very close to the curve of an unknown sample. FIG. 7 is a
graph of the absolute value of the normalized response to the
unknown sample minus the normalized linear combination fit for the
data depicted in FIG. 6. The sum of these errors yields the total
gross error, which in this example, is 3.798. The time dependent
response of the fuel cell from a second sample is depicted in FIG.
8 along with the de-convoluted ethanol and cigarette smoke
responses and the linear fit curve. FIG. 9 is a graph of the
absolute value of the normalized response to the unknown sample
minus the normalized linear combination fit for the data depicted
in FIG. 8. In this example, the gross error is 15.316. These are
acceptable errors for this analysis.
[0094] This example illustrates that the fuel cell test device can
be used to identify multiple organic components in a sample.
Additionally, the data presented below in Table 2 represents
results from 10 typical samples. The results were produced using
the fuel cell device described above. The results in Table 2
indicate that the fuel cell test devices, along with the equations
described above, can accurately determine the BrAC of an individual
who is consuming alcohol and smoking cigarettes. Thus, the fuel
cell test devices can accurately determine the presence and
relative amounts of multiple organic compounds in a sample. The
coefficient C in Table 2 is another notation for the parameter A
from Eq. 3 for these particular analytes.
TABLE-US-00002 TABLE 2 Test Results of Ten (10) Typical Samples #
Clean Breaths After Cigarette Ethanol Consumed C BrAC from Fit GE
Person X (Male) 0 2 oz. in ~43 min. 0.117 0.05 12.564 1 2.5 oz. in
~57 min 0.299 0.06 6.022 2 3 oz. in ~73 min 0.468 0.06 3.831 4 3
oz. in ~96 min 0.632 0.07 5.771 >100 (~10 min after last draw) 3
oz. in ~165 min 0.641 0.06 8.141 Person Y (Female) 0 1.5 oz. in ~29
min 0.130 0.05 10.063 1 2 oz. in ~48 min 0.543 0.08 5.684 2 2.5 oz.
in ~61 min 0.637 0.08 15.316 4 3 oz. in ~92 min 0.683 0.08 3.798
>300 (~30 min after last draw) 3 oz. in ~160 min 0.770 0.08
11.290
Example 5
Clean Breath Sample
[0095] This example demonstrates the fuel cell response to a
"clean" breath sample from a healthy individual. In determining the
presence and amount of acetone on a person's breath, a subject's
"clean" breath generally is taken into consideration.
[0096] Referring to FIG. 10, the fuel cell response to three
"clean" breath samples from a healthy individual is depicted. The
individual was a twenty-six year old male (non-smoker, had not been
drinking) who had eaten roughly two hours prior to providing the
breath samples. The three response curves have a similar shape to
each other although they differ in magnitude from each other.
Example 6
Acetone Measurements
[0097] This example is directed to the evaluation of the time
response curves for different acetone concentrations.
[0098] A fuel cell was constructed as described above. The acetone
samples were generated by bubbling air through an acetone solution
heated to 37.degree. C. to simulate body temperature using a
concentration of acetone in the bath of 27 mM or 140 mM (1 mL and 5
mL of acetone diluted with 500 mL of de-ionized water,
respectively). The time response curves of the fuel cell are
depicted for three separate samples at each of the two
concentrations in FIG. 11 for 27 mM and in FIG. 12 for 140 mM. The
curves indicate good reproducibility.
[0099] This example illustrates the unusual shape of the curve with
the negative component enabling identification of the acetone
present in a vapor sample. While the acetone signal is generally
smaller with 27 mM acetone relative to the curve for 140 mM
acetone, the signal is still characteristic of acetone with the
negative component to the response. The negative component
indicates that the acetone is undergoing a reduction reaction,
although the resulting reduction product may be subsequently
oxidized within the fuel cell. Nevertheless, the distinctive
negative peak can be used to analyze the breath of a person with
respect to detecting the presence and concentration of acetone in a
person's breath. The appropriate analytical techniques based on
these results are presented above. In particular, average curves
from the results in FIGS. 11 and 12 can be used to obtain standard
results for these particular concentrations for use in the analyses
above.
[0100] The embodiments above are intended to be illustrative and
not limiting. Additional embodiments are within the claims. In
addition, although the present invention has been described with
reference to particular embodiments, those skilled in the art will
recognize that changes can be made in form and detail without
departing from the spirit and scope of the invention. Any
incorporation by reference of documents above is limited such that
no subject matter is incorporated that is contrary to the explicit
disclosure herein.
* * * * *