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Spectroscopic Analysis for biological samples :
towards in situ sample analysis of body fluids
Gilwon Yoon
September 27, 2006
Seoul National University of Technology
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Spectra of water, Hb(RBC), albumin, glucose
from visible to NIR (water compensated)
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
0.00
0.05
0.10
0.15
0.5 mm pathlength, temperature control, 37
albumin, 8 g/dl
hemoglobin, 16.9 g/dl
glucose, 5g/dl
absorbance
wavelength (nm)
0
1
2
3
ab
sorbance,water
water
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Absorption spectrum of water
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Involved Key Technologies
Spectroscopic
detection
targetcomponent
Interfering
substances
inhomogeneous medium
Visible/IRLight source
Light interactionwith tissue
High S/N
electronic
detection
Chemometrics
Clinical testStatistical
analysis
Prediction of
concentration
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Spectroscopic Analysis Statistical Methods
Influence of measurement setup : Transmission or
reflection measurement
Influence of red blood cell (hemoglobin) in partial least
squares regression (PLSR) analysis
Independent Component Analysis (ICA) a methodwithout calibration process
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I. Influence of measurement setup :
Transmission or reflection measurement
(a ) (b)
Lightsource Mono-chromator
sli t
Detec torSample
Detec to r
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Comparison between reflectance and transmittance
Jeon, Hwang, Hahn, Yoon (2006), 11:1:014022, Journal of Biomedical Optics
83.5840.471100-1830
39.0726.461100-1830
2050-2392diffuse transmittance
(2mm thick sample)
43.514.502064-2338
26.772.881100-1800
24.693.221100-1800
2064-2338diffuse transmittance
(1mm thick sample)
192.0030.571850-2500
437.5415.911100-1850
275.4427.381100-2500
diffuse reflectance(10mm thick sample)
SEP
[mg/dl]
SEC
[mg/dl]
wavelength
region [nm]
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Diffuse reflectance between 1100 and 1850 nm (a) SECV with respect to the
optimal number of factor, (b) Loading vector of calibration model, (c)
Regression vector of calibration model, (d) Prediction of glucose illustrated
with the intralipid concentrations of sample solutions.
0 2 4 6 8 100
50
100
150
200
250
300
350
400
1000 1200 1400 1600 1800 2000-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
1000 1200 1400 1600 1800 2000
-60000
-40000
-20000
0
20000
40000
0 200 400 600 800 1000
0
200
400
600
800
1000
1200
1400
1600
s4.08
s4.08
s4.08s4.08
s4.08
s4.08
s4.08
s4.16
s4.16
s4.16
s4.16
s4.16
s4.16
s4.16
s4
s4
s4
s4
s4
s4
s4
(b)
SECV[mg/dl]
factor
(a)
loadingvector[a.u.]
wavelength [nm]
factor1
factor2
factor3
(c)
regressionvector[a.u.]
wavelength [nm]
(d)
SEP= 437.54 mg/dl
CV= 98.8%
ref
predictionpredictedglucose[mg/dl]
reference glucose [mg/dl]
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Diffuse transmittance with 1 mm thick samples (a) SECV with respect to the
optimal number of factor, (b) Loading vector of calibration model, (c)
Regression vector of calibration model, and (d) Prediction of glucose
concentrations.
0 2 4 6 8 100
50
100
150
200
250
300
350
400
1000 1200 1400 1600 2200 2400-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1000 1200 1400 1600 1800 2200 2400-10000
-8000
-6000
-4000
-2000
0
2000
4000
6000
8000
0 200 400 600 800 1000
0
200
400
600
800
1000
1200
s4.16
s4
s4
s4.16
s4
s4
s4.16s4
s4.08s4
s4.16
s4.08
s4.08
s4.16
s4.08
s4.08
s4.16
s4.08
s4.08
s4.16
s4
(b)
Prediction
1mm path length
SEP= 24.69 mg/dl
SECV[mg/dl]
factor
(a)
loadin
gvector[a.u.]
wavelength [nm]
factor1
factor2
factor3
(c)
regressionvector
[a.u.]
wavelength [nm]
(d)
predictedglucose
[mg/dl]
reference glucose [mg/dl]
ref
prediction
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II. Influence of red blood cell (hemoglobin) in partial
least squares regression (PLSR) analysis
Absorption becomes much stronger towards longer wavelengths
Dominance of hemoglobin
Interferences among the substances in blood or extracellular fluid
Effect of preprocessing methods
Biological Samples in the near infrared (1000 2500 nm)
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a) Whole blood spectra of
98 samples and saline
spectrum, b) Whole blood
spectra are correlated
with hemoglobin and
glucose concentrations at
each wavelength and
computed correlations
coefficients are shown.
1200 1400 1600 1800 2200
0.0
0.2
0.4
0.6
0.8
1.01200 1400 1600 1800 2200
0.0
0.3
0.6
0.9
1.21.5
1.8
2.1
2.4
b
Glucose
Hemoglobin
correlationc
oefficient(r)
wavelength (nm)
whole blood
Saline
1888 2044
a
absorbance(a.u.)
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What is a maximally achievable accuracy ?
The standard error of glucose prediction was 25.5 mg/dl
and the coefficient of variation in prediction was 11.2%.
Kim and Yoon (2006), 11: 041128, Journal of Biomedical Optics
15.933.8 (0.9603)213Hbmid
35.874.2 (0.8672)207HbhighHb
low
21.246.9 (0.9328)221Hblow
19.039.3 (0.9465)207HbhighHb
mid
22.048.7 (0.9279)221Hblow
10.823.1 (0.9817)213HbmidHb
high
11.225.5 (0.9764)228HbpreHbcal
VCPre
c [%]SEPa (rPre
b)mean value
of glucosePrediction set
Calibration
set
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III. Independent Component Analysis (ICA)
method without calibration process
Identification of pure, or individual, absorption spectra of
constituent components from the mixture spectra without a priori
knowledge of the mixture.
This method was tested with a two-component system consisting
of aqueous solution of both glucose and sucrose, which exhibit
distinct but closely overlapped spectra.
ICA combined with principal component analysis was able toidentify a spectrum for each component, the correct number of
components, and the concentrations of the components in the
mixture. This method does not need calibration process.
Hahn and Yoon (2006), in print, 45:32, November, Applied Optics
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Pure, or individual, water-subtracted absorption profiles
of Glucose (G) and Sucrose (S)
960 980 1000 1020 1040 1060 1080 1100 1120
Absorbance
S
G
Wavenumber (cm-1)
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25 measured mid-IR spectra for the mixtures of glucose and
sucrose. Water absorption was subtracted to enhance the
absorption profile of each component.
960 980 1000 1020 1040 1060 1080 1100 1120
0.0
0.2
0.4
0.6
0.8
1.0
Absorbance
Wavenumber(cm-1)
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Extracted pure-component spectra from measured IR spectra of
25. Pure and ICA represent pure-component absorption
spectrum and the ICA-method extracted absorption spectrum
respectively.
960 980 1000 1020 1040 1060 1080 1100 1120
0.2
0.4
0.6
0.8
1.0
1.2
960 980 1000 1020 1040 1060 1080 1100 1120
0.4
0.5
0.6
0.7
0.8
ICA
Pure
Glucose
Wavenumber(cm-1)
Absorbance
Absorbance
ICA
Pure
Sucrose
Wavenumber(cm-1)
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Scatter plot for the reference concentrations and ICs from
measured mid-IR spectra
1 2 3 4 50
1
2
3
4
1 2 3 4 5
-11
-10
-9
-8
IC1(a.u.)
IC2(a.u.)
Sucrose (a.u.)
Glucose(a.u.)
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Measurement geometry or setup loading factor analysis
can provide actual contribution of wavelength in prediction
Dominant absorber such as RBC(hemoglobin) and water in
near infrared effect substantially. A proper care is needed.
A new method that does not require no concentration
information and calibration process is introduced.
Summary in Spectroscopic analysis