Biophysics GYoon

8/14/2019 Biophysics GYoon

1/18

Spectroscopic Analysis for biological samples :

towards in situ sample analysis of body fluids

Gilwon Yoon

September 27, 2006

Seoul National University of Technology


2/18

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


3/18

Absorption spectrum of water


4/18

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


5/18

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


6/18

I. Influence of measurement setup :

Transmission or reflection measurement

(a ) (b)

Lightsource Mono-chromator

sli t

Detec torSample

Detec to r


7/18

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]


8/18

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]


9/18

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


10/18

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)


11/18

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.)


12/18

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


13/18

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


14/18

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)


15/18

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)


16/18

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)


17/18

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.)


18/18

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

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Biophysics GYoon