Spectroscopic Analysis for biological samples :

towards in situ sample analysis of body fluids

Gilwon Yoon

September 27, 2006

Seoul National University of Technology

Spectra of water, Hb(RBC), albumin, glucosefrom visible to NIR (water compensated)

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0.00

0.05

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0.15

0.5 mm pathlength, temperature control, 37

albumin, 8 g/dl

hemoglobin, 16.9 g/dl

glucose, 5g/dl

abso

rban

ce

wavelength (nm)

0

1

2

3

abso

rban

ce, w

ater

water

Absorption spectrum of water

Involved Key Technologies

Spectroscopicdetection

target component

Interferingsubstances

inhomogeneous medium

Visible/IR Light source

Light interaction with tissue

High S/N electronic detection

Chemometrics

Clinical testStatisticalanalysis

Prediction ofconcentration

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 method without calibration process

I. Influence of measurement setup :Transmission or reflection measurement

(a) (b)

Light source

Mono-chromator

slit

DetectorSample

Detector

Comparison between reflectance and transmittance

Jeon, Hwang, Hahn, Yoon (2006), 11:1:014022, Journal of Biomedical Optics

wavelength region [nm]

SEC[mg/dl]

SEP[mg/dl]

diffuse reflectance (10mm thick sample)

1100-2500 27.38 275.44

1100-1850 15.91 437.54

1850-2500 30.57 192.00

diffuse transmittance(1mm thick sample)

1100-1800 2064-2338

3.22 24.69

1100-1800 2.88 26.77

2064-2338 4.50 43.51

diffuse transmittance(2mm thick sample)

1100-1830 2050-2392

26.46 39.07

1100-1830 40.47 83.58

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.

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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

s4s4

s4

s4

s4

(b)S

EC

V [

mg

/dl]

factor

(a)

load

ing

vec

tor

[a.u

.]

wavelength [nm]

factor1 factor2 factor3

(c)

reg

ress

ion

vec

tor

[a.u

.]

wavelength [nm]

(d)

SEP= 437.54 mg/dlCV= 98.8%

ref prediction

pre

dic

ted

glu

cose

[m

g/d

l]

reference glucose [mg/dl]

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.

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0.25

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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)

Prediction1mm path lengthSEP= 24.69 mg/dl

SE

CV

[m

g/d

l]

factor

(a)

load

ing

vec

tor

[a.u

.]

wavelength [nm]

factor1 factor2 factor3

(c)

reg

ress

ion

vec

tor

[a.u

.]

wavelength [nm]

(d)

pre

dic

ted

glu

cose

[m

g/d

l]

reference glucose [mg/dl]

ref prediction

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)

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.

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b

Glucose

Hemoglobin

corr

elat

ion

co

effi

cien

t (r

)

wavelength (nm)

whole blood

Saline

1888 2044

a

abso

rban

ce (

a.u

.)

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

Calibration set

Prediction setmean valueof glucose

SEPa (rPreb) VCPre

c [%]

Hbcal Hbpre 228 25.5 (0.9764) 11.2

Hbhigh

Hbmid 213 23.1 (0.9817) 10.8

Hblow 221 48.7 (0.9279) 22.0

Hbmid

Hbhigh 207 39.3 (0.9465) 19.0

Hblow 221 46.9 (0.9328) 21.2

Hblow

Hbhigh 207 74.2 (0.8672) 35.8

Hbmid 213 33.8 (0.9603) 15.9

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 to identify 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

Pure, or individual, water-subtracted absorption profiles of Glucose (G) and Sucrose (S)

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Ab

so

rba

nc

e

S

G

Wavenumber (cm-1)

25 measured mid-IR spectra for the mixtures of glucose and sucrose. Water absorption was subtracted to enhance the

absorption profile of each component.

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Abs

orba

nce

Wavenumber(cm-1)

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.

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ICA

Pure

Glucose

Wavenumber(cm-1)

Abs

orba

nce

Abs

orba

nce

ICA

Pure

Sucrose

Wavenumber(cm-1)

Scatter plot for the reference concentrations and ICs from measured mid-IR spectra

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IC1(

a.u

.)

IC2(

a.u

.)

Sucrose (a.u.)

Glucose(a.u.)

Measurement geometry or setup – loading factor analysis ca

n provide actual contribution of wavelength in prediction

Dominant absorber such as RBC(hemoglobin) and water in n

ear infrared effect substantially. A proper care is needed.

A new method that does not require no concentration inform

ation and calibration process is introduced.

Summary in Spectroscopic analysis