Raman spectroscopy for measurement of blood analytes
Measurement of the concentrations of blood analytes presently
requires withdrawal of one of more blood samples and a measurement
process which often involves sample handling, such as serum extraction,
addition of various reagents and a delay in the diagnosis process.
Withdrawal of blood exposes personnel to biohazards and causes inconvenience
and pain to the patient. A non invasive measurement would revolutionize
medical diagnosis by providing analytes concentrations quickly,
painlessly and without the use of reagents. A non-invasive measurement
would be particularly beneficial where the results are needed quickly
or where measurements must be taken frequently. An obvious example
of this is the measurement of glucose concentration. Millions of
people with diabetes must measure their glucose level multiple times
per day to maintain their glucose level within prescribed limits
so as to reduce the serious long term consequences of this disease.
Non-invasive measurement of glucose is a goal of many institutions.
Many technologies are being investigated to reach this goal. Among
them are absorption spectroscopy, both by diffuse reflectance and
transmission, light polarization and light scattering.
Over the past several years, we have been investigating the use
of near-infrared (NIR) Raman Spectroscopy for measuring the concentrations
of blood analytes. The ultimate goal of this research is to develop
a transcutaneous method of measuring clinically important analytes
in the blood-tissue matrix by developing a basic understanding of
the scientific and engineering issues involved.
The strength of Raman spectroscopy lies in its sharp spectral features,
characteristic for each molecule. This strength is ideally suited
to blood analyte measurements, where there are many interfering
spectra, many of which are much stronger that that of blood analytes.
Raman’s sharp spectral features enable detection of blood
analyte spectra among these strong interfering spectra and in the
presence of a large fluorescence background. Figure 1 shows the
sharp spectral features of the glucose spectrum and how it is differentiated
from the spectra of the many components that exist in skin.
|Figure 1. The distinct spectral features,
characteristic for a back-ground subtracted Raman spectrum,
are here exemplified by a spectrum of glucose dissolved in water.
The typical Raman spectrum of skin is shown for comparison with
arrows indicating regions that clearly differentiate glucose
from the other components in skin. To allow comparison, the
glucose spectrum shown is 150 times the estimated size of the
spectrum that would be received from a concentration of 5mM
glucose in human skin.
However, to measure glucose in tissue is far more complex than
indicated by this figure for various reasons: a) the spectra from
typical physiological concentrations of glucose in skin are in the
order of several hundred times lower than the total spectrum from
skin, as shown in an example in Figure 1, b) even with excitation
at 830nm, the fluorescence background dominates the signal, as can
be seen in figure 2, c) Raman peaks from all other biomolecules
that are present overlap and therefore interfere and d) the optical
properties of the tissue as well as the probe depth/volume influence
|Figure 2. A signal collected from a transcutaneous
measurement, is shown in blue. It consists of a dominating fluorescence
background on top of which the Raman peaks from all present
biomolecules is superimposed. The extracted Raman signal is
shown in red.
Previous work determined that the measurements of blood
analytes in a serum matrix were feasible. From that foundation,
the Raman system was improved and then utilized to demonstrate the
feasibility of the measurement of Glucose, Urea, Triglyceride, Total
Protein, Albumin, Hemoglobin and Hematocrit in whole blood .
Based upon these successes, our focus moved to transcutaneous measurement
of analytes. To support this objective, the Raman system was further
improved to significantly increase its light collection and detection
efficiency. The system developed for this application is shown in
figure 3. The Raman light generated in the tissue is collected by
a paraboloidal mirror, designed for both wide-angle and large-area
light collection optimal for light being emitted from highly scattering
skin. A circular-to-linear shaped fiber bundle efficiently guides
the collected Raman light to the spectrograph and a large area CCD,
enabling recordings of Raman spectra with high sensitivity.
|Figure 3. Diagram of the high sensitivity
Raman spectroscopy system used for transcutaneous measurements
As an initial evaluation of the ability of Raman spectroscopy
to measure glucose transcutaneously, a series of spectra were collected
on human volunteers in conjunction with a glucose tolerance test.
This involves the intake of a high-glucose containing fluid (SUN-DEX),
after which the glucose levels were elevated to more than twice
that found under fasting conditions. Raman spectra, each measured
for 3 minutes, and reference glucose concentrations from blood samples
were measured periodically during the 2-2 ½ hour duration
of the procedure for each volunteer. A Hemocue glucose analyzer
provided the reference measurement for the blood analysis. The manufacturer’s
specification for precision is SD < 6 mg/dl.
The Raman spectra were extracted from the large fluorescence background
using a 5th order polynomial to fit the background. A calibration
algorithm was generated individually from the data from each volunteer
using the Partial Least Squares (PLS) regression method. Each calibration
algorithm was validated using hold-out-one cross validation.
A comparison of the predicted glucose concentrations to the corresponding
reference data from one of the volunteers is shown in figure 4.
The average error in the validated data (Standard Error of Validation,
SEV) is 9.8 mg/dl with an R^2 of 0.91.
|Figure 4. The left chart shows
the predicted glucose tracking the reference values for one
volunteer. The correlation of the same data is shown on the
right, with an average error of 9.8mg/dl and an R^2 of 0.91.
The procedure was applied individually to data from each of 16
volunteers and the validated prediction results combined into one
chart, shown in figure 5. For the data from all 16 volunteers, the
average prediction error is 13.2 mg/dl and the R^2 is 0.79.
|Figure 5. Cross validated results for 16
volunteers calibrated individually. The average prediction error
for this set is 13.2 mg/dl and the R2 is 0.79.
A question that occurs with this kind of procedure is whether the
calibration is based upon glucose. Raman Spectroscopy offers a way
to obtain an indication of whether glucose is an important factor
in this calibration; by comparing the calibration regression vector
to the spectrum of glucose. Figure 6 compares the regression vector
for the calibration shown in figure 4 to the spectrum of glucose
in water, scaled to fit on the same chart. Due to the sharp features
of Raman Spectroscopy, there are numerous peaks in the glucose spectrum
that appear in the regression vector.
Unlike many methods of measuring glucose, where there are valid
questions of whether glucose is being measured, the existence of
numerous glucose peaks in the regression vector developed from Raman
measurements provides direct evidence that glucose is being measured.
|Figure 6. The regression (B) vector for the
calibration shown in figure 4 and the spectrum of glucose, scaled
to fit on the same chart. Numerous peaks in the glucose spectrum
match peaks in the regression vector, indicating that glucose
in an important part of the calibration.
The data from this series of tests in volunteers has provided
a wealth of knowledge for us. We are continuing to analyze the data
to identify opportunities to improve results. From the analysis
and further testing of system characteristics, we have generated
a number of instrument improvements to be made and identified a
number of causes for error. Addressing those causes and making identified
instrument improvements are expected to decrease measurement error.
We have further analyzed the optical and stability characteristics
of our system. Based upon that, we have improved light collection
efficiency by over 30% by changing to a higher NA fiber in the bundle
that couples light to the spectrometer. We are also taking steps
to make our system more stable and developing techniques to accurately
and precisely measure and correct for the drifts that remain.
We are also developing new data processing techniques to extract
more information from our measurements. Our goal is to utilize all
the techniques we are learning to obtain reduced error levels in
an expended human volunteer study.
- "Measurement of blood analytes in turbid biological tissue
using near-infrared Raman spectroscopy", Tae-Woong Koo, Doctoral
thesis, Massachusetts Institute of Technology, 2001
- "Prospects for In Vivo Raman Spectroscopy", Eugene
B. Hanlon, Ramasamy Manoharan, Tae-Woong Koo, Karen E. Shafer,
Jason T. Motz, Maryann Fitzmaurice, John R. Kramer, Irving Itzkan,
Ramachandra R. Dasari, and Michael S. Feld, Physics in Medicine
and Biology 45(2), R1-R59 (2000)
- "Reagentless Blood Analysis by Near-Infrared Raman Spectroscopy",
Tae-Woong Koo, Andrew J. Berger, Irving Itzkan, Gary Horowitz,
and Michael S. Feld, Diabetes Technology & Therapeutics
1(2), 153-157 (1999).
- "Multicomponent Blood Analysis by Near-Infrared Raman Spectroscopy",
Andrew J. Berger, Tae-Woong Koo, Irving Itzkan, Gary Horowitz,
and Michael S. Feld, Applied Optics 38(13),
- "Measurement of Glucose in Human Blood Serum using Raman
Spectroscopy", Tae-Woong Koo, Andrew J. Berger, Irving Itzkan,
Gary Horowitz, and Michael S. Feld, IEEE-LEOS Newslette 12(2)
- "An Enhanced Algorithm for Linear Multivariate Calibration",
Andrew J. Berger, Tae-Woong Koo, Irving Itzkan, and Michael S.
Feld, Analytical Chemistry 70(3), 623-627
- "Measurements of analytes in whole blood by means of Raman
spectroscopy" Annika M. K. Enejder, Tae-Woong Koo, Jeankun
Oh, Gary L. Horowitz, Ramachandra R. Dasari, and Michael S. Feld,
SPIE's BIOS 2002, 19-25 January 2002, San Jose, California,
- "Blood Analysis by Raman Spectroscopy" Annika M. K.
Enejder, Tae-Woong Koo, Jeankun Oh, Martin Hunter, Slobodan Sasic,
Gary L. Horowitz, Michael S. Feld. Optics Letters 27,
2004-2006 ( 2002).
This research is funded by National Center for Research
Resources (National Institute of Health) and Bayer Diagnostics.