Sensors and Materials, Vol. 24, No. 6 (2012) 323–333
MYU Tokyo
S & M 0888

Determination of Concentrations of Glucose andHuman Serum Albumin in Mixtures in PhosphateBuffered Solution by Near-Infrared Spectroscopy
Mitsuhiro Ogawa*,**, Takehiro Yamakoshi, Kosuke Motoi, Koji Nishida,
Yasuhiro Yamakoshi1 and Ken-Ichi Yamakoshi
Graduate School of Natural Science and Technology, Kanazawa University,
Kakuma, Kanazawa, Ishikawa 920-1192, Japan
1yu.sys Corporation, 2-7-1 Ishibiki, Kanazawa, Ishikawa 920-0935, Japan(Received November 1, 2011; accepted March 6, 2012)
Key words: blood constituent measurement, glucose, human serum albumin, near-infrared
spectroscopy, reagentless measurement

Near-infrared (NIR) absorbance spectra of glucose and human serum albumin (HSA)
mixtures in phosphate-buffered solution (PBS) (pH=7.4) were investigated as the basisof reagentless blood constituent measurements. Diffuse reflectance spectra of powdered
glucose and HSA were also studied. Differential absorbance spectra taken relative to thespectrum of a reference PBS were measured in the wavelength range of 750–2,500 nm.
In the differential spectra of mixture sample solutions, positive peaks corresponding to
the HSA concentration could be observed, particularly in the range of 2,150–2,350 nm;
however, peaks corresponding to the glucose concentration could not be observed. Somepeaks visible in the diffuse reflectance spectra of powdered HSA could not be observedin the differential spectra. Measured NIR spectra were subjected to partial least-squares
regression and good predictions of both HSA and glucose concentrations were obtained.
Calibration for glucose in the concentration range of 0–300 mg/dl yielded a correlation
coefficient of 0.9965 and a standard error of prediction (SEP) of 8.3 mg/dl with a latentvariable of five. Calibration of HSA within the range of 2–4 g/dl yielded a correlation
coefficient of 0.9996 with a SEP of 0.023 g/dl and a latent variable of five. These results
suggest that reagentless measurements of glucose and HSA concentrations in humanserum are possible with NIR spectroscopy.

Corresponding author: e-mail: ogawa@his.teikyo-u.ac.jp
Present affiliation: Faculty of Science and Engineering, Teikyo University (1-1 Tokyosato-Dai,
Utsunomiya, Tochigi 320-5551, Japan)
*

**

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1.

Sensors and Materials, Vol. 24, No. 6 (2012)

Introduction

From a clinical viewpoint, blood substances can provide much valuable information
about the physiological status of an individual. Because of this importance, many
attempts to determine these substances have been reported over the past century.
Although current blood substance analyses mainly involve chemical and/or biologicalreactions with reagents, because of the high precision of such measurement, blood
constituent measurement without any reagents, also called “reagentless measurement”,
has been studied in tandem with the advancement of measurement instrumentation andapplied statistics.(1,2) Reagentless monitoring allows the possibility of measurement with
rapid response, real-time monitoring, cost-effectiveness, and simultaneous measurementof multiple constituents with only one device.(1–3) Thus, there have been many attempts
to realize this approach. An example of such reagentless measurement, with a long
history, is CO-oximetry, that is, the in vitro measurement of the oxygenation status ofhemoglobin contained in red blood cells using visible light and near-infrared (NIR)
radiation.(4,5) Furthermore, in vitro CO-oximetry has been expanded to the in vivo andnoninvasive measurement technique named “pulse oximetry”, which aims to measure theoxygenation status of a patient’s hemoglobin transcutaneously.(6) Attempting to add tothe success of these methods for hemoglobin oxygenation measurement, the developmentof reagentless techniques for blood constituents, other than those linked to oxygen, is
still ongoing.
In efforts to develop methods for the reagentless measurement of blood constituents,
researchers continue to explore the use of optical techniques, using visible and infrared
radiation, because such measurement procedures can be applied to blood, plasma, and/or
serum nondestructively. Among these efforts, many promising attempts using opticalabsorbance measurement have been reported in recent decades. In these previous
attempts, interest was mainly focused on clinically important blood constituents suchas total hemoglobin, glucose, protein, and cholesterol.(1,3,7–14) From a methodological
viewpoint, many recent advances in reagentless measurement have been based on the
combined power of NIR spectroscopy and chemometric analysis. Chemometrics is an
important field within applied chemistry, whereby chemical information is acquiredby data-driven means, using advanced statistical and applied mathematical methods,
and computing science. Although chemometric methods applied to quantitative NIR
spectroscopy have the potential to extract small components from the original measured
data, the extraction procedure is highly dependent on the data-driven means employed.
Consequently, the results that emerge could sometimes be unclear or inaccurate if there
are artifacts with spurious correlation.
Although there have been many reports of promising results of studies using NIR
spectroscopy, most of such studies have been highly dependent on chemometrics, inwhich several different statistical methods have been investigated. The mechanisms
and origins of the measurements are still unclear in many cases; thus, it is difficult,in practice, to compare those results and establish a standard calibration. Althoughmany previous attempts have been focused particularly on statistical analyses to form acalibration model, we consider that the data and parameters obtained, such as absorbanceSensors and Materials, Vol. 24, No. 6 (2012)325

spectra, should be discussed in more detail to establish a standard method of reagentlessmonitoring of blood substances. The adequacy of the results obtained from the diffuse
reflectance spectra of powdered blood constituents has been reported and there was animplicit assumption that spectral peaks in the powder state could be observed in aqueous
solutions, in serum and/or whole blood.(8) However, this implicit assumption had notbeen justified. Previously, we attempted to observe positive peaks in the absorbance
spectrum of human serum albumin (HSA) in phosphate-buffered solution (PBS)
using differential spectra from a reference solution spectrum and found clear peaks
corresponding to HSA concentration.(15) We also found that some peaks that could beobserved in diffuse reflectance spectra of powder-state HSA could not be observed, andlimited peaks in the powder state could be apparent in aqueous solution.
Moreover, we have recently proposed a novel noninvasive method of in vivo
measurement of blood glucose concentration using an NIR multiwavelength
photoplethysmogram (PPG), which was named pulse glucometry.(16–18) The measurement
principle of pulse glucometry is based on the fact that the glucose molecule has
absorption bands in the NIR region and the PPG is derived from the arterial volume
change of the blood vessel at the measurement site (e.g., fingertip). In vitro opticalreagentless measurement can be an important basis of the further development of this invivo pulse glucometry technique.
In this study, we tried to expand on our previous attempt and endeavored to observe
the spectral features of glucose and HSA mixture solutions, and then form a calibration
model for the determination of glucose and HSA concentrations in a solution.

2.

Materials and Methods

2.1

Sample preparation

Glucose powder (d(+)-glucose, 043-31163, Wako Pure Chemical Industries Ltd.,
Japan) and HSA (019-10503, Wako Pure Chemical Industries Ltd., Japan) were used in
the experiments. The purity of the HSA sample was proved to be greater than 95% byelectrophoresis.
Two groups of glucose and HSA samples were prepared. Glucose powder and HSA
samples were used for group I. Group II consisted of glucose and HSA powders in
PBS. PBS was prepared using purchased PBS powder (167-14491, Wako Pure Chemical
Industries Ltd., Japan) and pure water supplied from an ultrapure water production
system (Autopure WT100, Yamato Corp., Japan). After sterilizing PBS, its pH wasadjusted to 7.4, a typical pH of blood, serum, and plasma in a healthy human. The
concentrations of the prepared glucose and HSA in solution samples were in the range of
0 to 300 mg/dl and 2.0 to 4.0 g/dl, respectively, as shown in Table 1. The concentration
ranges of glucose and HSA cover the concentrations in the blood of a healthy humanand a diabetic patient. In addition to mixture solutions, pure glucose solutions of 100 to
500 mg/dl were prepared. To prepare sample solutions, glucose powder and HSA were
weighed using an electronic balance and the volumes of solutions were made up using
volumetric flasks.

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Sensors and Materials, Vol. 24, No. 6 (2012)

Table 1
Concentrations of glucose and human serum albumin in samples.
Albumin (g/dl)
2
3
4

Concentration
Glucose (mg/dl)
0, 50, 100, 150, 200, 250, 300
0, 50, 100, 150, 200, 250, 300
0, 50, 100, 150, 200, 250, 300

Albumin: human serum albumin

2.2

Instruments

2.3

Data processing and calibration model3.

Results

3.1

NIR spectra of glucose and HSA powders

A Fourier transform near-infrared (FT-NIR) spectrometer (Spectrum One NTS,
PerkinElmer Japan Co., Ltd, Japan) was used for absorbance measurements. The
measured wavelength range was 750–2,500 nm and the resolution was 1.66 nm. For
group I, diffuse reflectance absorption spectra of glucose and HAS powder were
measured using a diffuse reflectance cell. For group II, a flow cell of 1 mm optical path
length with quartz plate windows was used for measurement in the NIR region (Quartz
Glass Products, 6220-72011, GL Sciences Inc., Japan). For each sample in group II,absolute absorbance spectra (background measurement was obtained using no sample) of
samples and differential absorbance spectra relative to the spectrum of a reference PBS(pH 7.4; concentrations of glucose and HSA, 0.0 g/dl each) were measured. The room
temperature and solution sample temperatures were both kept at 27.0±0.3°C during themeasurements. In group II, the sequence of measurements was random.
The software “R” (version 2.12.2), its extension package “pls” (version 1.2-1),
and “RTisean” (version 3.0.11) were used for spectral data processing and forming
the calibration model.(19) All data from group II were processed using the SavitzkyGolay filter to eliminate noise and artifacts by conventional chemometry. Multivariate
regression analysis of group II samples was then performed to form a calibration modelfor estimating both glucose and HSA concentrations. In this study, a classical partial
least-squares (PLS) regression was applied to the spectral data after Savitzky-Golay
filtering to analyze data and form calibration models.

The measurements of glucose and HSA powder samples (group I) using the FTNIR spectrometer provided NIR diffuse-reflectance absorption spectra. Figure 1 shows
diffuse-reflectance absorption spectra of glucose and HSA powders in the 750–2,500
nm range. In these spectra, several positive (convex) peaks can be observed for both
samples. The difference between the forms of the spectra of glucose and HSA is clearlyobserved in Fig. 1. As was suggested in a previous paper,(3) the spectral peaks contain
information about the molecular features of glucose and HSA.

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Fig. 1. NIR diffuse-reflectance spectra of glucose and human serum albumin powders.

3.2

Absolute absorption of samples

3.3

Differential absorption spectra of glucose and HSA solutions from thespectrum of reference solution

Absolute absorption spectra of glucose and HSA mixture solutions were measured inthe 750–2,550 nm range. The forms of the spectra were similar to our previous results
for absolute absorption spectra of HSA sample solutions.(14) In the obtained spectra,
strong absorption peaks derived from water (H2O) absorption(20,21) were observed in the
1,450–1,900 nm range. Because such strong water absorption completely masks theabsorption of glucose and HSA, it is very difficult to observe any features of dissolved
glucose and HSA in the absolute absorption spectra.

Examples of differential absorption spectra of glucose and HSA solution samples
relative to a reference PBS spectrum in the 750–2,550 nm range are shown in Figs. 2 and 3.
Figure 2 shows differential absorption spectra of pure glucose solutions. Figure 3 showsdifferential absorption spectra of glucose and HSA mixture solutions. The baseline shift
on the measured spectra was removed by subtracting the absorbance at 1,200 nm. As
shown in Figs. 2 and 3, the band near 1,950 nm and over 2,400 nm cannot be measured
because of the strong water absorption in the band. The observed negative (concave)
peak at 1,450 nm is also considered to be caused by a change in the amount of waterin the sample. This is because samples with higher glucose and HSA concentrations
contain smaller amounts of water per unit volume. Consequently, samples with higher
glucose and HSA concentrations must have smaller absorptions in the water-dominant328

Sensors and Materials, Vol. 24, No. 6 (2012)

Fig. 2. Examples of NIR differential absorbance spectra of pure glucose in PBS. Differential
absorbances were measured against the reference PBS.

Fig. 3. Examples of near-infrared differential absorbance spectra of glucose and human serum
albumin mixtures in PBS. Differential absorbances were measured against the reference PBS.

Sensors and Materials, Vol. 24, No. 6 (2012)329

absorption regions such as near 1,450 nm. In contrast, a positive peak that corresponds
to glucose concentration can be observed in the 1,600–1,700 nm range in Fig. 2. In Fig. 3,
the clearly observable peaks in the 1,650–1,750 nm and 2,150–2,350 nm ranges should
be considered as corresponding to the HSA concentration of the samples, as in ourprevious results.(15) Additionally, the position of the positive peaks coincided well withthe position of the convex peaks on the diffuse reflectance spectra of the glucose andHSA powders. However, limited peaks for the powder state could be found in aqueous
solution as found in our previous study.(15) Convex peaks at approximately 1,600 and 2,100
nm can be observed in both Figs. 1 and 2. The peak at approximately 1,200 in Fig. 1
cannot be found in Fig. 2. The dependence of the positive peak on glucose concentration
cannot be observed from the absorbance spectra of glucose and HSA mixture solutions,because the NIR absorbance of glucose is weaker than that of HSA at the selectedconcentration.

3.4

Calibration models for determination of glucose and HSA concentrations

4.

Discussion

Classical PLS regression analysis was adopted to correlate the NIR differential spectrafrom samples with various glucose and HSA concentrations. First, the determination of
HSA concentration was attempted, because HSA has more intense absorption peaks thanglucose in our samples. The differential absorbance spectra at the 1,650–1,750 nm and2,250–2,250 nm ranges were selected as explanatory variables and HSA concentration as
the dependent variable for PLS analysis. Bands other than the observed positive peaks
were rejected from the analysis in advance. Differential absorbances of 122 wavelengths
were examined, and the leave-one-out cross-validation technique was applied to evaluate
the regression. As a result, the latent variable five in the PLS analysis was obtained as
the optimum parameter, and the regression provided a standard error of prediction (SEP)of 0.023 g/dl. Figure 4 shows the results of the leave-one-out cross-validation and thecorrelation coefficient between the actual HSA concentration and predicted concentration
is 0.9996. This result shows that HSA concentrations in the range expected in healthy
humans can be estimated, in the presence of glucose, on the basis of the selected bands.
Second, the determination of glucose concentration was attempted. The differential
absorbance spectra for the 1,550–1,750 nm range were selected for PLS regression.
Differential absorbances of 122 wavelengths were examined as explanatory variables.
The leave-one-out cross-validation technique was also applied, and the optimum latentvariable five for PLS yielded the regression with SEP of 8.3 mg/dl. Figure 5 shows theresults of the leave-one-out cross-validation, with a correlation coefficient of 0.9965.
This result shows that glucose concentrations in the range expected for human subjects
can be estimated in the presence of an interfering substance, HSA.

Concerning the comparison of diffuse reflectance spectra of glucose powder
with differential spectra of glucose solution, limited peaks of glucose powder were
only apparent in glucose solution, as in our previous result for HSA.(15) This finding
suggests the importance of an accurate absorbance measurement by differential spectral

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Sensors and Materials, Vol. 24, No. 6 (2012)

Fig. 4 (left). Calibration plot of estimated concentration of human serum albumin in mixtures ofsolutions against actual concentration based on leave-one-out cross-validation.
Fig. 5 (right). Calibration plot of estimated concentration of glucose in mixtures of solutions
against actual concentration based on leave-one-out cross-validation.

observation.
A recently proposed in vivo blood glucose measurement, pulse glucometry, isbased on an NIR multiwavelength PPG that can contain information on arterial blood
constituents. We previously attempted PPG spectral analysis using PLS regression,
principal component regression, and also support vector machine regression,(17,18) andobtained good estimates of blood glucose concentrations. However, current pulse
glucometry is dependent on multivariate analyses using multiwavelength PPG with about
100 wavelengths. Our final goal is to develop a new cost-effective and noninvasive
self-monitoring blood glucose (SMBG) device based on pulse glucometry to replace
the current finger-pricking SMBG meters and to expand it to other blood chemicalmeasurements. To achieve our final goal, neither a Fourier transform infrared (FT-IR)
spectrometer nor a diffraction-grating spectrometer can be used. One possibility for
actualization would be spectrophotometry using multiple light-emitting diodes (LEDs)
as light sources. In this case, an optical measurement of many wavelengths with very
narrow wavelength resolution, as in an FT-IR or diffraction-grating spectrometer, cannot
be expected. Therefore, we tried to form calibration models using a smaller numberof wavelengths with a wider wavelength resolution. Measured differential absorbance
was averaged within a predefined range and adopted as an explanatory variable in
PLS regression. This should simulate spectrometry using one or more LEDs. For

Sensors and Materials, Vol. 24, No. 6 (2012)331

determination of HSA concentration, differential absorbance was selected and averaged
within each of the ranges 1,650–1,700, 1,701–1,750, 2,150–2,200, and 2,201–2,250 nm,
and used as four explanatory variables. For determination of glucose concentration,differential absorbance within each of the ranges 1,550–1,600, 1,601–1,650, 1,651–1,700,
and 1,701–1,750 nm was selected, averaged, and again adopted as four explanatory
variables. As a result of the HSA concentration determination, the optimum latentvariable of four was obtained and the PLS regression provided an SEP of 0.0181 g/dl.
Figure 6 shows the results of the leave-one-out cross-validation, and the correlation
coefficient was 0.9998. In the determination of glucose concentration, the optimum
latent variable of four was obtained, and the PLS regression provided an SEP of 12.3 mg/
dl. Figure 7 shows the results of the leave-one-out cross-validation, with a correlation
coefficient of 0.9924. This result can form the basis of the design of an LED-based
spectroscopy system for in vivo blood glucose and HSA concentration measurement.
Li et al. conducted in vitro and in vivo blood glucose measurements by NIR
spectroscopy, and when combined with their sophisticated data-driven chemometrics
procedure, they found good results.(14) They selected informative spectral regions forFig. 6 (left). Calibration plot of estimated concentration of human serum albumin in mixtures ofsolutions against actual concentration using averaged absorbances based on leave-one-out crossvalidation.
Fig. 7 (right). Calibration plot of estimated concentration of glucose in mixtures of solutions
against actual concentration using averaged absorbances based on leave-one-out cross-validation.

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Sensors and Materials, Vol. 24, No. 6 (2012)prediction of HSA and glucose concentrations by their statistical method. Their reported
informative ranges were 5,907–5,663 cm−1 (1,692–1,765 nm wavelength) and 4,648–
4,323 cm−1 (2,151–2,313 nm) for HSA, and those ranges coincide well with our results.
In addition, their reported informative ranges of glucose were 6,080–5,700 cm−1 (1,664–
1,754 nm in wavelength) and 4,647–4,255 cm−1 (2,152–2,350 nm) and the former range
falls within our selected range for glucose prediction. Although our methodology wascompletely different in principle from that of Li et al., approximately the same results
could be obtained. This can possibly support the adequacy of our results and the resultsof Li et al.
Although we discussed a simple HSA solution previously(15) and a glucose and HSA
mixture solution in this paper, real blood, serum, and/or plasma are more complicatedand composed of many substances. In the next step, we plan to measure and examine
control plasma and real blood samples, then attempt to form a calibration model using asmall number of absorbances with a wider wavelength resolution.

5.

Conclusions

NIR absorbance spectra of glucose and HSA mixture sample solutions were discussed
as the basis of reagentless blood constituent measurement. Differential absorbance
spectra from the spectrum of a reference PBS were measured in the wavelength rangeof 750–2,500 nm. In the differential spectra of mixture sample solutions taken relative
to the reference spectrum, positive peaks corresponding to HSA concentration could
be observed, particularly in the 1,650–1,750 and 2,150–2,350 nm ranges; however,peaks corresponding to glucose concentration could not be observed. NIR spectra
were subjected to partial least-squares regression, and good predictions of the HSA
and glucose concentrations were obtained. These results are considered as the basis ofreagentless measurement of HSA and glucose concenstrations in human serum as well as
in vivo glucose and HSA concentration measurement.

Acknowledgements
This research was partially supported by the Ministry of Education, Culture, Sports,
Science and Technology, Japan through Grant-in-Aid for Scientific Research (A),
21240047, 2009–2011. The authors are grateful for the support.

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