Reagentless Near-Infrared Determination of Glucose in Whole Blood Using Multivariate Calibration*

D A V I D M. H A A L A N D , t M. R I E S R O B I N S O N , G A R Y W. K O E P P , E D W A R D V. T H O M A S , and R. P H I L I P E A T O N Sandia National Laboratories, Albuquerque, New Mexico 87185 (D.M.H., M.R.R., G.W.K., E.V.T.) and University of New Mexico School of Medicine, Albuquerque, New Mexico 87131 (M.R.R., R.P.E.)

Noninvasive monitoring of glucose in diabetic patients is feasible with the use of near-infrared spectroscopic measurements. As a step toward the final goal of the development of a noninvasive monitor, the near- infrared spectra (4250 to 6600 cm -I) of glucose-doped whole blood sam- ples were obtained along with reference glucose values. Glucose concen- trations and spectra of blood samples obtained from four subjects were subjected to multivariate calibration with the use of partial least-squares (PLS) methods. The cross-validated PLS standard errors of prediction for glucose concentration based on data obtained from each individual subject's blood samples averaged 33 mg/dL over the range from 3 to 743 mg/dL. Cross-validated standard errors for glucose concentration from PLS calibrations based on data from all four subjects were 39 mg/dL. However, when PLS models based upon three subjects' data were used for prediction on the fourth, glucose prediction abilities were poor. It is suggested that blood chemistry differences were sufficiently different for the four subjects to require that a larger number of subjects be included in the calibration for adequate prediction abilities to be obtained from near-infrared spectra of blood from subjects not included in the calibra- tion.

Index Headings: Near-infrared; Multivariate calibration; Partial least- squares; Glucose determination; Blood analysis.

INTRODUCTION

Monitoring of blood glucose levels in diabetic patients is important for the management of their disease. It is desirable that insulin-dependent diabetic subjects mon- itor their blood-glucose levels four or more times a day. However, since current glucose monitors require invasive procedures such as a finger stick to obtain blood samples, patient compliance is difficult to achieve. Increased mon- itoring of glucose levels would lead to improved control of a patient's blood glucose levels and would result in the eventual decrease in the significant complications of diabetes including kidney, nerve, eye, and heart disease. Thus, the development of a noninvasive glucose monitor would have significant impact in diabetes management.

A number of groups have shown that mid-infrared spectroscopy can be used in the reagentless determina- tion of glucose in blood or serum. 1-7 However, mid-in- frared spectroscopy cannot be used for noninvasive mea- surements since the absorption of water in this region of the spectrum is too high to achieve adequate penetration depths. Yet, near-infrared spectroscopy (14,000 to 4000

Received 1 June 1992. * This work was performed in part at Sandia National Laboratories

supported by the U.S. Department of Energy under Contract Number DE-AC04-76DP00789 and in part at the University of New Mexico School of Medicine Clinical Research Center under Grant 5M01RR997.

t Author to whom correspondence should be sent.

cm -1) is not affected by water to the same degree, and penetration depths of 1 mm to 1 cm are possible in tissue. Thus, if near-infrared spectroscopy can be used to de- termine glucose in whole blood, the potential exists for the determination of glucose noninvasively. Several groups have recently published results from the appli- cation of near-infrared spectroscopy to buffered water or serum samples in the determination of glucose con- centrations. 8~1~ This work describes our use of near-in- frared spectroscopy coupled with multivariate calibra- tion methods for the in vitro determination of glucose in whole blood.

EXPERIMENTAL

A Nicolet 800 FT-IR spectrometer equipped with a liquid-nitrogen-cooled InSb detector and a CaF2 beam- splitter was used in the determination of glucose in doped blood samples from four different subjects, two males and two females (one of the male subjects and one of the female subjects were diabetic patients). The near-infra- red source was a 100-W tungsten halogen light, and the spectra were collected at a resolution of 32 cm -1. Spectra were obtained in transmission mode. Separate air back- grounds were taken after each sample without the sample cell present. These single-beam background spectra were obtained with the Jacquinot iris in the spectrometer re- duced to prevent saturation of the InSb detector. The single-beam spectra of blood samples were obtained with the blood contained in a 1-mm-pathlength quartz cell. Each sample spectrum was ratioed to its respective back- ground, and the resultant transmission spectrum was converted to absorbance. The run order of the samples for each subject was randomized to ensure that any in- strumental drift would not be confounded with the mul- tivariate calibration models.

Whole blood samples were collected from each subject following a 12-h fast and placed in EDTA tubes to pre- vent coagulation of the blood. D-glucose in powdered form was then added to half the collected blood to form a sample with a high glucose concentration (650 to 750 mg/dL). The blood cells in the other half of the collected blood were allowed to metabolize much of the existing glucose in the blood to achieve blood at sub-physiological glucose levels. The high-glucose-concentration blood was then mixed by volume with each subject's low-glucose- concentration blood to form 5-mL aliquots with varying glucose concentration. The original doped and undoped blood samples were taken as the extreme high- and low-

Volume 46, Number 10, 1992 0003-7028/92/4610-157552.00/0 APPLIED SPECTROSCOPY 157,5 © 1992 Society for Applied Spectroscopy

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glucose-concentration samples, respectively. For each of the four subjects' blood, twenty samples were prepared, with glucose concentrations varying in the range between 3 and 743 mg/dL, and their near-infrared spectra were obtained. The temperatures of the blood samples were neither measured nor controlled during the collection of the spectra. A 2-mL portion of the blood from each sam- ple was centrifuged at the time its spectrum was taken, and the resulting plasma was frozen so that the glucose concentrations of the plasma could be determined with a glucose oxidase method employing a Beckman Instru- ments Astra glucose analyzer, as described previously) The standard deviation of repeated measurements of the same sample by the reference method is reported to be in the range from 2 to 4 % relative. However, other sources of systematic error may be present due to the consump- tion of glucose by bacteria, differences in blood chem- istry, evaporation, etc. Therefore, the total error present in the reference glucose concentration values is probably larger than the measured repeatability error.

The partial least-squares (PLS) method was used to develop a multivariate calibration model relating the spectral data to the glucose concentrations determined by the reference method. A Fortran software package, developed at Sandia National Laboratories and running on a VAX 3100 work station, was used in the analysis. The software includes cross-validation methods and con- tains several outlier detection methods to ensure the quality of the data used to construct the calibration mod- el. Outliers are those samples among the calibration or unknown samples that are different from the bulk of the calibration samples. Significant outliers were removed from the data set since their presence can greatly degrade the performance of the calibration model during predic- tion.

Absorbance and concentration data were mean-cen- tered prior to PLS analysis. Most of the results reported here were based upon analysis of the absorbance data. However, since large baseline variations were present within a given subject's spectra and between the four subjects' spectra, first derivatives of the absorbance spec- tra were also sometimes used in the PLS analysis. The

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use of derivatives has the ability to minimize the effects of baseline variations. The derivative spectra were esti- mated by differences between successive spectral data points. Another method to decrease sensitivity of the analysis to large baseline variations is to use principal component regression (PCR), eliminating those principal components in the regression that primarily relate to baseline variation. This method was also tested in the analysis of the multisubject data.

RESULTS AND DISCUSSION

A representative set of near-infrared spectra taken on the Nicolet 800 spectrometer for a single subject is pre- sented in Fig. 1. The spectra in Fig. I span the full range of glucose concentration from 3 to 645 mg/dL for this subject's blood. Only the spectral regions closely sur- rounding the analyzed region are shown. The blank re- gion centered at about 5100 cm -1 includes a strong water absorption band that was in the nonlinear range of the detector response and demonstrated significant vari- ability in intensity. Examination of Fig. 1 reveals that the spectral variations due to glucose are quite small.

The solid line in Fig. 2 represents the near-infrared spectrum of a 50 %-by-weight aqueous glucose sample in a 1-mm-pathlength quartz cell that has been ratioed to a pure-water single-beam spectrum obtained in the same cell. The positive features are due to glucose, while the negative features are due to water which has been dis- placed by the presence of the glucose. From Fig. 2, it is apparent that the glucose bands in the 4200-4850 cm -1 region exhibit the greatest structure and, therefore, the greatest specificity for glucose. We have found empiri- cally that model performance is improved if glucose bands from 4250 to 4850 and from 5500 to 6600 cm -1 are used for analysis. The spectral region between 4850 and 5500 is a water band that is too strongly absorbing and too variable in intensity to aid in the analysis. Inclusion of this intervening spectral region was found to degrade model performance. Also presented in Fig. 2 is the third weight-loading vector from the PLS model generated during the calibration based on all four subjects' data. It is clear that the third PLS weight vector represents the component of spectral variation that corresponds to

1576 Volume 46, Number 10, 1992

T A B L E I. Results of near-infrared determination of glucose in whole blood.

Cross- Number validated Ave. Abs. Number

Range of PLS SEP error of Subject (mg/dL) factors (mg/dL) (mg/dL) outliers

1 3-645 6 30.5 26.9 1 2 65-713 7 30.8 25.5 1 3 48-743 7 37.8 31.7 1 4 23-655 6 32.3 26.7 3

Average errors (1-4) 32.9 27.7

the glucose spectrum. As previously described, 12 the first PLS weight vector generally represents the best least- squares approximation to the pure-component spectrum as it exists in the calibration samples. However, in this case, where baseline variations are by far the greatest source of spectral variance, the spectrum of glucose does not appear until the third weight-loading vector. The first two weight-loading vectors correspond primarily to the baseline variations. The appearance of the glucose spectrum in the third PLS loading vector gives confi- dence that glucose is responsible for the prediction abil- ity of the PLS model. This is in spite of the fact that the glucose spectrum cannot be directly observed in the in- dividual near-infrared spectra of whole blood.

Initially the data from each subject were analyzed sep- arately. In each set of data, there was at least one outlier sample identified by spectral F-ratios greater than 3.12 Most of the outliers differed from the bulk of the cali- bration samples by having significantly different broad and featureless baselines. It is suspected that in these outlier cases the spectrometer iris opening of either the background or sample single-beam spectrum was not perfectly reproducible. Because of the limited number of these deviant spectra, they were identified as outliers not representative of the other calibration samples. The cross-validated results leaving one sample out of the cal- ibration at a time are presented in Table I for the in- dividual calibrations from each subject's data obtained on the Nicolet spectrometer. Included in the table are the range of glucose concentrations, the optimal number of PLS factors in the model, the standard error of pre- diction (SEP), the average absolute error, and the num- ber of outlier samples deleted from the calibration. The averaged errors for the four subjects are also given in Table I.

Because baseline variations were the major source of spectral variance in these spectra, analyses were also performed with differences between intensities of suc- cessive spectral data points. In this case, the resulting PLS calibration model is based on the unsmoothed spec- tral derivatives that are less sensitive to baseline varia- tions. In these cases, cross-validated SEPs for each sub- ject were inflated to the range of 70 to 85 mg/dL. Therefore, the decreased baseline variation of the deriv- ative spectra was insufficient to overcome the poorer sig- nal-to-noise ratios of the derivative spectra in this case where the spectra contain only broad features.

If simple cross-validation is performed over the com- plete set of data from all four subjects (i.e., one sample is left out at a time), we can demonstrate reasonable glucose prediction performance. With the outlier sam-

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Fla. 3. Cross-val idated P L S glucose predic t ions based on the near- infrared spec t ra of all four subjec ts ' whole blood. Cross-val idat ion was pe r fo rmed by leaving one s p e c t r u m out of the cal ibrat ion model a t a t ime.

ples deleted, the SEP and average absolute error are 38.6 mg/dL and 30.3 mg/dL, respectively, when PLS is used. Thus, the prediction errors are only slightly inflated when all four subjects are included in the analysis. Prediction of glucose concentration by this cross-validation method is illustrated in Fig. 3. Note, however, that cross-vali- dation implemented in this way does not provide a re- alistic assessment of how a method will perform when confronted with spectral data from the blood of a totally new subject. In order to make an assessment of how a method will perform with spectra of blood from a new subject, we resort to a different variation of cross-vali- dation whereby data from three subjects are used to develop a calibration model that is used to predict glu- cose concentrations based on spectra of blood samples from a fourth subject. If this form of cross-validation is used, the resulting prediction errors are quite large (SEP = 117 mg/dL). The increased error is due to inadequate modeling of the spectral variation resulting from blood chemistry differences between subjects. These differ- ences are so large that blood samples from only three subjects are insufficient to model the range of spectral variation present in a fourth subject's blood. The biggest differences in the spectra between subjects appear to be due to hematocrit (fraction of red blood cells by volume) differences, since the baselines approximately correlate with hematocrit levels. These hematocrit differences cause significant differences in optical throughput and in the scattering of the near-infrared radiation. Large differ- ences in cholesterol, triglyceride, and total protein levels were also noted between subjects. A single spectrum from each of the four subjects is plotted in Fig. 4 for samples at glucose concentrations of approximately 100 mg/dL. The relatively large differences in absorbance baselines approximately correlate with hematocrit levels in the blood. It appears that samples from a large number of subjects with diverse blood chemistries will be required in order to develop a global model that can be used to predict glucose concentrations for a wide variety of sub- jects.

Although the small number of subjects represented in these data made it difficult to adequately model the blood chemistry differences, it was possible to demonstrate tea-

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sonable prediction performance by eliminating the sub- ject with the most extreme hematocrit, total protein, cholesterol, and triglyceride levels (Subject 1). Appar- ently, these differences make this subject's spectral data inconsistent with other subjects' data. Adequate perfor- mance was demonstrated by using principal component regression without data from Subject 1. To reduce the deleterious effect of baseline variations on the prediction errors, we used only those principal components exhib- iting statistically significant correlation to glucose con- centration in the analysis. Here, cross-validation was ac- compl ished by using da ta from two subjects for constructing a model while predicting glucose concen- trations on samples from the third subject. The standard error of prediction for glucose concentration obtained by this form of cross-validation is 53 mg/dL. Model perfor- mance, as measured by this form of cross-validation, should improve if sufficient numbers of subjects are con- sidered.

CONCLUSIONS

It has been shown that glucose levels in whole blood can be determined with moderate precision with the use of PLS calibration models based upon near-infrared spectra obtained in the region from 4250 to 6600 cm -1. The PLS weight-loading vectors clearly show that the spectral features due to glucose are incorporated directly into the PLS model. Thus, near-infrared spectroscopy has the potential to be used as a reagentless method for glucose determination in whole blood. However, in order for this method to be insensitive to changes in the other components of blood, it is apparent that the calibrations

will have to be based upon blood samples taken from large numbers of subjects. Further improvements in method performance might be expected if the temper- ature of the samples is precisely controlled. Temperature variations can cause large variations in the spectrum of the water, which is the major component of blood and is a strong absorber in the near-infrared region. In ad- dition, further improvements in method performance might be possible with various pretreatments of the data that could include smoothing and filtering of the spectral data.

The near-infrared spectral region investigated here al- lows transmission through 1 to 2 mm of tissue. Therefore, noninvasive measurements using this spectral region could be achieved through the web between the thumb and index finger or possibly through the ear lobe. Non- invasive spectral measurement of glucose in diabetic sub- jects will be the topic of a future publication.

ACKNOWLEDGMENTS

The authors would like to acknowledge David K. Melgaard of J & M Systems, Inc. for implementing improvements in the multivariate calibration software. James C. Standefer of the University of New Mexico Medical School performed all the blood analyses including the plasma glucose determinations using the Beckman Astra instrument.

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