- Email: [email protected]
Quantitation of protein, creatinine, and urea in urine by near-infrared spectroscopy
ClinicalBiochemistry,Vol. 29, No. 1, pp. 11-19, 1996 Copyright© 1996 The CanadianSocietyof ClinicalChemists Printed in the USA. All rights reserved 0009-9120/96 $15.00 + .00 ELSEVIER
0009-9120(95)02011-A
Quantitation of Protein, Creatinine, and Urea in Urine by Near-Infrared Spectroscopy R. ANTHONY SHAW, 1 STEVEN KOTOWICH,1 HENRY H. MANTSCH) and MICHAEL LEROUX 2 1Institute for Biodiagnostics, National Research Council of Canada, 435 Ellice Avenue, Winnipeg, Manitoba, R3B 1Y6 Canada; and 2Department of Clinical Chemistry, Health Sciences Centre, 820 Sherbrook Street, Winnipeg, Manitoba, R2H 2A6 Canada Objectives: To determine the feasibility of near-infrared analysis for quantitating urea, creatinine, and protein in urine. Practical advantages of this method include ease of sample presentation and the absence of reagents or disposables. Design and Methods: The near-infrared methods were developed by first measuring the spectra of 123 different urine samples and, using independent clinical analyses, determining the protein, creatinine, and urea levels in each. Calibration models relating near-infrared spectroscopic features to those independently determined concentrations were optimized, and each model then validated using a set of 50 additional samples. Results: Standard errors of c,atibration were 14.4 mmol/L, 0.66 mmol/L, and 0.20 g/L, and standard errors of prediction 16.6 mmol/L, 0.79 mmol/L, and 0.23 g/L, respectively, for urea, creatinine, and protein. Conclu=ions: Near-infrared urea quantitation is as accurate as the reference method, enzymatic (urease) conductivity, used here for calibration. Creatinine analysis is slightly less accurate relative to the reference (Jaffc~ rate) method; however, these errors can be minimized by careful attention to factors affecting precision. The accuracy of the near-infrared protein analysis cannot approach that of the reference method; nevertheless, the technique is potentially useful for coarse screening and for quantifying protein levels abc,ve 0.3 g/L.
evolved into widespread use as an analytical method (1). In general terms, the method is suited for the detection of organic compounds to a level of about 0.1%, and quantification above this level. Factors contributing to the acceptance of N1R include: 1. No reagents or disposables are required; 2. Little or no sample preparation is necessary and only minimal technical expertise required of the operator; 3. The method lends itself readily to on-line or high-volume repetitive measurements; 4. Several analyte levels may be measured simultaneously from a single spectrum; 5. The m e a s u r e m e n t is n o n - d e s t r u c t i v e - - t h e sample may be saved and passed on for further measurements if required.
Correspondence: R. A n t h o n y Shaw, I n s t i t u t e for Biodiagnostics, N a t i o n a l Research Council of C a n a d a , 435 Ellice Avenue, W i n n i p e g , M a n i t o b a , R3B 1Y6 Canada. M a n u s c r i p t received A p r i l 11, 1995; r e v i s e d a n d accepted J u n e 15, 1995. Issued as NRCC p u b l i c a t i o n No. 34754.
Some clinical applications of near-infrared spectroscopy have been proposed (e.g., measuring serum composition) (see Refs. 2 and 3, and references quoted therein) and fecal fat content (4,5). In vivo measurements using fiber optics can be used to measure hemoglobin saturation (see Refs. 6 and 7), the oxidation state of cytochrome oxidase (8), or to estimate body composition (9), and several groups are engaged in research toward the development of a noninvasive NIR blood glucose sensor (see Ref. 10). The success of NIR spectroscopy in these areas prompted this study, to explore the applicability of the method in analyzing for the major organic components of urine. Constituents dissolved in urine include both organic compounds such as urea, creatinine, and protein, and ionic species such as sodium and potassium. Although NIR is unsuitable for the detection of metal ions, it appeared likely in our view that the method might be appropriate to quantitate urea and creatinine with precision and accuracy that meet or exceed currently accepted standards. In this report, we assess the ability of NIR spectroscopy to quantitate these two analytes over the concentration range typically encountered in routine clinical analysis. Normal urine protein lev-
CLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
11
K E Y WORDS: n e a r - i n f r a r e d spectroscopy; creatinine; urea; protein; urine; analysis; precision.
Introduction ith costs escalating and resources dwindling, providers of health care are struggling to allocate those resources in ~Lhemost effective way possible. One way of contributing toward this effort is to seek alternative, more etticient means of performing routine clinical analyses. With advances both in instrumentation and in chemometrics, near-infrared (NIR) spectroscopy has
W
SHAW ET
els, on the other hand, are below the ideal range for NIR analysis; however, proteinuria can lead to protein levels in the range suitable for NIR analysis. In the latter case, our aim is, therefore, to establish the suitability of NIR as 1) a screening test for proteinuria, and 2) a means of quantitating protein in those cases where proteinuria is indicated.
Materials and methods EXPERIMENTAL
One hundred and seventy-seven urine samples were analysed for urea, creatinine, and total protein. The enzymatic (urease) conductivity method was used for urea (11), with the sample diluted twenty-fold for analysis using the Astra 8 analyzer (Beckman Instruments Inc., CA, USA). Creatinine was determined by the Jaff~ rate method (12) using the same analyzer, and total protein using the benzethonium chloride reaction (13) with a Hitachi 717 analyzer (Boehringer Mannheim, Indianapolis, IN, USA). Near-infrared spectra were recorded for each of the 177 urine samples by using a rapid scanning NIRSystems model 6500 visible/near-infrared spectrometer with a 10 nm bandpass and 2 nm between data points. The transmission cell has a pathlength of 0.5 mm. For each sample, 64 scans were accumulated and ratioed against a reference spectrum (64 scans collected with no sample or cell in the beam) to provide the absorption spectrum. The absorption spectra themselves are often unsuitable for analytical applications. By pretreating the spectra, the weaker features that carry analytical information are brought into prominence, and spectral fluctuations that are uncorrelated to any analyte level (e.g., variations in the baseline level) are minimized. The method used here evaluates second derivative spectra numerically as described in Appendix A, using a segment of 10 nm, gap of 4 nm to obtain spectra used for both the urea and protein calibrations, and a segment of 8 nm, gap of 2 nm for spectra used in the creatinine calibration. QUANTITATION MODELS
Near-infrared analysis is a secondary technique requiring calibration against reference measurements. There are a variety of ways to achieve this, and the interested reader is referred to Reference (14) for a synopsis and (15) for a thorough description of these methods. The two models used in this paper are multiple wavelength linear regression and partial least squares, both of which are described in Refs (14) and (15). References 2 and 3 also contain very cogent descriptions of these two methods as applied to serum analysis. Here we give a brief outline of each.
Calibration The first step in developing any near-infrared calibration is to measure the spectra of a set of samples 12
AL.
that have been analyzed for the analytes of interest using independent reference methods. For this study, a total of 127 samples were included in the calibration set for each analyte. The spectral variations, whether they be individual absorption intensities or other features, are then correlated against the set of concentrations determined independently for each analyte. The relationship so derived is then tested for its predictive ability, and the optimal equation adopted as the final NIR model. In cases where the analyte concentration is relatively high, and there are spectral features that are free of interference from other absorbers, the spectral i n t e n s i t y is d i r e c t e d proportional to concentration. C = Ko + K i ' A ( k i )
[i]
where C is the concentration, A is the spectral response (e.g., absorbance or the intensity of some derivative of the absorption spectrum) at kl, and Ko and K1 the intercept and slope. Additional terms may be required as minor corrections to account for deviations from Beer's law relationship of Eqn [1]. The resulting relationship is referred to as a multiple wavelength linear regression (MLR, see Glossary) model. The partial least squares (PLS) model derives from the calibration spectra and reference analyte levels a set of factors that may be viewed as spectral building blocks; the number of factors is much smaller than the number of calibration spectra, yet a linear combination of PLS factors can account for all of the spectral variance that is correlated to the analyte level of interest. This relationship forms the basis of PLS modelling. In practice, the first step is to select the spectral region(s) most likely to show variability as the analyte level of interest varies (this will include, but will not necessarily be limited to, regions where the analyte absorbs). Taking the NIR spectra and the independently determined analyte levels as input, commercial software then can be used to evaluate PLS models that optimally relate the two. The standard error of calibration (SEC) is, then, a gauge of how well the NIR model fits the calibration data used to develop it. To gauge the accuracy expected for 'real world' samples outside the calibration set, each model must be subject to a validation procedure.
Validation In developing a MLR calibration model, it is always true that the SEC can be improved by adding more terms to a relationship of Eqn [1] above. The danger in doing so is that at some stage the calibration data becomes overfitted, and the predictive value of the model starts to deteriorate. To guard against this, spectra have been measured for an additional set of 50 samples, also independently analyzed for protein, urea, and creatinine. ConcentraCLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
QUANTITATION OF PROTEIN, CREATININE, A N D U R E A IN URINE
tions predicted for this validation set are then compared to the reference values, and the resulting standard error of prediction (SEP) is a measure of the predictive ability of the model, also commonly referred to as Sy/~. The PLS calibrations are validated in the same way, monitoring the SEC and SEP as additional factors are included. Cross-validation (see Glossary) was also used here for further validation of the two PLS models. The accuracy of each NIR model is summarized here by reporting a) scatter plots for both the calibration and validation sets, comparing NIR calculated analyte levels to 1;hose independently determined using reference methods (see Figure 1); b) standard errors of calibration and of prediction; c) linear regression (slope, intercept, and correlation coefficient) of NIR predicted vs. reference analytical results for the set of validation samples.
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Results and discussion
0.4N I R SPECTRA
Figure 2A shows the near-infrared/visible absorption spectrum of a typical urine sample. The overall appearance of the spectrum is dominated by water absorptions at 1450 and 1935 nm. The strongest absorptions that arise from organic compounds are found in the region from 2000 to 2500 nm. These absorptions correspond te molecular vibrations, typically combinations of X - H (C-H, O-H, or N-H) stretching vibrations with lower energy motions such as CH2, N-H, or O-H bending vibrations. At higher energy (shorter wavelength, 1400-2000 nm) are absorptions that are generally weaker than the combination bands. These bands are overtones of the X-H stretching bands, whose fundamental absorptions appear in the spectral range 2800-3600 nm (3571-2778 cm - t). Figure 3 shows 10 urine spectra, randomly selected to show the variability among the samples. Also plotted for comparison are 5 spectra of aqueous urea, 6 of creatinine, and 5 protein (albumin) solutions. For urea and creatinine, the solution spectra of Figure 3 represent concentrations that fall within the range typically encountered in urine. The majority of urine samples from healthy individuals con-
0.0--400
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12'00
16'00
2000
2400
wove length (nm) Figure 2 - - Absorption spectrum (A) of a typical urine sample in the visible (400-780 nm) and near-infrared (780-2500 nm) regions and the second derivative of that spectrum (B). Note: The weak negative feature at 2210 nm is due to an absorption of the glass cell. tain less than 0.15 g/L protein, so the plotted protein spectra span a concentration range t h a t is well above the normal range. The urea absorptions dominate the spectra of urine. In contrast, the primary creatinine absorption at 2285 nm is overlapped by other features. The same concern exists for protein; again, as a result of the low concentrations, it is clear that none of the protein absorption bands is strong enough to serve as a direct gauge of low protein levels in urine. NIR
ANALYSES
Urea
Figure 1 - - NIR calculatedversus referencelab values for urea calibrationand validation sets.
Because the strong features due to urea dominate the urine spectra, it is relatively straightforward to develop a single wavelength calibration using a MLR model of the type given in Eqn [1]. The optimal wavelength was found to be 2152 nm (Ko = 589, K1 = 61658), yielding a standard error of calibration of 16.5 mmol/L and a c o r r e l a t i o n coefficient r of - 0.988. The SEC can be improved by accounting for minor deviations from Beer's law that m a y arise from spectral interferences or other sources. In this case, a more flexible model m a y improve the accuracy of the calibration. For urea, we have used a relationship of the type
CLINICAL BIOCHEMISTRY,VOLUME 29, FEBRUARY1996
13
700 600 ¢= 500"o 400300~: 2001000
Calibration
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700 600
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400 3O0 200 1000 5;0 6;0 4~ o 1;0 2;0 300 400 5;0 6;0 700 Labmeasuredurea(mmol/L)
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Wove length (nm) Figure 3 -- Second derivative spectra in the 2000-2450 nm region (region encompassing the most intense combination bands) for urine specimens and aqueous solutions of the analytes of interest: A = 10 representative urine samples; B = 5 aqueous urea solutions ranging from 30 to 230 mmol/L; C = 6 aqueous creatinine solutions ranging from 2 to 10 mmol/L; D = aqueous protein solutions of concentration 0.2, 0.7, 1.7, 3.7, and 5.7 g/L. All spectra are plotted on a common intensity scale. C = K o + K1 • [A(}`I)/A(}`2)] + K 2 " A(}`3)
[2]
Keeping the primary wavelength }`1 = 2152 nm, the SEC for this model is 14.6 mmol/L (r = 0.990) for Ko = - 10, K1 = 68,/£2 = 128123, }`1 = 2152 nm, }`2 = 1194 nm, and }`3 = 1724 nm. The accuracy of this expression is demonstrated by a scatter plot of NIR calculated concentration vs. the concentration provided by the reference method, shown in Figure 1. To compare their accuracy for samples outside the calibration set, each of the two MLR models was then used to predict the urea concentrations from the NIR spectra of the 50 validation samples. The inclusion of the additional terms of Eqn [2] was clearly warranted by the improvement in the predictive power of this model as compared to the single wavelength algorithm of Eqn [1]; the SEP for Eqn [1] is 20.0 mmol/L (r = 0.974), and t h a t for Eqn 2 is 16.6 mmol/L (r = 0.982). It is informative to seek a rationale for the im14
E T A L.
provements t h a t the additional terms confer on the NIR a n a l y s i s . Two w o r t h w h i l e objectives a r e achieved by seeking a physical interpretation for these terms - - the model is placed on a sounder footing and the spectroscopic insights gained m a y be useful for subsequent NIR model development. The first correction to Eqn [1] was derived by retaining the 2152 nm term as the p r i m a r y wavelength and performing regressions of A(2152)/A(}`2) vs. Curea for all possible values of }`2. The optimal wavelength of 1194 nm is the value of }`2 t h a t provides the strongest correlation o f A ( 2 1 5 2 ) / A ( } ` 2) with urea concentration. The most common explanation for the effectiveness of such a denominator term is t h a t it corrects for variations in effective pathlength; for example, due to variability in the scattering properties of the sample. The wavelength of the denominator term, therefore, should correspond to a feature whose intensity is correlated to the effective pathlength, but uncorrelated from variations in urea concentration. This is, in fact, the case, because the 1194 nm term corresponds to the center of a weak water absorption. A question arises as to why this band should be favored over other more intense water absorptions. The most likely explanation is t h a t this band is at relatively high energy and, hence, is well removed from potential overlapping transitions of the dissolved species. The third wavelength }'3 at 1724 n m is situated on the shoulder of a protein overtone absorption. This additive term is likely correcting for contributions of the 2167 nm protein band to the intensity at 2152 nm. Again, the most effective correction is found by using a wavelength well removed from potential interferences. The model was further characterized by linear regression of the urea levels provided by reference analytical method (x) against NIR predicted concentrations (y) for the 50 validation samples. The regression yields y = 1.03x - 2.6, with a correlation coefficient r = 0.982. Additional statistics are provided in Table 1. A scatter plot of NIR predicted vs. reference urea values is included in Figure 1, which also shows the regression lines for both the calibration and validation data sets. TABLE 1
Summary of Calibration, Validation, and Linear Regression (y = A x + B) of Urea Analytes Measured Using the Reference Methods (x) vs. NIR Predicted Concentration (y) for 50 Validation Samples Urea
Creatinine
Protein
SEC rcalibration
14.6 mmol/L 0.990
0.68 mmol/L 0.982
0.20 g/L 0.992
SEP rvalidation A (slope) B (intercept) SEslop e S E i n t . . . . pt
16.6 mmol/L 0.982 1.03 - 2.6 0.028 6.5
0.79 mmol/L 0.977 0.953 0.30 0.031 0.24
0.23 g/L 0.988 0.923 0.034 0.020 0.034
CLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
QUANTITATION OF PROTEIN, CREATININE, AND UREA IN URINE
Creatinine
The MLR model is un,,mitable for creatinine. Variability in the levels of other components (i.e., matrix variations) produce fluctuations in spectral intensity that are of the same order of magnitude as the most intense creatinine absorption (see Figure 3). The most effective way of modelling such analytes is to use a 'full-spectrum' method. We have used the PLS method for both the creatinine and protein analyses. Some preliminary work was carried out before settling on the final NIR model; the objective was to assess how sensitive the NIR calibrations are to spectral pretreatment (i.e., to changes in the parameters used for the derivative calculations). This work, summarized in Appendix B, clearly shows that a segment of 8 mm is the optimal choice. The creatinine calibration is, therefore, based upon derivative spectra evaluated using a segment of 8 nm and gap of 2 nm. The final model was reached by selecting various wavelength regions in the derivative spectra, and then following trends in the SEC, SEP, and rootmean-square error of cross-validation (RMSCV, see Glossary) as up to 15 factors were included in the PLS model. The spectral range 2100-2400 nm was found to be optimal, as judged by a) the SEC, SEP, and RMSCV all decreasing regularly and finally reaching stable minima with the inclusion of additional factors, b) the fact that the SEP and SEC settled to similar values, and c) linear regression of NIR predicted vs. 'actual' creatinine levels for the validation samples yields a line whose slope and intercept are close to the ideal values. For 9 PLS factors, the standard error of calibration is 0.68 mmol/ L, (r = 0.982), and the SEP is 0.79 mmol/L (r = 0.977). Table 1 summarizes the regression of NIR predicted vs. lab values for the validation set, and the calibration and wdidation scatter plots are shown in Figure 4.
urine over a 24-hour period is considered to be cause for concern. This converts to a protein concentration of 0.06-0.2 g/L for typical daily urine output of 8002500 ml. NIR quantitation at these levels poses a challenge, as the precision limits of the NIR measurements themselves come into consideration. Nevertheless, it is informative to pursue the question of how accurately, and to what levels, protein may be quantified using NIR. To this end, we have evaluated two PLS models for protein, one including all samples in the calibration set, and a second including only those samples having protein concentration less than 1 g/L. The protein content for the samples in the calibration set ranged from 0 to 7 g/L, with the majority below 0.5 g/L (see Figure 5). One of the calibration spectra was confirmed to be a spectral outlier and was not used in the calibration - - large errors in the NIR calculated protein level (typically 4.7 g/L compared to 2.5 g/L from the reference method) were traced to a unique feature centered at 2064 nm in the spectrum of this particular specimen, very close to the dominant protein absorption at 2054 nm. With this spectrum removed, PLS calibration using 8 factors yielded a SEC of 0.20 g/L (r = 0.992), and a SEP of 0.23 g/L (r = 0.978). The optimal wavelength range was found to be 2000-2450 nm. The calibration and validation scatter plots are shown in Figure 6, and a summary of the regression of lab protein vs. NIR calculated values is included in Table 1. Below 1 g/L, where the majority of samples are found, this level of uncertainty becomes cause for 30
25
20
Protein
Serum proteins can be quantified using NIR spectroscopy with accuracy comparable to that of standard clinical chemistry methods (3). Urine protein levels, on the other hand, are normally 3 orders of magnitude lower--excretion of more than 0.15 g in 25-
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Figure 4 - - N I R calculated versus reference lab values for creatinine calibration and v a l i d a t i o n sets.
Figure 5 - - Protein concentrations for samples m a k i n g up the calibration set.
CLINICAL BIOCHEMISTRY:, VOLUME 29, FEBRUARY 1996
15
S H A W ET AL.
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Figure 6 - - NIR calculated versus reference lab values for protein calibration and validation sets. concern, particularly because the distinction between 'normal' urine from proteinuria is at about 0.1 g/L. The accuracy at these protein levels can be improved by deriving a new PLS calibration including only samples within the concentration range 0 - 1 g/L. The result, calibrating on 93 samples for which the protein concentration is less than 1 g/L, is a 10 factor model with a SEC of 0.109 g/L (r = 0.901) and SEP of 0.115 g/L (38 samples, r = 0.736). These correlation coefficients confirm that the accuracy is lower in this low concentration range. Nevertheless, this calibration remains sufficiently accurate that the NIR measurements m a y be useful as a protein screening test. This potential is supported, for example, by the observation that for the combined set of 173 samples the NIR predicted values separate 'low' (<0.15 g/L) from 'high' (>0.15 g/L) protein levels with an overall accuracy of 83% (sensitivity = 96%, specificity = 67%, positive predictive value = 78%, negative predictive value = 93%), and identifying and quantifying protein in samples of concentration greater than 0.33 g/L (3or, Or = 0.11 g/L) with 99.4% overall accuracy (one false-positive for the 173 samples examined here). ACCURACY
AND
PRECISION
In this section, we discuss the contributions of various factors influencing the accuracy and precision of the NIR determinations. As is the case for any secondary method, the accuracy of the NIR analysis cannot exceed that of the reference method against which it has been calibrated. This factor places a lower limit on the accuracy that can be expected for urea, creatinine, and protein analyses; in each the SEP m a y approach but cannot exceed Sy/x of the corresponding reference method. It is useful to summarize this and other sources of variance in the NIR analyses via
(SEP) 2
= Or2overalI = Or2reference Jr Or2modelling ~- Or2presentation "~- Or2S/N
SEPunlimited .. = [SEP2 precmlon
2
-- (Or presentation
+
Or2S/N)]1/2 [4]
Removing the influence of imprecision in this way reduces the S E P insignificantly for urea (from 16.6 to 16.3 mmol/L) and by less than 20% for creatinine (from 0.79 to 0.64 mmol/L). For protein, the overall S E P of 0.23 g/L is reduced to 0.14 g/L when the role of precision is factored out (Table 3). Unfortunately, the S/x values are unavailable for Y the reference methods we have used here; the analysis above indicates upper limits of 16.3 mmol/L, 0.64 mmol/L, and 0.14 mmol/L for urea, creatinine, and protein, respectively. The between-day preci-
[3]
where 'reference' refers to errors in the reference analyses (orreference= Sy/xreference), ,modelling to errors arising through the use of an imperfect calibra16
tion set 1, 'presentation' to variability in sample presentation, and 'S/A v to precision limits imposed by the signal-to-noise ratio of the NIR spectrometer. The last two terms Or2presentation + Or2S/N are factors that affect the precision of the NIR analyses, while the first two terms represent systematic errors. To assess the relative importance of these effects, we have performed a series of measurements to estimate the precision of NIR analyses for each of the three analytes. First, a series of 20 spectra were measured for one urine sample, with the instrument turned off and allowed to cool for at least 1 h between measurements. A second series of spectra was collected for the same sample, with all 20 spectra collected successively during a single day. Concentrations derived from each set of m e a s u r e m e n t s were then analysed to yield estimates of the between-day and within-day precision, respectively. Finally, a single aliquot was placed in the sample cell, and the cell left in the measurement position while 20 successive spectra were measured (a different sample was used for these measurements - - the specimen used for the previous measurements was no longer usable). The precison in analyte levels from these spectra is limited only by the signal-to-noise in the measurements. The results of these 3 trials are summarized in Table 2. The 'between-day' estimates of Table 2 approximate the precision available from the spectra in the calibration and validation sets. For example, for creatinine the SEP of 0.79 mmol/L implicitly incorporates an uncertainty of 0.46 mmol/L due to imprecision of the measurements, encompassing both variability in sample presentation (O'presentation) (i.e., first or last sample of the day, variations in the time between inserting the cell and acquiring the spectrum), and random errors due to finite signal-tonoise in the measurements (orS/N). We m a y now determine the role of these effects in the final analysis by removing their contribution to the overall SEP;
1Ideally, the samples should be evenly spaced through the concentration range expected, and the full range of
matrix variations should be represented in the calibration set. C L I N I C A L B I O C H E M I S T R Y , V O L U M E 29, F E B R U A R Y 1996
QUANTITATION OF PROTEIN, CREATININE, AND UREA IN URINE
TABLE 2 Precision Estimates for NIR-Predicted Concentrations
NIR Between-day~ x OC.V. (%) Within-day b x OC.V. (%)
Urea
Creatinine
Protein
(mmoUL)
(mmol/L)
(g/L)
350.9 3.1 0.8
5.0 0.46 9.2
0.68 0.18 26
354.8 2.2 0.6
5.6 0.38 7.1
0.56 0.11 19
236.]. 1.3 0.6
9.4 0.32 3.4
10.8 0.09 0.8
S / N limited ¢
x OC.V. (%)
Note: Between-day and within-day measurements were made using the same sample; signal-to-noise limited measurements used a different sample. a Values derived from 20 NIR measurements over 20 days. b Values derived from 20 consecutive NIR measurements (same urine sample as for within-day precision runs), using a fresh aliquot for each spectrum. Values derived from a single sample, allowed to reach thermal equilibrium and left standing in the spectrometer for 20 consecutive measurements. sion has been determined for each of the reference methods, and places the lower limits on Sy/x at 7.0 mmol/L, 0.25 mmol/L, and 0.01 g/L. For urea, the evidence above suggests that the NIR analyses are equivalent in accuracy to the reference values. The precision of NIR is clearly better than that of the reference method (3.1 vs. 7.0 mmol/ L), and for high-concentration components such as urea it is reasonable to expect that the accuracy of the MLR model will be limited only by the precision of the NIR measurement. The majority of the error in the urea quantitation is, therefore, attributed to the reference method. For creatinine, it is less clear to what extent scatter in the reference ana]Lyses contributes to the overall performance of NIR. It appears likely that the reference assays are somewhat more accurate than their NIR counterparts, and it is shown above that some of the discrepancy can be ascribed to the slightly lower precision in the NIR measurements. Given the importance of this analyte, we have carefully considered other potential sources of error in this feasibility study and how they might be minimized in routine NIR analyses. In a small number of cases, the large discrepancy between NIR and reference values suggests t h a t the sample m a y have unique characteristics 1;hat distinguish it from the majority of the sample population. For example, 2 of the spectra in the validation set contain clear distinguishing features in the vicinity of the primary creatinine absorption that evidently bias the creatinine predictions to lower values. One w a y of handling these spectral outliers is to CLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
include a pattern recognition routine in the quantitation software to identify them as such, and to use a separate NIR calibration for such samples (systematic errors of this type can be minimized by including in the calibration set a number of samples containing the interferents responsible for the unusual features). The majority of samples (i.e., those that remain once spectral outliers are selected out) may then be analyzed by using a calibration that is optimized for this majority. The potential of this approach is demonstrated by excluding these 2 spectral outliers and re-evaluating the SEP. The new estimate of 0.70 mmol/L represents an immediate improvement of better than 10% over the previous value. Finally, for protein, the evidence strongly indicates that limiting factors in the NIR analyses are the precision of the NIR measurement and modelling uncertainties of the type discussed above for creatinine. The SEP of 0.11 g/L, derived by calibrating (and validating) only on samples containing less than 1 g/L protein, is at or near the precision limit of the NIR measurements. S u m m a r y and c o n c l u s i o n s
The three a n a l y t e s chosen for this feasibility study can be quantified from the near-infrared spectra. The optimal models quantitate urea, creatinine, and protein levels with standard errors (Sy/x) of 16.5 mmol/L (1.0 g/L), 0.79 mmol/L (0.09 g/L), and 0.23 g/L respectively relative to the reference analytical methods. The NIR urea analysis is as accurate as the urease conductivity reference method used here to calibrate it. For creatinine, it is not clear to what extent uncertainty in the reference (Jaffd rate) method contributes to the overall SEP of 0.79 mmol/L. From our measurements of the NIR precision, and assessment of possible spectral outliers, it is clear that this SEP does not represent the limiting value achievable usTABLE 3 Contributions to the NIR Standard Errors of Prediction ({3-. . . . . ll = SEP) Ascribed to Precision ((o-presentation "~O-S/N)l/2), to Sy/x of the Reference Analytical Method (o-refe. . . . . ), and to Other Combined Uncertainties Inherent to the NIR Calibration Spectra and Method (o-modeling)
aow~.n
Urea (retool/L)
Creatinine (mmol/L)
Protein (g/L)
16.6
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b Signal-to-noise limited precision from Table 2. 17
SHAW
ing NIR (Figure 7). The situation with creatinine is that factors that are generally inconsequential are magnified in importance, mainly because the concentration range lies close to the detection limit of NIR. Although these factors are certainly not worth implementing as part of a feasibility study, we have shown t h a t significant benefits would likely result by minimizing precision errors - - for example, by using an autosampler - - and by using automated pattern recognition software to screen for spectral outliers. With these adaptations in place, it is not unreasonable to expect the SEP to fall below 0.5 mmol/L for NIR creatinine analysis. In the case of protein, it is not likely that the NIR analysis can approach the accuracy or precision of methods currently employed. Protein is quantified with an overall standard error of 0.23 g/L, with this figure dropping to 0.11 g/L for a separate calibration on only those samples having concentrations < 1 g/L. Although this falls short of the accuracy desired of a test for stringent screening at the 0.15 g/L level, it does indicate t h a t near infrared analysis may serve usefully as a coarse screening test. The protein determination is competitive with commonly used dipstick tests that screen at the 0.3 g/L level, is less likely to produce false-negatives that the dipstick test may produce for low-molecular-weight or nonalbumin proteins (16), and provides quantitative levels above that threshold. Finally, there are other urine analytes that might also be quantified or screened for by using NIR spectroscopy. For example, it is reasonable to expect that uric acid and glucose levels may be derived accurately enough to distinguish normal (<~5 mmol uric acid/24 h, ~<2 mmol glucose/24 h) from abnormally high levels. Indeed, a recent report shows that glucose in urine m a y be quantified by NIR in the concentration range of 50-350 mmol/L (17). By covering the visible range in the same measurement, it is also likely t h a t h e m a t u r i a and hemoglobinuria may be detectable through the visible absorptions of hemoglobin. In conclusion, it has been shown that NIR spec-
1.21.1
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Figure 7 - - Variation in standard errors of calibration (SEC) and prediction (SEP) for creatinine models, varying a) the segment used to evaluate the second derivative
spectra, and b) the number of PLS factors included in the models. All models are based upon the 2100-2400 nm spectral range. 18
ET AL.
troscopy may be used to quantitate urea and creatinine in urine with accuracy comparable to that of currently accepted clinical laboratory methods, and that NIR protein analysis m a y prove useful as a coarse screening test for proteinuria. With no reagents or disposables, no dilution step for any of these analyses, and the possibility of an automated system processing 60 samples/h, this method appears to have the potential to serve an effective addition to the clinical chemistry laboratory. Glossary Standard error of calibration (SEC): The rootmean-square of the residuals (XNm - X R E F ) for the samples in the calibration set. S t a n d a r d error of prediction (SEP): The rootmean-square of the residuals (XNIR -- XREF) for the samples in the validation set. Equivalent to Sy/x. Cross-validation: A method for gauging the accuracy of a near-infrared calibration. A NIR model is derived from a calibration set with a fraction 1 / N of samples excluded, and the model is t h e n used to predict values for those samples. The process is repeated 'N' times (for 'N' distinct subgroups) and the model accuracy gauged by the root-mean-square error of cross-validation (RMSCV) (i.e., the root-meansquare of (XNI R -- XRE F) for the predicted values). The model is considered to be optimized when the RMSCV is minimized or reaches a plateau where additional degrees of freedom in the model have no beneficial effect. (Note: When N is equal to the number of samples, this method is equivalent to the 'leave-one-out' method).
Appendix A Second derivatives of the absorption spectra were e v a l u a t e d n u m e r i c a l l y by u s i n g t h e following procedure: i) For each wavelength ki, the absorption intensities are averaged over a segment of 'n' nanometers (centered on ki) to give An(ki); ii) The second derivative at k i is calculated numerically from these average values, either: a) from adjacent data points (2 nanometers apart): 2nd derivative at ki = A n [ k i 2 A n [ k i] + A , [ ~ i + 2]
2] -
or b) including a gap of 'm' nanometers between data points: 2nd derivative at ki = A n [ k i -
m] -
2 A n [ k i] + A n [ k i + m]
The parameters 'n' and 'm' are referred to as the segment and the gap, respectively. Taking a segment average (n > 1) serves to smooth the spectra, and changing the gap alters the relative prominence of narrow vs. broad bands. CLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
QUANTITATION OF PROTEIN, CREATININE,AND UREA IN URINE
Appendix B As part of this study, we have explored various options in calculating the second derivative spectra, using as the arbiter the performance of the model t h a t results in the final analysis. This procedure was followed through only tbr creatinine; for urea, the concentration is high enough t h a t the relative importance of these effects is minor, and for protein prospective improvements are likely to be marginal in comparison to other uncertainties. PLS models were evaluated for creatinine by using second derivative spectra (123 calibration and 50 validation samples) t h a t differed only in the segment used for the derivative calculation, with the gap fixed at 2 nm (see Appendix A). The models have been characterized by monitoring both the SEC and SEP as a function of the number of PLS factors (Fig. 7). Two general observations emerge: (a) for a given number of PLS factors, the SEC improves regularly as the derivative segment is reduced; and (b) trends in the SEP show t h a t a short segment is strongly preferable. Guided by these conclusions, the final creatinine model was obtained by using derivatives calculated using a segment of 8 nm, gap of 2 nm.
References 1. Burns DA, Ciurczak EW (Eds.) Practical spectroscopy, Vol. 13; Handbook of near-infrared analysis. New York: Marcel Dekker, 1992. 2. Hall JW, Pollard A. Near-infrared spectrophotometry: A new dimension in clinical chemistry. Clin Chem 1992; 38: 1623-31. 3. Hall JW and Pollard A. Near-infrared spectroscopic determination of serum total proteins, albumin, globulins, and urea. Clin Biochem 1993; 26: 48390. 4. Peuchant E, Salles C, Jensen R. Value of a spectro-
CLINICALBIOCHEMISTRY,VOLUME 29, FEBRUARY 1996
5. 6. 7.
8. 9. 10.
11. 12. 13.
14. 15. 16. 17.
scopic "fecalogram" in determining the etiology of steatorrhea. Clin Chem 1988; 34: 5-8. Koumantakis G, Radcliff FJ. Estimating fat in feces by near-infrared reflectance spectroscopy. Clin Chern 1987; 34: 502-6. Jobsis FF. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 1977; 198: 1264-7. Takei Y, Edwards D, Lorek A, Peebles DM, Belai A, Cope M, Delpy DT, Reynolds EOR. Effects of N-o~nitro-L-arginine methyl ester on the cerebral circulation of newborn piglets quantified in vivo by NIR spectroscopy. Pediatr Res 1993; 34: 354-9. McCormick DC, Edwards AD, Brown GC, Wyatt JS, Potter A, Cope M, Delpy DT, Reynolds EOR. Pediat Res 1993; 33: 603-8. Elia M, Parkinson SA, Diaz E. Evaluation of nearinfrared interactance as a method for predicting body composition. Eur J Clin Nutr 1990; 44: 113-21. Small GW, Arnold MA, Marquardt LA. Strategies for coupling digital filtering with partial least-squares regression: Application to the determination of glucose in plasma by Fourier transform near-infrared spectroscopy. Anal Chem 1993; 65: 3279-89. Watson D. A note on the urease-catalysed hydrolysis of urea. Clin Chim Acta 1966; 14: 571-2. Larsen K. Creatine assay by a reaction-kinetic principle. Clin Chim Acta 1972; 41: 209-17. Luxton RW, Patel P, Keir G, Thompson EJ. A micromethod for measuring total protein in cerebrospinal fluid by using benzethonium chloride in microtiter plate wells. Clin Chem 1989; 35: 1731-4. Thomas EV. A primer on multivariate calibration. Anal Chem 1994; 66: 795A-804A. Martens H, Naes T. Multivariate Calibration. New York: John Wiley & Sons, 1989. J. Wallach. Interpretation of Diagnostic Tests. 5th Ed. Toronto: Little, Brown, and Company, 1992. van Toorenbergen AW. Assay of glucose in urine by near-infrared spectrophotometry. Clin Chem 1994; 40: 1788.
19
0009-9120(95)02011-A
Quantitation of Protein, Creatinine, and Urea in Urine by Near-Infrared Spectroscopy R. ANTHONY SHAW, 1 STEVEN KOTOWICH,1 HENRY H. MANTSCH) and MICHAEL LEROUX 2 1Institute for Biodiagnostics, National Research Council of Canada, 435 Ellice Avenue, Winnipeg, Manitoba, R3B 1Y6 Canada; and 2Department of Clinical Chemistry, Health Sciences Centre, 820 Sherbrook Street, Winnipeg, Manitoba, R2H 2A6 Canada Objectives: To determine the feasibility of near-infrared analysis for quantitating urea, creatinine, and protein in urine. Practical advantages of this method include ease of sample presentation and the absence of reagents or disposables. Design and Methods: The near-infrared methods were developed by first measuring the spectra of 123 different urine samples and, using independent clinical analyses, determining the protein, creatinine, and urea levels in each. Calibration models relating near-infrared spectroscopic features to those independently determined concentrations were optimized, and each model then validated using a set of 50 additional samples. Results: Standard errors of c,atibration were 14.4 mmol/L, 0.66 mmol/L, and 0.20 g/L, and standard errors of prediction 16.6 mmol/L, 0.79 mmol/L, and 0.23 g/L, respectively, for urea, creatinine, and protein. Conclu=ions: Near-infrared urea quantitation is as accurate as the reference method, enzymatic (urease) conductivity, used here for calibration. Creatinine analysis is slightly less accurate relative to the reference (Jaffc~ rate) method; however, these errors can be minimized by careful attention to factors affecting precision. The accuracy of the near-infrared protein analysis cannot approach that of the reference method; nevertheless, the technique is potentially useful for coarse screening and for quantifying protein levels abc,ve 0.3 g/L.
evolved into widespread use as an analytical method (1). In general terms, the method is suited for the detection of organic compounds to a level of about 0.1%, and quantification above this level. Factors contributing to the acceptance of N1R include: 1. No reagents or disposables are required; 2. Little or no sample preparation is necessary and only minimal technical expertise required of the operator; 3. The method lends itself readily to on-line or high-volume repetitive measurements; 4. Several analyte levels may be measured simultaneously from a single spectrum; 5. The m e a s u r e m e n t is n o n - d e s t r u c t i v e - - t h e sample may be saved and passed on for further measurements if required.
Correspondence: R. A n t h o n y Shaw, I n s t i t u t e for Biodiagnostics, N a t i o n a l Research Council of C a n a d a , 435 Ellice Avenue, W i n n i p e g , M a n i t o b a , R3B 1Y6 Canada. M a n u s c r i p t received A p r i l 11, 1995; r e v i s e d a n d accepted J u n e 15, 1995. Issued as NRCC p u b l i c a t i o n No. 34754.
Some clinical applications of near-infrared spectroscopy have been proposed (e.g., measuring serum composition) (see Refs. 2 and 3, and references quoted therein) and fecal fat content (4,5). In vivo measurements using fiber optics can be used to measure hemoglobin saturation (see Refs. 6 and 7), the oxidation state of cytochrome oxidase (8), or to estimate body composition (9), and several groups are engaged in research toward the development of a noninvasive NIR blood glucose sensor (see Ref. 10). The success of NIR spectroscopy in these areas prompted this study, to explore the applicability of the method in analyzing for the major organic components of urine. Constituents dissolved in urine include both organic compounds such as urea, creatinine, and protein, and ionic species such as sodium and potassium. Although NIR is unsuitable for the detection of metal ions, it appeared likely in our view that the method might be appropriate to quantitate urea and creatinine with precision and accuracy that meet or exceed currently accepted standards. In this report, we assess the ability of NIR spectroscopy to quantitate these two analytes over the concentration range typically encountered in routine clinical analysis. Normal urine protein lev-
CLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
11
K E Y WORDS: n e a r - i n f r a r e d spectroscopy; creatinine; urea; protein; urine; analysis; precision.
Introduction ith costs escalating and resources dwindling, providers of health care are struggling to allocate those resources in ~Lhemost effective way possible. One way of contributing toward this effort is to seek alternative, more etticient means of performing routine clinical analyses. With advances both in instrumentation and in chemometrics, near-infrared (NIR) spectroscopy has
W
SHAW ET
els, on the other hand, are below the ideal range for NIR analysis; however, proteinuria can lead to protein levels in the range suitable for NIR analysis. In the latter case, our aim is, therefore, to establish the suitability of NIR as 1) a screening test for proteinuria, and 2) a means of quantitating protein in those cases where proteinuria is indicated.
Materials and methods EXPERIMENTAL
One hundred and seventy-seven urine samples were analysed for urea, creatinine, and total protein. The enzymatic (urease) conductivity method was used for urea (11), with the sample diluted twenty-fold for analysis using the Astra 8 analyzer (Beckman Instruments Inc., CA, USA). Creatinine was determined by the Jaff~ rate method (12) using the same analyzer, and total protein using the benzethonium chloride reaction (13) with a Hitachi 717 analyzer (Boehringer Mannheim, Indianapolis, IN, USA). Near-infrared spectra were recorded for each of the 177 urine samples by using a rapid scanning NIRSystems model 6500 visible/near-infrared spectrometer with a 10 nm bandpass and 2 nm between data points. The transmission cell has a pathlength of 0.5 mm. For each sample, 64 scans were accumulated and ratioed against a reference spectrum (64 scans collected with no sample or cell in the beam) to provide the absorption spectrum. The absorption spectra themselves are often unsuitable for analytical applications. By pretreating the spectra, the weaker features that carry analytical information are brought into prominence, and spectral fluctuations that are uncorrelated to any analyte level (e.g., variations in the baseline level) are minimized. The method used here evaluates second derivative spectra numerically as described in Appendix A, using a segment of 10 nm, gap of 4 nm to obtain spectra used for both the urea and protein calibrations, and a segment of 8 nm, gap of 2 nm for spectra used in the creatinine calibration. QUANTITATION MODELS
Near-infrared analysis is a secondary technique requiring calibration against reference measurements. There are a variety of ways to achieve this, and the interested reader is referred to Reference (14) for a synopsis and (15) for a thorough description of these methods. The two models used in this paper are multiple wavelength linear regression and partial least squares, both of which are described in Refs (14) and (15). References 2 and 3 also contain very cogent descriptions of these two methods as applied to serum analysis. Here we give a brief outline of each.
Calibration The first step in developing any near-infrared calibration is to measure the spectra of a set of samples 12
AL.
that have been analyzed for the analytes of interest using independent reference methods. For this study, a total of 127 samples were included in the calibration set for each analyte. The spectral variations, whether they be individual absorption intensities or other features, are then correlated against the set of concentrations determined independently for each analyte. The relationship so derived is then tested for its predictive ability, and the optimal equation adopted as the final NIR model. In cases where the analyte concentration is relatively high, and there are spectral features that are free of interference from other absorbers, the spectral i n t e n s i t y is d i r e c t e d proportional to concentration. C = Ko + K i ' A ( k i )
[i]
where C is the concentration, A is the spectral response (e.g., absorbance or the intensity of some derivative of the absorption spectrum) at kl, and Ko and K1 the intercept and slope. Additional terms may be required as minor corrections to account for deviations from Beer's law relationship of Eqn [1]. The resulting relationship is referred to as a multiple wavelength linear regression (MLR, see Glossary) model. The partial least squares (PLS) model derives from the calibration spectra and reference analyte levels a set of factors that may be viewed as spectral building blocks; the number of factors is much smaller than the number of calibration spectra, yet a linear combination of PLS factors can account for all of the spectral variance that is correlated to the analyte level of interest. This relationship forms the basis of PLS modelling. In practice, the first step is to select the spectral region(s) most likely to show variability as the analyte level of interest varies (this will include, but will not necessarily be limited to, regions where the analyte absorbs). Taking the NIR spectra and the independently determined analyte levels as input, commercial software then can be used to evaluate PLS models that optimally relate the two. The standard error of calibration (SEC) is, then, a gauge of how well the NIR model fits the calibration data used to develop it. To gauge the accuracy expected for 'real world' samples outside the calibration set, each model must be subject to a validation procedure.
Validation In developing a MLR calibration model, it is always true that the SEC can be improved by adding more terms to a relationship of Eqn [1] above. The danger in doing so is that at some stage the calibration data becomes overfitted, and the predictive value of the model starts to deteriorate. To guard against this, spectra have been measured for an additional set of 50 samples, also independently analyzed for protein, urea, and creatinine. ConcentraCLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
QUANTITATION OF PROTEIN, CREATININE, A N D U R E A IN URINE
tions predicted for this validation set are then compared to the reference values, and the resulting standard error of prediction (SEP) is a measure of the predictive ability of the model, also commonly referred to as Sy/~. The PLS calibrations are validated in the same way, monitoring the SEC and SEP as additional factors are included. Cross-validation (see Glossary) was also used here for further validation of the two PLS models. The accuracy of each NIR model is summarized here by reporting a) scatter plots for both the calibration and validation sets, comparing NIR calculated analyte levels to 1;hose independently determined using reference methods (see Figure 1); b) standard errors of calibration and of prediction; c) linear regression (slope, intercept, and correlation coefficient) of NIR predicted vs. reference analytical results for the set of validation samples.
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Results and discussion
0.4N I R SPECTRA
Figure 2A shows the near-infrared/visible absorption spectrum of a typical urine sample. The overall appearance of the spectrum is dominated by water absorptions at 1450 and 1935 nm. The strongest absorptions that arise from organic compounds are found in the region from 2000 to 2500 nm. These absorptions correspond te molecular vibrations, typically combinations of X - H (C-H, O-H, or N-H) stretching vibrations with lower energy motions such as CH2, N-H, or O-H bending vibrations. At higher energy (shorter wavelength, 1400-2000 nm) are absorptions that are generally weaker than the combination bands. These bands are overtones of the X-H stretching bands, whose fundamental absorptions appear in the spectral range 2800-3600 nm (3571-2778 cm - t). Figure 3 shows 10 urine spectra, randomly selected to show the variability among the samples. Also plotted for comparison are 5 spectra of aqueous urea, 6 of creatinine, and 5 protein (albumin) solutions. For urea and creatinine, the solution spectra of Figure 3 represent concentrations that fall within the range typically encountered in urine. The majority of urine samples from healthy individuals con-
0.0--400
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wove length (nm) Figure 2 - - Absorption spectrum (A) of a typical urine sample in the visible (400-780 nm) and near-infrared (780-2500 nm) regions and the second derivative of that spectrum (B). Note: The weak negative feature at 2210 nm is due to an absorption of the glass cell. tain less than 0.15 g/L protein, so the plotted protein spectra span a concentration range t h a t is well above the normal range. The urea absorptions dominate the spectra of urine. In contrast, the primary creatinine absorption at 2285 nm is overlapped by other features. The same concern exists for protein; again, as a result of the low concentrations, it is clear that none of the protein absorption bands is strong enough to serve as a direct gauge of low protein levels in urine. NIR
ANALYSES
Urea
Figure 1 - - NIR calculatedversus referencelab values for urea calibrationand validation sets.
Because the strong features due to urea dominate the urine spectra, it is relatively straightforward to develop a single wavelength calibration using a MLR model of the type given in Eqn [1]. The optimal wavelength was found to be 2152 nm (Ko = 589, K1 = 61658), yielding a standard error of calibration of 16.5 mmol/L and a c o r r e l a t i o n coefficient r of - 0.988. The SEC can be improved by accounting for minor deviations from Beer's law that m a y arise from spectral interferences or other sources. In this case, a more flexible model m a y improve the accuracy of the calibration. For urea, we have used a relationship of the type
CLINICAL BIOCHEMISTRY,VOLUME 29, FEBRUARY1996
13
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Wove length (nm) Figure 3 -- Second derivative spectra in the 2000-2450 nm region (region encompassing the most intense combination bands) for urine specimens and aqueous solutions of the analytes of interest: A = 10 representative urine samples; B = 5 aqueous urea solutions ranging from 30 to 230 mmol/L; C = 6 aqueous creatinine solutions ranging from 2 to 10 mmol/L; D = aqueous protein solutions of concentration 0.2, 0.7, 1.7, 3.7, and 5.7 g/L. All spectra are plotted on a common intensity scale. C = K o + K1 • [A(}`I)/A(}`2)] + K 2 " A(}`3)
[2]
Keeping the primary wavelength }`1 = 2152 nm, the SEC for this model is 14.6 mmol/L (r = 0.990) for Ko = - 10, K1 = 68,/£2 = 128123, }`1 = 2152 nm, }`2 = 1194 nm, and }`3 = 1724 nm. The accuracy of this expression is demonstrated by a scatter plot of NIR calculated concentration vs. the concentration provided by the reference method, shown in Figure 1. To compare their accuracy for samples outside the calibration set, each of the two MLR models was then used to predict the urea concentrations from the NIR spectra of the 50 validation samples. The inclusion of the additional terms of Eqn [2] was clearly warranted by the improvement in the predictive power of this model as compared to the single wavelength algorithm of Eqn [1]; the SEP for Eqn [1] is 20.0 mmol/L (r = 0.974), and t h a t for Eqn 2 is 16.6 mmol/L (r = 0.982). It is informative to seek a rationale for the im14
E T A L.
provements t h a t the additional terms confer on the NIR a n a l y s i s . Two w o r t h w h i l e objectives a r e achieved by seeking a physical interpretation for these terms - - the model is placed on a sounder footing and the spectroscopic insights gained m a y be useful for subsequent NIR model development. The first correction to Eqn [1] was derived by retaining the 2152 nm term as the p r i m a r y wavelength and performing regressions of A(2152)/A(}`2) vs. Curea for all possible values of }`2. The optimal wavelength of 1194 nm is the value of }`2 t h a t provides the strongest correlation o f A ( 2 1 5 2 ) / A ( } ` 2) with urea concentration. The most common explanation for the effectiveness of such a denominator term is t h a t it corrects for variations in effective pathlength; for example, due to variability in the scattering properties of the sample. The wavelength of the denominator term, therefore, should correspond to a feature whose intensity is correlated to the effective pathlength, but uncorrelated from variations in urea concentration. This is, in fact, the case, because the 1194 nm term corresponds to the center of a weak water absorption. A question arises as to why this band should be favored over other more intense water absorptions. The most likely explanation is t h a t this band is at relatively high energy and, hence, is well removed from potential overlapping transitions of the dissolved species. The third wavelength }'3 at 1724 n m is situated on the shoulder of a protein overtone absorption. This additive term is likely correcting for contributions of the 2167 nm protein band to the intensity at 2152 nm. Again, the most effective correction is found by using a wavelength well removed from potential interferences. The model was further characterized by linear regression of the urea levels provided by reference analytical method (x) against NIR predicted concentrations (y) for the 50 validation samples. The regression yields y = 1.03x - 2.6, with a correlation coefficient r = 0.982. Additional statistics are provided in Table 1. A scatter plot of NIR predicted vs. reference urea values is included in Figure 1, which also shows the regression lines for both the calibration and validation data sets. TABLE 1
Summary of Calibration, Validation, and Linear Regression (y = A x + B) of Urea Analytes Measured Using the Reference Methods (x) vs. NIR Predicted Concentration (y) for 50 Validation Samples Urea
Creatinine
Protein
SEC rcalibration
14.6 mmol/L 0.990
0.68 mmol/L 0.982
0.20 g/L 0.992
SEP rvalidation A (slope) B (intercept) SEslop e S E i n t . . . . pt
16.6 mmol/L 0.982 1.03 - 2.6 0.028 6.5
0.79 mmol/L 0.977 0.953 0.30 0.031 0.24
0.23 g/L 0.988 0.923 0.034 0.020 0.034
CLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
QUANTITATION OF PROTEIN, CREATININE, AND UREA IN URINE
Creatinine
The MLR model is un,,mitable for creatinine. Variability in the levels of other components (i.e., matrix variations) produce fluctuations in spectral intensity that are of the same order of magnitude as the most intense creatinine absorption (see Figure 3). The most effective way of modelling such analytes is to use a 'full-spectrum' method. We have used the PLS method for both the creatinine and protein analyses. Some preliminary work was carried out before settling on the final NIR model; the objective was to assess how sensitive the NIR calibrations are to spectral pretreatment (i.e., to changes in the parameters used for the derivative calculations). This work, summarized in Appendix B, clearly shows that a segment of 8 mm is the optimal choice. The creatinine calibration is, therefore, based upon derivative spectra evaluated using a segment of 8 nm and gap of 2 nm. The final model was reached by selecting various wavelength regions in the derivative spectra, and then following trends in the SEC, SEP, and rootmean-square error of cross-validation (RMSCV, see Glossary) as up to 15 factors were included in the PLS model. The spectral range 2100-2400 nm was found to be optimal, as judged by a) the SEC, SEP, and RMSCV all decreasing regularly and finally reaching stable minima with the inclusion of additional factors, b) the fact that the SEP and SEC settled to similar values, and c) linear regression of NIR predicted vs. 'actual' creatinine levels for the validation samples yields a line whose slope and intercept are close to the ideal values. For 9 PLS factors, the standard error of calibration is 0.68 mmol/ L, (r = 0.982), and the SEP is 0.79 mmol/L (r = 0.977). Table 1 summarizes the regression of NIR predicted vs. lab values for the validation set, and the calibration and wdidation scatter plots are shown in Figure 4.
urine over a 24-hour period is considered to be cause for concern. This converts to a protein concentration of 0.06-0.2 g/L for typical daily urine output of 8002500 ml. NIR quantitation at these levels poses a challenge, as the precision limits of the NIR measurements themselves come into consideration. Nevertheless, it is informative to pursue the question of how accurately, and to what levels, protein may be quantified using NIR. To this end, we have evaluated two PLS models for protein, one including all samples in the calibration set, and a second including only those samples having protein concentration less than 1 g/L. The protein content for the samples in the calibration set ranged from 0 to 7 g/L, with the majority below 0.5 g/L (see Figure 5). One of the calibration spectra was confirmed to be a spectral outlier and was not used in the calibration - - large errors in the NIR calculated protein level (typically 4.7 g/L compared to 2.5 g/L from the reference method) were traced to a unique feature centered at 2064 nm in the spectrum of this particular specimen, very close to the dominant protein absorption at 2054 nm. With this spectrum removed, PLS calibration using 8 factors yielded a SEC of 0.20 g/L (r = 0.992), and a SEP of 0.23 g/L (r = 0.978). The optimal wavelength range was found to be 2000-2450 nm. The calibration and validation scatter plots are shown in Figure 6, and a summary of the regression of lab protein vs. NIR calculated values is included in Table 1. Below 1 g/L, where the majority of samples are found, this level of uncertainty becomes cause for 30
25
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Protein
Serum proteins can be quantified using NIR spectroscopy with accuracy comparable to that of standard clinical chemistry methods (3). Urine protein levels, on the other hand, are normally 3 orders of magnitude lower--excretion of more than 0.15 g in 25-
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Figure 5 - - Protein concentrations for samples m a k i n g up the calibration set.
CLINICAL BIOCHEMISTRY:, VOLUME 29, FEBRUARY 1996
15
S H A W ET AL.
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O-
Labmeasuredprotein(g/L)
Figure 6 - - NIR calculated versus reference lab values for protein calibration and validation sets. concern, particularly because the distinction between 'normal' urine from proteinuria is at about 0.1 g/L. The accuracy at these protein levels can be improved by deriving a new PLS calibration including only samples within the concentration range 0 - 1 g/L. The result, calibrating on 93 samples for which the protein concentration is less than 1 g/L, is a 10 factor model with a SEC of 0.109 g/L (r = 0.901) and SEP of 0.115 g/L (38 samples, r = 0.736). These correlation coefficients confirm that the accuracy is lower in this low concentration range. Nevertheless, this calibration remains sufficiently accurate that the NIR measurements m a y be useful as a protein screening test. This potential is supported, for example, by the observation that for the combined set of 173 samples the NIR predicted values separate 'low' (<0.15 g/L) from 'high' (>0.15 g/L) protein levels with an overall accuracy of 83% (sensitivity = 96%, specificity = 67%, positive predictive value = 78%, negative predictive value = 93%), and identifying and quantifying protein in samples of concentration greater than 0.33 g/L (3or, Or = 0.11 g/L) with 99.4% overall accuracy (one false-positive for the 173 samples examined here). ACCURACY
AND
PRECISION
In this section, we discuss the contributions of various factors influencing the accuracy and precision of the NIR determinations. As is the case for any secondary method, the accuracy of the NIR analysis cannot exceed that of the reference method against which it has been calibrated. This factor places a lower limit on the accuracy that can be expected for urea, creatinine, and protein analyses; in each the SEP m a y approach but cannot exceed Sy/x of the corresponding reference method. It is useful to summarize this and other sources of variance in the NIR analyses via
(SEP) 2
= Or2overalI = Or2reference Jr Or2modelling ~- Or2presentation "~- Or2S/N
SEPunlimited .. = [SEP2 precmlon
2
-- (Or presentation
+
Or2S/N)]1/2 [4]
Removing the influence of imprecision in this way reduces the S E P insignificantly for urea (from 16.6 to 16.3 mmol/L) and by less than 20% for creatinine (from 0.79 to 0.64 mmol/L). For protein, the overall S E P of 0.23 g/L is reduced to 0.14 g/L when the role of precision is factored out (Table 3). Unfortunately, the S/x values are unavailable for Y the reference methods we have used here; the analysis above indicates upper limits of 16.3 mmol/L, 0.64 mmol/L, and 0.14 mmol/L for urea, creatinine, and protein, respectively. The between-day preci-
[3]
where 'reference' refers to errors in the reference analyses (orreference= Sy/xreference), ,modelling to errors arising through the use of an imperfect calibra16
tion set 1, 'presentation' to variability in sample presentation, and 'S/A v to precision limits imposed by the signal-to-noise ratio of the NIR spectrometer. The last two terms Or2presentation + Or2S/N are factors that affect the precision of the NIR analyses, while the first two terms represent systematic errors. To assess the relative importance of these effects, we have performed a series of measurements to estimate the precision of NIR analyses for each of the three analytes. First, a series of 20 spectra were measured for one urine sample, with the instrument turned off and allowed to cool for at least 1 h between measurements. A second series of spectra was collected for the same sample, with all 20 spectra collected successively during a single day. Concentrations derived from each set of m e a s u r e m e n t s were then analysed to yield estimates of the between-day and within-day precision, respectively. Finally, a single aliquot was placed in the sample cell, and the cell left in the measurement position while 20 successive spectra were measured (a different sample was used for these measurements - - the specimen used for the previous measurements was no longer usable). The precison in analyte levels from these spectra is limited only by the signal-to-noise in the measurements. The results of these 3 trials are summarized in Table 2. The 'between-day' estimates of Table 2 approximate the precision available from the spectra in the calibration and validation sets. For example, for creatinine the SEP of 0.79 mmol/L implicitly incorporates an uncertainty of 0.46 mmol/L due to imprecision of the measurements, encompassing both variability in sample presentation (O'presentation) (i.e., first or last sample of the day, variations in the time between inserting the cell and acquiring the spectrum), and random errors due to finite signal-tonoise in the measurements (orS/N). We m a y now determine the role of these effects in the final analysis by removing their contribution to the overall SEP;
1Ideally, the samples should be evenly spaced through the concentration range expected, and the full range of
matrix variations should be represented in the calibration set. C L I N I C A L B I O C H E M I S T R Y , V O L U M E 29, F E B R U A R Y 1996
QUANTITATION OF PROTEIN, CREATININE, AND UREA IN URINE
TABLE 2 Precision Estimates for NIR-Predicted Concentrations
NIR Between-day~ x OC.V. (%) Within-day b x OC.V. (%)
Urea
Creatinine
Protein
(mmoUL)
(mmol/L)
(g/L)
350.9 3.1 0.8
5.0 0.46 9.2
0.68 0.18 26
354.8 2.2 0.6
5.6 0.38 7.1
0.56 0.11 19
236.]. 1.3 0.6
9.4 0.32 3.4
10.8 0.09 0.8
S / N limited ¢
x OC.V. (%)
Note: Between-day and within-day measurements were made using the same sample; signal-to-noise limited measurements used a different sample. a Values derived from 20 NIR measurements over 20 days. b Values derived from 20 consecutive NIR measurements (same urine sample as for within-day precision runs), using a fresh aliquot for each spectrum. Values derived from a single sample, allowed to reach thermal equilibrium and left standing in the spectrometer for 20 consecutive measurements. sion has been determined for each of the reference methods, and places the lower limits on Sy/x at 7.0 mmol/L, 0.25 mmol/L, and 0.01 g/L. For urea, the evidence above suggests that the NIR analyses are equivalent in accuracy to the reference values. The precision of NIR is clearly better than that of the reference method (3.1 vs. 7.0 mmol/ L), and for high-concentration components such as urea it is reasonable to expect that the accuracy of the MLR model will be limited only by the precision of the NIR measurement. The majority of the error in the urea quantitation is, therefore, attributed to the reference method. For creatinine, it is less clear to what extent scatter in the reference ana]Lyses contributes to the overall performance of NIR. It appears likely that the reference assays are somewhat more accurate than their NIR counterparts, and it is shown above that some of the discrepancy can be ascribed to the slightly lower precision in the NIR measurements. Given the importance of this analyte, we have carefully considered other potential sources of error in this feasibility study and how they might be minimized in routine NIR analyses. In a small number of cases, the large discrepancy between NIR and reference values suggests t h a t the sample m a y have unique characteristics 1;hat distinguish it from the majority of the sample population. For example, 2 of the spectra in the validation set contain clear distinguishing features in the vicinity of the primary creatinine absorption that evidently bias the creatinine predictions to lower values. One w a y of handling these spectral outliers is to CLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
include a pattern recognition routine in the quantitation software to identify them as such, and to use a separate NIR calibration for such samples (systematic errors of this type can be minimized by including in the calibration set a number of samples containing the interferents responsible for the unusual features). The majority of samples (i.e., those that remain once spectral outliers are selected out) may then be analyzed by using a calibration that is optimized for this majority. The potential of this approach is demonstrated by excluding these 2 spectral outliers and re-evaluating the SEP. The new estimate of 0.70 mmol/L represents an immediate improvement of better than 10% over the previous value. Finally, for protein, the evidence strongly indicates that limiting factors in the NIR analyses are the precision of the NIR measurement and modelling uncertainties of the type discussed above for creatinine. The SEP of 0.11 g/L, derived by calibrating (and validating) only on samples containing less than 1 g/L protein, is at or near the precision limit of the NIR measurements. S u m m a r y and c o n c l u s i o n s
The three a n a l y t e s chosen for this feasibility study can be quantified from the near-infrared spectra. The optimal models quantitate urea, creatinine, and protein levels with standard errors (Sy/x) of 16.5 mmol/L (1.0 g/L), 0.79 mmol/L (0.09 g/L), and 0.23 g/L respectively relative to the reference analytical methods. The NIR urea analysis is as accurate as the urease conductivity reference method used here to calibrate it. For creatinine, it is not clear to what extent uncertainty in the reference (Jaffd rate) method contributes to the overall SEP of 0.79 mmol/L. From our measurements of the NIR precision, and assessment of possible spectral outliers, it is clear that this SEP does not represent the limiting value achievable usTABLE 3 Contributions to the NIR Standard Errors of Prediction ({3-. . . . . ll = SEP) Ascribed to Precision ((o-presentation "~O-S/N)l/2), to Sy/x of the Reference Analytical Method (o-refe. . . . . ), and to Other Combined Uncertainties Inherent to the NIR Calibration Spectra and Method (o-modeling)
aow~.n
Urea (retool/L)
Creatinine (mmol/L)
Protein (g/L)
16.6
0.79
0.23
3.1
0.46
0.18
1.3 2.8
0.32 0.33
0.09 0.16
16.3
0.64
0.14
(O'presentation -~-
O-S{N)'/2a
O-S/N
O-p..... ration (O'referenc e -~-
O-modeling)1/2
a Between-day precision estimates from Table 2.
b Signal-to-noise limited precision from Table 2. 17
SHAW
ing NIR (Figure 7). The situation with creatinine is that factors that are generally inconsequential are magnified in importance, mainly because the concentration range lies close to the detection limit of NIR. Although these factors are certainly not worth implementing as part of a feasibility study, we have shown t h a t significant benefits would likely result by minimizing precision errors - - for example, by using an autosampler - - and by using automated pattern recognition software to screen for spectral outliers. With these adaptations in place, it is not unreasonable to expect the SEP to fall below 0.5 mmol/L for NIR creatinine analysis. In the case of protein, it is not likely that the NIR analysis can approach the accuracy or precision of methods currently employed. Protein is quantified with an overall standard error of 0.23 g/L, with this figure dropping to 0.11 g/L for a separate calibration on only those samples having concentrations < 1 g/L. Although this falls short of the accuracy desired of a test for stringent screening at the 0.15 g/L level, it does indicate t h a t near infrared analysis may serve usefully as a coarse screening test. The protein determination is competitive with commonly used dipstick tests that screen at the 0.3 g/L level, is less likely to produce false-negatives that the dipstick test may produce for low-molecular-weight or nonalbumin proteins (16), and provides quantitative levels above that threshold. Finally, there are other urine analytes that might also be quantified or screened for by using NIR spectroscopy. For example, it is reasonable to expect that uric acid and glucose levels may be derived accurately enough to distinguish normal (<~5 mmol uric acid/24 h, ~<2 mmol glucose/24 h) from abnormally high levels. Indeed, a recent report shows that glucose in urine m a y be quantified by NIR in the concentration range of 50-350 mmol/L (17). By covering the visible range in the same measurement, it is also likely t h a t h e m a t u r i a and hemoglobinuria may be detectable through the visible absorptions of hemoglobin. In conclusion, it has been shown that NIR spec-
1.21.1
# of PLS factors -=~ 9 ~10 --e-- 11
1.0-
~
1.0
o908-
e
g
Q. 0.9 LU 03 0.8
~
.7-
(D
0.6 2
0.5
0.7
I12
116
S
210
Derivative segment
Figure 7 - - Variation in standard errors of calibration (SEC) and prediction (SEP) for creatinine models, varying a) the segment used to evaluate the second derivative
spectra, and b) the number of PLS factors included in the models. All models are based upon the 2100-2400 nm spectral range. 18
ET AL.
troscopy may be used to quantitate urea and creatinine in urine with accuracy comparable to that of currently accepted clinical laboratory methods, and that NIR protein analysis m a y prove useful as a coarse screening test for proteinuria. With no reagents or disposables, no dilution step for any of these analyses, and the possibility of an automated system processing 60 samples/h, this method appears to have the potential to serve an effective addition to the clinical chemistry laboratory. Glossary Standard error of calibration (SEC): The rootmean-square of the residuals (XNm - X R E F ) for the samples in the calibration set. S t a n d a r d error of prediction (SEP): The rootmean-square of the residuals (XNIR -- XREF) for the samples in the validation set. Equivalent to Sy/x. Cross-validation: A method for gauging the accuracy of a near-infrared calibration. A NIR model is derived from a calibration set with a fraction 1 / N of samples excluded, and the model is t h e n used to predict values for those samples. The process is repeated 'N' times (for 'N' distinct subgroups) and the model accuracy gauged by the root-mean-square error of cross-validation (RMSCV) (i.e., the root-meansquare of (XNI R -- XRE F) for the predicted values). The model is considered to be optimized when the RMSCV is minimized or reaches a plateau where additional degrees of freedom in the model have no beneficial effect. (Note: When N is equal to the number of samples, this method is equivalent to the 'leave-one-out' method).
Appendix A Second derivatives of the absorption spectra were e v a l u a t e d n u m e r i c a l l y by u s i n g t h e following procedure: i) For each wavelength ki, the absorption intensities are averaged over a segment of 'n' nanometers (centered on ki) to give An(ki); ii) The second derivative at k i is calculated numerically from these average values, either: a) from adjacent data points (2 nanometers apart): 2nd derivative at ki = A n [ k i 2 A n [ k i] + A , [ ~ i + 2]
2] -
or b) including a gap of 'm' nanometers between data points: 2nd derivative at ki = A n [ k i -
m] -
2 A n [ k i] + A n [ k i + m]
The parameters 'n' and 'm' are referred to as the segment and the gap, respectively. Taking a segment average (n > 1) serves to smooth the spectra, and changing the gap alters the relative prominence of narrow vs. broad bands. CLINICAL BIOCHEMISTRY, VOLUME 29, FEBRUARY 1996
QUANTITATION OF PROTEIN, CREATININE,AND UREA IN URINE
Appendix B As part of this study, we have explored various options in calculating the second derivative spectra, using as the arbiter the performance of the model t h a t results in the final analysis. This procedure was followed through only tbr creatinine; for urea, the concentration is high enough t h a t the relative importance of these effects is minor, and for protein prospective improvements are likely to be marginal in comparison to other uncertainties. PLS models were evaluated for creatinine by using second derivative spectra (123 calibration and 50 validation samples) t h a t differed only in the segment used for the derivative calculation, with the gap fixed at 2 nm (see Appendix A). The models have been characterized by monitoring both the SEC and SEP as a function of the number of PLS factors (Fig. 7). Two general observations emerge: (a) for a given number of PLS factors, the SEC improves regularly as the derivative segment is reduced; and (b) trends in the SEP show t h a t a short segment is strongly preferable. Guided by these conclusions, the final creatinine model was obtained by using derivatives calculated using a segment of 8 nm, gap of 2 nm.
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CLINICALBIOCHEMISTRY,VOLUME 29, FEBRUARY 1996
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