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Plasma protein contents determined by fourier-transform infrared spectrometry.

Fourier-transform infrared (FT-IR) spectrometry is a global, sensitive, and highly reproducible physicochemical analytical technique that identifies structural moieties of biomolecules on the basis of their IR absorption (1, 2). Because a biomolecule is determined by its unique structure, each biomolecule will exhibit a unique FT-IR spectrum, representing the vibrations of its structural bonds (3). Furthermore, every biomolecule present in the sample will exhibit more or less specific FT-IR absorption peaks (4). Thus, a plasma FT-IR spectrum will exhibit absorption peaks related to its major components. FT-IR analytical applications have allowed determination of blood contents using various materials and sample preparations. Concentrations of glucose (1, 2, 5, 6), total proteins, creatinine, urea, triglycerides (1), and cholesterol (2) in blood, plasma, or serum have been determined with clinical accuracy. However, extensive sample preparation, manipulation of spectra, and mathematical treatments have been necessary to obtain such results. Clinical analyses require methods that involve minimal sample manipulations because every handling step may be a source of quantitative error, affecting the predictive performance of the method used (7, 8).

With FT-IR spectrometry, fluctuations in absorption related to water content because of environmental conditions (e.g., variations in air temperature, humidity, and atmospheric pressure) have necessitated manipulation of samples and/or spectra (or both) (1, 2, 5, 6, 8). Recently, we described FT-IR analysis of glucose concentrations in dried serum samples (9). By avoiding water absorption in serum spectra, we obtained results that correlated well with those obtained by the glucose oxidase method (10); sample manipulations were limited to a precise dilution with water and desiccation under moderately reduced pressure after deposition of 35 [micro]L of this solution onto a multiple sample holder. No other manipulations of the sample or the spectrum were necessary for obtaining highly reproducible results. The characteristic IR absorption peaks for glucose were determined before those of biomolecules exhibiting more intense absorption contributions to serum FT-IR spectra (namely, proteins); we choose this approach because the glucose absorption appears in a specific spectral area, the [nu]C--O absorption region (1300-900 [cm.sup.-1]), in which major protein absorption is absent.

However, this analytical method is rather limited. Only ~80 spectral ranges, i.e., deformations such as peaks, shoulders, and bands, are found on a FT-IR spectrum, whereas plasma contains thousands of biomolecules at various concentrations. We previously had shown that subtraction of the spectral contribution of a previously determined metabolite allowed subsequent analysis, e.g., subtraction of the glucose spectrum allowed determination of lactatemia (11). These results for lactate were in good agreement with an enzymatic comparison method but could not have been obtained before this subtraction because the glucose absorption partially obscured the lactate absorption in the complete serum FT-IR spectra of subjects. Glucose and lactate concentrations could be determined because the broadest protein IR absorption peaks (for [nu]C [??] O and [delta]N-H: 1700-400 [cm.sup.-1]) were outside the spectral region in which most characteristic absorption peaks for glucose and lactate were found ([nu]C--O: 1300-900 [cm.sup.-1]). We now apply this methodology to the analysis of plasma proteins, the major component of plasma (accounting for ~75 g/L of the 100 g/L of dried matter present in plasma).

Materials and Methods


Venous blood samples from 35 anonymous healthy blood donors, admitted to hospital for routine analyses, were used for this study. Although these subjects did not present with pathological situations, several presented with some protein concentrations outside the physiological range for healthy adults (Table 1). The mean ([+ or -] SD) age of these subjects was 35.4 [+ or -] 5.1 years (range, 27-43 years). Blood was sampled between 0730 and 0830 after an overnight fast of 10 h. Blood was drawn into sterile and gel-barrier collection tubes (6-mL red-brown top and 7-mL red top Vacutainer Tubes; Becton Dickinson). One gel-barrier collection tube was used for FT-IR measurements. Venous blood was sampled by an antecubital venipuncture of the right arm through a Teflon catheter. Samples collected in gel-barrier collection tubes were centrifuged immediately for 10 min at 40008; plasma was collected in the red-brown top tubes and stored at -20[degrees]C before analysis.

Venous blood samples from 14 age-matched patients (37.1 [+ or -] 4.4 years; range, 30-44 years) admitted to hospital for severe infection (n = 4), renal disorder (n = 4), or anemia (n = 6) were used for validation of the methodology implemented for the 35 blood samples from the relatively healthy subjects.


Glucose and urea concentrations were measured with an accepted enzymatic method on a Dax 48 analyzer and using calibrators and controls from Bayer. Lactate was also measured by an enzymatic method (Microzym-L; SGI). Protein concentrations were determined immunonephelometrically with a BN II apparatus and calibrators and controls from Dade Behring, except that apolipoproteins [A.sub.1], B, and [C.sub.3] (Apo-[A.sub.1], Apo-B, Apo-[C.sub.3]) and IgD were evaluated by radial immunodiffusion with reagents from Daiichi Pure Chemicals Co. (for the apolipoproteins) and Dade Behring (for IgD).


The method for acquiring spectra has been described elsewhere (9). Briefly, after samples returned to room temperature (~15 min at 20-25[degrees]C), 20-[micro]L aliquots were diluted with 80 [micro]L of water. The diluted samples were homogenized with an agitator (Vortex Reax 2000; Heidolf) at 10008 for 10 s. Thirty-five microliters of each solution was deposited exactly within the cell limits of a zinc selenide wheel with 15 sample cells (Bruker). The loaded wheel was subsequently placed in a low-pressure [266 Pa (2 mmHg)] drier to evaporate the water in the sample (45 min), after which a KBr cover was attached to protect the wheel. The sample-bearing wheel was then inserted into the analysis compartment of a Bruker IFS 28/B spectrometer equipped with a Globar (MIR) source (7 V), a KBr beam splitter, and a DTGS/B detector (18-28[degrees]C) from Bruker. Beam diameter at the sample location was 6 mm. In all experiments, we used a resolution of 2.0 [cm.sup.-1] and performed 32 scans for data acquisition. All analyses were performed in triplicate on three successive wheels.


FT-IR spectra obtained for pure components (98-99% purity; Sigma-Aldrich) were used as comparison FT-IR spectra. These were the spectra used for all subtractions of biomolecule absorption peaks. The concentration of the dried matter in plasma is ~100 g/L. We previously demonstrated (9,11) that FT-IR analyses of plasma diluted fourfold with water provided a higher signal-to-noise ratio (determined in the 2100-2200 [cm.sup.-1] region, where no absorption attributable to plasma biomolecules is found) and greater signal reproducibility for successive analyses. Because these fourfold-diluted solutions contained ~20 g/L dried matter, we used pure component solutions ranging in concentration from 12 to 28 g/L to obtain FT-IR spectra that would be comparable in absorption values to those of the plasma FT-IR spectra. Application of pure component solutions onto the analytical wheel, desiccation, and FT-IR spectrum acquisition were performed exactly as described for plasma FT-IR spectrum acquisitions. For every biomolecule, linearity between the absorption signals detected and these concentrations was assessed by total area integration over 4000-500 [cm.sup.-1], representing the whole FT-IR absorption range for the biomolecules (with P <0.01).


The method for iterative determination of biomolecule concentrations (Fig. 1) was assessed as follows:

(1) Spectral ranges (peaks, bands, shoulders) were chosen as approximate absorption bands within the plasma FT-IR spectra. The precise abscissa limits for the spectral ranges were assigned using a subroutine of OPUS 3.0 software (Bruker). In brief, once two wave-numbers had been selected outside the absorption band, the software found the lowest absorption values that allowed the drawing of a line, no other point on which was in common with the spectrum curve.

(2) For each defined spectral range common to the 35 plasma FT-IR spectra of our experiments, the area between the line drawn and the spectrum was integrated by the software and expressed in arbitrary units (U).

(3) A correlation matrix was used to compare all integration results with the concentrations of all biomolecule measured with the comparison clinical analytical methods. The spectral range that led to the greatest correlation between integration values and the results obtained by the comparison method for each of the biomolecules considered was selected for subsequent use.

(4) A series of accurately determined calibration solutions of the selected biomolecule allowed us to acquire and integrate the spectral area of the pure compound over the previously determined spectral range. These results were expressed as the biomolecule concentration per arbitrary unit of spectral area: g * [L.sup.-1] * [U.sup.-1].

(5) For each FT-IR plasma spectrum, the concentration of the selected biomolecule was then calculated using its absorption value, based on the spectrum of the pure component, and the spectral area integration result obtained in step 2.

(6) The contributing spectrum of the biomolecule, determined from its pure component spectrum and its plasma concentration (as measured with a comparison method), was subsequently subtracted from the plasma FT-IR spectrum.

(7) The resulting spectrum was then saved and subjected to the same iterative method (steps 1 to 6) to determine the concentration of the next biomolecule to be analyzed. This process was repeated for each of the biomolecules being evaluated.


Data are expressed as the mean [+ or -] SD. In each clinical reference data series, the dispersion [mean dispersion (CV), expressed as a percentage] around the mean value was calculated. When the CV exceeded 5%, the concentration values were considered significantly heterogeneous (P <0.05). Linear regressions were performed to compare the data series. The results obtained by the clinical analytical method and the spectral data were considered comparable when P was <0.05. When the data were comparable, dispersion data around the regression lines were estimated by the mean standard error of prediction:


[S.sub.x|y] = [square root of (term)] [[summation]([x.sub.i] - [[mu].sup.2]) / n]

where [x.sub.i] corresponds to the predicted analyte value, [mu], is the value determined by the comparison method, and n is the total number of samples in the prediction data set. For results to be acceptable, the regression line had to show a slope close to 1 and an intercept close to 0. Differences between methods were tested using a standard t-test for paired data.



The plasma concentrations of glucose, lactate, urea, and proteins for the first set of 35 venous blood samples are presented in Table 1. Immunoglobulins, haptoglobin, [[alpha].sub.1]-acid glycoprotein, and Apo-[C.sub.3] exhibited the broadest distribution of concentrations (CV = 8-10%). This heterogeneity in plasma protein content allowed us to assess the analytical potential of FT-IR spectrometry for determining concentrations of successive biomolecules.


For the set of 35 plasma FT-IR spectra, the mean signal-to-noise ratio in the 2100-2200 [cm.sup.-1] region was 687 [+ or -] 19. The baselines between repeated samples were also homogeneous, varying by 0.011 [+ or -] 0.006 at 4000 [cm.sup.-1]. The between-run CV was 1.3% for triplicate FT-IR measurements. The mean plasma FT-IR spectrum area was 472 [+ or -] 45 U (integration range, 4000-500 cm 1). Results were comparable for the set of FT-IR spectra from the plasma samples from 14 patients, except that the mean plasma FT-IR spectrum area for the latter was 461 [+ or -] 76 U (integration range, 4000-500 [cm.sup.-1]).


For each series of pure component solutions, we studied the correlations between the corresponding 4000-500 [cm.sup.-1] spectral area and the biomolecule concentration. The correlations were 0.95-0.99 for all analytes evaluated in the concentration range 12-28 g/L (Table 2). The FT-IR spectra for the biomolecules presented important IR absorption dissimilarities, even for biomolecules belonging to the same families, e.g., apolipoproteins (Fig. 2) and IgG (Fig. 3).


The iterative procedure for determining the concentrations of biomolecules was tested for the set of 35 venous blood samples. Table 3 presents, in the order of analysis of the biomolecules, details on the spectral ranges used for integration (left and right abscissa limits), the FT-IR absorption data for the pure components in the same spectral ranges used to measure the concentrations of the biomolecules in plasma, the statistical significance of any correlation with results by the comparison methods, and a summary of the statistical indices for linearity between methods. After multiple analyses of the highest statistical values found in the correlation matrices, only one sequence of biomolecule analysis was found to give results that provided sufficient agreement with the 35 results obtained with the clinically used comparison methods.


The closest relationship between integration results and the values obtained by the clinical comparison methods was for albumin, the biomolecule present in the highest concentration in plasma. This relationship involved the spectral area located between 1600 and 1488 [cm.sup.-1] ([delta]N--H region; 23.3 [+ or -] 0.3 U vs 40.9 [+ or -] 3.8 g/L; r = 0.99; P <0.001). Albumin represents 65% of plasma proteins and 45% of the total plasma mass. The FT-IR absorption of albumin between 1600 and 1488 [cm.sup.-1] was 1.76 g * [L.sup.-1] * [U.sup.-1]. The albumin concentration measured by FT-IR spectrometry was 41.0 [+ or -] 3.9 g/L (correlation with the comparison method, r = 0.99; P <0.001). Regression analysis of the two methods showed a slope of 1.00 with an intercept of 0.17 g/L (Fig. 4). The spectral contribution of albumin (4000-500 [cm.sup.-1]) represented 39.9% of the total plasma FT-IR spectral area. Subtracting the albumin absorption left a spectral area of 230 [+ or -] 21 U (Fig. 5).


Evaluation of the remaining area showed that glucose was the second biomolecule whose concentration closely correlated with absorption area integration values (5.2 [+ or -] 0.4 mmol/L; [nu]C--O--C absorption at 1033-997 cm -1 = 7.27 mmol * [L.sup.-1] * [U.sup.-1]; plasma spectral area at 1033-997 [cm.sup.-1] = 0.71 [+ or -] 0.05 U). The glucose concentration measured by FT-IR spectrometry was 5.22 [+ or -] 0.17 mmol/L (correlation with the comparison method, r = 0.97; P <0.001; regression slope = 0.99; intercept = 0.04 mmol/L). Regardless of whether the albumin absorption was subtracted before or after glucose concentration analysis in the resulting spectra, the results for glucose were in accord with those from our previous study (9).


For subsequent spectra obtained after the spectral contributions of specific biomolecules were subtracted, the greatest correlations were found between the concentrations measured by the comparison method and the results obtained by spectral integration in the following descending order: fibrinogen, Ig[G.sub.2], lactate, Ig[G.sub.2], [[alpha].sub.1]-antitrypsin, [[alpha].sub.2]-macroglobulin, transferrin, Apo-[A.sub.1], urea, Apo-B, IgM, Apo-[C.sub.3], IgA, Ig[G.sub.4], Ig[G.sub.3], IgD, haptoglobin, and [[alpha].sub.1]-acid glycoprotein (Tables 2 and 3). Urea was included in this analysis because it exhibits strong absorption bands between 1800 and 1100 [cm.sup.-1], a spectral range in which IgM. Ig[G.sub.3], and Ig[G.sub.4] are also examined. In the 1500-1300 [cm.sup.-1] region, several of the spectral ranges used for determining biomolecule concentrations were very similar, i.e., those for fibrinogen, haptoglobin, Ig[G.sub.1], IgA, and IgM. Utilization of these ranges was possible, however, because the absorption bands for the pure compounds were not found in exactly the same locations ([[lambda].sub.max], and [epsilon]) on the spectrum for each of the other pure components (Fig. 6).

Haptoglobin was one the last biomolecules analyzed in our sequence and presented important concentration variations among subjects. Nevertheless, although fibrinogen, Ig[G.sub.1], IgA, and IgM concentrations were determined before those of haptoglobin, we obtained very good relationships between spectral and clinical reference values for these five biomolecules. Finally, after subtraction for 20 biomolecules, which corresponded to 69 [+ or -] 4 g/L of the dried matter in plasma, the mean total spectral area of the remaining spectrum was 114.5 [+ or -] 11.2 U, i.e., 24.2% of the initial plasma FT-IR spectral area (Fig. 7).


The results obtained for plasma samples from the 14 patients are presented in Table 4. Patients presented with broad concentration heterogeneities for albumin, IgA, IgD, Ig[G.sub.1], Ig[G.sub.2], Ig[G.sub.3], Ig[G.sub.4], IgM, urea, Apo-B. Apo-[C.sub.3], and haptoglobin. By comparing these differences in plasma contents with the results obtained for the first set of plasma samples, we could test the validity of the sequence used for biomolecule analysis. For this set of 14 plasma samples, we used exactly the same spectral ranges and sequence order that were used for the previous analyses of 35 plasma samples. As shown in Table 4, all concentration values obtained correlated well with the concentrations determined by the comparison methods.





We previously demonstrated that FT-IR spectrometry could determine glucose and lactate concentrations with clinical accuracy in the 1300-900 [cm.sup.-1] absorption region (9,11). These measurements were facilitated by the absence of marked absorption by proteins, mainly albumin, within this spectral region. This opportunity has to be related to the absence of glycosylation of albumin, this protein being by far the most abundant compound in plasma. However, until now, no more than a few characteristic absorption peaks have been used successfully for measuring the individual concentrations of several biomolecules in a sample. Only statistical methods, such as multivariate regression, allowed determination of concentrations of other biomolecules in plasma, using FT-IR spectra. These methods looked for a strong relationship between a wide spectral region (500 [cm.sup.-1] or higher) that contains several absorption bands for a biomolecule and the concentration of the molecule in the analyzed sample (1, 2). The aim of multivariate regression methods is to correlate variations in blood FT-IR absorption spectra with the known changes in the absorption spectrum of a pure component when the concentration of the pure component varies within samples. Such methods have been used to determine the plasma concentrations of total proteins (1) and cholesterol (2). However, a strong limitation of these methods is that a direct analysis, i.e., one showing any characteristic absorption of the biomolecules in the spectrum, has been unavailable. Indeed, defined concentrations of biomolecules could not be determined as had been done for glucose or lactate. In the FT-IR spectra of complex biological samples, such as plasma, the absorption spectra of some biomolecules may first be subtracted to uncover other absorption patterns. This has been clearly shown for the absorption pattern of lactate in the 1300-900 [cm.sup.-1] spectral region, which is obscured by glucose absorption (11 ). Lactate absorption in this spectral region could be exploited only after the absorption attributable to glucose had been subtracted.


Our aim in this study was to determine the concentrations of various proteins in plasma on the basis of their most characteristic IR absorption peaks. For albumin, the best correlation with results obtained by a comparison method was found using the [delta]N-H absorption region (1600-1480 [cm.sup.-1]) common to all plasma proteins. However, the physiological concentration of albumin is 37-45 g/L, and the biomolecule that contributes the most intense absorption to plasma FT-IR spectra after albumin is Ig[G.sub.1], the concentration of which usually is 6-8 g/L. We found a high correlation between Ig[G.sub.1] concentrations measured by the comparison method and spectral data for the 1419-1361 [cm.sup.-1] absorption region, which is outside of the spectral region used for determining the albumin concentration.

We determined an absorption-related sequence for the biomolecules to be subtracted from plasma FT-IR spectra; that is, the concentrations of several biomolecules could be determined only after the absorption spectra of other biomolecules had been subtracted, even when the absorption contributed to the plasma FT-IR spectrum by the molecules subtracted earlier was several-fold less intense than the absorption contributed by the remaining biomolecules. This was clearly illustrated by the plasma concentrations of the biomolecules determined in the following order: glucose (~1 g/L), fibrinogen (~3 g/L), lactate (~0.3 g/L), and Ig[G.sub.1] (~8 g/L). Despite its high concentration, Ig[G.sub.1] could not be determined before the glucose, fibrinogen, and lactate IR absorption peaks were subtracted. Glucose absorbs strongly in the 1419-1361 [cm.sup.-1] spectral region, which is also the most specific region for Ig[G.sub.1] absorption in plasma. Similarly, fibrinogen exhibits a strong absorption centered at 1395 [cm.sup.-1], and lactate exhibits a weak absorption centered at 1399 [cm.sup.-1]. Furthermore, we were unable to find another specific Ig[G.sub.1] absorption peak in any other spectral region. Indeed, before we could ascertain which absorption region could be used for determining the Ig[G.sub.1] concentration in plasma, the glucose, fibrinogen, and lactate absorption spectra had to be subtracted. The sequence we found for determining biomolecule concentrations was the one yielding the best correlation with the spectral range areas calculated with respect to the resulting spectra for the other biomolecules. Multivariate calibration methods, such as partial least squares and principal component regression, effectively remove the spectral features of each determined analyte sequentially, similar to the method we have proposed. However, by subtracting the spectral contribution of each biomolecule determined from the complete FT-IR spectrum for plasma, we were able to determine the concentrations of many more biomolecules than could be measured by other FT-IR spectrometry methods.

Importantly, the FT-IR spectra of biomolecules belonging to the same families contained important differences, as was observed for the IgGs. This reflects structural specificities for these biomolecules; e.g., between 1300 and 900 [cm.sup.-1] ([nu]C--O region), spectral differences reflect differences in the sugar content. Thus, we were able to determine the concentration of each IgG form in plasma according to the FT-IR spectrum. This was also true for apolipoproteins, for which three different spectral regions were used, representing the different FT-IR absorption spectra of these biomolecules.

Another important finding of this study is that the concentrations of several biomolecules, notably those for fibrinogen, haptoglobin, Ig[G.sub.1], IgA, and IgM, could be determined using spectral ranges that were very close to one another, i.e., 1500-1300 [cm.sup.-1], but that differed as to the exact locations and intensities of the absorption peaks. Even when haptoglobin concentrations exhibited several-fold variations between subjects, we were able to determine the concentrations of the other biomolecules with clinical accuracy in the previous steps. Indeed, the spectral ranges we used exhibited slight but sufficiently characteristic differences in the 1500-1300 [cm.sup.-1] absorption region, leading to determination of the concentrations of these biomolecules. For the last biomolecule determined in this series, [[alpha].sub.1]-acid glycoprotein, the results correlated well (r = 0.96) with the values obtained by the comparison method.

We tested the sequence order we found for determining biomolecule concentrations by assaying plasma samples from 14 patients in which the concentrations of several proteins varied widely. We also assayed these samples with the comparison methods for all biomolecules determined by the FT-IR method. The results demonstrated that the spectral ranges we used to determine biomolecule concentrations reflected structural absorption peaks sufficiently characteristic of these biomolecules.

Moreover, the FT-IR absorption spectra of the biomolecules analyzed on the basis of sequential plasma spectra were not altered by successive subtractions of absorption. After the individual absorption peaks for 20 biomolecules were subtracted from the complete FT-IR spectrum of plasma, the resulting spectra were not noise. We therefore consider it possible to continue to use this method to determine the concentrations of additional biomolecules, namely, triglycerides, cholesterol esters, amino acids, and fatty acids.

In conclusion, the present study has demonstrated that FT-IR spectrometry is a useful tool for determining concentrations of multiple biomolecules in microsamples of plasma.

We are indebted to the Conseil Regional d'Aquitaine and the Federation Francaise de Rubgy for financial support and technical assistance.


(1.) Shaw RA, Kotowich S, Leroux M, Mantsch HH. Multivariate serum analysis using mid-infrared spectroscopy. Ann Clin Biochem 1998;35:624-32.

(2.) Budinova G, Salva J, Volka K. Application of molecular spectroscopy in the mid-infrared region to the determination of glucose and cholesterol in whole blood and in blood serum. Appl Spectrosc 1997;51:631-5.

(3.) Gremlich HU. Infrared and Raman spectroscopy. In: Ullmann's encyclopedia of industrial chemistry, Vol. B5. Weinheim, Switzerland: [NU]CH, 1994:429-69.

(4.) Sockalingum GD, Bouhedja W, Pina P, Allouch P, Bloy C, Manfait M. FT-IR spectroscopy as an emerging method for rapid characterization of microorganisms. Cell Mol Biol 1998;44:261-9.

(5.) Ward KJ, Haaland DM, Robinson MR, Eaton RP. Quantitative infrared spectroscopy of glucose in blood using partial least-square analyses. Proc SPIE Int Soc Opt Eng 1989;1145:607-8.

(6.) Werner GH, Bocker D, Haar H-P, Kuhr H-J, Mischler R. Multicomponent assay for blood substrate in human sera and haemolysed blood by mid-infrared spectroscopy; in Infrared spectroscopy: new tools in medicine. Proc SPIE Int Soc Opt Eng 1998;3257:91-100.

(7.) Ward KJ, Haaland DM, Robinson MR, Eaton RP. Post-prandial blood glucose determination by quantitative mid-infrared spectroscopy. Appl Spectrosc 1992;46:959-65.

(8.) Heise HM, Bittner A. Investigation of experimental errors in the quantitative analysis of glucose in human blood plasma by ATR-IR spectroscopy. J Mol Struct 1995;348:21-4.

(9.) Petibois C, Rigalleau V, Melin A-M, Perromat A, Cazorla G, Gin H, Deleris G. Determination of glucose in dried serum samples by Fourier-transform infrared spectroscopy. Clin Chem 1999;45:1530-5.

(10.) Bergmeyer HU. Methods of enzymatic analysis. New York: Academic Press, 1974:496pp.

(11.) Petibois C, Melin A-M, Perromat A, Cazorla G, Deleris G. Glucose and lactate concentrations determination on single microsamples by Fourier-transform infrared spectroscopy. J Lab Clin Med 2000;135:210-5.


[1] INSERM U443, Equipe de Chimie Bio-Organique, [2] Faculte des Sciences du Sport et de l'Education Physique, and [3] Department de Biochimie Medicale et Biologie Moleculaire, Laboratoire de Biochimie, CHU Bordeaux, Universite Victor Segalen Bordeaux 2, 146 rue Leo Saignat, 33076 Bordeaux, France.

*Author for correspondence. Fax 33-5-5757-1002; e-mail

Received June 5, 2000; accepted January 12, 2001.
Table 1. Plasma concentrations of glucose, lactate, urea, and various
proteins as determined by comparison methods.

 Concentration, (a) g/L

Biomolecule Mean [+ or -] SD Min

Glucose 5.20 [+ or -] 0.16 5.0
Lactate 1.72 [+ or -] 0.31 1.11
Urea 6.07 [+ or -] 1.26 4.13
Albumin 40.9 [+ or -] 3.8 36.8
Fibrinogen 3.37 [+ or -] 0.43 2.34
Transferrin 2.44 [+ or -] 0.30 1.8
Haptoglobin 0.94 [+ or -] 0.45 0.16
[[alpha].sub.1] -Acid glycoprotein 0.79 [+ or -] 0.22 0.48
[[alpha].sub.1] -Antitrypsin 2.89 [+ or -] 0.63 1.7
[[alpha].sub.2] -Macroglobulin 2.05 [+ or -] 0.50 1.1
IgA 1.88 [+ or -] 0.69 0.26
IgD 0.37 [+ or -] 0.13 0.18
I9[G.sub.1] 7.51 [+ or -] l.91 4.1
I9[G.sub.2] 2.53 [+ or -] 0.54 1.1
I9[G.sub.3] 0.61 [+ or -] 0.21 0.2
I9[G.sub.4] 0.53 [+ or -] 0.25 0.1
Igm 1.20 [+ or -] 0.66 0.20
Apo-[A.sub.1] 1.52 [+ or -] 0.21 1.2
Apo-B 0.93 [+ or -] 0.17 0.55
APO-[c.sub.3] 0.48 [+ or -] 0.17 0.17

 Concentration, (a) g/L

Biomolecule Max CV, %

Glucose 5.6 0.84
Lactate 2.23 2.4
Urea 9.33 7.7
Albumin 50.5 5.6
Fibrinogen 4.15 2.4
Transferrin 3.0 7.4
Haptoglobin 2.02 20
[[alpha].sub.1] -Acid glycoprotein 1.47 13
[[alpha].sub.1] -Antitrypsin 4.0 6.7
[[alpha].sub.2] -Macroglobulin 3.3 8.4
IgA 2.99 11
IgD 0.71 12
I9[G.sub.1] 13.2 8.7
I9[G.sub.2] 3.4 9.6
I9[G.sub.3] 1.2 12
I9[G.sub.4] 1.2 21
Igm 2.69 14
Apo-[A.sub.1] 2.1 6.8
Apo-B 1.27 4.7
APO-[c.sub.3] 0.81 9.1

(a) Concentrations for glucose, lactate, and urea given as mmol/L.

Table 2. Relationships between concentrations and
corresponding 4000-500 cm-1 total spectral areas
obtained from series of pure component solutions.

Biomoleculee r Slope Intercept

Albumin 0.97 18.80 0.22
[alpha] 1 Acid glycoprotein 0.98 19.80 -0.41
[alpha] 1 Antitrypsin 0.99 22.20 -0.3
[alpha] 2 Macroglobulin 0.99 19.90 0.28
Apo-[A.sub.1] 0.96 19.50 0.17
Apo-B 0.95 24.70 -0.02
APO-[C.sub.3] 0.98 20.40 0.11
Fibrinogen 0.99 22.10 0.21
Glucose 0.97 28.50 0.19
Haptoglobin 0.95 18.70 -0.22
IgA 0.96 21.50 -0.37
1gD 0.99 18.50 -0.39
Ig[G.sub.1] 0.97 20.00 -0.13
Ig[G.sub.2] 0.99 18.50 -0.1
Ig[G.sub.3] 0.96 21.40 0.28
Ig[G.sub.4] 0.96 24.40 0.31
Igm 0.97 19.70 -0.12
Lactate 0.94 29.40 -0.44
Transferrin 0.96 21.90 0.19
Urea 0.99 30.50 -0.23

(a) n = 15 analyses per biomolecule concentration. For each
biomolecule, the mean of the spectra for the 20 g/L solution was used
as the comparison spectrum for spectra subtractions.

Table 3. Statistical indices between comparison method values and
FT-IR spectrometry values.

 FT-IR spectrometry data

Biomolecule (a) Left Right g x [L.sup.-1]
 x [U.sup.-1]

Albumin 1600 1488 1.76
Glucose 1062 997 7.27 (c)
Fibrinogen 1428 1363 2.42
Ig[G.sub.2] 1652 1622 3.72
Lactate 1134 1115 65.46 (c)
Ig[G.sub.1] 1419 1361 9.07
[[alpha].sub.1] -Antitrypsin 712 691 48.4
[[alpha].sub.2] -Macroglobulin 1116 983 1.98
Transferrin 1338 1292 12.39
Apo-[A.sub.1] 1484 1427 1.81
Urea 1652 1632 35.01 (c)
Apo-B 2862 2835 6.95
Igm 1428 1360 1.96
APO-C3 1676 1660 8.09
IgA 1418 1372 5.31
Ig[G.sub.4] 1538 1505 6.89
Ig[G.sub.3] 1652 1628 4.29
IgD 1479 1464 21.8
Haptoglobin 1428 1378 3.35
[[alpha].sub.1] -Acid
 glycoprotein 1337 1279 2.77

 FT-IR spectrometry data

 Mean [+ or -] SD

Biomolecule (a) Area, U g/L

Albumin 23.3 [+ or -] 0.3 41.0 [+ or -] 3.9
Glucose 0.71 [+ or -] 0.05 5.22 [+ or -] 0.17 (d)
Fibrinogen 1.38 [+ or -] 0.17 3.36 [+ or -] 0.46
Ig[G.sub.2] 0.74 [+ or -] 0.11 2.57 [+ or -] 0.57
Lactate 0.026 [+ or -] 0.007 1.80 [+ or -] 0.36 (d)
Ig[G.sub.1] 0.83 [+ or -] 0.23 7.66 [+ or -] 2.08
 -Antitrypsin 0.059 [+ or -] 0.015 2.89 [+ or -] 0.68
 -Macroglobulin 1.05 [+ or -] 0.26 2.11 [+ or -] 0.51
Transferrin 0.19 [+ or -] 0.02 2.45 [+ or -] 0.31
Apo-[A.sub.1] 0.91 [+ or -] 0.11 1.55 [+ or -] 0.22
Urea 0.17 [+ or -] 0.04 6.25 [+ or -] 1.39 (d)
Apo-B 0.13 [+ or -] 0.01 0.96 [+ or -] 0.17
Igm 0.61 [+ or -] 0.25 1.27 [+ or -] 0.65
APO-C3 0.062 [+ or -] 0.028 0.52 [+ or -] 0.19
IgA 0.36 [+ or -] 0.11 1.88 [+ or -] 0.70
Ig[G.sub.4] 0.075 [+ or -] 0.031 0.54 [+ or -] 0.26
Ig[G.sub.3] 0.14 [+ or -] 0.05 0.63 [+ or -] 0.23
IgD 0.019 [+ or -] 0.006 0.38 [+ or -] 0.16
Haptoglobin 0.29 [+ or -] 0.14 0.98 [+ or -] 0.47
[[alpha].sub.1] -Acid
 glycoprotein 0.33 [+ or -] 0.08 0.82 [+ or -] 0.23


Biomolecule (a) r (b) [S.sub.xly] Slope Intercept

Albumin 0.98 0.67 1.00 0.67
Glucose 0.95 0.05 (c) 0.99 0.04 (d)
Fibrinogen 0.97 0.10 1.04 -0.16
Ig[G.sub.2] 0.97 0.18 1.01 0.02
Lactate 0.94 0.11 (c) 1.09 -0.08 (d)
Ig[G.sub.1] 0.98 0.35 1.07 -0.35
 -Antitrypsin 0.96 0.18 1.04 -0.14
 -Macroglobulin 0.98 0.22 1.01 0.03
Transferrin 0.97 0.08 0.99 0.02
Apo-[A.sub.1] 0.97 0.05 1.06 -0.06
Urea 0.97 0.33 (c) 1.07 -0.25 (d)
Apo-B 0.96 0.05 0.96 0.07
Igm 0.98 0.13 0.97 0.11
APO-C3 0.97 0.04 1.06 0.004
IgA 0.97 0.16 0.99 -0.003
Ig[G.sub.4] 0.95 0.08 0.93 0.08
Ig[G.sub.3] 0.95 0.07 1.03 0.01
IgD 0.92 0.05 1.11 -0.03
Haptoglobin 0.99 0.08 1.04 0.01
[[alpha].sub.1] -Acid
 glycoprotein 0.96 0.07 0.99 0.02

(a) Data are presented in order of analysis of the biomolecules in the
iterative method.

(b) P <0.001 if r >0.99; P <0.005 if r >0.95.

(c) mmol x [L.sup.-1] x [U.sup.-1].

(d) mmol/L.

Table 4. Results obtained for the plasma samples from 14 patients.

 Concentration, g/L

 Comparison method

Biomolecule (a) Mean [+ or -] SD Min Max

Albumin 43.1 [+ or -] 8.5 32 61
Glucose 5.06 [+ or -] 1.26 (c) 3.6 (c) 8.4 (c)
Fibrinogen 2.61 [+ or -] 0.84 1.4 4.2
Ig[G.sub.2] 2.91 [+ or -] 2.31 0.5 8.0
Lactate 1.24 [+ or -] 0.60 (c) 0.5 (c) 2.8 (c)
Ig[G.sub.1] 7.16 [+ or -] 5.19 1.8 22.0
-Antitrypsin 1.69 [+ or -] 0.37 1.09 2.90
-Macroglobulin 1.79 [+ or -] 0.59 0.83 2.94
Transferrin 1.87 [+ or -] 0.49 0.81 2.58
Apo-[A.sub.1] 1.17 [+ or -] 0.49 0.3 2.18
Urea 11.7 [+ or -] 7.9 (c) 1.7 (c) 25.5 (c)
Apo-B 0.90 [+ or -] 0.44 0.19 1.87
Igm 2.73 [+ or -] 3.10 0.31 10.40
APO-[C.sub.3] 0.16 [+ or -] 0.10 0.03 0.33
IgA 3.41 [+ or -] 1.95 0.69 6.06
Ig[G.sub.4] 0.24 [+ or -] 0.23 0.01 0.80
Ig[G.sub.3] 1.07 [+ or -] 0.60 0.22 2.00
IgD 0.67 [+ or -] 0.37 0.13 1.11
Haptoglobin 1.79 [+ or -] 1.26 0.49 4.35
[[alpha].sub.1] -Acid
 glycoprotein 1.48 [+ or -] 0.48 0.80 2.31

 Concentration, g/L

 FT-IR spectrometry data

Biomolecule (a) Mean [+ or -] SD Min Max

Albumin 42.8 [+ or -] 8.6 31.4 61.3
Glucose 4.93 [+ or -] 1.21 (c) 3.18 (c) 7.99 (c)
Fibrinogen 2.59 [+ or -] 0.80 1.51 4.01
Ig[G.sub.2] 2.82 [+ or -] 2.27 0.47 7.46
Lactate 1.18 [+ or -] 0.57 (c) 0.45 (c) 2.61 (c)
Ig[G.sub.1] 7.40 [+ or -] 5.60 1.61 23.6
-Antitrypsin 1.67 [+ or -] 0.57 0.89 2.61
-Macroglobulin 1.79 [+ or -] 0.63 0.86 3.05
Transferrin 1.91 [+ or -] 0.51 0.81 2.67
Apo-[A.sub.1] 1.17 [+ or -] 0.52 0.24 2.33
Urea 11.7 [+ or -] 7.8 (c) 1.54 (c) 25.4 (c)
Apo-B 0.90 [+ or -] 0.43 0.22 1.71
Igm 2.64 [+ or -] 3.16 0.33 11.1
APO-[C.sub.3] 0.16 [+ or -] 0.10 0.02 0.37
IgA 3.23 [+ or -] 1.90 0.56 6.04
Ig[G.sub.4] 0.23 [+ or -] 0.23 0.01 0.86
Ig[G.sub.3] 1.08 [+ or -] 0.65 0.30 2.21
IgD 0.69 [+ or -] 0.42 0.11 1.23
Haptoglobin 1.73 [+ or -] 0.30 0.45 4.89
[[alpha].sub.1] -Acid
 glycoprotein 1.44 [+ or -] 0.49 0.61 2.19


Biomolecule (a) r (b) SVY

Albumin 0.98 1.66
Glucose 0.98 0.22 (c)
Fibrinogen 0.97 0.19
Ig[G.sub.2] 1.00 0.21
Lactate 0.97 0.15 (c)
Ig[G.sub.1] 0.96 0.56
 -Antitrypsin 0.94 0.20
 -Macroglobulin 0.98 0.12
Transferrin 0.94 0.14
Apo-[A.sub.1] 0.99 0.08
Urea 0.97 0.78 (c)
Apo-B 0.96 0.13
Igm 0.93 0.35
APO-[C.sub.3] 0.96 0.03
IgA 0.94 0.23
Ig[G.sub.4] 0.97 0.05
Ig[G.sub.3] 0.95 0.13
IgD 0.94 0.15
Haptoglobin 0.97 0.23
[[alpha].sub.1] -Acid
 glycoprotein 0.95 0.16

(a) Data are presented in order of analysis of the biomolecules in
the iterative method.

(b) P <0.01 if r >0.98; P <0.05 if r >0.92.

(c) mmol/L.
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Article Details
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Title Annotation:Automation and Analytical Techniques
Author:Petibois, Cyril; Cazorla, Georges; Cassaigne, Andre; Deleris, Gerard
Publication:Clinical Chemistry
Date:Apr 1, 2001
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