Anal. Chem. 1998, 70, 226-231
Automated Multivariate Calibration in Sequential Injection-Fourier Transform Infrared Spectroscopy for Sugar Analysis R. Schindler, M. Watkins, R. Vonach, B. Lendl,* and R. Kellner†
Vienna University of Technology, Getreidemarkt 9/151 A-1060 Wien, Austria R. Sara
A° bo Akademi University and University of Turku, Centre for Biotechnology, Turku, Finland
Two approaches for automated preparation of a multicomponent calibration solution in sequential injection analysis (SIA) using one single standard for each component are proposed. The key to both approaches is the generation of highly reproducible injection sequences in the flow system. In the first approach, single standards of three different components are stacked in the holding coil in well-defined sequences. Upon flow reversal and transport to the detector, the stacked zones disperse into each other producing exactly defined mixtures of the three standards which can be recorded as a function of time. The second approach is based on complete mixing of the three single standards in different ratios prior to detection. For this purpose, repeated short sequences of small volumes of standard and distilled water are aspirated into the holding coil, which results in a homogeneous calibration solution upon flow reversal and transport to the detector. The proposed approaches have been applied to simultaneous determination of glucose, fructose, and sucrose in aqueous standards as well as in soft drinks using Fourier transform infrared spectroscopic detection and multivariate partial least-squares data analysis. The obtained results were compared with external reference methods to confirm the results obtained by the multivariate SIA-FTIR technique. Typical deviations from the results obtained by the reference method were in the order of 2.5, 4.4, and 2.8 g/L (2.5, 4.1, and 2.1% of total sugar) for glucose, fructose, and sucrose, respectively. The concept of sequential injection analysis (SIA) introduced in 19901,2 has improved flow injection analysis (FIA) in many ways, especially concerning the robustness of the resulting analysis systems. Due to “keyboard-based” liquid handling, SIA manifolds can easily be adapted to various analytical problems without physical reconfiguration only by simple changes in the controlling program. A rapidly increasing number of publications reflects the interest in this technique for various applications like biochemical assays,3-5 process control purposes,6-8 or environmental analysis.9,10 All applications rely on the long-term stability of timing † Deceased, October 8, 1997. (1) Ruzicka, J.; Marshall, G. D. Anal. Chim. Acta 1990, 237, 329. (2) Ruzicka, J.; Marshall, G. D.; Christian, G. D. Anal. Chem. 1990, 62, 1861. (3) Ruzicka, J.; Gu ¨ beli, T. Anal. Chem. 1991, 63, 1680.
226 Analytical Chemistry, Vol. 70, No. 2, January 15, 1998
and flow rates in SIA systems, but the possibility to develop new analytical features was only rarely realized.11-13 In FIA, exploitation of the controlled dispersion process and precise fluid manipulation for analytical purposes were the cornerstones of new techniques14 like stopped flow,15 reversal flow,16 electronic dilution,17 and single standard calibration.18 The underlying fundamental process is generation of a concentration gradient by injection of a sample with reproducible dispersion into an unsegmented carrier stream. By evaluation of several points of a single gradient corresponding to different dispersion values, it is possible to construct a calibration curve from one single standard injection17 or the dilution of the analyte can be adjusted in order to fit into the linear range of the detector used.18 Zone penetration19 enables interference studies20 and application of the standard addition method19 by dispersion-controlled mixing of sample with standards and interferents, respectively. The flow rate gradient technique was introduced for generation of complex flow patterns,21 adding the advantage that for each element of the fluid, the concentration of one species can be varied while keeping the overall reaction time constant.22 Single standard calibration was also performed in SIA systems,23 using the tailing edge of a large sample volume (4) Guzman, M.; Pollema, C.; Ruzicka, J.; Christian, G. D. Talanta 1993, 40, 81. (5) Hansen, E. H.; Winther, B. W. S. K.; Drabol, H. Talanta 1994, 41, 1881. (6) Masini, J. C.; Baxter, P. J.; Detwiler, K. R.; Christian, G. D. Analyst 1995, 120, 1583. (7) Schuhmann, W.; Wohlschla¨ger, H.; Huber, J.; Schmidt, H.-L.; Stadler, H. Anal. Chim. Acta 1995, 315, 113. (8) Baxter, P. J.; Christian, G. D.; Ruzicka, J. Analyst 1994, 119, 1807. (9) Liu, S.; Dasgupta, P. K. Anal. Chim. Acta 1995, 308, 281. (10) Oms, M. T.; Cerda´, A.; Cerda´, V. Anal. Chim. Acta 1995, 315, 321. (11) Pollema, C.; Ruzicka, J. Analyst 1993, 118, 1235. (12) Pollema, C.; Ruzicka, J. Anal. Chem. 1994, 66, 1825. (13) Peterson, K. L.; Logan, B. K.; Christian, G. D.; Ruzicka, J. Anal. Chim. Acta 1997, 337, 99. (14) Ruzicka, J. Anal. Chim. Acta 1992, 261, 3. (15) Ruzicka, J.; Hansen, E. H. Anal. Chim. Acta 1979, 106, 207. (16) Rı´os, A.; Luque de Castro, M. D.; Valca´rcel, M. Anal. Chem. 1988, 60, 1540. (17) Olson, S.; Ruzicka, J.; Hansen, E. H. Anal. Chim. Acta 1982, 136, 101. (18) Tyson; J. F.; Idris; A. B. Analyst 1981, 106, 1025. (19) Fang, Z.; Harris, J. M.; Ruzicka, J.; Hansen, E. H. Anal. Chem.. 1985, 57, 1457. (20) Hansen, E. H.; Ruzicka, J.; Krug, F. J.; Zagatto, E. A. Anal. Chim. Acta 1983, 148, 111. (21) Marcos, J.; Rı´os, A.; Valca´rcel, M.. Trends Anal. Chem. 1992, 11, 373. (22) Lendl, B.; Rı´os, A.; Valca´rcel, M.; Grasserbauer, M. Anal. Chim. Acta 1994, 289, 187. S0003-2700(97)00415-0 CCC: $15.00
© 1998 American Chemical Society Published on Web 01/15/1998
stored in a dilution conduit. It is important to note that the controlled-dispersion process can also be applied to standards of different species simultaneously, so that each element of fluid of the recorded peak represents a different, but well-defined, mixture of the introduced standards. However, in FIA, the generation of different injection sequences would require a physical rearrangement of the FIA manifold for each sequence. This situation is significantly different in the case of SIA as here only small changes in the controlling software are required to create different interpenetrating dispersion profiles of the standards used. Mixtures based on chasing dispersion profiles are of special interest considering the application of multidimensional detectors in flow systems such as diode array UV-visible or FTIR spectrometers for the purpose of simultaneous multicomponent analysis based on chemometric data analysis. For the successful application of multivariate calibration techniques, exactly defined mixtures of standards of different species are a fundamental prerequisite as they are required to construct a multivariate calibration model,24,25 which can be used to obtain qualitative and quantitative information of unknown samples. One obvious obstacle for a more general use of multivariate data analysis26 is the tedious and timeconsuming manual preparation of the calibration solutions. Furthermore, manual preparation of calibration solutions is also prone to errors because of human failure. As a consequence, frequent recalibration during long-term operation is often omitted, resulting in the fact that drifts in the instrument response or changes in the process environment cannot be corrected for.27 Several efforts have been made to overcome this problem, including sophisticated chemometric procedures,27,28 but it is clear that frequent recalibration of the analysis system would be the best way to improve the situation. The two techniques presented here are expected to improve this situation significantly, and this is illustrated with the example of the simultaneous determination of glucose, fructose, and sucrose in aqueous standards as well as in soft drinks using a simple SIA system with molecule-specific mid-FTIR detection. The hyphenation of SIA and FTIR spectroscopy is furthermore considered to give a significant improvement of FTIR instrument performance as then complex multivariate recalibrations can also be executed whenever necessary in a fully automated and computer-controlled way. Since the first papers dealing with FIA-FTIR were published in 1985,29,30 FIA-FTIR has developed from a simple approach for automated sample introduction to a valuable tool for solving complex analytical problems. In most FIA-FTIR methods reported so far, FTIR spectroscopic measurements are performed in organic solvents like CHCl3 or n-hexane for direct determination of selected organic molecules using single wavenumbers31 for taking the analytical readout. For the analysis of aqueous solutions both
solid-phase32 and liquid-liquid extraction33 have been performed prior to measurement in the organic phase. Direct reaction-based FIA-FTIR determination of enzyme substrates,34,35 enzyme activities,36 and inorganic phosphate37 in complex aqueous matrixes was also shown by recording FTIR spectra before and after a selective reaction of the analyte and evaluation of the matrix-independent difference spectrum. However, chemometric data evaluation has been introduced only for two problems, namely, analysis of acetone/ethanol/tetrahydrofuran mixtures38 and simultaneous determination of acetylsalicylic acid/paracetamol/caffeine in pharmaceutical products.39
(23) Baron, A.; Guzman, M.; Ruzicka, J.; Christian, G. D. Analyst 1992, 117, 1839. (24) Beebe, K. R.; Kowalski, B. R. Anal. Chem. 1987, 59, 1007A. (25) Lorber, A.; Kowalski, B. R. J. Chemom. 1991, 2, 67. (26) Haaland, D. M.; Thomas, E. V. Anal. Chem. 1988, 60, 1193. (27) Wang, Y.; Veltkamp, D. J.; Kowalski, B. R. Anal. Chem. 1991, 63, 2750. (28) Rius, A.; Callao, M. P.; Ferre´, J.; Rius, F. X. Anal. Chim. Acta 1997, 337, 287. (29) Morgan, D. K.; Danielson, N. D.; Katon, J. E. Anal. Lett. 1985, 18 (A16), 1979. (30) Curran, D. J.; Collier, W. G. Anal. Chim. Acta 1985, 177, 259. (31) Gallignani, M.; Garrigues, S.; de la Guardia, M. Analyst 1994, 119, 653.
(32) Garrigues, S.; Vidal, M. T.; Gallignani, M.; de la Guardia, M. Analyst 1994, 119, 659. (33) Daghbouche, Y.; Garrigues, S.; de la Guardia, M. Analyst 1996, 121, 1031. (34) Rosenberg, E.; Kellner, R. J. Mol. Struct. 1993, 294, 9. (35) Kellner, R.; Lendl, B.; Wells, I.; Worsfold, P. J. Appl. Spectrosc. 1997, 51, 227. (36) Schindler, R.; Lendl, B.; Kellner, R. Analyst 1997, 122, 531. (37) Vonach, R.; Lendl, B.; Kellner, R. Analyst 1997, 122, 525. (38) Guzman, M.; Ruzicka, J.; Christian, G. D.; Shelley, P. Vibr. Spectrosc. 1991, 2, 1. (39) Bouhsain, Z.; Garrigues, S.; De la Guardia, M. Analyst 1996, 121, 1935. (40) Lendl, B.; Schindler, R.; Frank, J.; Kellner, R.; Drott, J., Laurell, T. Anal. Chem. 1997, 69, 2877.
EXPERIMENTAL SECTION Reagents. Sucrose, fructose, and glucose standards were prepared by dissolving the sugar (p.a., Merck) in distilled water. Apparatus. The SIA system, designed to reach a high degree of simplicity, was set up with a Cavro XP 3000 syringe pump (syringe size 500 µL) and a Valco six-port selection valve equipped with an electric microactuator. Polytetrafluoroethane (PTFE) (i.d.: 0.3, 0.5, and 0.8 mm) tubings and fittings were obtained from Global FIA (Gig Harbor, WA). The setup was controlled by an IBM-compatible PC (486, 33 MHz), using the AnalySIA software package from the Turku Centre for Biotechnology (A° bo Akademi University and University of Turku). The exact timing between the sample injection and the recording of spectra was of key importance for the method and was achieved by TTL-level signals between the syringe pump and the FTIR spectrometer. HPLC reference measurements were carried out using a Sarasep CAR-Ca 300 × 7.8 mm analytical column (Inovex GmbH, Vienna, Austria) equipped with a differential refractometer (Knauer GmbH, Berlin, Germany). Control Software. The control program AnalySIA is a special Windows-based software designed for sequential injection analysis. It is a script language interpreter easily interfacable to different hardware devices like pumps and valves. It supports userdefinable variables and subroutines, heavily encouraging modular programming. Due to the modularity, even large and complex SIA methods are easily manageable. Miniaturized Fiber-Optic Flowthrough Cell. A home-made fiber-optic flow cell was used as previously described.40 Briefly, two AgClxBr1-x fiber pieces (diameter, 1 mm) with plane-parallel faces were assembled in a PTFE block coaxially to each other with a gap of 30 µm between the fiber tips. The flow was directed through this gap. By means of this flowthrough microcell (probed volume, 25 nL), the peak profile could be scanned with high resolution using a conventional SIA manifold. IR Data Acquisition. A Bruker IFS 88 FTIR spectrometer equipped with a high-sensitivity narrow-band MCT detector was used for spectra acquisition. The peak profile was obtained using the routine for LC/GC-IR measurements available within the
Analytical Chemistry, Vol. 70, No. 2, January 15, 1998
227
Figure 1. Mid-IR spectra of fructose, glucose, and sucrose (50 g/L each).
OPUS spectrometer software. The spectra were continuously recorded at a spectral resolution of 8 cm-1, coadding 128 scans for each spectrum. The rapid scan (80 kHz, HeNe frequency) acquisition of spectra permitted the recording and storage of one spectrum in ∼8 s. Blackman-Harris three-term apodization was used throughout. Before each peak, a new background spectrum was recorded coadding 300 scans when pumping distilled water through the flow cell. To limit the amount of data recorded during experiments, only the relevant spectral range (1400-900 cm-1) was stored. Data Analysis. In carbohydrate analysis based on FTIR spectroscopy, multivariate data analysis such as multiple linear regression (MLR), principal component regression (PCR), and partial least-squares regression (PLS) are often proposed for simultaneous determination of several compounds.41-43 Application of multivariate data analysis is essential because of the similarity of the mid-IR spectra of carbohydrates in general and sugars especially (Figure 1). For successful multivariate data analysis, a large set of calibration samples is usually necessary to construct a robust calibration model which can then be applied to the analysis of food samples or to monitoring of biotechnological processes. In this work, the PLS technique was chosen for simultaneous determination of glucose, fructose, and sucrose because it was reported to be the most appropriate technique for multivariate sugar analysis.37 The quantitative analysis routine available within the OPUS spectrometer software was used for data evaluation. RESULTS AND DISCUSSION Setup and Characterization of the SIA Manifold. The simple SIA-FTIR system was set up as depicted in Figure 2. A fundamental study of the influence of the sample volume and flow velocity on the SIA peak profile was carried out. For this purpose, a single standard was aspirated in the holding coil and propelled to the detector after flow reversal. A 100 mM sucrose standard was used as a “dye”, integrating the spectral area in the range of 963 and 1182 wavenumbers. The resulting SIA-FTIR-grams are depicted in Figure 3. The S1/2 volume of the SIA manifold was determined to be 21.2 µL. The SIA parameters (injection volume (41) Lene` Mirouze, F. d.; Boulou, J. C.; Dupuy, N.; Meurens, M.; Huvenne, J. P.; Legrand, P. Appl. Spectrosc. 1993, 47, 1187. (42) Bellon-Maurel, V.; Vallat, C.; Goffinet, D. Appl. Spectrosc. 1995, 49, 556. (43) Picque, D.; Lefier, D.; Grappin, R.; Corrieu, G. Anal. Chim. Acta 1993, 279, 67.
228 Analytical Chemistry, Vol. 70, No. 2, January 15, 1998
Figure 2. SIA-FTIR manifold: holding coil, 0.5 mm i.d., 250 cm; S1-3, standard solutions; W, water; mixing coil, 0.3 mm i.d., 30 cm.
Figure 3. Influence of SIA parameters: (A) influence of injection speed; (B) influence of sample volume.
and flow velocity) were chosen such that at least 10 FTIR spectra at different dispersion values ranging from approximately 1 to 10 could be recorded from the tailing edge of the SIA peak. The corresponding values were 75 µL () 3.4S1/2), and 75 µL/min for the injection volume and flow velocity, respectively. With these parameters, a dispersion coefficient of 1.09 was obtained for the peak maximum; furthermore, up to 12 spectra could be recorded within 120 s during the peak decay, covering a dispersion coefficient range from 1.09 to 6.10. As can be seen from the
Table 1. Statistic Data for the Calibration Curves of Single Sugar Determinations sucrose gradient calibration manual glucose fructose sucrose injection calibration range (mmol‚L-1) regression coefficient r slope, b (AUa‚mmol-1‚L) intercept, a (AU) residual std dev, s (AU) std dev of the method, sx0 (mmol‚L-1) mean RSD of the calibration points (%, n ) 6) a
30-200 0.9994 0.150 0.336 0.2517 1.717
30-200 0.9994 0.081 -0.125 0.1413 1.754
15-100 0.9994 0.233 0.065 0.2031 0.8725
10-100 0.9994 0.2325 0.079 0.2641 1.136
0.77
1.61
1.62
1.58
Figure 4. Dispersion profiles for injection sequence: 40 µL of glucose, 40 µL of fructose, and 40 µL of sucrose.
AU, arbitrary units.
preliminary studies, higher sample volumes only give broader peak maxima whereas the tailing edges remain practically identical, hence only reducing sample throughput without giving additional information. In a following study (data not shown), the dispersion profiles obtained when using glucose or fructose as “dye” molecules instead of sucrose were investigated, showing no deviation from the peak profile obtained with sucrose. Single-Standard Calibration. The aim of this study was to verify the feasibility of single-standard calibration using the SIAFTIR system. For this purpose, calibration curves for glucose, fructose, and sucrose were determined using 200 mM sugar standards for glucose and fructose and a 100 mM standard for sucrose and exploiting the controlled-dispersion process in the SIA-FTIR system. As the analytical readout of the single-sugar spectra, the area between 1182 and 963 cm-1 with a straight baseline between the two points was calculated. Each single standard was measured six times, recording eight FTIR spectra at instances corresponding to dispersion coefficients of 1.09, 1.12, 1.18, 1.27, 1.48, 1.72, 2.19, 2.96, 4.17, and 6.10. The results obtained are listed in Table 1. The relative standard deviation of multiple determinations ranged between 0.3 and 3.1% reflecting the high precision achievable with the fully automated SIA-FTIR system. Furthermore, FTIR spectra of a set of manually prepared sucrose standards were recorded and evaluated in the same way as the spectra obtained by the single-standard calibration procedure using the SIA-FTIR system. Comparison (F test) of the precision of the obtained two calibration curves for sucrose analysis showed no significant difference at a 99% confidence level. An evaluation of the calibration curves by regression analysis showed no statistically significant (95% confidence level) variation between the methods.44 Multiple Single-Standard Calibration for Multicomponent Analysis. Two different techniques for automated preparation of calibration solutions using one single standard for each component were investigated. Both techniques, although different in principle, rely on the high precision and long-term stability of the automated SIA-FTIR system and enable a fully automated preparation of a large set of calibration solutions containing the analytes in different well-defined concentrations. In the application shown here, the three sugars generally present in soft drinks, (44) Wegscheider, W. In Accreditation and Quality Assurance in Analytical Chemistry; Gu ¨ nzler, H., Ed.; Springer-Verlag: Berlin, Heidelberg, New York, 1996.
namely, glucose, fructose, and, sucrose have been chosen as model analytes. Calibration via Gradient Mixing. In this technique, single plugs of the standards were aspirated into the holding coil. Flow reversal and transport to the detector produced a flow pattern of chasing dispersion zones which resulted in well-defined mixtures of the introduced standards eluting as a function of time. In order to take advantage of the so-generated mixtures, the dispersion profile of each single standard in an injection sequence must be known. Five different injection sequences (10 µL/10 µL/10 µL; 40 µL/40 µL/40 µL; 40 µL/10 µL/40 µL, 100 µL/10 µL/100 µL and 40 µL/40 µL/100 µL) were chosen and programmed so that they could be executed in a fully automated way. As an example, the three dispersion profiles obtained in sequence 2 (40 µL/40 µL/40 µL) when aspirating sucrose and carrier (instead of glucose and fructose standards) are shown (Figure 4). The other four injection sequences were studied in the same way so that for each sequence the dispersion profile of each of the three introduced standards was known. A complete run (containing all five sequences permutating the order of standard aspiration) resulted in 500 spectra of exactly defined calibration mixtures containing all three standards, ready for PLS calibration. This procedure is expected to be especially suitable for difficult problems with highly correlated spectra as a considerable profit from a large calibration set can be expected. In the system under study, only 32 spectra were taken and used in the PLS calculation, however. The spectra cover a range of from 0 to 120 g/L for each sugar in approximately equal concentrations steps. The feasibility of this technique was confirmed by comparing the dispersion profiles obtained from the calibration procedure using the sucrose standard alone with those calculated form an injection sequence containing all three standards using the established PLS calibration (Figure 4). Calibration via Complete Mixing. In this technique, small plugs of standards and distilled water were aspirated in repeated sequences into the holding coil before flow reversal and transported to the detector. The aspiration sequences were designed so that each plug of a concentrated standard was followed by distilled water to achieve the desired dilution. In order to achieve mixing of the three so-prepared standards, 27 sequences of the three different standards were aspirated. In such a way, each injection delivered a homogeneous mixture of the premixed standards. Eventual inhomogenities were leveled off by accumulation of 128 scans during the passage of the plug through the detector. The piston pump worked well down to 5 µL as the smallest aspirated volume. Each sugar was measured at three Analytical Chemistry, Vol. 70, No. 2, January 15, 1998
229
Table 2. Results for PLS Calibrations method of sample preparation calibration range (g/L) RMSECVa (g/L) for three ranks fructose glucose sucrose determination coeff R2 for calibration with 3 ranks fructose glucose sucrose
gradient mixing
complete mixing
0-120
0-50
1.98 1.07 2.18
0.95 0.76 1.38
99.72 99.93 99.56
99.82 99.89 99.61
a RMSECV, Root-mean-square error of cross validation; RMSECV i (ci - ci)2/l)1/2, where cˆj are predicted sample values, ci are ) (∑ i)1 independently determined sample values, and l is the number of internal samples.
levels (0, 25, 50 g/L), so the total concentration of sugar never exceeded 150 g/L. A total of 32 samples were prepared in the described way and subjected to PLS calibration. Construction of the PLS Calibration Model. Two PLS calibrations (corresponding to both ways of spectra generation) were established with the help of the OPUS software in the following manner: (1) The wavenumber range was determined using correlograms and property-weighted spectra (PWS). Correlograms investigate the relationship of the concentrations of one substance to the absorbances in the mixtures and give normalized values between +1 and -1. High values indicate the existence of a relationship whereas values near zero imply a random relationship. Property-weighted spectra furthermore include the variance of the spectral intensity detecting strongly varying spectral regions. The chosen spectral region should have high correlation values and significant PWS signals. In the case of the three investigated sugars, the spectral range from 971 to 1206 cm-1 was found to fulfill these conditions. (2) For data preprocessing, mean centering proved to give the best performance. Other methods like first and second derivatives or range scaling were tested but did not improve the calculations in terms of root mean standard error of cross validation (RMSECV) values. (3) The optimum order (number of factors) of the PLS model was determined by a “leave one out” calculation using the predicted residual sum of squares (PRESS) function of the OPUS software. One of the calibration standards is removed, and a PLS calibration is performed with the remaining standards. This calibration is then used to predict the property value of the standard left out, and this result is compared with the known concentration value. True mean quadratic predictive error RMSECV is calculated. The optimum number of factors is given by the minimum RMSECV value. Three factors were found to be optimal for each sugar in both calibration models. (4) The calibration was performed with the parameters determined and tested with an independent set of samples. The figures of merit of the so-established PLS calibrations are listened in Table 2. Both PLS calibrations were applied to the analysis of independently prepared test sets containing 19 and 14 samples for the gradient mixing and complete mixing techniques, respectively. The results are displayed in Figure 5, where the 230 Analytical Chemistry, Vol. 70, No. 2, January 15, 1998
Figure 5. Predicted vs real values: a, intercept; b, slope; r, regression coefficient; error of prediction EP (%) ) sx × 100/mean concentration.
predicted values vs the true values are plotted. No evidence for systematic errors and no significant differences between the two investigated methods were found. Application to Real Samples. The applicability of the proposed method to the analysis of real samples was shown by simultaneous determination of glucose, fructose, and sucrose in soft drinks. The concentrations found were compared with those obtained by HPLC analysis using an ion-exchange column and a refractive index detector. Each soft drink was analyzed six times, using the routine outlined for single-component determination. With this method, several spectra at different degrees of dilution were obtained. Therefore, spectra with concentrations for all sugars within the PLS calibration range could be obtained by one single injection. Spectra taken at D ) 1.50 met this requirement for all investigated samples. Tedious manual dilution was thus omitted, the only sample preparation being the removal of carbon dioxide by 5 min ultrasonic treatment. The concentration values obtained by the PLS algorithm were multiplied by the dispersion factor and are listed together with the reference values in Table 3. Differences between the two methods which occur especially at low concentration levels are supposed to result from the fact that pure sugar standards were used for the PLS calibration. By coupling the effluent from the HPLC column to the FTIR spectrometer, additional compounds like taurin (an amino acid often added to so-called “energy drinks”) and ethanol could be identified in some drinks. These compounds, in combination with organic acids and higher carbohydrates often present in soft drinks in the gram per liter scale, also exhibit absorbances in the investigated spectral region. The amount of interference is dependent on the spectral overlap between analytes and interfering
Table 3. Analysis of Real Samples Using the Newly Developed Calibration Methodsa HPLC (g/L)
PLS/complete mixing (g/L) (RSD %, n ) 6)
Almdudler Blaue Sau Icetea Fanta (Pink Grapefruit) Full Speed apple juice
60.2 9.3 68.3 48.8 11.2 16.5
59.6 (2.2) 7.2 (8.7) 70.0 (2.9) 56.3 (2.1) 9.4 (4.3) 14.1 (2.1)
Almdudler Blaue Sau Icetea Fanta (Pink Grapefruit) Full Speed apple juice
16.0 42.5 8.9 31.6 56.7 59.7
Almdudler Blaue Sau Icetea Fanta (Pink Grapefruit) Full Speed apple juice Almdudler Blaue Sau Icetea Fanta (Pink Grapefruit) Full Speed apple juice
PLS/gradient mixing (g/L) (RSD %, n ) 6)
difference between PLS methods (g/L)
difference gradient mixing and HPLC (g/L)
difference complete mixing and HPLC (g/L)
Sucrose 57.7 (2.0) 10.2 (6.8) 67.0 (2.9) 57.0 (2.8) 14.7 (4.0) 16.6 (1.8)
2.0 3.1 3.0 0.7 5.3 2.5
2.5 0.9 1.3 8.2 3.5 0.1
-0.6 -2.1 1.7 7.5 1.8 2.4
15.7 (3.4) 49.1 (1.2) 8.9 (3.8) 36.7 (1.8) 67.3 (1.2) 64.4 (1.1)
Fructose 13.4 (3.6) 48.3 (1.2) 6.2 (3.9) 34.0 (1.9) 64.9 (2.6) 64.1 (1.1)
2.3 0.9 2.6 2.7 2.4 0.3
2.6 5.8 2.7 2.4 8.2 4.4
0.3 6.6 0.0 5.1 10.6 4.7
16.1 57.4 8.8 31.2 56.8 20.8
16.5 (4.5) 57.6 (1.6) 9.4 (3.5) 34.5 (2.9) 62.1 (1.2) 23.1 (1.4)
Glucose 17.2 (4.0) 58.5 (1.6) 10.1 (3.9) 36.2 (3.0) 60.7 (3.2) 23.3 (1.3)
0.6 0.9 0.7 1.7 1.3 0.2
1.1 1.1 1.3 5.0 3.9 2.5
0.4 0.2 0.6 3.3 5.3 2.3
92.3 109.2 86.0 111.6 124.7 97.0
91.9 (4.0) 113.9 (1.5) 88.2 (2.8) 127.6 (2.2) 138.8 (1.0) 101.6 (1.2)
Total Sugar 88.3 (3.5) 117.0 (1.5) 83.3 (3.0) 127.2 (2.1) 140.4 (3.7) 104.0 (1.2)
3.6 3.1 4.9 0.4 1.5 2.4
4.0 7.8 2.7 15.6 15.7 7.0
0.4 4.7 2.2 16.0 14.1 4.6
a RSD, relative standard deviation (%). HPLC results are for a single injection, the standard deviation of the method is 0.108, 0.112, and 0.073 g/L for sucrose, fructose, and glucose, respectively.
substances in the calibration wavenumber range (971-1206 cm-1). In combination with the high content of total sugar, these interferences account for the differences between the two methods which are for all samples maximally 7 (for sucrose and fructose) and 4.5% (for glucose), respectively, in terms of total sugar. The sample throughput in the SIA-FTIR system is 15 samples/ h, including all washing cycles between the samples. Frequently applied methods for the simultaneous determination of glucose, fructose, and sucrose in soft drinks such as HPLC or enzymatic test kits are significantly slower, requiring typically 30 min or more for the analysis of a single sample. SUMMARY AND CONCLUSIONS The hyphenation of sequential injection analysis and Fourier transform infrared spectroscopy presented here was developed to establish and apply partial least-squares calibrations for the simultaneous determination of glucose, fructose, and sucrose in soft drinks using one single standard for each sugar. The developed SIA-FTIR system has the advantage of the highly reproducible flow manipulation and control characteristic of SIA and of the molecule-specific information contained in mid-IR spectra. The determination was based on the direct chemometric evaluation of the mid-IR spectra of the untreated samples and is therefore different from the commonly used enzymatic methods.35 The principles of controlled dispersion and controlled timing, the main features of FIA and SIA, were exploited to create well-defined calibration solutions using one single standard for each component. Two techniques based upon interpenetrating dispersion
zones were introduced. The first one, termed gradient calibration, is a logical extension of the known principle of single-standard calibration. With this technique, spectra of different ratios of the introduced standards are obtained upon one single injection as a function of time. The second technique is based upon complete mixing of diluted standards prior to detection. The ratio of the standards remains the same during the whole SIA peak. Both techniques, although different in principle, share the advantage that once a SIA-FTIR system is built up and the flow pattern is programmed a complete recalibration or a new calibration for another three analytes can be executed from the keyboard, requiring only one single standard for each component. As the calibration procedures are carried out in an automated way, almost free from human failure, the presented techniques are supposed to significantly enhance the performance of multidimensional detectors in general and FTIR spectroscopy especially in simultaneous multicomponent analysis in terms of speed, robustness, and cost efficiency. ACKNOWLEDGMENT This research was sponsored by the Austrian Fonds zur Fo¨rderung der wissenschaftlichen Forschung within the project “µ-FIA-FTIR-systems” P11338O ¨ CH. Received for review April 21, 1997. Accepted October 14, 1997.X AC970415B X
Abstract published in Advance ACS Abstracts, December 1, 1997.
Analytical Chemistry, Vol. 70, No. 2, January 15, 1998
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