Journal of Chromatography A, 1216 (2009) 4868–4876

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Journal of Chromatography A

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Second-order multivariate calibration procedures applied to high-performanceliquid chromatography coupled to fast-scanning fluorescence detection for thedetermination of fluoroquinolones

F. Canada-Canadaa,∗, J.A. Arancibiab,∗, G.M. Escandarb, G.A. Ibanezb, A. Espinosa Mansillaa,A. Munoz de la Penaa, A.C. Olivierib

a Department of Analytical Chemistry, Faculty of Sciences, University of Extremadura, 06071 Badajoz, Spainb Instituto de Química Rosario (CONICET-UNR), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000, Rosario, Argentina

a r t i c l e i n f o

Article history:Received 30 October 2008Received in revised form 26 March 2009Accepted 9 April 2009Available online 16 April 2009

Keywords:High-performance liquid chromatographyFluorescence emission detectionSecond-order multivariate calibrationFluoroquinolones

a b s t r a c t

Different second-order multivariate calibration algorithms, namely parallel factor analysis (PARAFAC),N-dimensional partial least-squares (N-PLS) and multivariate curve resolution-alternating least-squares(MCR-ALS) have been compared for the analysis of four fluoroquinolones in aqueous solutions, includingsome human urine samples (additional four fluoroquinolones were simultaneously determined by uni-variate calibration). Data were measured in a short time with a chromatographic system operating in theisocratic mode. The detection system consisted of a fast-scanning spectrofluorimeter, which allows oneto obtain second-order data matrices containing the fluorescence intensity as a function of retention timeand emission wavelength. The developed approach enabled us to determine eight analytes, some of themwith overlapped profiles, without the necessity of applying an elution gradient, and thus significantlyreducing both the experimental time and complexity. The study was employed for the discussion of thescopes of the applied second-order chemometric tools. The quality of the proposed technique coupled toeach of the evaluated algorithms was assessed on the basis of the figures of merit for the determinationof fluoroquinolones in the analyzed water and urine samples. Univariate calibration of four analytes led

−1

to limits of detection in the range 20–40 ng mL and root mean square errors for the validation samplesin the range 30–60 ng mL−1 (corresponding to relative prediction errors of 3–8%). The ranges for second-order multivariate calibration (using PARAFAC and N-PLS) of the remaining four analytes were: limit of

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detection, 2–8 ng mL−1, ro

. Introduction

Quinolones and fluoroquinolones are an important groupf broad-spectrum synthetic antibacterial agents derived fromalidixic acid which inhibit bacterial growth by interfering withhe bacterial enzyme DNA gyrase [1]. The fluoroquinolones areuinolones with a fluorine atom at position 6 of the quinolone naph-hyridine or benzoaxazine ring system. The introduction of thisroup enhances its antibacterial activity [2]. They are used in botheterinary and human medicine. Their main excretion pathway is

rinary and low amounts are found in plasma [3].

Numerous high-performance liquid chromatographic (HPLC)ethods have been reported describing the analysis of single asell as various combinations of quinolones and fluoroquinolones

∗ Corresponding authors.E-mail addresses: floricanada@gmail.com (F. Canada-Canada),

arancib@fbioyf.unr.edu.ar (J.A. Arancibia).

021-9673/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.chroma.2009.04.033

ean square errors, 3–50 ng mL−1 and relative prediction errors, 1–5%.© 2009 Elsevier B.V. All rights reserved.

in different matrices, such as biological fluids [4–10], pharmaceuti-cals [6,9–11], edible animal products [10,12–18] and environmentalsamples [10,19–21]. Most of the proposed methods listed above useeither UV or fluorescence detection, one of them employs electro-chemistry detection [17] and remaining ones mass spectrometry[18,19]. Recently, capillary electrophoresis methods for analyzingfluoroquinolones in biological and food matrices have also beenreported [22–24].

An ideal chromatographic experiment should lead to perfectseparation of the analytes, which can be directly determined fromthe resulting chromatograms. Sometimes, however, it is not possi-ble to achieve perfect separation, either because of the complexityof the samples or because faster chromatographic runs are pre-ferred. In such situations, overlapping peaks result, and multivariate

data analysis can be used for achieving selectivity by mathematicalmeans. With these multivariate techniques, it is intended to avoidproblems derived from co-elution of compounds, taking advantageof the mathematical separation of the instrumental signals [25].Five recent reviews deal with the application of N-way calibration

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F. Canada-Canada et al. / J. Chr

trategies to analytical problems [26–31], and most of them refero quantitative chromatographic analyses [28–31]. The informationrovided by the second-order signals, together with an adequateecomposition of the generated three-way data sets, enables oneo identify the analyte, even in the presence of interferences not

odelled in the calibration stage. This is known as the second-orderdvantage [25].

The combination of chromatographic data with spectroscopicechniques, such as HPLC with UV–vis diode-array detection (DAD)r fast-scanning luminescence detection (FSLD) is able to yieldpectral-time second-order data, and second-order multivariatealibration can be performed when full selectivity in the chro-atographic separation is not achieved, even in the presence of

nexpected components. Additional benefits are decreasing timesf analysis and less solvent consumption, because gradient pro-rams can be avoided.

Two recent reports deal with the advantages and drawbacksssociated with the combination of multivariate calibration andhromatography [32,28]. Pertinent references on the successfulata processing of HPLC-DAD information are: the determina-ion of low levels of pesticides and phenolic compounds in rivernd wastewater samples [33], three sulphonamide derivatives inorcine kidney [34,35], lidocaine and prilocaine [36], and eightetracycline antibiotics [37,38].

Surprisingly, very few literature works concern HPLC-FSLDecond-order data: the pioneering work of Appellof and David-on using a videofluorimeter as liquid chromatographic detector39], the use of N-dimensional partial least-squares (N-PLS) [40]nd a comparative analysis of N-dimensional partial least-squaresnd parallel factor analysis (PARAFAC) for the determination ofolycyclic aromatic hydrocarbons using fast-scanning fluorescencepectroscopic detection [41] and a method of determination of aixture of 12 naphthalenesulfonates and naphthalenedisulfonates

y N-PLS [42].As regards the HPLC multi-component determination of several

fluoro)quinolones, gradient programs of mobile phases have beenmployed [6,7,9–13,15,16,19–21]. In these cases, the analysis timeignificantly increased compared to those carried out under iso-ratic conditions, as the continuous change on the mobile phaseomposition, which is inherent to the utilization of a gradient mode,mplies a larger stabilization time of the chromatographic system.

Indeed, (fluoro)quinolones show a native fluorescence with twoxcitation bands: a broad one (300–350 nm) and a second one withigher absorptivity, centered at 245–290 nm, as well as a widemission band at 440–500 nm. This opens up the possibility ofoth sensitive and selective determination, as well as the acqui-ition of the excitation or emission spectra as a function of theetention time, data which can be conveniently analyzed usingecond-order calibration models. In this work, the simultaneousetermination of eight (fluoro)quinolones by LC-FSLD under iso-ratic conditions is described, with the advantages of reducing thexperimental time (from ca. 20 to 10 min) and complexity (fromH gradient to isocratic). Furthermore, the use of several second-rder calibration methods is compared for the analysis of fourf them: PARAFAC [43], multivariate curve resolution-alternatingeast-squares (MCR-ALS) [44] and N-PLS [45] (for certain sam-les requiring the help of residual bilinearization, RBL, to achievehe second-order advantage) [46]. The assayed analytes were:ipemidic acid (PIPE), belonging to the first generation of quinolonentibiotics; marbofloxacin (MARBO), ofloxacin (OFLO), norfloxacinNOR), ciprofloxacin (CIPRO), enrofloxacin (ENRO) and lomefloxacin

LOME) belonging to the second generation; and danofloxacinDANO) belonging to the third generation. These analytes were cho-en due to their importance in human medicine (second generationuoroquinolones) and also in veterinary medicine (MARBO, ENRO,nd DANO).

gr. A 1216 (2009) 4868–4876 4869

2. Experimental

2.1. Reagents and solutions

Marbofloxacin (Riedel de Haën), ciprofloxacin, enrofloxacin, cit-ric acid (Fluka), pipemidic acid, ofloxacin, norfloxacin, lomefloxacinand danofloxacin (Sigma), salicylic acid, gentisic acid, and sali-cyluric acid (Aldrich) were obtained from Sigma–Aldrich (Spain).Acetonitrile and methanol, HPLC-grade, and acetic acid were pur-chased from Merck (Spain). Sodium hydroxide was supplied byScharlau (Spain). Ultrapure water provided by a Milli-Q purificationsystem was used. Solvents, artificial and real samples used to per-form the chromatographic technique were filtered through 0.22 �mnylon filter membranes before each injection.

Fluoroquinolone stock solutions (100–500 �g mL−1) were pre-pared dissolving the exact amount of the corresponding compoundin 50 mmol L−1 acetic acid aqueous solution. These solutions werestored at 4 ◦C and were stable for at least a month. Methanol stocksolutions of salicylic acid, gentisic acid and salicyluric acid at con-centrations of 600 �g mL−1 were also prepared.

2.2. Apparatus and software

HPLC was carried out on a Hewlett-Packard Model 1100 LCequipped with quaternary pump, degasser, manual six-way injec-tion valve, and a rapid multi-scan fluorescence detector (Agilent1100 G1321A FLD). A 20.0 �L loop was employed to introducethe sample onto a Nova-Pak C18 column (4 �m average particlesize, 150 mm × 3.9 mm ID, Waters Millipore). Data acquisition andinstrument control were performed on the HP CHEMSTATION forLC software package Rev.A.06.03.

The data matrices were collected with the excitation wavelengthfixed at 290 nm, using emission wavelengths from 355 to 650 nmeach 1 nm, and times from 0 to 11 min each 0.04 min. In this way,the emission-time matrices were of size 296 × 270. These matriceswere then saved in ASCII format, and transferred to a PC based onAMD Athlon dual core microprocessor for subsequent manipula-tion.

The routines employed for second-order multivariatecalibration are all available on the Internet: PARAFAC andN-PLS at http://www.models.life.ku.dk/source/, MCR-ALS athttp://www.ub.es/gesq/mcr/mcr.htm and N-PLS/RBL, includingthe graphical interface of the MVC2 toolbox which implements bothPARAFAC and N-PLS/RBL at http://www.chemometry.com/Index/Links%20and%20downloads/Programs.html. All of them are writ-ten in MATLAB 7.0 [47]. An in-house MATLAB routine was employedfor the alignment of the chromatograms in the time dimension,based on the work of Prazen et al. [48].

2.3. HPLC procedure

The mobile phase was a mixture of acetonitrile, methanol and10 mmol L−1 citrate buffer at pH 3.5 (8.5:10.5:81 v/v/v). The flowrate was maintained at 1.5 mL min−1. Each chromatographic deter-mination, performed under isocratic conditions, was accomplishedin 11 min.

2.4. Calibration and validation samples

Univariate calibration curves (peak area vs. concentration) wereconstructed for the quantification of PIPE, MARBO, CIPRO and LOME.

Solutions for calibration curves were prepared by convenient dilu-tions of the standard solutions (using mobile phase, i.e., a mixtureof acetonitrile, methanol and 10 mmol L−1 of a citrate buffer withpH 3.5, in the proportion 8.5:10.5:81 v/v/v), in order to obtain con-centrations in the range 0.1–2.0 �g mL−1 for each compound.

4870 F. Canada-Canada et al. / J. Chromatogr. A 1216 (2009) 4868–4876

Table 1Calibration concentrations employed for quantitation of the overlapping pairsOFLO–NOR and DANO–ENRO.a.

Sample OFLO NOR DANO ENRO

1 0.105 1.036 0.102 1.0822 1.505 1.036 0.981 1.0823 0.805 0.095 0.540 0.1614 0.805 1.975 0.540 1.9975 0.311 0.371 0.230 0.4306 1.299 0.371 0.847 0.4307 0.311 1.697 0.230 1.7318 1.299 1.697 0.847 1.7319 0.805 1.036 0.540 1.082

10 0.000 1.036 0.000 1.082

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2

2

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2

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Fig. 1. (A) Three-dimensional plot of a typical chromatogram of a sample containing

11 0.805 0.000 0.540 0.000

a All values are given in �g mL−1.

The experimental procedure corresponding to the three-waynalysis for OFLO and NOR was developed preparing a calibra-ion set of 11 samples. Nine of these samples corresponded to theoncentrations provided by a two-factor central composite design,nd the remaining two samples only contained each of the stud-ed analytes at the average calibration concentration. The testedoncentrations were in the ranges 0.0–1.5 and 0.0–2.0 �g mL−1

or OFLO and NOR, respectively. A similar calibration set wasonstructed for DANO–ENRO, in which the tested concentrationsere 0.0–1.0 �g mL−1 (DANO) and 0.0–2.0 �g mL−1 (ENRO). The

pecific calibration concentrations are provided in Table 1, theanges were established on the basis of Ref. [9] and the analysisf the linear fluorescence-concentration range for each analyte.alidation test sets were prepared for both systems, employingoncentrations different than those used for calibration and fol-owing random designs, i.e., choosing the validation concentrationsy generating random numbers, equally distributed within each ofhe calibration ranges for each of the analytes. Calibration and val-dation samples were prepared by measuring appropriate aliquotsf standard solutions, placing them in 25.00 mL volumetric flasksn order to obtain the desired concentrations, and completing tohe mark with mobile phase. All these samples were prepared byriplicate. Injection into the chromatographic system (three injec-ions per sample) was made in random order and in differentays.

.5. Real samples

.5.1. Water samplesTap and river water samples were prepared by spiking them

ith the standard solutions of the eight fluoroquinolones. Eachpiked water (10 mL) was placed in a 25.00 mL flask and mobilehase was added to the mark. In this way, the final concentra-ion of each fluoroquinolone in these samples were in the range.2–1 �g mL−1.

.5.2. Urine samplesUrine samples taken from different healthy voluntaries were

piked with stock solutions of MARBO and also with potentialnterferences (salicylic, salicyluric and gentisic acids). Taking intoccount that the therapeutic urine fluoroquinolone concentra-ion are about 200 �g mL−1 [49], the concentration of MARBO

as varied from 40 to 160 �g mL−1. On the other hand, therine concentrations of salicylic acid (230 �g mL−1), salicyluric acid200 �g mL−1) and gentisic acid (50 �g mL−1) also corresponded tohose found under therapeutic conditions [50]. Volumes of 250 �Lf spiked urine samples were then placed in 25.00 mL volumetricasks, which were filled to the mark with mobile phase.

the studied fluoroquinolones. (B) The corresponding two-dimensional contour plot.Concentrations are as follows (all in mg L−1): PIPE, 1.497; MARBO, 1.497; OFLO, 0.805;NOR, 1.975; CIPRO, 1.503; LOME, 1.352; DANO, 0.540; ENRO, 1.997.

3. Results and discussion

3.1. General considerations

Fig. 1 shows three-dimensional and contour plots of the com-plete landscape of fluorescence intensity as a function of emissionwavelength and retention time for a mixture of the eight studiedfluoroquinolones. As can be seen, there are regions where singleanalytes respond, with almost no interference from other sam-ple components, i.e., in the range of 2–4.5 min and 6–8 min. Theseanalytes can be accurately quantitated by univariate calibrationof chromatographic peak areas vs. analyte concentration. How-ever, there are also regions where the simple univariate approachcannot be applied, because of extensive overlapping. This occursin the regions 4.5–6 and 8–11 min. In addition, unexpected sam-ple components may appear in real samples which may overlapto any of the peaks shown in Fig. 1. In these latter cases, multi-variate calibration (either first-order calibration of emission data

or second-order calibration of emission-retention time data) mayprovide a suitable scenario for the quantitation of both pairs of ana-lytes. Second-order multivariate calibration is known to provideincreased sensitivity and selectivity over any first-order counter-

F. Canada-Canada et al. / J. Chromato

Fig. 2. (A and B) Selected contour plot in the spectral-time regions for OFLO and NOR(as indicated for each analyte) in two different chromatographic runs. (C) Alignedchromatogram (B) with respect to (A). The dotted lines show the correspondingfluorescence signal vs. time plots, and the vertical solid line serves as guide for theeye.

Table 2Analytical parameters for the univariate and multivariate calibrations.

Univariate calibration of PIPE, MARBO, CIPR

PIPE MARBO

Linear range (�g mL−1) 0.136–1.997 0.076Slopeb 51.1 (3) 92.1 (3Interceptb 0.8 (4) −0.6 (4LODc (�g mL−1) 0.04 0.02LOQd (�g mL−1) 0.14 0.08RSDe (%) 3.2 [0.9] 1.1 [0

Second-order calibration of OFLO, NOR, DANO and ENRO

OFLO NOR

A B A B

Sensitivity 520 521 406 407LOD (�g mL−1) 0.004 0.004 0.005 0.005LOQ (�g mL−1) 0.012 0.012 0.015 0.015Linear range (�g mL−1) 0.012–1.505 0.015–1.975

A: calculated for PARAFAC according to Ref. [58].B: calculated for N-PLS according to Ref. [58].

a The number of data for each calibration curve corresponds to five different concentrab The corresponding standard deviations, in the last significant figure, are given in parec Limit of detection calculated according to Ref. [54].d Limit of quantitation calculated as (10/3.3) × LOD.e Relative standard deviation for the concentrations (�g mL−1) given in square brackets

gr. A 1216 (2009) 4868–4876 4871

part [51]. More interestingly, in the presence of unexpected samplecomponents, which may overlap to any of the peaks shown inFig. 1, only second-order calibration using suitable algorithms canbe applied, because of the need of achieving the second-orderadvantage.

Adequate data processing algorithms for second-order data,which can in principle be applied to cases where selectivity is notcomplete, are: (1) PARAFAC, (2) N-PLS, with or without RBL depend-ing on the presence or absence of sample interference, and (3)MCR-ALS. It is important to note that PARAFAC requires that thedata show the property of trilinearity, which can be lost if chro-matographic retention times are not exactly reproducible. In orderto restore the trilinearity which is lost by lack of reproducibilityin retention times, several procedures are available to align thelatter, causing all chromatograms for a set of samples to have iden-tical retention time profiles for a given analyte [48]. In the caseof our HPLC-FSLD data, Fig. 2A and B shows the chromatographicpeaks for OFLO and NOR for two different runs (correspondingto two different samples, hence having different contours), andFig. 2C the result of retention time alignment. The latter wasaccomplished on the basis of Ref. [48], whose basic philosophy isthe singular value decomposition analysis of an augmented datamatrix composed of two HPLC-FSLD matrices: one used as refer-ence and a given test data matrix. The latter is digitally movedwith respect to the former one, until a minimum in the so-calledresidual variance is obtained, which indicates that the number ofsignificant principal components (the mathematical pseudo-rankof the augmented matrix) equals the number of chemical con-stituents.

Besides chromatographic alignment, some signal pre-processing was needed before PARAFAC analysis, for example,in the presence of a blank background signal, which is significantlyreduced if mean-centering is applied. Unwanted constant signalsfrom the background can be removed from all data matrices bydigital subtraction of the mean calibration matrix, as described, forexample, in Ref. [52]. Finally, restrictions were applied during the

least-squares PARAFAC fitting, i.e., non-negativity in the spectraland retention time profiles.

As regards N-PLS, in principle this algorithm does not require tri-linearity to be strictly fulfilled. However, we have found that much

O and LOMEa

CIPRO LOME

–1.996 0.091–2.000 0.121–1.797) 347 (1) 315 (2)) −8 (2) −8 (2)

0.03 0.040.09 0.12

.9] 1.1 [1.1] 2.7 [0.7]

DANO ENRO

A B A B

2749 2736 401 4020.002 0.002 0.008 0.0070.006 0.006 0.024 0.0210.006–0.981 0.024–1.997

tion levels, with three replicates for each level.ntheses.

.

4872 F. Canada-Canada et al. / J. Chromatogr. A 1216 (2009) 4868–4876

Table 3Predicted concentrations (�g mL−1) for fluoroquinolones in validation samples.

PIPE MARBO OFLO NOR CIPRO LOME DANO ENRO

Taken 1.955 1.544 0.388 0 1.454 0.338 0.514 0.233

Found

Univariate calibration 1.98 (3) 1.576 (7) 1.51 (2) 0.36 (2)PARAFAC 0.386 (7) −0.005 (3) 0.513 (2) 0.21 (3)N-PLS 0.389 (4) −0.006 (3) 0.515 (3) 0.22 (3)MCR-ALS 0.405 (2) 0.018 (5) 0.465 (1) 0.48 (6)

Taken 0.541 0.745 0.278 1.183 0.444 0.538 0.146 1.414

Found

Univariate calibration 0.540 (6) 0.747 (4) 0.443 (8) 0.547 (5)PARAFAC 0.234 (5) 1.17 (2) 0.147 (4) 1.409 (7)N-PLS 0.233 (4) 1.18 (3) 0.147 (3) 1.387 (7)MCR-ALS 0.251 (2) 1.16 (6) 0.265 (2) 1.18 (8)

Taken 0.126 0.119 0.578 1.877 0.182 0.722 0.818 0

Found

Univariate calibration 0.15 (2) 0.120 (3) 0.190 (9) 0.74 (2)PARAFAC 0.581 (8) 1.87 (5) 0.82 (1) 0.00 (9)N-PLS 0.574 (7) 1.82 (8) 0.83 (2) 0.0 (1)MCR-ALS 0.578 (7) 1.85 (6) 0.713 (4) 0.58 (8)

Taken 0.341 0.871 0.736 0.130 1.050 1.344 0.374 0.582Univariate calibration 0.36 (1) 0.905 (8) 1.11 (1) 1.39 (1)PARAFAC 0.74 (2) 0.123 (2) 0.376 (6) 0.60 (4)N-PLS 0.73 (1) 0.124 (2) 0.384 (6) 0.59 (4)MCR-ALS 0.735 (6) 0.156 (4) 0.391 (4) 0.8 (2)

Taken 1.414 1.901 1.489 1.428 1.220 0.115 0.730 1.231

Found

Univariate calibration 1.46 (1) 1.95 (2) 1.28 (2) 0.149 (2)PARAFAC 1.48 (1) 1.42 (2) 0.732 (4) 1.22 (5)N-PLS 1.47 (2) 1.42 (2) 0.729 (4) 1.23 (5)MCR-ALS 1.460 (7) 1.39 (2) 0.739 (7) 1.36 (2)

Taken 0.957 0.855 0.792 1.672 0.800 1.498 0.648 1.060

Found

Univariate calibration 0.96 (2) 0.856 (8) 0.80 (2) 1.52 (6)PARAFAC 0.790 (6) 1.660 (9) 0.652 (9) 0.99 (3)N-PLS 0.785 (5) 1.657 (8) 0.652 (8) 1.00 (3)MCR-ALS 0.788 (7) 1.64 (1) 0.65 (1) 1.18 (8)

Taken 0.749 1.148 0.119 0.938 0.590 0.518 0.934 0.424

Found

Univariate calibration 0.72 (1) 1.15 (2) 0.59 (2) 0.521 (7)PARAFAC 0.120 (4) 0.94 (2) 0.934 (7) 0.30 (2)N-PLS 0.115 (9) 0.95 (2) 0.934 (6) 0.35 (3)MCR-ALS 0.142 (2) 0.93 (1) 0.835 (4) 0.85 (5)

Taken 0.499 0.531 0 0.514 0.929 0.991 0.251 1.620

Found

Univariate calibration 0.51 (2) 0.538 (5) 0.93 (2) 1.00 (5)PARAFAC 0.000 (2) 0.518 (3) 0.252 (4) 1.61 (3)N-PLS 0.003 (1) 0.513 (3) 0.252 (4) 1.59 (3)MCR-ALS 0.016 (3) 0.53 (1) 0.378 (2) 1.36 (6)

Taken 1.173 0.364 1.140 0.816 1.155 1.728 0.555 1.914

Found

Univariate calibration 1.18 (2) 0.354 (6) 1.19 (2) 1.95 (6)PARAFAC 1.133 (7) 0.79 (1) 0.56 (1) 1.87 (4)N-PLS 1.132 (9) 0.79 (1) 0.552 (9) 1.86 (4)MCR-ALS 1.110 (7) 0.817 (3) 0.61 (7) 1.8 (1)

Taken 1.290 1.188 0.871 0.359 1.923 1.144 0 0.874

F

Univariate calibration 1.28 (4) 1.201 (4) 1.93 (3) 1.28 (9)(5)(6)1)

bl

lmcpcrrapopf

oundPARAFAC 0.882N-PLS 0.886MCR-ALS 0.87 (

etter results are obtained after matrix alignment, and hence thisatter procedure was applied before N-PLS processing.

Finally, MCR-ALS has been designed to take proper account ofack of reproducibility in the temporal direction, by resorting to

atrix augmentation in the latter dimension. In principle, thishemometric procedure would allow to avoid the most complexre-processing step, which is the time alignment (mean-centeringannot be applied because it is not compatible with non-negativityestrictions which are usually employed in MCR-ALS). This algo-ithm was thus applied in order to check whether chromatographic

lignment could be avoided. The results were satisfactory for oneair of analytes (OFLO and NOR), but discouraging for the remainingne (MARBO and ENRO). In the latter case, strong spectral overlap-ing in the emission dimension occurs, which may be problematicor proper resolution with MCR-ALS.

0.340 (5) −0.007 (5) 0.924 (7)0.343 (5) 0.0001 (1) 0.892 (6)0.38 (1) 0.087 (2) 0.85 (3)

Specific details on the determination of each analyte are pro-vided below.

3.2. Univariate determination of PIPE, MARBO, CIPRO and LOME

As discussed above, PIPE, MARBO, CIPRO and LOME can be quan-titated using univariate calibration based on the correspondingpeak areas, because they do not appear overlapped with other sam-ple components. Table 2 shows the figures of merit corresponding tothe univariate calibration curves for these analytes (see Refs. [53,54]

for their definitions). As to the concentration ranges to be studiedfor each analyte, we based our selection on a previous work on thesubject [9] and also on the consideration of the linear concentra-tion ranges for each studied component. The linearity for the curveswas tested applying the F-test recommended by IUPAC [53], which

F. Canada-Canada et al. / J. Chromatogr. A 1216 (2009) 4868–4876 4873

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Table 4Root mean square errors (RMSEs) and relative error of predictions (REPs) for valida-tion samples.

Univariate calibration PARAFAC N-PLS MCR-ALS

PIPERMSE 0.027REP 3.21

MARBORMSE 0.028REP 3.28

OFLORMSE 0.015 0.017 0.019REP 2.04 2.29 2.58

NORRMSE 0.012 0.022 0.023REP 1.33 2.30 2.44

CIPRORMSE 0.039REP 4.51

LOMERMSE 0.059REP 7.60

DANORMSE 0.003 0.005 0.080REP 0.66 1.06 16.4

ENRORMSE 0.051 0.038 0.28REP 5.19 3.89 28.7

RMSE (�g mL−1) =

[1

I∑(cact − cpred)2

]1/2

where I is the number of prediction

tion was performed in the wavelength emission ranges 384–589

TP

RT

F

TT

F

TT

F

ig. 3. (A) Emission spectra of OFLO (solid line) and NOR (dashed line). (B) Emissionpectra of DANO (solid line) and ENRO (dashed line). All spectra were registered athe excitation wavelength of 290 nm, with intensities normalized to unit length.

ompares the variance corresponding to the regression residualsith the mean variance of the replicates for the calibration points.

he obtained figures of merit (Table 2) are satisfactory, with detec-ion limits on the order of 20–40 ng mL−1, and RSDs of 1–3%. The

able 5redicted values (�g mL−1) for the determination of fluoroquinolones in water samples.

PIPE MARBO OFLO

iver waterb

aken 0.141 0.190 0.316

ounda

Univariate calibration 0.143 (3) 0.206 (3)PARAFAC 0.331 (4)N-PLS 0.336 (4)MCR-ALS 0.37 (6)

ap waterc

aken 0.216 0.158 0.139

ounda

Univariate calibration 0.206 (7) 0.160 (2)PARAFAC 0.142 (2)N-PLS 0.148 (1)MCR-ALS 0.157 (7)

ap waterd

aken 0.412 0.218 0.464

ounda

Univariate calibration 0.41 (1) 0.213 (5)PARAFAC 0.47 (1)N-PLS 0.48 (1)MCR-ALS 0.478 (3)

a Experimental standard deviation, in the last significant figure, from triplicate sampleb Guadiana River (Extremadura, Spain).c From Badajoz City (Extremadura, Spain).d From Guarena (Extremadura, Spain).

I

1

samples, cact and cpred are the actual and predicted concentrations, respectively;REP (%) = 100 × RMSE/c, where c is the mean calibration concentration.

predicted concentrations for these four fluoroquinolones in the val-idation samples and the corresponding statistical results are shownin Tables 3 and 4, respectively.

3.3. Multivariate determination of OFLO, NOR, DANO and ENRO

The mutual overlap in the OFLO–NOR and DANO–ENRO pairscan be distinguished in Fig. 1, a fact which hinders their directdetermination by univariate calibration. Thus second-order calibra-

and 379–544 nm and retention times of 4.5–5.9 and 7.9–11 min forOFLO–NOR and DANO–ENRO pairs, respectively. For this purpose,calibration samples were prepared following a central compos-ite design, plus additional samples with each analyte at its mean

NOR CIPRO LOME DANO ENRO

0.286 0.303 0.307 0.277 0.3740.366 (5) 0.335 (5)

0.299 (3) 0.309 (3) 0.392 (7)0.297 (3) 0.320 (3) 0.386 (5)0.32 (1) 0.323 (1) 0.485 (7)

0.163 0.404 0.269 0.196 0.3120.459 (8) 0.30 (1)

0.167 (2) 0.210 (2) 0.32 (1)0.163 (2) 0.223 (2) 0.311 (6)0.193 (5) 0.229 (2) 0.38 (2)

0.551 0.485 0 0.380 0.4990.49 (2) –

0.547 (7) 0.399 (8) 0.503 (9)0.544 (8) 0.407 (8) 0.500 (9)0.572 (3) 0.408 (4) 0.57 (3)

analysis between parenthesis.

4 omatogr. A 1216 (2009) 4868–4876

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874 F. Canada-Canada et al. / J. Chr

alibration concentration. Central composite designs are usuallymployed in multivariate calibration of mixtures in order to providesuitably representative set of training samples, minimizing the

umber of required experiments [55]. They consist of a two-levelull factorial design with 2k points (k is the number of chemical com-onents), a star design with 2k points and a central point. The setmployed for validation contained the analytes at concentrationshich were selected at random from the corresponding calibration

anges, a methodology which is commonly used for generating testamples having concentrations different than those employed foralibration [52].

The number of components when PARAFAC was applied waselected by the so-called core consistency analysis (CORCONDIA)56,57]. The estimated number of components for both pairs usinghis technique was 2, which can be justified taking into accounthe presence of two different analyte signals in the selected region.mploying PARAFAC with two components and applying the pre-rocessing discussed above, figures of merit were estimated aseported in Table 2 for the four overlapping analytes. The valuesere computed according to Ref. [58].

With the purpose of estimating the number of optimum latentariables for N-PLS, leave-one-sample-out cross-validation waserformed [59]. The optimum number of factors is estimatedy calculating the ratios F(A) = PRESS(A < A*)/PRESS(A), whereRESS = �(ci,act − ci,pred)2, A is a trial number of factors, A* corre-ponds to the minimum PRESS, and ci,act and ci,pred are the actualnd predicted concentrations for the ith sample left out of the cal-bration during cross-validation, respectively. Then, the numberf factors leading to a probability of less than 75% that F > 1 areelected. This analysis led to the conclusion that the latter num-er is 2 which, as in the PARAFAC case, can be justified taking intoccount the presence of two different signals in the selected region.igures of merit were estimated as reported in Table 2 for the fourverlapping analytes [58].

Finally, for the application of MCR-ALS, the number of com-onents was estimated by principal component analysis of theugmented data matrices, composed of each test sample data andll calibration data matrix for each of the studied pair of analytes.his algorithm does also require the introduction of estimated spec-ral profiles for each sample component. This was done by resortingo the so-called purest variables furnished by SIMPLISMA (simplenteractive self-modelling mixture analysis) methodology [60], a

ultivariate curve resolution algorithm which extracts pure com-onent spectra from a series of spectra of mixtures of varyingomposition. During the ALS fitting, restrictions of non-negativityn spectral profiles and non-negativity and unimodality in timerofiles were imposed.

Table 3 shows the nominal and predicted concentration resultsorresponding to the application of PARAFAC, N-PLS and MCR-ALSo the same test of validation samples, and Table 4 displays thetatistical indicators (root mean square errors and relative errorsf predictions) corresponding to the predictions of Table 3. Inoth tables it can be appreciated that the concentrations for theFLO–NOR pair seem to be well predicted by the three algorithms. If

he elliptical joint confidence region (EJCR) is analyzed for the slopend intercept of the plot of found vs. nominal concentrations [61],e conclude that all the ellipses (at 95% confidence level) include

he theoretically expected point (1, 0), except in the case of MCR-ALSnalysis of NOR.

PARAFAC and N-PLS predictions for the second pair DANO–ENROre in good agreement with the nominal concentration values

Tables 3 and 4), a fact which is confirmed by the EJCR analysisor accuracy, i.e., the elliptical joint confidence region for slope andntercept contains the ideal (1, 0) point. On the other hand, MCR-LS results for this system are rather poor (Tables 3 and 4, the EJCRtudy indicates that the ellipse for MCR-ALS was significantly larger

Fig. 4. Two-dimensional contour plots. River water (A). Standard mixture sample offluoroquinolones (B). Spiked river water sample with fluoroquinolones. Concentra-tions are as follows (all in mg L−1): PIPE, 0.768; MARBO, 1.152; OFLO, 0.660; NOR,0.672; CIPRO, 1.398; LOME, 0.711; DANO, 0.121; ENRO, 0.610.

than those for PARAFAC and N-PLS). The significance of the com-parison of RMSEP (root mean square error of prediction) values forboth DANO and ENRO by using PARAFAC and MCR in the validationsamples was tested using the randomization approach describedby van der Voet [62]. The obtained probability values (0.0015 and0.0025 for DANO and ENRO, respectively), which are lower than0.05, indicate significant differences between both employed algo-

rithms. This fact could be ascribed to the strong degree of spectraloverlapping in the fluorescence emission dimension, in compar-ison with the OFLO–NOR system (Fig. 3). In fact, the correlationcoefficients for the OFLO–NOR overlapping is 0.583 vs. 0.994 forthe DANO–ENRO system.

F. Canada-Canada et al. / J. Chromatogr. A 1216 (2009) 4868–4876 4875

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Table 6Predicted values for the determination of MARBO in urine samples.a.

Nominal (�g mL−1) PARAFAC N-PLS/RBL MCR-ALSFound (�g mL−1) Found (�g mL−1) Found (�g mL−1)

0.400 0.34 (1) 0.34 (1) 0.450 (5)0.697 0.600 (4) 0.600 (4) 0.706 (9)0.720 0.640 (2) 0.633 (3) 0.900 (9)1.200 1.10 (5) 1.10 (5) 1.37 (8)1.600 1.43 (3) 1.42 (3) 1.80 (5)0.800 0.70 (1) 0.70 (1) 0.880 (4)

RMSE (�g mL−1)b 0.10 0.11 0.13REP (%)b 12 13 15

ig. 5. Chromatographic selected region of a urine sample containing salicylic acidsolid lines) and MARBO (dotted lines). CSA = 2.30 �g mL−1, CMARBO = 1.49 �g mL−1.

.4. Real samples

With the purpose of testing the applicability of the investigatedethods, the analysis of different kinds of waters as performed.

ecause these samples did not contain fluoroquinolones, they werepiked with analytes and a recovery study was carried out. Althoughhese drugs are frequently found in waters at concentrations below× 10−3 �g mL−1, higher levels (>0.1 �g mL−1) can be measured inaters near discharges from hospitals and drug manufacturers [63].

hese latter levels can be directly investigated with the proposedechnique without the necessity of using a larger sample injectionr a pre-concentration step. We have taken samples from a riverGuadiana) on its way through a city of 200,000 inhabitants (Bada-oz City, Spain). In this river, untreated water is discharged fromifferent backgrounds (urban, industry, hospitals, factories, etc.), so

t is likely to present potential interferences. On the other hand, theap water could also contain unexpected compounds due to negli-ence on the stages of purification. The results obtained under ourxperimental conditions are shown in Table 5. All analytical resultsre reasonably good (including the analysis of the correspondinglliptical joint regions), except those for the DANO–ENRO system,hose raw data were processed by MCR-ALS. In Fig. 4 the contourlots of the complete landscape of fluorescence intensity, corre-ponding to river water (A), a standard mixture of the eight studieduoroquinolones (B) and a river water sample spiked with the sameoncentration of fluoroquinolones (C), are showed.

Another real matrix analyzed in the present work was humanrine. It was found that a normal urine matrix does not pro-uce interference in the fluoroquinolone determination when theresent technique is applied to this type of samples. However, whenhe determination is carried out in the presence of compoundshich may be found in urine of patients administered with aspirin,

uch as salicylic acid and their metabolites (i.e., salicyluric and gen-isic acids), an interference may be observed from these fluorescentomponents. However, the corresponding retention times are suchhat only salicylic acid interferes in the time window correspondingo MARBO (retention times: gentisic acid, 1.92 min, salicyluric acid,.28 min, salicylic acid, 3.68 min, MARBO, 3.88 min). The overlap-

ing between the signals for this fluoroquinolone and salicylic acidan be observed in Fig. 5. Therefore, in this case MARBO requireschievement of the second-order advantage in order to be accu-ately quantitated. This can be done employing any of the threelgorithms discussed above, i.e., PARAFAC and N-PLS/RBL using

a Experimental standard deviation, in the last significant figure, from triplicatesample analysis between parenthesis.

b See Table 4.

aligned matrix data, and MCR-ALS using raw matrix data. In thiscase, the selected wavelength emission and retention time rangeswere 379–564 and 3.3–4.7 min, respectively. Table 6 shows theobtained results, which are seen to be satisfactory in view of thecomplexity of the analyzed samples.

4. Conclusions

The combination of emission fluorescence–retention timematrices with selected second-order algorithms allowed the suc-cessful determination of fluoroquinolones in samples with andwithout interferences. Both PARAFAC and N-PLS/RBL algorithmsyield good results for all the investigated systems where signifi-cantly signal overlap is detected, provided they are fed with suitablypre-processed data, particularly in what concerns the alignmentof the chromatographic profiles in the retention time dimension.MCR-ALS did also produce reasonably accurate results in one ofthe analyzed systems, even if raw data are processed. However,extensive spectral overlapping seriously affects the MCR-ALS pre-dictions in the remaining binary system when no alignment orpre-processing is carried out.

Acknowledgements

The authors gratefully acknowledge Agencia Espanola de Coop-eración Internacional (AECI), Project A/6576/06, Ministerio deCiencia e Innovacion of Spain (Project CTQ 2008-06657/BQU), Agen-cia Nacional de Promoción Científica y Tecnológica (Project PAE22204), Consejo Nacional de Investigaciones Científicas y Técnicas,and Universidad Nacional de Rosario for financially supporting thiswork. Profs. Rasmus Bro and Romà Tauler are thanked for provid-ing access to the MATLAB routines for PARAFAC and N-PLS (Bro) andMCR-ALS (Tauler) on the Internet.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.chroma.2009.04.033.

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