Fluoroquinolone antibiotic determination in bovine milk using capillary liquid chromatography with diode array and mass spectrometry detection

Journal of Food Composition and Analysis 28 (2012) 99–106

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Original Research Article

Fluoroquinolone antibiotic determination in bovine milk using capillary liquid chromatography with diode array and mass spectrometry detection J.A. Ruiz-Viceo, N. Rosales-Conrado, V. Guille´n-Casla, L.V. Pe´rez-Arribas, M.E. Leo´n-Gonza´lez *, L.M. Polo-Dı´ez Analytical Chemistry Department, Faculty of Chemistry, Complutense University of Madrid, E-28040 Madrid, Spain

A R T I C L E I N F O

A B S T R A C T

Article history: Received 22 February 2012 Received in revised form 4 August 2012 Accepted 12 August 2012

A fast and sensitive method has been developed for the determination of five fluoroquinolones and a quinolone in commercial bovine milk by capillary-LC–DAD–MS after simple extraction method based on milk deproteinization with 15% trichloroacetic acid. Separation was carried out in a Zorbax SB-C18 column (150 mm  0.5 mm, 5 mm) using a mixture of acetonitrile–5 mM ammonium formate (pH 3.7) as mobile phase in gradient elution mode. The sample was prepared in a 5 mM aqueous ammonium formate buffer for focusing purposes and 20 mL were injected. Flow rate and temperature were set at 20 mL min1 and 25 8C, respectively. Method validation was performed according to the European Commission Decision 657/2002/EC. Sample detection limits were between 2.8 and 25 mg kg1, and good linearity was observed up to 250 mg kg1 for all analytes. Acceptable and constant recoveries of 96% for enrofloxacin, 77% for its metabolite ciprofloxacin, and 64% for flumequine, with RSDs (n = 4) lower than 8%, were obtained at the 100 mg kg1 maximum level permitted in milk. Recoveries between 70% and 83% were obtained when difloxacin, sarafloxacin and ofloxacin (not allowed in animals that produce milk for human consumption) were added to milk samples at levels in the range 75–150 mg kg1. Decision limit (CCa), detection capability (CCb), repeatability, within-laboratory reproducibility and robustness were compliant with European Union regulations. ß 2012 Elsevier Inc. All rights reserved.

Keywords: Quinolones Capillary liquid chromatography Mass spectrometry On column focusing techniques Milk Method validation Veterinary residues in food Agricultural practices and nutrition Food safety Food analysis Food composition

1. Introduction Quinolones (Qns) are a family of antibacterial agents used in human and veterinary medicine. The 6-fluorinated piperazinyl derivatives, fluoroquinolones (FQs), have a wide range of antibacterial activities and have been increasingly used in veterinary medicine because of their effectiveness in treating bacteria infections (Tang et al., 2009). Veterinary drugs have become an integral part of livestock production and play an important role in maintaining animal welfare, mainly for disease prevention, curing infections, controlling the risk of transmitting disease to humans and increasing the productive capacity of animals. The widespread use of quinolones in agriculture has resulted in the potential presence of residues of these compounds in foodstuffs of animal origin. The use of fluoroquinolones in lactating breeding animals may leave residues in milk and tissues.

* Corresponding author at: Analytical Chemistry Department, Faculty of Chemistry, UCM, Avda. Complutense s/n, E-28040 Madrid, Spain. Tel.: +34 91 394 4196; fax: +34 91 394 43 29. E-mail address: [email protected] (M.E. Leo´n-Gonza´lez). 0889-1575/$ – see front matter ß 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jfca.2012.08.003

Therefore, animal food may be a potential hazard for consumers, causing allergic reaction and also leading to the emergence of drug resistant bacteria. Considering the food safety, some antibiotics are prohibited for breeding livestock by a number of countries, such as China, Australia, Canada, USA and EU member states (Gao et al., 2011). Meanwhile, to protect the health of consumers and minimize the potential harm, maximum residue limits (MRLs) of the antibiotic have been established by the European Union (Commission Decision 37/2010/CE) (European Commission Regulation, 2010) and USA Food and Drug Administration (USDA, 2012). In the case of milk, these MRLs range from 30 mg kg1 for danofloxacin to 100 mg kg1 for the sum of enrofloxacin and its metabolite ciprofloxacin; therefore, the determination of these compounds at trace levels requires sensitive analytical methods to comply with the current legislation. Many analytical methods for the determination of fluoroquinolone residues in food using different extraction procedures, clean-up conditions and detection principles are available. The most commonly employed methods to determine Qns are based on liquid chromatography with different detection techniques, such as UV (Gao et al., 2011), electrochemical detection (Rodrı´guez Ca´ceres et al., 2010), fluorescence (Cho et al., 2010; Herrera-Herrera et al.,

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2009; Lombardo-Agu¨ı´ et al., 2011; Rambla-Alegre et al., 2011; Rodrı´guez et al., 2010; Zhen et al., 2010), mass spectrometry (MS) (Bogialli et al., 2009; Krebber et al., 2009; Tang et al., 2009) and chemiluminescence (Li et al., 2011). The main problem concerning the determination of residues of Qns in food derived from animals is sample treatment. Different procedures have been proposed to improve the cleanup and pre-concentration step of the antibiotics from milk samples. Thus, liquid-phase extraction (Cho et al., 2010), Quick, Easy, Cheap Effective, Rugged and Safe (QuEChERS) methodology (Lombardo-Agu¨ı´ et al., 2011), molecular imprinted-SPE (Lombardo-Agu¨ı´ et al., 2011; Zhen et al., 2010), solid phase extraction (SPE) (Herrera-Herrera et al., 2009; Tang et al., 2009), and ionic liquid-based homogeneous liquid–liquid microextraction (Gao et al., 2011) were used. Moreover, some authors studied the improvement of the separation of Qns in milk from interfering matrix compounds using ionic liquids (HerreraHerrera et al., 2009) or surfactants (Rambla-Alegre et al., 2011) as mobile phase additives and in sample preparation. The high specificity and sensitivity of MS significantly reduced problems with interfering matrix compounds. Nevertheless, since the MS detector is matrix sensitive, a clean-up is usually required before the chromatographic determination. SPE (Tang et al., 2009) is commonly used for the separation of matrix compounds. This type of clean-up is a multi-step process that requires the conditioning of the SPE cartridge, extract addition, rinsing of the cartridge and elution of the analytes. Afterwards the eluate is usually evaporated and dissolved in an appropriate solvent. Another strategy to optimize this time-consuming step is the use of turbulent flow chromatography (TFC) coupled to MS (Krebber et al., 2009). In this technique, sample preparation is made by loading the sample to an extraction column using a high flow rate of typically 4–6 mL min1. However, high volumes of organic solvents are required for the clean-up step. On the other hand, LC–MS is a powerful technique to determine Qns in complex samples, although low flow rates are usually needed for electrospray ionization (ESI). Capillary liquid chromatography (cLC) handles very low flow rates, which are compatible with MS detection. In reversed-phase mode, sensitivity of cLC can be increased by making use of focusing techniques, injecting large solution volumes with very low elution strength (low organic solvent composition) (Leo´n-Gonza´lez et al., 2010). These techniques allow the analyte preconcentration in a small plug at the head of the capillary column, thus minimizing the broadening of chromatographic peaks. Furthermore, cLC shows several advantages over conventional HPLC, such as better resolution, lower detection limits and lower solvent consumption, which makes it more environmentally friendly (Leo´n-Gonza´lez et al., 2010; Lombardo-Agu¨ı´ et al., 2011). In this paper, a sensitive method based on cLC–DAD–MS has been developed for the determination of six Qns, using a simple and fast sample preparation procedure combined with the injection of large volumes of sample. These veterinary drugs have been selected considering that some of them, such as difloxacin (DIF), sarafloxacin (SARA) and ofloxacin (OFL), are not allowed in animals that produce milk for human consumption. For the rest of fluoroquinolones, MRLs have been established by the EU legislation (European Union Commission Regulation, 2010). Optimization of cLC injection conditions has been carried out using experimental design methodologies based on response surface analysis (RSA) (Almeida et al., 2008). This method has been applied to determine Qns in commercial bovine milk, and it was evaluated according to the guidelines established in the 2002/657/ EC European Union Commission Decision (European Union Commission Decision, 2002; Ortiz et al., 2010).

2. Materials and methods 2.1. Chemicals All reagents and solvents were of analytical grade and purified water from a Milli-Q system was used (Millipore, Bedford, MA, USA). Methanol and acetonitrile of gradient-HPLC grade, formic acid, trichloroacetic acid (Scharlau, Barcelona, Spain), ammonium formate and ammonium hydroxide (Fluka–Sigma–Aldrich, St. Louis, MO, USA) were employed. Quinolone standards were used as received from commercial sources. Ofloxacin (OFL, 99% pure) and flumeqine (FLU, 99% pure) were supplied by Sigma (Sigma–Aldrich, Seelze, Germany). Ciprofloxacin (CIP, 99.9% pure), enrofloxacin (ENR, 99.1% pure), sarafloxacin (SAR, 97.2% pure) and difloxacin (DIF, 98.4% pure) were provided by Fluka (Sigma–Aldrich, St. Louis, MO, USA). Chemical structures of studied Qns are shown in Fig. 1. Analyte stock solutions of OFL, FLU and ENR (200 mg L1) were prepared in acetonitrile. Stock solutions of SAR and DIF (200 mg L1) were prepared in methanol, CIP stock solution (200 mg L1) was prepared by initial dissolution in 100 mL of 1 M sodium hydroxide and further dilution to 25 mL with methanol. All solutions were stored in the dark at 4 8C for three months maximum. Fresh working standard solutions were prepared daily in mobile phase or in buffer solution by suitable dilution of stock solutions as required. 2.2. Equipment and software Chromatographic analysis by cLC was performed by an Agilent cLC instrument Mod. 1100 Series (Agilent Technologies, Madrid, Spain) which was equipped with a G1376A binary capillary pump, a G1379A degasser and a G1315B diode array detector (DAD) (500 nL, 10 nm path-length). Mass spectrometry detection was carried out using an Agilent 6120 Quadrupole LC–MS. The instrument was operated using an ESI source in positive mode and a microelectrospray nebulizer. Nitrogen was used as both drying and nebulizer gas. The drying gas temperature was set at 325 8C and the gas flow rate at 0.80 L min1. The nebulizer pressure was fixed at 225 kPa, and the capillary ESI voltage at 3.0 kV. Data acquisition and processing were made using the Agilent Chemstation software package for Microsoft Windows. Several external stainless steel loops with volumes of 5, 10 and 20 mL were placed into a manual Rheodyne1 injection valve (Cotati, CA, USA). Reversed-phase separations were made on a Zorbax SB-C18 column (150 mm  0.5 mm, 5 mm) supplied by Agilent (Agilent Technologies, Madrid, Spain). It was thermostated during the chromatographic run by employing a MISTRAL programmable oven supplied by Spark Holland (Emmem, The Netherlands). A Unicen centrifuge model 21 supplied by Ortoalresa (Madrid, Spain) was used for protein centrifugations. The software package Statgraphics Plus version 5.0, running under Windows XP, was employed for the application of statistical tools. 2.3. Procedures 2.3.1. cLC–DAD–MS method Separation was carried out using a mixture of acetonitrile– 5 mM ammonium formate buffer at pH 3.7 as the mobile phase. The multistep gradient used was acetonitrile–ammonium formate buffer 5 mM 18:82 (v/v) for 8 min, then a linear increase to 70% acetonitrile for another 5 min, and a final isocratic step at 70% acetonitrile till the end of the chromatogram. Flow rate and temperature were set at 20 mL min1 and 25 8C, respectively.

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Fig. 1. Structures of the studied quinolones and pKa values.

Wavelength for the UV-diode array detection was fixed at 250 nm for FLU, 283 nm for CIP, ENR, SAR, DIF and 300 nm for OFL. Regarding MS detection, the full-scan mass spectrum was acquired for each analyte with identification purposes in automatic mode. ESI source operated in the positive ionization mode, using (M+H(+ adducts as the molecular ions. These ions were monitored for each parent compound working in selected ion monitoring (SIM). Quantitative analyses were performed using ions at m/z 262 for FLU, m/z 332 for CIP, m/z 360 for ENR, m/z 386 for SAR, m/z 400 for DIF and m/z 362 for OFL. To improve sensitivity, large injection volumes (20 mL) of sample prepared in ammonium formate buffer 5 mM aqueous solution were employed. Optimization of the focusing conditions was done by using a personalized multifactorial experimental design. Even though in a strictly multifactorial design the intermediate volume would be 12.5 mL, since not all possible loop volumes are commercially available, injection volumes were fixed at 5, 10 and 20 mL. To obtain the response function, two factors including composition of injection solution (buffer/ methanol and water/methanol mixtures) and injection volume (5–20 mL) were considered. All the experiments were carried out at 25 8C using 20 mL min1 as the flow rate, with standard solutions of 100 mg L1 prepared as needed. Responses studied were expressed as peak area and peak width. Chromatographic analyses were carried out according to the designed experiments (Table 1). 2.3.2. Preparation of milk samples Samples (5 g) were shaken with 0.5 mL of 15% trichloroacetic acid aqueous solution and were centrifuged at 4200 min1 for 25 min. The supernatant was collected, and the precipitate was rinsed and centrifuged again with 1 mL of ammonium formate buffer (5 mM, pH 3.7). The new supernatant was collected, and the precipitate was rinsed and centrifuged again (4200 min1, 15 min) with 0.5 mL of ammonium formate buffer (5 mM, pH 3.7), and then with 0.5 mL of 50 mM ammonium hydroxide. The resulting

solution from combined extracts was shaken, passed through a 0.45 mm nylon filter and directly injected in the cLC system. 2.3.3. Method validation The method was validated according to the criteria specified in the European Union Commission Decision (European Union Commission Decision, 2002) using spiked milk samples. The method developed was evaluated in terms of selectivity, linearity, decision limit (CCa), detection capability (CCb), limits of detection and quantification, recoveries, accuracy and precision.

Table 1 Plan of experiments for factorial design of the focalization optimization. Experiment

Factors Injection volume*, mL

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 a

20 20 10 10 10 10 5 5 20 20 10 10 10 10 5 5

(1) (1) (0.33) (0.33) (0.33) (0.33) (1) (1) (1) (1) (0.33) (0.33) (0.33) (0.33) (1) (1)

Composition of injection solution Methanola, %

Buffer aqueous solution/pure water

15 0 15 0 0 0 15 0 15 0 15 0 0 0 15 0

Buffer Buffer Buffer Buffer Buffer Buffer Buffer Buffer Water Water Water Water Water Water Water Water

(1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1)

Normalized values of studied factors are given in brackets.

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Linearity ranges were determined from spiked milk samples at 10 concentration levels in the range 10–275 mg kg1. The results were analyzed by linear regression using peak area ratios. 3. Results and discussion 3.1. Optimization of chromatographic conditions 3.1.1. Previous assays In order to establish the best chromatographic conditions for Qns separation, several parameters such as mobile phase, buffer composition, pH, column temperature, flow rate and gradient elution program were studied. Compositions of the mobile phases were based on those proposed in several papers (Rodrı´guez et al., 2010; Bogialli et al., 2009; Krebber et al., 2009) Thus, different mixtures acetonitrile/ammonium formate buffer at different pH values (between 3.0 and 6.3) were tested. Taking into account the instrumental limitations of the MS capillary nebulizer, the ammonium formate concentration in the mobile phase was fixed at 5 mM to encourage ionization. Firstly, the isocratic separation on the Zorbax SB-C18 column was individually studied for each analyte using acetonitrile ratios in the mobile phase between 15% and 50% and a flow rate of 20 mL min1. In this range of organic solvent composition, retention factors depend on pH values, but, in any case, the experiments showed that to achieve a reasonable peak separation of the OFL-CIP and SAR-DIF critical pairs, mobile phases with high aqueous content and pH 3.7 were required. In general, changes in the elution order were not produced. However, considering the analyte pKa values (Fig. 1), retention times were affected when the buffer pH was changed to 6.3, showing a considerable increase in the retention times except for FLU. From this study, it was concluded that the best conditions were achieved by using a linear elution gradient with a mobile phase consisting of ammonium formate (solvent A) and acetonitrile (solvent B), as described in Section 2.3.1. The influence of the column temperature was studied under the optimized elution gradient. A negligible effect on the separation was observed when temperatures between 20 8C and 30 8C were employed. Several flow rates, between 10 and 20 mL min1, were also tested, and a decrease in retention times for all analytes (and therefore, in their peak widths) was observed when flow rate was increased. As a

compromise between resolution, separation time and peak width, a flow rate of 20 mL min1 was finally selected. 3.1.2. Experimental design The loss of sensitivity derived from the small volumes or masses injected when capillary columns are used can be avoided by the use of the so-called on column focusing modes by injection of larger sample volumes (Leo´n-Gonza´lez et al., 2010). The dispersion caused by injection of high sample volumes can be expected to be dependent on several parameters, including composition of injection solution, pH and nature of the analytes injected. Consequently, injection volume and sample focusing conditions were assumed to be the most important factors to optimize experimental conditions that provide better sensitivity. The optimization study of the focusing conditions was done by using multifactorial experimental design. To obtain the response function, two factors including injection volume (fixed at 5, 10 and 20 mL) and composition of injection solution (with or without buffer solution and methanol) were studied for each selected factor. The composition of the buffer solution was selected taking into account the optimal conditions described in Section 3.1.1. All the experiments were carried out at 25 8C and 20 mL min1 flow rate, using standard solutions of 100 mg L1 prepared with the convenient mobile phase. A total of 16 experiments and 6 replicates, corresponding to the central zone of the studied domain, were carried out for each analyte as described in Table 1. Chromatographic parameters such as peak area and peak width (w1/2) were measured in all tested conditions. In general, a linear increase in the peak areas with the injection volume was observed for all the Qns studied. The injection of water solution produced larger peak widths and loss of resolution between the peaks corresponding to OFL, CIP, ENR, due to the contribution of acid– base equilibriums. Narrower peaks were observed in all studied conditions when the focusing solution contained ammonium formate buffer at pH 3.7. The injection of water with methanol ratios of 15% produced slightly lower peak areas and narrower peaks. The 20 mL injection loop provided the highest sensitivity without significantly increasing retention time and w1/2. Therefore, this injection volume was selected as the optimum for separation because sensitivity was improved and a reasonable separation between OFL, CIP and ENR was obtained.

Fig. 2. Estimated response surfaces (normalized) of OFL for peak area with (a) pure water or (b) buffer solution and for peak width (w1/2) with (c) pure water or (d) buffer solution.

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In order to easily visualize the most important effects on peak area and w1/2, estimated response surfaces were obtained for each quinolone. These three-dimensional graphs showed more or less distorted planes with some factor interactions of small importance. The increase in sensitivity is significant in all cases, but is especially important for the first eluted compounds. As an example, response surfaces obtained for OFL can be observed in Fig. 2. It shows a linear increase of peak area as the injection volume increases in both water/methanol (Fig. 2(a)) and buffer/ methanol mixtures (Fig. 2(b)), and a slow increase of peak width for buffer/methanol mixtures as the injection volume increases. To get the optimum chromatographic conditions multiple response analyses (MRA) were carried out. The optimization criterion was maximum peak area and minimum w1/2 to determine the combination of experimental factors which simultaneously optimize the two studied responses and, therefore, maximize the desirability function over the experimental selected region. As a compromise, an injection volume of 20 mL and a composition of the injection solution of ammonium formate buffer 5 mM at pH 3.7 were selected. 3.2. Optimization of sample preparation Due to the effect of high protein content in milk, several methods previously reported (Tang et al., 2009; Gao et al., 2011; Herrera-Herrera et al., 2009; Li et al., 2011) for the determination of Qns in milk involve protein precipitation. In order to achieve good and reproducible analyte recoveries, preliminary experiments were carried out to select the amount of sample, washing solution of the precipitate and nature of the filter. Recovery studies were conducted by spiking milk samples with 100 mg kg1 of the analytes. All experiments were made by triplicate and the quinolone quantification was performed following the procedure described in Section 2.3.1. Protein precipitation was carried out with 1 mL of 15% (w/v) trichloroacetic acid solution over 10 g or 5 g of sample. Best recoveries for OFL, CIP and ENR were obtained when 5 g of

103

sample was used. To optimize the Qns recovery, several washing solutions for protein precipitate were evaluated. When washing solutions of 0.5 mL of 15% (w/v) trichloroacetic acid were used, recoveries between 39% and 68% were obtained for SAR and OFL, respectively. When a 5 mM ammonium formate buffer solution at pH 3.7 was used (0.5 mL), a considerable increase in the recoveries was observed for all analytes (55–90%) except for FLU (30%). Taking into account the acid–base characteristics of Qns, a second washing step was considered. Thus, 0.5 mL of a 50 mM ammonium hydroxide solution produced a high recovery increase for FLU (68%), while recoveries for other Qns were between 72% and 99%. Finally, the nature of the filter (PTFE and nylon) used before the injection into the cLC–DAD–MS system was evaluated. When PTFE filters were used, a significant decrease in Qns recoveries was observed, suggesting that these analytes might be retained in the filter. Therefore, nylon filters were selected. 3.3. Validation study The proposed method was used to determine the six quinolones in spiked milk samples. Different commercial samples were obtained from local supermarkets. We found no evidence of the presence of fluoroquinolone residues in any of the real samples analyzed at the method detection levels. 3.3.1. Ruggedness and specificity Reproducibility of the retention time, expressed as relative standard deviation (RSD), was between 0.1% and 0.7% for intra-day assays and between 0.7% and 3% for inter-day assays. The potential matrix effect in the suppression/enhancement of the analyte ESI/MS signal was studied. Some authors suggest that organic compounds present in the sample in concentrations exceeding 105 M may compete with the analyte for access to the droplet surface for gas phase emission. Other hypothesis is that, when the sample contains non-volatile matrix components, droplets are prevented from reaching their critical radius and

Fig. 3. cLC–MS chromatogram (extracted ion mode) obtained for a spiked bovine milk sample (addition of 50 mg kg1 for FLU and 100 mg kg1 for the others).

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surface field, and hence a reduction of the ion signal for an analyte occurs. As a result of these matrix effects, the response of a standard analyte in pure solvent can differ significantly from that in matrix sample (Junza et al., 2011). The matrix effect was evaluated by comparing the slopes of calibration curves of Qns standard solutions with those obtained with solutions prepared by addition of Qns to milk samples after deproteinization. Firstly, an Ftest was applied to compare the standard deviations of the slopes of both calibration methods. In most cases, no significant differences between them at a 95% confidence level were found. Secondly, a Student’s t-test comparison was done. This test showed that there was a statistically significant difference between the slopes at the 95.0% confidence level for CIP, ENR, SAR and FLU. Therefore, it could be concluded that there is a slight matrix effect at the studied concentration levels. In order to eliminate or compensate for this possible matrix effect, standard addition to milk samples after deproteinization (matrix matched calibration) was selected to determine Qns. The matrix effect was calculated using the modified version of the equation described by Matuszewki (Junza et al., 2011)

matrix e f fect; % ¼



 A milk  1  100 A standard

where A milk is the area for the analyte in milk and A standard is the area for the compound in standard solution. Thus, a matrix effect of 18%, 8%, 5% and 2% was observed for CIP, ENR, SAR and FLU, respectively. Fig. 3 shows the total ion mass chromatogram (extract ion mode) obtained for a milk sample spiked with 100 mg kg1 of each fluoroquinolone and 50 mg kg1 of FLU. 3.3.2. Linearity range Linearity (n = 10) was evaluated using calibration curves prepared in a blank milk sample spiked with target Qns. As summarized in Table 2, all compounds presented good linearity in the concentration range studied, with correlation coefficients (r2) higher than 0.9906 for both DAD and MS detection. 3.3.3. Detection and quantification limits Limits of detection (LODs) and quantification (LOQs) were evaluated from calibration graphs analyzing blank samples spiked

Fig. 4. cLC–DAD chromatogram (a) bovine milk sample at 250 nm, (b) spiked bovine milk sample at 250 nm, (c) bovine milk sample at 283 nm and (d) spiked bovine milk sample at 283 nm (addition of 50 mg kg1 for FLU and 100 mg kg1 for the others).

with Qns. LODs and LOQs were defined as 3 and 10 times the signal-to-noise ratio (S/N), respectively. These parameters were estimated experimentally by analysis of a series of decreasing concentrations of analyte solutions. By considering the MLRs established for quinolones in bovine milk, the proposed method proved to be sensitive enough to analyze DIF and FLU by cLC–DAD (Fig. 4), while the other quinolones can be quantified only at higher concentration levels. All Qns could be determined by cLC–MS because LOD and LOQ values obtained (Table 2) were below the MLRs established for these compounds by the EU legislation (European Union Commission Regulation, 2010).

Table 2 Quality parameters obtained for blank samples spiked with Qns by cLC–DAD–MS.

c

1

MRL , mg kg cLC–DAD l, nm Linearity range, mg kg1 Correlation coefficient (n = 10) LOD, mg kg1 LOQ, mg kg1 Precisiona (RSD, %)

cLC–MS m/z Linearity range, mg kg1 Correlation coefficient (n = 10) LOD, mg kg1 LOQ, mg kg1 Precisionb (RSD, %) CCa, (g kg1 CCb, (g kg1 a b c

For a concentration of 200 mg kg1. For a concentration of 100 mg kg1. EU legislation.

Intraday (n = 4) Interday (n = 12)

Intraday (n = 4) Interday (n = 12)

OFL

CIP

ENR

SAR

DIF

FLU

–

100

100

–

–

50

300 110–275 0.9990 30 110 6 9

283 82–275 0.9990 25 85 8 16

283 110–275 0.9990 30 110 6 8

283 110–275 0.9920 30 110 5 5

283 22–275 0.9920 6 22 5 6

250 22–275 0.9960 6 22 3 14

362 20–275 0.9906 6 20 2.5 10 18 24

332 20–275 0.9970 3 10 5.3 7.7 7 16

360 20–275 0.9910 6 20 5.0 5.5 9 22

386 30–275 0.9949 6 20 3.7 4 14 18

400 30–275 0.9908 6 20 0.98 4.5 10 23

262 10–275 0.9908 2.4 8 3.3 4.0 12 16

J.A. Ruiz-Viceo et al. / Journal of Food Composition and Analysis 28 (2012) 99–106 Table 3 Recovery study in milk samples.

Taking these considerations into account, the recoveries obtained by the developed methodology would be acceptable.

Analyte

Added, mg kg1

Recoveriesa, Mean  RSD (%)

OFL

75 100 150 75 100 150 75 100 150 75 100 150 75 100 150 50 75 100

75  7 76  5 83  5 69  6 77  8 88  5 92  6 96  6 91  6 71  5 78  6 70  6 82  5 78  8 78  8 68  6 69  6 64  7

CIP

ENR

SAR

DIF

FLU

a

105

n = 3 for each spiked level.

3.3.4. Precision and accuracy Precision was verified for each fluoroquinolone as the intra- and inter-day variability following the ICH document on validation methodology (Rambla-Alegre et al., 2012). It was expressed as RSD (%) and evaluated in terms of peak area at two concentration levels (200 mg kg1 or 100 mg kg1). The obtained results are summarized in Table 2. As can be seen, the intra-day variation (n = 4) was between 0.98% and 5.3% for MS detection. RSD values were between 4.0% and 10% for inter-day precision (n = 12, three successive days and four replicates each) when MS detection was used. The accuracy of the method was assessed by a recovery test. Table 3 includes the recoveries calculated as the mean of three determinations for each spiked level. These levels were either equal to or slightly above or below the legislated levels for fluoroquinolones permitted in milk samples. As can be seen, for CIP, ENR and FLU the recoveries were in the range 64–96% with RSDs between 5% and 8% at the 100 mg kg1 level, and were between 75% and 82% for OFL, SAR and DIF at the minimum level analyzed, which was 75 mg kg1. According to the AOAC manual for the Peer-Verified Methods program (AOAC, 1998) and to other authors (Rambla-Alegre et al., 2012), the acceptability of a given set of recovery and precision results depend on the spiked concentration levels of the analytes.

3.3.5. Robustness The robustness of an analytical method is the measurement of the capacity to remain unaffected by small, but deliberate variations in the method parameters, and it provides an indication of the reliability during normal use. The most convenient way to determine the robustness of a method is by using chemometric experimental design procedures (RamblaAlegre et al., 2012). Concentration of organic modifier, concentration and pH of buffer solution are chromatographic parameters that have important effects on the separation of fluoroquinolones as stated in Section 3.1. The robustness study of the chromatographic conditions was done by using personalized multifactorial experimental design. To obtain the response function, three factors including pH (3.5–3.9), buffer concentration of the mobile phase (3–7 mM) and organic modifier concentration (17–19%) and two replicates of each experiment were considered, and two levels were studied for each selected factor. In this range of organic solvent composition, areas and retention times of the analytes studied depend mainly on pH values and buffer concentration; therefore, in order to obtain the experimental response, only these two factors were considered. In the studied domain and for all targets, neither significant differences in the peak areas nor variability in the retention times were observed. To evaluate the sensitivity, peak area was selected as the experimental response. Responses (A) were correlated with experimental factors x1 (buffer concentration), x2 (pH) and; x1x2 (buffer–pH interaction) by means of a first-order general polynomial equation: A ¼ a0 þ a1 x1 þ a2 x2 þ a3 x1 x2

(1)

Coefficients a0–a3 represent the weight of each studied factor (x1 and x2) and their interactions (x1x2) on the area. The mathematical models, correlation coefficients (r2) and standard estimation errors (SEE) for peak area, as well as Durbin–Watson statistic test to determine if there are significant correlations, are shown in Table 4. The coefficient which showed higher weight for all analytes studied was a2, which corresponds to pH. Regarding the interaction term and the buffer concentration term, both showed an important weight in the peak area of OFL, CIP and FLU. The results of the equations shown in Table 4 indicate that the selected factors remain sufficiently unaffected by the slight variations of these parameters (RSD between 1.5% and 2.6%). As

Table 4 Fitted parameter values obtained from the mathematical model described in Eq. (1). Peak area

a0

a1

a2

a3

R2

SEE

Durbin–Watson statistic (p-value)

Predicted areaa

Experimental areaa

OFL

488

1.75418 (0.3032)

628

631

0.997

7.38

1.96519 (0.3963)

621

624

ENR

230

0.997

6.79

1.64092 (0.2569)

326

321

SAR

94

0.997

1.78

2.13158 (0.3526)

119

120

DIF

207

0.999

2.19

2.03448 (0.3965)

295

300

FLU

654

62 (0.0001) 63 (0.0001) 0.12 (0.9618) 4.0 (0.0079) 4.5 (0.0103) 42 (0.0001)

3.67

485

18 (0.00001) 18 (0.0057) 91 (0.00001) 22 (0.0001) 81 (0.00001) 104 (0.00001)

0.999

CIP

59 (0.00001) 53 (0.0002) 4.1 (0.1843) 1.0 (0.2102) 2.0 (0.0823) 53 (0.00001)

0.999

3.67

2.04489 (0.3917)

855

851

Coefficients a0–a3 represents the weight of each studied factor and their interactions in the selected response. SEE, standard error of estimation. p-Values are given in brackets (in bold are presented the p-values 0.05 indicating the effects significantly different from 0 at the 95% confidence level). a Optimal conditions.

106

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expected, pH variation had influence on the peak area of the studied compounds (except for CIP), while pH and buffer concentration–pH interaction affect peak area of OFL, CIP and FLU. p-Values of the Durbin–Watson statistic which are higher than 0.05 show no evidence of serial autocorrelation in the residuals. The areas predicted by the model in the optimal conditions are close to those obtained experimentally (Table 4). 3.3.6. Decision limit (CCa) and detection capability (CCb) In the 2002/656/EU European Union Decision (European Union Commission Decision, 2002) the CCa is defined as the limit at and above which it can be concluded with an error probability of a that a sample is non-compliant, and CCb is defined as the smallest content of the substance that may be detected, identified and/or quantified in a sample with an error probability of b. These probabilities a and b are imposed and, in quantitative methods, should be equal to 0.05 for substances with a maximum residue limit of 0.01 for banned substances. Table 2 shows CCa and CCb values with an error of b = 5% for ENR, CIP and FLU (probability of false compliance  5%) and b = 1% for OFL, SAR and DIF (banned substances in milk samples). The obtained values indicate that the established limits can be detected in the milk samples studied. 4. Conclusions The proposed cLC–DAD–MS method has been found to be useful to determine quinolones in milk samples. The employment of high injection volumes together with injection solutions of low elution strength has allowed the simultaneous determination of quinolone residues in bovine milk with high sensitivity at a few mg kg1 levels. This cLC method provides shorter analysis times than other published methods, due to the possibility of injecting milk samples directly into the chromatographic system with no previous treatment other than protein precipitation and filtration. It is worth mentioning that it uses low flow rates (mL min1) and low amounts of organic solvents, producing little waste and being an environmentally friendly technique, cost-saving and more suitable for coupling with MS detection. Validation according to Commission Decision (EC) No. 2002/657/EC has provided satisfactory results in terms of sensitivity, linearity and accuracy at low mg kg1 levels. Acknowledgement The present work has received financial support from the project CTQ2011-26684, funded by the Spanish Ministerio de Economı´a y Competitividad, Secretarı´a de Estado de Investigacio´n, Desarrollo e Innovacio´n. References Almeida Bezerra, M., Erthal Santelli, R., Padua Oliveira, E., Silveira Villar, L., Ame´lia Escaleira, L., 2008. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76, 965–977. AOAC Peer-Verified Methods Program, 1998. Manual on Policies and Procedures. Arlington, VA, USA.

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