Development of a Method for the Analysis of Multiclass Antibiotic Residues in Milk Using QuEChERS and Liquid Chromatography

Abstract

A precise and simplified method of sample preparation for the simultaneous quantification of the antibiotics β-lactam, macrolide, tetracycline, sulfonamide, and quinolone in bovine milk was developed. The central composite design of response surface methodology was used to design and optimize the method for the determination of six different antibiotic residues in milk. The recovery of each antibiotic was studied using a quick, easy, cheap, effective, rugged, and safe (QuEChERS) method. Octadecylsilane (C18), primary secondary amine (PSA), and sodium acetate (Na acetate) were the main factors affecting the recovery of each antibiotic. After optimization, the maximum predicted recovery rate was 84.18% for erythromycin under the optimized conditions of 101.20 mg C18, 52.00 mg PSA, and 1.01 g Na acetate. The recovery rates of the five other antibiotic residues ranged from 86.09% to 115.99%. The results suggested that modified QuEChERS could effectively be implemented in the analysis of antibiotic residues in milk.

Introduction

Antibiotics research has played an important role in dairy cattle management for the treatment of microbial infections or diseases (e.g., mastitis and influenza) (Benito-Pena et al., 2006; Abdel-Rehim, 2010). However, the incorrect use of antibiotics may result in drug residues in milk and, beyond the potential detrimental effects to human health or the environment, may also interfere with fermented milk product processing. Therefore, the development of a sensitive, rapid analytical method that allows the determination of several classes of drug at trace levels in milk is necessary (Fonseca et al., 2009; Granja et al., 2012; Nada et al., 2012).

Milk is a very important food for humans, and supplies all the energy and nutrients needed for the proper growth and development of the neonate (Nagpal et al., 2012). During recent decades, there has been increasing interest in the development of different milk safety measures to ensure safe and high-quality milk and milk products.

When milk samples are analyzed, the most difficult and time-consuming task is their treatment to extract these substances from complex matrices, such as high-fat and high-protein food (Glowka and Karazniewicz-Lada, 2007; Huang et al., 2012; Xie et al., 2012; Xiao et al., 2013). In recent years, analytical scientists have been seeking efficient processes with reduced solvent use for the extraction and purification of antibiotics residue (AD) (Beltran et al., 2007; Gajda et al., 2013). Several new methods, such as solid-phase extraction (Bailon-Perez et al., 2009; Camara et al., 2013), accelerated solvent extraction (Rouviere et al., 2012; Wang and Gardinali, 2012), matrix solid-phase dispersion (Pavlovic et al., 2012; Karageorgou et al., 2013), supercritical fluid extraction (Yang et al., 2012; Zhang et al., 2012), and solid-phase microextraction (Hallier et al., 2013; Melo et al., 2013) have been introduced for the detection of ADs.

Since 2003, the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method, developed by Anastassiades, Lehotay, Stajnbaher, and Schenck, has been recognized for its applicability in the simultaneous analysis of a large number of pesticides, mainly in a variety of natural agricultural food matrices (Anastassiades et al., 2003; Pereira Lopes et al., 2012; Chen et al., 2013; Zhang et al., 2013). Several QuEChERS methods have been developed for the determination of multiple ADs (Ferrer et al., 2010; Martinez Vidal et al., 2010; Lombardo-Aguei et al., 2011; Leon et al., 2012; Parab et al., 2012; Arroyo-Manzanares et al., 2014; Fernandes et al., 2015). Despite these successes, many difficulties arise when the QuEChERS are applied to high-fat food–milk for the analysis of ADs. Milk contains 3.3–4.7% fat components (Fox and McSweeney, 2006), and ADs may bind to lipoproteins and the extraction solvents to form emulsions and foams (Huang et al., 2006). To our knowledge, the QuEChERS method has not been optimized for use in the determination of multiclass ADs in fatty foods using response surface methodology (RSM).

The methods of RSM have been widely used to optimize diverse food-processing analysis parameters and in chromatographic studies (Cruz et al., 2010; Morais et al., 2014). However, they were seldom used in the optimization of sample preparation parameters. The aim of this research was to describe the development of a modified QuEChERS method to detect trace levels of six major ADs in milk combined with the RSM design and central composite design (CCD) to optimize the conditions of various factors used in sample preparation more easily.

Materials and Methods

Reagents and materials

Erythromycin, penicillin G, tetracycline hydrochloride, sulfamethoxazole, ciprofloxacin hydrochloride, and penicillin V analytical standards (purity >99%) were supplied by Sigma-Aldrich (São Paulo, Brazil). Acetonitrile (ACN) and formic acid were high-performance liquid chromatography (HPLC) grade from Merck (Shanghai, China). Analytical reagent grade anhydrous sodium sulfate (Na₂SO₄), acetic acid, ammonia, and sodium acetate (Na acetate) were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Water was purified with a water purification system (resistivity 18.2 MΩcm; Millipore, Billerica, MA). Octadecylsilane (C18) and primary secondary amine (PSA) were obtained from Bonna-Agela Technologies (Tianjin, China).

Preparation of standards

Individual aqueous antibiotic stock solutions containing 1000.00 mg/L of the target compounds were prepared monthly and stored at 4°C. Intermediate aqueous working standards mixtures, containing 1000.00 μg/L of each antibiotic, were used to prepare the working standard solutions containing spiking extracted blanks at 6 concentration levels. The corresponding amounts of the calibration points were 10.00, 20.00, 50.00, 100.00, 150.00, and 200.00 μg/L.

Milk samples

Pasteurized milk samples were obtained from local supermarkets (Shanghai, China). For validation purposes, milk samples were first analyzed by liquid chromatography–tandem mass spectrometry (LC-MS/MS) to verify the absence of the antibiotics studied. Known concentrations of ADs and internal standards were then added to the milk samples.

Sample preparation–QuEChERS

Figure 1 shows the sample preparation procedure for LC-MS/MS: 10.00±0.05 g of homogenized milk sample was placed into a 50-mL centrifuge vial with a screw cap. Five different ADs and an internal standard used for the recovery test were added at a concentration of 20 mg/L; the vial was capped and mixed on a rotator for 30 min. For extraction, 15 mL of ACN in 1% acetic acid and anhydrous Na₂SO₄ were added and vortexed for 2 min. The tube was centrifuged for 5 min at 10,000×g 4°C. The ACN layer was transferred to another tube, and 10 mL ACN with 1% ammonia water were added and vortexed for 2 min. The tube was centrifuged for 5 min at 10,000×g at 4°C. The two ACN layers were mixed and diluted with ACN to a volume of 25 mL, and 1000 μL of supernatant was transferred to an autosampler vial for LC-MS/MS analysis.

FIG. 1.

Sample preparation used the quick, easy, cheap, effective, rugged, and safe method (QuEChERS). ACN, acetonitrile; PSA, primary secondary amine; LC-MS/MS, liquid chromatography–tandem mass spectrometry.

Instrumentation

An Agilent 1200 series high-performance liquid chromatography system (Agilent Technologies, Santa Clara, CA) was coupled to a triple-quadruple mass spectrometer 3200 series (AB Sciex Pte. Ltd., Rahway, NJ) using electrospray ionization (ESI) in positive mode. Ionization and mass spectrometric conditions were optimized for each antibiotic by infusing at a flow rate of 5 μL/min flow rate a solution at a suitable concentration prepared in ACN:water (50:50, vol/vol) containing 0.07% formic acid. The specific MS/MS parameters for each antibiotic are shown in Table 1. Analytical instrument control, data acquisition, and treatment were performed using Analyst software version 1.5 mode (AB Sciex Pte. Ltd.).

Table 1.

Retention Time Windows (RTW) and Tandem Mass Spectrometry Parameters for the Selected Antibiotics

Antibiotic	RTW (min)	Precursor ion (m/z)	DP	EP	CEP	Product ion (m/z) ^a	CE	CXP
Ciprofloxacin	2.49–2.75	332.5	53.8	5.9	12.9	288.3 ^Q	26.7	9.3
						245.2 ^I	35.3	18.0
Tetracycline	2.81–3.04	445.3	54.1	4.5	22.0	154.2 ^Q	38.0	1.7
						410.2 ^I	28.0	14.9
Erythromycin	6.52–7.50	734.6	54.4	5.8	33.0	158.3 ^Q	44.0	2.4
						576.2 ^I	29.3	20.1
Sulfamethoxazole	8.53–8.89	254.2	48.9	8.0	18.0	156.0 ^Q	26.0	2.6
						108.2 ^I	38.9	0.7
Penicillin G	11.98–12.26	373.1	52.9	3.6	18.1	198.2 ^Q	22.0	1.4
						214.1 ^I	21.8	6.0
Penicillin V	13.88–14.14	351.0	37.2	3.4	17.9	160.1 ^Q	12.9	1.4
						192.1 ^I	11.2	6.8

Product ions: (Q) transition used for quantification; (I) transition employed to complete the identification.

DP, declustering potential; EP, entrance potential; CEP, collision cell entrance potential; CXP, collision cell exit potential; CE, collision energy.

Chromatography

Chromatographic separations were performed on a XSELECT C18 analytical column (250-mm length; 4.6-mm internal diameter; 5-μm particle diameter). The C18 column was run at 35°C. The mobile phase comprised two components: water with 0.07% formic acid (eluent A) and ACN (eluent B) at a flow rate of 1.0 mL/min. The gradient profile was first started at 80% of eluent A and decreased linearly to 50% within 10 min, then started at 50% of eluent A and decreased linearly to 5% within 3 min. This composition was maintained for a further 2 min before being returned to the initial conditions within 1 min, followed by a re-equilibration time of 10 min. The injection volume was 20 μL.

Validation procedure

The analytical characteristics evaluated were sensitivity, linearity, trueness through recovery studies, intraday and interday precision, uncertainty, limits of detection (LODs) and limits of quantification (LOQs). Spiking extracted blanks at 6 concentration levels between 10.00 and 200.00 μg/L linearity were tested using matrix-matched calibration. The calculations for the LODs were based on the standard deviation of y-intercepts of regression analysis (σ) and the slope (S), using the following equation: LOD=3σ/S. The LOQs were calculated by the equation LOQ=10σ/S. Recovery and repeatability (intraday precision) tests were performed on spiking blanks at 50 μg/L, using 6 replicates for each AD in 1 d.

Experimental design for RSM

The water solubility of anhydrous Na₂SO₄ at 20°C is 195 g/L, indicating that at least 2 g of anhydrous Na₂SO₄ should be used for a 10-g sample. During the sample preparation process, PSA, C18, and Na acetate have a dissolving effect on milk-fat globules, which could affect recovery rates. Therefore, the amounts of C18, PSA, and Na acetate used in this study were determined by the RSM variables. A five-level, three-factor factorial CDD was determined by Design-Expert 7.0 (Minneapolis, MN). The variables and the amounts added were as follows: the amount of C18 (X₁) ranged from 40 to 200 mg and △X was 40, while the amounts of PSA (X₂) ranged from 10 to 90 mg and △X was 20, and those of Na acetate (X₃) ranged from 0.2 to 1.8 g and △X was 0.4 (△X is the increment of each experimental factor value corresponding to 1 unit of each coded variable). The actual variable was coded to facilitate multiple regression analysis.

Design-Expert 7.0.0 Trial (State-Ease Inc., Minneapolis, MN) was used to design the experiment and analyze the results. The quality of the fitted model was expressed by the coefficient of determination R², and its statistical significance was checked by an F-test. Seventeen experimental settings with three factors and five levels were generated by the principles of RSM using Design-Expert 7.0.0. Table 2 indicates the coded and CCD-processed variables for the optimization of the QuEChERS method for milk samples. A quadratic polynomial regression model was assumed for predicting the Y variable-recoveries of the antibiotics (%). The model proposed for the response of Y fitted equation was as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}Y = \beta_0 \sum_{i = 1}^{k} \beta_i X_i + \sum_{i = 1}^{k} \beta_{ii} {X_i^2} + \sum _{i , j = 1 ( i \neq j ) }^{k} \beta_{ij} X_i X_j\tag{1}\end{align*} \end{document}

where k is the number of variables, Y is the response (recovery of antibiotics [%]), β₀ is the coefficient term, β_i is the linear term, β_ii is the coefficient of squared term, β_ji is the coefficient of interaction effect, and X_i and X_j are the coded values of variables i and j, respectively (amounts of C18 (X₁], PSA [X₂], and Na acetate [X₃]). A software quality of the fitted model was expressed by the coefficient of determination R², and its statistical significance was checked by an F-test.

Table 2.

Recovery Test Results for Each Antibiotic Using the Central Composite Design Analysis and Relative Standard Deviation (RSD) for the Different Studied Samples

	Coded values of the independent variables			Process values of the independent variables			Recovery % (RSD%) ^a
Run number	X₁ ^b	X₂ ^c	X₃ ^d	X₁ ^b	X₂ ^c	X₃ ^d	Ciprofloxacin	Tetracycline	Erythromycin	Sulfamethoxazole	Penicillin G	Penicillin V
1	0	0	−2	120	50	0.2	54 (8)^e	66 (11)	70 (13)	87 (15)	68 (8)	79 (12.6)
2	0	0	0	120	50	1.0	81 (5)	102 (12)	86 (12)	97 (17)	98 (8)	116 (12)
3	−1	−1	−1	80	30	0.6	54 (5)	70 (12)	67 (12)	68 (17)	66(9)	80 (13)
4	0	0	2	120	50	1.8	56 (6)	81 (14)	71 (13)	78 (17)	61 (9)	101 (13)
5	0	0	0	120	50	1.0	84 (6)	100 (14)	87 (12)	100 (16)	88 (7)	107 (12)
6	1	1	1	160	70	1.0	54 (6)	75 (13)	66 (13)	76 (16)	78 (8)	95 (13)
7	1	1	−1	160	70	0.6	54 (6)	71 (13)	79 (13)	88 (17)	83 (9)	91 (12)
8	0	0	0	120	50	1.0	89 (6)	98 (13)	80 (12)	94 (16)	98 (8)	113 (12)
9	0	0	0	120	50	1.0	84 (6)	101 (12)	82 (13)	101 (17)	94 (8)	120 (12)
10	−1	1	1	80	70	1.4	54 (7)	95 (13)	75 (14)	90 (16)	75 (9)	89 (13)
11	0	0	0	120	50	1.0	86 (6)	103 (12)	79 (12)	106 (17)	81 (8)	117 (12)
12	1	−1	1	160	30	1.4	82 (6)	78 (14)	77 (14)	90 (16)	63 (10)	105 (13)
13	2	0	0	200	50	1.0	70 (7)	77 (12)	59 (14)	77 (16)	49 (10)	93 (13)
14	−1	1	−1	80	70	0.6	78 (7)	85 (13)	81 (13)	97 (16)	81 (9)	78 (13)
15	0	−2	0	120	10	1.0	59 (7)	71 (15)	61 (13)	66 (17)	65 (9)	91 (14)
16	−2	0	0	40	50	1.0	69 (7)	87 (14)	77 (13)	70 (18)	68 (9)	80 (12)
17	0	2	0	120	90	1.0	51 (7)	82 (14)	59 (14)	82 (16)	64 (9)	87 (14)
18	1	−1	−1	160	30	0.6	67 (5)	75 (13)	64 (14)	93 (17)	76 (7)	93 (13)
19	−1	−1	1	80	30	1.4	66 (6)	84 (13)	77 (13)	84 (16)	80 (8)	85 (13)
20	0	0	0	120	50	1.0	83 (6)	98 (12)	84 (12)	96 (17)	87 (8)	116 (12)

Number of replicates=6.

Amount of C18 (mg).

Amount of primary secondary amine (mg).

Amount of Na acetate (g).

Intraday precision is given in parentheses as RSD (n=6).

Results and Discussion

Optimization of MS/MS detection and chromatographic separation

For each AD, the mass spectrometer was optimized to provide the best responses for quantification. To achieve high sensitivity, the MS parameters of each individual analyte were infused as a standard solution of 500 μg/L in a mixture of 0.1% aqueous formic acid solution/ACN (50/50, vol/vol) directly into the mass spectrometer. All ADs were tested using both atmospheric pressure chemical ionization and ESI probes with positive/negative ionization mode. The ESI probe in positive mode showed the best results in term of sensitivity, ruggedness, and handling and maintenance. The MS parameters, such as declustering potential, entrance potential, collision cell entrance potential, collision cell exit potential, and collision energy, were optimized for each AD to obtain the maximum sensitivity, good ruggedness, and easy handling and maintenance (Table 1). Each AD was characterized by its retention time and by two precursor-product ion transitions. The most abundant product ion was used for quantification, whereas the second most abundant was used to complete the identification. The dwell time established for each transition was 0.2 s. Under the experimental conditions, [M+H]⁺ ions were found to be the most abundant for all the ADs. Ion source parameters, source temperature, curtain gas, ion spray voltage, and GAS 1 and GAS 2 were optimized once chromatographic conditions were established, resulting in the optimum values indicated in the Instrumentation section. Table 1 shows the MS/MS transitions for quantification and confirmation as well as cone voltages and collision energy values optimized for each of the selected compounds.

ADs have commonly been analyzed by LC-MS/MS using aqueous formic acid solution and ACN as the mobile phase. The separation method was therefore optimized for these solvents. With respect to the chromatographic conditions, standard aqueous/ACN (50/50, vol/vol) solutions of ADs were used during the optimization of chromatographic separation. The mobile phase comprised 0.1% aqueous formic acid solution (solvent A) and ACN (solvent B). The gradient was studied to determine the best separation, peak shape, and sensitivity, to obtain the best separation in the shortest time, and was finally found to be a rising gradient up to 50% of ACN for a good separation and elution of the greatest amount of retained analyte. The percentage of ACN was increased up to 90% after the elution of retained analyte to elute other possible components included in the final sample extract. The two acids (formic and acetic acid) in solvent A were evaluated. Formic acid gave better results than acetic acid, and the aqueous mobile phase with 0.07% formic acid gave the highest signals and peak shape. The temperature of the column was studied between 25°C and 45°C, and 35°C was selected to give the best results, because it provided the highest peak height and area with the best peak shape and good run time. For all ADs, the LOQs were low enough to quantify the analytes below their maximum residue limits. Table 3 summarizes the results obtained for the analytes that could be quantified within the samples. Figure 2 shows a representative chromatogram obtained when a blank milk sample was spiked at 50 μg/L with the selected ADs, from which it can be seen that clean extracts with no interference were obtained.

FIG. 2.

Chromatograms of a spiked milk sample at 50 μg/L.

Table 3.

Matrix-Matched Calibration Curves and Performance Characteristics of Liquid Chromatography–Tandem Mass Spectrometry Method Using the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) Treatment for the Analysis of Antibiotics Residue (AD) in Milk Samples

AD	Linear regression equation	R2	LOD (μg/L)	LOQ (μg/L)
Ciprofloxacin	Y=75.1x+458	0.995	0.4	0.2
Tetracycline	Y=33.3x+131	0.998	1	0.2
Erythromycin	Y=5010x+31400	0.998	1	0.5
Sulfamethoxazole	Y=211x–329	0.997	3	0.5
Penicillin G	Y=94x+1160	0.999	0.5	0.1
Penicillin V	Y=181x+363	0.999	0.5	0.1

R2, coefficient of determination; LOD, limits of detection; LOQ, limits of quantification.

Sample preparation method optimization by RSM

RSM has been used to evaluate the effects of multiple parameters on response variables (Ghadge and Raheman, 2006; Tiwari et al., 2007; Jeong et al., 2012). The CCD of RSM was used to design and optimize extraction and clean-up methods for the six ADs. Experimental runs were planned according to a 3-factor CCD on the basis of coded levels from 3 independent variables, resulting in 20 experimental sets. The recoveries after sample preparation under different conditions are summarized in Table 2, and vary with the AD. The amounts of C18 (X₁), PSA (X₂), and Na acetate (X₃) were investigated in the ranges of 40–200 mg, 10–90 mg, and 0.2–1.8 g, respectively. To determine the optimal conditions for sample preparation methods of ADs and the relationship between the recovery and the significant variables, analyses of variance were performed through a joint test of three parameters. Erythromycin was selected to demonstrate the regression coefficient values calculated for the recovery of an antibiotic.

Among the linear, quadratic, and cross-product forms of the independent variables, the coefficient constants X₁, X₂, X₃, X₁ ², X₂ ², and X₃ ² were significant at the level of p<0.05. Therefore, after the response recovery rates were experimentally determined under the 20 sets of conditions, the regression coefficients for the recoveries were calculated by RSREG analysis and a polynomial regression model equation was fitted as follows: Y=83.38659 – 3.19750X₁+0.80250X₂+0.47750X₃ – 3.54295X₁ ² – 5.40545X₂ ² – 2.90045X₃ ²−0.86750 X₁X₂ – 0.35750X₁X₃ – 5.17250X₂X₃, where X₁ is the amount of C18 added, X₂ is the amount of PSA added, and X₃ is the amount of Na acetate added (in relation to erythromycin). Based on the regression coefficients and the p value, we concluded that the linear and quadratic terms for the amounts of C18 (X₁), PSA (X₂), and Na acetate (X₃) had significant effects on the recoveries of antibiotic (p<0.05) and that there were no negligible factors. The greatest recovery of AD corresponded to samples with intermediate levels of C18, PSA, and Na acetate.

To investigate the influence of matrix effect (ME) on the determination of each antibiotic by QuEChERS treatment method, Eq. (2) was employed (Andrija et al., 2012). The obtained results are given in Table 4. \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} \rm {ME {(\%) }} = & \frac {\rm peak \ area \ of \ post \ extraction - \ peak \ area \ of \ pure \ solution } { \rm peak \ area \ of \ pure \ solution } \\ & \times \ 100 \tag{2}\end{align*} \end{document}

Table 4.

Matrix Effect on Determination for Each Antibiotic Using the Central Composite Design Analysis and Relative Standard Deviation (RSD) for the Different Studied Samples

	Coded values of the independent variables			Process values of the independent variables			Recovery % (RSD%) ^a
Run number	X₁ ^b	X₂ ^c	X₃ ^d	X₁ ^b	X₂ ^c	X₃ ^d	Ciprofloxacin	Tetracycline	Erythromycin	Sulfamethoxazole	Penicillin G	Penicillin V
1	0	0	−2	120	50	0.2	−46 (8)^e	−34 (11)	−30 (13)	−13 (15)	−32 (8)	−21 (12.6)
2	0	0	0	120	50	1	−19 (5)	2 (12)	−14 (12)	−3 (17)	−2 (8)	16 (12)
3	−1	−1	−1	80	30	0.6	−46 (5)	−30 (12)	−33 (12)	−32 (17)	−34 (9)	−20 (13)
4	0	0	2	120	50	1.8	−44 (6)	−19 (14)	−29 (13)	−22 (17)	−39 (9)	1 (13)
5	0	0	0	120	50	1	−16 (6)	0 (14)	−13 (12)	0 (16)	−12 (7)	7 (12)
6	1	1	1	160	70	1	−46 (6)	−25 (13)	−34 (13)	−24 (16)	−22 (8)	−5 (13)
7	1	1	−1	160	70	0.6	−46 (6)	−29 (13)	−21 (13)	−12 (17)	−17 (9)	−9 (12)
8	0	0	0	120	50	1	−11 (6)	−2 (13)	−20 (12)	−6 (16)	−2 (8)	13 (12)
9	0	0	0	120	50	1	−16 (6)	1 (12)	−18 (13)	1 (17)	−6 (8)	20 (12)
10	−1	1	1	80	70	1.4	−46 (7)	−5 (13)	−25 (14)	−10 (16)	−25 (9)	−11 (13)
11	0	0	0	120	50	1	−14 (6)	3 (12)	−21 (12)	6 (17)	−19 (8)	17 (12)
12	1	−1	1	160	30	1.4	−18 (6)	−22 (14)	−23 (14)	−10 (16)	−37 (10)	5 (13)
13	2	0	0	200	50	1	−30 (7)	−23 (12)	−41 (14)	−23 (16)	−51 (10)	−7 (13)
14	−1	1	−1	80	70	0.6	−22 (7)	−15 (13)	−19 (13)	−3 (16)	−19 (9)	−22 (13)
15	0	−2	0	120	10	1	−41 (7)	−29 (15)	−39 (13)	−34 (17)	−35 (9)	−9 (14)
16	−2	0	0	40	50	1	−31 (7)	−13 (14)	−23 (13)	−30 (18)	−32 (9)	−20 (12)
17	0	2	0	120	90	1	−49 (7)	−18 (14)	−41 (14)	−18 (16)	−36 (9)	−13 (14)
18	1	−1	−1	160	30	0.6	−33 (5)	−25 (13)	−36 (14)	−7 (17)	−24 (7)	−7 (13)
19	−1	−1	1	80	30	1.4	−34 (6)	−16 (13)	−23 (13)	−16 (16)	−20 (8)	−15 (13)
20	0	0	0	120	50	1	−17 (6)	−2 (12)	−16 (12)	−4 (17)	−13 (8)	16 (12)

Number of replicates=6.

Amount of C18 (mg).

Amount of primary secondary amine (mg).

Amount of Na acetate (g).

Intraday precision is given in parentheses as RSD (n=6).

Comparison of Tables 2 and 4 shows that recovery results are better, while matrix effect results are better. Thus, using recovery results given illustrate the efficiency of pretreatment method.

Optimized conditions for each antibiotic

The maximum recovery of each AD was determined using contour plots of the model obtained. According to Figure 3, the optimized conditions of the three reagents (C18, PSA, and Na acetate) for each antibiotic were as follows: 160.00 mg C18, 32.00 mg PSA, and 1.25 g Na acetate for ciprofloxacin; 96.00 mg C18, 57.80 mg PSA, and 1.14 g Na acetate for tetracycline; 101.20 mg C18, 52.00 mg PSA, and 1.01 g Na acetate for erythromycin; 119.20 mg C18, 58.20 mg PSA, and 0.83 g Na acetate for sulfamethoxazole; 115.2 mg C18, 53.2 mg PSA, and 0.95 g Na acetate for penicillin G; and 133.2 mg C18, 47.4 mg PSA, and 1.14 mg Na acetate for penicillin V.

FIG. 3.

Contour maps of the recoveries of the antibiotics for varying amounts of C18, primary secondary amine (PSA), and Na acetate: (A) ciprofloxacin, (B) tetracycline, (C) erythromycin, (D) sulfamethoxazole, (E) penicillin G, and (F) penicillin V.

The results of the recovery tests by CCD for each AD are shown in Table 2. The maximum observed recovery rate was >80% for all ADs, and ranged, under all conditions tested, as follows: ciprofloxacin, 50.92–88.75%; tetracycline, 65.65–101.97%; erythromycin, 58.52–86.62%; sulfamethoxazole, 65.92–105.6%; penicillin G, 48.92–98.11%; and penicillin V, 77.95–117.35%.

The RSM graphs of ADs are shown in Figure 3. The amount of C18 was the most important factor affecting the recoveries of the ADs, which appeared to be high over the entire range of the variables C18, PSA, and Na acetate, with little variation.

Three experiments of each AD under optimal sample preparation conditions were carried out. The recoveries of ADs were as follows: ciprofloxacin, 86.09%; tetracycline, 101.68%; erythromycin, 84.18%; sulfamethoxazole, 100.38%; penicillin G, 92.38%; and penicillin V, 115.99%.

Optimized conditions for six types of AD

The optimized condition of the three reagents (C18, PSA, and Na acetate) for the 6 ADs was 117.20 mg C18, 51.00 mg PSA, and 1.03 g Na acetate. Not only were individual optimized conditions successfully obtained for each antibiotic, but maximum recovery of all ADs was also obtained under a single set of pretreatment conditions.

Three experiments under for six types of AD were carried out. The results obtained are given in Table 5.

Table 5.

Recovery Test Results for Each Antibiotic Under Optimal Pretreatment Conditions Analysis and Relative Standard Deviation (RSD) for the Different Studied Samples

ADs	20 (μg/L)	50 (μg/L)	100 (μg/L)
Ciprofloxacin	74% (9)^a	84% (6)	85% (7)
Tetracycline	88% (14)	100% (13)	95% (12)
Erythromycin	70% (15)	84% (13)	90% (14)
Sulfamethoxazole	82% (17)	99% (15)	97% (17)
Penicillin G	82% (11)	92% (10)	93% (9)
Penicillin V	99% (13)	113% (12)	111% (12)

Intraday precision and interday precisions are given in parentheses as RSD (n=6, on 3 days).

ADs, antibiotics residue.

Sample analysis

The method developed was applied to determine the ADs in three milk samples obtained from local supermarkets in China. The results obtained are given in Table 6 and Figure 4. Traces of ADs (<LOQ) were observed in two samples.

FIG. 4.

Chromatograms of an S2 milk sample.

Table 6.

Concentration of Antibiotics Residue (μg/L) Found in Real Samples

Milk samples	Ciprofloxacin	Tetracycline	Erythromycin	Sulfamethoxazole	Penicillin G	Penicillin V
S1	<LOQ	—	—	—	—	—
S2	—	<LOQ	—	—	—	—
S3	—	—	—	—	—	—

LOQ, limits of quantification.

Conclusions

In this study, optimized sample treatment conditions were established by RSM and were used to optimize a QuEChERS method to extract and clean up six types of AD found in milk. For these ADs, the modified QuEChERS method achieved acceptable results, and the observed maximum recovery rates were >80% for all ADs. When the optimum conditions for each AD were determined by RSM, the predicted range of recoveries was increased to 84.18–115.99%. Furthermore, when the optimum condition for all ADs was determined by RSM, the predicted range of recoveries was increased to 83.80–114.11%. We established a relationship between the recovery of AD and the amount of C18, PSA, and Na acetate. Our models predicted the maximum recovery of each AD in terms of the amount of C18, PSA, and Na acetate, which will be useful in designing new sample pretreatments with optimum conditions that meet sample analysis.

Footnotes

Acknowledgments

This study was supported by The 12th Five Years Key Programs for Science and Technology Development of China (2012BAD12B08 and 2012BAK17B14).

Disclosure Statement

No competing financial interests exist.

References

Abdel-Rehim

. Recent advances in microextraction by packed sorbent for bioanalysis. J Chromatogr A, 2010; 1217:2569–2580.

Anastassiades

, Lehotay

, Stajnbaher

, Schenck

. Fast and easy multiresidue method employing acetonitrile extraction/partitioning and “dispersive solid-phase extraction” for the determination of pesticide residues in produce. J AOAC Int, 2003; 86:412–431.

Andrija

, Helena

, Milena

J-S

, Predrag

. Evaluation of matrix effect in determination of some bioflavonoids in food samples by LC–MS/MS method. Talanta, 2012; 99:780–790.

Arroyo-Manzanares

, Gámiz-Gracia

, García-Campaña Ana

. Alternative sample treatments for the determination of sulfonamides in milk by HPLC with fluorescence detection. Food Chem, 2014; 143:459–464.

Bailon-Perez

, Garcia-Campana

, del Olmo-Iruela

, Gamiz-Gracia

, Cruces-Blanco

. Trace determination of 10 beta-lactam antibiotics in environmental and food samples by capillary liquid chromatography. J Chromatogr A, 2009; 1216:8355–8361.

Beltran

, Caro

, Marce

, Cormack

PAG

, Sherrington

, Borrull

. Synthesis and application of a carbamazepine-imprinted polymer for solid-phase extraction from urine and wastewater. Anal Chim Acta, 2007; 597:6–11.

Benito-Pena

, Partal-Rodera

, Leon-Gonzalez

, Moreno-Bondi

. Evaluation of mixed mode solid phase extraction cartridges for the preconcentration of beta-lactam antibiotics in wastewater using liquid chromatography with UV-DAD detection. Anal Chim Acta, 2006; 556:415–422.

Camara

, Gallego-Pico

, Garcinuno

, Fernandez-Hernando

, Durand-Alegria

, Sanchez

. An HPLC-DAD method for the simultaneous determination of nine beta-lactam antibiotics in ewe milk. Food Chem, 2013; 141:829–834.

Chen

, Yin

, Song

, Liu

, Zheng

, Xing

, Liu

. Determination of pesticide residues in ginseng by dispersive liquid-liquid microextraction and ultra high performance liquid chromatography-tandem mass spectrometry. J Chromatogr B, 2013; 917:71–77.

10.

Cruz

, Faria

JAF

, Walter

EHM

, Andrade

, Cavalcanti

, Oliveira

CAF

, Granato

. Processing optimization of probiotic yogurt containing glucose oxidase using response surface methodology. J Dairy Sci, 2010; 93:5059–5068.

11.

Fernandes

FCB

, Silva

, Rufino

, Pezza

. Screening and determination of sulphonamide residues in bovine milk samples using a flow injection system. Food Chem, 2015; 166:309–315.

12.

Ferrer

, Agueera

, Mezcua

, Fernandez-Alba

, Mack

, Anastassiades

, Gamon

. Efficiency evaluation of the main multiresidue methods used in Europe for the analysis of amitraz and its major metabolites. J AOAC Int, 2010; 93:380–388.

13.

Fonseca

, Cruz

, Faria

JAF

, Silva

, Moura

MRL

, Carvalho

LMJ

. Antibiotic residues in Brazilian UHT milk: A screening study. Ciênc Tecnol Aliment, 2009; 29:451–453.

14.

Fox

, McSweeney

PLH

. Advanced Dairy Chemistry, 1 ^st ed. New York: Springer, 2006.

15.

Gajda

, Posyniak

, Zmudzki

, Tomczyk

. Determination of doxycycline in chicken fat by liquid chromatography with UV detection and liquid chromatography-tandem mass spectrometry. J Chromatogr B, 2013; 928:113–120.

16.

Ghadge

, Raheman

. Process optimization for biodiesel production from mahua (Madhuca indica) oil using response surface methodology. Bioresource Technol, 2006; 97:379–384.

17.

Glowka

, Karazniewicz-Lada

. Determination of roxithromycin in human plasma by HPLC with fluorescence and UV absorbance detection: Application to a pharmacokinetic study. J Chromatogr B, 2007; 852:669–673.

18.

Granja

RHMM

, Lima

, Salerno

, Wanschel

ACBA

. Validation of a liquid chromatography with ultraviolet detection methodology for the determination of sulfonamides in bovine milk according to 20 02/657/EC. Food Control, 2012; 28:304–308.

19.

Hallier

, Noirot

, Medina

, Leboeuf

, Cavret

. Development of a method to determine essential oil residues in cow milk. J Dairy Sci, 2013; 96:1447–1454.

20.

Huang

, Zheng

, Fu

, Yao

, Tao

, Ren

. UPLC-ESI-MS/MS for determining trans- and cis-vitamin K-1 in infant formulas: Method and applications. Eur Food Res Technol, 2012; 235:873–879.

21.

Huang

, Lin

, Yu

, Feng

. Determination of fluoroquinolones in eggs using in-tube solid-phase microextraction coupled to high-performance liquid chromatography. Anal Bioanal Chem, 2006; 384:1228–1235.

22.

Jeong

I-S

, Kwak

B-M

, Ahn

J-H

, Jeong

S-H

. Jeong. Determination of pesticide residues in milk using a QuEChERS-based method developed by response surface methodology. Food Chem, 2012; 133:473–481.

23.

Karageorgou

, Myridakis

, Stephanou

, Samanidou

. Multiresidue LC-MS/MS analysis of cephalosporins and quinolones in milk following ultrasound-assisted matrix solid-phase dispersive extraction combined with the quick, easy, cheap, effective, rugged, and safe methodology. J Sep Sci, 2013; 36:2020–2027.

24.

Leon

, Roca

, Igualada

, Martins

CPB

, Pastor

, Yusa

. Wide-range screening of banned veterinary drugs in urine by ultra high liquid chromatography coupled to high-resolution mass spectrometry. J Chromatogr A, 2012; 1258:55–65.

25.

Lombardo-Aguei

, Gamiz-Gracia

, Cruces-Blanco

, Garcia-Campana

. Comparison of different sample treatments for the analysis of quinolones in milk by capillary-liquid chromatography with laser induced fluorescence detection. J Chromatogr A, 2011; 1218:4966–4971.

26.

Martinez Vidal

, Garrido Frenich

, Aguilera-Luiz

, Romero-Gonzalez

. Development of fast screening methods for the analysis of veterinary drug residues in milk by liquid chromatography-triple quadrupole mass spectrometry. Anal Bioanal Chem, 2010; 397:2777–2790.

27.

Melo

, Mansilha

, Pinho

, Ferreira

IMPLVO

. Analysis of pesticides in tomato combining QuEChERS and dispersive liquid-liquid microextraction followed by high-performance liquid chromatography. Food Anal Methods, 2013; 6:559–568.

28.

Morais

, Morais

, Cruz

, Bolini

HMA

. Development of chocolate dairy dessert with addition of prebiotics and replacement of sucrose with different high-intensity sweeteners. J Dairy Sci, 2014; 97:2600–2609.

29.

Nada

, Ilija

, Igor

, Jelena

, Ruzica

. Implication of food safety measures on microbiological quality of raw and pasteurized milk. Food Control, 2012; 25:728–731.

30.

Nagpal

, Behare

, Kumar

, Mohania

, Yadav

, Jain

, Menon

, Parkash

, Marotta

, Minelli

, Henry

CJK

, Yadav

. Milk, milk products, and disease free health: An updated overview. Crit Rev Food Sci, 2012; 52:321–333.

31.

Parab

, Amritkar

. Development and validation of a procedure for determination of sulfonamide residues in pasteurized milk using modified QuEChERS method and liquid chromatography/tandem mass spectrometry. J AOAC Int, 2012; 95:1528–1533.

32.

Pavlovic

, Perisa

TPM

, Babic

. Optimization of matrix solid-phase dispersion for liquid chromatography tandem mass spectrometry analysis of 12 pharmaceuticals in sediments. J Chromatogr A, 2012; 1258:1–15.

33.

Pereira Lopes

, Cazoria Reyes

, Romero-Gonzalez

, Martinez Vidal

, Garrido Frenich

. Multiresidue determination of veterinary drugs in aquaculture fish samples by ultra high performance liquid chromatography coupled to tandem mass spectrometry. J Chromatogr B, 2012; 895:39–47.

34.

Rouviere

, Bulete

, Cren-Olive

, Arnaudguilhem

. Multiresidue analysis of aromatic organochlorines in soil by gas chromatography-mass spectrometry and QuEChERS extraction based on water/dichloromethane partitioning: Comparison with accelerated solvent extraction. Talanta, 2012; 93:336–344.

35.

Tiwari

, Kumar

, Raheman

. Biodiesel production from jatropha (Jatropha curcas) with high free fatty acids: An optimized process. Biomass Bioenerg, 2007; 31:569–575.

36.

Wang

, Gardinali

. Analysis of selected pharmaceuticals in fish and the fresh water bodies directly affected by reclaimed water using liquid chromatography-tandem mass spectrometry. Anal Bioanal Chem, 2012; 404:2711–2720.

37.

Xiao

, Dramou

, Xiong

, He

, Yuan

, Dai

, Li

, He

, Peng

, Li

. Preparation of molecularly imprinted polymers on the surface of magnetic carbon nanotubes with a pseudo template for rapid simultaneous extraction of four fluoroquinolones in egg samples. Analyst, 2013; 138:3287–3296.

38.

Xie

, Han

, Hou

, Wang

, Qian

, Xi

. Simultaneous determination of multiveterinary drug residues in pork meat by liquid chromatography-tandem mass spectrometry combined with solid phase extraction. J Sep Sci, 2012; 35:3447–3454.

39.

Yang

C-H

, Yang

C-S

, Hwang

M-L

, Chang

C-C

, Li

R-X

, Chuang

L-Y

. Antimicrobial activity of various parts of Cinnamomum cassia extracted with different extraction methods. J Food Biochem, 2012; 36:690–698.

40.

Zhang

J-M

, Wu

Y-L

, Lu

Y-B

. Simultaneous determination of carbamate insecticides and mycotoxins in cereals by reversed phase liquid chromatography tandem mass spectrometry using a quick, easy, cheap, effective, rugged and safe extraction procedure. J Chromatogr B, 2013; 915:13–20.

41.

Zhang

, Liu

, Cui

, Pan

, Zhang

, Chen

. A review of sample preparation methods for the pesticide residue analysis in foods. Cent Eur J Chem, 2012; 10:900–925.

Development of a Method for the Analysis of Multiclass Antibiotic Residues in Milk Using QuEChERS and Liquid Chromatography–Tandem Mass Spectrometry

Abstract

Introduction

Materials and Methods

Reagents and materials

Preparation of standards

Milk samples

Sample preparation–QuEChERS

Instrumentation

Chromatography

Validation procedure

Experimental design for RSM

Results and Discussion

Optimization of MS/MS detection and chromatographic separation

Sample preparation method optimization by RSM

Optimized conditions for each antibiotic

Optimized conditions for six types of AD

Sample analysis

Conclusions

Footnotes

Acknowledgments

Disclosure Statement

References