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Article

Multi-Residue Determination of 244 Chemical Contaminants in Chicken Eggs by Liquid Chromatography-Tandem Mass Spectrometry after Effective Lipid Clean-Up

by
Lin Zhang
1,2,
Qi Jia
1,2,
Guangqin Liao
1,2,
Yongzhong Qian
1,2 and
Jing Qiu
1,2,*
1
Key Laboratory of Agro-Product Quality and Safety, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
Key Laboratory of Agri-Food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Agriculture 2022, 12(6), 869; https://doi.org/10.3390/agriculture12060869
Submission received: 14 May 2022 / Revised: 11 June 2022 / Accepted: 13 June 2022 / Published: 16 June 2022
(This article belongs to the Section Agricultural Product Quality and Safety)
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Versions Notes

Abstract

:
In this study, we aimed to establish a multi-residue analytical method for the simultaneous detection of chemical contaminants in eggs. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we developed an analytical method that can separate 244 compounds (including β-agonists (25), imidazole and benzimidazoles (31), sulfonamides (22), antihistamines (10), β-lactam (5), insecticides (7), quinolones (24), non-steroidal anti-inflammatory drugs (13), and steroidal hormones (38)) within 30 min. A new enhanced matrix removal-lipid (EMR-Lipid) material was used as a purified sorbent in the QuEChERS clean-up method. Excellent linearity (r > 0.9905) was achieved. Additionally, recoveries ranged between 51.33% and 118.28%, with repeatability (RSDr) and reproducibility (RSDwR) in the range of 1.01–14.22% and 1.08–14.96%, respectively. In all of the compounds, low limits of quantification (LOQs) ≤ 5 μg kg−1 were found. Meanwhile, the detection limit (CCα) and detection capability (CCβ) were 1.88–40.60 μg kg−1 and 2.85–407.19 μg kg−1, respectively. In conclusion, the evaluated method was shown to provide reliable screening, quantification, and identification of 244 multi-class chemicals in eggs and was successfully applied in real samples.

1. Introduction

Chemical contaminant residues in agricultural products are one of the most serious problems that can affect food safety and quality [1,2]. Usually, veterinary drugs are used at therapeutic levels or as food additives to maintain animals’ health and promote animal growth [3]. For example, antibiotics such as quinolones are widely used by farmers against the growth of various microorganisms [4]. Other families of veterinary drugs, such as anthelmintics [5] and coccidiostats [6], can be used to kill or inhibit the growth of microscopic protozoan parasites. Furthermore, the fipronil egg scandal, which occurred in the European Union in 2017, showed that pesticides could be detected in animal-derived foods [7]. The use of these products has been shown to enhance the risk of residues in tissues or eggs and create potential hazards for human health, such as drug resistance and allergies, poisoning, carcinogenicity, or teratogenicity through the paths of the environment and the food chain [8,9,10]. Many researchers have focused on the development of detection methods for poultry tissue such as muscle, liver, and eggs (Table S1). Bilandzic et al. developed a method to detect 178 pesticides in eggs using C18 sorbent (C18) and primary secondary amine (PSA) as clean-up sorbents and obtained good accuracy [11]. Dasenaki et al. developed a multi-residue method for the determination of 115 veterinary drugs in eggs. For over 80% of the compounds, the recoveries were between 50% and 120%. However, the time taken for sample preparation was too long because the lipids and remaining proteins needed to be precipitated at 23 °C for 12 h [3]. Ping et al. used a method for the simultaneous determination of 169 veterinary drugs in chicken eggs, and their results did not suggest that pesticides or other chemical contaminants may exist in eggs [12]. To summarize, all of these methods require a pretreatment procedure that is relatively tedious, they can be quite time-consuming, and they have low detection sensitivity for some analytes. Additionally, using these methods, it is not possible to provide a technical guarantee of the quick and efficient identification of a plurality of mixed pollutants such as pesticides, veterinary drugs, and illegal additives that may exist in eggs at present. Additionally, these methods cannot meet the requirements of quality and safety supervision under new guidelines [13]. Hence, the development of an efficient and universal analytical method is important to ensure the quality and safety of eggs and to protect consumers’ health.
At present, the methods used for multi-residue detection in animal-derived foods are mainly liquid chromatography-tandem mass spectrometry (LC-MS/MS) [14,15], liquid chromatography–time of flight mass spectrometry (LC-TOF/MS) [16,17], as well as gas chromatography-mass spectrometry (GC-MS) [18,19]. Gasification must be performed when analyzing samples using GC-MS; thus, the detection of thermal instability, polarity, and macromolecular compounds is limited. Comparatively, LC-MS/MS combines the advantages of chromatography and mass spectrometry and has dramatically increased sensitivity and selectivity, making it a more general and powerful method for qualitative and quantitative analysis. Additionally, it has been widely used for the detection of chemical contaminants in animal-derived foods. For example, the method used by Jadhav et al. [20] was shown to simultaneously detect 78 drugs and 238 pesticides, including penicillins, quinolones, and tetracyclines, in bovine milk using ultra-fast liquid chromatography-tandem mass spectrometry (UFLC-MS/MS). Meanwhile, Jamie et al. developed an accurate quantitative analytical method to examine the mycotoxin, hormone, and fat-soluble vitamin content in hen egg yolks using a Shimadzu LCMS-8040 (Shimadzu Scientific Instruments, Inc., Columbia, MD, USA) triple quadrupole instrument [21]. LC-MS/MS can also be used for the detection of β-agonists in pork meat [22,23], as well as fipronil in chicken egg and muscle [24], multi-class antibiotic residues [25] and pesticides [26] in honey, and quinolones and tetracyclines in aquatic products [27,28]. All of these methods have shown relatively high accuracy and sensitivity. Additionally, these methods indicate that most analytes ranging from non-polar organochlorine pesticides to polar drugs are suitable for LC-MS/MS analysis. It is a relatively advanced and popular detection method worldwide.
In addition to advanced instruments, the sample pretreatment process is also a crucial link in the analysis of chemical contaminant residues, which can directly decrease the interference of impurities and improve the detection sensitivity and selectivity to obtain satisfactory analysis results. The QuEChERS methodology, introduced by Anastassiades et al. in 2003, is a green chemical extraction and clean-up method used for the detection of veterinary residues [29]. d-SPE sorbents are always used in clean-up methods. For example, C18 and PSA can be used to determine the levels of fipronil and its metabolites [30] and other pesticides in chicken eggs [31]. Zirconium-dioxide-based sorbents have been widely applied in the extract purification of fat-rich samples by different researchers [32,33,34].
In this work, different traditional d-SPE sorbents mixtures (C18, PSA, and ZrO2), as well as enhanced matrix removal-lipid (EMR-Lipid), a novel selective sorbent for lipid removal [35], were compared in order to obtain a sensitive, high throughput, and reliable method for screening multiple drugs in eggs. The method covered a wide range of compounds, including veterinary drugs, pesticides, additives, and other drugs. The recovery and matrix effect was fully evaluated to select the optimal extract conditions. Linearity, limits of detection (LODs), quantification (LOQs), the detection limit (CCα), and detection capability (CCβ) analyses were also carried out for method validation according to the SANTE/11813/2017 [36] and the EU Commission Decision 2002/675/EC [37]. This method provides a powerful method for the Ministry of Agriculture and Rural Affairs to monitor a wide range of potential hazards and guarantee the quality and safety of agro-products in China.

2. Materials and Methods

2.1. Chemicals and Reagents

All standard solutions for chemical contaminants were purchased from Alta Scientific Co., Ltd. (Tianjin, China), which were configured, respectively, with concentrations of 100 mg L−1 according to the categories listed in Table S2. and stored at −20 °C. Working standard solutions needed to be prepared before use at the concentration of 5 mg L−1, and they were prepared for each group of analytes by dissolving an appropriate amount of standard stock solutions in methanol.
High-performance liquid chromatography (HPLC)-grade acetonitrile (>99.5%) was supplied by Fisher Scientific (Fair Lawn, NJ, USA), and MS-grade methanol was obtained from Merck (Darmstadt, Germany). Formic acid (>96%) and ammonium acetate (>99%) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Ultra-pure water was provided by a Milli-Q purification apparatus (Millipore Direct-Q UV, Bedford, MA, USA). Bond Elut dSPE Enhanced Matrix Removal EMR–Lipid (p/n 5982-1010) and Bond Elut EMR-Lipid Polish Pouch, anhydrous MgSO4 only (p/n 5982–0102) were purchased from Agilent Technologies (Santa Clara, CA, USA).

2.2. Sample Preparation

Firstly, 5.00 g of homogenized egg sample were weighed (accurate to 0.01 g) into a 50 mL centrifuge tube, and 10 mL of acetonitrile solution containing 5% formic acid were accurately added. Then, the tissue was dispersed by shaking it manually (for approximately 10 s), and then it was vortexed (2000 rpm) for 5 min and centrifuged for 5 min (4000 rpm).
Then, 3.0 mL of 5 mM ammonium acetate solution were added to the 15 mL EMR-Lipid dSPE tube and were shaken for 2 min using a vortex to create an activated EMR-Lipid dSPE tube. A total of 7.0 mL of acetonitrile extract from the first step were transferred to the activated EMR-Lipid dSPE tube, and then they were mixed by shaking, vortexed for 2 min, and centrifuged for 5 min (4000 rpm). All of the supernatant was poured into an empty 50.0 mL centrifuge tube. Then, the EMR polish powder bag was added to the supernatant, which was shaken rapidly and vortexed for 2 min (2000 rpm), and then centrifuged for 5 min (4000 rpm). Finally, 1 mL of supernatant was drained using a disposable syringe and filtered through a 0.2 μm organic filter membrane into the sample vial for analysis.

2.3. LC-MS/MS Analysis

An Agilent 1290 Infinity II LC system (Agilent Technologies, Santa Clara, CA, USA) was used. A gradient elution program separated the analytes on a 150 × 3.0 mm, 1.8 μm Zorbax Eclipse XDB-C18 column (Agilent Technologies, Santa Clara, CA, USA). The elution solvents were 2 mM ammonium acetate in water containing 0.2% formic acid (A) and 0.2% formic acid in methanol (B) with the following gradient: initial, 5% B; 0–0.5 min, 5% B; 0.5–3 min, 15% B; 3–10 min, 40% B; 10–18 min, 100% B; 18–22 min, 100% B; 22–22.1 min, 5% B. The post-run time was 3 min. The column temperature was maintained at 40 °C, and the injection volume and the mobile phase flow rate were 2 μL and 0.4 mL min−1, respectively. Typical chromatograms are shown in Figure 1.
MS spectrometry analysis was performed on an Agilent 6470 triple quadrupole mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). The dynamic multiple reaction monitoring modes (DMRM) and fast real-time to switch electrospray ion source positive (ESI+) and electrospray ion source negative (ESI−) were used in order to ensure the correct number of compounds were detected in a single run while reducing its time. The instrument conditions were as follows: gas temperature, 250 °C; gas flow, 7 L/min; nebulizer gas, 35 psi; capillary voltage, 3.5 kV; sheath gas heater, 325 °C; sheath gas flow, 11 L/min. The fragmentor voltage and collision energy of each compound are presented in Table S2.

2.4. Method Validation

An analyte was identified when the following parameters were achieved: (1) at least 2 product ions were detected, as defined by the SANTE/11813/2017; (2) the retention time of 2 product ions in the extract corresponded with the calibration standard within a tolerance of ±0.1 min; (3) the ion ratio between multiple reaction monitoring(MRM) 1 and MRM2 from sample extracts was within ±30% (relative) in accordance with the calibration standards. The selectivity was determined by analyzing blank samples from different resources, and the result indicated that there was no endogenous interference showing similar properties with analytes.
In addition, measured parameters including trueness, repeatability (RSDr) and reproducibility (RSDwR), linearity, the limit of quantitation (LOQ), the limit of detection (LOD), decision limit (CCα), detection capability (CCβ), and matrix effect (ME) were evaluated by conducting the recovery experiment at three concentrations (10, 50, and 100 μg kg−1) with six replicates for each level on three separate days. A six-point calibration curve was constructed for all the compounds injected in duplicates for each batch. All of the results were in accordance with the guideline of SANTE/11813/2017 as well as the EU Commission Decision 2002/675/EC for validating methods, in which mean recoveries should be in the range of 70−120% with RSDr of ≤20% and RSDwR of ≤20%. The linearity of the calibration curves for each analyte was studied by injecting calibration solutions at concentrations of 0.1, 0.5, 1, 2, 5, 10, 25, 50, and 100 μg L−1 in both acetonitrile and matrix extracts. The matrix effect (ME) was calculated using the calibration curve of the solvent standard and matrix-matched standards. The calculation formula is as follows:
ME% = (bmatrix/bsolvent − 1) × 100%
where bmatrix and bsolvent are the slopes of the compound in the calibration curves of the matrix and solvent, respectively.

3. Results and Discussion

3.1. Optimization of Pretreatment Methods

3.1.1. Selection of Extract Solvent

To reduce the handing procedure and achieve compounds with high throughput, a universal extract method based on QuEChERS needed to be selected. Various factors can impact the efficiency of extraction. Based on previous reports, the acetonitrile extraction system has generally been used with a mixture of different amounts of water or formic acid (FA) [38,39,40,41]. The addition of water aids the extraction of hydrophilic compounds. As shown in our previous study, by using a mixture of MeOH/water (3:2, v/v) with 1% acetic acid, a maximum rate of recovery between 70% and 120% [42] was achieved in eggs. However, for multi-residue extraction, this method was also shown to cause the incomplete extraction of hydrophobic compounds as well as cloudy and foaming sample extraction due to the low solubility of proteins and lipids in water. In addition, when appropriate formic acid or acetic acid was added to acetonitrile, the ionization efficiency was improved, and the recovery rate was also increased due to the weak alkalinity of most compounds. In this study, we investigated four extraction solutions, including 1% acetic acid in acetonitrile and 1%, 5%, and 10% formic acid in acetonitrile. Meanwhile, the number of compounds was compared with recovery rates in the range of 50–130% and 70–120% in the spiking level of 100 μg kg−1, respectively.
As shown in Figure 2, when 1% acetic acid in acetonitrile and 1% formic acid (FA) in acetonitrile were used, there were 122 and 136 compounds with recovery rates between 70 and 120%, respectively. In particular, the recovery rates of quinolones that were extracted with formic acid in acetonitrile were higher than those extracted with acetic acid. Interestingly, there were no significant differences in the three ratios of formic acid in acetonitrile. The number of compounds with recovery rates between 50% and 130% was approximately equivalent in these three extracts. Especially when extracted with 5% formic acid in acetonitrile, the recovery rates of 158 compounds were between 51.74% and 105.66%, nearly 90% of them were ranged from 70.42% to 105.66%, which means that the extraction method that used 5% formic acid in acetonitrile was shown to enable the maximum number of compounds to meet the recovery range. Therefore, in this study, 5% formic acid in acetonitrile was chosen as an extract solvent.

3.1.2. Selection of Clean-Up Method

To obtain a better recovery result for more compounds, the clean-up method is fundamental. Traditional QuEChERS methods use C18 and PSA as dispersive solid-phase extraction (d-SPE) clean-up sorbents and add different amounts of magnesium sulfate (MgSO4) to remove water [15,43]. Research has shown that PSA can remove interferences such as fatty acids, organic acids, and pigments effectively, while C18 can reasonably remove non-polar components, such as lipids and waxes [44]. However, all of these d-SPE sorbents are non-selective, as their mechanisms are hydrophobic interactions between interferences and sorbents [35]. They not only trap and remove unwanted impurities but also cause the loss of hydrophobic analytes. Zirconia (ZrO2) is another type of material that utilizes Lewis acid/base interactions to selectively retain fatty non-polar interferers. It was successfully used to analyze 30 pesticides in milk, which significantly reduced matrix interference [45]. Meanwhile, it was shown to retain analytes of interest such as tetracyclines, fluoroquinolones, and macrolides [46]. EMR-Lipid is an original chemistry sorbent. By combining size exclusion and hydrophobic interactions mechanism, it selectively removes straight, unbranched hydrocarbon chains and lipid-like molecules in fatty foods such as animal tissue and edible oil [19]. As these sorbents have different characteristics, four clean-up combinations were compared to determine the most efficient clean-up method. The combinations were as follows: (A) 50 mg of ZrO2 + 50 mg of C18, (B) 50 mg of PSA + 50 mg of C18, (C) 150 mg of MgSO4 + 50 mg of PSA + 50 mg of C18, and (D) EMR-Lipid. The recovery rates were calculated, and the number of compounds per 10 percent interval was counted, starting from 50% to 130%, and the distribution of the different recovery rates of the compounds is shown in Figure 3.
Compared to the combination of 50 mg of PSA and 50 mg of C18, when MgSO4 was added, the number of compounds with recovery rates between 70% and 120% was increased to 164. However, 90% of sulfonamides obtained a poor recovery rate that was below 50%, which means adding MgSO4 can strongly affect the extraction of sulfonamides. This result is in accordance with the results of the study performed by Chunna Guo et al., which indicated that when d-SPE was used with a combination of PSA and C18, an obvious improvement in the recovery rates of sulfonamides was seen [47]. In addition, when ZrQ2 combined with C18 instead of PSA was used for purification, there was a slight increase in recovery rates between 50% and 130%. Most importantly, recovery rates in the range of 70–120% (especially between 80% and 90%) were dramatically increased. When EMR-Lipid was used, recovery rates between 50% and 130% and 70% and 120% increased to a total number of 226 and 158, respectively. Additionally, all of the recovery rates of sulfonamides after clean-up with EMR were in the range of 67.51% to 91.69%, which was better than the combination of 50 mg of PSA and 50 mg of C18. Zhao’s results also showed that the use EMR-Lipid cartridge clean-up for the analysis of 39 veterinary drugs in meat matrices could effectively absorb lipids in the matrix, thus improving recovery rates [35]. Above all, the results indicated that EMR-Lipid is an effective sorbent that can simultaneously enable multi-residue screening and obtain satisfactory quantitative results. Hence, EMR-Lipid was chosen as a clean-up sorbents for the following optimization.
After cleaning up with EMR-Lipid sorbent, it is essential to remove the redundant water. Thus the effects of EMR polish and sodium chloride (NaCl) on chemical contaminants were compared. As shown in Figure S1, in the spiked concentration of 100 μg kg−1, the number of compounds used by EMR Polish with recovery rates of 50–130% (198) and 70–120% (179) were both higher than the number of compounds used by NaCl. So, the EMR Polish powder package was finally selected to achieve better results.

3.2. Method Validation

3.2.1. Matrix Effects

In this study, matrix inhibition and matrix enhancement were observed in different compounds in eggs (Figure 4). There were 203 compounds with matrix effects between −18.9% (budesonide) and 19.9% (pefloxacin), which indicated no noticeable matrix enhancement or inhibition; 8 compounds had strong matrix-enhancing effects, namely ampicillin, ciprofloxacin, desloratadine, flubendazole, maduramycin, nigericin, norfloxacin, and pirbuterol acetate. Among them, the maximum enhancement effect of desloratadine was 94.4%; 33 compounds had apparent matrix inhibition, mostly benzimidazoles, and the value was between −68.1% (Nimorazole) and −20.4% (Miconazole). The effect of the matrix could effectively be reduced by minimizing possible interference, improving the chromatographic separation ability, changing the strength of the mobile phase, and changing gradient conditions. Meanwhile, the use of internal standards can effectively correct the ion suppression (or enhancement) induced by co-eluting components present in the sample extracts and improve the quantitative accuracy. However, the internal standard is expensive, and this method involves too many different kinds of drugs; it is difficult to find a suitable internal standard that meets a large number of drug screening conditions. Therefore, matrix-matched calibration standards were used to equalize the response enhancement or exhibition for calibration standards and sample extracts.

3.2.2. Linearity

The regression equation and the correlation coefficient were calculated according to the standard curve of the peak area value and the corresponding solution concentration ratio (μg L−1). The matrix-matched standard curve showed excellent linearity in the entire range (0.1, 0.5, 1, 2, 5, 10, 25, 50, and 100 μg L−1) and the Scorrelation coefficient (r) of all drugs was higher than 0.9905. The specific linear equations and correlation coefficients are shown in Table S3.

3.2.3. LOQs and LODs

The mixture of chemical contaminants was added to samples, and the fortified concentration was 0.2, 0.5, 1, 2, 5, 10, 25, 50, and 100 μg kg−1. The pretreatment method described above was used for the sample for the LC-MS/MS analysis. The LODs and LOQs of this method were determined by the signal-to-noise (S/N) ratios of 3 and 10, with the recovery of LOQs in the range of 50% to 130%, respectively. As is shown in Table 1, the LODs and LOQs ranged from 0.2 to 2 μg kg−1 and 0.5 to 5 μg kg−1, respectively. The LODs of 193 compounds were 0.2 μg kg−1, and the LOQs of 162 compounds were 0.5 μg kg−1. This method showed a high sensitivity for the determination of chemical contaminants.

3.2.4. Accuracy and Precision

The accuracy of the experiments was reflected by the average recovery rate in three fortified concentrations with six replicates. The results of the recovery study are provided in Table 1. When the fortified concentration was 10, 50, and 100 μg kg−1, the recovery rates were 51.33–117.83%, 52.29–118.28%, and 52.65–110.66%, respectively. This result is also shown in Figure 5; about 150, 60, and 30 compounds had recovery rates in the range of 70–120%, 60–70%, and 50–60% at three fortified concentrations, respectively (Figure 5a), and similar trends seemed to occur in all three spiked concentrations (Figure 5b). Above all, for those compounds with recovery rates in the range of 70–120%, this method can be directly used for quantitative calculation. Meanwhile, for those compounds with recovery rates under 70% after qualitative detection with this method, other purification methods or related internal standards should be used for more accurate quantification.
The precision of this method was determined by calculating the repeatability and within-laboratory reproducibility, which were expressed as the RSDr and RSDwR for three fortified levels (n = 6) in the egg matrix. The relevant results are presented in Table 1. In eggs, the values of RSDr and RSDwR were 1.01–14.22% and 1.08–71014.96%. It could be observed that the RSDs were always lower than 20% for all of the chemical contaminants in eggs. The results indicated that this advanced method has excellent precision and reliability.

3.2.5. CCα and CCβ

Due to the large number of compounds, the calculation of CCα and CCβ, according to the method specified by the European Union, is time-consuming and costly. Based on this, the formula for calculating CCα and CCβ was modified [17,43,48]. Among the 244 compounds for eggs, there are only four drugs with maximum residue limits (MRLs) set by the EU, namely flubendazole, lincomycin, oxytetracycline, and tylosin; the set values are 400 μg kg−1, 50 μg kg−1, 200 μg kg−1, and 200 μg kg−1, respectively. Thus, for these four compounds, the CCα = MRL + 1.64 × SDMRL and CCβ = CCα + 1.64 × SDMRL. Most compounds do not have an MRL. The formula is CCα = LOQ + 1.64 × SDLOQ and CCβ = CCα + 1.64 × SDLOQ. The CCα and CCβ values are shown in Table 1, and the values were 1.88–40.60 μg·kg−1 and 2.85–407.19 μg·kg−1, respectively.

3.3. Analysis of Real Egg Samples

To prove the effectiveness of the validated method, 40 eggs from different cities in China were tested using the developed method after being shelled and homogenized. The mass spectrometry conditions were checked, such as the peak of each compound ion pair, the ion ratio, and retention time deviation, which did not exceed ±0.1 min, to determine whether the samples were positive. The ion chromatograms of compounds detected in actual samples are shown in Figure 6.
The results showed that a total of six compounds were detected in 10 egg samples. In three different samples, clopidol at the concentration of 21.16 μg kg−1, oblixacin at the concentration of 10.98 μg kg−1, and danofloxacin at the concentration of 2.36 μg kg−1, were found. Enrofloxacin was detected in two egg samples at concentrations of 3.70 μg kg−1 and 4.64 μg kg−1, respectively. Tilmicosin was detected in two egg samples, and the residual concentrations were 19.26 μg kg−1 and 20.26 μg kg−1, while trimethoprim had the highest detection frequencies, which was detected in three samples with values of 25.06 μg kg−1, 33.80 μg kg−1, and 41.78 μg kg−1, respectively. The European Union has a zero-tolerance principle for enrofloxacin, danofloxacin, trimethoprim, and tilmicosin residues in eggs [49]. These results are in agreement with other studies in which enrofloxacin [50] and trimethoprim [11] were frequently detected. However, both the EU and CAC, as well as China, do not stipulate limitations for clopidol and oblixacin quantities in eggs. Its application to actual samples proved that this method has excellent practicality.

4. Conclusions

In this study, an extraction method with 5% FA in acetonitrile (ACN) and a clean-up method with EMR-lipid material was used with LC-MS/MS to simultaneously detect a total of 244 compounds, thus significantly expanding the range of chemical contaminants monitored in eggs. The compounds included chemicals that are banned and restricted in eggs and may have residual risks following human consumption. In addition, it was shown that the method achieves good separation of the 244 compounds within 30 min. This method was validated according to the guidelines of SANTE/11813/2017 and the EU Commission Decision 2002/675/EC. The results showed that the method had good sensitivity and accuracy. The method was successfully applied to egg samples obtained from different cities, and six compounds were detected, indicating that the method is suitable for monitoring multi-class residues in eggs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture12060869/s1, Figure S1: Effect of EMR and NaCl on the number of chemical contaminants in the recovery of 50–130% and 70–120%; Table S1: Methods for the determination of veterinary drugs and pesticides in eggs [11,31,40,51,52,53,54,55,56,57]; Table S2: The mass spectrometry parameters of 244 chemical contaminants; Table S3: Regression Equation and correlation coefficient of 244 compounds in matrix standard curve.

Author Contributions

Conceptualization, Y.Q. and J.Q.; methodology, L.Z. and J.Q.; formal analysis, L.Z. and G.L.; data curation, L.Z. and Q.J.; writing—original draft preparation, L.Z.; writing—review and editing, L.Z., J.Q. and Q.J.; funding acquisition, Y.Q. and J.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [Coordinated Research Project from International Atomic Energy Agency] grant number [D52041] and [Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences] grant number [CAAS-ASTIP-IQSTAP].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data shown in this study are contained within the article.

Acknowledgments

The authors are thankful to the Chinese Academy of Agricultural Sciences and the reviewers for their comments and suggestions to the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Agriculture 12 00869 g001 550
Figure 1. Typical chromatograms of 244 chemical contaminants in egg samples at the spiked concentration of 50 μg kg−1.
Figure 1. Typical chromatograms of 244 chemical contaminants in egg samples at the spiked concentration of 50 μg kg−1.
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Agriculture 12 00869 g002 550
Figure 2. The number of compounds with recoveries between 50 and 130 (%) and 70 and 120 (%) using different extraction solvents. Abbreviations: AA: acetic acid, FA: formic acid, ACN: acetonitrile.
Figure 2. The number of compounds with recoveries between 50 and 130 (%) and 70 and 120 (%) using different extraction solvents. Abbreviations: AA: acetic acid, FA: formic acid, ACN: acetonitrile.
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Figure 3. Number of compounds in recovery ranges with different clean-up methods.
Figure 3. Number of compounds in recovery ranges with different clean-up methods.
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Figure 4. Matrix effects of 244 chemical contaminants in egg.
Figure 4. Matrix effects of 244 chemical contaminants in egg.
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Figure 5. Distribution of recovery at the fortified concentrations of 10, 25 and 100 μg kg−1. (a) Number of compounds in recovery ranges at the fortified concentrations of 10, 25 and 100 μg kg−1; (b) Trends of recoveries at the fortified concentrations of 10, 25 and 100 μg kg−1.
Figure 5. Distribution of recovery at the fortified concentrations of 10, 25 and 100 μg kg−1. (a) Number of compounds in recovery ranges at the fortified concentrations of 10, 25 and 100 μg kg−1; (b) Trends of recoveries at the fortified concentrations of 10, 25 and 100 μg kg−1.
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Agriculture 12 00869 g006 550
Figure 6. The ion chromatograms of the compounds detected in the actual sample.
Figure 6. The ion chromatograms of the compounds detected in the actual sample.
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Table
Table 1. The results of method validation for determination of 244 chemical contaminants in egg.
Table 1. The results of method validation for determination of 244 chemical contaminants in egg.
Compound NameFortified Concentration (n = 6)LOQLODCCαCCβ
10 μg kg−150 μg kg−1100 μg kg−1
RecoveryRSDrRSDwRRecoveryRSDrRSDwRRecoveryRSDrRSDwR
Unit%μg kg−1
2-Aminobenzimidazole71.83.73.875.55.56.073.92.13.05.02.011.217.4
2-Mercaptobenzimidazole54.79.811.758.29.411.556.47.010.45.02.016.127.2
2-Methyl-4 (5)-nitroimidazole56.94.06.755.65.56.159.83.84.02.01.05.18.2
2-Methyl-5-nitroimidazole58.81.61.761.02.22.464.41.51.60.50.212.724.9
3-Methoxytyramine (3-MT)52.51.92.053.82.73.060.81.41.90.50.211.422.4
4-Acetamido antipyrine72.32.53.275.04.06.471.52.32.40.50.215.530.5
4-Formylamino antipyrine65.52.12.463.14.14.364.71.61.80.50.24.58.6
4-Nitroimidazole97.02.02.597.64.75.993.16.08.60.50.222.945.3
4-Nitrophenol69.89.19.265.96.76.764.93.34.05.02.011.618.1
5-Hydroxymebendazole67.43.13.970.03.94.667.64.97.80.50.25.310.0
5-Hydroxy-thiabendazole65.91.51.866.92.32.669.11.92.40.50.212.223.9
5-Nitrobenzimidazole69.42.72.772.41.22.168.85.07.82.01.05.18.1
Albendazole69.22.02.472.55.86.769.84.06.10.50.29.919.3
Albendazole sulfone71.23.13.975.33.94.677.64.97.80.50.25.310.0
Alclomethasone dipropionate73.03.74.173.53.64.873.03.84.30.50.218.736.9
Amantadine76.22.02.575.63.95.375.73.84.00.50.214.728.9
Amcinonide79.56.37.681.74.86.973.83.43.70.50.215.630.7
Ampicillin58.48.511.153.810.211.253.37.69.35.02.012.920.9
Antipyrine77.51.51.978.61.31.383.11.62.00.50.213.526.5
Azaperol76.42.33.577.81.21.978.61.72.30.50.24.17.7
Azaperone76.13.13.276.82.13.079.52.33.00.50.214.027.5
Bambuterol86.81.92.284.41.92.787.21.41.80.50.28.115.6
Beclomethasone73.44.56.173.93.54.069.23.36.21.00.514.728.3
Beclomethasone dipropionate82.77.49.083.82.44.384.33.53.50.50.225.650.7
Benzimidazole76.73.54.380.33.54.680.51.21.72.01.07.412.7
Betamethasone76.83.43.875.73.73.977.12.62.70.50.218.736.8
Betamethasone valerate86.25.77.471.86.69.776.14.44.90.50.24.48.3
Betamethasone dipropionate85.32.83.181.54.04.480.73.14.00.50.216.231.9
Bromchlorbuterol77.11.82.180.61.52.184.41.82.50.50.219.438.2
Brompheniramine55.33.64.258.04.24.457.43.13.60.50.28.115.6
Budesonide84.25.26.073.45.78.271.31.43.80.50.210.119.7
Cambendazole65.72.63.168.12.72.867.35.07.50.50.25.911.3
Carazolol86.31.71.887.82.73.386.01.11.60.50.23.36.1
Carbadox104.24.05.9107.84.26.0108.12.53.11.00.512.524.1
Carbamazepine76.11.11.374.62.62.881.11.31.80.50.24.99.3
Carbofuran74.01.82.471.33.04.777.61.31.90.50.214.728.8
Cefapirin104.47.07.491.311.912.4110.73.34.72.01.014.627.2
Cefotaxime101.66.78.1101.47.19.0105.41.93.15.02.015.926.8
Ceftiofur72.85.25.776.66.77.283.410.012.42.01.08.715.3
Chlordimeform70.94.54.975.33.44.480.53.75.52.01.07.713.4
Chlormadinone acetate77.36.87.474.24.75.976.91.63.90.50.218.035.5
Chloroprocaine72.12.02.071.62.32.872.41.31.40.50.213.927.4
Chlorpheniramine58.32.63.253.94.55.855.01.52.80.50.27.915.2
Chlorpromazine75.82.03.674.82.73.474.12.43.40.50.23.15.7
Cinchocaine80.52.32.481.82.53.888.32.02.20.50.210.119.7
Cinoxacin61.33.13.559.72.62.960.12.73.00.50.211.021.5
Ciprofloxacin74.51.82.175.61.62.378.41.64.32.01.04.57.0
Clenbuterol84.12.02.583.01.72.484.71.31.60.50.26.412.3
Clenbuterol hydroxymethyl76.92.02.282.01.52.881.41.11.20.50.26.712.9
Clencyclohexerol73.71.72.180.71.82.383.51.61.90.50.210.420.3
Clenhexerol80.81.72.281.32.02.380.21.62.20.50.26.712.8
Clenisopenterol76.82.63.277.92.53.376.71.82.00.50.25.29.8
Clenpenterol78.93.94.683.11.12.683.31.21.90.50.28.616.6
Clenproperol75.51.92.177.82.02.581.11.71.90.50.27.314.1
Clindamycin72.14.14.971.23.54.373.21.11.20.50.27.815.1
Clobetasol 17-propionate79.14.85.583.63.33.977.23.95.20.50.219.137.6
Clobetasone 17-butyrate83.45.05.680.62.73.276.04.65.50.50.223.145.7
Clopidol72.51.12.773.51.22.778.81.81.90.50.29.618.8
Clorprenaline71.72.72.771.62.13.072.12.42.40.50.27.514.5
Cortisone66.38.69.064.53.24.464.42.12.30.50.26.111.8
Coumaphos96.11.61.791.24.56.983.94.35.80.50.27.013.5
Cyproheptadine77.61.72.477.62.93.078.42.93.90.50.210.721.0
Danofloxacin76.22.82.977.92.52.776.31.61.70.50.24.17.7
Dapsone61.91.61.766.62.02.163.11.82.20.50.28.516.5
Deflazacort82.93.94.879.02.83.680.74.75.30.50.225.149.7
Demeclocycline58.25.86.156.99.710.367.37.39.15.02.018.632.2
Desloratadine74.74.36.970.77.29.474.34.05.72.01.07.112.3
Desoxycarbadox51.33.34.152.33.04.655.03.74.90.50.25.410.2
Dexamethasone77.85.45.772.34.66.774.33.33.90.50.218.035.4
Dichlorvos59.94.14.760.95.97.664.61.61.65.02.010.415.7
Diflorasone diacetate71.55.36.377.06.08.580.24.45.50.50.26.713.0
Difloxacin62.43.94.066.63.63.769.94.97.10.50.216.532.5
Dimetridazole71.42.83.271.43.04.474.12.22.50.50.227.354.1
Diphenhydramine83.61.01.783.12.62.982.21.31.60.50.24.48.3
Doxepin76.85.36.580.23.84.479.97.511.12.00.210.218.4
Econazole67.84.96.069.93.94.269.25.28.10.50.212.123.6
Enoxacin73.51.61.673.23.75.672.71.41.95.02.09.013.1
Enrofloxacin64.81.51.766.12.22.869.24.67.00.50.216.332.1
Epitestosterone65.13.13.664.52.62.666.23.94.30.50.212.624.7
Eprinomectin71.66.17.586.611.812.574.26.28.12.01.010.719.5
Febantel85.71.31.783.53.75.176.34.25.70.50.24.58.5
Fenbendazole56.42.94.258.44.36.359.23.64.00.50.28.316.0
Fenoterol59.61.61.858.72.62.964.12.93.20.50.24.07.5
Fenthion sulfoxide74.02.03.578.65.48.272.62.22.50.50.210.420.2
Fleroxacin71.12.02.273.12.02.371.31.31.60.50.25.19.7
Florfenicol82.97.810.481.314.215.087.510.010.15.02.020.235.4
Flubendazole85.72.02.689.34.75.784.65.56.82.00.2403.6407.2
Fluconazole64.93.94.769.36.16.768.76.27.12.00.23.04.0
Fludrocortisone 21-acetate75.69.09.372.710.310.672.07.38.65.02.016.227.3
Fludroxycortide70.48.710.171.04.96.170.22.53.02.01.016.931.8
Flumequine62.62.83.363.22.32.463.02.53.30.50.24.99.2
Flumethasone68.710.512.775.16.29.673.44.95.90.50.214.227.9
Flumethasone pivalate76.74.95.786.24.35.281.64.04.50.50.29.418.3
Flunixin71.01.31.474.53.24.075.84.45.42.01.03.65.2
Fluocinolone acetonide66.65.46.873.14.25.768.06.78.51.00.58.816.6
Fluoromethalone73.32.64.871.78.412.273.94.67.82.01.05.18.1
Fluoxetine86.92.53.086.62.12.989.61.52.00.50.29.518.5
Fluphenazine78.64.44.980.92.43.080.53.14.00.50.211.221.8
Fluticasone propionate87.03.74.979.42.43.578.15.25.70.50.219.438.3
Formoterol75.71.11.179.23.03.278.41.11.10.50.29.318.2
Gatifloxacin56.03.54.353.63.23.656.62.43.20.50.24.58.6
Gemifioxacin53.44.77.856.13.84.060.42.94.72.01.06.010.0
Griseofulvin81.62.83.076.32.22.479.12.22.50.50.213.927.3
Halcinonide73.39.810.570.84.76.270.13.74.70.50.29.418.2
Halofuginone64.93.94.664.63.74.069.01.21.40.50.210.821.2
Haloperidol77.71.21.279.52.62.776.81.52.20.50.26.913.4
2-Hydroxymethyl-1-methyl-5-nitroimidazole (HMMNI)70.01.92.372.84.05.179.62.03.72.00.23.04.1
Hydrocortisone72.24.55.371.33.33.771.13.33.72.01.07.312.6
Hydroxy-ipronidazole72.62.53.571.52.23.472.24.87.42.00.24.97.7
Hydroxyzine75.33.24.575.02.52.975.61.72.40.50.29.218.0
Imipramine77.42.73.080.02.13.880.92.12.60.50.25.911.2
Indoprofen54.52.02.558.38.710.757.02.93.20.50.210.821.2
Ipronidazole73.92.72.877.92.73.878.41.21.60.50.210.420.4
Isoxsuprine84.41.21.386.51.82.887.31.51.50.50.27.614.8
Ivermectin80.29.410.388.35.36.785.92.12.40.50.222.945.3
Ketoconazole56.42.43.155.05.66.758.04.46.52.00.24.26.4
Ketoprofen65.06.67.465.12.53.165.03.84.10.50.29.318.1
Ketotifen83.32.22.481.22.73.081.61.11.80.50.28.416.3
Labetalol72.62.04.072.23.23.379.12.13.10.50.27.815.1
Levamisole78.42.82.982.91.72.684.41.92.30.50.25.711.0
Lidocaine/Diocaine77.01.71.878.61.82.681.01.72.10.50.25.410.3
Lincomycin71.31.82.472.56.48.481.310.613.10.50.255.160.2
Invisible malachite green (LMG)69.81.82.167.54.55.969.15.67.82.00.24.36.7
Lomefloxacin72.02.93.174.83.24.172.41.61.60.50.25.310.0
Loratadine68.71.52.266.21.43.064.54.54.90.50.210.420.3
Lornoxicam55.44.34.558.97.07.357.04.05.32.00.25.99.8
Maduramycin82.911.911.992.04.77.186.93.33.50.50.221.943.4
Marbofloxacin63.32.53.864.64.05.770.63.65.10.50.211.121.8
Mebendazole64.72.83.168.15.16.367.15.17.72.00.25.07.9
Mebendazole-amine (HMEB)61.31.72.464.85.27.162.14.06.02.00.24.46.8
Mefenamic acid54.51.51.757.65.58.254.83.94.71.00.59.918.8
Megestrol acetate74.43.23.677.62.02.977.35.86.80.50.220.640.6
Melengestrol acetate81.23.23.775.42.32.873.34.95.20.50.215.029.6
Melitracene82.61.52.977.53.43.677.22.83.70.50.211.222.0
Meloxicam68.52.83.168.24.67.069.43.05.52.00.25.79.3
Metaproterenol71.47.28.974.32.13.781.02.32.70.50.217.835.2
Methylprednisolone74.63.84.676.64.35.175.93.14.00.50.225.650.7
Methylprednisolone 21-acetate94.75.15.393.76.06.684.55.47.10.50.26.211.8
Methyltestosterone71.34.04.274.84.97.778.96.27.10.50.213.426.2
Metronidazole64.92.22.866.22.12.765.16.09.30.50.216.432.4
Miconazole62.91.11.264.82.84.264.44.16.30.50.27.514.5
Hydroxy metronidazole (MNZOH)55.24.24.758.12.52.761.25.78.82.01.06.110.3
Mometasone furoate116.16.98.2114.71.81.9107.76.77.10.50.220.340.1
Monensin69.31.71.869.75.17.770.31.81.80.50.22.75.0
Moxifloxacin61.91.94.354.92.93.453.61.21.82.00.513.525.1
Nabumetone93.32.93.888.53.43.783.45.46.30.50.29.017.6
N-Acetyl dapson63.88.910.763.03.54.869.74.95.40.50.212.424.2
Nadifloxacin58.23.43.953.26.27.252.94.05.40.50.217.033.4
Nafcillin77.37.29.482.513.113.586.39.911.25.02.011.317.5
Naftifine59.76.79.161.811.413.166.05.67.12.01.013.525.1
Nalidixic acid65.71.32.464.13.03.066.11.63.20.50.214.728.9
Nandrolone78.83.63.875.64.04.576.64.85.80.50.211.823.2
Naproxen58.15.76.554.04.15.260.84.75.25.02.09.213.4
Nequinate55.92.83.156.44.36.356.63.84.10.50.221.342.1
Nigericin75.81.82.172.62.23.074.51.82.30.50.27.213.9
Nimorazole78.79.210.280.610.111.778.47.28.22.00.213.424.9
Norfloxacin74.01.62.272.21.61.771.85.27.40.50.210.821.2
Ofloxacin72.71.41.776.22.42.673.15.17.70.50.215.430.3
Oleandomycin83.61.21.886.54.15.481.91.11.20.50.213.726.9
Orbifloxacin76.93.03.171.13.84.475.13.14.10.50.27.113.8
Oxaprozin56.22.73.355.43.13.153.23.94.80.50.218.636.7
Oxfendazole83.86.47.290.19.09.684.47.18.60.50.211.923.3
Oxibendazole62.61.62.564.23.44.064.15.15.70.50.24.38.1
Oxolinic acid71.02.03.775.56.88.874.311.514.22.01.02.42.8
Oxytetracycline (OTC)77.34.75.175.61.82.471.02.94.65.02.0202.7205.5
Paracetamol85.32.12.488.12.22.381.71.11.40.50.25.510.6
Pefloxacin72.22.42.573.75.97.279.55.27.00.50.216.933.2
Penbutolol87.61.42.879.84.26.277.32.12.40.50.210.420.2
Phenacetin79.81.51.878.52.32.580.31.92.02.00.22.53.0
Phenylbutazone79.06.19.074.18.29.786.65.86.20.50.28.716.8
Phenylethanolamine A88.21.61.987.52.02.694.52.12.20.50.21.93.3
Pipemidic acid75.34.14.876.32.43.672.12.73.42.01.06.310.5
Pirbuterol acetate52.73.33.852.54.95.268.65.17.75.00.28.812.6
Piroxicam64.55.68.365.74.65.366.94.67.52.00.210.719.4
Prednicarbate77.94.04.474.44.85.173.53.94.30.50.29.819.1
Prednisolone62.01.82.057.13.44.059.14.85.20.50.215.230.0
Prednisone74.25.85.976.64.85.578.91.83.82.00.57.012.0
Procainamide58.11.62.553.16.910.363.21.71.80.50.27.213.9
Procaine/Novocaine60.22.44.658.49.511.655.12.24.72.01.04.46.7
Procaterol61.53.23.864.64.55.471.81.73.72.00.25.79.5
Progesterone92.41.11.787.83.75.381.66.27.52.00.522.643.3
Promethazine60.94.96.261.74.35.363.01.02.82.00.28.114.2
Propetamphos58.910.511.063.29.010.952.79.29.65.00.214.724.5
Propionylpromazine78.11.21.279.33.74.079.72.03.40.50.28.516.4
Propranolol80.22.12.573.85.06.988.63.85.52.01.012.623.2
Propyl thiouracil64.36.57.164.67.48.367.65.05.35.01.012.019.0
Ractopamine76.12.83.379.92.93.582.21.11.40.50.222.043.5
Ritodrine83.73.53.681.53.85.171.83.74.50.50.219.037.5
Robenidine54.82.94.557.84.75.954.02.05.65.00.29.113.1
Ronidazole75.91.52.473.03.84.372.81.93.42.00.23.95.7
Salbutamol78.14.45.372.23.24.176.71.12.60.50.23.66.6
Salmeterol70.94.45.271.24.25.673.23.03.00.50.29.819.1
Sarafloxacin76.21.01.172.84.05.874.43.94.02.00.53.85.6
Secnidazole66.33.63.769.32.22.967.01.85.10.50.23.66.7
Sineptina73.33.23.979.26.77.785.34.85.52.01.06.611.3
Sotalol73.41.62.873.43.33.377.32.22.60.50.221.843.1
Sparfloxacin85.42.42.574.14.45.081.62.22.20.50.23.76.9
Sulfabenzamide73.42.93.277.23.24.573.93.54.90.50.23.76.9
Sulfachloropyridazine57.44.76.062.16.87.461.03.15.52.00.26.410.8
Sulfadiazine61.72.82.965.43.84.563.41.15.50.50.28.516.4
Sulfadimethoxine66.61.72.067.94.65.966.31.44.30.50.25.29.9
Sulfadimidine64.32.73.667.13.84.066.33.43.40.50.211.221.9
Sulfadoxine67.52.83.469.23.94.369.15.66.40.50.26.813.1
Sulfamerazine60.43.03.063.53.03.064.95.45.80.50.26.813.1
Sulfameter60.92.83.962.73.03.062.45.77.50.50.211.522.6
Sulfamethizole57.24.96.061.94.75.562.83.35.12.00.27.613.2
Sulfamethoxazole57.25.37.461.32.33.059.95.77.12.00.28.915.8
Sulfamethoxypyridazine64.83.84.169.62.73.769.34.25.70.50.210.821.2
Sulfamonomethoxine63.02.83.364.44.85.264.16.07.82.00.24.97.7
Sulfamoxol62.35.37.165.12.13.262.56.87.40.50.214.127.7
Sulfanilamide73.12.42.983.25.46.884.11.31.90.50.28.115.6
Sulfanitran70.34.65.075.18.611.477.26.69.75.01.014.624.2
Sulfaphenazole68.82.63.870.42.32.870.22.25.00.50.210.119.7
Sulfapyrazole71.21.72.271.33.74.471.65.35.30.50.210.720.9
Sulfapyridine61.42.83.663.53.54.063.85.57.40.50.29.117.6
Sulfaquinoxaline56.76.36.863.26.17.660.95.65.90.50.213.025.5
Sulfathiazole53.03.74.656.13.53.956.95.67.45.00.28.010.9
Sulfisomidine60.92.83.363.52.52.667.55.05.62.00.54.87.7
Sulindac70.54.04.772.94.15.673.81.92.72.00.516.931.7
Sulphacetamide117.83.94.0118.32.83.1106.51.31.30.50.23.15.8
Sulpiride72.51.63.277.84.46.683.45.36.00.50.29.017.4
Tenoxicam62.42.93.461.63.74.965.14.25.12.00.25.58.9
Terbutaline85.23.23.382.49.012.172.73.84.20.50.218.035.5
Terfenadine84.81.03.584.63.85.383.62.43.40.50.26.713.0
Testosterone62.91.12.363.33.43.758.04.55.30.50.28.115.7
Tetracaine55.44.75.853.47.010.953.82.83.40.50.24.38.1
Thiabendazole59.91.11.362.22.92.965.05.55.72.00.23.14.2
Tilmicosin95.66.97.181.17.58.481.72.52.52.00.212.823.7
Tinidazole63.22.32.565.53.63.666.64.75.00.50.25.610.7
Tolfenamic acid75.73.84.276.95.16.572.23.14.02.01.07.312.5
Tolmetin75.46.97.176.07.69.676.53.54.82.00.210.418.7
Toltrazuril74.93.44.271.51.92.071.79.912.85.02.07.29.3
Toltrazuril sulfone88.64.35.180.01.92.591.26.58.75.02.012.820.7
Toltrazuril-sulfoxide73.75.05.776.73.85.277.16.06.82.01.010.018.0
Tosufloxacin55.25.05.462.211.714.066.88.311.05.00.215.926.8
Triamcinolone acetonide81.53.26.781.03.33.581.82.54.30.50.27.815.1
Triclabendazole54.63.03.957.22.62.757.44.65.95.00.27.39.6
Trimethoprim74.11.11.280.72.92.981.71.11.40.50.211.221.8
Tulobuterol80.71.81.881.22.52.782.11.62.00.50.24.79.0
Tylosin74.23.34.678.72.63.281.31.21.90.50.2206.4212.8
Valnemulin66.96.17.356.34.86.060.86.47.95.02.010.115.1
Virginiamycin M162.13.94.268.93.33.866.11.83.00.50.211.722.9
Xylazine76.11.51.777.32.93.578.11.21.40.50.29.217.9
Zolpidem77.21.01.179.32.42.578.21.91.90.50.23.97.4
Abbreviations: RSDr: repeatability, RSDwR: reproducibility, LOD: limit of detection, LOQ: limit of quantitation, CCα: decision limit, CCβ: detection capability.
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MDPI and ACS Style

Zhang, L.; Jia, Q.; Liao, G.; Qian, Y.; Qiu, J. Multi-Residue Determination of 244 Chemical Contaminants in Chicken Eggs by Liquid Chromatography-Tandem Mass Spectrometry after Effective Lipid Clean-Up. Agriculture 2022, 12, 869. https://doi.org/10.3390/agriculture12060869

AMA Style

Zhang L, Jia Q, Liao G, Qian Y, Qiu J. Multi-Residue Determination of 244 Chemical Contaminants in Chicken Eggs by Liquid Chromatography-Tandem Mass Spectrometry after Effective Lipid Clean-Up. Agriculture. 2022; 12(6):869. https://doi.org/10.3390/agriculture12060869

Chicago/Turabian Style

Zhang, Lin, Qi Jia, Guangqin Liao, Yongzhong Qian, and Jing Qiu. 2022. "Multi-Residue Determination of 244 Chemical Contaminants in Chicken Eggs by Liquid Chromatography-Tandem Mass Spectrometry after Effective Lipid Clean-Up" Agriculture 12, no. 6: 869. https://doi.org/10.3390/agriculture12060869

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