*3.3. Performance of the ic-ELISA Analysis*

Simple sample pretreatment will directly affect the analytical results of the ELISA. A large number of compounds in the sample matrix often have a strong interfering effect on the assay [19]. In previous studies, ethyl acetate was added to animal tissues for extraction and needed to be incubated for 2 h. After nitrogen blowing, it was degreased with hexane and diluted with PBS. Other studies showed that samples can be extracted by sodium hydroxide and acetonitrile, and diluted with diluent (methanol: PBS = 5:95) [23,24]. In this study, sodium hydroxide, ethyl acetate and acetonitrile were selected for the rapid extraction of DMEQ from the actual samples. This extraction method is faster and more efficient than previous methods.

To characterize the detection performance of the assay, we measured the LODs, LOQs, accuracy, and precision of the assay. Table 3 shows the results of 20 different blank samples, with the LODs and LOQs of samples ranging from 0.47–0.58 μg/kg and 0.61–0.90 μg/kg, respectively. The recoveries of the above samples spiked with DMEQ at the levels of 1 × LOQ, 2 × LOQ, and 4 × LOQ are listed in Table 3, which were in the range of 73.7% to 107.8%. The CVs were less than 10.8%, and we can find that the swine and chicken livers have the highest CVs. This is due to the different metabolic pathways of MEQ in different species and the differences in the enzymes involved [25,26]. All of the above results indicate that the analytical method developed in this study is highly sensitive, with good sample pretreatment and a low coefficient of variation.

**Table 3.** LOD, LOQ, and CVs of the samples spiked with DMEQ.


<sup>1</sup> "*n*" means the number of parallel detection.

In order to determine the stability of this method, a study was investigated to correlate the stability of the coating antigen, the antibodies, and the DMEQ standard used in the assay system. The coated enzyme plates and the prepared antibodies were placed in a 37 ◦C thermostat and taken out for ELISA validation on days 0, 2, 4, 6, and 8, respectively. The stability of the method was evaluated using the titer and the IC50 value of the analysis as evaluation criteria. The results in Figure 5A,B show that the titer of the enzyme plate and the antibody remained above 80%, and the IC50 fluctuated within 20% after 8 d of storage at 37 ◦C. According to the empirical Arrhenius equation, this implies that the enzyme plates and antibodies can be stored for at least 12 months at 4 ◦C. We also measured the IC50 of the standard concentration of DMEQ at 4 ◦C every month. The results showed that the standard solution of DMEQ can still be stored at 4 ◦C for at least 6 months and keep the IC50 stable (Figure 5C).

**Figure 5.** Stability testing of DMEQ–AOAA–OVA coating antigen (**A**), antibody (**B**), and DMEQ standard solution (**C**).

In addition, the reliability of the method was verified by analyzing the four spiked chicken samples (0.5, 1.0, 2.0, and 4.0 μg/kg) by ic-ELISA and LC–MS/MS, respectively. Figure 6 shows the fitted correlation coefficient (R2) of 0.9973 between this analytical method and the instrumental analysis results in chicken. This demonstrates the reliability of the established ic-ELISA method for DMEQ, which provides strong technical support for the monitoring of Qx drug residues in food.

**Figure 6.** Correlation of LC–MS/MS and ic-ELISA for the analysis of spiked chicken samples.

#### **4. Conclusions**

In this study, we creatively synthesized a novel hapten to generate mAb against DQx. Based on the mAb, we firstly developed an ic-ELISA method to detect the residues of Qx in animal-derived foods, which was rapid, accurate, and sensitive. The simple analytical method reduces the sample pretreatment time, ensures higher efficiency, and meets the requirements for Qx residue analysis. The prepared mAb and the developed ic-ELISA method can monitor the residues of Qx in animal-derived foods to ensure food safety and human health.

**Author Contributions:** Conceptualization, J.X., W.S. and M.L.; methodology, H.L., W.S. and M.L.; software, J.X.; validation, J.X., W.S., M.L., J.L. and X.H.; formal analysis, W.S. and M.L.; investigation, W.S. and M.L.; data curation, W.S. and M.L.; writing—original draft preparation, W.S., J.X. and M.L.; writing—review and editing, J.X. and D.P.; visualization, W.S. and M.L.; supervision, D.P.; and funding acquisition, D.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors are grateful to the Hubei University Student Innovation and Entrepreneurship Training Program (S202110504022), the Fundamental Research Funds for the Central Universities (2662022DKPY007), and the HZAU-AGIS Cooperation Fund (SZYJY2022024).

**Data Availability Statement:** Data is contained within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.
