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IntroductionDifenoconazole (DIFE) is a common pesticide used in citrus cultivation; excessive intake can cause neurological damage to the organism, and the existing colloidal gold immunochromatographic test strips cannot meet the requirements for the detection of citrus samples.MethodsDifenoconazole test strip was prepared based on the colloidal gold immunochromatographic technique (GICT), and its application in citrus samples was investigated; with colloidal gold (CG) as the probe, the optimization of GICT parameters, and the determination of reaction method, the immunochromatographic test strips for the detection of DIFE in citrus was developed, and the limit of detection (LOD), specificity, accuracy, and stability of the test strips were verified.ResultsThe results showed that the visual detection limit of the prepared colloidal gold immunochromatographic test strips was 0.2 mg/kg and the quantitative range was 0.06–0.6 mg/kg, and the test strips could specifically identify DIFE and have no cross-reaction with other common triazole pesticides. The detection method established in this study was verified by the GC–MS method, and the detection results achieved good consistency (R2 > 0.98).ConclusionThe test strips developed in this study have good performance and can be used for highly sensitive detection of citrus samples.

Enrofloxacin (ENR) is a veterinary antibiotic used to treat bacterial infections in livestock. It chiefly persists in foods and dairy products, which in turn pose severe risks to human health. Hence it is very important to detect the ENR in foods and dairy products to safeguard human health. Herein, we attempted to develop a single-step detection lateral flow immunochromatographic assay (LFIA) using gold nanoparticles (AuNPs) for the rapid and on-site detection of ENR in milk samples. An anti-enrofloxacin monoclonal antibody (ENR-Ab) was conjugated with AuNPs for the specific detection of ENR in milk samples. For sensitivity improvement, many optimization steps were conducted on LFIA test strips. The visual limit of detection (vLOD) was found to be 20 ng/ml with a cut-off value of 50 ng/ml in the milk samples. The obtained LOD and cut-off value were within the safety limit guidelines of the Ministry of food and drug safety, South Korea. The test strip showed negligible cross-reactivity with ENR analogs, and other components of antibiotics, this indicates the high specificity of the LFIA test strip towards ENR. The designed test strip showed good reliability. The visual test results can be seen within 10 min without the need for special equipment. Therefore, the test strip can be employed as a potential detection strategy for the qualitative on-site detection of enrofloxacin in milk samples.

Introduction: Difenoconazole (DIFE) is a common pesticide used in citrus cultivation; excessive intake can cause neurological damage to the organism, and the existing colloidal gold immunochromatographic test strips cannot meet the requirements for the detection of citrus samples., Methods: Difenoconazole test strip was prepared based on the colloidal gold immunochromatographic technique (GICT), and its application in citrus samples was investigated; with colloidal gold (CG) as the probe, the optimization of GICT parameters, and the determination of reaction method, the immunochromatographic test strips for the detection of DIFE in citrus was developed, and the limit of detection (LOD), specificity, accuracy, and stability of the test strips were verified., Results: The results showed that the visual detection limit of the prepared colloidal gold immunochromatographic test strips was 0.2 mg/kg and the quantitative range was 0.06-0.6 mg/kg, and the test strips could specifically identify DIFE and have no cross-reaction with other common triazole pesticides. The detection method established in this study was verified by the GC-MS method, and the detection results achieved good consistency ( R 2  > 0.98)., Conclusion: The test strips developed in this study have good performance and can be used for highly sensitive detection of citrus samples., Competing Interests: YW was employed by Beijing Kwinbon Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Gong, Liu, Zhu, Zhang, Zhao, Yin, Liu, Wang and Wan.)

Enrofloxacin (ENR) is a veterinary antibiotic used to treat bacterial infections in livestock. It chiefly persists in foods and dairy products, which in turn pose severe risks to human health. Hence it is very important to detect the ENR in foods and dairy products to safeguard human health. Herein, we attempted to develop a single-step detection lateral flow immunochromatographic assay (LFIA) using gold nanoparticles (AuNPs) for the rapid and on-site detection of ENR in milk samples. An anti-enrofloxacin monoclonal antibody (ENR-Ab) was conjugated with AuNPs for the specific detection of ENR in milk samples. For sensitivity improvement, many optimization steps were conducted on LFIA test strips. The visual limit of detection (vLOD) was found to be 20 ng/ml with a cut-off value of 50 ng/ml in the milk samples. The obtained LOD and cut-off value were within the safety limit guidelines of the Ministry of food and drug safety, South Korea. The test strip showed negligible cross-reactivity with ENR analogs, and other components of antibiotics, this indicates the high specificity of the LFIA test strip towards ENR. The designed test strip showed good reliability. The visual test results can be seen within 10 min without the need for special equipment. Therefore, the test strip can be employed as a potential detection strategy for the qualitative on-site detection of enrofloxacin in milk samples., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alhammadi, Yoo, Sonwal, Park, Umapathi, Oh and Huh.)