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土壤样品中72种抗生素检测方法研究

Determination of 72 Antibiotics in Soil Samples

  • 摘要: 针对土壤中抗生素残留引发的环境与健康风险,以及现有检测方法在同步分析多类抗生素时存在的灵敏度低、抗干扰能力弱、前处理效率不高等问题,本文建立了一种超高效液相色谱-串联质谱(UPLC-MS/MS)同时检测土壤中72种抗生素的分析方法。该方法涵盖磺胺类、喹诺酮类、四环素类、大环内酯类、氯霉素类、β-内酰胺类及其他类共七大类化合物,通过系统优化质谱参数与色谱条件,采用正负离子切换扫描与同步多反应监测(sMRM)模式,提高检测灵敏度与准确性。在样品前处理中,采用乙腈-EDTA-McIlvaine缓冲液作为提取剂,经过超声-振荡-离心三次提取手段后,方法验证结果表明,目标抗生素在5~300μg/L范围内线性良好(R2>0.99),方法检出限为0.06~0.57μg/kg,加标回收率在51.0%~110.4%,相对标准偏差为1.24%~7.76%。本方法应用于实际城区土壤样品检测,检出六大类共24种抗生素,以喹诺酮类和磺胺类为主,回收率多数高于70%。本方法在同步检测化合物种类数、前处理效率与方法适用性方面优于现有技术,为土壤抗生素污染监测与风险评估提供了可靠分析方法。

     

    Abstract: In response to the environmental and health risks posed by antibiotic residues in soil, as well as the challenges of existing detection methods, which include low sensitivity, weak anti-interference ability, and inefficient sample preparation when simultaneously analyzing multiple classes of antibiotics, an ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was established for the simultaneous detection of 72 antibiotics in soil. This method encompasses seven major classes of compounds: sulfonamides, quinolones, tetracyclines, macrolides, chloramphenicol, β-lactams, and others. By systematically optimizing the mass spectrometry parameters and chromatographic conditions, the method enhances detection sensitivity and accuracy through positive and negative ion switching scans and simultaneous multiple reaction monitoring (sMRM) mode. In the sample preparation process, a novel extraction agent comprising acetonitrile-EDTA-McIlvaine buffer was utilized. After three extraction steps involving ultrasound, oscillation, and centrifugation, method validation results indicated that the target antibiotics exhibited good linearity within the range of 5 to 300μg/L (R2>0.99), with detection limits ranging from 0.06 to 0.57μg/kg and spiked recovery rates between 51.0% and 110.4%, along with relative standard deviations of 1.24% to 7.76%. This method was successfully applied to the detection of actual urban soil samples, identifying a total of 24 antibiotics across six major classes, predominantly consisting of quinolones and sulfonamides, with most recovery rates exceeding 70%. This study presents a reliable analytical method that outperforms existing technologies in terms of simultaneous detection of compound varieties, sample preparation efficiency, and method applicability, thereby providing a robust analysis for monitoring soil antibiotic pollution and assessing associated risks.

     

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