为定量检测水产品中的组胺（Hist）、尸胺（Cad）和腐胺（Put），该研究构建了一种基于氮硫掺杂碳点和二氧化锰纳米花的复合材料（N,S-CDs@MnO₂）及二胺氧化酶（DAO）的新型荧光生物传感器。通过对合成材料的表征验证了N,S-CDs@MnO₂的成功合成，通过对N,S-CDs@MnO₂-DAO传感器检测条件的优化实现了对目标生物胺（BAs）的定量检测。结果表明，该荧光生物传感器对Hist、Cad和Put的检测线性范围均为0~200 μmol·L^-1，检出限（LOD）分别为0.59、0.65和0.85 μmol·L^-1，方法灵敏度良好。对具有潜在干扰的氨基酸、无机盐离子和还原性物质无显著响应，方法选择性良好。应用于实际水产样品的检测中，加标回收率在93.17%~105.34%之间，相对标准偏差（RSD）小于5.83%，且在鱼、虾和贝类样品的检测中，传感器与HPLC法检测结果的相对偏差在10%以内，方法准确性良好。因此，开发的N,S-CDs@MnO₂-DAO荧光生物传感器对Hist、Cad和Put的检测具有良好的灵敏度、选择性和准确性，能够为水产品安全和质量控制提供有力工具。

A novel fluorescent biosensor was developed for the quantitative detection of histamine (Hist), cadaverine (Cad), and putrescine (Put) in seafood. This biosensor was based on the composite material of N,S co-doped carbon dots and manganese dioxide nanoflowers (N,S-CDs@MnO₂) and diamine oxidase (DAO). The successful synthesis of N,S-CDs@MnO₂ was confirmed through the characterization of the synthetic material, and the quantitative detection of target biogenic amines (BAs) was achieved by optimizing the detection conditions of the N,S-CDs@MnO₂-DAO sensor. The results showed that the linear detection ranges of the fluorescence biosensor for Hist, Cad and Put were all 0~200 μmol·L^-1, and the detection limits (LOD) were 0.59, 0.65 and 0.85 μmol·L^-1, respectively, with good method sensitivity. There was no significant response to amino acids, inorganic salt ions, or reducing substances with potential interference and with good method selectivity. For the detection of actual seafood, the spiked recovery rate was between 93.17% and 105.34%, and the relative standard deviation (RSD) was less than 5.83%. In the detection of fish, shrimp, and shellfish samples, the relative deviation between the detection results of the sensor and the HPLC method was less than 10%, indicating good accuracy of the method. Therefore, the developed N,S-CDs@MnO₂-DAO fluorescent biosensor had good sensitivity, selectivity, and accuracy for the detection of Hist, Cad and Put, and was suitable as an effective tool for the safety and quality control of seafood.

国家重点研发计划项目；山东省博士后基金