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冬虫夏草微滴式数字聚合酶链式反应定量检测方法的建立及应用

Establishment and Application of Droplet Digital PCR Quantitative Detection Method for Cordyceps Sinensis
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摘要 目的建立一种定量检测冬虫夏草菌的微滴式数字聚合酶链式反应(ddPCR)检测方法,探讨其定量检测冬虫夏草的可行性。方法使用冬虫夏草菌特异性引物和探针建立ddPCR检测方法,对冬虫夏草及其常见伪品进行鉴别,对其特异性和自制混伪品进行检测和评估,并采用该方法对市场流通的冬虫夏草产品中的冬虫夏草菌成分进行检测。结果ddPCR检测方法对冬虫夏草菌具有良好的检测特异性,且在掺伪0%~70%范围内定量检测的准确性高;此外,对冬虫夏草及其制剂市售样品的检测结果显示,该方法可用于冬虫夏草的定量检测。结论ddPCR检测方法可成功检测市售冬虫夏草产品中的冬虫夏草菌成分,具有市场应用价值,可为含冬虫夏草成分的保健食品和药品提供定量和鉴伪检测依据。 Objective A droplet digital PCR(ddPCR)method for quantitative detection of Cordyceps mycelial was established,and its feasibility in quantitative detection for Cordyceps sinensis was investigated.Methods With the detection method of ddPCR established by using the specific primers and probes of Cordyceps mycelial to identify Cordyceps sinensis and its common counterfeits,its specificity and self-made counterfeits were tested and evaluated.In addition,the method was used to detect the Cordyceps mycelial components in the Cordyceps sinensis products in the market.Results There was good specificity in the detection methid of ddPCR for the detection of Cordyceps mycelial,with high accuracy in the quantitative detection of adulteration in the range of 0%~70%.In addition,the test results of Cordyceps sinensis and its preparations on the market samples show that the method can be used for quantitative detection of Cordyceps sinensis.Conclusion The Cordyceps mycelial components in Cordyceps sinensis products on the market can be successfully detected by ddPCR,which has market application value,and can provide quantitative and identification basis for the health food and drugs containing Cordyceps sinensis components.
作者 潘映秋 夏慧丽 洪亮 李兆奎 卢启寰 周晶莹 Pan Yingqiu;Xia Huili;Hong Liang;Li Zhaokui;Lu Qihuan;Zhou Jingying(Taizhou Institute of Drug Inspection,Taizhou Food Inspection and Testing Center,Taizhou Zhejiang 317700,China)
出处 《医疗装备》 2023年第8期32-35,共4页 Medical Equipment
基金 台州市科技计划项目(1901ky84)。
关键词 冬虫夏草菌 冬虫夏草 微滴式数字聚合酶链式反应 定量 真伪 Cordyceps mycelial Cordyceps sinensis Droplet digital PCR Quantitative Authenticity
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