Quantitative Analysis of Pork in Adulterated Beef Products by Droplet Digital Polymerase Chain Reaction
LIU Libing1,2, SHI Ruihan1,2, XIANG Jialin1, SUN Xiaoxia1,2, FU Qi1, WANG Jinfeng1, ZHOU Wei3, WANG Suhua4, GUO Chunhai1,2, WANG Jianchang1,2,*
1.Shijiazhuang Customs District, Shijiazhuang 050051, China; 2.Hebei Academy of Inspection and Quarantine, Shijiazhuang 050051, China; 3.Hebei Food Inspection and Research Institute, Shijiazhuang 050200, China; 4.Wenzhou Customs District, Wenzhou 325000, China
Abstract：According to the national standard GB/T 25165?2010, specific primers and probes targeting bovine growth hormone gene (GH) and porcine prion protein gene (PRNP), respectively were synthesized. The ratio of bovine GH gene copy number per unit mass of beef to porcine PRNP gene per unit mass of pork was derived to be a constant value theoretically, which was analyzed and verified by droplet digital polymerase chain reaction (ddPCR). This value was used to quantitatively detect added pork components in beef samples and pork components in adulterated commercial beef products. The results showed that ddPCR was highly accurate and repeatable. When beef added with 5%–99% was detected by ddPCR, the absolute error was less than 1.28%, the coefficient of variation less than 6.5%, and the recovery of pork 99.09%–102.80%. Pork components were detected in 4 out of 20 beef products; the content of pork was 29.19%–98.15% in three of the four and 0.12% (lower than the limit of detection of ddPCR, 5%) in the remaining one. The above results demonstrated that the developed ddPCR method is highly applicable for the quantitative determination of pork components in adulterated commercial beef products.
刘立兵，石蕊寒，项佳林，孙晓霞，付 琦，王金凤，周 巍，王素华，郭春海，王建昌. 应用微滴式数字聚合酶链式反应定量检测牛肉制品中的猪源性成分[J]. 肉类研究, 2018, 32(9): 29-34.
LIU Libing, SHI Ruihan, XIANG Jialin, SUN Xiaoxia, FU Qi, WANG Jinfeng, ZHOU Wei, WANG Suhua, GUO Chunhai, WANG Jianchang. Quantitative Analysis of Pork in Adulterated Beef Products by Droplet Digital Polymerase Chain Reaction. Meat Research, 2018, 32(9): 29-34.
2018, Vol. 32 
