An effective droplet digital PCR method for identifying and quantifying meat adulteration in raw and processed food of beef (Bos taurus) and lamb (Ovis aries)

Abstract

Meat adulteration caused by economic intentions has long been concerning food safety. Accurate quantification of meat products can distinguish between artificial adulteration and unintentional contamination during its processing or packaging. After determining the species-specific constant and the correlation between mass fraction and copy number of each species, we developed an effective approach-based droplet digital PCR (ddPCR) platform that can identify target species with high detection sensitivity: 13 copies of beef, 6 copies of lamb, 13 copies of pork, 13 copies of chicken, 6 copies of duck, and 6 copies of turkey. Using this method, a level as low as 1% of the adulterated ingredients blended in beef and lamb was accurately quantified. Following the addition of reference species, several quantitative equations were constructed for simultaneous analysis of different species in commercial processed products; even the animal components with a minimum content of 0.5% can be quantified to judge whether the label ingredients are fraudulent. This suggests the feasibility of the proposed strategy for the accurate identification and quantification of animal-derived adulteration according to the processing degree and food commodity.