Molecular Studies to Identify to Commercial Fraud in Processed Meat in the Egyptian Market

Document Type : New and original researches in the field of Microbiology.

Authors

1 Department of Department of Zoology, Faculty of Science, Menoufia University, Shebin El-Kom, Egypt

2 Department of Nutrition and Food Science, Faculty of Home Economics, Menoufia University, Shebin El-Kom, Egypt

Abstract

Background: The rising consumption of meat products such as luncheon meat and kofta in large urban areas has underscored the importance of quality control and accurate labeling. With the increasing cost of red meat and the difficulty of detecting adulteration in ground beef, it is likely that cheaper animal and plant proteins are being substituted for beef in these products. Objective: This study aimed to evaluate the accuracy of labeling in beef luncheon and premium kofta products. A sensitive and rapid uniplex-PCR technique was employed to detect the presence of pig, mouse, dog, cat, and chicken meats-species considered indicators of commercial fraud in such products. Methodology: Samples were collected from markets in Shebin El Kom City, Egypt, including five brands of premium kofta and five brands of luncheon meat made with beef. Species-specific primers were used to optimize uniplex-PCR for beef, chicken, pig, mouse, cat, and dog meat detection. Mitochondrial genes and RNA were targeted for the specific and qualitative identification of cat, dog, and rodent (mouse or rat) tissues in food. Results: the results revealed the presence of undeclared meats-including pig, cat, chicken, mouse, and dog—in the tested kofta and luncheon products. Although 5% of the samples were free from pig residues, the majority were contaminated with unlisted animal meats. Notably, one brand of luncheon meat contained no beef at all. Conclusion: The uniplex-PCR assay is a rapid and dependable diagnostic tool that can be routinely employed by quality control laboratories to detect meat fraud and ensure food authenticity.

Keywords

Main Subjects

Files

Share

How to cite

Statistics