Development of a DNA metabarcoding method for the identification of animal sources in pet food.

The increasing popularity of pets worldwide has highlighted the critical need for transparency and accuracy in the pet food industry, particularly due to rising concerns about pet health, food fraud, and regulatory compliance. This study aims to develop a robust DNA barcoding and metabarcoding methodology, for identifying animal species in commercial pet food products by integrating Sanger sequencing and Next-Generation Sequencing (NGS) techniques. It focused on the use of mitochondrial markers (COI and miniCYTB targets) to evaluate ingredient authenticity and detect adulteration. The study was conducted on 100 pet food samples, comprising single-protein and regular diet products from diverse brands. DNA extraction, quantification, PCR amplification, and sequencing were applied. Sanger sequencing was performed on single-protein samples only, and limitations were encountered with the COI target, while miniCYTB provided higher amplification success and specificity. However, NGS using Illumina platforms, which was performed on all pet food samples, enabled high-throughput analysis and identified extensive species mislabeling which was not detected with Sanger sequencing. The results revealed mislabeling across 82.36% of single-protein samples, and 75.76% with regular diet samples, exposing prevalent substitution with undeclared species like chicken and pork. It was also noticed that high-value products were substituted with lower-cost ingredients. These findings emphasize economic motivations behind fraud, raising concerns about regulatory gaps and consumer trust. The developed methodology proved effective in uncovering misrepresentation and demonstrated its value in improving food safety and transparency in the pet food supply chain. However, it also showed that improvements in the data analysis part are needed for a better specificity of results. The thesis concludes that DNA barcoding and metabarcoding serve as powerful tools for pet food authentication and broader applications in food safety. Future work should address database limitations and expand this approach to other food sectors, contributing to global efforts to combat food fraud and protect consumer interests.