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Abstract

<jats:p>The intentional or accidental adulteration of meat products with undeclared animal species continues to pose significant concerns regarding consumer trust, food safety, and regulatory compliance. Conventional detection methods are often based on organoleptic or biochemical assessments, which lack the sensitivity and specificity required to identify trace levels of adulterants reliably. This study aimed to develop and validate an efficient, cost-effective, and reproducible molecular method for the detection of horse meat (Equus caballus L.) in processed food products using PCR-based amplification of mitochondrial DNA. A highly specific pair of primers was designed in silico to target a 346 bp fragment of the cytochrome oxidase subunit I (COX1) gene, using a reference sequence stored in GenBank (X79547.1). The primers underwent in-house validation in accordance with Good Laboratory Practice (GLP) standards. Validation included specificity testing against DNA from ten different species and matrices (including bovine, porcine, ovine, poultry, plant, and fungal sources), demonstrating exclusive amplification of horse-derived DNA. Sensitivity testing using serial dilutions of horse DNA revealed a detection threshold of 0.005 ng/µl, while the method’s limit of detection (LOD) in mixed DNA matrices was determined at 0.005%. The robustness of the method was evaluated on samples subjected to various food processing techniques, including grinding, boiling, drying, seasoning, and frying. Despite DNA degradation caused by processing, successful amplification was achieved in all cases. The method was further tested on commercial meat products from the Romanian market, correctly identifying declared horse meat and confirming its absence in products labelled as beef or mixed meats. This validated molecular method provides a rapid, specific, and sensitive approach for detecting horse meat in complex food matrices, supporting its future implementation in official food quality and authenticity control protocols.</jats:p>

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Keywords

meat food products detection method

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