Nuclear DNA barcodes for cod identification in mildly-treated and processed food products

• Main Authors: Paracchini, Valentina, Petrillo, Mauro, Lievens, Antoon, Dafni-Maria Kagkli, Angers-Loustau, Alexandre
• Format: Text
• Language: unknown
• Published: Taylor & Francis 2019
• Subjects: Biochemistry; Genetics; FOS Biological sciences; Molecular Biology; 59999 Environmental Sciences not elsewhere classified; FOS Earth and related environmental sciences; Ecology; 69999 Biological Sciences not elsewhere classified; Inorganic Chemistry; FOS Chemical sciences; atlantic cod; Gadus morhua
• Online Access: https://dx.doi.org/10.6084/m9.figshare.7579919; https://tandf.figshare.com/articles/Nuclear_DNA_barcodes_for_cod_identification_in_mildly-treated_and_processed_food_products/7579919

Gadoids are a group of fish with historical importance in the fishing industry. The high demand for cod is one of the reasons why cod products are often mislabelled, and numerous observations have been made on the replacement of Atlantic cod ( Gadus morhua ) by cheaper species or its illegal capture in contravention of fish quotas. Fish species identification is traditionally based on morphological features, but this may be difficult in case of heat-treated or processed products, or where the species look similar, as in the Gadoid group. DNA-based approaches (using either nuclear or mitochondrial DNA) are most commonly used in this case, due to their high specificity and to the high resilience of the target molecules to food processing techniques. In this article, we identified, using an automated screening approach, novel barcode regions and their associated primers in the nuclear genome, to be used for the efficient identification of Gadoids. The barcode regions were tested on official and commercial samples, raw or mildly treated products, like frozen, or salted, as well as pre-cooked complex mixtures and processed samples, using next-generation sequencing (NGS) technique. The method proposed could complement existing fish identification strategies in establishing an efficient framework to detect and prevent frauds along the food chain.