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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/171720

Estimation of the number of Anisakis larvae in commercial fish using a descriptive model based on real-time PCR

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BACKGROUND: Seafood parasitation by Anisakis (Anisakidae) larvae has been reported in most of the oceans and seas worldwide.The presence of these nematodes in commonly consumed fish represents a potential hazard for consumers as they can provoke gastrointestinal symptoms and allergic reactions. In the present work, the capacity of a SYBR Green qPCR protocol to quantify Anisakis larvae in commercial fish was evaluated using experimentally spiked samples with different numbers (0-50) of A. simplex third-stage larvae (L3). To verify the agreement of the obtained results, 25 naturally infected fish specimens of Atlantic blue whiting underwent a parallel visual inspection. RESULTS: The logarithmic behavior of the Cq data obtained from the experimentally spiked samples allowed the development of a descriptive mathematical model that correlates the Cq value with the number of Anisakis larvae (R2 = 0.9908, CV = 2.37%). In the commercial blue whiting specimens there was a high correlation between the results of the molecular technique and the visual inspection (R2 = 0.9912); the Bland-Altman analysis showed that 94% of the differences were within the limits of agreement (−4.98 and 6.68), indicating the reliability of the descriptive mathematical model based on the SYBR Green qPCR technique. CONCLUSION: The descriptive function presented based on the SYBR Green qPCR assay is promising as a sensitive and accurate tool for measuring the Anisakis larval load in commercial fish, with a potential application not only in the food industry but also in prevention programs for public health.

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GODÍNEZ GONZÁLEZ, Carla, et al. Estimation of the number of Anisakis larvae in commercial fish using a descriptive model based on real-time PCR. Journal of the Science of Food and Agriculture. 2020. ISSN 0022-5142. [consulted: 13 of June of 2026]. Available at: https://hdl.handle.net/2445/171720

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