ReviewFood and waterborne parasitology2026
Methods for the detection of anisakid material in different food matrices: A systematic review.
Review in Food and waterborne parasitology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Anisakid nematodes are common foodborne parasitic nematodes with an important implication for seafood safety and public health. On top of usual symptoms resulting from infestation with live larvae, these nematodes have allergenic proteins that may cause allergic reactions even in the absence of live larvae. Current methods of detection in industrial settings are mainly limited to gross visual detection and candling, as required by EU legislation. These have a limited sensitivity and do not detect macroscopically invisible allergens that persist after larval removal. In scientific settings, highly sensitive methods such as UV-press and artificial digestion are commonly used, but also don't detect allergens. Alternative detection methods may be useful in seafood processing industry, as well as in scientific settings. A systematic review of detection methods for trace anisakid material has been performed. PubMed, Web of Science, Scopus, and Google Scholar were used as databases. Only English papers with a focus on anisakid detection in food matrices were included, while methods based on visual detection were excluded. A total of 25 articles were identified, and methods were categorized by detection principle. Out of the detection methods, 10 were DNA-based, 14 immunochemistry-based, and 5 mass spectrometry-based (MS-based) methods. There was a lack of standardized reporting of the detection performance parameters which hinders thorough cross comparison. We recommend a chemiluminescent sandwich ELISA method for routine analysis of allergens in food. For larval DNA detection, a LAMP method seems the most promising for fast routine DNA detection. When trace detection of anisakid material is needed, MS-based methods could be most effective.
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