{"refrec":{"BRefID":310950,"RR":"<b>David, F.</b> (2019). A worldwide reliable indicator to differentiate wild vs. farmed Penaeid shrimps based on 207 fatty acid profiles. <i>Food Chemistry 292</i>: 247-252. <a href=\"https://dx.doi.org/10.1016/j.foodchem.2019.04.042\" target=\"_blank\">https://dx.doi.org/10.1016/j.foodchem.2019.04.042</a>","BEntID":303311,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Food Chemistry 292</i>: 247-252. <a href=\"https://dx.doi.org/10.1016/j.foodchem.2019.04.042\" target=\"_blank\">https://dx.doi.org/10.1016/j.foodchem.2019.04.042</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"David, F.","OrigTitleTranslFlag":0,"Authorstringtrunc":"David, F.","Englishabstract":"Shrimps and prawns are especially subject to food fraud, which has consequences not only on the economy but also represents a potential risk for public health. Fatty acids (FA) of Penaeid shrimps have been largely explored in the literature, and although they are unable to discriminate shrimps geographical origin or species, they might provide an interesting tool to distinguish their production method (wild vs. farmed). The present study is based on a literature compilation of Penaeid shrimp FA profiles encompassing all continents and 28 species. It reveals that the ratio of FA 18:2ω6 + FA 18:3ω3 / FA 16:1ω7 can differentiate wild vs. farmed Penaeid shrimps with 100% accuracy within the 207 FA profiles of the dataset considered. Assuming a normal distribution of the dataset, 94.4% of the farmed shrimps population is expected to exhibit a ratio above 2.92, and 99.7% of the wild shrimps population is expected to fall below 2.92.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"A worldwide reliable indicator to differentiate wild vs. farmed Penaeid shrimps based on 207 fatty acid profiles","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2024-12-10 01:32:52.928458","timezone_type":1,"timezone":"+01:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"Food authenticity; Food authenticity; Class-modelling analysis","OtherDescriptors":null,"Notes":null,"AnaPub":2019,"MonPub":null,"DateUpdate":"2019-05-13","DateCreate":"2019-05-13","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000466346600033","VABBcode":null,"OpenAcc":0,"DOI":"10.1016/j.foodchem.2019.04.042"},"refs":null,"anarec":{"AnaID":310950,"PubliDate":2019,"Pagination":"247-252","XtraPublOfAnaID":null,"ISBN":null,"Volume":"292","Issue":null,"BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":45225,"SerRR":"Food Chemistry. 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