title: Multi-element fingerprinting as a tool in origin authentication of four east China marine species.
abstract: The contents of 25 elements in 4 types of commercial marine species from the East China Sea were determined by inductively coupled plasma mass spectrometry and atomic absorption spectrometry. The elemental composition was used to differentiate marine species according to geographical origin by multivariate statistical analysis. The results showed that principal component analysis could distinguish samples from different areas and reveal the elements which played the most important role in origin diversity. The established models by partial least squares discriminant analysis (PLS-DA) and by probabilistic neural network (PNN) can both precisely predict the origin of the marine species. Further study indicated that PLS-DA and PNN were efficacious in regional discrimination. The models from these 2 statistical methods, with an accuracy of 97.92% and 100%, respectively, could both distinguish samples from different areas without the need for species differentiation.
authors.affiliation: College of Food Science and Biological Engineering, Zhejiang Gongshang Univ, Hangzhou, 310035, People's Republic of China.
authors.forename: Lipan
authors.initials: L
authors.lastname: Guo
authors.forename: Like
authors.initials: L
authors.lastname: Gong
authors.forename: Yanlei
authors.initials: Y
authors.lastname: Yu
authors.forename: Hong
authors.initials: H
authors.lastname: Zhang
mesh_terms: D002681:China; D016002:Discriminant Analysis; D005504:Food Analysis / Q000379:methods*; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D017747:Seafood / Q000032:analysis*; D014131:Trace Elements / Q000032:analysis*
chemical_list: D014131:Trace Elements
keywords: ICP-MS; marine species; multi-element fingerprinting; multivariate statistics; regional discrimination