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Multi‐element Fingerprinting as a Tool in Origin Authentication of Four East China Marine Species
Author(s) -
Guo Lipan,
Gong Like,
Yu Yanlei,
Zhang Hong
Publication year - 2013
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/1750-3841.12302
Subject(s) - principal component analysis , partial least squares regression , linear discriminant analysis , inductively coupled plasma mass spectrometry , probabilistic logic , statistical analysis , mass spectrometry , statistics , chemistry , mathematics , chromatography
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.