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Chemotaxonomic study of Chrysobalanus icaco Linnaeus (Chrysobalanaceae) using ultra‐high performance liquid chromatography coupled with diode array detection fingerprint in combination with multivariate analysis
Author(s) -
Paracampo Nádia Elígia Nunes Pinto,
Prance Ghillean Tolmie,
Poppi Ronei Jesus,
da Silva José Alberto Fracassi
Publication year - 2017
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.201601444
Subject(s) - partial least squares regression , chromatography , linear discriminant analysis , fingerprint (computing) , mass spectrometry , principal component analysis , electrospray ionization , high performance liquid chromatography , analytical chemistry (journal) , multivariate statistics , chemistry , artificial intelligence , mathematics , computer science , statistics
We investigated a strategy for the chemotaxonomy study of Chrysobalanus icaco Linnaeus (Chrysobalanaceae) based on ultra‐high performance liquid chromatography coupled with diode array detection fingerprint in combination with multivariate analysis. Two models using principal component analysis and partial least squares discriminant analysis were developed, and the samples could be successfully classified into two classes: Class 1 (red morphotype) and Class 2 (white and black morphotypes). Furthermore, ultra‐high performance liquid chromatography coupled with diode array and electrospray ionization tandem mass spectrometry was used to identify the main compounds responsible for class separation. The partial least squares discriminant analysis model accurately classified the C. icaco samples using an external validation subset with prediction ability of 100% and revealed the existence of two chemotypes. The most important finding obtained in this study is that the three morphotypes distinguished by the mature fruit color (white, red, and black) are not all phytoequivalent to each other.