Predictive and interpolative biplots applied to canonical variate analysis in the discrimination of vegetable oils by their fatty acid composition

• Type: Article in International Scientific Journal
• Date: 2004

Authors

Alves, MR (Author) Other; Oliveira, MB (Author) FFUP

Journal

Title: Journal of Chemometrics

Vol. 18

Pages: 393-401

ISSN: 0886-9383

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 9 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-000-8SC

DOI: 10.1002/cem.884

Abstract (EN): The fatty acid profiles of 120 commercial unblended peanut, corn, soybean and sunflower vegetable oils and 17 commercial brands of blended edible oils were determined by HRGC/FID/capillary column, including several cis and trans isomers of mono-, di- and tri-unsaturated fatty acids as well as fatty acids with an odd number of carbon atoms. Although many statistical techniques may show some usefulness in the description and analysis of the data obtained, predictive biplots applied to canonical variate analysis prove to be a very useful way of carrying out interpretations and an important aid in building up models, while interpolative biplots display great advantages in the utilization of models for classification purposes on a day-to-day basis. Furthermore, these biplots require only a modest understanding of statistical tools, since all judgements are made regarding original fatty acids and original measuring units. Copyright (c) 2005 John Wiley T Sons, Ltd.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9