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Analysis and characterization of edible oils by chemometric methods
Author(s) -
Giacomelli Liliana M.,
Mattea Miguel,
Ceballos Claudio D.
Publication year - 2006
Publication title -
journal of the american oil chemists' society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.512
H-Index - 117
eISSN - 1558-9331
pISSN - 0003-021X
DOI - 10.1007/s11746-006-1204-0
Subject(s) - principal component analysis , edible oil , fingerprint (computing) , vegetable oil , pattern recognition (psychology) , chemometrics , mathematics , food science , statistics , artificial intelligence , chromatography , chemistry , computer science
Chemometric techniques have been used to group samples with similar features as well as to discriminate among experimental data on edible oils. The objective of this study was to provide a simple method for differentiating vegetable oil types and to classify unknown samples using analytical techniques commonly used in the edible oil industry. We used principal component analysis to study the relationship between FA composition, tocopherol levels, CIF (Commission Internationale de l'Eclairage) parameters, and a photometric color index. The total variance in the original data matrix was established mainly by three principal components. Data processing allowed the oil samples to be identified and created a 2‐D map as a fingerprint of the oil types. This analysis can be used successfully to differentiate vegetable oil types and classify them as crude or refined oils.