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Computational estimation of the composition of fat/oil mixtures containing interesterifications from gas and liquid chromatography data
Author(s) -
Vliet Martin H.,
Kempen Geert M. P.,
Reinders Marcel J. T.,
Ridder Dick
Publication year - 2005
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/s11746-005-1132-z
Subject(s) - noise (video) , multiplicative function , composition (language) , extension (predicate logic) , biological system , computer science , chromatography , mathematics , chemistry , artificial intelligence , mathematical analysis , linguistics , philosophy , image (mathematics) , biology , programming language
A mathematical framework is introduced that relates analytical data to the composition of fat and oil mixtures. Within this framework, the noise characteristics of four common analytical techniques [FAME, FAME2‐pos, CN (carbon number), and AgLC] were investigated and modeled by both additive and multiplicative noise terms. The fat blend recognition (FBR) performance was investigated under these two types of noise, both qualitatively and quantitatively. Furthermore, an extension is proposed that makes it possible to detect interesterifications of unknown mixtures, which was impossible before. The proposed procedure is divided into a qualitative estimation stage, which is focused on identifying the raw materials (RM), followed by a quantitative estimation stage, which is focused on quantifying the levels of the RM identified. We compared two qualitative strategies and four quantitative methods for their ability to correctly estimate simulated mixtures under the noise characteristics determined. The comparison of methods was extended to actual mixtures, revealing promising results. Our analysis presents multiple directions for further adulteration and FBR studies.