Computational estimation of the composition of fat/oil mixtures containing interesterifications from gas and liquid chromatography data

• Published in: Journal of the American Oil Chemists' Society 2005-10, Vol.82 (10), p.707-716
• Main Authors: Vliet, Martin H., Kempen, Geert M. P., Reinders, Marcel J. T., Ridder, Dick
• Format: Article
• Language: English
• Subjects: Adulteration; Biological and medical sciences; chromatographic data; Chromatography; expectation maximization; fat blend recognition; Fat industries; Food industries; Fundamental and applied biological sciences. Psychology; interesterifications; Lagrange multipliers; Liquid chromatography; Mathematical analysis; oil mixtures; Oils & fats; Raw materials; vegetable fats and oils; Vegetable oils
• ISSN: 0003-021X; 1558-9331
• DOI: 10.1007/s11746-005-1132-z

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.