Prediction of gas chromatographic retention of polyunsaturated fatty acid methyl esters

• Published in: Journal of Chromatography A Vol. 1110; no. 1; pp. 171 - 180
• Main Authors: Mjøs, Svein A., Grahl-Nielsen, Otto
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 31.03.2006; Elsevier
• Subjects: Analytical chemistry; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography, Gas - methods; Equivalent chain lengths; Esters - analysis; Exact sciences and technology; FAME; Fatty acid methyl esters; Fatty Acids, Unsaturated - analysis; Gas chromatographic methods; Isomerism; Partial least squares regression; Polyunsaturated fatty acids; Reproducibility of Results; Sensitivity and Specificity; Temperature; Equivalent chain lengths; FAME; Partial least squares regression; Polyunsaturated fatty acids; Fatty acid methyl esters; Statistical analysis; Coupled method; Chromatographic retention; Chain length; Prediction; Polyunsaturated fatty acid; Modeling; Gas chromatography; Retention index; PLS regression; Property structure relationship; Fatty acid methyl ester; Mass spectrometry; Chemometrics
• ISSN: 0021-9673
• DOI: 10.1016/j.chroma.2006.01.092

Multivariate regression models were applied to predict retention indices as equivalent chain lengths (ECL) for methylene-interrupted polyunsaturated fatty acids. Simple molecular descriptors, the chain length, the number of double bonds and the position of the double bond system, were used as predictors. The merits of different variable combinations were evaluated. For general models, it was necessary to include the distance from the double bond system to both the carbonyl group (Δ-position) and the methyl end of the fatty acid ( n-position). The best accuracy was found for models including higher order terms of Δ and n. For models restricted to n-3 and n-6 isomers, it was not necessary to include the n-position among the variables. The highest residuals for the most accurate models were below 0.06 ECL units, and root mean square error of prediction was below 0.030. The ECL data was achieved by three different temperature programs on a cyanopropyl column.