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Optimization of industrial‐scale deodorization of high‐oleic sunflower oil via response surface methodology
Author(s) -
Martinčič Vito,
Golob Janvit,
de Greyt Wim,
Verhé Roland,
Knez Sergej,
Van Hoed Vera,
Žilnik Ljudmila Fele,
Potočnik Klemen,
Hraš Andreja Rižner,
Ayala José Vila
Publication year - 2008
Publication title -
european journal of lipid science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.614
H-Index - 94
eISSN - 1438-9312
pISSN - 1438-7697
DOI - 10.1002/ejlt.200700194
Subject(s) - phytosterol , response surface methodology , sunflower oil , organoleptic , process engineering , oleic acid , process optimization , sunflower , pulp and paper industry , environmental science , materials science , food science , mathematics , chemistry , chromatography , engineering , biochemistry , combinatorics , environmental engineering
Optimization of industrial‐scale deodorization of high‐oleic sunflower oil (HOSO) via response surface methodology is presented in this study. The results of an experimental program conducted on an industrial‐scale deodorizer were analyzed statistically. Predictive models were derived for each of the oil quality indicators (QI) in dependence on the studied variable deodorization process parameters. The deodorization behavior of some minor components was analyzed on a pilot‐scale deodorizer. For comparison, a similar experimental program was also performed on the laboratory‐scale. The results of this study demonstrate that optimization of the deodorization process requires a suitable compromise between often mutually opposing demands dictated by different oil QI. The production of HOSO with top‐quality organoleptic and nutritional values (high tocopherol and phytosterol contents and low free and trans fatty acid contents) and high oxidative stability demands deodorization temperatures in the range between 220 and 235 °C and a total sparge steam above 2.0% (wt/wt in oil). The response surface methodology provides the tools needed to identify the optimum deodorization process conditions. However, the laboratory‐scale experiments, while showing similar response characteristics of QI in dependence on the process parameters and thus helpful as a guide, are of limited value in the optimization of an industrial‐scale operation.