Optimization of industrial‐scale deodorization of high‐oleic sunflower oil via response surface methodology

植物甾醇 响应面法 葵花籽油 感官的 工艺工程 油酸 工艺优化 向日葵 制浆造纸工业 环境科学 材料科学 食品科学 数学 化学 色谱法 工程类 生物化学 组合数学 环境工程
作者
Vito Martinčič,Janvit Golob,Wim De Greyt,Roland Verhé,Sergej Knez,Vera Van Hoed,Ljudmila Fele Žilnik,Klemen Potočnik,Andreja Rižner Hraš,José Vila Ayala
出处
期刊:European Journal of Lipid Science and Technology [Wiley]
卷期号:110 (3): 245-253 被引量:17
标识
DOI:10.1002/ejlt.200700194
摘要

Abstract 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.
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