4.1 Article

Process optimization for the supercritical carbon dioxide (SC-CO2) extraction of wheat germ oil with respect to yield, and phosphorous and tocol contents using a Box Behnken design

Journal

GRASAS Y ACEITES
Volume 69, Issue 3, Pages -

Publisher

CONSEJO SUPERIOR INVESTIGACIONES CIENTIFICAS-CSIC, INST GRASA
DOI: 10.3989/gya.0102181

Keywords

Box Behnken design; Oil yield; Phosphorous content; Supercritical carbon dioxide extraction; Tocol contents; Wheat germ

Funding

  1. Council of Scientific and Indusial Research (CSIR)

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The supercritical carbon dioxide (SC-CO2) extraction technique has emerged as one of the best possible alternatives to organic solvent (hexane) extraction. However, very limited information is available on process optimization for this extraction technique and the lack of available engineering data is causing the slow growth of this technique. In the present investigation, SC-CO(2 )extraction was carried out to extract the oil from wheat germ under various operating conditions and the oil samples were characterized for properties such as phosphorous and tocol contents (vitamin E). A three-level Box Behnken design from response surface methodology was applied to optimize the SC-CO2 extraction parameters such as pressure, temperature and CO2 flow rate with an objective to obtain high oil yield, rich tocol contents and low phosphorous content. The process parameters were maintained between 30 to 50 MPa, 40 to 60 degrees C and a flow rate of 10 to 30 g.min(-1) in a Box Behnken design matrix. Three different second order polynomial models were obtained for oil yield, phosphorous content and tocol contents with high R-2 values. The optimum conditions were found to be 50 M Pa, 60 degrees C and 30 g.min(-1)where the predicted oil yield, phosphorous content and tocol contents were found to be 8.87%, 31.86 mg.Kg(-1) and 2059.92 mg.Kg(-1) respectively. Under the optimum conditions, the experimental oil yield, phosphorous content and tocol contents obtained were found to be very close to the values predicted by the model.

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