2014
DOI: 10.1016/j.foodchem.2013.08.024
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Determination of volatile components of saffron by optimised ultrasound-assisted extraction in tandem with dispersive liquid–liquid microextraction followed by gas chromatography–mass spectrometry

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Cited by 89 publications
(34 citation statements)
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“…Chloroform was found to be the best solvent for the extraction of safranal. Ultrasound assisted extraction in conjunction with dispersive liquid–liquid microextraction (DLLME) was chosen for the extraction of safranal by Sereshti and co‐workers . The extraction parameters, such as composition of extraction solvent, volume of preconcentration solvent and duration of sonication were optimized by using a half‐fraction factorial central composite design (CCD).…”
Section: Analysis Of Saffron Apocarotenoidsmentioning
confidence: 99%
See 1 more Smart Citation
“…Chloroform was found to be the best solvent for the extraction of safranal. Ultrasound assisted extraction in conjunction with dispersive liquid–liquid microextraction (DLLME) was chosen for the extraction of safranal by Sereshti and co‐workers . The extraction parameters, such as composition of extraction solvent, volume of preconcentration solvent and duration of sonication were optimized by using a half‐fraction factorial central composite design (CCD).…”
Section: Analysis Of Saffron Apocarotenoidsmentioning
confidence: 99%
“…Apart from HPLC, GC hyphenated with MS or not, has been also used over the years for the determination of safranal considering its volatile nature .…”
Section: Analysis Of Saffron Apocarotenoidsmentioning
confidence: 99%
“…The organic solvent is not retained by the highly polar silica surface and so is readily eluted from the cartridge. [47][48][49] …”
Section: Liquid-liquid Extractionmentioning
confidence: 99%
“…Because traditional one-factor-at-a-time optimization methods cannot examine the interaction of variables, experimental designs, such as central composite design (CCD) and the Box-Behnken design (BBD), have been widely adopted to evaluate the primary effect as well as the interactions and significance of each variable [31,45,46]. Because traditional one-factor-at-a-time optimization methods cannot examine the interaction of variables, experimental designs, such as central composite design (CCD) and the Box-Behnken design (BBD), have been widely adopted to evaluate the primary effect as well as the interactions and significance of each variable [31,45,46].…”
Section: Introductionmentioning
confidence: 99%