Headspace Liquid-Phase Microextraction Followed by Gas Chromatography–Mass Spectrometry for Determination of Furanic Compounds in Baby Foods and Method Optimization Using Response Surface Methodology

Development of counter current salting-out homogenous liquid–liquid extraction for isolation and preconcentration of some pesticides from aqueous samples

“…LPME was developed as a solvent-minimized sample pretreatment procedure that is inexpensive, and since a very little solvent is used, there is minimal exposure to toxic organic solvents [19,20]. However, this method suffers from some disadvantages as follows: fast stirring tends to form air bubbles [21], extraction is time-consuming and equilibrium can not be attained after a long time in most cases [22]. To overcome these disadvantages, Rezaee et al developed a novel liquid phase microextraction technique termed dispersive liquidliquid microextraction (DLLME) [23], which is based on a ternary component solvent system.…”

An efficient method for the simultaneous determination of furan, 2-methylfuran and 2-pentylfuran in fruit juices by headspace solid phase microextraction and gas chromatography–flame ionisation detector

“…The detection limit of both furan and 2-methylfuran by isotope dilution method were 0.05 ng/g (Becalski et al, 2010). Habibi et al used headspace liquid-phase microextraction (HS-LPME) coupled with gas chromatography-mass spectrometry for the analysis of furan, 2-methylfuran and 2,5-dimethylfuran (Habibi, Mohammadi, Hoseini, Mohammadi, & Azadniya, 2013) and the detection limits for furan and 2-methylfuran were 0.038 and 0.028 ng/mL respectively. The detection limit of 2-pentylfuran was 0.23 ng/mL in this study and no data was found on the detection limit of this compound in literature.…”

Development of an Aqueous Polyethylene Glycol-Based Extraction and Recovery Method for Almond (Prunus armeniaca L.) Protein

“…Therefore, MAE has been widely applied to the extraction of various ingredients from different matrices (Liu et al 2012;Hadi et al 2015;Ramezani et al 2015). Over recent years, response surface methodology (RSM) has been extensively used to develop, improve, and optimize extractions of the chemical substances (Liu et al 2013;Habibi et al 2013;Lee et al 2014;Sarfarazi et al 2015;Barizao et al 2015). The prime advantage of RSM is the ability to use statistical modeling and analysis to simulate the extraction process, determine significant variables, and estimate optimal extraction conditions, to give the maximal yield of target responses.…”