Optimization of dispersive liquid-liquid microextraction (DLLME) based on the solidification of floating organic drop (SFO) coupled with ultrasonic-assisted extraction (UAE) for the extraction recovery of deca-bromodiphenyl ether (BDE-209) from surficial sediments

This paper aims to optimize dispersive liquid-liquid microextraction based on the solidification of floating organic drop coupled with ultrasonic-assisted extraction (UAE-DLLMESFO) of brominated diphenyl ether (BDE-209) in surficial sediment samples through genetic algorithm neural network (GANN) model and high-performance liquid chromatography (HPLC). A GANN model was established based on the orthogonalized experiment of BDE-209. The average relative deviation between the values predicted by GANN model and the experimental ones was 5.83%. GANN model established was combined with genetic algorithm to optimize the UAE-DLLMESFO and the optimum DLLME conditions were then validated by experiments. The results indicated that the optimal UAEDLLME-SFO for BDE-209 is to use 0.54 g of sediment sample, 13.75 mL of acetone, 10 min of UAE, 0.53 mL of disperser solvents, 35 mu L of undecanol, 0% of NaCl and 14.24 min of DLLME, and the extraction recovery (ER) of BDE-209 from sediments is approximately 90% with an increase of 7.59% through this GANN model.