Applications and opportunities of experimental design for the dispersive liquid–liquid microextraction method – A review

Abstract

Nowadays, the trend to simplify and miniaturize sample preparation methods has resulted in the development of effective and low-cost microextraction techniques that utilize a very small volume of the extracting phase. Among them, the liquid–liquid microextraction (LLME) method is a simple and effective sample pre-treatment technique applicable to numerous analytical methods. A related miniaturized and environmentally friendly extraction technique, dispersive liquid–liquid microextraction (DLLME), has been developed within the last decade and shows a very high enrichment factor and very low solvent consumption compared to other liquid- or even solid-phase extraction methods. The inclusion of several effective parameters in DLLME and its variants has increased the need for optimization to obtain the best possible extraction results. In fact, experimental design and optimization of performance conditions are the most important applications of chemometrics in analytical chemistry. Thus, design of experiments (DoE) helps us to determine the best model of the relationship between variables, as well as the optimal experimental conditions. Here, a comprehensive review of recent advancements in the use of DoE methodologies including full factorial, fractional factorial, Plackett–Burman, orthogonal array, central composite, Box–Behnken, Doehlert, and D-optimal designs to optimize DLLME applications is provided. In addition, the preponderance and drawbacks of each optimal method are discussed. The overall purpose of this review is to present a general overview of the different DoEs that are currently used to optimize DLLME for various matrices and analytes.