Experimental design of supercritical fluid extraction – A review

Abstract

Supercritical fluid extraction (SFE), a sustainable green technology leads a wide range of applications since the past decade. Like many other processes, SFE is sometimes criticized for its large number of factors which need to be properly adjusted before every single run. Experimental design and proper statistical analysis with small number of trials in adjusting the SFE parameters become popular in this regard. This paper is aimed to review the common experimental designs that are frequently used in the SFE process. Utilizations of different experimental designs in SFE with the intention of either screening the most influential factors or optimizing the selected factors are briefly reviewed. Strategies and recommendation addressing the choice of appropriate design, constructing design matrix, experimental trial and data analysis are discussed in this paper. For more application oriented readers of SFE, an effective and easy chart on choosing proper experimental design and a list of experimental design software are also included.

Optimización de la extracción de antioxidantes de Salvia officinalis L. con CO2 supercrítico

“…Ejemplos representativos de estos procedimientos los constituyen los trabajos publicados sobre Vetiveria zizanioides (Danh, et al, 2009), Myrtus communis (Pereira, et al, 2013;Ghasemi, et al, 2011), Satureja hortensis (Khajeh, 2011), Pimpinella affinis (Dashtianeh, 2013), y Piper nigrum (Bagheri, et al, 2014). Debido al gran número de variables a explorar, se requiere una primera etapa de tamizaje para seleccionar un conjunto reducido de factores que optimicen el dato observable de interés, que, usualmente, se trata del rendimiento (Sharif, et al, 2014).…”

“…Ejemplos representativos de estos procedimientos los constituyen los trabajos publicados sobre Vetiveria zizanioides (Danh, et al, 2009), Myrtus communis (Pereira, et al, 2013;Ghasemi, et al, 2011), Satureja hortensis (Khajeh, 2011), Pimpinella affinis (Dashtianeh, 2013), y Piper nigrum (Bagheri, et al, 2014). Debido al gran número de variables a explorar, se requiere una primera etapa de tamizaje para seleccionar un conjunto reducido de factores que optimicen el dato observable de interés, que, usualmente, se trata del rendimiento (Sharif, et al, 2014).En este trabajo se buscaron las condiciones de extracción de hojas y tallos de Salvia officinalis con CO 2 supercrítico que permitieran maximizar un dato observable combinando el rendimiento de la extracción, el contenido de polifenoles (según el método de Folin-Ciocalteu) y la capacidad antioxidante del extracto (según el método ORAC, oxygen radical absorbance capacity). Se efectuó una primera etapa de tamizaje basada en un diseño factorial fraccionado 2 7-2 , seguida de la optimización empírica por el método simplex (Nelder y Mead, 1965) de cuatro factores.…”

Empirical Model for Optimizing Shea Butter Extraction in an Unbaffled Vessel Equipped with an Impeller

“…Response surface methodology (RSM) belongs to a statistical method of process optimization which has been found to be more popular and effective than the conventional method. RSM requires minimum experimental runs to determine the effect of the process input variables and their interactions as well as to identify the optimum input conditions that will generate the optimum yield [14]. RSM allows the use of derivative based algorithm that can generate a smooth function that minimizes an error and improve a convergence of the optimization process [15].…”

A Simplified Method to Estimate Sc-CO2 Extraction of Bioactive Compounds from Different Matrices: Chili Pepper vs. Tomato By-Products

“…Different phenomena as phase equilibrium, mass transfer, and flow of Sc-CO 2 through packed beds, are differently involved in the kinetic models reported in the literature [29]. In many cases, the extraction of the first fraction of extracts is essentially limited by its solubility, whereas the extraction rate of the remaining fraction is limited by internal diffusion through the matrix [30].…”