Question

Describe the process of supercritical fluid extraction of natural products

Answer 1

Different species for which Supercritical fluid extraction is used are categorized as: Seeds, Roots, Flowers, Leaves, Herbs, Meat, Algee, Stem, Fractionation of Oil components

Design of Experiment (DOE) is a well-planned, systematic and
organized method used to determine the functional relationship
between the various input parameters (Xs) that affect the output
parameters (Ys) of the process as:

Y=f(X)

Building a design through DOE helps to carefully select a small number of experiments that are to be performed under controlled conditions. This design is obtained in such a way that its analysis will lead to valid statistical inferences. There are a few interrelated steps in building a design as:
I. To define an objective for the research e.g. sort out important input and output variables for a process.
II. To define the important input design variables those will be controlled during the experiment and its maximum and minimum levels.
III. To define the response variables e.g. how to measure the response that will describe the results of experimental runs (response variables).
IV. One design has been chosen from all the available standard designs which suits to the objective e.g. number of input design variables and can be performed at a reasonable cost.
There are various well-known classes of standard designs available for DOE. Once, the objective, number and nature of design variables, nature of the responses and number of experimental runs are decided, the experimental design can be generated using various software like Minitab, Matlab, Design Expert, etc.
DOE has been mainly classified into four categories such as:

• Factorial design(full or fractional)
• Mixture design
• Response surface method
• Taguchi method

Amongst all the categories of DOE, RSM is essentially a set of mathematical and statistical methods for experimental design. RSM is capable of evaluating the effects of variables and searching optimum conditions of variables required to predict targeted responses. It is also used extensively in industrial research where large number of variables affect the process9). It is a well suited approach to study the primary and interactive effects of distinct variables and also for optimization of the process. Many authors followed the RSM for the optimization of extraction of vegetable oil from the various seed materials with supercritical carbon dioxide 10)
Various design techniques, which fall under RSM, are

• Three-level factorial design (FD)
• Central composite design (CCD) and
• Box-Behnken design (BBD)

The three-level design is denoted as 3k factorial design and used by several authors to optimize the experimental runs11). It means that „k‟ factors are considered, each at three levels. Usually, these are referred to as low, intermediate and high levels as -1, 0 and +1, respectively.
Central composite design (CCD)12,13) is another design of the RSM, which is often recommended when the design plans for sequential experimentation. CCD consists of factorial or cube points, axial or star points and center points. There are supercritical fluid extraction method14).
2k cube points, where k is the number of factors/input parameters. The cube portion and its center points may serve as a preliminary stage where the first-order linear model can be fitted. However, these points can also be used for the fitting of a second-order (quadratic) model.

Box-Behnken design (BBD)16,17,18) is one of the classes of rotatable or nearly rotatable second-order designs, which is based on three-level incomplete factorial designs. The number of experiments (N) required for the development of BBD is defined as N=2k (k−1) +Co, (where, k is number of factors and Co is the number of central points). For comparison, the number of experiments for a CCD is N=2k +2k +C0. Therefore, CCD provides less number of experiments for given factors and central points.