Volume 10 Part 1 Article 83
Title: Use of Response Surface Methodology (Rsm) for Experimentation in Mushroom Culture. Development of a Delayed Release Nutrient
Authors: R.B. Holtz and V. Miroyan
Response Surface Methodology (RSM) is a relatively new statistical modeling technique that has proven valuable to research scientists faced with multivariable experimental designs.
The basic statistical program is based on the Taylor Expansion Series. This series used the experimental data to determine the effects of the variables and provides calculated coefficients for each variable. The equation addresses three major areas of consideration.
(i) linear effects of the variables
(ii) second order effects of the variables
(iii) interactions of the variables
A common problem faced by most experimenters is the inability to test all variables in a single series of experiments. One variable is usually tested per experiment. Responses to different variables are then combined or optimized by “trial and error”, intuition, or experience. The vast area of interactive effects cannot be addressed by these techniques. Often success is achieved by traditional means ; however, optimization can only be achieved when all responses are tested against the interactive effects of the variables.
Therefore, experimental models such as RSM have been developed to aid the researcher with multivariable problems. Models are designed so that a small number of experiments are selected to represent the total universe of the experimental design. Intermediate values are then extrapolated mathematically to connect the points. This is the principle behind the RSM modeling technique. RSM allows the experimenter to select a matrix of variables and levels of variables, then execute a designated set of randomized experiments. The data is then analyzed by the computer.Please login to download the PDF for this proceeding.