POTENTIAL DIFFICULTIES WITH RSM

It must be remembered that RSM uses multiple regression techniques to determine the coefficients for the Taylor expansion equation which best fits the data. The RSM does not determine the function which describes the data. The Taylor equation only approximates the true function. The RSM process fits one of a series of curves to the data. Most RSM programs use only the first and second order terms of the Taylor equation to the data, which limits the number of curves available to fit the data. The first order T aylor equation is a linear model. Therefore, the only curves available are a series of straight lines. The second order Taylor equation is a nonlinear model where two types of curves are available a peak or a saddle surface. Over a narrow range, these curves will approximate the true function that exists in nature but they are not necessarily the function that describes the response. 

Although RSM is a rapid method for determining optimum conditions for a process, caution must be used when interpreting the results. Always remember the quote by Mark Twain, There are liars, damn liars, and statisticians. Unless the RSM output is used properly, it is easy to make this quote true. RSM will always give the user a number. The question remains as to how good is that number and what does it mean Some of the important statistical values which should be considered in evaluating the RSM output are listed below. 

Degree of Saccliarification AHglogliicosiaase Level HELD CONSTANT AT. 1800 Tewperatiire HELD CONSTANT AT 36.00 Total Carbohydrate HELD CONSTANT AT 17.50 

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