Experimental domain, multivariate

The method of steepest ascent determines the direction from an initial experimental domain which has the steepest slope upwards along the response surface, and hence point towards the optimum conditions. A series of experiments can then be run along this steepest ascent vector. This will lead to rapid improvements. The method was introduced by Box and Wilson[l] and was the first method for systematic multivariate optimization experiments in chemistry. 

Are the standardization samples representative of the calibration samples The second important point is the nature of the standardization samples used. For some standardization algorithms, successful standardization results can only be obtained if the representativity of the standardization samples is satisfactory. This is the case for multivariate algorithms such as the two-block PLS [43,44], the direct and PDS [22] or the algorithms based on neural networks [45,46]. Therefore, these algorithms can only be applied when the experimental calibration domain is well covered by the standardization samples [24]. 

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