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The multivariate optimization data (Additional file 1) were further analyzed by linear regression with the instrument signal as the dependent variable, and it was found that the single important factor influencing the peak height is extraction speed.
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These variables were accurately optimized using multivariate optimization strategies.
The extraction procedure was optimized using the multivariate optimization approach following a two-stage process.
The variables of interest in the SDME process were optimized using a multivariate optimization approach.
In the second half of the course, the above are used to build iterative methods for nonlinear systems and for multivariate optimization.
Multivariate optimization can be used to improve the determination of the maximum adsorption capacity.
In recent years, multivariate optimization has played an increasing role in analytical method development.
Experimental designs were applied for multivariate optimization of the experimental conditions of RP-HPLC method.
Details of the multivariate optimization procedure and results are presented in the paper.
Additionally it should be noted that multivariate optimization does not mean multi-objective optimization.
By using any multivariate optimization algorithm, the set of hyperparameters θ can be estimated analytically.
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