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Flame conditions were optimized by a multivariate D-optimal design, using as response the sum of the analyte addition calibration slopes and their standard deviations.
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Next, to achieve global optimum a multivariate D-optimal design was used.
Robustness testing of the method, performed by a D-optimal design, and system suitability criteria allowed a control strategy to be designed.
Subsequently, the model was validated in laboratory media within the no growth region by a response surface D-optimal design.
For this purpose, the HS-SPME sampling method for the volatile compounds of A. camphorata in submerged culture was optimised by a D-optimal design.
Electrochemical studies by using a D-optimal mixture design in Design-Expert software revealed an optimized composition of 60% graphite, 14.2% paraffin, 10.8% MWCNT and 15% IL.
Even by using a D-optimal input, not all parameters were found to have a significant effect on model predictions.
Three operation parameters (vent time, vent flow and vent pressure) were optimised by applying a D-optimal experimental design.
A- and D-optimal designs are typically not E-optimal.
Existence is proved for A-optimal, D-optimal and Q-optimal minimax designs.
New A- and D-optimal (and efficient) designs are provided.
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