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Uncovering Interactions in Plackett-Burman Screening Designs Applied to Analytical Systems. A Monte Carlo Ant Colony Optimization Approach

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Uncovering Interactions in Plackett-Burman Screening Designs Applied to Analytical Systems. A Monte Carlo Ant Colony Optimization Approach

Alejandro C Olivieri et al. Talanta.

Abstract

Screening of relevant factors using Plackett-Burman designs is usual in analytical chemistry. It relies on the assumption that factor interactions are negligible; however, failure of recognizing such interactions may lead to incorrect results. Factor associations can be revealed by feature selection techniques such as ant colony optimization. This method has been combined with a Monte Carlo approach, developing a new algorithm for assessing both main and interaction terms when analyzing the influence of experimental factors through a Plackett-Burman design of experiments. The results for both simulated and analytically relevant experimental systems show excellent agreement with previous approaches, highlighting the importance of considering potential interactions when conducting a screening search.

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