Uncovering interactions in Plackett–Burman screening designs applied to analytical systems: A Monte Carlo ant colony optimization approach

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.