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About

Samantha Dunkerley’s research lies at the intersection of analytical chemistry and laboratory automation, where she pioneered methods for simultaneous drug analysis and robotic control systems. Her most-cited work, “The simultaneous determination of caffeine, aspirin and paracetamol by principal components regression using automatic dilution and calibration” (1997), introduced a robust chemometric approach to resolve overlapping spectral signals in common analgesic formulations. By coupling principal components regression with automated dilution, she enabled precise, high-throughput quantification of these three active ingredients—a critical advance for pharmaceutical quality control. This methodology, cited 13 times across two publications, demonstrated how multivariate calibration could streamline routine analysis without physical separation. Dunkerley also contributed to the design of intelligent laboratory robotics, as seen in “A general robot control system for automatic chemical analysis” (1997, 5 citations) and “A general robotics operating system for a linear track robot” (1996, 2 citations). These works established flexible, modular control architectures that allowed chemists to automate complex, multi-step analytical procedures with minimal reprogramming. Her combined focus on chemometrics and robotics reflects an early vision of integrated, intelligent laboratories—a concept now central to modern high-throughput screening and process analytical technology.