My doctoral research at Erasmus University Rotterdam focused on improving decision-making in product innovation by making sensory tests more robust, predictive, and cost-effective.
I applied Signal Detection Theory and Thurstonian modelling to develop better tools for identifying subtle but meaningful differences between products — differences that truly matter to consumers. This work led to more sensitive test protocols and new ways to calibrate trained sensory panel results with consumer responses.
The methods developed have since been applied in business to optimize product testing, reduce unnecessary trials, and strengthen evidence-based decisions in R&D pipelines.

My research focusses on strengthening the methodological foundation of sensory and consumer research, especially in industrial settings. Central to this work is the use of Signal Detection Theory (SDT) to make testing more sensitive, reliable, and decision-relevant.
Key themes include:
Advancing discrimination testing: optimizing A-Not-A, 2-AFC, and same-different protocols, and introducing SDT metrics like d-prime to compare test power and guide method selection.
From difference to preference: developing new SDT-based methods for consumer research, such as DOSD – a directional version of A-Not-A using "Satisfied? Yes/No" with sureness ratings – as an alternative to conventional hedonic scales.
Efficient profiling tools: evaluating mDFA (multivariate difference-from-attribute) as a lightweight alternative to traditional descriptive methods like QDA and Spectrum, particularly useful in fast-paced innovation work.

Full summaries and links to each publication are on my LinkedIn publications page