Current facets (Pre-Master)
PhD defence of Danielle van Hout on Thursday 23 January 2014
On Thursday 23 January 2014 Danielle van Hout will defend her PhD thesis entitled 'Measuring Meaningful Differences - Sensory testing based decision making in an industrial context; applications of signal detection theory and Thurstonian modelling'. Supervisor is Professor Patrick Groenen (Erasmus School of Economics) and Professor Garmt Dijksterhuis (University of Copenhagen). Other members of the Doctoral Committee are Professor Roy Thurik (Erasmus School of Economics), Professor Rolf Zwaan (Faculty of Social Sciences, Erasmus University Rotterdam) and Professor Kees de Graaf (Wageningen University and Research centre).
Time and location
The PhD defence will take place in the Senate Hall of Erasmus University Rotterdam and will start at 13.30 hrs.
About Danielle van Hout
Danielle van Hout was born on the 31 May 1972 in Kerkrade, The Netherlands. After completing her secondary school at the Sancta Maria College in Kerkrade, she studied Food and Business at the Hogeschool Zuyd, in Heerlen.
This study aimed to deliver food marketers with broad skills in the field of Food Science, Marketing and Economics. During this study she conducted two internships on sensory evaluation topics. The first was to set-up descriptive sensory panel for Verkade Chocolates, and the second to implement a sensory quality system at Campina – Melkunie, Mona.
After graduation in 1994, Danielle started working at Unilever Research and Development in Vlaardingen as a sensory panel leader. Through the years she has been taken on various different roles in the research organisation; sensory team leader, global innovation project leader, expertise team leader, and her current role is that of science leader in the global Strategic Science group. During her 18 years career in Unilever she studied and implemented many different sensory and consumer tests for the various research and innovation projects. In particular in the area of signal detection theory and Thurstonian modelling she collaborates with various leading academic scientists, researching more effective sensory and consumer test methods. Many of the results are published in peer-review journals.
Abstract of 'Measuring Meaningful Differences - Sensory testing based decision making in an industrial context; applications of signal detection theory and Thurstonian modelling'
In the Fast Moving Consumer Goods industry, results from sensory research form the basis for many important business decisions. Examples of such decisions are whether to launch new products, change existing products in order to make them more healthy or sustainable, or whether to continue with specific novel technological developments. To make good quality decisions, it is important that the sensory methods used are fast, accurate and deliver robust results.
Signal detection theory and Thurstonian modelling can improve the effectiveness of sensory research, and these theories have been applied to one specific type of methods; sensory difference tests. Sensory difference tests are used to measure small differences between products, and can be used to answer important questions like: “Are these two products similar in taste?”, “Does this new ingredient make the product different?”, and “Will our consumers be able to notice the differences?”
Two signal detection applications have been investigated. The first application is to compare test methods and identify how to optimize them, as there are many methods available that largely differ in performance. With this knowledge, more effective methods can be selected or specifically designed. The second application is to integrate results from different studies to improve the effectiveness of sensory testing in general, for example by relating sensory differences detected by a trained panel “In Lab” to differences found by consumers “In Home”. Such knowledge can make future studies more predictive of what really matters to consumers, and improve the quality of decision making based on sensory results whilst reducing the amount of testing required.