Measuring Meaningful Differences: Sensory testing based decision making in an industrial context; applications of signal detection theory and Thurstonian modelling Defended on Thursday, 23 January 2014

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.

Keywords

signal detection; decision making; discrimination tests; A-Not A; 2-AFC; d´ (d-prime); cognitive strategy; operational power; sequence effects; familiarization; learning effects; FMCG


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