Abstract:

 

Product reformulations in response to cost or health initiatives have pushed discrimination testing back to the forefront of sensory science. While many experimental approaches are possible (protocol, sample size, subjects’ expertise level), test power and consumer relevance are two essential elements of this type of investigation. It has been shown in numerous published theoretical and applied pieces of research that different experimental settings will vary broadly on their ability to detect sensory differences, calling for the need of an overall framework allowing reliable methodological comparisons and experimental approach recommendations. Over the past 30 years, Thurstonian modeling has gradually been shown to exhibit the needed characteristics for such a framework. This resulted in notable advances such as understanding why the triangle test generally has low power, providing meaningful and consistent sample size recommendations across methodologies, and identifying the tetrad test as a promising method for unspecified testing.

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The first for this two-part webinar will introduce the elements of Thurstonian modeling from a practitioner’s perspective. Please note that while the theory is based on mathematical modeling of human perception, no advanced technical background is required to attend and benefit from this webinar as the concepts are presented in a visual and user-friendly manner. In particular, we will show how this approach takes products’ inherent variability into account and discuss protocols’ specific decision rules that result in power differences. This in turn will be used to describe how Thurstonian modeling helps us measure sensory differences between existing products and reformulations, and will illustrate how it can be used to determine the size of consumer relevant differences. This webinar is intended for a general audience of sensory professionals and graduate students. No detailed technical knowledge is assumed.

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A digital recording of this webinar is available for purchase ($289). 

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