Sensory-informed experimental design and modelling time-intensity

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The design of new products for consumer markets has undergone a major transformation over the last 50 years. Traditionally, inventors would create a new product that they thought might address a perceived need of consumers. Such products tended to be developed to meet the inventors own perception and not necessarily that of consumers. The social consequence of a top-down approach to product development has been a large failure rate in new product introduction. By surveying potential customers, a refined target is created that guides developers and reduces the failure rate. Today, however, the proliferation of products and the emergence of consumer choice has resulted in the identification of segments within the market. Consumers were originally segmented on demographics but the proportion of disposable income has increased so much for people in the developed world that choice is now based upon liking rather than financial means. Therefore, understanding your target market is essential for building successful products. Understanding your target market typically involves conducting a product category assessment, where 12 to 30 commercial products are tested with consumers to create a preference map. Every consumer gets to test every product in a complete-block design; however, several classes of products do not lend themselves to such approaches. These tend to be "high-fatigue" products, such as beverage alcohol products, intensely flavoured products, such as coffee or chocolate, and highly seasoned products, such as salsa or curry. The work outlined herein will see the development of methodologies that will allow incomplete-block designs to be tailored for high-fatigue data such that 'similar' products are not tasted consecutively. Work will also be carried out on the development of statistical methods for modelling consumer reaction to a product over the time course of a tasting; e.g., as one tastes strawberry jam, how does the sweetness of the flavour sensation change over time. These novel statistical approaches will allow the extraction of otherwise hidden information from time-intensity data.