Exploring taffy product consumption experiences using a multi-attribute time–intensity (MATI) method

• Classical time-intensity is extended with MATI (multi-attribute time-intensity).
• Data was collected with descriptive and consumer panels on taffy chews.
• MATI offers concurrent evaluation of multiple intensity and hedonic attributes.
• Some analytics are novel and avoid limitations in other time–intensity analytics.
• MATI statistical methods offer panel and panelist performance assessment.

Taffy is a popular food form for delivery of functional ingredients but requires formulation that delivers acceptable flavor and texture throughout the entire product consumption experience. Because consumer-relevant flavor and texture changes occur throughout the mastication process for this type of product, it is useful to apply a sensory time–intensity methodology during chew-down for product optimization. Classical time–intensity methods are not efficient approaches for rapid development timelines as they are generally limited to single attributes per run. A multi-attribute time–intensity (MATI) application has been developed and applied to ‘pace’ respondents through multiple attributes and cycles within a run, thereby offering an efficient means to capture key flavor and texture attributes over time.MATI is a natural extension of the classical time–intensity methodology, from one attribute to multiple attributes and from intensity attributes to both intensity and hedonic attributes. Many advanced statistical techniques can be used for analyzing the MATI data. The techniques include turning raw discrete data into smooth MATI curves; the bootstrap method for estimations of the parameters of MATI curves and the standard errors of the estimators; HANOVA, an adaptive analysis of variance for high-dimensional data, for comparisons of groups of MATI curves; and the intraclass correlation coefficient (ICC) and multivariate intraclass correlation coefficient (MICC) for assessing performance of a panel and panelists. A combination of MATI results from a trained sensory descriptive panel and consumers was used to deliver product category understanding, provide detailed understanding of what flavors and textures consumers enjoy and formulation optimization guidance.