Parametric modeling of time intensity data collected on product prototypes generated from a fractional factorial experiment to quantify sources of texture variability

Trained panels have been used to evaluate the sensory properties of food products for a number of years. Time-intensity sensory methodologies have been developed to identify and quantify the temporal sensory properties of foods and beverages. The data collected is represented in a time dependent intensity curve. Over the years, several multivariate data analysis techniques have been proposed to characterize time-intensity curves. One specific technique, fitting a parametric model to individual respondent curves. has been recently proposed. The model pararmeters quantify meaningful characteristics of the time-intensity Curves: Lip and down slopes. times at which the curves reach and begin descent from the peak height, and the peak height itself. The use of statistical experimental designs to direct the creation of product prototypes so that the effects of ingredient levels and/or processing condition charities can be statistically modeled has become prevalent in the food industry in the last decade. Use of these designs in projects for cost reduction. quality improvement and variation reduction has helped to make the product development process more scientific and efficient. A common application of these designs in the product development process has been with consumer acceptance measures Lis responses to determine optimal product formulations. This paper discusses how the combination of the two methodologies has been used to identify ingredient levels and/or processing conditions that most affect product texture variability (product texture in this case being a temporal phenomenon). The parametric model fitting process, assessment of respondent repeatability and reproducibility. and the statistical modeling of the time-intensity response Curve parameters with respect to the statistical experimental design are described in detail. A discussion of how the resultant modeling directed future product development efforts demonstrates the utility of pairing these methodologies. (C) 2003 Elsevier Science Ltd. All rights reserved.