Blending response surface methodology and principal components analysis tomatch a target product

Highly trained sensory panels have long been used to evaluate food products on perhaps dozens of attributes. Principal components analysis is one of a number of multivariate data analysis techniques commonly used in analyzing sensory panel data. More recently, response surface designs have been used to direct the creation of product prototypes so that the effects of ingred ient levels and/or processing conditions can be modeled. This paper will di scuss how the two methodologies have been used together in projects where t he goal is to identify ingredient levels and/or processing conditions that best match a target product's sensory profile. Some unique problems arise w hen analyzing and interpreting the results of response surface models when the number of responses is quite large. This paper will explain how some of these problems have been addressed through the detailed discussion of the development of a cost reduced product. Six ingredients were systematically varied in a response surface design to create 48 prototypes. The prototypes and the target product were then measured on 33 sensory attributes. Design selection, data collection, response surface modeling, rotated principal c omponents analysis and the use of both desirability and distance functions to identify ingredient level combinations that meet the product development objectives will be discussed in detail using the data analyses from this p roject. Recommendations for next steps in the product development process w ill also be given. (C) 2001 Elsevier Science Ltd. All rights reserved.