Challenges for data analysis in flavour science

Abstract

Publisher Summary The analysis of data from instrumental and sensory flavor analyses and related data types often pose special challenges for the data analyst. This may be due to the richness of information in the data or due to special artifacts that need to be handled. The chapter presents experiences from a workshop. In this workshop, some of the basic and more advanced tools for handling various types of data were illustrated. The chapter presents an overview of multivariate methods with a description of analysis of sensory data. New methods for handling GC and electronic nose data are described. Multivariate data analysis uses all available data simultaneously - exactly as in human pattern recognition. Multivariate data analysis also enables an exploratory approach to data. The techniques can be taken one-step further: calibration can be extended to multivariate calibration and relations between complex data matrices can be described. One type of variables can be predicted from other types—typically from measurements that are more easily available, more unspecific and more complex. A very important issue using these techniques is to distinguish between causality and correlation. A typical sensory experiment includes a number of panelists, a number of samples and a number of replications. Simple 3-way mixed model ANOVA is typically carried out for each variable, the main issue being a test for product differences.