R. Bro, P. M. B. Brockhoff, and Thomas Skov. Challenges for data analysis in flavour science. Flavor Science: Recent Advances and Trends. W. L. P. Bredie and M. A. Petersen (Eds). Elsevier. 619-621, 2006. Challenges for data analysis in flavour science Rasmus Bro (chairman) a , Per M. Bruun Brockhoff b and Thomas Skov a a Department of Food Science, Royal Veterinary and Agricultural University, Copenhagen, Denmark; b Informatics and Mathematical Modelling, Technical University of Denmark, Kongens Lyngby, Denmark 1. INTRODUCTION The analysis of data from instrumental and sensory flavour 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 artefacts that need to be handled. In this workshop, some of the basic and more advanced tools for handling various types of data were illustrated. First, an overview of multivariate methods was given, followed by a description of analysis of sensory data. Finally, new methods for handling GC and electronic nose data were described. 2. MULTIVARIATE ANALYSIS Rasmus Bro started to point out why we need multivariate analysis. Multivariate data analysis uses all available data simultaneously - exactly as in human pattern recognition. For most interesting problems the information is in the relation between variables. Examples were given on how Principal Component Analysis can make interpretation of data tables easier. Even simple questions as ‘Which of the samples are most similar?’ can be very difficult to answer by looking at a data table in a univariate way. Multivariate data analysis also enables an exploratory approach to data. No previous hypotheses are needed - all data can be analysed and new information can be found. The techniques can be taken one step further: calibration can be extended to multi- variate 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. The following examples