Time dependence of the aroma pattern emitted by an encapsulated essence studied by means of electronic noses and chemometric analysis

This work studies the changes along days of the aroma released from a flavour encapsulated in a polysaccharide gel matrix using the electronic nose methodology. The purpose is to explore the capacity of the sensor array to assign a pattern of aroma to the corresponding release day within a total period of five days. Different procedures of data treatment and analysis are compared in order to achieve the maximum of information of the system under study in conditions where the number of measurements is limited. Raw and normalized sensor signals are processed using various unsupervised and supervised data analysis algorithms such as Principal Component Analysis, Kohonen-Self Organizing Maps, Cluster Analysis, Multiple Discriminant Analysis and two types of Artificial Neural Networks (BP-ANN and RBF-ANN). Accurate assignation of the number of release days is obtained with a successful classification up to four classes associated to samples at increasing days of aroma release. The relative advantages and drawbacks of the different procedures and data manipulations are discussed.