Evaluation of the signal response of the electronic nose measured on oregano and lovage samples using different methods of multivariate analysis

In case of spices or crude drugs of medicinal- and aromatic plant origin, sensory characteristics, especially odour, has great commercial importance. The instrumental sensory analysis the so-called 'electronic nose' has proved to be a significant, new and quick method in chemometry. The sensor signal responses (data recorded by the electronic nose instrument) of the electronic nose were evaluated by statistical methods, including principal component analysis (PCA) and canonical discriminant analysis (CDA) and the combination of these methods by applying the discriminant analysis on the first eight principal components. The aim of this paper is the comparative analysis of the above evaluation methods as data processing tools of the sensor signal response of the electronic nose (chemosensor array). The essential oil of oregano (<i>Origanum vulgare </i>subsp. <i>hirtum</i>) selected line No. 10 was compared to the oil distilled from the selected line No. 11; and dried root samples of lovage (<i>Levisticum officinale</i>) harvested at different times from the two- and three-year-old population, were investigated with electronic nose (NST-3320, AppliedSensor Sweden AB). Principal component analysis, as a first step of the evaluation, did not clearly distinguish either oregano or lovage samples. Further statistical evaluation of the original sensor signal responses of the electronic nose with canonical discriminant analysis improved the separation power of the model. The best separation could be achieved by the combination of the two methods, whereby canonical discriminant analysis was applied to the first eight principal components, which described 99% of the differences. In all cases more than 92%, while in several experiments 100% of cross-validated grouped cases were classified correctly. Based on the results, the application of the electronic nose and the combination of multivariate methods, PCA and CDA, could be an appropriate tool either for identification of cultivar to accelerate selection process or to distinguish crude drugs of different age or different harvesting period.