AROMA DISCRIMINATION BY PATTERN-RECOGNITION ANALYSIS OF RESPONSES FROM SEMICONDUCTOR GAS SENSOR ARRAY

A semiconductor gas sensor array was applied to discriminating coffee aromas, essential oils, and volatile compounds with different functional groups. To standardize sample introduction and to remove excess ethanol from volatile mixtures, headspace concentration utilizing a porous polymer trap was incorporated into the sensing system. Distinctive differences were not observed among response patterns of samples due to the nonselectivity of semiconductor gas sensors. Pattern recognition techniques such as discriminant analysis and cluster analysis were applied to the normalized response patterns. Two ground coffees, Coffea arabica and C. robusta, and freeze-dried and spray-dried commercial instant coffees were clearly separated by cluster analysis and linear discriminant analysis. A combination of three sensors was sufficient to perfectly discriminate the four coffee samples. Two clusters corresponding to a citrus group and other fruits were shown by cluster analysis of essential oils. Clustering of compounds was partly based on their chemical structure.