Abstract
An electronic nose based on metal oxide semiconductor (MOS) sensors has been used to identify liquors with excessive methanol. The technique for a square wave temperature modulated MOS sensor was applied to generate the response patterns and a back-propagation neural network was used for pattern recognition. The parameters of temperature modulation were optimized according to the difference in response features of target gases (methanol and ethanol). Liquors with excessive methanol were qualitatively and quantitatively identified by the neural network. The results showed that our electronic nose system could well identify the liquors with excessive methanol with more than 92% accuracy. This electronic nose based on temperature modulation is a promising portable adjunct to other available techniques for quality assurance of liquors and other alcoholic beverages.