‘Sensory analysis’ of Chinese vinegars using an electronic nose

The aim of this paper was to establish a gas-sensing fingerprint database of Chinese vinegars and identify them individually for their quality control. Seventeen commercial Chinese vinegars were analyzed by an electronic nose containing nine doped nano-ZnO thick film gas sensors. The type, raw materials, total acidity, fermentation method and production area of the vinegars were selected as the sensory attributes to establish the fingerprints. Back-propagation artificial neural networks (BP-ANNs) incorporating with K nearest neighbors (KNN) were used to perform the identification. The identification accuracy of the vinegars was 89.7%. It is proved that using the attributes to establish the vinegar gas-sensing fingerprints is flexible and robust and the electronic nose technology is more promising for Chinese vinegars quality control.