A method to discriminate different factories of Zhenjiang aromatic vinegar by means of vacuum distillation and UV-visible spectroscopy （190-800 nm） was developed. A relation has been established between the absorption spectra and the aroma of Zhenjiang aromatic vinegar. The dataset consists of a total of 122 samples of Zhenjiang aromatic vinegar; five absorptions at 195, 235, 250, 275 and 310 nm wavelength were chose, which were applied as characteristic values of spectra plot. First, the data was analyzed with principal component analysis （PCA）. It appeared to provide the reasonable clustering of different factories of Zhenjiang aromatic vinegar. Meanwhile PCA compressed hundreds of spectral data into a small quantity of principal components which described the body of the spectra; the five absorptions were applied as inputs to a back propagation artificial neural network with four hidden layer. One hundred eight samples from seven different factories were selected randomly, then they were used to build BP-ANN model. This model has been used to predict the varieties of 14 unknown samples; the recognition rate of 100% was achieved. The organoleptic results demonstrated that there was different between Hengshun Zhenjiang aromatic vinegar and other six factories samples, which matched well with UV-visible spectroscopy analysis results.
Zhenjiang aromatic vinegar
principal component analysis
back propagation neural network