[Discrimination of varieties of yellow wines using Vis/NIR spectroscopy]

In order to achieve the rapid discrimination of the varieties of yellow wines, the spectral curves of yellow wines were obtained by Vis/NIR spectroscopy, and the principal component analysis (PCA) was applied to perform the clustering analysis. The principal components (PCs) extracted by PCA were employed as the inputs of the BP neural networks, and then a discrimination model was built. The first 6 PCs were regarded as the new eigenvectors to accelerate the training speed and to improve the precision of the model. Fifteen samples from each variety and a total of 45 samples were selected randomly as the prediction sets. The remaining 145 samples were used as the training sets to build the training model which is validated by the samples of the prediction sets. The result error was set to be +/- 0.1, and the results indicated that only one sample exceeded the threshold value, therefore the recognition rate of 97.78% and an excellent precision were achieved. So the discrimination method studied in the present paper played a good role in the classification and discrimination, and offered a new approach to the rapid discrimination of the varieties of yellow wines.