Study on discrimination of four Chinese brand spirits based on ICP-AES coupled the principal component and decision tree analysis

The potential of ICP- AES for metal element profiling of Chinese spirit samples was examined. Sixteen elements in fifty six spirits samples representing four varieties of brands( Wuliangye,Lang Liquor,Quanxing,Wujinchun) were determined.The set of data was employed to construct a sample class prediction model based on z- score standardization followed by principal component analysis( PCA) and decision tree analysis( DT),which was employed to explore the structure of the data and construct classification and prediction model. The first principal component explained 40.3% of variance while the top ten components explained 96.3% of variance which was employed to construct the DT model. The validated DT model based on 5- fold cross- validation enabled correct classification of 97.6% of samples,and other 15 spirit samples could be predict correctly. The Wuliangye,Lang Liquor,Quanxing,Wujinchun could be classified intensively.