Identification of different aromatic Chinese liquors by colorimetric artificial nose
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Abstract:
A potential instrument, the colorimetric nose, is developed to identify the fragrances of five different Chinese liquors. Firstly, in order to minimize error, the RGB values of the raw data are preprocessed using a threshold before further analysis. The output of the artificial nose is then analyzed by hierarchical routing cluster analysis (HCA), principal component analysis (PCA) and support vector machine (SVM). It is found that HCA can perform correctly classify fragrances into five different classes. However, using the first three components identified by PCA analysis, representing 80.79% of the variance, the five individual fragrances can be reliably distinguished. Finally, it also shows that the five constituent fragrant liquors can also be reliably classified with 100% accuracy by SVM. These results show that the colorimetric artificial nose, a simple and efficient detection and identification tool, has great potential to identify different constituent fragrant liquors reliably well.
