Classification Method of Liquor Quality Based on Time and Frequency Spectrum Characteristics

In order to establish a fast and accurate method for liquor quality identification, this paper uses the machine learning method to model liquor of different quality. To extract the characteristics of different quality liquor, we analyzed different quality liquor with the ion mobility spectroscopy, constructed the feature vectors based on the signal of ion mobility spectroscopy, and classified different quality liquor. The ion mobility spectroscopy signals of liquor samples were obtained using the Excellims GA2100 electrospray ionization mobility spectrometry (ESI-IMS). Each ion mobility spectroscopy signal is a time series signal with its intensity varying with time. In the aspect of feature extraction, the time-domain characteristic peaks of the original data were extracted. Fourier transform is performed on the data of ion mobility spectroscopyfor more comprehensive characteristics, and the characteristic peaks in the frequency domain were extracted. At the same time, in order to express the characteristics of signal change, the spectral entropy and zero-crossing rate of ion mobility spectroscopy were calculated, and the N X 9 dimensional feature matrix was constructed; Then, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensions of the features. The cumulative contribution rate of the first three-dimensional features of PCA to the overall features is 95%. By contrast, the cumulative contribution rate of the first two-dimensional features of LDA is 95%. Therefore, LDA is chosen as the feature dimension reduction method; Finally, a support vector machine (SVM) , a nonlinear classifier in machine learning, was used to classify liquor ion mobility spectrum data. The experimental results show that the correct classification rate of the SVM method is 100% in the classification of real liquor and liquor with added alcohol; The correct classification rate of the SVM method is 99. 7% in the six classifications of real liquor and five kinds of fake liquor with 10% , 20% , 30% , 40% and 50% alcohol concentration respectively. In addition, this paper compared the results of classification of ion mobility spectroscopy of liquor samples by logistic regression analysis (LRM) , fuzzy C-means (FCM) and k-nearest neighbor (KNN). The results show that the SVM method based on spectrum feature vector can accurately distinguish the real liquor and the liquor with added alcohol, which provides a new detection method for identifying liquor quality.