An e-Nose Fingerprint-Based OC-OCC Classification Approach for Authenticity Identification of Liquors

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Abstract
Herein, an original algorithm, the model-free one-class open-style circular coding (OC-OCC) approach is proposed to identify the authenticity of Chinese liquors based on an electronic nose system. When measuring liquors with an e-nose, the liquor odors were transformed into the corresponding odor fingerprint, which is composed of the response curves of the gas sensor array. In the training stage, only the positive samples, i.e., the true liquors’ odor fingerprints were used to train algorithms. For feature extraction, the response curve of each gas sensor was averagely divided into 20 segments, followed by a median average of the response voltages in each segment to form the voltage features. A circular feature set is subsequently constructed using the voltage features of each segment, and the circular codes are generated by arranging features of different sensors in ascending order. In the identification stage, the testing samples with the same circular codes as the trained true (positive) liquor type are predicted as true samples, otherwise as the fake (negative) samples. The proposed method yields average accuracy of 98.7% and 96.1% for two public datasets, superior to three classical one-class classification algorithms, such as the one-class support vector machine (OC-SVM), isolation forest (IF), and the one-class k-nearest neighbor (OC-KNN).
Funding Information
  • National Natural Science Foundation of China (62203321, 22208080)

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