Classifying Beers With Memristor Neural Network Algorithm in a Portable Electronic Nose System - Zafar, Imran; Cui, Yuanhui; Bai, Qinghao; Yang, Yanqing (Frontiers in Physics, 2022)
Quality control and counterfeit product detection have become exceedingly important due to the vertical market of beers in the global economy. China is the largest producer of beer globally and has a massive problem with the counterfeit alcoholic beverages. In this research, a modular electronic nose system with 4 MOS gas sensors was designed for collecting the models from four different brands of Chinese beers. A sample delivery subsystem was fabricated to inject and clean the samples. Software-based data acquisition subsystem was programmed to record the time-dependent chemical responses in 28 different models. A back propagation neural network based on a memristor was proposed to classify the quality of the beers. Collected data from the electronic nose system were then used to train, validate and test the created memristor back propagation neural network model. Over 70 tests with changes in the setup parameters, feature extraction methods, and neural network parameters were performed to analyze the classification performance of electronic nose hardware and neural network. Samples collected from 28 experiments showed the deviation of 9% from mean value. The memristor back propagation network was able to classify four brands of Chinese beers with 88.3% of classification accuracy. Because the memristor neural network algorithm is easy to fabricate in hardware, it is reasonable to design an instrument with low cost and high accuracy in the near future.