A Non-contact Alcohol Measurement Method Based on Neural Network Correction Algorithm

This paper presents a non-contact method for the measurement of alcohol gas emission based on the neural network correction algorithm, to mitigate the influence of external factors on the measurement process. The proposed method combines the characteristics of alcohol gas absorption in the infrared spectrum and the nonlinear processing method of the back propagation(BP) neural network algorithm. The algorithm considers the influence of temperature and humidity on light intensity during the gas absorption process and trains it as the input to the neural network and measurement parameters. Simultaneously, the proposed algorithm is compared with the data fitting algorithm, and the experimental results show that this algorithm achieves better results.