Recognition Algorithm for Detection of Precursor Chemicals by Semiconductor Gas Sensor Array Under Dynamic Measurement

Abstract

Narcotic and psychotropic drugs seriously endanger human health and the stable development of society. In order to snipe drugs from the source, it is extremely significant to detect precursor chemicals. In this article, the precursor chemicals and interference samples are measured by the semiconductor gas sensor array under dynamic measurement and static measurement. The principal component analysis (PCA), support vector machine (SVM), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -nearest neighbor (KNN) algorithms are used for qualitative and quantitative analyses of the acquired data. In addition, the qualitative result of two measurement methods using the SVM and KNN algorithms are given. For quantitative analysis, support vector regression (SVR) is used, and the sum of the mean absolute error (MAE) in the dynamic measurement is better than in static measurement. Besides, the stability experiments are conducted about a month later. It is found that the qualitative recognition in dynamic measurement is better than static measurement under interference and no interference samples. It reveals that the dynamic measurement of the sensor array has certain advantages under qualitative analysis and is more suitable for the real-time detection of precursor chemicals.