Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas sensor with dynamic measurement and PCA weak separation

Abstract

With the rampant drug crime, the detection of drug-producing chemicals has put forward the great demand of multi-type and multi-concentration on-line rapid detection. The rapid development of dynamic measurement for semiconductor gas sensors provides a solution to this problem. However, the mutual blend of type and concentration information negatively affects sensor performance. In this paper, principal component analysis (PCA) was used for weak separation of type and concentration; k-Nearest Neighbor (KNN) was used for qualitative recognition; polynomial regression was used for quantitative recognition. The physical meaning of the dynamic response signal after PCA transformation was first proposed: PC1 has a weak concentration meaning; the combination of PC2, PC3, and PC4 has a weak type meaning. Based on the weak separation, the stepwise recognition method of qualitative classification and quantitative regression was first used to improve the recognition rate, the resolution and the generalization performance of the sensor. Using the inverse transformation of PCA, the principle of PCA and the method of ideal data verified the feasibility of this method. The qualitative and quantitative recognition of various drug-producing chemicals had been realized, which is a new way of on-line rapid sensor detection for drug-producing chemicals.