Application of Optimization Algorithm for Discrete Particle Swarm and Principal Component Projection Analysis in Chromatographic Fingerprint Analysis of Cigarette Smoke

In order to create distinguishable chromatogram models for mainstream cigarette smoke samples of similar style, the GC-TOFMS chromatographic sections of 38 smoke samples of two sets of the same brand cigarettes were processed with optimization algorithm for discrete particle swarm(DPSO), and the resultant optimized chromatographic sections were analyzed further with principal component projection analysis (PCPA). The results showed that from the DPSO optimized chromatographic sections, the chromatograms of the 38 smoke samples were basically distinguished apart by PCPA. PCPA combining with DPSO can be used in fingerprint chromatogram analysis of cigarette smoke.