离散粒子群优化算法和主成分投影分析在卷烟烟气色谱指纹分析中的应用

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

摘要: 为实现风格相似的卷烟主流烟气样本的色谱分类建模,采用离散粒子群优化算(DPSO)法处理两种同品牌卷烟产品的38个烟气样本的GC/TOFMS色谱区间,并对得到的优化色谱区间进行了主成分投影分析(PCA)。结果表明,在DPSO的优化色谱区间上,PCA投影分析基本上可将这38个烟气样本的色谱信号分类。PCA投影分析结合DPSO法可用于卷烟烟气指纹图谱的判别分析。

Abstract: 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.