Abstract Determining quantitative molecular composition of atmospheric particles is required for assessing their environmental and health impacts. The presented algorithm was designed to analyse numerous Raman spectra of metal-rich atmospheric particles. Multivariate curve resolution-alternating least squares procedure (MCR-ALS) has been applied to resolve complex data from Raman microanalysis by means of a computer-assisted analytical procedure called Single Particle Analysis (SPA). The SPA – contrary to Raman mapping – provides data in which each single particle is assigned to a single spectrum, in the group with a statistically significant size. During the procedure, the relative contributions of individual compounds in the recorded Raman spectra have been specified. Grouping and relationship determination of the collected data have been performed by hierarchical cluster analysis (HCA) and principal component analysis (PCA). A new methodology is proposed to quantitatively determine the molecular composition and chemical mixing of single airborne particles based on the data from the automated Raman microspectrometry measurements.

中文翻译：

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结合拉曼显微光谱和化学计量学，确定大气气溶胶粒子的定量分子组成和混合状态

摘要 确定大气颗粒的定量分子组成是评估其环境和健康影响所必需的。所提出的算法旨在分析大量富含金属的大气粒子的拉曼光谱。多元曲线分辨率交替最小二乘法 (MCR-ALS) 已被应用于通过称为单粒子分析 (SPA) 的计算机辅助分析程序来解析来自拉曼微量分析的复杂数据。SPA——与拉曼映射相反——提供数据，其中每个单个粒子被分配到单个光谱，在具有统计显着大小的组中。在此过程中，已指定记录的拉曼光谱中各个化合物的相对贡献。已通过层次聚类分析 (HCA) 和主成分分析 (PCA) 对收集的数据进行分组和关系确定。基于自动拉曼显微光谱测量的数据，提出了一种新的方法来定量确定单个空气传播颗粒的分子组成和化学混合。