期刊
ANALYTICAL CHEMISTRY
卷 81, 期 19, 页码 8074-8084出版社
AMER CHEMICAL SOC
DOI: 10.1021/ac901272b
关键词
-
资金
- Universidad Nacional de Rosario
- CONICET (Consejo Nacional de Investigaciones Cientificas y Tecnicas) [PIP 1950]
- ANPCyT (Agencia Nacional de Promocion Cientifica y Tecnologica) [PAE-22204]
Multivariate calibration coupled to high-performance liquid chromatography-fast scanning fluorescence spectroscopy (HPLC-FSFS) was employed for the analysis of 10 selected polycyclic aromatic hydrocarbons (PAHs), six of which correspond to heavy PAHs. The goal of the present study was the successful resolution of a system even in the presence of real interferences. Second-order HPLC-FSFS data matrices were obtained in a short time with a chromatographic system operating in isocratic mode. The difficulties in aligning chromatographic bands in complex systems, such as the ones presented here, are discussed. Two second-order calibration algorithms which do not require chromatographic alignment were selected for data processing, namely, multivariate curve resolution-alternating least-squares (MCR-ALS) and parallel factor analysis 2 (PARAFAC2). These algorithms did also achieve the second-order advantage, and therefore they were able to overcome the problem of the presence of unexpected interferences. The study was employed for the discussion of the scopes of the applied second-order chemometric tools, demonstrating the superiority of MCR-ALS to successfully resolve this complex system. The quality of the proposed techniques was assessed on the basis of the analytical recoveries from different types of water and olive oil samples after solid-phase extraction. The studied concentration ranges in water samples were 5.6 x 10(-3)-0.20 ng mL(-1) for heavy PAHs and 0.036-0.80 ng mL(-1) for light PAHs, while in oil samples the PAHs concentrations were 0.13-9.6 and 2.3-49.5 ng mL(-1) for heavy and light PAHS, respectively. All real samples were analyzed in the presence of the studied interferences.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据