Isolation and Identification of Environmental Pollutants from Complex Mixtures Using Effect-directed Analysis and Preparative Capillary Gas Chromatography
Author | : Cornelia Meinert |
Publisher | : |
Total Pages | : 119 |
Release | : 2011 |
ISBN-10 | : OCLC:837867140 |
ISBN-13 | : |
Rating | : 4/5 (40 Downloads) |
Download or read book Isolation and Identification of Environmental Pollutants from Complex Mixtures Using Effect-directed Analysis and Preparative Capillary Gas Chromatography written by Cornelia Meinert and published by . This book was released on 2011 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerous compounds are continuously released into the environment in large quantities e.g. solvents, dyes and varnishes, herbicides, insecticides, as well as many biologically active compounds such as hormones and antibiotics. They are suspicious as the cause of adverse effects to biota, including mutagenicity, carcinogenicity, and endocrine disruption. To ensure this problem to be minimized in the future, hazard chemicals need to be identified in the environment. A simple analytical screening, of e.g. water bodies, is insufficient to detect harmful compounds. To facilitate the assignment of adverse effects of complex environmental mixtures, the combination of chemical fractionation, specific biological endpoint detection and chemical identification combined with qualitative structure-activity relationships (QSAR) should be applied known as Effect-directed analysis (EDA). However, toxicant identification in isolated fractions is still one of the biggest challenges in EDA, since these fractions often remain complex even after extensive HPLC-based fractionation. Therefore, preparative capillary gas chromatography (pcGC) - a method that holds promise to overcome this problem - is presented here. The resolving power of capillary gas chromatography compared to LC suggests to include this technique into effect-directed fractionation procedures. Moreover, toxicity confirmation using computer tools based on substructure identification and structure generation combined with QSAR models is used in this study to establish reliable cause-effect relationships.