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Environ Sci Technol

Title:		Chemical indices and methods of multivariate statistics as a tool for odor classification
Author(s):		Mahlke IT; Thiesen PH; Niemeyer B;
Address:		"Helmut-Schmidt- University / University of the Federal Armed Forces Hamburg Institute of Thermodynamics, 22043 Hamburg, Germany. ingo.mahlke@hsu-hamburg.de"
Journal Title:		Environ Sci Technol
Year:		2007
Volume:		41
Issue:		7
Page Number:		2414 - 2421
DOI:		10.1021/es060512z
ISSN/ISBN:		0013-936X (Print) 0013-936X (Linking)
Abstract:		"Industrial and agricultural off-gas streams are comprised of numerous volatile compounds, many of which have substantially different odorous properties. State-of-the-art waste-gas treatment includes the characterization of these molecules and is directed at, if possible, either the avoidance of such odorants during processing or the use of existing standardized air purification techniques like bioscrubbing or afterburning, which however, often show low efficiency under ecological and economical regards. Selective odor separation from the off-gas streams could ease many of these disadvantages but is not yet widely applicable. Thus, the aim of this paper is to identify possible model substances in selective odor separation research from 155 volatile molecules mainly originating from livestock facilities, fat refineries, and cocoa and coffee production by knowledge-based methods. All compounds are examined with regard to their structure and information-content using topological and information-theoretical indices. Resulting data are fitted in an observation matrix, and similarities between the substances are computed. Principal component analysis and k-means cluster analysis are conducted showing that clustering of indices data can depict odor information correlating well to molecular composition and molecular shape. Quantitative molecule describtion along with the application of such statistical means therefore provide a good classification tool of malodorant structure properties with no thermodynamic data needed. The approximate look-alike shape of odorous compounds within the clusters suggests a fair choice of possible model molecules"
Keywords:		"Classification/*methods Cluster Analysis Gases/*chemistry Models, Chemical Molecular Structure Multivariate Analysis Odorants/*analysis Organic Chemicals/*analysis/chemistry Principal Component Analysis Volatilization *Waste Products;"
Notes:		"MedlineMahlke, Ingo T Thiesen, Peter H Niemeyer, Bernd eng Research Support, Non-U.S. Gov't 2007/04/19 Environ Sci Technol. 2007 Apr 1; 41(7):2414-21. doi: 10.1021/es060512z"