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A new receptor model: A direct trilinear decomposition followed by a matrix reconstruction
Author(s) -
Zeng Yousheng,
Hopke Philip K.
Publication year - 1992
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180060205
Subject(s) - table (database) , matrix (chemical analysis) , set (abstract data type) , sampling (signal processing) , data matrix , data set , decomposition , algorithm , computer science , complex matrix , data mining , statistics , biological system , mathematics , chemistry , clade , biochemistry , organic chemistry , filter (signal processing) , chromatography , biology , gene , computer vision , programming language , phylogenetic tree
In many cases, monitoring data for ambient airborne particles can be organized in the form of a three‐way data table with one way for chemical species, one for sampling periods and one for sites. A direct trilinear decomposition followed by a matrix reconstruction (DTDMR) is developed to analyze such a data table as a whole. The three‐way data set is composed into three two‐way matrices by a direct trilinear decomposition (DTD). The column vectors of each of the matrices are called ‘source profiles’, ‘emission patterns’ and ‘site coefficients’ respectively. Particulate sources are identified by examining both their source profiles and emission patterns. After the sources have been identified, emission patterns and site coefficients are used to produce a three‐way matrix that gives estimates of mass contributions of sources to the samples collected at every site in every period. By simulation study, not only has the method been verified, but a good indicator has been found that shows the number of factors (i.e. sources) in the system. Unlike other receptor models, DTDMR does not require source profile data and does not involve trial‐and‐error procedures. Since DTDMR identifies sources based on variations in two dimensions, it has a higher potential to distinguish two sources that have similar chemical compositions. The DTDMR model has provided excellent results with simulated data and has been applied in a real world three‐way data set.