Positive matrix factorization applied to a curve resolution problem

✍ Scribed by Yu-Long Xie; Philip K. Hopke; Pentti Paatero

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 84 KB
• Volume: 12
• Category: Article
• ISSN: 0886-9383
• DOI: 10.1002/(sici)1099-128x(199811/12)12:6<357::aid-cem523>3.0.co;2-s

✦ Synopsis

Positive matrix factorization (PMF) is a least squares approach to factor analysis which was originally developed for environmental data analysis and has been applied to several problems in resolving sources of environmental pollutants. PMF has been used as both a two-way and three-way data analysis tool. In this investigation, threeway data arrays were used to explore the ability of PMF in curve resolution. Pulsed gradient spin echo (PGSE) nuclear magnetic resonance (NMR) data were measured for spectral mixtures where the concentrations of the compounds decay exponentially. Three-way data arrays were constructed by packing different parts of the data from single experiments and were analyzed with three-way PMF to obtain the NMR spectra, decay profiles and the self-diffusion coefficients of constituents.