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|Title:||Band-target entropy minimization. A robust algorithm for pure component spectral recovery. Application to complex randomized mixtures of six components||Authors:||Widjaja, E.
|Issue Date:||1-Sep-2003||Citation:||Widjaja, E., Li, C., Chew, W., Garland, M. (2003-09-01). Band-target entropy minimization. A robust algorithm for pure component spectral recovery. Application to complex randomized mixtures of six components. Analytical Chemistry 75 (17) : 4499-4507. ScholarBank@NUS Repository. https://doi.org/10.1021/ac0263622||Abstract:||A newly developed self-modeling curve resolution method, band-target entropy minimization (BTEM), is described. This method starts with the data decomposition of a set of spectroscopic mixture data using singular value decomposition. It is followed by the transformation of the orthonormal basis vectors/loading vectors into individual pure component spectra one at a time. The transformation is based in part on some seminal ideas borrowed from information entropy theory with the desire to maximize the simplicity of the recovered pure component spectrum. Thus, the proper estimate is obtained via minimization of the proposed information entropy function or via minimization of derivative and area of the spectral estimate. Nonnegativity constraints are also imposed on the recovered pure component spectral estimate and its corresponding concentrations. As its name suggests, in this method, one targets a spectral feature readily observed in loading vectors to retain, and then combinations of the loading vectors are searched to achieve the global minimum value of an appropriate objective function. The major advantage of this method is its one spectrum at a time approach and its capability of recovering minor components having low spectroscopic signals. To illustrate the application of BTEM, spectral resolution was performed on FT-IR measurements of very highly overlapping mixture spectra containing six organic species with a two-component background interference (air). BTEM estimates were also compared with the estimates obtained using other self-modeling curve resolution techniques, i.e., SIMPLISMA, IPCA, OPA-ALS, and SIMPLISMA-ALS.||Source Title:||Analytical Chemistry||URI:||http://scholarbank.nus.edu.sg/handle/10635/66469||ISSN:||00032700||DOI:||10.1021/ac0263622|
|Appears in Collections:||Staff Publications|
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