Weighted two-band target entropy minimization for the reconstruction of pure component mass spectra: Simulation studies and the application to real systems

A method is proposed, on the basis of a recently developed algorithm—Band Target Entropy Minimization (BTEM)—to reconstruct mass spectra of pure components from mixture spectra. This method is particular useful in dealing with spectral data with discrete features (like mass spectra). Compared to the original BTEM, which has been applied to differentiable spectros-copies such as Fourier-transfer infrared spectroscopy (FTIR), ultraviolet (UV), Raman, and nuclear magnetic resonance (NMR), the latest modifications were obtained through: (1) Reformulating the objective function using the peak heights instead of their derivatives; (2) weighting the abstract vector VT to reduce the effect of noise; (3) using a two-peak targeting strategy (tBTEM) to deal with strongly overlapping peaks; and (4) using exhaustive search to locate all the component spectra. A set of 50 multi-component mass spectra was generated from ten reference experimental pure component spectra. Many of the compounds chosen have common MS fragments and therefore, many of the pure component spectra have considerable intensity in same data channels. In addition, a set of MS spectra from a real system with four components was used to examine the newly developed algorithm. Successful reconstruction of the ten component spectra of the simulated system and the four component spectra of the real system was rapidly achieved using the new tBTEM algorithm. The advantages of the new algorithm and its implication for rapid system identification of unknown mixtures are readily apparent.

[1]  Peter de B. Harrington,et al.  Interactive Self-Modeling Mixture Analysis of Ion Mobility Spectra , 1997 .

[2]  Chris W. Brown,et al.  Self-Modeling Mixture Analysis by Interactive Principal Component Analysis , 2000 .

[3]  Edmund R. Malinowski,et al.  Factor Analysis in Chemistry , 1980 .

[4]  Marc Garland,et al.  The Rh4(CO)12-catalyzed hydroformylation of 3,3-dimethylbut-1-ene promoted with HMn(CO)5. Bimetallic catalytic binuclear elimination as an origin for synergism in homogeneous catalysis. , 2003, Journal of the American Chemical Society.

[5]  A novel approach to the retrieval of the mass spectrum of a mixture , 2000 .

[6]  E. A. Sylvestre,et al.  Self Modeling Curve Resolution , 1971 .

[7]  S Kawata,et al.  Constrained nonlinear method for estimating component spectra from multicomponent mixtures. , 1983, Applied optics.

[8]  E. A. Sylvestre,et al.  Curve Resolution Using a Postulated Chemical Reaction , 1974 .

[9]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[10]  Jan Havrda,et al.  Quantification method of classification processes. Concept of structural a-entropy , 1967, Kybernetika.

[11]  Marc Garland,et al.  An improved algorithm for estimating pure component spectra in exploratory chemometric studies based on entropy minimization , 1998 .

[12]  Marc Garland,et al.  Semi-Batch Homogeneous Catalytic In-Situ Spectroscopic Data. FTIR Spectral Reconstructions Using Band-Target Entropy Minimization (BTEM) without Spectral Preconditioning , 2002 .

[13]  Marc Garland,et al.  Pure component reconstructions using entropy minimizations­and variance–weighted piecewise–continuous spectral regions. Application to the unstable experimental system Co2(CO)8/Co4(CO)12 , 2000 .

[14]  Wee Chew,et al.  Spectral Pattern Recognition of in Situ FT-IR Spectroscopic Reaction Data Using Minimization of Entropy and Spectral Similarity (MESS): Application to the Homogeneous Rhodium Catalyzed Hydroformylation of Isoprene , 2003, Applied spectroscopy.

[15]  Gene H. Golub,et al.  Matrix computations , 1983 .

[16]  Satosi Watanabe,et al.  Pattern recognition as a quest for minimum entropy , 1981, Pattern Recognit..

[17]  I. Vidavsky,et al.  Comparing similar spectra: From similarity index to spectral contrast angle , 2002, Journal of the American Society for Mass Spectrometry.

[18]  Yuqi Feng,et al.  Preparation and Characterization of Alkylphosphonate-Modified Magnesia-Zirconia Composite for Reversed-Phase Liquid Chromatography , 2000 .

[19]  W. Windig.  Spectral data files for self-modeling curve resolution with examples using the Simplisma approach , 1997 .

[20]  J. N. Kapur.  Maximum-entropy models in science and engineering , 1992 .

[21]  Marc Garland,et al.  Pure component spectral reconstruction from mixture data using SVD, global entropy minimization, and simulated annealing. Numerical investigations of admissible objective functions using a synthetic 7‐species data set , 2002, J. Comput. Chem..

[22]  Wee Chew,et al.  Rhodium tetracarbonyl hydride: the elusive metal carbonyl hydride. , 2002, Angewandte Chemie.

[23]  M. Garland,et al.  Spectral reconstruction of in situ FTIR spectroscopic reaction data using band-target entropy minimization (BTEM): application to the homogeneous rhodium catalyzed hydroformylation of 3,3-dimethylbut-1-ene using Rh4(CO)12 , 2003 .

[24]  D. Massart,et al.  Orthogonal projection approach applied to peak purity assessment. , 1996, Analytical chemistry.

[25]  Wee Chew,et al.  Band-target entropy minimization (BTEM): An advanced method for recovering unknown pure component spectra. Application to the FTIR spectra of unstable organometallic mixtures , 2002 .

[26]  Peter Terwiesch,et al.  On the Number of Observable Species, Observable Reactions and Observable Fluxes in Chemometric Studies and the Role of Multichannel Integration , 1997 .

[27]  Marc Garland,et al.  Fourier transform Raman spectral reconstruction of inorganic lead mixtures using a novel band‐target entropy minimization (BTEM) method , 2003 .

[28]  Willem Windig,et al.  The resolution of mixtures using data from automated probe mass spectrometry , 1995 .