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Software for Interactive Curve Resolution using SIMPLISMA

Software for Interactive Curve Resolution using SIMPLISMA. Andrey Bogomolov , Michel Hachey, and Antony Williams. SIMPLISMA is… . SIMPL e-to-Use Intuitive I nteractive Operator is involved in the process S elf-modeling No prior information is required M ixture A nalysis. Willem Windig.

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Software for Interactive Curve Resolution using SIMPLISMA

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  1. Software for Interactive Curve Resolution using SIMPLISMA Andrey Bogomolov, Michel Hachey, and Antony Williams

  2. SIMPLISMA is… • SIMPLe-to-Use • Intuitive • Interactive • Operator is involved in the process • Self-modeling • No prior information is required • Mixture • Analysis

  3. Willem Windig SIMPLISMA Reference: [1] W. Windig and J. Guilment, Anal. Chem.65 (1991),1425.

  4. SIMPLISMA is a Multivariate Curve Resolution Algorithm • Extract pure componentspectra from a series of spectroscopic observations of a mixture while the component concentrations vary • Obtain component concentration profiles for processes evolving in time • Detect the number of mixture components

  5. assumptions General Curve Resolution Problem

  6. Curve Resolution and PCA

  7. Practical Applications • Qualitative characterization of unknown mixtures • Interactive process monitoring • Studying chemical reactions’ kinetics and mechanisms • Obtaining equilibrium constants • Resolving co-eluting signals in hyphenated chromatography (HPLC/DAD) • Quantitative analysis (calibration is required)

  8. Self-Modeling Curve Resolution Algorithms • Evolving Factor Analysis (EFA) • Window/Subwindow Factor Analysis (WFA/SFA) • Iterative Target Transformation Factor Analysis (ITTFA) • Rank Annihilation Factor Analysis (RAFA) • Direct Exponential Curve Resolution Algorithm (DECRA) by W. Windig • and more…

  9. Self-Modeling Basic Steps (Factor-Based Methods) • Deducing the number of components (PCA) • Obtaining initial curve estimates • Iterative improvement using system-specific constraints

  10. SIMPLISMA is a Purity-Based Approach • A pure variable represents the component concentration profile • Find a pure variable for each component • Solve for the component spectra by means of regression • How to find pure variables?

  11. Purity Function • Purity Function • Mean • Standard Deviation

  12. Purity-Corrected Standard Deviation

  13. Overestimated Purity Problem

  14. Overestimated Purity Problem • Purity tends to the infinity when the mean approaches zero • Offset  serves to compensate for this effect • Offset is usually defined as % of the mean

  15. Deducing the Number of Components • Shape of Residuals • Shape of the Resolved Curves • Shape of Purity and Purity-Corrected Standard Deviation Spectra • TSI vs LSQ plot • Cumulative %Variance • IND Function

  16. SIMPLISMA Result

  17. SIMPLISMA with 2nd Derivative • The algorithm assumes that each component has pure variable • Often, in real-world mixtures this requirement is not met • Inverted 2nd derivative may help!

  18. Live Data Example

  19. Advantages of SIMPLISMA • Interactive: unlike black-box algorithms, lets a human interfere • Intuitive: spectrum-like curves are easily interpreted by spectroscopists • Fast: does not perform time-consuming iterative improvements • Flexible: does not use prior assumptions about spectral and curve shapes

  20. Limitations and Workarounds • Real purity is unknown => assess purity by other algorithms • No variance—no component => more experiments to make it vary • Too complex data => try to split

  21. CONCLUSION • SIMPLISMA is a curve resolution program designed for use by spectroscopic experts • Commercial implementation has been transformed into a chemical software interface • Therefore, the hurdles to widespread usage have been overcome!

  22. Acknowledgments • Willem Windig for the invention • Eastman Kodak for licensing the SIMPLISMA algorithm • Yuri Zhukov and Alexey Pastutsan, the ACD/Labs programmers • Antony Williams and Michel Hachey, colleagues and co-authors

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