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VL in material science Chemical Imaging: A typical VL application

Institute for Atomic and Molecular Physics. VL in material science Chemical Imaging: A typical VL application. Ron M.A. Heeren IBM meeting, September 19 th , 2001 WTCW Amsterdam. Outline. Chemical imaging: how-what-where VL needs of the experimentalist

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VL in material science Chemical Imaging: A typical VL application

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  1. Institute for Atomic and Molecular Physics VL in material scienceChemical Imaging: A typical VL application Ron M.A. Heeren IBM meeting, September 19th, 2001 WTCW Amsterdam

  2. Outline • Chemical imaging: how-what-where • VL needs of the experimentalist • An example: Scrutinizing Rembrandt in multiple dimensions • An industrial application of FTIR • Summary

  3. Amide-CO Pb/Cl 100 mm Mass microscopy Optical microscopy Infrared microscopy Chemical Imaging Spatial detail Molecular detail

  4. Common features of chemical imaging methods • Multi-channel detection • Large data sets • High information density Y (mm) l (nm), n(cm-1), m/z (D) X (mm) But how do we extract information from these datasets ?

  5. Chemical imaging Determine local chemical surface composition with a spatial resolution better than 5 micrometer UV-VIS imaging spectroscopy FTIR imaging spectroscopy Quantification of spectral variations Functional group distribution Imaging Mass spectrometry Atomic and Molecular identification Three imaging techniques for biomolecular surface analysis

  6. Applied chemical imaging Topics and 3rd party interest • Tissue samples : NKI, AMC, UvA • Paint cross-sections : Oranjezaal, De Mayerne program • Pharmaceutical systems : Organon, (UU) • Polymer laminates : Akzo-Nobel, Shell • Food Industry : DSM-Gist, Unilever

  7. The “Data Cube” • Design a Data model with prerequisites : • All 3-, 2- or 1-dimensional data can be stored : FTIR, m-beam, Trift, NMR, VIS, MS,… • Generic tools will be used for extracting Information block containing all important parameters (generic data) +

  8. VL needs of the experimentalist Location independent (on-line) availability Measurement (raw data) • Storage and handling of large datasets (UC) • Fast retrieval of data and information (OK) • Rapid data processing = processing power Processing tools (UC) • Meta-data generation (PCA and CCA) (OK) Up one abstraction level • Meta-data database (UC) • Correlation between different data sets (different analytical techniques, meta-data processing, ext./int. database-linking) (OK) (OK = completed, UC = Under Construction)

  9. The virtual laboratory environment VLAM

  10. Development of End-user applications • Consistent user interface • First stage: Implementation of standard functions for (FTIR) data processing • Second stage: Implementation of PCA, database searching, …. • Third stage: Integration with other databases (m-beam,…)

  11. The VLAM –GUI, The user’s View

  12. The anatomy of a painting: Scrutinizing Rembrandt in multiple dimensions The anatomy lesson of dr. Tulp, Rembrandt (1632), Mauritshuis, Inv nr. 146

  13. Cross-section from inclusion on paint surface on corps • 146/b01 Dr. Tulp

  14. TOF-SIMS images FTIR Images C2H3 + UV Fluorescence Visual image Pb + SEM Cl - Aliphatic Carboxylate Carbonate

  15. 100 x 73 355 27 325 147 39 Lead 55 Pb-carboxylate+ @ m/z 460-465 43 341 208 399 310 369 221 69 383 463 429 321 337 413 281 14 83 0 50 100 150 200 250 300 350 400 450 Positive ion TOF-SIMS spectrum of an embedded paint cross-section Silicones Mass (m/z)

  16. Information density related issues • Sharing data with other researchers • Answers often in details or combination of details • Manual “Needle in haystack” approach not workable with Mbytes of data (e.g. One FTIR data cube = 4096 spectra, one MS imaging datacube = 65536 spectra) • One technique alone only provides part of the analytical picture • But always a strong correlation between location and spectral information exists • Need for “fast” automated unsupervised data processing tools I.e. chemometrics

  17. FTIR raw data MS raw data Meta data generation (PCA) Meta data generation (PCA) Reduced metadata set Reduced metadata set Improvedmetadata set Improved metadata set VL chemical imaging data experiment Experiment 1 Experiment 2 To metadata database VL Exp.1 VL Exp.2 VL Result 3 VL Result 4 VL Result 2 VL Result 1 Metadata correlation analysis (CCA) VL Exp.3 + 4

  18. TOF-SIMS Imaging FTIR Imaging (+) (+) (-) (-) (+) (+) (-) (-) Score images Reconstructed mass spectra Score images Reconstructed FTIR spectra ACTIONS Rebin Determine Overlap Principal Component Analysis 400mmx400mm 64x64 pixels 3-D Dataset 300mmx300mm 256x256 pixels 3-D Dataset

  19. Calculate Cross Correlation matrix C using principal components 1 to 10 from each dataset : Canonical Correlation Analysis Actions CV1 EV 0.736 15.9 % var CV1 EV 0.736 5.6 % var CV2 EV 0.382 5.7 % var CV2 EV 0.382 2.1 % var Pos R=0.858 Neg Pos R=0.618 Neg Score images Score images Reconstructed mass spectra Reconstructed FTIR spectra

  20. F o r e v e r y d a y n e e d s o f p e o p l e e v e r y w h e r e Infrared spectroscopy on detergent systems and fabrics(An industrial application) Ewoud van Velzen (Central Analytical Sciences, URV)

  21. Infrared and detergents F o r e v e r y d a y n e e d s o f p e o p l e e v e r y w h e r e Infrared analysis is generally applied on a wide range of detergent (related) systems e.g. washing powders, tablets, ingredients, stains, fabrics, packing materials in the framework of: 1- Performance and quality control, 2- Stain removal studies, 3- Washing effects, 4- Micro-structural behavior, 5- Competitor analysis, etc.

  22. F o r e v e r y d a y n e e d s o f p e o p l e e v e r y w h e r e Multivariate Data Analysis: Statistics Molecules Recognition Human Interpretation Data Block Shape Spectral Instrumental access Mean Spatial Direct interaction with data Statistical Interpretation Compute power Statistical tools & Algorithms VLAM Variance Correlation

  23. F o r e v e r y d a y n e e d s o f p e o p l e e v e r y w h e r e Schematic representation Collecting spectra 1740 cm-1 Peak selection 64*64 FPA MCT detector 400 µm Image 1 400 µm 1740 cm-1 Image 2 3340 cm-1 3340 cm-1

  24. F o r e v e r y d a y n e e d s o f p e o p l e e v e r y w h e r e Microstructure analysis of detergents 2920 cm-1 Aliphatic nCH2 (Anions, Non-Ionics, soap...) Image of a detergent particle Field of view = 400*400 µm Bedding material (polyacrylate) Image peak Image peak 3350 cm-1 hyroxylic nOH (Free water) 1560 cm-1 carboxylate nCOO- (Soap)

  25. F o r e v e r y d a y n e e d s o f p e o p l e e v e r y w h e r e Microstructure analysis of fabrics (I) Cross-section of shirt fabrics, consisting of three different fiber systems. Visual Image Polyacrylic bedding material Elastics IR Image, 1534 cm-1 (dNH/uCN) 100*100 µm area

  26. F o r e v e r y d a y n e e d s o f p e o p l e e v e r y w h e r e Microstructure analysis of fabrics (II) A small piece of shirt fabrics ……. …….. between two diamond windows in a pressure cell. Visual Image 1050 cm-1 (cotton) 1550 cm-1 (protein/skin ?) 1735 cm-1 (polyester)

  27. Summary • The VL renders a new experimental research tool (for chemical imaging) • Combine information to gain new insights • Information extraction from large data sets facilitated (GRID and tools) • Possibilities for fast dissemination of information. A tool for collaboration

  28. Acknowledgements • The VLAM team • Gert Eijkel • Anne Frenkel • David Groep • Bob Hertzberger • AMOLF E&I support • Royal cabinet of paintings Mauritshuis (J. Wadum and P. Noble) • ICES-KIS II • FOM-AMOLF • NWO

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