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Improvements on an ATR-IR Spectrometer

Improvements on an ATR-IR Spectrometer. By Dylan Wilks Union College March, 2005. Acknowledgments. Paul Wilks , President, Wilks Enterprise, Inc. Don Lavery , Senior Engineer, Wilks Enterprise, Inc. Palma Catravas , Senior Project Advisor, Professor of Engineering Union College

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Improvements on an ATR-IR Spectrometer

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  1. Improvements on an ATR-IR Spectrometer By Dylan Wilks Union College March, 2005

  2. Acknowledgments • Paul Wilks, President, Wilks Enterprise, Inc. • Don Lavery, Senior Engineer, Wilks Enterprise, Inc. • Palma Catravas, Senior Project Advisor, Professor of Engineering Union College • Michael Rudko, Professor of Engineering Union College • Seyfollah Maleki, Professor of Physics, Union College

  3. Introduction • Infrared spectrometry has been used for over fifty years to identify substances • Useful since it requires only a small sample and does not destroy the sample • New devices are becoming much smaller, allowing them to be used in the field or factory

  4. “Attenuated Total Reflection” (ATR) Explained • A sample is placed on the ZnSe crystal • An infrared source emits light into the crystal • The light reflects on the inside of the crystal, eventually reaching the variable filter detector

  5. “Attenuated Total Reflection” (ATR) Explained • Each time the IR light reflects off the sample side, energy is absorbed by the sample • The amount of energy absorbed by the sample is used to generate a waveform

  6. Project Definition Improve upon the Wilks Enterprise VFA spectrometer through both hardware and software modifications, and conduct experiments that will lead to future improvements.

  7. Project Definition, Cont The project was divided into five sections: • Design a spectral search algorithm. • Reduce thermal cross-talk through software design to improve output spectral quality. • Conduct IR source experiments to categorize impact of source setup on spectral quality. • Comparison of sample grinding techniques: a. output spectral quality b. particle size categorization

  8. 1. Spectral Search Algorithm • An algorithm that compares an unknown spectrum to a library of known spectra was desired. • 2 designs were implemented: Mean Square Average, and Factor Analysis • Both algorithms were implemented in MATLAB

  9. 1. Spectral Search Algorithm: Mean Square Average • A simple approach that takes the squared difference between each pixel from a library spectrum and the unknown spectrum. • The squared differences are then averaged, and the library spectra with the lowest mean square average is returned as the most likely match.

  10. 1. Spectral Search Algorithm: Factor Analysis • Factor Analysis is a method that allows for spectral search improvement by considering only the important parts of the spectra • The data is represented as vectors, which are placed in matrix form. Matrices are then reduced to include only important information through “singular value decomposition” • At this point the algorithm was less accurate than the mean square average approach.

  11. 2. Thermal Cross-talk Reduction • The detector consists of 64 pixels • When one pixel is heated by infrared light, the pixels adjacent to the heated pixel will heat up as well. This is thermal cross-talk. • An algorithm was implemented to compensate for this

  12. 3. IR Source and Detector Experiments Tested effects of lens vs. reflector light collimation on pixel cross-talk and energy Experimented with different source setups, and different detector arrangements Experiments led to a modification of the source arrangement from 5 sources to 3 with hemispherical lenses

  13. 4a. Sample Grinding:Output Spectral Quality • Spectra were taken From samples ground with both a ceramic and agate mortar and pestle • Both techniques generated similar spectral quality spectra

  14. 4b. Sample Grinding:Particle Size Categorization Agate Ground Asprin, magnified 100x, scale = 1um • Particle size distributions were found for pills ground in an agate mortar and pestle and a ceramic mortar and pestle • The particle size distribution was similar for both grinding techniques, ~ 10-50 um clumps of particles Ceramic Ground Asprin, magnified 100x, scale = 1um

  15. Conclusions • Design a spectral search algorithm: successful, 2 algorithms implemented 2. Reduce thermal cross-talk through software design to improve output spectral quality: successful, algorithm increased spectral quality 3. Conduct IR source experiments to categorize impact of source setup on spectral quality: found ideal source setup of 3 sources with lenses 4. Comparison of sample grinding techniques: a. output spectral quality: both techniques generated similar spectra b. particle size categorization: 10-50 micron particle size distribution for both techniques

  16. Summary The experiments and improvements completed in this project have increased the resolution, as well as the marketability of the Wilks Enterprise VFA spectrometer. This spectrometer provides an approach to infrared spectroscopy that is reliable, accurate and small enough to use in the field, rather than in a lab.

  17. CARB BAND-> Low Carb Guinness?

  18. Questions?

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