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
Experimented with different source setups, and different detector arrangements
Tested effects of lens vs. reflector light collimation on pixel cross-talk and energy
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, ~ 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. Low Carb Guinness?
CARB BAND->

18. Questions?