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• Michael Rudko, Professor of Engineering

Union College• Seyfollah Maleki, Professor of Physics, Union

College

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

“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

“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

Project Definition

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

Project Definition, ContThe project was divided into five sections:

1. Design a spectral search algorithm.2. Reduce thermal cross-talk through software

design to improve output spectral quality.3. Conduct IR source experiments to categorize

impact of source setup on spectral quality.4. Comparison of sample grinding techniques:

a. output spectral qualityb. particle size categorization

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

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.

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.

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

3. IR Source and Detector Experiments

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

Tested effects of lens vs. reflector light collimation on pixel cross-talk and energy

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

4b. Sample Grinding:Particle Size Categorization

•The particle size distribution was similar for both grinding techniques, ~ 10-50 um clumps of particles

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

Ceramic Ground Asprin, magnified 100x, scale = 1um

Conclusions1. Design a spectral search algorithm:

successful, 2 algorithms implemented2. 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 lenses4. 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

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.

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