In what ways is on-line digital data acquisition superior to the old methods such as the chart recorder? Some of the advantages are obvious, such as archival storage and retrieval of data and post-run replotting with adjustable scale expansion. Even more important, however, there is the possibility of performing post-run data analysis and signal processing. There are a large number of computer-based numerical methods that can be used to reduce noise, improve the resolution of overlapping peaks, compensate for instrumental artifacts, test hypotheses, optimize measurement strategies, diagnose measurement difficulties, and decompose complex signals into their component parts. These techniques can often make difficult measurements easier by extracting more information from the available data. Many of these techniques are based on laborious mathematical procedures that were not practical before the advent of computerized instrumentation. It is important for chemistry students to appreciate the capabilities and the limitations of these modern signal processing techniques.
In the chemistry curriculum, signal processing may be covered as part of a course on instrumental analysis (1, 2), electronics for chemists (3), laboratory interfacing (4), or chemometrics (5). The purpose of this paper is to give a general introduction to some of the most widely used signal processing techniques and to give illustrations of their applications in analytical chemistry. This essay covers only basic topics related to one-dimensional time-series signals, not two-dimensional data such as images, and is limited to only basic mathematics. For more advanced topics and for a more rigorous treatment of the underlying mathematics, refer to the extensive literature on signal processing (6, 10), statistics in analytical chemistry (11, 12), and chemometrics (8, 9).
This tutorial makes use of a freeware signal-processing application called SPECTRUM that was used to produce many of the illustrations. The animated videos and several additional examples were developed in Matlab, a high-performance commercial numerical computing environment and programming language that is widely used in research. Many of these techniques can also be performed in spreadsheets (11).
1. Douglas A. Skoog, Principles of Instrumental Analysis, Third Edition,
Saunders, Philadelphia, 1984. Pages 73-76.
2. Gary D. Christian and James E. O'Reilly, Instrumental Analysis, Second
Edition, Allyn and Bacon, Boston, 1986. Pages 846-851.
3. Howard V. Malmstadt, Christie G. Enke, and Gary Horlick, Electronic
Measurements for Scientists, W. A. Benjamin, Menlo Park, 1974. Pages
816-870.
4. Stephen C. Gates and Jordan Becker, Laboratory Automation using the IBM
PC, Prentice Hall, Englewood Cliffs, NJ, 1989.
5. Muhammad A. Sharaf, Deborah L Illman, and Bruce R. Kowalski, Chemometrics, John Wiley and Sons, New York, 1986.
6. Peter D. Wentzell and Christopher D. Brown, Signal Processing in Analytical Chemistry, in Encyclopedia of Analytical Chemistry, R.A. Meyers (Ed.), p. 9764–9800, John Wiley & Sons Ltd, Chichester, 2000 (http://myweb.dal.ca/pdwentze/papers/c2.pdf)
7. Constantinos E. Efstathiou, Educational Applets in Analytical Chemistry, Signal Processing, and Chemometrics. (http://www.chem.uoa.gr/Applets/Applet_Index2.htm)
8. A. Felinger, Data Analysis and Signal Processing in Chromatography, Elsevier Scice (19 May 1998).
9. Matthias Otto, Chemometrics: Statistics and Computer Application in Analytical Chemistry, Wiley-VCH (March 19, 1999). Some parts viewable in Google Books.
10. Steven
W. Smith, The Scientist and Engineer's Guide to Digital Signal
Processing. (Downloadable chapter by chapter in PDF format from http://www.dspguide.com/pdfbook.htm). This is a much more general treatment of the topic.
11. Robert de Levie, How to use Excel in Analytical Chemistry and in General Scientific Data Analysis, Cambridge University Press; 1 edition (February 15, 2001), ISBN-10: 0521644844. PDF excerpt .
12. Scott Van Bramer, Statistics for Analytical Chemistry, http://science.widener.edu/svb/stats/stats.html.
13. Numerical Analysis for Chemical Engineers, Taechul Lee (http://www.cheric.org/ippage/e/ipdata/2001/13/lecture.html)
14. Educational Matlab GUIs, Center for Signal and Image Processing (CSIP), Georgia Institute of Technology. (http://users.ece.gatech.edu/mcclella/matlabGUIs/)
15. Digital Signal Processing Demonstrations in Matlab, Jan Allebach, Charles Bouman, and Michael Zoltowski, Purdue University (http://www.ecn.purdue.edu/VISE/ee438/demos/Demos.html)