[Introduction]  [Signal arithmetic]  [Signals and noise]   [Smoothing]   [Differentiation]  [Peak Sharpening]  [Harmonic analysis]   [Fourier convolution]  [Fourier deconvolution]  [Fourier filter]   [Peak area measurement]  [Linear Least Squares]  [Multicomponent Spectroscopy]  [Iterative Curve Fitting]  [Hyperlinear quantitative absorption spectrophotometry] [Appendix and Case Studies]  [Peak Finding and Measurement]  [iPeak]   [iSignal]  [Peak Fitters]   [iFilter]  [iPower]  [List of downloadable software]  [Interactive tools]

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Introduction

The interfacing of measurement instrumentation to small computers for the purpose of online data acquisition has now become standard practice in the modern laboratory for the purposes of performing signal processing and data analysis and storage, using a large number of digital computer-based numerical methods that are used to transform signals into more useful forms, detect and measure peaks, reduce noise, improve the resolution of over-lapping 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 even practical before the advent of computerized instrumentation. But in recent decades, computer storage and digital processing has become far less costly and literally millions of times more capable, reducing the cost of raw data and making complex computer-based signal processing techniques more practical and necessary. It is important to appreciate the abilities, as well as the limitations, of these techniques. As Erik Brynjolfsson and Andrew McAfee wrote in The Second Machine Age (W. W. Norton, 2014): "...many types of raw data are getting dramatically cheaper, and as data get cheaper, the bottleneck increasingly is the ability to interpret and use data". 

This essay covers only basic topics related to one-dimensional time-series signals, not two-dimensional data such as images. It uses a pragmatic approach and is limited to mathematics only up to the most elementary aspects of calculus, statistics, and matrix math. (For the math phobic, know that this essay does not dwell on the math and that it contains more than twice as many figures as equations). Data processing without math? Not really! Math is essential, just as it is for the technology of cell phones, GPS, digital photography, the Web, and computer games. But you can get started using these tools without understanding all the underlying math and software details. Seeing it work makes it more likely that you'll want to understand how it works. But in the long run, it's not enough just to know how to operate the software, any more than knowing how to use a word processor or a MIDI sequencer makes you a good author or musician. 

Why do I title this document "signal processing" rather than "data processing"? By "signal" I mean the x,y numerical data recorded by scientific instruments as time-series, where x may be time or another quantity like energy or wavelength, as in the various forms of spectroscopy. "Data" is a more general term that includes categorical data as well. In other words, I'm oriented to data that you would plot in a spreadsheet using the "scatter" chart type rather than bar or pie charts. 

Some of the examples come from my own areas of research in analytical chemistry, but these techniques have been used in a wide range of application areas. My software has been cited in over 250 journal papers, theses, and patents, covering fields from industrial, environmental, medical, engineering, earth science, space, military, financial, agriculture, and even music and linguistics. Suggestions and experimental data sent by hundreds of readers from their own work has helped shape my writing and software development. Much effort has gone into making this document concise and understandable; it has been highly praised by many readers.

This tutorial makes considerable use of  Matlab, a high-performance commercial and proprietary numerical computing environment and "fourth generation" programming language that is widely used in research (14, 17, 19, 20), and Octave, a free Matlab alternative that runs almost all of the programs and examples in this tutorial. There is a good reason why this language is so massively popular in science and engineering; it's powerful, fast, and relatively easy to learn, you can download thousands of useful user-contributed functions, it can interface to C, C++, Java, Fortran, and Python, and it's extensible to symbolic computing and model-based design for dynamic and embedded systems. There are many code examples in this text that you can Copy and Paste and modify into the Matlab/Octave command line, which is especially convenient if you can split your screen between the two.

My old 90s-era freeware signal-processing application for Macintosh, called SPECTRUM, was also used to produce some of the illustrations. Most of the techniques covered in this work can also be performed in spreadsheets (11, 22, 23) such as Excel or OpenOffice Calc. Octave and the OpenOffice Calc (LibreOffice Calc) spreadsheet program can be downloaded without cost from their respective web sites.

All of the Matlab/Octave scripts and functions, the SPECTRUM program, and all of the spreadsheets used here can all be downloaded from this site at no cost; they have received extraordinarily positive feedback from users.

If you are unfamiliar with Matlab/Octave, read these sections about basics and functions and scripts for a quick start-up. These are not really general-purpose programming languages like C++ or Python; rather, they are specifically suited to matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages - essentially the needs of numerical computing by scientists and engineers. Matlab and Octave are more loosely typed and are less well structured in a formal sense than other languages, and thus they tend to be more favored by scientists and engineers and less well liked by computer scientists and professional programmers.

At the present time, this work does not cover image processing, wavelet transforms, pattern recognition, or factor analysis. For more advanced topics and for a more rigorous treatment of the underlying mathematics, refer to the extensive literature on signal processing and on statistics and chemometrics.

This site had its origin in one of the experiments in a course called "Electronics and Computer Interfacing for Chemists" that I developed and taught at the University of Maryland in the 80's and 90's. The first Web-based version went up in 1995. Subsequently it has been revised and greatly expanded based on feedback from users. It is still a work in progress and, as such, benefits from feedback from readers and users.


This work is dedicated to the Joy of Uncompetitive Purposefulness.

"...in our culture of competitive self-comparison, we can choose to amplify each other’s accomplishments because there is, after all, enough to go around."  Maria Popova 

"People are generally better persuaded by the reasons which they have themselves discovered than by those which have come into the mind of others."
Blaise Pascal

"...producing technologies, and then teaching them to others, ... pushes humankind ahead". David Premack

"A computer does not substitute for judgment any more than a pencil substitutes for literacy. But writing without a pencil is no particular advantage."
Robert McNamara

  "...in the course of looking deeply within ourselves, we may challenge notions that give comfort before the terrors of the world....supporters of superstition and pseudoscience are human beings with real feelings, who, like the skeptics, are trying to figure out how the world works and what our role in it might be. Their motives are in many cases consonant with science." Carl Sagan, in The Demon-Haunted World: Science as a Candle in the Dark.

"...[be] full of wonder, generously open to every notion, [dismiss] nothing except for good reason, but at the same time, and as second nature, [demand] stringent standards of evidence, ...[applied] with at least as much rigor to what [you] hold dear as to what [you] are tempted to reject with impunity." Carl Sagan


References
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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)

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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.

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15. Jan Allebach, Charles Bouman, and Michael Zoltowski, Digital Signal Processing Demonstrations in Matlab, Purdue University (http://www.ecn.purdue.edu/VISE/ee438/demos/Demos.html)

16. Chao Yang , Zengyou He  and Weichuan Yu, Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis, http://www.biomedcentral.com/1471-2105/10/4

 17. Michalis Vlachos, A practical Time-Series Tutorial with MATLAB, http://alumni.cs.ucr.edu/~mvlachos/PKDD05/PKDD05_Handout.pdf

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19. Tobin A. Driscoll, A crash course in Matlab, http://www.math.umn.edu/~lerman/math5467/matlab_adv.pdf

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21. Martin van Exter, Noise and Signal Processing, http://www.physics.leidenuniv.nl/sections/cm/ip/Onderwijs/SVR/bestanden/noise-final.pdf

22. Scott Sinex, Developer's Guide to Excelets,  http://academic.pgcc.edu/~ssinex/excelets/

23. R. de Levie, Advanced Excel for scientific data analysis, Oxford University Press, New York (2004)

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26. “Estimation of Atomic Absorption Line Widths in Air-Acetylene Flames by Transmission Profile Modeling”, T. C. O'Haver and Jing-Chyi Chang, Spectrochim. Acta 44B, 795-809 (1989)
 
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30. “Numerical Error Analysis of Derivative Spectroscopy for the Quantitative Analysis of Mixtures”, T. C. O'Haver and G. L. Green, Anal. Chem. 48, 312 (1976).

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34. Introduction to Signal Processing in Analytical Chemistry”, T. C. O'Haver, J. Chem. Educ. 68 (1991)

35. Applications of Computers and Computer Software in Teaching Analytical Chemistry”, T. C. O'Haver, Anal. Chem. 68, 521A (1991).

36. The Object is Productivity”, T. C. O'Haver, Intelligent Instruments and Computers March-April, 1992, p 67-70.  

37. Analysis software for spectroscopy and mass spectrometry, Spectrum Square Associates ( http://www.spectrumsquare.com/).

38. Fityk, a program for data processing and nonlinear curve fitting. (http://fityk.nieto.pl/)

39. Peak fitting in Origin (http://www.originlab.com/index.aspx?go=Products/Origin/DataAnalysis/PeakAnalysis/PeakFitting)   

40. IGOR Pro 6, software for signal processing and peak fitting (http://www.wavemetrics.com/index.html)

41. PeakFIT, automated peak separation analysis (http://www.sigmaplot.com/products/peakfit/peakfit.php)

42. OpenChrom, open source software for chromatography and mass spectrometry. (http://www.openchrom.net/main/content/index.php)

43.  W. M. Briggs, Do not smooth times series, you hockey puck!, http://wmbriggs.com/blog/?p=195

44.  Nate Silver, The Signal and the Noise: Why So Many Predictions Fail-but Some Don't , Penguin Press, 2012. ISBN 159420411X .  A much broader look at "signal" and "noise". Worth reading.

45. Stats Tutorial - Instrumental Analysis and Calibration, David C. Stone, Dept. of Chemistry, U. of Toronto,  http://www.chem.utoronto.ca/coursenotes/analsci/stats/index.html

46. Streamlining Digital Signal Processing: A Tricks of the Trade Guidebook, Richard G. Lyons, John Wiley & Sons, 2012.

47. http://physics.nist.gov/PhysRefData/ASD/ and http://www.astm.org/Standards/C1301.htm

48. Curve fitting to get overlapping peak areas (http://matlab.cheme.cmu.edu/2012/06/22/curve-fitting-to-get-overlapping-peak-areas/#13)

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50. Nicole K. Keppy, Michael Allen, Understanding Spectral Bandwidth and Resolution in the Regulated Laboratory, Thermo Fisher Scientific Technical
Note: 51721. http://www.analiticaweb.com.br/newsletter/02/AN51721_UV.pdf

51. Martha K. Smith, "Common mistakes in using statistics", http://www.ma.utexas.edu/users/mks/statmistakes/TOC.html

52. Jan Verschelde, “Signal Processing in MATLAB”, http://homepages.math.uic.edu/~jan/mcs320s07/matlec7.pdf

53. Howard Mark and Jerome Workman Jr, “Derivatives in Spectroscopy”, Spectroscopy 18 (12). p.106.

54. Jake Blanchard, Comparing Matlab to Excel/VBA, https://blanchard.ep.wisc.edu/PublicMatlab/Excel/Matlab_VBA.pdf

55. Ivan Selesnick, "Least Squares with Examples in Signal Processing", http://eeweb.poly.edu/iselesni/lecture_notes/least_squares/

56. Tom O'Haver, " Is there Productive Life after Retirement?", Faculty Voice, University of Maryland, April 24, 2014.  DOI: 10.13140/2.1.1401.6005; URL: http://imerrill.umd.edu/facultyvoice1/?p=3231

57. http://www.dsprelated.com/, the most popular independent internet resource for Digital Signal Processing (DSP) engineers around the world.

58. John Denker, "Uncertainty as Applied to Measurements and Calculations", http://www.av8n.com/physics/uncertainty.htm

59. T. C. O'Haver, Teaching and Learning Chemometrics with Matlab, Chemometrics and Intelligent Laboratory Systems 6, 95-103 (1989).

60. Allen B. Downey, "Think DSP", Green Tree Press, 2014. (164-page PDF download). Python code instruction using sound as a basis.

61. M. Farooq Wahab, et. al," Salient Sub-Second Separations", Anal. Chem. 2016, 88, 8821−8826. 

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This page is part of "A Pragmatic Introduction to Signal Processing", created and maintained by Prof. Tom O'Haver , Department of Chemistry and Biochemistry, The University of Maryland at College Park. Comments, suggestions and questions should be directed to Prof. O'Haver at toh@umd.edu.
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