1. Introduction

The interfacing of analytical instrumentation to small computers for the purpose of on-line data acquisition has now become almost standard practice in the modern chemistry laboratory. Using widely-available, low-cost microcomputers and off-the-shelf add-in components, it is now easier than ever to acquire large amounts of data quickly in digital form.

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).