5: Signals and Noise
- Page ID
- 332628
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When we try to calibrate an analytical method or to optimize an analytical system, our ability to do so successfully is limited by the uncertainty, or noise, in our measurements and by background signals that interfere with our ability to measure the signal of interest to us. In this chapter we will consider how we characterize noise, example of sources of noise, and ways to clean up our data by decreasing the contribution of noise to our measurements and by correcting for the presence of background signals.
- 5.1: The Signal-to-Noise Ratio
- When we make a measurement it is the sum of two parts, a determinate, or fixed contribution that arises from the analyte and an indeterminate, or random, contribution that arises from uncertainty in the measurement process. We call the first of these the signal and we call the latter the noise. There are two broad categories of noise: that associated with obtaining samples and that associated with making measurements. Our interest here is in the latter.
- 5.2: Sources of Instrumental Noise
- Noise is a random fluctuation in the signal that limits our ability to detect the presence of the underlying signal. There are a variety of ways in which noise can enter into our measurements. In this chapter, we consider sources of noise that arise from the instruments we use to make measurements. We call these sources of instrumental noise.
- 5.3: Signal-to-Noise Enhancement
- There are two broad approaches we can use to improve the signal-to-noise ratio: hardware and software. Hardware approaches are built into the instrument and include decisions on how the instrument is set-up for making measurements and how the signal is processed by the instrument. Software solutions are computational approaches in which we manipulate the data either while we are collecting it or after data acquisition is complete.