Evaluation of Measurement Precision from Stationary Baseline Noise in Instrumental Analyses

This paper provides two approaches to estimate the standard deviation of measurements from baseline noise in instrumental output when (i) in theory, the noise can be approximated by a well-established random process in statistics and mathematics, referred to as a stationary process and (ii) in practice, the baseline noise is the predominant source of measurement error. For the first approach proposed, a general evaluation equation for measurement precision, when the baseline noise can be treated as a stationary process, is derived as a function of the process autocorrelations and process variance of the noise. In particular, for the second approach, when the baseline noise is a mixed random process of white noise and a first order autoregressive (AR(1)) process, the corresponding equation for the precision is also derived. The equations derived in the present paper include some results published elsewhere as special cases. For illustration, an example is presented.