The quality control of measured data

In digital, computerized analytical measurement raw data signal samples are collected at a given frequency. In two-dimensional analytical methods the analytical information is produced by different algorithms, which can be the integrated peak area, the height of the peak, or the extreme values of the measured signal (for example the retention time in chromatography). The analytical information thus produced is always biased since an element of 'noise' is inevitable [1 and 2-]. Recent advances in microelectronics, coupled with cheaper technology, promise a new generation of analytical instruments. There are several analytical areas where microprocessors offer new, or wider, capabilities [3], for example qualification of measurements by estimating the standard deviation of the random error term during measurement. The application of constant signal-to-noise ratio (SNR) strategy is a way of determining the efficiency of a measurement procedure [4]. However, there is no previously reported method for calculating standard deviation of random errors ofthe measured signal samples generated by two-dimensional analytical methods. This paper describes such a method. The signal samples of an analytical measurement contain signal and noise components from different sources. Random errors cannot be determined directly; however, the standard deviation of the random errors can be estimated as follows: