Baseline drift correction

The baseline of the atomic peak can steadily increase its absorbance because of instrumental malfunction, drift, a sample constituent giving rise to proportional interferences or an atomisation problem. The general appearance is that the atomic peaks are stacked , but this arrangement is not related to the concentration of the analyte. Baseline correction is a general practice in atomic spectrometry to measure accurately the height or the total area under the peak. In multivariate analysis, the procedure is the same in order to start all atomic peaks in the same absorbance position, in general at zero scale. The most basic baseline correction is offset correction, in which a constant value is subtracted to all absorbances that define the atomic peak (Figure 4.10a). 

What constitutes a significant difference between two spectra When the differences are small, the answer depends on sample preparation and sample stability as well as accuracy of concentration determination, identification of and compensation for drift in the spectrometer, correct baseline correction, absence of bubbles in the sample, reproducible cleanliness of the cuvette, and the level of general handling procedures. Ultimately, an assessment of significance depends on the experience, competence, and confidence of the operator. 

Subtraction of reference Linear drift subtraction Normalization Averaging Linearization Differential measurements Baseline correction Relative signals Signal quality redundancy Transform functions, e.g., antilog... 

The most common way to deal with the problem of stochastic drift is to modulate the exposure of the analyte to the sensor and to synchronously detect the sensor response. When the analyte is off (i.e., the sensor is zeroed ), the sensor signal can be recorded as the baseline value. Drift-corrected signals can be obtained by subtracting the baseline signal from that recorded when the analyte is on. If the frequency of the on/off modulation is much higher than the frequency of the baseline drift, then this scheme results in dramatically improved stability in the measured data. An implicit requirement in this measurement strategy is that the response kinetics of the sensitive film/analyte combination be sufficiently fast to allow on/off modulation at the desired frequency. 

A careful inspection of Equation 58 and Figure 9 leads to the following statement If a signal with linear drifting baseline and first order baseline noise is integrated, then the optimum baseline correction interval is infinite if the integration time is greater than four times the time constant of the noise otherwise, the optimum correction interval is zero. In the last case the use of two correction points on both sides of a peak is sufficient. 

Figure 2.52. Comparison of (1) in situ ATR and (2) ex situ DRIFTS spectra of same chalcopy-rite sample <30 r m size contacted with ethyl xanthate solution of pH 9.5 of 7.6 x 10 M. Baseline correction was done for both reflection spectra. Adaptated, by permission, from J. A. Mieiczarski, J. M. Cases, and O. Barres, J. Colloid Interface Sci. 178, 740 (1996), p. 744, Fig. 5. Copyright 1996 Academic Press.

DOSY NMR experiments are popular for analysing diffusional properties of mixtures. Quality of the results is affected by experimental factors such as baseline drift, peak shift and phase shift. Huo et al proposed a series of pre-processing operations to reduce the experimental distortions, including baseline correction and reference deconvolution to remove frequency and phase shifts. The corrected data can be successfully analysed with a combination of multivariate curve resolution with non-linear least square regression. 

To leam that the magnitude of the current peak in cyclic voltammetry (after suitable correction for baseline drift, where applicable) is proportional to analyte concentration according to the Randles-Sev5ik equation. 

Figure 2.9 and Table 2.1 summarize the effects of a number of common row pretreatments, discussed below, in correcting for unwanted signal variations such as addictive (baseline shifts), multiplicative (baseline drifts), and global intensity variation effects. 

Detector sensitivity is one of the most important properties of the detector. The problem is to distinguish between the actual component and artifact caused by the pressure fluctuation, bubble, compositional fluctuation, etc. If the peaks are fairly large, one has no problem in distinguishing them however, the smaller the peaks, the more important that the baseline be smooth, free of noise and drift. Baseline noise is the short time variation of the baseline from a straight line. Noise is normally measured "peak-to-peak" i.e., the distance from the top of one such small peak to the bottom of the next. Noise is the factor which limits detector sensitivity. In trace analysis, the operator must be able to distinguish between noise spikes and component peaks. For qualitative purposes, signal/noise ratio is limited by 3. For quantitative purposes, signal/noise ratio should be at least 10. This ensures correct quantification of the trace amounts with less than 2% variance. The baseline should deviate as little as possible from a horizontal line. It is usually measured for a specified time, e.g., 1/2 hour or one hour and called drift. Drift usually associated to the detector heat-up in the first hour after power-on. 

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