Spectral drift, causes

SPECTRAL LIBRARY ASSEMBLY 5.4.1 Causes of Spectral Drift... 

An internal standard is needed to compensate for differences in physical properties (such as viscosity) between the calibration standard and the test samples and drift caused by thermal changes in the laboratory that will affect the instrument optics. An appropriate internal standard element should not be naturally present in the test samples in appreciable concentrations and should not present spectral interferences with any analyte. In addition, the internal standard should be a strong emitter so that its relative concentration can be kept low, and be as chemically similar to the analyte as possible. 

Note that Hanack et al. measured the PL of thin films, while Detert and Sugiono measured PL in solution. Interchain interactions will be more significant in the thin film, which may cause the magnitude of the spectral drift to change. 

As mentioned, stellar spectra with right circular polarization obtained in the first exposure and left circular polarization in the second exposure are projeeted in turn on the same section of the CCD detector. Thus, errors in the flat-fielding procedure for two spectra with opposite eireular polarization are practically the same and do not affect the calculation of GMF in the ease of weak magnetic fields. Additionally, this observational technique automatieally allows us to rale out shifts of spectral lines caused by inaccurate adjustment of the CCD plane to the focal plane of the spectrograph and instrumental drift of contours of spectral lines during the second exposure relative to the first one. 

Short- and long-term drift in the spectral output can be caused by several factors drift in the output of the infrared light source or of the electronics, aging of the beam splitter, and changes in the levels of contaminants (water, CO2, etc.) in the optical path. These problems are normally eliminated by rapid, routine calibration procedures. 

As was the case for PCR, we see that the PLS spectral residuals for a sample will be higher whenever there is something in the data that introduces a mode of variation into the spectrum that was not present in any of the training samples used to develop the basis space. The anomolous variation could be caused by instrument drift, an unexpected interfering component, a misaligned sample cell, or whatever. We can use this property of residuals as an indicator that can signal... 

The major causes of spectral variation were (1) instrumental drift, as Goodacre and Kell realized, but also (2) sample history, as discussed above. In particular, variations in the supplier or even the batch of tryptic soy agar (TSA) used for cell culturing led to spectral variations that differed in degree among disparate species. This phenomenon was attributed to the differential metabolic capabilities of the species with respect to the changed nutrients. 

On the other hand, cationic species existing in the solid do not always exist as independent species in solutions, but do solvate with some solvent molecules. This then causes spectral shifts in the observed frequencies, vdrich are in some cases larger than expected by the mere change of phase and thus in turn complicate the identification and assignment of the drifted frequencies, both in the oii nal solid or in solution. 

As has been previously demonstrated in Eqs. (3.6) and (3.7), the sensitivity of the instrument for the analyte line is proportional to the intensity of the excitation source and is also a function of the spectral distribution of the source. Anything that changes the intensity or spectral distribution of the excitation source will cause a change in the analyte sensitivity. If the changes are uncontrolled or unpredictable, then analytical errors due to drift are encountered. Some of the sources of drift are inherent to the instrument design and cannot be controlled by the operator. On the other hand, there are techniques that can minimize the importance of the drifts inherent in the instrument. 

The most stringent need for wavenumber axis calibration is in determinations based on band position. For this reason, qualitative analyses are likely to be affected by drifts or inaccuracy in the wavenumber axis [14]. Likewise, quantitative determinations based on band position, such as strain in diamond films [6], will be affected similarly. Other quantitative analyses may also be affected by band-position error. It is common to use the raw spectral intensities (intensity at every wavenumber) in a multivariate analysis. Although this approach can be very powerful, any unexpected shift in wavenumber calibration can cause severe error in the model. In essence, the spectral pattern to which the model has been trained has been shifted. The mathematics of the model are expecting a particular relationship of intensity between adjacent variables (wavenumbers) and cannot usually account for shifts [31], To some extent, multivariate models can be desensitized to inaccuracy and imprecision by assuring that the calibration samples also exhibit some of the same shifting features, but model sensitivity may suffer as a result. Although not in common use, other deconvolution methods have been introduced which may be applicable to removing shift effects of inaccurate wavenumber calibrations [37]. 

DRIFTS has equally been applied to the identification of HPLC separated fractions [152]. On the other hand, the characterisation of spots on TLC plates is not particularly well suited to DRIFT some spectral regions are heavily obscured and interactions between the sample and the substrate cause wavenumber shifts. Subtraction of the substrate spectrum from the diffuse reflectance spectrum of 20 /xg of Irganox 1076 on a cellulose plate did not reveal useful information [152]. 

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