Automatic baseline functions

Carries out a fully automatic baseline correction using the degree of the polynom for the baseline function last defined during interactive baseline correction (see chapter 5). 

FT-Raman spectra were carried out using Perkin-Elmer spectrometer equipped with Nd YAG laser source. Spectra were accumulated from 64 scans at a resolution of 4cm. An optical bench alignment was performed before each Raman measurements to ensure that e spectrometer was fine-tuned and the detector signal maximized. FT-IR spectra were recorded using Perkin-Elmer spectrometer equipped with diamante crystal for attenuated total reflection (ATR). The FT-IR or Raman spectra were smoothed and their baselines were corrected using the automatic smooth and the automatic baseline correct functions of the built-in software of the spectrophotometer. Then, the intensities of the interested peaks were measured. 

Many automatic baseline correction routines that may be applied without operator intervention are available. These routines may be applied by default for operations such as spectral searching. Automatic basehne functions typically use linear or polynomial baseline fits in regions of the spectmm where no peaks are detected. 

Trend data also must be adjusted for maintenance and repair activities. Figure 44.34 illustrates an average trend curve that indicates a sharp rise in vibration levels. It also reflects that, after repair, the levels drop radically. At this point, all baseline and reference values should be reset. If this does not occur, the automatic trending capabilities of the computer-based system do not function properly. 

EM affects the linewidth and is often also known as a line broadening function LB. A positive value of LB (here 0.8 and 1.9 Hz) broadens the lines, a negative value (here -0.3 Hz) sharpens them however, never forget that we are only modifying the information present, so that a decrease in the line-width is automatically accompanied by an increase in the baseline noise. This becomes clear immediately when we see the spectra of the OCH2 multiplet shown in Fig. 6. 

The program fits the intensity at these point values to a polynomial (up to 5th order) function and then subtracts the polynomial function from the whole dataset. This is repeated for each ID slice (row or column) of the 2D data matrix. More sophisticated methods calculate the baseline points automatically and use functions other than polynomials. For example, a program called FLATT (by Kurt Wiithrich) is very effective at removing horizontal or vertical streaks resulting from baseline curvature in rows or columns of the data matrix. Especially with NOESY and ROESY databaseline correction is essential to getting clean 2D displays and plots. 

A spline function was used to automatically correct the baseline in these examples. 

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