Abstract
Spectral soft modelling is a straightforward approach
to analyse vibrational spectra obtained from process
monitoring using IR or Raman spectroscopy. However,
when unexpected species, i.e. species that have not
been included in the calibration, contribute to the signal,
the data evaluation may lead to signifi cant errors in the
determined mixture composition. We present a simple
procedure that allows detection of such unexpected
species and in many cases their identifi cation as well.
The evaluation approach is based on a systematic and
dynamic piecewise spectral fi tting algorithm facilitating
the quantifi cation of the calibrated species and the
detection of unexpected ones.
to analyse vibrational spectra obtained from process
monitoring using IR or Raman spectroscopy. However,
when unexpected species, i.e. species that have not
been included in the calibration, contribute to the signal,
the data evaluation may lead to signifi cant errors in the
determined mixture composition. We present a simple
procedure that allows detection of such unexpected
species and in many cases their identifi cation as well.
The evaluation approach is based on a systematic and
dynamic piecewise spectral fi tting algorithm facilitating
the quantifi cation of the calibrated species and the
detection of unexpected ones.
| Original language | English |
|---|---|
| Pages (from-to) | 54-57 |
| Number of pages | 4 |
| Journal | Chimica Oggi |
| Volume | 30 |
| Issue number | 3 |
| Publication status | Published - May 2012 |